Patent Application
20170107577
THIS INVENTION relates to cancer. More particularly, this invention relates to methods of determining the aggressiveness of cancers, prognosis of cancers and/or predicting responsiveness to anti-cancer therapy.
Hormone receptors (ER and PR) and HER2 are standard biomarkers used in clinical practice to aid the histopathological classification of breast cancer and management decisions. Hormone receptor (HR)− and HER2− positive tumors benefit from tamoxifen and anti-HER2 therapies, respectively. On the other hand, there are currently no targeted drug therapies for management of triple negative breast cancer (TNBC), which lacks expression of HR/HER2. TNBCs are more sensitive to chemotherapy than HR-positive tumors because they are generally more proliferative, and pathological complete responses (pCR) after chemotherapy are more likely in TNBC than in non-TNBC1,2. Paradoxically, TNBC is associated with poorer survival than non-TNBC, due to more frequent relapse in TNBC patients with residual disease1,2. Only 31% of TNBC patients experience pCR after chemotherapy3, emphasizing the need for targeted therapies.
Transcriptome profiling has been used to dissect the heterogeneity of breast cancer into five intrinsic ‘PAM50’ subtypes; Luminal A, Luminal B, Basal-like, HER-2 and normal-like subtypes that relate to clinical outcomes4-8. Several gene signatures have been developed to predict outcome or response to treatment including: MammaPrint9, OncotypeDx10,11, Theros12-15. These commercial signatures rely on models that select genes based on clinical phenotypes such as tumor response or survival time. Notwithstanding their clinical utilities, these models fail to identify core biological mechanisms for the phenotypes of interest. Recently, an approach based on biological function-driven gene coexpression signatures, “attractor metagenes”, has been applied to the prediction of survival in certain cancers. However such approaches are at an early stage and much work needs to be done to develop this attractor metagene analysis in relation to cancers in general and also for specific cancers.
The present invention relates to the comparison of expression levels of a plurality of differentially expressed genes from one or a plurality of functional metagenes, including a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune system metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene; wherein the comparison of expression level of a plurality of genes in these metagenes is used to facilitate determining the aggressiveness of certain cancers. This comparison may also, or alternatively, assist in providing a cancer prognosis for a patient. The invention also relates to predicting the responsiveness of a cancer to an anti-cancer treatment by determining an expression level of one or a plurality of genes associated with one or a plurality of the aforementioned twelve functional metagenes.
The invention further relates to the comparison of expression levels of a specific signature of differentially expressed proteins to facilitate or assist in determining the aggressiveness of a particular cancer, a prognosis for a cancer patient and/or predicting responsiveness to an anti-cancer treatment. One or both of these comparisons may also be integrated with the aforementioned comparison of the expression levels of the plurality genes from one or a plurality of the aforementioned functional metagenes in determining cancer aggressiveness, prognosis and/or treatment.
In a first aspect, the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In a second aspect, the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a more favourable cancer prognosis.
In one embodiment of the above aspects, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are selected from one of the aforesaid metagenes. In an alternative embodiment, the one or plurality of overexpressed genes and/or one or the plurality of underexpressed genes are selected from a plurality of the aforesaid metagenes.
Suitably, for the method of the above aspects the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and/or the Multiple Networks metagene comprise one or a plurality of genes listed in Table 21.
In a third aspect, the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level
In a fourth aspect, the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a more favourable cancer prognosis.
In one embodiment of the third and fourth aspects, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are selected from one of the aforesaid metagenes. In an alternative embodiment, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are selected from a plurality of the aforesaid metagenes.
Suitably, the Metabolism metagene, the Signalling metagene, the Development and Growth metagene, the Chromosome Segregation/Replication metagene, the Immune Response metagene and/or the Protein Synthesis/Modification metagene comprise one or a plurality of genes listed in Table 22.
In particular embodiments of the method of the third and fourth aspects, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are from one or a plurality of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene.
In a fifth aspect, the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes associated with chromosomal instability and/or an expression level of one or a plurality of underexpressed genes associated with estrogen receptor signalling in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed genes associated with chromosomal instability compared to the one or plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level expression level of the one or plurality of overexpressed genes associated with chromosomal instability compared to the one or plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In a sixth aspect, the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes associated with chromosomal instability and/or an expression level of one or a plurality of underexpressed genes associated with estrogen receptor signalling in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed genes associated with chromosomal instability compared to the one or plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or plurality of overexpressed genes associated with chromosomal instability compared to the one or plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with a more favourable cancer prognosis.
In certain embodiments, the genes associated with chromosomal instability are of a CIN metagene. Non-limiting examples include genes selected from the group consisting of ATP6V1C1, RAP2A, CALM1, COG8, HELLS, KDM5A, PGK1, PLCH1, CEP55, RFC4, TAF2, SF3B3, GP1, PIR, MCM10, MELK, FOXM1, KIF2C, NUP155, TPX2, TTK, CENPA, CENPN, EXO1, MAPRE1, ACOT7, NAE1, SHMT2, TCP1, TXNRD1, ADM, CHAF1A and SYNCRIP. Preferably, the genes are selected from the group consisting of: MELK, MCM10, CENPA, EXO1, TTK and KIF2C.
In certain embodiments, the genes associated with estrogen receptor signalling are of an ER metagene. Non-limiting examples include genes selected from the group consisting of: BTG2, PIK3IP1, SEC14L2, FLNB, ACSF2, APOM, BIN3, GLTSCR2, ZMYND10, ABAT, BCAT2, SCUBE2, RUNX1, LRRC48, MYBPC1, BCL2, CHPT1, ITM2A, LRIG1, MAPT, PRKCB, RERE, ABHD14A, FLT3, TNN, STC2, BATF, CD1E, CFB, EVL, FBXW4, ABCB1, ACAA1, CHAD, PDCD4, RPL10, RPS28, RPS4X, RPS6, SORBS1, RPL22 and RPS4XP3. Preferably, the genes are selected from the group consisting of: MAPT and MYB.
In certain embodiments, the method of the fifth and sixth aspects further including the step of comparing an expression level of one or a plurality of other overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and/or an expression level of one or a plurality of other underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the other overexpressed genes compared to the other underexpressed genes indicates or correlates with higher aggressiveness of the cancer and/or a less favourable cancer prognosis; and/or a lower relative expression level of the other overexpressed genes compared to the other underexpressed genes indicates or correlates with lower aggressiveness of the cancer and/or a more favourable cancer prognosis compared to a mammal having a higher expression level.
In one embodiment, the one or plurality of other overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or plurality of other underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
Suitably, the comparison of the expression level of the overexpressed genes associated with chromosomal instability and/or the expression level of the underexpressed genes associated with estrogen receptor signalling is integrated with the comparison of the expression level of the one or plurality of other overexpressed genes and/or the expression level of the one or plurality of other underexpressed genes to derive a first integrated score.
In a seventh aspect, the invention provides a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In an eighth aspect, the invention provides a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a more favourable cancer prognosis compared to a mammal having a higher expression level.
In one embodiment of the seventh and eighth aspects, the one or plurality of overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment of the seventh and eighth aspects, the one or plurality of underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In particular embodiments, the method of the first, second, third, fourth, fifth, sixth, seventh and eighth aspects further includes the step of comparing an expression level of one or a plurality of overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and/or an expression level of one or a plurality of underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the overexpressed proteins compared to the underexpressed proteins indicates or correlates with higher aggressiveness of the cancer and/or a less favourable cancer prognosis; and/or a lower relative expression level of the overexpressed proteins compared to the underexpressed proteins indicates or correlates with lower aggressiveness of the cancer and/or a more favourable cancer prognosis compared to a mammal having a higher expression level.
Suitably, the comparison of the expression level of the one or plurality of overexpressed proteins and/or the expression level of the one or plurality of underexpressed proteins is to thereby derive an integrated score. In one particular embodiment, the comparison of the expression level of the one or plurality of overexpressed proteins and/or the expression level of the one or plurality of underexpressed proteins is integrated with:
In particular embodiments, the second, third, fourth, fifth and/or sixth integrated score are derived, at least in part, by addition, subtraction, multiplication, division and/or exponentiation.
In a preferred embodiment, the first, second and/or third integrated scores are derived, at least in part, by exponentiation wherein the comparison of the expression level of the other overexpressed genes and the expression level of the other underexpressed genes is raised to the power of
In a ninth aspect, the invention provides a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and/or an expression level of one or a plurality of underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In a tenth aspect, the invention provides a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and/or an expression level of one or a plurality of underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with a more favourable cancer prognosis compared to a mammal having a higher expression level.
In an eleventh aspect, the invention provides method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein an altered or modulated relative expression level of the overexpressed genes compared to the underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
Suitably, for the present aspect the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and/or the Multiple Networks metagene comprise one or a plurality of genes listed in Table 21.
In a twelfth aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes in one or a plurality of cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or a plurality of metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein an altered or modulated relative expression level of the overexpressed genes compared to the underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment of the eleventh and twelfth aspects, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are selected from one of the metagenes. In an alternative embodiment, the one or plurality of overexpressed genes and/or the one or plurality of underexpressed genes are selected from a plurality of the metagenes.
Suitably, the Metabolism metagene, the Signalling metagene, the Development and Growth metagene, the Chromosome Segregation/Replication metagene, the Immune Response metagene and/or the Protein Synthesis/Modification metagene comprise one or a plurality of genes listed in Table 22.
In particular embodiments, the one or plurality of overexpressed genes and the one or plurality of underexpressed genes are from one or a plurality of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene.
According to the method of the eleventh and twelfth aspects, the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes includes comparing an average expression level of the one or plurality of overexpressed genes and/or an average expression level of the one or plurality of underexpressed genes. This may include calculating a ratio of the average expression level of the one or plurality of overexpressed genes and the average expression level of the one or plurality of underexpressed genes. Suitably, the ratio provides an aggressiveness score which is indicative of, or correlates with, cancer aggressiveness and a less favourable prognosis. Alternatively, the step of comparing an expression level of one or a plurality of overexpressed genes and/or an expression level of one or a plurality of underexpressed genes includes comparing the sum of expression levels of the one or plurality of overexpressed genes and/or the sum of expression levels of the one or plurality of underexpressed genes. This may include calculating a ratio of the sum of expression levels of the one or plurality of overexpressed genes and/or the sum of expression levels of the one or plurality of underexpressed genes.
In a thirteenth aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of determining an expression level of one or a plurality of genes associated with chromosomal instability in one or a plurality of non-mitotic cancer cells of the mammal, wherein a higher expression level indicates or correlates with relatively increased responsiveness of the cancer to the anti-cancer treatment
Suitably, the one or plurality of genes associated with chromosomal instability are selected from the group consisting of: TTK, CEP55, FOXM1 and SKIP2 and/or any CIN genes listed in Table 4.
In a fourteenth aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes associated with chromosomal instability and/or an expression level of one or a plurality of underexpressed genes associated with estrogen receptor signalling in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed genes associated with chromosomal instability compared to the one or plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In certain embodiments, the genes associated with chromosomal instability are of a CIN metagene. Non-limiting examples include genes selected from the group consisting of: ATP6V1C1, RAP2A, CALM1, COG8, HELLS, KDM5A, PGK1, PLCH1, CEP55, RFC4, TAF2, SF3B3, GP1, PIR, MCM10, MELK, FOXM1, KIF2C, NUP155, TPX2, 11K, CENPA, CENPN, EXO1, MAPRE1, ACOT7, NAE1, SHMT2, TCP1, TXNRD1, ADM, CHAF1A and SYNCRIP. Preferably, the genes are selected from the group consisting of: MELK, MCM10, CENPA, EXO1, TTK and KIF2C.
In certain embodiments, the genes associated with estrogen receptor signalling are of an ER metagene. Non-limiting examples include genes selected from the group consisting of: BTG2, PIK3IP1, SEC14L2, FLNB, ACSF2, APOM, BIN3, GLTSCR2, ZMYND10, ABAT, BCAT2, SCUBE2, RUNX1, LRRC48, MYBPC1, BCL2, CHPT1, ITM2A, LRIG1, MAPT, PRKCB, RERE, ABHD14A, FLT3, TNN, STC2, BATF, CD1E, CFB, EVL, FBXW4, ABCB1, ACAA1, CHAD, PDCD4, RPL10, RPS28, RPS4X, RPS6, SORBS1, RPL22 and RPS4XP3. Preferably, the genes are selected from the group consisting of: MAPT and MYB.
Suitably, the method of this aspect further includes the step of comparing an expression level of one or a plurality of other overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and/or an expression level of one or a plurality of other underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3 in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of other overexpressed genes compared to the one or plurality of other underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment, the one or plurality of other overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or plurality of other underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In certain embodiments, the comparison of the expression level of the one or plurality of other overexpressed genes and/or the expression level of the one or plurality of other underexpressed genes is integrated with the comparison of the expression level of the one or plurality of overexpressed genes associated with chromosomal instability and/or the expression level of the one or plurality of underexpressed genes associated with estrogen receptor signalling to derive a first integrated score, which is indicative of, or correlates with, responsiveness of the cancer to the anti-cancer treatment. By way of example, the first integrated score may be derived, at least in part, by addition, subtraction, multiplication, division and/or exponentiation. Preferably, the integrated score is derived by exponentiation, wherein the comparison of the expression level of the one or plurality of other overexpressed genes and the expression level of the one or plurality of other underexpressed genes is raised to the power of the comparison of the expression level of the one or plurality of overexpressed genes associated with chromosomal instability and the expression level of the one or plurality of underexpressed genes associated with estrogen receptor signalling.
In a fifteenth aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment, the one or plurality of overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or plurality of underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
Suitably, the method of the eleventh, twelfth, thirteenth, fourteenth and fifteenth aspects further includes the step of comparing an expression level of a one or a plurality of overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and/or an expression level of one or a plurality of underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
Suitably, the comparison of the expression level of the one or plurality of overexpressed proteins and/or the expression level of the one or plurality of underexpressed proteins is to thereby derive an integrated score. In one particular embodiment, the comparison of the expression level of the one or plurality of overexpressed proteins and/or the expression level of the one or plurality of underexpressed proteins is integrated with:
In particular embodiments the first, second, third, fourth, fifth and/or sixth integrated score are derived, at least in part, by addition, subtraction, multiplication, division and/or exponentiation.
In a preferred embodiment, the first, second and/or third integrated scores are derived, at least in part, by exponentiation wherein the comparison of the expression level of the other overexpressed genes and/or the expression level of the other underexpressed genes is raised to the power of
In a sixteenth aspect, the invention provides method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed proteins selected from the group consisting of DVL3, PM-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and/or an expression level of one or a plurality of underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed proteins compared to the one or plurality of underexpressed proteins indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
Suitably, the anticancer treatment of the eleventh, twelfth, thirteenth, fourteenth, fifteenth and sixteenth aspects is selected from the group consisting of endocrine therapy, chemotherapy, immunotherapy and a molecularly targeted therapy. In certain embodiments, the anticancer treatment comprises an anaplastic lymphoma kinase (ALK) inhibitor, a BCR-ABL inhibitor, a heat shock protein 90 (HSP90) inhibitor, an epidermal growth factor receptor (EGFR) inhibitor, a poly (ADP-ribose) polymerase (PARP) inhibitor, retinoic acid, a B-cell lymphoma 2 (Bcl2) inhibitor, a gluconeogenesis inhibitor, a p38 mitogen-activated protein kinase (MAPK) inhibitor, a mitogen-activated protein kinase kinase 1/2 (MEK1/2) inhibitor, a mammalian target of rapamycin (mTOR) inhibitor, a phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitor, an insulin-like growth factor 1 receptor (IGF1R) inhibitor, a phospholipase C-γ (PLCγ) inhibitor, a c-Jun N-terminal kinase (JNK) inhibitor, a p21-activated kinase-1 (PAK1) inhibitor, a spleen tyrosine kinase (SYK) inhibitor, a histone deacetylase (HDAC) inhibitor, a fibroblast growth factor receptor (FGFR) inhibitor, an X-linked inhibitor of apoptosis (XIAP) inhibitor, a polo-like kinase 1 (PLK1) inhibitor, an extracellular-signal-regulated kinase 5 (ERK5) inhibitor and combinations thereof.
Suitably, the method of the eleventh, twelfth, thirteenth, fourteenth, fifteenth and sixteenth aspects further includes the step of administering to the mammal a therapeutically effective amount of the anticancer treatment. Preferably, the anticancer treatment is administered when the altered or modulated relative expression level indicates or correlates with relatively increased responsiveness of the cancer to the anti-cancer treatment.
In a seventeenth aspect, the invention provides a method of predicting the responsiveness of a cancer to an immunotherapeutic agent in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of ADORA2B, CD36, CETN3, CFDP1, KCNG1, LAMA3, NAE1, MAP2K5, PGK1, SF3B3, STAU1 and TXN and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of APOBEC3A, BTN2A2, BCL2, CAMK4, FBXW4, CAMSAP1, CARHSP1, GSK3B, HCFC1R1, PSEN2, MYB and ZNF593, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the immunotherapeutic agent.
Suitably, the immunotherapeutic agent is an immune checkpoint inhibitor. Preferably, the immune checkpoint inhibitor is or comprises an anti-PD1 antibody or an anti-PDL1 antibody.
In an eighteenth aspect is provided a method of predicting the responsiveness of a cancer to an epidermal; growth factor receptor (EGFR) inhibitor in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of NAE1, GSK3B, TAF2, MAPRE1, BRD4, STAU1, TAF2, PDCD4, KCNG1, ZNRD1-AS1, EIF4B, HELLS, RPL22, ABAT, BTN2A2, CD1B, ITM2A, BCL2, CXCR4, and ARNT2 and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of CD1C, CD1E, CD1B, KDM5A, BATF, EVL, PRKCB, HCFC1R1, CARHSP1, CHAD, KIR2DL4, ABHD5, ABHD14A, ACAA1, SRPK3, CFB, ARNT2, NDUFC1, BCL2, EVL, ULBP2, BIN3, SF3B3, CETN3, SYNCRIP, TAF2, CENPN, ATP6V1C1, CD55 and ADORA2B in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the EGFR inhibitor.
In a nineteenth aspect is provided a method of predicting the responsiveness of a cancer to a multikinase inhibitor in a mammal, said method including the step of comparing an expression level of one or a plurality of overexpressed genes selected from the group consisting of SCUBE, CHPT1, CDC1, BTG2, ADORA2B and BCL2, and/or an expression level of one or a plurality of underexpressed genes selected from the group consisting of NOP2, CALR, MAPRE1, KCNG1, PGK1, SRPK3, RERE, ADM, LAMA3, KIR2DL4, ULBP2, LAMA4, CA9, and BCAP31, in one or a plurality of cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the multikinase inhibitor.
Suitably, for the method of the seventeenth, eighteenth and nineteenth aspects, a higher relative expression level of the one or plurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a relatively increased responsiveness of the cancer to the immunotherapeutic agent, EGFR inhibitor or multikinase inhibitor; and/or a lower relative expression level of the one or aplurality of overexpressed genes compared to the one or plurality of underexpressed genes indicates or correlates with a relatively decreased responsiveness of the cancer to the immunotherapeutic agent, EGFR inhibitor and/or multikinase inhibitor.
In some embodiments, the method of the seventeenth, eighteenth and nineteenth aspects further includes the step of administering to the mammal a therapeutically effective amount of the immunotherapeutic agent, the EGFR inhibitor or the multikinase inhibitor respectively. Preferably, the immunotherapeutic agent, the EGFR inhibitor or the multikinase inhibitor is administered when the altered or modulated relative expression level indicates or correlates with relatively increased responsiveness of the cancer to the immunotherapeutic agent, the EGFR inhibitor or the multikinase inhibitor respectively.
Suitably, for the methods of the aforementioned aspects, the step of comparing an expression level of one or a plurality ofoverexpressed genes or proteins and an expression level of one or a plurality of underexpressed genes or proteins, includes comparing an average expression level of the one or plurality of overexpressed genes or proteins and an average expression level of the one or plurality of underexpressed genes or proteins. This may include calculating a ratio of the average expression level of the one or plurality of overexpressed genes or proteins and the average expression level of the one or plurality of underexpressed genes or proteins. Suitably, the ratio provides an aggressiveness score which is indicative of, or correlates with, cancer aggressiveness and a less favourable prognosis. Alternatively, the step of comparing an expression level of one or a plurality of overexpressed genes and an expression level of one or a plurality of underexpressed genes or proteins, includes comparing the sum of expression levels of the one or plurality of overexpressed genes or proteins and the sum of expression levels of the one or plurality of underexpressed genes or proteins. This may include calculating a ratio of the sum of expression levels of the one or plurality of overexpressed genes or protein and the sum of expression levels of the one or plurality of underexpressed genes or proteins.
In certain embodiments of the aforementioned methods, the mammal is subsequently treated for cancer.
In a twentieth aspect, the invention provides a method for identifying an agent for use in the treatment of cancer including the steps of:
(i) contacting a protein product of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and/or KCNG1 with a test agent; and
(ii) determining whether the test agent, at least partly, reduces, eliminates, suppresses or inhibits the expression and/or an activity of the protein product.
Suitably, the agent possesses or displays little or no significant off-target and/or nonspecific effects.
Preferably, the agent is an antibody or a small organic molecule.
In a twenty first aspect, the invention provides an agent for use in the treatment of cancer identified by the method of the eighteenth aspect.
In a twenty second aspect, the invention provides a method of treating a cancer in a mammal, including the step of administering to the mammal a therapeutically effective amount of an agent identified by the method of the eighteenth aspect.
Preferably, for the invention of the twentieth, twenty first and twenty second aspects, the cancer has an overexpressed gene selected from the group consisting of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1, KCNG1 and any combination thereof.
Suitably, the method of the aformentioned aspects further includes the step of determining, assessing or measuring the expression level of one or plurality of the overexpressed genes, the underexpressed genes, the overexpressed proteins and/or the underexpressed proteins described herein.
Suitably, the mammal referred to in the aforementioned aspects and embodiments is a human.
In certain embodiments of the invention of the aforementioned aspects, the cancer includes breast cancer, lung cancer inclusive of lung adenocarcinoma and lung squamous cell carcinoma, cancers of the reproductive system inclusive of ovarian cancer, cervical cancer, uterine cancer and prostate cancer, cancers of the brain and nervous system, head and neck cancers, gastrointestinal cancers inclusive of colon cancer, colorectal cancer and gastric cancer, liver cancer inclusive of hepatocellular carcinoma, kidney cancer inclusive of renal clear cell carcinoma and renal papillary cell carcinoma, skin cancers such as melanoma and skin carcinomas, blood cell cancers inclusive of lymphoid cancers and myelomonocytic cancers, cancers of the endocrine system such as pancreatic cancer and pituitary cancers, musculoskeletal cancers inclusive of bone and soft tissue cancers, although without limitation thereto. By way of example, breast cancer includes aggressive breast cancers and cancer subtypes such as triple negative breast cancer, grade 2 breast cancer, grade 3 breast cancer, lymph node positive (LN+) breast cancer, HER2 positive (HER2+) breast cancer and ER positive (ER+) breast cancer, although without limitation thereto.
Unless the context requires otherwise, the terms “comprise”, “comprises” and “comprising”, or similar terms are intended to mean a non-exclusive inclusion, such that a recited list of elements or features does not include those stated or listed elements solely, but may include other elements or features that are not listed or stated.
The indefinite articles ‘a’ and ‘an’ are used here to refer to or encompass singular or plural elements or features and should not be taken as meaning or defining “one” or a “single” element or feature.
The present invention is at least partly predicated on the discovery that there are genes that are associated with tumor aggressiveness and poor clinical outcome based on meta-analysis of published gene expression profiling. More particularly, the overexpression and/or underexpression of these genes (see Table 21) was found to be associated with poor survival in breast cancer. Network analysis using the Ingenuity Pathway Analysis (IPA®) software identified a number of networks or metagenes within these survival-associated genes that possess distinct biological functions as outlined in Table 21. A smaller subset of genes from each network or metagene which consistently associated with patient survival were then selected. The list of these genes and their corresponding functions are shown in Table 22. These genes were divided into six functional metagenes or networks.
The present invention is also at least partly predicated on the discovery that there are genes that are commonly de-regulated in particular subgroups that exemplify aggressive clinical behavior in triple-negative breast cancer (TNBC). More particularly, this is evident in TNBC compared to non-TNBC and normal breast, tumors associated with distant metastasis and/or death compared to their respective counterparts. Initially, a list of 206 recurrently deregulated genes was found to be particularly enriched for chromosomal instability (CIN) and estrogen receptor signaling (ER) metagenes. An aggressiveness score based on the ratio of the expression level of a CIN metagene relative to an ER metagene has been shown to identify aggressive tumors regardless of molecular subtype and clinico-pathologic indicators. Furthermore, depletion of proteins involved in kinetochore binding or chromosome segregation could be therapeutic and significantly reduced the survival of TNBC cell lines in vitro, particularly with regard to TTK. TTK inhibition with small molecule inhibitor affected the survival of TNBC cell lines. Also, TTK mRNA and protein levels were associated with aggressive tumor phenotypes. Mitosis-independent expression of TTK protein was prognostic in TNBC and other aggressive breast cancer subgroups, suggesting that protection of CIN/aneuploidy drives aggressiveness and treatment-resistance. The combination of TTK inhibition with chemotherapy was effective in vitro in the treatment of cells that overexpress TTK, thus providing a therapeutic treatment for the protected CIN phenotype.
Additionally, the present invention is at least partly predicated on the discovery of a second signature of altered gene expression, including 21 overexpressed genes and 7 underexpressed genes, that is highly prognostic in patients with ER− breast cancer, TNBC and basal-like breast cancer (BLBC). Indeed, integration of this 28 gene signature with the aforementioned aggressiveness score or gene signature produces an integrated score which is prognostic in breast cancer independent of ER status. Furthermore, the integrated score was prognostic in cancer broadly irrespective of the cancer type, as well as in specific types of cancer in addition to breast cancer, such as lung adenocarcinoma. Moreover, the 28 gene signature and the integrated score were both shown to be predictive of response to chemotherapy in breast cancer patients, as well as identify those ER+ lymph node positive breast cancer patients who would benefit from endocrine therapy. Altered expression of the signatures described herein was also predictive of sensitivity in cancer cell lines and clinically to a range of anticancer therapeutics, and in particular, molecularly targeted inhibitors.
The inventors of the present invention have also identified a protein signature that is highly prognostic in a range of cancers, including breast cancer and lung adenocarcinoma. Furthermore, this protein signature may be integrated with the aforementioned 28 gene signature and aggressive gene signature to provide a robust prognostic indicator in cancer that was shown to outperform known clinicopathological indicators.
In one aspect, the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein: a higher relative expression level of the plurality of the overexpressed genes compared to the plurality of the underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the plurality of the overexpressed genes compared to the plurality of the underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In a further aspect, the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein: a higher relative expression level of the plurality of overexpressed genes compared to the plurality of underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the plurality of overexpressed genes compared to the plurality of underexpressed genes indicates or correlates with a more favourable cancer prognosis.
In one embodiment of the above aspects, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from one of the metagenes. In an alternative embodiment, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from a plurality of the metagenes.
Suitably, for the method of the above aspects the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and/or the Multiple Networks metagene comprise one or more genes listed in Table 21.
In another aspect, the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein: a higher relative expression level of the plurality of the overexpressed genes compared to the plurality of the underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the plurality of the overexpressed genes compared to the plurality of the underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level
In yet another aspect, the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein: a higher relative expression level of the plurality of overexpressed genes compared to the plurality of underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the plurality of overexpressed genes compared to the plurality of underexpressed genes indicates or correlates with a more favourable cancer prognosis.
Suitably, the Metabolism metagene, the Signalling metagene, the Development and Growth metagene, the Chromosome Segregation/Replication metagene, the Immune Response metagene and/or the Protein Synthesis/Modification metagene comprise one or more genes listed in Table 21.
In particular embodiments of the method of the two aforementioned aspects, the plurality of overexpressed genes and the plurality of underexpressed genes are from one or more of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene. According to the method of the above aspects, the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes includes comparing an average expression level of the plurality of overexpressed genes and an average expression level of the plurality of underexpressed genes. This may include calculating a ratio of the average expression level of the plurality of overexpressed genes and the average expression level of the plurality of underexpressed genes. Suitably, the ratio provides an aggressiveness score which is indicative of, or correlates with, cancer aggressiveness and a less favourable prognosis. Alternatively, the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes includes comparing the sum of expression levels of the plurality of overexpressed genes and the sum of expression levels of the plurality of underexpressed genes. This may include calculating a ratio of the sum of expression levels of the plurality of overexpressed genes and the sum of expression levels of the plurality of underexpressed genes.
For the purposes of this invention, by “isolated” is meant material that has been removed from its natural state or otherwise been subjected to human manipulation. Isolated material may be substantially or essentially free from components that normally accompany it in its natural state, or may be manipulated so as to be in an artificial state together with components that normally accompany it in its natural state. Isolated material may be in native, chemical synthetic or recombinant form.
As used herein a “gene” is a nucleic acid which is a structural, genetic unit of a genome that may include one or more amino acid-encoding nucleotide sequences and one or more non-coding nucleotide sequences inclusive of promoters and other 5′ untranslated sequences, introns, polyadenylation sequences and other 3′ untranslated sequences, although without limitation thereto. In most cellular organisms a gene is a nucleic acid that comprises double-stranded DNA.
Non-limiting examples of genes are set forth herein, particularly in Tables 4, 21 and 22, which include Accession Numbers referencing the nucloetide sequence of the gene, or its encoded protein, as are well understood in the art.
The term “nucleic acid” as used herein designates single- or double-stranded DNA and RNA. DNA includes genomic DNA and cDNA. RNA includes mRNA, RNA, RNAi, siRNA, cRNA and autocatalytic RNA. Nucleic acids may also be DNA-RNA hybrids. A nucleic acid comprises a nucleotide sequence which typically includes nucleotides that comprise an A, G, C, T or U base. However, nucleotide sequences may include other bases such as inosine, methylycytosine, methylinosine, methyladenosine and/or thiouridine, although without limitation thereto.
Also included are, “variant” nucleic acids that include nucleic acids that comprise nucleotide sequences of naturally occurring (e.g., allelic) variants and orthologs (e.g., from a different species). Preferably, nucleic acid variants share at least 70% or 75%, preferably at least 80% or 85% or more preferably at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleotide sequence disclosed herein.
Also included are nucleic acid fragments. A “fragment” is a segment, domain, portion or region of a nucleic acid, which respectively constitutes less than 100% of the nucleotide sequence. A non-limilting example is an amplification product or a primer or probe. In particular embodiments, a nucleic acid fragment may comprise, for example, at least 10, 15, 20, 25, 30 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475 and 500 contiguous nucleotides of said nucleic acid.
As used herein, a “polynucleotide” is a nucleic acid having eighty (80) or more contiguous nucleotides, while an “oligonucleotide” has less than eighty (80) contiguous nucleotides. A “probe” may be a single or double-stranded oligonucleotide or polynucleotide, suitably labeled for the purpose of detecting complementary sequences in Northern or Southern blotting, for example. A “primer” is usually a single-stranded oligonucleotide, preferably having 15-50 contiguous nucleotides, which is capable of annealing to a complementary nucleic acid “template” and being extended in a template-dependent fashion by the action of a DNA polymerase such as Taq polymerase, RNA-dependent DNA polymerase or Sequenase™. A “template” nucleic acid is a nucleic acid subjected to nucleic acid amplification.
It will be appreciated that the “overexpressed” genes or proteins referred to herein are genes or proteins that are expressed at a higher level in a cancer cell or tissue compared to a corresponding normal or otherwise non-cancerous cell or tissue or reference/control level or sample.
It will be appreciated that the “underexpressed” genes or proteins referred to herein are genes or proteins that are expressed at a lower level in a cancer cell or tissue compared to a corresponding normal or otherwise non-cancerous cell or tissue or reference/control level or sample.
In certain embodiments, the “overexpressed” and “underexpressed” genes referred to herein may form, or be components of, a metagene.
As used herein, a “metagene” is a grouping, cohort or network of a plurality of different genes that display a common, shared or aggregate expression profile, expression level or other expression characteristics that associate with, or are indicative of, a particular function or phenotype. Non-limiting examples include a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene. Table 21 provides non-limiting examples of genes that are components of the aforementioned twelve metagenes. Further non-limiting examples include a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene. Table 22 provides non-limiting examples of genes that are components of the aforementioned six metagenes.
In particular embodiments, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from one of the metagenes. In this regard, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from the same metagene. By way of example, the plurality of overexpressed genes or the plurality of underexpressed genes may be only from one of the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and the Multiple Networks metagene. In a further example, both the plurality of overexpressed genes and the plurality of underexpressed genes may be only from one of the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and the Multiple Networks metagene.
Alternatively, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from a plurality of the metagenes described herein.
By “aggressiveness” and “aggressive” is meant a property or propensity for a cancer to have a relatively poor prognosis due to one or more of a combination of features or factors including: at least partial resistance to therapies available for cancer treatment; invasiveness; metastatic potential; recurrence after treatment; and a low probability of patient survival, although without limitation thereto.
Cancers may include any aggressive or potentially aggressive cancers, tumours or other malignancies such as listed in the NCI Cancer Index at http://www.cancer.gov/cancertopics/alphalist, including all major cancer forms such as sarcomas, carcinomas, lymphomas, leukaemias and blastomas, although without limitation thereto. These may include breast cancer, lung cancer inclusive of lung adenocarcinoma, cancers of the reproductive system inclusive of ovarian cancer, cervical cancer, uterine cancer and prostate cancer, cancers of the brain and nervous system, head and neck cancers, gastrointestinal cancers inclusive of colon cancer, colorectal cancer and gastric cancer, liver cancer, kidney cancer, skin cancers such as melanoma and skin carcinomas, blood cell cancers inclusive of lymphoid cancers and myelomonocytic cancers, cancers of the endocrine system such as pancreatic cancer and pituitary cancers, musculoskeletal cancers inclusive of bone and soft tissue cancers, although without limitation thereto.
In certain embodiments, cancers include breast cancer, bladder cancer, colorectral cancer, glioblastoma, lower grade glioma, head & neck cancer, kidney cancer, liver cancer, lung adenocarcinoma, acute myeloid leukaemia, pancreatic cancer, adrenocortical cancer, melanoma and lung squamous cell carcinoma.
Breast cancers include all aggressive breast cancers and cancer subtypes such as triple negative breast cancer, grade 2 breast cancer, grade 3 breast cancer, lymph node positive (LN+) breast cancer, HER2 positive (HER2+) breast cancer and ER positive (ER+) breast cancer, although without limitation thereto.
As used herein, “triple negative breast cancer” (TNBC) is an often aggressive breast cancer subtype lacking or having significantly reduced expression of estrogen receptor (ER) protein, progesterone receptor (PR) protein and HER2 protein. TNBC and other aggressive breast cancers are typically insensitive to some of the most effective therapies available for breast cancer treatment including HER2-directed therapy such as trastuzumab and endocrine therapies such as tamoxifen and aromatase inhibitors.
As used herein, a gene expression level may be an absolute or relative amount of an expressed gene or gene product inclusive of nucleic acids such as RNA, mRNA and cDNA and protein.
As would be appreciated by the skilled artisan, the present invention need not be limited to comparing the expression level of the overexpressed genes and/or proteins with the expression level of the underexpressed genes and/or proteins provided herein. Accordingly, in particular embodiments, the expression level of the overexpressed and/or underexpressed genes and/or proteins is compared to a control level of expression, such as the level of gene and/or protein expression of a “housekeeping” gene in one or more cancer cells, tissues or organs of the mammal.
In further embodiments, the expression level of the overexpressed and/or underexpressed genes and/or proteins is compared to a threshold level of expression, such as a level of gene and/or protein expression in non-aggressive cancerous tissue. A threshold level of expression is generally a quantified level of expression of a particular gene or set of genes, including gene products thereof. Typically, an expression level of a gene or set of genes in a sample that exceeds or falls below the threshold level of expression is predictive of a particular disease state or outcome. The nature and numerical value (if any) of the threshold level of expression will vary based on the method chosen to determine the expression the one or more genes or proteins used in determining, for example, a prognosis, the aggressiveness and/or response to anticancer therapy, in the mammal. In light of this disclosure, any person of skill in the art would be capable of determining the threshold level of gene/protein expression in a mammal sample that may be used in determining, for example, a prognosis, the aggressiveness and/or response to anticancer therapy, using any method of measuring gene or protein expression known in the art, such as those described herein. In one embodiment, the threshold level is a mean and/or median to expression level (median or absolute) of the overexpressed and/or underexpressed genes and/or proteins in a reference population, that, for example, have the same cancer type, subgroup, stage and/or grade as said mammal for which the expression level is determined. Additionally, the concept of a threshold level of expression should not be limited to a single value or result. In this regard, a threshold level of expression may encompass multiple threshold expression levels that could signify, for example, a high, medium, or low probability of, for example, progression free survival.
By “protein” is meant an amino acid polymer. The amino acids may be natural or non-natural amino acids, D- or L-amino acids as are well understood in the art. As would be appreciated by the skilled person, the term “protein” also includes within its scope phosphorylated forms of a protein (i.e., phosphoproteins).
Also provided are protein “variants” such as naturally occurring (eg allelic variants) and orthologs. Preferably, protein variants share at least 70% or 75%, preferably at least 80% or 85% or more preferably at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with an amino acid sequence disclosed herein.
Also provided are protein fragments, inclusive of peptide fragments thqat comprise less than 100% of an entire amino acid sequence. In particular embodiments, a protein fragment may comprise, for example, at least 10, 15, 20, 25, 30 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375 and 400 contiguous amino acids of said protein.
A “peptide” is a protein having no more than fifty (50) amino acids.
A “polypeptide” is a protein having more than fifty (50) amino acids.
It would be appreciated that in addition to comparing the expression levels of one or more genes or proteins, the methods of the present invention may further include the step of determining, assessing, evaluating, assaying or measuring the expression level of one or more of the overexpressed genes, the underexpressed genes, the overexpressed proteins and/or the underexpressed proteins described herein. The terms “determining”, “measuring”, “evaluating”, “assessing” and “assaying” are used interchangeably herein and may include any form of measurement known in the art, such as those described hereinafter.
Determining, assessing, evaluating, assaying or measuring nucleic acids such as RNA, mRNA and cDNA may be performed by any technique known in the art. These may be techniques that include nucleic acid sequence amplification, nucleic acid hybridization, nucleotide sequencing, mass spectroscopy and combinations of any these.
Nucleic acid amplification techniques typically include repeated cycles of annealing one or more primers to a “template” nucleotide sequence under appropriate conditions and using a polymerase to synthesize a nucleotide sequence complementary to the target, thereby “amplifying” the target nucleotide sequence. Nucleic acid amplification techniques are well known to the skilled addressee, and include but are not limited to polymerase chain reaction (PCR); strand displacement amplification (SDA); rolling circle replication (RCR); nucleic acid sequence-based amplification (NASBA), Q-β replicase amplification; helicase-dependent amplification (HAD); loop-mediated isothermal amplification (LAMP); nicking enzyme amplification reaction (NEAR) and recombinase polymerase amplification (RPA), although without limitation thereto. As generally used herein, an “amplification product” refers to a nucleic acid product generated by a nucleic acid amplification technique.
PCR includes quantitative and semi-quantitative PCR, real-time PCR, allele-specific PCR, methylation-specific PCR, asymmetric PCR, nested PCR, multiplex PCR, touch-down PCR and other variations and modifications to “basic” PCR amplification.
Nucleic acid amplification techniques may be performed using DNA or RNA extracted, isolated or otherwise obtained from a cell or tissue source. In other embodiments, nucleic acid amplification may be performed directly on appropriately treated cell or tissue samples.
Nucleic acid hybridization typically includes hybridizing a nucleotide sequence (typically in the form of a probe) to a target nucleotide sequence under appropriate conditions, whereby the hybridized probe-target nucleotide sequence is subsequently detected. Non-limiting examples include Northern blotting, slot-blotting, in situ hybridization and fluorescence resonance energy transfer (FRET) detection, although without limitation thereto. Nucleic acid hybridization may be performed using DNA or RNA extracted, isolated, amplified or otherwise obtained from a cell or tissue source or directly on appropriately treated cell or tissue samples.
It will also be appreciated that a combination of nucleic acid amplification and nucleic acid hybridization may be utilized.
Determining, assessing, evaluating, assaying or measuring protein levels may be performed by any technique known in the art that is capable of detecting cell- or tissue-expressed proteins whether on the cell surface or intracellularly expressed, or proteins that are isolated, extracted or otherwise obtained from the cell of tissue source. These techniques include antibody-based detection that uses one or more antibodies which bind the protein, electrophoresis, isoelectric focussing, protein sequencing, chromatographic techniques and mass spectroscopy and combinations of these, although without limitation thereto. Antibody-based detection may include flow cytometry using fluorescently-labelled antibodies that bind the protein, ELISA, immunoblotting, immunoprecipitation, in situ hybridization, immunohistochemistry and immuncytochemistry, although without limitation thereto. Suitable techniques may be adapted for high throughput and/or rapid analysis such as using protein arrays such as a TissueMicroArray™ (TMA), MSD MultiArrays™ and multiwell ELISA, although without limitation thereto.
In certain embodiments, a gene expression level may be assessed indirectly by the measurement of a non-coding RNA, such as miRNA, that regulate gene expression. MicroRNAs (miRNAs or miRs) are post-transcriptional regulators that bind to complementary sequences in the 3′ untranslated regions (3′ UTRs) of target mRNA transcripts, usually resulting in gene silencing. miRNAs are short RNA molecules, on average only 22 nucleotides long. The human genome may encode over 1000 miRNAs, which may target about 60% of mammalian genes and are abundant in many human cell types. Each miRNA may alter the expression of hundreds of individual mRNAs. In particular, miRNAs may have multiple roles in negative regulation (e.g., transcript degradation and sequestering, translational suppression) and/or positive regulation (e.g., transcriptional and translational activation). Additionally, aberrant miRNA expression has been implicated in various types of cancer.
In this regard, an average expression level, or alternatively a sum of the expression levels, may be calculated for the plurality of overexpressed genes and for the plurality of underexpressed genes, to thereby produce or calculate a ratio.
Accordingly, determining cancer aggressiveness and/or a prognosis for a cancer patient in certain embodiments of the present invention further includes determining the ratio of the expression level (e.g. an average or sum of the expression level) of the plurality of overexpressed genes to the expression level (e.g. an average or sum of the expression level) of the plurality of underexpressed genes.
In another aspect of the invention relates to a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes associated with chromosomal instability and an expression level of a plurality of underexpressed genes associated with estrogen receptor signalling in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the plurality of overexpressed genes associated with chromosomal instability compared to the plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level expression level of the plurality of overexpressed genes associated with chromosomal instability compared to the plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In yet another aspect of the invention relates to a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes associated with chromosomal instability and an expression level of a plurality of underexpressed genes associated with estrogen receptor signalling in the mammal, wherein: a higher relative expression level of the plurality of overexpressed genes associated with chromosomal instability compared to the plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the plurality of overexpressed genes associated with chromosomal instability compared to the plurality of underexpressed genes associated with estrogen receptor signalling indicates or correlates with a more favourable cancer prognosis.
Non-limiting examples of genes in a chromosomal instability (CIN) metagene include ATP6V1C1, RAP2A, CALM1, COG8, HELLS, KDM5A, PGK1, PLCH1, CEP55, RFC4, TAF2, SF3B3, GP1, PIR, MCM10, MELK, FOXM1, KIF2C, NUP155, TPX2, TTK, CENPA, CENPN, EXO1, MAPRE1, ACOT7, NAE1, SHMT2, TCP1, TXNRD1, ADM, CHAF1A and SYNCRIP genes, although without limitation thereto; and an estrogen receptor signalling (ER) metagene may comprise BTG2, PIK3IP1, SEC14L2, FLNB, ACSF2, APOM, BIN3, GLTSCR2, ZMYND10, ABAT, BCAT2, SCUBE2, RUNX1, LRRC48, MYBPC1, BCL2, CHPT1, ITM2A, LRIG1, MAPT, PRKCB, RERE, ABHD14A, FLT3, TNN, STC2, BATF, CD1E, CFB, EVL, FBXW4, ABCB1, ACAA1, CHAD, PDCD4, RPL10, RPS28, RPS4X, RPS6, SORBS1, RPL22 and RPS4XP3 genes, although without limitation thereto. Table 4 provides further examples of genes that are components of a CIN metagene or that are components of an ER metagene.
An average expression level may be calculated for the CIN metagene and for the ER metagene, to thereby produce or calculate a ratio.
Alternatively, a sum of expression levels may be calculated for the CIN metagene and for the ER metagene, to thereby produce or calculate a ratio.
In certain embodiments, a higher or increased ratio of the average or sum of expression levels of a CIN metagene relative to an ER metagene is associated with, correlates with or is indicative of, higher or increased cancer aggressiveness.
Thus, some embodiments of the invention provide an “aggressiveness score” which is the ratio of CIN metagene expression level (e.g. average or sum of expression of CIN genes) to an ER metagene expression level (e.g average or sum of expression of ER genes).
Accordingly, embodiments of the aforementioned aspects of the invention include determining, assessing or measuring an expression level of a plurality of overexpressed genes associated with chromosomal instability and determining, assessing or measuring an expression level of a plurality of underexpressed genes associated with estrogen receptor signalling. In this regard, reference is made to Table 4 which provides a listing of 206 genes that include genes associated with chromosomal instability and genes associated with estrogen receptor signalling. Preferably, the chromosomal instability genes are of a CIN metagene, comprising genes such as ATP6V1C1, RAP2A, CALM1, COG8, HELLS, KDM5A, PGK1, PLCH1, CEP55, RFC4, TAF2, SF3B3, GP1, PIR, MCM10, MELK, FOXM1, KIF2C, NUP155, TPX2, 11K, CENPA, CENPN, EXO1, MAPRE1, ACOT7, NAE1, SHMT2, TCP1, TXNRD1, ADM, CHAF1A and SYNCRIP, although without limitation thereto. In one preferred embodiment, the chromosomal instability genes are selected from the group consisting of MELK, MCM10, CENPA, EXO1, TTK and KIF2C. Preferably, the estrogen receptor signalling genes are of an ER metagene comprising genes such as BTG2, PIK3IP1, SEC14L2, FLNB, ACSF2, APOM, BIN3, GLTSCR2, ZMYND10, ABAT, BCAT2, SCUBE2, RUNX1, LRRC48, MYBPC1, BCL2, CHPT1, ITM2A, LRIG1, MAPT, PRKCB, RERE, ABHD14A, FLT3, TNN, STC2, BATF, CD1E, CFB, EVL, FBXW4, ABCB1, ACAA1, CHAD, PDCD4, RPL10, RPS28, RPS4X, RPS6, SORBS1, RPL22 and RPS4XP3, although without limitation thereto. In one preferred embodiment, the estrogen receptor signalling genes are selected from the group consisting of MAPT and MYB.
In certain embodiments, the method of the aforementioned two aspects further includes the step of comparing an expression level of one or more other overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and an expression level of one or more other underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more other overexpressed genes compared to the one or more other underexpressed genes indicates or correlates with higher aggressiveness of the cancer and/or a less favourable cancer prognosis; and/or a lower relative expression level of the one or more other overexpressed genes compared to the one or more other underexpressed genes indicates or correlates with lower aggressiveness of the cancer and/or a more favourable cancer prognosis compared to a mammal having a higher expression level.
In one embodiment, the one or more other overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or more other underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In this regard, an average expression level, or alternatively a sum of the expression levels, may be calculated for the one or more other overexpressed genes and for the one or more other underexpressed genes, to thereby produce or calculate a ratio.
Accordingly, determining cancer aggressiveness and/or a prognosis for a cancer patient in certain embodiments of the present invention further includes determining the ratio of the expression level (e.g. an average or sum of the expression level) of the one or more other overexpressed genes to the expression level (e.g. an average or sum of the expression level) of the one or more other underexpressed genes.
Detection and/or measurement of expression of the one or more other overexpressed genes and the one or more other underexpressed genes may be performed by any of those methods or combinations thereof described herein (e.g measuring mRNA levels or an amplified cDNA copy thereof and/or by measuring a protein product thereof), albeit without limitation thereto.
Suitably, the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling is integrated with the comparison of the expression level of the one or more other overexpressed genes and the expression level of the one or more other underexpressed genes to derive a first integrated score. In particular embodiments, this may include deriving the first integrated score, at least in part, by addition, subtraction, multiplication, division and/or exponentiation.
By way of example, the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling may be added to, subtracted from, multiplied by, divided by and/or raised to the power of the comparison of the expression level of the one or more other overexpressed genes and the expression level of the one or more other underexpressed genes to derive the first integrated score. Alternatively, the comparison of the expression level of the one or more other overexpressed genes and the expression level of the one or more other underexpressed genes may be added to, subtracted from, multiplied by, divided by and/or raised to the power of the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling to derive the first integrated score.
In a particular preferred embodiment, the first integrated score is derived by exponentiation, wherein the comparison of the expression level of the one or more other overexpressed genes and the expression level of the one or more other underexpressed genes is raised to the power of the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling.
As would be appreciated by the skilled person, the other overexpressed and underexpressed genes described herein may not necessarily be associated with chromosomal instability and estrogen receptor signalling respectively.
In a further aspect, the invention provides a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or more overexpressed genes, wherein the one or more overexpressed genes are selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and an expression level of one or more underexpressed genes, wherein the one or more underexpressed genes are selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or more underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In yet another aspect, the invention provides a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or more overexpressed genes, wherein the one or more overexpressed genes are selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and an expression level of one or more underexpressed genes, wherein the one or more underexpressed genes are selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with a more favourable cancer prognosis compared to a mammal having a higher expression level.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or more underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In particular embodiments, the method of the aforementioned aspects further includes the step of comparing an expression level of one or more overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and an expression level of one or more underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with higher aggressiveness of the cancer and/or a less favourable cancer prognosis; and/or a lower relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with lower aggressiveness of the cancer and/or a more favourable cancer prognosis compared to a mammal having a higher expression level.
As would be appreciated by the skilled artisan, the expression level of one or more of the overexpressed proteins and/or one or more of the underexpressed proteins described herein may include one or more phosphorylated forms of said proteins (i.e., a phosphoprotein). In one embodiment, EIF4EBP1 is or comprises one or more phosphoproteins selected from the group consisting of pEIF4EBP1S65, pEIF4EBP1T37, pEIF4EBP1T46 and pEIF4EBP1T70. In one embodiment, EGFR is or comprises one or more phosphoproteins selected from the group consisting of pEGFRY1068 and pEGFRY1173. In one embodiment, HER3 is or comprises pHER3Y1289. In one embodiment, AKT1 is or comprises one or more phosphoproteins selected from the group consisting of pAKT1S473 and pAKT1T308. In one embodiment, NFKB1 is or comprises pNFKB1S536 In one embodiment, HER2 is or comprises pHER2Y1248. In one embodiment, ESR1 is or comprises pESR1S118. In one embodiment, PEA15 is or comprises pPEA15S116. In one embodiment, RPS6 is or comprises one or more phosphoproteins selected from the group consisting of pRPS6S235, pRPS6S236, pRPS6S240 and pRPS6S244.
An average or sum of the expression levels may be calculated for the overexpressed genes, the underexpressed genes, the overexpressed proteins and/or the underexpressed proteins, to thereby produce or calculate a ratio.
Thus, in certain embodiments of the present invention determining cancer aggressiveness and/or a prognosis for a cancer patient includes determining (i) the ratio of the expression level (e.g. an average or sum of the expression level) of the one or more overexpressed genes to the expression level (e.g. an average or sum of the expression level) of the one or more underexpressed genes; and/or (ii) the ratio of the expression level (e.g. an average or sum of the expression level) of the one or more overexpressed proteins to the expression level (e.g. an average or sum of the expression level) of the one or more underexpressed proteins.
Detection and/or measurement of expression of the overexpressed proteins and the underexpressed proteins may be performed by any of those methods or combinations thereof hereinbefore described, albeit without limitation thereto.
Suitably, the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins is to thereby derive an integrated score. In one particular embodiment, the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins is integrated with:
In particular embodiments, the second, third, fourth, fifth and/or sixth integrated scores are derived, at least in part, by addition, subtraction, multiplication, division and/or exponentiation. By way of example, the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins may be added to, subtracted from, multiplied by, divided by and/or raised to the power of (i) the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling; or (ii) the first integrated score. Alternatively, the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling or the first integrated score may be added to, subtracted from, multiplied by, divided by and/or raised to the power of the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins.
In a further aspect, the invention provides a method of determining the aggressiveness of a cancer in a mammal, said method including the step of comparing an expression level of one or more overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and an expression level of one or more underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with higher aggressiveness of the cancer; and/or a lower relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with lower aggressiveness of the cancer compared to a mammal having a higher expression level.
In a related aspect, the invention provides a method of determining a cancer prognosis for a mammal, said method including the step of comparing an expression level of one or more overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and an expression level of one or more underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with a less favourable cancer prognosis; and/or a lower relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with a more favourable cancer prognosis compared to a mammal having a higher expression level.
In particular embodiments of the two aforementioned aspects, one or more of the overexpressed proteins and/or one or more of the underexpressed proteins are or comprise a phosphoprotein hereinbefore described.
An average or sum of the expression levels may be calculated for the one or more overexpressed proteins and the one or more underexpressed proteins, to thereby produce or calculate a ratio as hereinbefore described.
This information with respect to the aggressiveness and/or prognosis of a patient's cancer may prove useful to a physician and/or clinician in determining the most effective course of treatment. A determination of the likelihood for a cancer relapse or of the likelihood of metastasis can assist the physician and/or clinician in determining whether a more conservative or a more radical approach to therapy should be taken. As such, a prognosis may provide for the selection and classification of patients who are predicted to benefit from a given therapeutic regimen.
Accordingly, another aspect of the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene, wherein an altered or modulated relative expression level of the overexpressed genes compared to the underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
As would be understood by the skilled person, the relative expression level of a gene or protein may be deemed to be “altered” or “modulated” when the expression level is higher/increased or lower/decreased when compared to a control or reference sample or expression level, such as a threshold level. In one embodiment, a relative expression level may be classified as high if it is greater than a mean and/or median relative expression level of a reference population and a relative expression level may be classified as low if it is less than the mean and/or median relative expression level of the reference population. In this regard, a reference population may be a group of subjects who have the same cancer type, subgroup, stage and/or grade as said mammal for which the relative expression level is determined.
Suitably, for the present aspect the Carbohydrate/Lipid Metabolism metagene, the Cell Signalling metagene, the Cellular Development metagene, the Cellular Growth metagene, the Chromosome Segregation metagene, the DNA Replication/Recombination metagene, the Immune System metagene, the Metabolic Disease metagene, the Nucleic Acid Metabolism metagene, the Post-Translational Modification metagene, the Protein Synthesis/Modification metagene and/or the Multiple Networks metagene comprise one or more genes listed in Table 21.
In a related aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes and an expression level of a plurality of underexpressed genes in one or more cancer cells, tissues or organs of the mammal, wherein the overexpressed genes and the underexpressed genes are from one or more metagenes selected from the group consisting of a Metabolism metagene, a Signalling metagene, a Development and Growth metagene, a Chromosome Segregation/Replication metagene, an Immune Response metagene and a Protein Synthesis/Modification metagene, wherein an altered or modulated relative expression level of the overexpressed genes compared to the underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment of the two aforementioned aspects, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from one of the metagenes. In an alternative embodiment, the plurality of overexpressed genes and/or the plurality of underexpressed genes are selected from a plurality of the metagenes.
Suitably, the Metabolism metagene, the Signalling metagene, the Development and Growth metagene, the Chromosome Segregation/Replication metagene, the Immune Response metagene and/or the Protein Synthesis/Modification metagene comprise one or more genes listed in Table 22.
In particular embodiments, the plurality of overexpressed genes and the plurality of underexpressed genes are from one or more of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene.
In a related aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of determining an expression level of one or more genes associated to with chromosomal instability (CIN) in one or more cancer cells of the mammal, wherein a higher expression level indicates or correlates with relatively increased responsiveness of the cancer to the anti-cancer treatment.
As will be described in more detail, overexpression of some CIN genes may be predictive of the responsiveness of a cancer to an anti-cancer treatment, particularly although not exclusively when overexpressed by non-mitotic cancer cells. In this context, by “non-mitotic” means that the cancer cell is not in the mitotic or “M phase” of the cell cycle. Preferably, the non-mitotic cancer cells are in interphase. Broadly, any overexpressed CIN gene set forth Table 4 may be predictive of the responsiveness of a cancer to an anti-cancer treatment. In particular embodiments, the CIN gene is selected from the group consisting of: TTK, CEP55, FOXM1 and SKIP2. In a particularly preferred embodiment, the CIN gene is selected from the group consisting of: TTK, CEP55, FOXM1 and SKIP2 and the cancer is breast cancer. In this regard, the inventors have shown that “bulk” measurements of extracted CIN gene mRNA or encoded protein do not provide a useful indication of whether overexpression of the CIN gene may be predictive of the responsiveness of a cancer to an anti-cancer treatment. More particularly, detection of CIN gene expression by individual cancer cells, particularly non-mitotic or interphase cancer cells, provides a more powerful indication of the responsiveness of a cancer to an anti-cancer treatment.
As previously described, detection and/or measurement of expression of the CIN gene may be performed by measuring RNA (e.g mRNA or an amplified cDNA copy thereof) or by measuring a protein product of a CIN gene. In a particularly preferred embodiment, a protein product of a CIN gene is detected or measured by immunohistochemistry. Typically, although not exclusively, a preferred immunohistochemistry method includes binding an antibody to the protein product of a CIN gene expressed by a cell or tissue and subsequent detection of the bound antibody. By way of example only, the antibody may be unlabelled, directly labelled with an enzyme such as horseradish peroxidase, alkaline phosphatase or glucose oxidase or directly labelled with biotin or digoxigenin. In embodiments where the antibody is unlabelled, a secondary antibody (labelled such as described above) may be used to detect the bound antibody. Biotinylated antibodies may be detected using avidin complexed with an enzyme such as horseradish peroxidase, alkaline phosphatase or glucose oxidase. Suitable enzyme substrates include diaminobanzidine (DAB), permanent red, 3-ethylbenzthiazoline sulfonic acid (ABTS), 5-bromo-4-chloro-3-indolyl phosphate (BCIP), nitro blue tetrazolium (NBT), 3,3′,5,5′-tetramethyl benzidine (TNB) and 4-chloro-1-naphthol (4-CN), although without limitation thereto.
In a further aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of a plurality of overexpressed genes associated with chromosomal instability and an expression level of a plurality of underexpressed genes associated with estrogen receptor signalling in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the overexpressed genes associated with chromosomal instability compared to the underexpressed genes associated with estrogen receptor signalling indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In certain embodiments, the genes associated with chromosomal instability are of a CIN metagene. Non-limiting examples include genes selected from the group consisting of: ATP6V1C1, RAP2A, CALM1, COG8, HELLS, KDM5A, PGK1, PLCH1, CEP55, RFC4, TAF2, SF3B3, GP1, PIR, MCM10, MELK, FOXM1, KIF2C, NUP155, TPX2, 11K, CENPA, CENPN, EXO1, MAPRE1, ACOT7, NAE1, SHMT2, TCP1, TXNRD1, ADM, CHAF1A and SYNCRIP. In one preferred embodiment, the chromosomal instability genes are selected from the group consisting of MELK, MCM10, CENPA, EXO1, TTK and KIF2C.
In certain embodiments, the genes associated with estrogen receptor signalling are of an ER metagene. Non-limiting examples include genes selected from the group consisting of: BTG2, PIK3IP1, SEC14L2, FLNB, ACSF2, APOM, BIN3, GLTSCR2, ZMYND10, ABAT, BCAT2, SCUBE2, RUNX1, LRRC48, MYBPC1, BCL2, CHPT1, ITM2A, LRIG1, MAPT, PRKCB, RERE, ABHD14A, FLT3, TNN, STC2, BATF, CD1E, CFB, EVL, FBXW4, ABCB1, ACAA1, CHAD, PDCD4, RPL10, RPS28, RPS4X, RPS6, SORBS1, RPL22 and RPS4XP3. In one preferred embodiment, the estrogen receptor signalling genes are selected from the group consisting of MAPT and MYB.
Suitably, the method of this aspect further includes the step of comparing an expression level of one or more other overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and an expression level of one or more other underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3 in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more other overexpressed genes compared to the one or more other underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment, the one or more other overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or more other underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In certain embodiments, the comparison of the expression level of the one or more other overexpressed genes and the expression level of the one or more other underexpressed genes is integrated with the comparison of the expression level of the plurality of overexpressed genes associated with chromosomal instability and the expression level of the plurality of underexpressed genes associated with estrogen receptor signalling to derive a first integrated score as described herein, which is indicative of, or correlates with, responsiveness of the cancer to the anti-cancer treatment.
In another related aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or more overexpressed genes selected from the group consisting of CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1, and an expression level of one or more underexpressed genes selected from the group consisting of BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3, in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of ABHD5, ADORA2B, BCAP31, CA9, CAMSAP1, CARHSP1, CD55, CETN3, EIF3K, EXOSC7, GNB2L1, GRHPR, GSK3B, HCFC1R1, KCNG1, MAP2K5, NDUFC1, PML, STAU1, TXN and ZNF593.
In one embodiment, the one or more underexpressed genes are selected from the group consisting of BTN2A2, ERC2, IGH, ME1, MTMR7, SMPDL3B and ZNRD1-AS1.
In particular embodiments, the method of the five aforementioned aspects further includes the step of comparing an expression level of one or more overexpressed proteins selected from the group consisting of DVL3, PM-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and an expression level of one or more underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or more cancer cells, tissues or organs of the mammal, wherein: a higher relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with higher aggressiveness of the cancer and/or a less favourable cancer prognosis; and/or a lower relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with lower aggressiveness of the cancer and/or a more favourable cancer prognosis compared to a mammal having a higher expression level.
In particular embodiments, one or more of the overexpressed proteins and/or one or more of the underexpressed proteins are or comprise a phosphoprotein hereinbefore described.
An average or sum of the expression levels may be calculated for the overexpressed genes, the underexpressed genes, the overexpressed proteins and/or the underexpressed proteins, to thereby produce or calculate a ratio, as hereinbefore described.
Detection and/or measurement of expression of the overexpressed proteins and the underexpressed proteins may be performed by any of those methods or combinations thereof hereinbefore described, albeit without limitation thereto.
Suitably, the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins is to thereby derive an integrated score. In one particular embodiment, the comparison of the expression level of the one or more overexpressed proteins and the expression level of the one or more underexpressed proteins is integrated with:
In particular embodiments, the second, third, fourth, fifth and/or sixth integrated scores are derived, at least in part, by addition, subtraction, multiplication, division and/or exponentiation, as hereinbefore described.
In a further related aspect, the invention provides a method of predicting the responsiveness of a cancer to an anti-cancer treatment in a mammal, said method including the step of comparing an expression level of one or more overexpressed proteins selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1, and an expression level of one or more underexpressed proteins selected from the group consisting of VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6, in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more overexpressed proteins compared to the one or more underexpressed proteins indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
In particular embodiments, one or more of the overexpressed proteins and/or one or more of the underexpressed proteins are or comprise a phosphoprotein hereinbefore described.
It will be appreciated from the foregoing that the invention provides methods that determine the aggressiveness of a cancer, facilitate providing a cancer prognosis for a patient and/or predict the responsiveness of a cancer to an anti-cancer treatment. Particular, broad embodiments of the invention include the step of treating the patient following determining the aggressiveness of the cancer, providing a cancer prognosis and/or predicting the responsiveness of the cancer to anti-cancer treatment. Accordingly, these embodiments relate to using information obtained about the aggressiveness of the cancer, the cancer prognosis and/or the predicted responsiveness of the cancer to anti-cancer treatment to thereby construct and implement an anti-cancer treatment regime for the patient. In a preferred embodiment, this is personalized to a particular patient so that the treatment regime is optimized for that particular patient.
Cancer treatments may include drug therapy, chemotherapy, antibody, nucleic acid and other biomolecular therapies, radiation therapy, surgery, nutritional therapy, relaxation or meditational therapy and other natural or holistic therapies, although without limitation thereto. In particular embodiments, the cancer therapy may target aneuploidy or aneuploid tumours and/or chromosomal instability.
Generally, drugs, biomolecules (e.g antibodies, inhibitory nucleic acids such as siRNA) or chemotherapeutic agents are referred to herein as “anti-cancer therapeutic agents”. In some embodiments relating to breast cancer, the anti-cancer treatment may include HER2-directed therapy such as trastuzumab and endocrine therapies such as tamoxifen and aromatase inhibitors. In other or alternative embodiments, the therapy may include administration of inhibitors of CIN genes or CIN gene products, such as one or more of those listed in Table 4. It will be appreciated that inhibition of the CIN gene product TTK using the specific inhibitor AZ3146 was effective against TNBC cell lines. Furthermore, siRNA-mediated knockdown of the CIN genes 11K, TPX2, NDC80 and PBK was effective against TNBC cell lines.
In certain embodiments, the cancer treatment may be directed at genes or gene products other than those listed in Tables 4, 10, 21 and/or 22. By way of example, the cancer treatment may target genes or gene products such as PLK171,72 or others73-76 to thereby target aneuploid tumours or tumour cells.
Suitably, when considering (i) the relative expression of one or more of the overexpressed genes of the 29 gene signature (i.e., CAMSAP1, CETN3, GRHPR, ZNF593, CA9, CFDP1, VPS28, ADORA2B, GSK3B, LAMA4, MAP2K5, HCFC1R1, KCNG1, BCAP31, ULBP2, CARHSP1, PML, CD36, CD55, GEMIN4, TXN, ABHD5, EIF3K, EIF4B, EXOSC7, GNB2L1, LAMA3, NDUFC1 and STAU1) when compared to one or more of the underexpressed genes of the 30 gene signature (i.e., BRD8, BTN2A2. KIR2DL4. ME1, PSEN2, CALR, CAMK4, ITM2C, NOP2, NSUN5, SF3B1, ZNRD1-AS1, ARNT2, ERC2, SLC11A1, BRD4, APOBEC3A, CD1A, CD1B, CD1C, CXCR4, HLA-B, IGH, KIR2DL3, SMPDL3B, MYB, RLN1, MTMR7, SORBS1 and SRPK3); (ii) the relative expression of one or more of the overexpressed proteins (i.e., DVL3, PAI-1, VEGFR2, INPP4B, EIF4EBP1, EGFR, Ku80, HER3, SMAD1, GATA3, ITGA2, AKT1, NFKB1, HER2, ASNS and COL6A1) when compared to one or more of the underexpressed proteins (i.e., VEGFR2, HER3, ASNS, MAPK9, ESR1, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6); and/or (iii) the first, second, third and/or fourth integrated score, the anticancer therapeutic agent is selected from the group consisting of a chemotherapy, an endocrine therapy, immunotherapy and a molecularly targeted therapy. In certain embodiments, the anticancer treatment comprises an ALK inhibitor (e.g., TAE684), an Aurora kinase inhibitor (e.g., Alisertib, AMG-900, BI-847325, GSK-1070916A, ilorasertib, MK-8745, danusertib), a BCR-ABL inhibitor (e.g., Nilotinib, Dasatinib, Ponatinib), a HSP90 inhibitor (e.g., Tanespimycin (17-AAG), PF0429113, AUY922, Luminespib, ganetespib, Debio-0932), an EGFR inhibitor (e.g., Afatinib, Erlotinib, Lapatinib, cetuximab), a PARP inhibitor (e.g., ABT-888, AZD-2281), retinoic acid (e.g., all-trans retinoic acid or ATRA), a Bcl2 inhibitor (e.g., ABT-263), a gluconeogenesis inhibitor (e.g., metformin), a p38 MAPK inhibitor (e.g., BIRB0796, LY2228820), a MEK1/2 inhibitor (e.g., trametinib, cobimetinib, binimetinib, selumetinib, pimasertib, refametinib, TAK-733), a mTOR inhibitor (e.g., BEZ235, JW-7-25-1), a PI3K inhibitor (e.g., Idelalisib, buparlisib/apelisib, copanlisib, GSK-2636771, pictilisib, AMG-319, AZD-8186), an IGF1R inhibitor (e.g., BMS-754807, dalotuzumab, ganitumab, linsitinib), a PLCγ inhibitor (e.g., U73122), a JNK inhibitor (e.g., SP600125), a PAK1 inhibitor (e.g., IPA3), a SYK inhibitor (e.g., BAY613606), a HDAC inhibitor (e.g., Vorinostat), an FGFR inhibitor (e.g., Dovitinib), a XIAP inhibitor (e.g., Embelin), a PLK1 inhibitor (e.g., Volasertib, P-937), an ERK5 inhibitor (e.g., XMD8-92), a MPS1/TTK inhibitor (e.g., BAY-1161909) and any combination thereof.
By way of example, patients with a high relative expression level of one or more overexpressed genes, such as those of the 21 gene signature, when compared to one or more underexpressed genes, such as those of the 7 gene signature, a high relative expression level of one or more overexpressed proteins when compared to one or more underexpressed proteins and/or a high integrated score described herein are more likely to respond favourably, such as a pathological complete response, when treated with chemotherapy. In this regard, non-limiting examples of chemotherapy include a pyrimidine analogue (e.g., 5-fluorouracil, capecitabine), a taxane (e.g., paclitaxel), an anthracycline (e.g., doxorubicin, epirubicin), an anti-folate drug (e.g., the dihydrofolate reductase inhibitor methotrexate), an alkylating agent (e.g., cyclophosphamide) or any combination thereof. It would be appreciated that the chemotherapy may be administered as adjuvant, neoadjuvant and/or as standard therapy, alone or in combination with other anticancer therapeutics.
Additionally, in certain embodiments, patients with a high relative expression level of one or more overexpressed genes, such as those of the 29 gene signature, when compared to one or more underexpressed genes, such as those of the 30 gene signature, a high relative expression level of one or more overexpressed proteins when compared to one or more underexpressed proteins and/or a high integrated score described herein may be more likely to respond favourably to (i.e., be more sensitive to) inhibition of HSP90, EGFR, IGF1R, mTOR, PI3K, p38 MAPK, PLCγ, JNK, PAK1, ERK5, XIAP, PLK1 and/or MEK1/2 and may be less likely to respond favourably to (i.e., be less sensitive to) anticancer treatment with an ALK inhibitor, a BCR-ABL inhibitor, a PARP inhibitor, retinoic acid, a Bcl2 inhibitor, a gluconeogenesis inhibitor, a p38 MAPK inhibitor, an FGFR inhibitor, a SYK inhibitor, a HDAC inhibitor and/or an IGF1R inhibitor.
It will also be understood that the gene and protein signatures described herein may be used to identify those poorer prognosis patients, such as those with larger and/or higher grade tumours, who may benefit from one or more additional anticancer therapeutic agents to the typical or standard anti-cancer treatment regime for that particular patient group. By way of example, ER+ breast cancer patients with or without lymph node involvement with a high integrated score, and hence a relatively poor prognosis, are more likely to respond favourably to or benefit from chemotherapy and/or endocrine therapy. This may include an improved survival and/or reduced likelihood of tumour recurrence and/or metastasis for these patients.
In certain embodiments, for patients with a high relative expression level of the overexpressed genes of the 21 gene signature when compared to the underexpressed genes of the 7 gene signature and/or a high integrated score, the cancer treatment may be directed at those genes or gene products listed in Tables 13, 15, 16 and 17.
Additionally, for patients with a high relative expression level of the overexpressed proteins when compared to the underexpressed proteins and/or a high integrated score the cancer treatment may be directed at one or more of those proteins listed in Table 19.
It would be appreciated that those methods described herein for predicting the responsiveness of a cancer to an anti-cancer treatment, such as an immunotherapeutic agent, may further include the step of administering to the mammal a therapeutically effective amount of the anticancer treatment. In a preferred embodiment, the anticancer treatment is administered when the altered or modulated relative expression level indicates or correlates with relatively increased responsiveness of the cancer to the anti-cancer treatment.
Methods of treating cancer may be prophylactic, preventative or therapeutic and suitable for treatment of cancer in mammals, particularly humans. As used herein, “treating”, “treat” or “treatment” refers to a therapeutic intervention, course of action or protocol that at least ameliorates a symptom of cancer after the cancer and/or its symptoms have at least started to develop. As used herein, “preventing”, “prevent” or “prevention” refers to therapeutic intervention, course of action or protocol initiated prior to the onset of cancer and/or a symptom of cancer so as to prevent, inhibit or delay or development or progression of the cancer or the symptom.
The term “therapeutically effective amount” describes a quantity of a specified agent sufficient to achieve a desired effect in a subject being treated with that agent. For example, this can be the amount of a composition comprising one or more agents that binds one or more of the overexpressed and/or underexpressed genes or gene products thereof described herein, necessary to reduce, alleviate and/or prevent a cancer or cancer associated disease, disorder or condition. In some embodiments, a “therapeutically effective amount” is sufficient to reduce or eliminate a symptom of a cancer. In other embodiments, a “therapeutically effective amount” is an amount sufficient to achieve a desired biological effect, for example an amount that is effective to decrease or prevent cancer growth and/or metastasis.
Ideally, a therapeutically effective amount of an agent is an amount sufficient to induce the desired result without causing a substantial cytotoxic effect in the subject. The effective amount of an agent useful for reducing, alleviating and/or preventing a cancer will be dependent on the subject being treated, the type and severity of any associated disease, disorder and/or condition (e.g., the number and location of any associated metastases), and the manner of administration of the therapeutic composition.
Suitably, the anti-cancer therapeutic agent is administered to a mammal as a pharmaceutical composition comprising a pharmaceutically-acceptable carrier, diluent or excipient.
By “pharmaceutically-acceptable carrier, diluent or excipient” is meant a solid or liquid filler, diluent or encapsulating substance that may be safely used in systemic administration. Depending upon the particular route of administration, a variety of carriers, well known in the art may be used. These carriers may be selected from a group including sugars, starches, cellulose and its derivatives, malt, gelatine, talc, calcium sulfate, liposomes and other lipid-based carriers, vegetable oils, synthetic oils, polyols, alginic acid, phosphate buffered solutions, emulsifiers, isotonic saline and salts such as mineral acid salts including hydrochlorides, bromides and sulfates, organic acids such as acetates, propionates and malonates and pyrogen-free water.
A useful reference describing pharmaceutically acceptable carriers, diluents and excipients is Remington's Pharmaceutical Sciences (Mack Publishing Co. N.J. USA, 1991), which is incorporated herein by reference.
Any safe route of administration may be employed for providing a patient with the composition of the invention. For example, oral, rectal, parenteral, sublingual, buccal, intravenous, intra-articular, intra-muscular, intra-dermal, subcutaneous, inhalational, intraocular, intraperitoneal, intracerebroventricular, transdermal and the like may be employed. Intra-muscular and subcutaneous injection is appropriate, for example, for administration of immunotherapeutic compositions, proteinaceous vaccines and nucleic acid vaccines.
Dosage forms include tablets, dispersions, suspensions, injections, solutions, syrups, troches, capsules, suppositories, aerosols, transdermal patches and the like. These dosage forms may also include injecting or implanting controlled releasing devices designed specifically for this purpose or other forms of implants modified to act additionally in this fashion. Controlled release of the therapeutic agent may be effected by coating the same, for example, with hydrophobic polymers including acrylic resins, waxes, higher aliphatic alcohols, polylactic and polyglycolic acids and certain cellulose derivatives such as hydroxypropylmethyl cellulose. In addition, the controlled release may be effected by using other polymer matrices, liposomes and/or microspheres.
Compositions of the present invention suitable for oral or parenteral administration may be presented as discrete units such as capsules, sachets or tablets each containing a pre-determined amount of one or more therapeutic agents of the invention, as a powder or granules or as a solution or a suspension in an aqueous liquid, a non-aqueous liquid, an oil-in-water emulsion or a water-in-oil liquid emulsion. Such compositions may be prepared by any of the methods of pharmacy but all methods include the step of bringing into association one or more agents as described above with the carrier which constitutes one or more necessary ingredients. In general, the compositions are prepared by uniformly and intimately admixing the agents of the invention with liquid carriers or finely divided solid carriers or both, and then, if necessary, shaping the product into the desired presentation.
The above compositions may be administered in a manner compatible with the dosage formulation, and in such amount as is pharmaceutically-effective. The dose administered to a patient, in the context of the present invention, should be sufficient to effect a beneficial response in a patient over an appropriate period of time. The quantity of agent(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof, factors that will depend on the judgement of the practitioner.
In particular embodiments of the hereinbefore described methods, the cancer is breast cancer and the one or more overexpressed proteins are selected from the group consisting of DVL3, VEGFR2, INPP4B, EIF4EBP1, EGFR, HER3, SMAD1, NFKB1 and HER2 and the one or more underexpressed proteins are selected from the group consisting of ASNS, MAPK9, YWHAE, RAD50, PGR, COL6A1, PEA15 and RPS6.
In particular embodiments of the hereinbefore described methods, the cancer is lung cancer, such as lung adenocarcinoma, wherein:
(i) the one or more overexpressed genes are selected from the group consisting of GNB2L1, TXN, KCNG1, BCAP31, GSK3B, FOXM1, ZNF593, EXO1, KIF2C, TTK, MELK, CENPA, TPX2, CA9, GRHPR, HCFC1R1,CEP55, MCM10, CENPN and CARHSP1, and the one or more underexpressed genes are selected from the group consisting of BTN2A2, MTMR7, ZNRD1-AS1, MAPT and BTG2; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, PAI-1, Ku80, GATA3, ITGA2 and AKT1, and the one or more underexpressed proteins are selected from the group consisting of ESR1.
In particular embodiments of the hereinbefore described methods, the cancer is kidney cancer, such as renal clear cell carcinoma, wherein:
(i) the one or more overexpressed genes are selected from the group consisting of EIF3K, ADORA2B, KCNG1, BCAP31, EXOSC7, FOXM1, CD55, ZNF593, KIF2C, TTK, MELK, CENPA, TPX2, CEP55, PML, CENPN and CARHSP1, and the one or more underexpressed genes are selected from the group consisting of BCL2 and MAPT; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, PAI-1 and EIF4EBP1, and the one or more underexpressed proteins are selected from the group consisting of HER3, MAPK9, ESR1 and RAD50.
In particular embodiments of the hereinbefore described methods, the cancer is melanoma, such as skin cutaneous melanoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of EIF3K, ADORA2B, GSK3B, EXOSC7, FOXM1, EXO1, KIF2C, CENPA, TPX2, CAMSAP1, MCM10 and ABHD5 and the one or more underexpressed genes are selected from the group consisting of BCAP31, BTN2A2, SMPDL3B, MTMR7, ME1 and BTG2; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of PAI-1, EIF4EBP1, EGFR, HER3 and Ku80 and the one or more underexpressed proteins are selected from the group consisting of ASNS, MAPK9 and ESR1.
In particular embodiments of the hereinbefore described methods, the cancer is endometrial cancer, such as uterine corpus endometrioid carcinoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of GNB2L1, EIF3K, KCNG1, BCAP31, GSK3B, EXOSC7, FOXM1, ZNF593, EXO1, KIF2C, MAP2K5, TTK, MELK, GRHPR, and PML, and the one or more underexpressed genes is MYB; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, INPP4B, EIF4EBP1 and ASNS and the one or more underexpressed proteins are selected from the group consisting of MAPK9, ESR1 and YWHAE.
In particular embodiments of the hereinbefore described methods, the cancer is ovarian adenocarcinoma and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of GNB2L1, EIF3K, TXN, ADORA2B, KCNG1, GSK3B, STAU1, MAP2K5, and HCFC1R1, and the one or more underexpressed genes are selected from the group consisting of BTN2A2, and ZNRD1-AS1; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of PAI-1 and VEGFR2 and the one or more underexpressed proteins are selected from the group consisting of ASNS, MAPK9, ESR1, YWHAE and PGR.
In particular embodiments of the hereinbefore described methods, the cancer is head and neck cancer, such as head and neck squamous cell carcinoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of GNB2L1, TXN, ADORA2B, KCNG1, CD55, ZNF593, NDUFC1, and HCFC1R1, and the one or more underexpressed genes are selected from the group consisting of BTN2A2, and MTMR7; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of PAI-1, INPP4B, EGFR, HER3, SMAD1, GATA3, ITGA2 and COL6A1 and the one or more underexpressed proteins are selected from the group consisting of VEGFR2 and ASNS.
In particular embodiments of the hereinbefore described methods, the cancer is colorectal cancer, such as colorectal adenocarcinoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of EIF3K, TXN, CD55, NDUFC1, HCFC1R1, and PML, and the one or more underexpressed genes are selected from the group consisting of BTN2A2, SMPDL3B, and ME1; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, PAI-1, INPP4B, EIF4EBP1, EGFR and HER3 and the one or more underexpressed proteins are selected from the group consisting of ASNS, MAPK9, YWHAE, RAD50 and PEA15.
In particular embodiments of the hereinbefore described methods, the cancer is glioma, such as lower grade glioma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of TXN, BCAP31, STAU1, PML, CARHSP1, and BTN2A2; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, PAI-1, VEGFR2, Ku80, SMAD1 and NFKB1 and the one or more underexpressed proteins are selected from the group consisting of ESR1, YWHAE and PGR.
In particular embodiments of the hereinbefore described methods, the cancer is bladder cancer, such as urothelial carcinoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of ADORA2B, KCNG1, STAU1, MAP2K5, and CAMSAP1, and the one or more underexpressed genes are selected from the group consisting of GNB2L1, EIF3K, TXN, BCAP31, EXOSC7, CD55, NDUFC1, GRHPR, CETN3, BTN2A2, SMPDL3B, and ERC2; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, VEGFR2, Ku80, SMAD1 and AKT1 and the one or more underexpressed proteins is ASNS.
In particular embodiments of the hereinbefore described methods, the cancer is lung cancer, such as lung squamous cell carcinoma, and wherein:
(i) the one or more overexpressed genes are selected from the group consisting of GNB2L1, ZNF593, and SMPDL3B, and the one or more underexpressed genes are selected from the group consisting of GSK3B, MAP2K5, NDUFC1, CAMSAP1, ABHD5, and ME1; and/or
(ii) the one or more overexpressed proteins are selected from the group consisting of DVL3, PAI-1, VEGFR2, INPP4B, EGFR and GATA3 and the one or more underexpressed proteins is ASNS.
In particular embodiments of the hereinbefore described methods, the cancer is adrenocortical carcinoma, and wherein:
the one or more overexpressed genes are selected from the group consisting of GNB2L1, EIF3K, TXN, ADORA2B, KCNG1, BCAP31, FOXM1, ZNF593, EXO1, KIF2C, MAP2K5, TTK, MELK, CENPA, TPX2, GRHPR, CEP55, MCM10, and CENPN, and the one or more underexpressed genes are selected from the group consisting of MTMR7, BCL2, MAPT, MYB, and STC2.
In particular embodiments of the hereinbefore described methods, the cancer is kidney renal papillary cell carcinoma and wherein:
the one or more overexpressed genes are selected from the group consisting of GNB2L1, ADORA2B, KCNG1, GSK3B, FOXM1, CD55, EXO1, KIF2C, STAU1, TTK, MELK, CENPA, TPX2, CA9, CEP55, and MCM10, and the one or more underexpressed genes are selected from the group consisting of SMPDL3B, and BCL2.
In particular embodiments of the hereinbefore described methods, the cancer is pancreatic ductal adenocarcinoma and wherein:
the one or more overexpressed genes are selected from the group consisting of EIF3K, ADORA2B, GSK3B, EXOSC7, FOXM1, CD55, EXO1, STAU1, CAMSAP1, and CETN3 and the one or more underexpressed genes are selected from the group consisting of BTN2A2, SMPDL3B, MTMR7, ME1, BCL2, and ERC2.
In particular embodiments of the hereinbefore described methods, the cancer is liver hepatocellular carcinoma and wherein:
the one or more overexpressed genes are selected from the group consisting of GNB2L1, TXN, EXOSC7, and CA9, and the one or more underexpressed genes is MTMR7.
In particular embodiments of the hereinbefore described methods, the cancer is cervical squamous cell carcinoma and/or endocervical adenocarcinoma and wherein:
the one or more overexpressed genes are selected from the group consisting of STAU1, CA9, and ME1 and the one or more underexpressed genes are selected from the group consisting of EIF3K, TXN, BCAP31, EXOSC7, and ZNRD1-AS1.
Furthermore, in certain embodiments, patients with a high relative expression level of one or more overexpressed genes, such as those of the 29 gene signature, when compared to one or more underexpressed genes, such as those of the 30 gene signature, a high relative expression level of one or more overexpressed proteins when compared to one or more underexpressed proteins and/or a high integrated score as described herein may be more likely to respond favourably to immunotherapy.
Accordingly, one aspect provides a method of predicting the responsiveness of a cancer to an immunotherapeutic agent in a mammal, said method including the step of comparing an expression level of one or more overexpressed genes selected from the group consisting of ADORA2B, CD36, CETN3, KCNG1, LAMA3, MAP2K5, NAE1, PGK1, STAU1, CFDP1, SF3B3 and TXN, and an expression level of one or more underexpressed genes selected from the group consisting of APOBEC3A, BCL2, BTN2A2, CAMSAP1, CAMK4, CARHSP1, FBXW4, GSK3B, HCFC1R1, MYB, PSEN2 and ZNF593, in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the immunotherapeutic agent.
In one embodiment the one or more overexpressed genes are selected from the group consisting of ADORA2B, CETN3, KCNG1, MAP2K5, STAU1 and TXN, and/or an expression level of one or more underexpressed genes are selected from the group consisting of BTN2A2, CAMSAP1, CARHSP1, GSK3B, HCFC1R1, and ZNF593.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of ADORA2B, CD36, KCNG1, LAMA3, MAP2K5, NAE1, PGK1, STAU1, CFDP1, and SF3B3 and/or an expression level of one or more underexpressed genes are selected from the group consisting of APOBEC3A, BCL2, BTN2A2, CAMK4, FBXW4, PSEN2 and, MYB.
It would be understood for particular embodiments of the present aspect that one or more other overexpressed genes and/or one or more other underexpressed genes from one or more of a Carbohydrate/Lipid Metabolism metagene, a Cell Signalling metagene, a Cellular Development metagene, a Cellular Growth metagene, a Chromosome Segregation metagene, a DNA Replication/Recombination metagene, an Immune System metagene, a Metabolic Disease metagene, a Nucleic Acid Metabolism metagene, a Post-Translational Modification metagene, a Protein Synthesis/Modification metagene and a Multiple Networks metagene. such as those listed in Table 21, may be included in the step of comparing an expression level of one or more overexpressed genes and an expression level of one or more underexpressed genes.
Insofar as they relate to cancer, immunotherapy or immunotherapeutic agents use or modify the immune mechanisms of a subject so as to promote or facilitate treatment of a cancer. In this regard, immunotherapy or immunotherapeutic agents used to treat cancer include cell-based therapies, antibody therapies (e.g., anti-PD1 or anti-PDL1 antibodies) and cytokine therapies. These therapies all exploit the phenomenon that cancer cells often have subtly different molecules termed cancer antigens on their surface that can be detected by the immune system of the cancer subject. Accordingly, immunotherapy is used to provoke the immune system of a cancer patient into attacking the cancer's cells by using these cancer antigens as targets.
Non-limiting examples of immunotherapy or immunotherapeutic agents include adalimumab, alemtuzumab, basiliximab, belimumab, bevacizumab, BMS-936559, brentuximab, certolizumab, cituximab, daclizumab, eculizumab, ibritumomab, infliximab, ipilimumab, lambrolkizumab, mepolizumab, MPDL3280A muromonab, natalizumab, nivolumab, ofatumumab, omalizumab, pembrolizumab, pexelizumab, pidilizumab, rituximab, tocilizumab, tositumomab, trastuzumab, ustekinumab, abatacept, alefacept and denileukin diftitox. In particular preferred embodiments, the immunotherapeutic agent is an immune checkpoint inhibitor, such as an anti-PD1 antibody (e.g., pidilizumab, nivolumab, lambrolkizumab, pembrolizumab), an anti-PDL1 antibody (e.g., BMS-936559, MPDL3280A) and/or an anti-CTLA4 antibody (e.g., ipilimumab).
As would be appreciated by the skilled artisan, immune checkpoints refer to a variety of inhibitory pathways of the immune system that are crucial for maintaining self-tolerance and for modulating the duration and/or amplitude of an immune response in a subject. Cancers can use particular immune checkpoint pathways as a major mechanism of immune resistance, particularly against T cells that are specific for tumour antigens. Accordingly, immune checkpoint inhibitors include any agent that blocks or inhibits the inhibitory pathways of the immune system. Such inhibitors may include small molecule inhibitors or may include antibodies, or antigen binding fragments thereof, that bind to and block or inhibit immune checkpoint receptors or antibodies that bind to and block or inhibit immune checkpoint receptor ligands. By way of example, immune checkpoint receptors or receptor ligands that may be targeted for blocking or inhibition include, but are not limited to, CTLA-4, 4-1BB (CD137), 4-1BBL (CD137L), PDL1, PDL2, PD1, B7-H3, B7-H4, BTLA, HVEM, TIM3, GALS, LAG3, TIM3, B7H3, B7H4, VISTA, KIR, 2B4, CD160 and CGEN-15049. Illustrative immune checkpoint inhibitors include tremelimumab (CTLA-4 blocking antibody), anti-OX40, PD-L1 monoclonal Antibody (Anti-B7-H1; MEDI4736), MK-3475 (PD-1 blocker), nivolumab (anti-PD1 antibody), pidilizamab (CT-011; anti-PD1 antibody), BY55 monoclonal antibody, AMP224 (anti-PDL1 antibody), BMS-936559 (anti-PDL1 antibody), MPLDL3280A (anti-PDL1 antibody), MSB0010718C (anti-PDL1 antibody) and yervoy/ipilimumab (anti-CTLA-4 checkpoint inhibitor), albeit without limitation thereto.
In one embodiment, the method of predicting the responsiveness of a cancer to an immunotherapeutic agent, may further include the step of administering to the mammal a therapeutically effective amount of the immunotherapeutic agent.
In a related aspect is provided a method of predicting the responsiveness of a cancer to an EGFR inhibitor in a mammal, said method including the step of comparing an expression level of one or more overexpressed genes selected from the group consisting of NAE1, GSK3B, TAF2, MAPRE1, BRD4, STAU1, TAF2, PDCD4, KCNG1, ZNRD1-AS1, EIF4B, HELLS, RPL22, ABAT, BTN2A2, CD1B, ITM2A, BCL2, CXCR4, and ARNT2 and an expression level of one or more underexpressed genes selected from the group consisting of CD1C, CD1E, CD1B, KDM5A, BATF, EVL, PRKCB, HCFC1R1, CARHSP1, CHAD, KIR2DL4, ABHD5, ABHD14A, ACAA1, SRPK3, CFB, ARNT2, NDUFC1, BCL2, EVL, ULBP2, BIN3, SF3B3, CETN3, SYNCRIP, TAF2, CENPN, ATP6V1C1, CD55 and ADORA2B in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the EGFR inhibitor.
It would be appreciated that the EGFR inhibitor may be any known in the art, including monoclonal antibody and small molecule inhibitors thereof, such as those hereinbefore described. In particular embodiments, the EGFR inhibitor is or comprises erlotinib and/or cetuximab.
In certain embodiments, the cancer is or comprises lung cancer, colorectal cancer or breast cancer.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of NAE1, GSK3B, and TAF2 and/or the one or more underexpressed genes are selected from the group consisting of CD1C, CD1E, CD1B, KDM5A, BATF, EVL, PRKCB, HCFC1R1, CARHSP1, CHAD, KIR2DL4, ABHD5, ABHD14A, ACAA1, SRPK3, and CFB.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of MAPRE1, BRD4, STAU1, TAF2, GSK3B, PDCD4, KCNG1, ZNRD1-AS1, EIF4B and HELLS and/or the one or more underexpressed genes are selected from the group consisting of ARNT2, NDUFC1, BCL2, ABHD14A, EVL, ULBP2, and BINS.
In one embodiment, the one or more overexpressed genes are selected from the group consisting of RPL22, ABAT, BTN2A2, CD1B, ITM2A, BCL2, CXCR4, and ARNT2 and/or the one or more underexpressed genes are selected from the group consisting of SF3B3, CETN3, SYNCRIP, TAF2, CENPN, ATP6V1C1, CD55 and ADORA2B.
In a related aspect is provided a method of predicting the responsiveness of a cancer to a multikinase inhibitor in a mammal, said method including the step of comparing an expression level of one or more overexpressed genes selected from the group consisting of SCUBE, CHPT1, CDC1, BTG2, ADORA2B and BCL2, and an expression level of one or more underexpressed genes selected from the group consisting of NOP2, CALR, MAPRE1, KCNG1, PGK1, SRPK3, RERE, ADM, LAMA3, KIR2DL4, ULBP2, LAMA4, CA9, and BCAP31, in one or more cancer cells, tissues or organs of the mammal, wherein an altered or modulated relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with relatively increased or decreased responsiveness of the cancer to the EGFR inhibitor.
Multikinase inhibitors typically work by inhibiting multiple intracellular and/or cell surface kinases, some of which may be implicated in tumor growth and metastatic progression of a cancer, thus decreasing tumor growth and replication. It would be appreciated that the multikinase inhibitor may be any known in the art, including small molecule inhibitors, such as those hereinbefore described. Non-limiting examples of multikinase inhibitors include sorafenib, trametinib, dabrafenib, vemurafenib, crizotinib, sunitinib, axitinib, ponatinib, ruxolitinib, vandetanib, cabozantinib, afatinib, ibrutinib and regorafenib. In a particular embodiment, the multikinase inhibitor is or comprises sorafenib.
In one embodiment, the cancer is or comprises lung cancer.
Suitably, with regard to predicting the responsiveness of a cancer to an immunotherapeutic agent, an EGFR inhibitor or a multikinase inhibitor, a higher relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with a relatively increased responsiveness of the cancer to the agent or inhibitor; and/or a lower relative expression level of the one or more overexpressed genes compared to the one or more underexpressed genes indicates or correlates with a relatively decreased responsiveness of the cancer to the agent or inhibitor.
In a further aspect, the invention provides a method for identifying an agent for use in the treatment of cancer including the steps of:
(i) contacting a protein product of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and/or KCNG1 with a test agent; and
(ii) determining whether the test agent, at least partly, reduces, eliminates, suppresses or inhibits the expression and/or an activity of the protein product.
Suitably, the cancer is of a type hereinbefore described, albeit without limitation thereto. Preferably, the cancer has an overexpressed gene selected from the group consisting of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and KCNG1 and any combination thereof, Suitably, the agent possesses or displays little or no significant off-target and/or nonspecific effects.
Preferably, the agent is an antibody or a small organic molecule.
In embodiments relating to antibody inhibitors, the antibody may be polyclonal or monoclonal, native or recombinant. Well-known protocols applicable to antibody production, purification and use may be found, for example, in Chapter 2 of Coligan et al., CURRENT PROTOCOLS IN IMMUNOLOGY (John Wiley & Sons NY, 1991-1994) and Harlow, E. & Lane, D. Antibodies: A Laboratory Manual, Cold Spring Harbor, Cold Spring Harbor Laboratory, 1988, which are both herein incorporated by reference.
Generally, antibodies of the invention bind to or conjugate with an isolated protein, fragment, variant, or derivative of the protein product of one or more of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and KCNG1. For example, the antibodies may be polyclonal antibodies. Such antibodies may be prepared for example by injecting an isolated protein, fragment, variant or derivative of the protein product into a production species, which may include mice or rabbits, to obtain polyclonal antisera. Methods of producing polyclonal antibodies are well known to those skilled in the art. Exemplary protocols which may be used are described for example in Coligan et al., CURRENT PROTOCOLS IN IMMUNOLOGY, supra, and in Harlow & Lane, 1988, supra.
Monoclonal antibodies may be produced using the standard method as for example, described in an article by Köhler & Milstein, 1975, Nature 256, 495, which is herein incorporated by reference, or by more recent modifications thereof as for example, described in Coligan et al., CURRENT PROTOCOLS IN IMMUNOLOGY, supra by immortalizing spleen or other antibody producing cells derived from a production species which has been inoculated with one or more of the isolated protein products and/or fragments, variants and/or derivatives thereof.
Typically, the inhibitory activity of candidate inhibitor antibodies may be assessed by in vitro and/or in vivo assays that detect or measure the expression levels and/or activity of the protein products of one or more of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and KCNG1 in the presence of the antibody.
In embodiments relating to small organic molecule inhibitors, this may involve screening of large compound libraries, numbering hundreds of thousands to millions of candidate inhibitors (chemical compounds including synthetic, small organic molecules or natural products, for example) which may be screened or tested for biological activity at any one of hundreds of molecular targets in order to find potential new drugs, or lead compounds. Screening methods may include, but are not limited to, computer-based (“in silico”) screening and high throughput screening based on in vitro assays.
Typically, the active compounds, or “hits”, from this initial screening process are then tested sequentially through a series of other in vitro and/or in vivo tests to further characterize the active compounds. A progressively smaller number of the “successful” compounds at each stage are selected for subsequent testing, eventually leading to one or more drug candidates being selected to proceed to being tested in human clinical trials.
At the clinical level, screening a test agent may include obtaining samples from test subjects before and after the subjects have been exposed to a test compound. The levels in the samples of the protein product of the overexpressed genes may then be measured and analysed to determine whether the levels and/or activity of the protein products change after exposure to a test agent. By way of example, protein product levels in the samples may be determined by mass spectrometry, western blot, ELISA and/or by any other appropriate means known to one of skill in the art. Additionally, the activity of the protein products, such as their enzymatic activity, may be determined by any method known in the art. This may include, for example, enzymatic assays, such as spectrophotometric, fluorometric, calorimetric, chemiluminescent, light scattering, microscale thermophoresis, radiometric and chromatographic assays.
It would be appreciated that subjects who have been treated with test agents may be routinely examined for any physiological effects which may result from the treatment. In particular, the test agents will be evaluated for their ability to decrease cancer likelihood or occurrence in a subject. Alternatively, if the test agents are administered to subjects who have previously been diagnosed with cancer, they will be screened for their ability to slow or stop the progression of the cancer as well as induce disease remission.
In a particular embodiment, the invention may provide a “companion diagnostic” whereby the one or more genes that are detected as having elevated expression are the same genes that are targeted by the anti-cancer treatment.
In a related aspect, the invention provides an agent for use in the treatment of cancer identified by the method hereinbefore described.
Suitably, the cancer is of a type hereinbefore described, albeit without limitation thereto. Preferably, the cancer has an overexpressed gene selected from the group consisting of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1, KCNG1 and any combination thereof.
In another related aspect, the invention provides a method of treating a cancer in a mammal, including the step of administering to the mammal a therapeutically effective amount of an agent hereinbefore described.
In this regard, test agents that are identified of being capable of reducing, eliminating, suppressing or inhibiting the expression level and/or activity of a protein product of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1 and/or KCNG1 may then be administered to patients who are suffering from or are at risk of developing cancer. For example, the administration of a test agent which inhibits or decreases the activity and/or expression of the protein product of one or more of the aforementioned genes may treat the cancer and/or decrease the risk cancer, if the increased activity of the biomarker is responsible, at least in part, for the progression and/or onset of the cancer.
Suitably, the cancer is of a type hereinbefore described, albeit without limitation thereto. Preferably, the cancer has an overexpressed gene selected from the group consisting of GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7, COG8, CFDP1, KCNG1 and any combination thereof.
All computer programs, algorithms, patent and scientific literature referred to herein is incorporated herein by reference.
For the present invention, the database accession number or unique identifier provided herein for a gene or a protein, such as those presented in Tables 4, 5, 10, 15, 16, 17 and 18, as well as the gene and/or protein sequence or sequences associated therewith, are incorporated by reference herein.
So that preferred embodiments of the invention may be fully understood and put into practical effect, reference is made to the following non-limiting examples.
We performed a meta-analysis of global gene expression data in the Oncomine™ database19 (Compendia Bioscience, MI) using a primary filter for breast cancer (130 datasets), sample filter to use clinical specimens and dataset filters to use mRNA datasets with more than 151 patients (22 datasets). Patients of all ages, gender, disease stages or treatments were included. Three additional filters were applied to perform three independent differential analyses: (1) triple negative (TNBC cases vs. non-TNBC cases, 8 datasets49-56; (2) metastatic event analysis at 5 years (metastatic events vs. no metastatic events, 7 datasets53,54,57-61) and (3) survival at 5 years (patients who died vs. patients who survived, 7 datasets49,54,56,58,61-63). Deregulated genes were selected based on the median p-value of the median gene rank in overexpression or underexpression patterns across the datasets (
Pathway analysis was performed using the Ingenuity Pathway Analysis® (Ingenuity Systems®, CA). For pathway analysis in IPA®, we used only direct relationships. After pathway analysis, we set to identify the minimum gene list that recapitulates the aggressiveness 206 gene list. We used the METABRIC dataset to perform statistical filtering in the BRB-ArrayTools software to derive the minimum gene list as follows: (1) the correlation of each gene in the CIN metagene and the ER metagene to the metagene itself was determined by quantitative trait analysis using the Pearson's correlation coefficient (univariate p-value threshold of 0.001); (2) the association of each gene with overall survival using univariate Cox proportional hazards model (univariate test p-value <0.001); and (3) the fold-change of gene expression between high aggressiveness score tumors and low aggressiveness score tumors was calculated for each gene. We selected genes with Pearson's correlation coefficient >0.7 to the metagenes, strongest survival association and more than 2-fold deregulation between high and low agressiveness score tumors. The METABRIC dataset and four publically available datasets were used to validate the 8-genes score. The four datasets (GSE2506653, GSE349465, GSE299015, GSE203466) were analyzed as described previously67.
Breast cancer cell lines were obtained from ATCC™ (VA, USA) and cultured as per ATCC™ instructions. All cell lines were regularly tested for mycoplasma and authenticated using STR profiling. For the siRNA screen, siRNA solutions (Shanghai Gene Pharma, China) were used to transfect cells (MDA-MB-231, SUM159PT and Hs578T) with 10 nM of respective siRNA using Lipofectamine® RNAiMAX (Life Technologies, CA, USA). For drug treatments, docetaxel and the TTK inhibitor AZ3146 were purchased from Selleck Chemicals LLC (TX, USA) and diluted in DMSO. Six days after siRNA knockdown or after drug treatments the survival of cells in comparison to control was determined using the CellTiter 96® Assay as per manufacturer instructions (Promega Corporation, WI, USA). For immunoblotting, standard protocols were used and membranes were probed with antibodies against TTK (anti-MPS1 mouse monoclonal antibody [N1] ab11108 (Abcam, Cambridge), and γ-tubulin (Sigma-Aldrich®) then developed using chemiluminescence reagent plus (Milipore, MA, USA). Flow cytometry to quantify apoptosis was performed using Annexin V-Alexa488 and 7-AAD (Life Technologies) as per manufacturer instruction using BD FACSCanto II™ flow cytometer (BD Biosciences, CA, USA).
The Brisbane Breast Bank collected fresh breast tumor samples from consenting patients; the study was approved by the local ethics committees. Tissue microarrays (TMAs) were constructed from duplicate cores of formalin-fixed, paraffin-embedded (FFPE) breast tumor samples from patients undergoing resection at the Royal Brisbane and Women's Hospital between 1987 and 1994. For biomarker analysis, whole tumor sections or TMAs (depending on the marker) were stained with antibodies against ER, PR, Ki67, HER2, CK5/6, CK14, EGFR and TTK (Table 8), and scored by trained Pathologists. The Vectastain® Universal ABC kit (Vector laboratories, CA) was used for signal detection according to the manufacturer's instructions. Stained sections were scanned at high resolution (ScanScope Aperio, Leica Microsystems, Wetzlar, Germany), and then images were segmented into individual cores for analysis using Spectrum software (Aperio). Survival and other clinical data were collected from the Queensland Cancer Registry and original diagnostic Pathology reports, and in addition we performed an internal histopathological review (SRL) of representative tumor sections from each case, stained with H&E. For analysis of HER2-amplification TMAs were analyzed using HER2 CISH. Criteria for assigning prognostic subgroups in this study are summarized in
Statistical analyses were prepared using GraphPad® Prism v6.0. The types of tests used are stated in Figure Legends. Univariate and multivariate Cox proportional hazards regression analyses were performed using MedCalc for Windows, version 12.7 (MedCalc Software, Ostend, Belgium).
We performed a meta-analysis of published gene expression data, irrespective of platform, using the Oncomine™ database19 (version 4.5). We compared the expression profiles of 492 TNBC cases vs. 1382 non-TNBC cases in 8 datasets and found 1600 overexpressed and 1580 underexpressed genes in the TNBC cases (cutoff median p-value across the 8 datasets <1×10−5 from a Student's t-test,
We compared the 206 genes from the above analysis, we called the “aggressiveness gene list” (Table 4), to the recently described metagene attractors16,17 and found that 45 of the overexpressed genes were in the CIN metagene, whereas 19 of the underexpressed genes were in the ER metagene (
Interestingly, tumors of various subtypes scored higher than the median aggressiveness score (line in box plots in
One Network of Direct Interactions in the Aggressiveness Gene List Associates with Patient Survival
We performed network analysis on the aggressiveness gene list using the Ingenuity Pathway Analysis (IPA®) and found a network with direct interactions between 97 of the 206 deregulated genes (
Next, we explored the 8-genes score for prognosis in several molecular and histological settings in the METABRIC dataset. The survival of patients with tumors with wild-type TP53 were stratified by the 8-genes score (
To exclude the possibility that the aggressiveness score—calculated using the 206 genes or the 8 genes—was redundant; we performed multivariate Cox-proportional hazards model analysis in the METABRIC dataset (with Illumina platform) in comparison to conventional clinical variables and current gene signatures. As detailed in Table 1, the aggressiveness scores significantly associated with patient survival when compared with conventional variables and outperformed MammaPrint9, OncotypeDx10,11, proliferation/cell cycle16,20 and CIN20 signatures. Moreover, our aggressiveness scores outperformed the CIN4 classier23 which was recently developed from the CIN signature.
We validated the six CIN and two ER genes in univariate survival association using the online tool Kaplan-Meier (KM)-plotter24 (Tables 6 & 7) which has the gene expression and survival data of more than 2000 patients (but are not part of the METABRIC dataset). We found that the collective expression of the six overexpressed genes (MELK, MCM10, CENPA, EXO1, TTK and KIF2C) significantly associated with relapse free survival (RFS) and distant metastasis free survival (DMFS) in all patients, ER+ patients, lymph node negative (LN−) or positive (LN+) patients (Table 6). The two underexpressed genes (MAPT and MYB) also significantly associated with RFS and DMFS in these patient groups (Table 7).
More importantly, we performed multivariate survival analysis of the 8-genes score in four datasets (with Affymetrix platform from the Gene Expression Omnibus [GEO]; GSE2990, GSE3494, GSE2034 and GSE25066). Again, the score was significantly associated with survival in a multivariate Cox-proportional hazards model in every dataset tested (
The overexpressed genes in the CIN metagene are involved in or regulate mitosis, spindle assembly and checkpoint, kinetochore attachment, chromosome segregation and mitotic exit. Thus it is not surprising that several of the overexpressed genes are targets for molecular inhibitors, such as CDK125,26 and AURKA/AURKB27 and have been trialed pre-clinically and clinically28. To this end, we performed siRNA depletion against 25 genes of the CIN metagene in three TNBC cell lines, MDA-MB-231, SUM159PT and Hs578T. We found that knockdown of four genes (11K, TPX2, NDC80 and PBK) consistently affected the survival of these cells (
To further study the potential of TTK as therapeutic target, we investigated TTK expression at the mRNA and protein levels in breast cancer patients. We analyzed the correlation of TTK mRNA expression, dichotomized at the median, with clinicopathological indicators in the METABRIC dataset of 2000 patients (Table 2). High TTK mRNA expression associated with younger age of tumor diagnosis, larger tumor size, higher tumor grade, higher Ki67 expression, TP53 mutations, an ER/PR negative tumor phenotype, HER2 positivity and TNBC. Based on PAM50 subtyping, high TTK mRNA was associated with luminal B, HER2-enriched and basal-like tumors.
We also analyzed TTK expression in a cohort of breast cancer patients (406 patients) by IHC. TTK and its activity is detected at all stages of the cell cycle, however, it is upregulated during mitosis29. Thus, we observed TTK staining in non-mitotic cells to define high TTK levels (score of 3) in order to exclude the bias of elevated TTK level during mitosis. Similar to TTK mRNA, high TTK protein level (Table 3) associated with high tumor grade, high Ki67 expression and TNBC status (particularly basal TNBC). Moreover, in agreement with the TTK mRNA associations with the PAM50 intrinsic subtypes, high TTK protein was observed in HER2-positive and proliferative ER+/HER2− tumors (most related to luminal B) but low TTK protein in non-proliferative ER+/HER2− tumors (most related to luminal A). In addition to these associations with aggressive phenotypes, we also found that high TTK protein significantly associated with aggressive histological features including ductal histology, pushing tumor border, lymph node involvement, nuclear pleomorphism, lymphocytic infiltration and higher mitotic scores (Table 3). Altogether, like the high aggressiveness score from the 206 or 8 genes, high level of TTK mRNA and protein span across breast cancer subtypes marking aggressive behavior.
We examined the association of TTK protein level with patient survival and found that breast tumors with high TTK staining (category 3) had worse survival than other staining groups at 5 years (
There is also reason to believe that the metagene signature may work for other cancers, such as lung cancer.
In
We also find that the 8-genes score (Aggressiveness score) stratifies the survival of all cancers collectively in the TCGA data better than the OncotypeDx (
This meta-analysis of gene expression in the Oncomine™ database identified a list of 206 was enriched with two core biological functions/metagenes; chromosomal instability (CIN) and ER signaling. We calculated the aggressiveness score, the ratio of CIN to ER metagenes, which associated with overall survival of breast cancer. A core of eight genes (six CIN genes and two ER signaling genes) was representative and recapitulated the correlations with outcome from the 206 genes. The score from the six CIN genes to the 2 ER signaling genes, 8-genes score, associated with survival in several breast cancer datasets. Our aggressiveness scores outperformed conventional variable and published signatures in multivariate survival analysis. Particularly in ER+ tumors, some cases have survival as poor as that of the aggressive HER2+ and TNBC subtypes. Our data suggest that the interplay of cancer-related biological functions, namely CIN and ER signaling, are better predictors of phenotypes than single genes or single functions. This notion is in line with recent studies showing that the interaction of biologically-driven predictors provide better prognosis16,17,30. Recently, all ER− tumors were described to have a high level of CIN metagene, however, it was not clear that ER+ tumors could be described as low CIN tumors16. In our study, we clarify that ER+ disease contains a considerable fraction of tumors that have high level of CIN genes and that the relationship between CIN and ER genes is a powerful predictor of survival in these patients.
The fidelity of chromosome segregation is ensured by the proper attachment of the microtubules from the mitotic spindle to the kinetochores of chromosomes in a tightly regulated process and CIN refers to the missegregation of whole chromosomes thus producing aneuploidy31. Using aneuploidy as a surrogate marker for CIN, Carter et al developed a gene signature and found that this “CIN signature” predicts clinical outcome in multiple cancers20. More recently, a minimal gene set that captures the CIN signature, CIN4 (AURKA, FOXM1, TOP2A and TPX2) was described as the first clinically applicable qPCR derived measure of tumor aneuploidy from FFPE tissue. Since Grade 2 tumors heterogeneous characteristics in terms of clinical outcome, the significance of the CIN4 classier is the stratification of Grade 2 tumors into good and poor prognosis groups23. Our aggressiveness scores were prognostic in all tumor grades and disease stages (stages I-III and lymph node negative and positive) and outperformed the CIN signature and the CIN4 classier in multivariate survival analysis in the METABRIC dataset. Strikingly, but in agreement with previous studies32,33, the prognostication using the CIN metagene and our aggressiveness scores from gene expression levels were restricted to ER+ disease but not in the TNBC or HER2 subtypes. This may be explained that ER− tumors have a high level of CIN metagene as per our results and published previously16. However, our results with TTK protein level clearly demonstrate that TNBC, HER2, high grade, lymph node positive and proliferative tumors contain subgroups with high TTK levels exclusive of mitotic cells and have poorer survival than those with low TTK expression or TTK expression in mitotic cells. We propose that there are two types of high expression of CIN genes that may not be clearly differentiated by mRNA expression studies. One form of elevated CIN genes relates to high level of mitosis and proliferation whereas the second form that we measured by IHC exclusive of mitotic cells is driven by another aggressive phenotype; protection of aneuploidy and genomic instability. The recent study of the CIN4 classifier lends support to our proposition. In this study, using flow cytometry to measure aneuploidy by DNA content, the authors found that a substantial proportion of tumors with high CIN4 scores have a normal DNA ploidy and that a significant proportion of aneuploid cases had low CIN4 score23.
Chromosome missegregation and aneuploidy enhance genetic recombination and defective DNA damage repair34 to drive a “mutator phenotype” required for oncogenesis35. Genomic instability caused by deregulated mitotic spindle assembly checkpoint (SAC) and aneuploidy has been termed “non-oncogene addiction”36,37. It is tempting to suggest that CIN and aneuploidy are exploited by breast cancer stem cells which are high in TNBC38 due to the link between cancer stem cells, aneuploidy and therapy resistance39,40. This is supported by studies that implicate several genes involved in the SAC and chromosome segregation in tumor initiation, progression and cancer stem cells, e.g. AURKA in ovarian cancer41, MELK/FOXM1 in glioblastoma42,43, MELK44 and MAD245 in breast cancer and SKP2 in several cancers46. The role of CIN genes to protecting aneuploidy could provide an insight to the paradox that TNBC show a better response to chemotherapy due to higher level of proliferation, yet these tumors have poorer outcome. We propose that resistance in TNBC could be attributed to the ability of aneuploid cells to adapt and drive recurrence. At least in vivo, chemotherapy has been shown to induce the proliferation quiescent aneuploid cells as a mechanism for therapy resistance39. We envisage that the high level of the CIN metagene in TNBC, particularly genes involved in chromosome segregation, is protective of this state. Indeed, one study found that a high level of TTK is protective of aneuploidy in breast cancer cells and its silencing reduces the tumorigenicity of breast cancer cell lines in vivo47. Our results from the patient cohort demonstrate that high TTK protein expression exclusive of mitosis was indeed prognostic aggressive tumors and support the concept that protection from aneuploidy and genomic instability is an aggressive phenotype that drives poor outcome.
Our results with the TTK molecular inhibitor, in agreement with published studies using siRNA depletion47,48, supports the idea of targeting chromosomal segregation in tumors with a high CIN phenotype as a therapeutic strategy. We also suggest that while TTK is high in TNBC as previously described47,48, a considerable proportion of non-TNBC tumors that display aggressive features also show an elevated level of CIN genes, and would benefit from such targeted therapies. To our knowledge the combination of sub-lethal doses of taxanes with TTK inhibition has not been investigated so far in breast cancer, but in other cancers33,50-53. Our results reveal that TTK inhibition indeed sensitizes breast cancer cells with high TTK to docetaxel.
Referring particularly in
In conclusion, our study emphasizes that classification of breast cancer based on biological phenotypes facilitates understanding the drivers of oncogenic phenotypes and therapeutic potentials. Importantly, our studies demonstrate that IHC assessment of CIN genes, exemplified by TTK here; provide better characterization and understanding for the contribution of CIN to tumor aggressiveness and prognosis.
Throughout this specification, the aim has been to describe the preferred embodiments of the invention without limiting the invention to any one embodiment or specific collection of features. Various changes and modifications may be made to the embodiments described and illustrated herein without departing from the broad spirit and scope of the invention.
All computer programs, algorithms, patent and scientific literature referred to herein is incorporated herein by reference in their entirety.
Nature reviews 2006; 6: 321-330.
#Chi square test (GraphPad ® Prism. ns: not significant)
Meta-analysis of global gene expression in TNBC
We performed a meta-analysis of global gene expression data in the Oncomine™ database [37] (Compendia Bioscience, Ann Arbor, Mich.) using a primary filter for breast cancer (130 datasets), sample filter to use clinical specimens and dataset filters to use mRNA datasets with more 151 patients (22 datasets). Two additional filters were applied to perform two independent differential analyses. The first differential was metastatic event analysis at 5 years (metastatic events vs. no metastatic events, 7 datasets [51, 56-61]) and the second differential analysis was survival at 5 years (patients who died vs. patients who survived. 7 datasets [39, 57, 59, 61-64]). Deregulated genes were selected based on the median p-value of the median gene rank in overexpression or underexpression patterns across the datasets for each of the two differential analyses.
Deriving the 28-Signature (the TN Signature) The online tool KM-Plotter [38] which collates gene expression data from Affymterix platform for more than 40(K) breast cancer patients were used for developing the 28-gene signature. From the deregulated genes in primary tumors which led to metastatic or death events within 5 years discovered in the meta-analysis in Oncomine™, 166 genes were common in both survival events. These genes were then interrogated one by one in KM-Plotter restricting the univariate survival analysis to ER− or BLBC subtypes. Genes which significantly associated with relapse-free survival (RFS). distant metastasis-free survival (DMFS) or overall survival (OS) in either ER− or BLBC subtypes were short selected. The 96 genes that were significant in this filtering where then sorted for their level of significance as well as the prevalence of significance across the different survival outcomes (RFS. DMFS and OS) and across ER− and BLBC subtypes. Based on this sorting, six groups of gene lists were obtained with different levels of survival association (Table 14). Each of these groups were then used as a metagene and the average expression of genes in each group was investigated for association with survival in KM-Plotter in ER and BLBC subtypes. Based on these analysis, four groups were selected and two were excluded. Furthermore, for two groups, the top 4 and 3 genes were found to be more prognostic than the rest of the group and these were selected. In total, the 7 genes (which their downregulation associates with poor survival) from these two groups and 21 genes (which their upregulation associates with poor survival) in the other two groups were selected to test for association with survival in KM-Plotter. These 28 genes showed the highest association with survival as a gene signature compared to any single gene in the original list or any groups from this list. These 28 genes were selected as the triple negative (TN) signature and was subjected to validation as described below.
Three large breast cancer gene expression datasets were used for validation. The Research Online Cancer Knowledgebase (ROCK) dataset [40] (GSE47561; n=1570 patients) and the homogenous TNBC dataset [32] (GSE31519; n=579 TNBC patients) were obtained from Gene Expression Omnibus (GEO) and the data was imported into BRB-ArrayTools [65] (V4.2, Biometric Research Branch, NCI, Maryland, USA) with built in R Bioconductor packages. The Cancer Genome Atlas (TCGA) dataset [39]; using the Illumina HiSeq RNA-Seq arrays (n=1106 patients) or the Agilent custom arrays (Agilent 04502A-07-3) on 597 patients of the 1106 total patients, were obtained from the UCSC Genome Browser [66, 67]. The TN signature was investigated in each of these datasets where a score was devised to quantify the signature; the TN score=average expression of the 21 genes whose overexpression associated with poor survival÷average expression of the 7 genes whose underexpression associated with poor survival. The TN score for each tumor in each dataset was calculated and tumors were assigned as high or low TN score tumors by dichotomy across the median TN score in each dataset. In some cases, tertiles of the TN score in each dataset were used to classify tumors as high, intermediate or low TN score tumors and in other cases the quartiles of the TN score were used to classify tumors in the 1st, 2nd, 3rd or 4th quartiles. The survival of patients in high (over the median, last tertile of the 4th quartile) vs. low TN score groups was compared. Survival analyses were constructed using GraphPad® Prism v6.0 (GraphPad Software, CA, USA) and the Log-rank (Mantel-Cox) Test was used for statistical comparisons of survival curves.
Association of the TN Score and Signatures with Pathological Complete Responses (pCR) after Neoadjuvant Chemotherapy and Response to Endocrine Therapy
Datasets which performed gene expression profiling prior to neoadjuvant chemotherapy or endocrine therapy alone were obtained from GEO. The datasets used in this study for neoadjuvant chemotherapy and recorded pathological complete response (pCR) include: GSE18728 [42], GSE50948 [43], GSE20271 [44], GSE20194 [45]. GSE22226 [41, 46], GSE42822 [47] and GSE23988 [48]. For datasets which performed gene expression profiling prior to endocrine therapy (tamoxifen) and recorded patient survival include: GSE6532 [25] and GSE17705 [51]. These datasets using the Affymetrix gene expression array platforms were imported into BRB-ArrayTools and normalized as described previously [68]. Each tumor in the datasets were assigned as high or low score for our signatures as described in the previous sections. The rate of pCR after chemotherapy or the survival of patients after endocrine therapy were compared between high score tumors and low score tumors using GraphPad® Prism.
Global gene expression comparison was carried out to compare tumors with high TN or iBCR scores to those with low TN or iBCR scores to characterize additional differences between these tumors and identify deregulated genes which could be suitable as for drug targeting. These comparisons were carried out in the large cohort of 1570 patients in the ROCK dataset and BRB-ArrayTools was used to perform the Class Comparison test. The two classes were high vs. low score tumors and the parameters selected in this plugin in ArrayTools were as follows: Type of univariate test used=Two-sample T-test; Class variable=TN score (high or low) or iBCR score (high or low); fold-change cutoff=1.5 fold; Permutation p-values for significant genes were computed based on 10000 random permutations and Nominal significance level of each univariate test: 0.05. The results from these analyses are shown in Tables 13 and 15-17.
Integration of the Agro and TN Signatures in the integrated Breast Cancer Recurrence (iBCR) Score
We previously published the Aggressiveness (Agro) signature and score also from meta-analysis and extensive validation and show that this signature is prognostic in ER+ breast cancer [36]. To test whether the Agro signatures could be integrated with the TN signature (prognostic in ER breast cancer) to produce an integrated test that is independent of ER status, several integration methods were investigated. The hypothesis behind the integration methods was to identify a direct relationship that can describe the relationship between the TN and Agro scores in both ER− and ER+ breast cancer subtypes that is also in direct relationship with the integrated score. In other words, the integrated score would retain the information from each the Agro and TN scores relevant to their prognostic value in ER+ and ER− breast cancers, respectively. The ROCK dataset was used to test the different methods of integration and the performance of these methods in the stratification of survival of ER+ and ER− breast cancer. The addition or subtraction of the scores produced a direct relationship between the TN and Agro score and the produced integrated score (
Two large studies which treated large panels of cancer cell lines with large panels of anticancer drugs were investigated to determine whether cell lines with high Agro, TN or iBCR scores show different sensitivity to particular anticancer drugs in comparison to cancer cell lines with low Agro, TN or iBCR scores. Briefly, the datasets of gene expression profiling from Genentech (mRNA Cancer Cell Line Profiles GSE10843), Pfizer (Pfizer Molecular Profile Data for Cell Line GSE34211) and Broad Institute/Novartis (Cancer Cell Line Encyclopedia [CCLE] GSE3613) were obtained from GEO and imported into ArrayTools as described earlier. The Agro, TN and iBCR scores for all the cell lines profiled were calculated and cell lines were assigned as high or low for each of the scores based on dichotomy across the median in each dataset. For cell lines which were profiled in more than one dataset, the average scores were used. Using this data, the sensitivity of cancer cell lines with high and low Agro, TN or iBCR scores was compared to those with low scores to anticancer drugs was investigated in two studies [49, 50]. Drugs which had significantly different IC50 in high score cell lines compared to low score cell lines are described herein. Statistical significance was determined from unpaired two-tailed t-test using GraphPad® Prism.
Univariate and multivariate Cox proportional hazards regression analyses were performed using MedCalc for Windows, version 12.7 (MedCalc Software, Ostend, Belgium).
We performed a meta-analysis of published gene expression data, irrespective of platform or breast cancer subtype, using the Oncomine™ database [37] (version 4.5). We were able to compared the expression profiles of primary breast tumors from 512 patients who developed metastases vs. 732 patients who did not develop metastases at 5 years (7 datasets in total) to identify 500 overexpressed genes and 500 underexpressed genes in the metastasis cases (cutoff median p-value across the datasets <0.05 from a Student's t-test,
The 166 deregulated genes in primary breast tumors that associated with poor outcome discovered from the Oncomine™ meta-analysis were interrogated using KM-Plotter. The overexpression of 31 genes and the underexpression of 65 genes associated with RFS, DMFS or OS of BLBC or ER− breast cancer (Table 14). Based on the level of significance in univariate survival analysis and the prevalence of this significance across the different disease outcomes (RFS, DMFS and OS), a list of 21 overexpressed and 7 underexpressed genes (Table 1) were shortlisted as a signature with the strongest association with survival in both BLBC and ER breast cancer subtypes (
The 28-gene signature, the TN signature, was then validated in multivariate survival analysis in two breast cancer cohorts, the homogenous TNBC dataset [32] and the Research Online Cancer Knowledgebase (ROCK) dataset [40]. We devised a score to quantify trends in the TN signature, the TN score, which is calculated as the ratio of the average expression of the 21 overexpressed genes to that of the 7 underexpressed genes. Dichotomy across the median TN score stratified the survival of TNBC (
While the discovery of the signature in Oncomine™ included datasets using the Affymterix, Illumina and Agilent platforms, the training and validation above was limited to the Affymterix platform. Thus, we validated the TN score in The Cancer Genome Atlas (TCGA) dataset [39] which used the lumina HiSeq RNA-seq platform. As shown in
The TN Score and the Likelihood of pCR after Chemotherapy
Chemotherapy is a standard therapy for ER− breast cancer and the only mode of therapy for ER−HER2− (TNBC) breast cancer. Although, pathological complete response (pCR) differs by receptor status, it remains highly predictive of survival within the different breast cancer subtypes [41]. Given the association of the TN score with outcome in TNBC, BLBC and ER− breast cancer, we questioned whether this score is also associated with pCR after chemotherapy. To this end. we analyzed publically available datasets of neoadjuvant chemotherapy trials which recorded pCR and performed pre-treatment gene expression profiling. As shown in
The overexpressed genes in the TN signature contains novel genes which have limited literature describing their function, particularly in cancer. These genes includes GRHPR, NDUFC1, CAMSAP1, CETN3, EIF3K, STAU1, EXOSC7 and KCNG1. These genes are novel candidates for future studies to investigate the effect of their knockdown on the survival of ER− or TNBC breast cancer cell lines. In addition, we took two approaches to identify possible therapeutic strategies envisioned by the TN signature to benefit the poor survival of patients identified by this signature. First, we compared the global gene expression profile of TNBC/BLBC tumors with high TN score to those with low TN score. Secondly, we analyzed published pre-clinical studies which treated cancer cell lines with panels of molecularly targeted drugs to determine whether cell lines with high TN score display sensitive to particular drugs. In the first approach, a class comparison between the global gene expression profiles of BLBC or ER− tumors with high TN score to those with low TN score was carried out in the ROCK dataset. In comparison to low TN score BLBC tumors, high TN score BLBC tumors overexpressed 171 probes and underexpressed 251 probes (Table 15). In a similar analysis, high TN score ER− tumors overexpressed 307 probes and underexpressed 332 probes (Table 16). Of the overexpressed probes, 87 probes (82 genes) were commonly overexpressed in high TN score BLBC and ER− breast cancer compared to low TN score counterparts. Of the 87 probes, 39 probes were prognostic in BLBC and ER− breast cancer (marked in bold in Table 15). More importantly, the 87 probes include genes which encode several kinases, enzymes and ion channels which could be targets or current for future drug development for the treatment of the high TN score tumors that have poor outcome.
In the second approach, published studies which surveyed panels of molecular drugs against, cancer cell lines were analyzed. The Cancer Cell Line Encyclopedia (CCLE) study [50] investigated the pharmacological profiles for 24 anticancer drugs across 479 cancer cell lines which were also profiled with gene expression arrays. We calculated the TN score for each cell line in this study and compared the sensitivity of these cell lines to the anticancer drugs according to the TN score. Cancer cell lines with high TN score were less sensitive to inhibition of ALK (TAE684) and BCR-ABL (Nilotinib) but more sensitive to the inhibition of HSP90 (Tanespimycin [17-AAG]) and EGFR (Erlotinib or Lapatinib) (
We have recently published the aggressiveness gene signature/score (Agro score) [36] from a meta-analysis in Oncomine™ and validated that this score is prognostic in ER+ breast cancer at the gene level. ER− breast cancer, BLBC and TNBC almost consistently express high level of the Agro score thus this signature was not prognostic in these subtypes. We further showed that one of these genes, TTK/MPS1, is upregulated in TNBC cell lines and some ER− negative cell lines, and that TTK is a therapeutic target in these cell lines. Moreover, we showed that the TTK protein level by immunohistochemistry (IHC) is prognostic in very aggressive subgroups of breast cancer including high grade, proliferative tumors, lymph node positive, TNBC and HER2+ subtypes [36]. The integration of the TN gene signature (prognostic in ER−/BLBC/TNBC) and the Agro gene signature (prognostic in ER+) would allow one integrated signature and score which will be prognostic in breast cancer irrespective of subtypes. As detailed in the methods section, the addition, subtraction, multiplication or division of the TN and Agro scores were investigated in the ROCK dataset to identify a direct relationship that would retain the information provided from each of the scores. A linear relationship was observed by the addition or subtraction of the TN and Agro scores (
The iBCR Score and the Likelihood of pCR after Chemotherapy
The association of the iBCR score with patient survival and the likelihood of pCR after chemotherapy was investigated in the ISPY-1 trial (GSE22226). The RFS of ER−/MER2− patients was stratified by iBCR score better than the TN score alone (
As shown in the summary from these four studies in Table 12, of the total 183 ER HER2− patients, 120 patients (65.6%) had high iBCR score and of these 54 patients (29.5%) achieved pCR while 66 patients (36.1%) did not achieve pCR. The larger number of patients with high iBCR score that did not achieving pCR (66/120, 55%) and that recurrence may be observed on high iBCR score patients after pCR (55/120, 45%) could explain the poorer survival of high iBCR score ER−HER2− patients (40-50% survival at 10 years in
The iBCR Score and the Treatment of ER+ Breast Cancer
ER+ breast cancer patients are treated with endocrine therapy, particularly tamoxifen. When these patients are lymph node positive (N0), adjuvant chemotherapy is also included. For lymph node negative (N0) ER+ patients, decision to include chemotherapy is less certain as good prognosis patients (small and lower grade tumors) would be over-treated if chemotherapy is included whereas poorer prognosis patients (larger and higher grade tumors) would be under-treated if chemotherapy is not included. This clinical decision has been the motivation for the development of Oncotype Dx® recurrence score, the MammaPrint and more recently the PAM50 risk of recurrence score. We have previously published that the Agro score outperformed the Oncotype Dx and the MammaPrint tests in multivariate survival analysis in the METABRIC dataset of 2000 patients [36] This finding is further supported by direct comparison of the Agro score to Oncotype Dx (
The iBCR Score Predicts Therapies for ER−/HER2− and ER+ and Breast Cancer Subtypes
The overexpressed genes in the Agro and TN signature contain targetable genes which could be useful for therapeutic intervention against the high iBCR tumors which have poor survival after the standard treatments. Similar to the analysis performed for the TN signature above, we took two approached to identify additional possible targets in the high iBCR score breast tumors. In the first approach, a class comparison between the global gene expression profiles of ER+ or ER− tumors with high iBCR score to those with low iBCR score was carried out in the ROCK dataset. The produced gene-list (1178 probes, data not shown) was then filtered by comparison to normal breast tissue which was also profiled in this dataset. In comparison to low iBCR score tumors and normal breast tissue, high iBCR score tumors overexpressed 204 probes (181 genes) and underexpressed 124 probes (116 genes) (Table 17). Of the 181 overexpressed genes, 134 genes were specifically upregulated in high iBCR score ER+ vs. normal breast and low iBCR ER+ and 95 genes were specifically upregulated in high iBCR score ER− vs. normal breast and low iBCR ER−. As shown in Table 13, 49 genes were uniquely upregulated in high iBCR score ER− tumors compared to low score iBCR score ER− tumors and normal breast tissue. Similar comparison revealed that high iBCR score ER+ tumors have unique upregulation of 86 genes. High iBCR score ER and ER+ tumors commonly overexpressed 46 genes in comparison to low score iBCR counterparts and normal breast tissue. These genes encode several kinases, enzymes and ion channels which could be targets for current or future drug development for the treatment of the high iBCR score tumors with poor outcome. Of the downregulated probes, a particularly interesting hit was the micro-RNA (miRNA) hsa-mir-568 (9.3- and 2.2-fold downregulated in high iBCR score ER− vs. normal breast and low iBCR score ER−, respectively; 5.6- and 2.9-fold downregulated in high iBGR score ER+ vs. normal breast and low iBCR score ER+, respectively). This downregulated miRNA in the high iBCR score tumors targets several of the upregulated genes in these tumors, particularly those which are upregulated compared to normal breast tissue (Table 18). This miRNA could be a genomic-based treatment against high iBCR score breast cancers.
IS In the second approach, again similar to the above analysis for the TN score, published studies of drug screens were analyzed for the association of the iBCR score with sensitivity of cancer cell lines to anti-cancer drugs. In the CCLE study (
Sensitivity of Breast Cancer Cell Lines to Targeted Inhibitors According to the iBCR Score
Breast cancer cell lines (10 cell lines); BT-549, MDA-MB-231, MDA-MB-436, MDA-MB-468, BT-20, Hs.578T, BT-474, MCF-7, T-47D, and ZR-75-1, were cultured in the absence or presence of escalating doses of 24 anti-cancer drugs. The survival of cells was determined six days in comparison to untreated cells using the MTS/MTA assay. The response of the cell lines to the drugs was analyzed in GraphPad® Prism using a dose response curve to calculate the log10 of IC50 (IC50 is the dose required to kill 50% of the cells). Sensitivity was presented as the −log10[IC50]. This drug screen which we published previously (Al-Ejeh et al., Oncotarget, 2014) was re-analyzed according to the iBCR score. The gene expression datasets of 51 breast cancer cell lines by Neve et al. (Cancer Cell, 2006), was analyzed to calculate the Agro and TN scores for each cell line to calculate the iBCR score. Each cell line was assigned as low of high iBCR score by dichotomy across the median of all the cell lines in the Neve et al. dataset. Based on the low or high iBCR score classification, the sensitivity of the 10 cell lines used in our screen was compared between high iBCR score cell lines (5 cell lines) to low iBCR score cell lines (5 cell lines). As shown in
Our meta-analysis of gene expression datasets in the Oncomine™ database has previously identified a signature, the Aggressiveness signature (Agro signature), which was prognostic in ER+ breast cancer. We validated one of the genes in this signature, TTK/MPS1, by IEC and found that TTK positivity in interphase cells (exclusive of mitotic cells) was prognostic in highly aggressive breast cancers such as high grade, high grade and lymph node positive and highly proliferative (Ki67 positive) cases [36]. In this study, we used our meta-analysis approach to identify a second signature, the triple negative signature (TN signature), which was highly prognostic in ER−, TNBC and BLBC subtypes. The TN signature outperformed all standard clincopatholical indicators in multivariate survival analysis and also outperformed published signatures in ER− breast cancer. We were also able to integrate the Agro signature (prognostic in ER+ breast cancer) to produce the integrated Breast Cancer Recurrence (iBCR) test. The two signatures and the iBCR were validated in large independent cohorts of breast cancer studies irrespective of the gene expression arrays used indicating the experimenter/technology independence of our signatures. Importantly, both the Agro and TN signatures and the iBCR test associated with response and outcome after endocrine therapy for ER+ and neoadjuvant chemotherapy for ER: and ER+ breast cancers. Moreover, by comparison of the global gene expression profiles of high iBCR score tumors to low iBCR score tumors, we were able to identify several overexpressed targets which can be used for the targeted therapy of these poor prognosis patients who are not really benefiting from the current treatment standards. In addition, mining of large preclinical studies of drug screens against cancer cell lines showed that the signatures and iBCR score predict higher sensitivity of cell lines to particular drugs. Thus. the signatures and the iBCR test could be used as a companion diagnostic to direct targeted therapies to those patients who would benefit from these treatments to increase their low survival rates. Altogether, our studies have not only extensively illustrated the potential of our signatures in personalized medicine, but may also shed light for future studies to understand the underlying mechanisms for the aggressiveness of tumors that the iBCR test identified that lead to poor survival To date, there is an unmet medical need for the prognostication of ER− breast cancer and the development of effective therapies against these tumors particularly when lacking HER2 expression. Chemotherapy remains to be the only standard therapy in these patients and the response rate after chemotherapy in the neoadjuvant setting is reported as 31% in ER HER2− (TNBC) patients [9]. Identifying patients who would truly benefit from chemotherapy would aid clinicians to determine patients who may require longer or additional treatment regimens including investigational clinical trial enrolment. Our signatures and the iBCR score predict higher pCR after chemotherapy in patients who have high scores compared to those with low score. The low score patients have better survival and may not require additional therapy. On the other hand, despite the higher pCR in high score patients, this patient subgroup still has poor survival and recurrences were present even after achieving pCR in high score patients when we analyzed the data from the 1SPY-1 trial. Our results from comparative analysis and mining pre-clinical drug screens identified several targets and sensitivity to drugs in development. Thus, ER− and particularly TNBC patients with high scores for our signatures/iBCR test may benefit from the inclusion of therapies envisioned by these signatures to increase their survival rates. Such clinical development will depend on future prospective validation of our signatures and the iBCR test in clinical trials and pre-clinical studies.
In ER+ breast cancer, three commercial tests exist for clinical decisions to spare or include adjuvant chemotherapy with the standard endocrine therapy; Oncotype Dx®, MammaPrint® and Prosigna®. These have been validated for ER+ lymph node negative (N0) breast cancer patients treated with endocrine therapy whether patients with high risk according to these tests are recommended for adjuvant chemotherapy. Our signatures and the iBCR test outperformed these tests in a direct comparison in ER+ N0 patient-survival after tamoxifen therapy. Moreover, our tests also predicted the response of ER+ patients to chemotherapy and importantly could predict sensitivity to targeted therapies. The current commercial tests do not have this capability. Importantly, our signatures and the iBCR test was also prognostic in the subgroup with unmet need, ER+ lymph node positive breast cancer (ER+ N1). The survival of these patients was stratified to poor and good prognosis groups by our signatures and iBCR test which also informed whether these patients are benefiting from endocrine therapy. Clinical validation of our signatures and the iBCR test along with validation of drug sensitivity predictions would aid the development of new treatment regimens for ER+ patients who are at high risk of relapse or metastatic spread after the current treatment standards.
The comparison of aggressive ER− tumors identified by our signatures to their counterparts and to normal breast tissue identified several kinases, enzymes (redox particularly) and potassium channels which could inform new directions in developing targeted treatments against ER− breast cancer. On the other hand, for aggressive ER+ tumors identified by our signatures, although targets were not restricted to cell cycle and proliferation, these functions were notably enriched. This high proliferation profile could explain the higher pCR in these tumors after chemotherapy as proliferative tumors would be more responsive to chemotherapeutics. Nonetheless, we have previously clarified that the overexpressed genes in the Agro signature, thus the iBCR test, are genes that are involved in kinetochore binding and chromosome segregations and that the signature is prognostic even in proliferative tumors (high Ki67 expression) [36]. Deregulation of genes involved in chromosome segregation would produce aneuploidy and chromosomal instability (CIN) [52]. At least in viva, chemotherapy has been shown to induce the proliferation quiescent aneuploid cells as a mechanism for therapy resistance [53]. In support of the notion that high Agro Score is related to aneuploidy, analysis of the copy number variations (CNVs) TCGA data showed that high Agro score tumours, compared to low Agro score tumors, have high level of CNVs, particularly those involving whole chromosomes or chromosome arms (
In conclusion, our meta-analysis in Oncomine™ and extensive subsequent validation and analysis have developed novel signatures and an integrated genomic test for the prognosis of breast cancer and prediction of response to standard treatments irrespective of ER status. The novel signatures and their integration also have the potential as companion diagnostic tests for several classes of targeted therapies in breast cancer patients who suffer poor survival. Future validation and clinical development of our signatures and the iBCR test holds a great potential and impact on personalized and precision medicine for breast cancer. Finally, it should be noted that the iBCR test has value in the prognosis of several other cancers (
HELLS
TOP2A
CDK1
STIP1
BUB1B
LRP8
NUDT21
WHSC1
The iBCR test described herein was developed from a meta-analysis of gene expression profiles of breast cancer. This test is based on the expression of 43 genes which are prognostic as a signature in breast cancer irrespective of subtype. This test was also found to be prognostic in lung adenocarcinoma. Patients with high iBCR score have much poorer overall survival than patients with low iBCR score.
In the current study. The Cancer Genome Atlas (TCGA) datasets for several cancer types were investigated for three purposes. First, to determine the differences in at the protein level between high iBCR score breast cancer cases to low iBCR score breast cancer cases. This comparison was also carried out for lung adenocarcinoma. Secondly. to determine whether deregulated proteins/phosphoproteins between high and low iBCR score tumours are prognostic. Finally, the prognostic value of the iBCR mRNA signature and associated protein signature are prognostic in other cancer types profiled by the TCGA.
As shown in
Similar analysis in the lung adenocarcinoma TCGA dataset identified proteins/phosphoproteins based on the iBCR mRNA signature which are prognostic as a protein signature (
Table 19 summarises the 43 genes at the mRNA level and 2 proteins/phosphoproteins in the iBCR test. The components which were prognostic in breast cancer (
In conclusion, the iBCR test including the mRNA and protein components (Table 19) is a highly prognostic test in all cancers tests. This test identifies aggressive human cancers and is enriched for protein-protein interactions (
The study by Westin et al. (Lancet Oncol, 2014. vol 15(1)) performed gene expression profiling on 18 follicular lymphoma patients before receiving pidilizumab in combination with rituximab. The expression of the genes in the iBCR signature was investigated for association with progression free survival (PFS) in these patients. Twelve genes showed a strong association with PFS (
The data presented here indicate the iBCR test can be a companion diagnostic for certain immunotherapy which is not surprising since the TN component includes several immune related genes in addition to genes involved in redox reactions and kinases.
A meta-analysis was performed in Oncomine™ using breast cancer datasets irrespective of subtypes or gene expression array platforms used. The global gene expression profiles of breast tumors that led to metastatic or death event within 5 years were compared to those that did not and the top overexpressed (OE) and underexpressed genes (UE) in these comparisons were selected. The commonly deregulated genes in the primary tumors that led to metastatic and death events (depending on the annotation of each dataset) were then interrogated using the online tool KM-Plotter™ (n>4000 patients with some overlap with the datasets in Oncomine™). Genes which associated with relapse-free survival of breast cancer patients were selected.
The 860 genes identified from this analysis were then subjected to network analysis using the Ingenuity Pathway Analysis (IPA®) software to identify functional networks within this gene list (see Table 21),
These 860 genes identified from the meta-analysis were then filtered for genes with the highest association with patient survival in each of the eleven functional networks. From this, the selected 133 genes (listed in Table 22) from the eleven functional networks are shown in
ARHGEF3
ATP6V0A1
AGBL2
ABCA8
KIF5C
ZNF211
ASAH1
ATP6V1C1
ARFRP1
APBB2
AP3B1
ASB1
COX4I1
ARNT2
ART4
MADD
DYNC1LI2
ATP2A2
DHRS7
CCR1
ATHL1
MAPT
ESRP1
BRD8
EPCAM
DST
BCL2
MIER2
GMPS
BTG2
HN1
EEF1A1
BEND5
MIS18A
GPI
BTN2A2
IDH3A
LUZP1
CABYR
MR1
HCCS
C1QB
IDH3G
MYBPC1
CASP10
N4BP1
HCFC1R1
CERS6
LAMTOR2
PIP
CHPT1
NEDD4L
KCNG1
CYP2C9
LAMTOR3
S1PR1
CYBRD1
OGN
NAPG
ELOVL2
MATR3
SNED1
ERC2
PRKCB
NDRG1
ELOVL5
NPR3
TAZ
FHL5
PROL1
NDUFB6
ERBB4
NRIP1
TP63
GAB1
RERE
NDUFS6
FLNB
PFKP
ADORA2B
GDNF
SETBP1
NME1
HIF3A
RAP2A
CMC4
GLRB
SGCD
OIP5
KIR2DL4
SLC16A3
DDX39A
GOLGB1
SGSM2
PGAM1
LRP2
TK1
GAPDH
GOSR1
SLC45A2
PIR
LRP8
VDAC1
GSK3B
GPR12
SOD2
PRRG1
ME1
RAPGEF6
HIF1A
HLA-B
SPAG8
RTCA
NCOA1
RBM38
HSPA14
ITM2A
SPG20
S100A11
NR1H3
SEC14L2
LAMA4
KIAA0247
SSPN
SMS
PBXIP1
SRSF5
MAP2K5
KIAA0430
SSX2
TARS
PIK3IP1
STARD13
STX18
XBP1
TRAK2
PSEN2
TRAK1
ZC3H14
TRAPPC10
ZMYM5
ASF1B
SLC11A1
BCAP31
AFF1
AURKB
BBS1
SMARCA2
BYSL
ATP1A2
BUB1
CCL13
SNX1
CCNA2
CDC14A
BUB1B
CCND2
SORL1
CCNE2
CDC27
BUB3
CDKN2A
SPDEF
CDC25A
CSPG4
C20orf24
DIRAS3
STAT5B
CDC45
FOXK2
CCNB1
DIXDC1
TAOK3
CDC6
MAGI1
CCNB2
DOCK1
TGOLN2
CDCA3
MLLT10
CDC20
DOK1
THPO
CDCA8
MTUS1
CDK1
EPOR
TIMELESS
CHEK1
NUP62
CENPE
FLT3
TNN
DERL1
NXF1
CENPF
FOSB
TNXB
DHFR
PKMYT1
CKS1B
GGA2
TYRO3
E2F8
RAPGEF2
CKS2
HAVCR1
ULK2
ECT2
SLC25A12
FOXM1
IL1RAPL1
VPS39
GINS3
SLC8A1
KIF2C
IL6ST
PIM1
RAD51
KIF4A
NUP93
JAK2
POLD1
RRM2
MAD2L1
NUSAP1
LEPR
PLK4
SKP2
MXI1
NUTF2
LIG1
PSMD10
UBE2C
NCAPG
PLK1
LZTFL1
MCM6
ULBP2
NDC80
PRC1
MTF1
MELK
WDHD1
NUP155
PTTG1
PCM1
MMP1
IL1RAP
TPX2
SPC25
PIK3R4
MYBL2
MCM10
TTK
TACC3
POU6F1
ORC6
MCM2
ZWINT
NF1
PDAP1
MCM4
ALDH3A2
ADRM1
ABCA1
DTX3
SARM1
PBK
ACOT7
ATAD5
BIRC5
AHSG
DYNC2H1
SIRT3
PFDN5
ANP32E
ATF5
CARHSP1
ANK3
EFCAB6
SMPDL3B
PSMA2
APOBEC3B
BLM
CENPA
APOBEC3A
EFNB3
SNN
RNASE4
CAST
BRD4
CENPI
BATF
ERAP1
TTC28
RNF141
CCT5
BRF2
CENPN
BECN1
EVL
WFDC2
S100A9
CCT6A
BTN3A2
CENPU
BUD31
FBXO41
ZMYM6
SHMT2
CCT7
CLASP2
DLGAP5
C2
FBXW4
ZNF516
SLC7A5
CD36
FANCA
ERCC6L
C3
FCGBP
IGHG3
SOX11
CD55
FBLN1
EXO1
CACNA1D
FCGR1A
IGHM
TBPL1
CDK8
KIF18B
FANCI
CARD10
FCGR1B
IGK
TCP1
CHD1
NPR2
H2AFX
CD163
FOS
IGKC
TOPORS
CXCL8
PLXNA3
H2AFZ
CD1A
FRZB
IGSF9B
TREM1
DHCR7
PSMD2
IMPDH2
CD1B
GAS7
IL16
TXN
DSCC1
STC2
MAPRE1
CD1C
GCH1
KCNMA1
TXNRD1
ELF3
TCF3
MSH6
CD22
GLI3
KIF13B
WNT5A
GEMIN4
TCF7L1
PML
CD68
GPRASP1
KL
GM2A
TCF7L2
POMP
CD80
GREB1
LAD1
GPSM2
TXNIP
PSMB4
CDK5R1
IGH
LAT
GSPT1
RYBP
PSMB5
CFB
IGHG1
LFNG
HMGB3
TOP2A
PSMB7
CHL1
NBPF10
MED12
HMMR
UBE2A
PSMD14
CIITA
NUMA1
MOG
HNRNPAB
UBE2B
PSMD3
CR1
PDE6B
MX2
HPSE
PSMD7
CRP
PGR
MCCC2
HRASLS
CST3
PHLDA2
MRPL12
IDH2
CXCL14
PPY
NAE1
KIAA0101
CXCR4
RLN2
NXN
LGALS1
AASS
ENOSF1
MMRN2
SESN1
CALM1
NME1-
ABCC8
FAM105A
MPP2
SFI1
CAMSAP1
NME2
ACAP2
FAM117A
MYO19
SLC35A2
CETN3
PARPBP
ACSF2
FAM120A
N4BP2L1
SLC6A5
CFAP20
PGK1
AHCYL1
FAM129A
NBEA
SLCO1A2
CMC2
PLCH1
ALDH1A2
FAM49B
NCAPD3
SPATA6
CNOT8
RAB22A
ANKHD1-
FAM86B1
NDUFAF5
TBRG4
COG8
SFXN1
EIF4EBP3
FCER1A
NFATC1
TCTN1
COQ9
SHMT1
ANKRD11
GCC2
NOP2
TLDC1
CORO1C
SMC4
APOM
GLTSCR2
NSUN5
TLE4
DKC1
SNRPA1
ARL3
GTPBP2
OSBPL1A
TMC6
DONSON
STIL
BIN3
HAUS5
PADI1
TSKS
EMC8
SUGCT
BSDC1
HDC
PDK3
TSR1
ENY2
TMEM208
BTD
HOOK2
PHF8
TTC12
FKBP3
TPD52L2
BTN2A1
HOXA4
PIEZO1
VAMP1
GGH
TRIP13
BTN3A3
HPN
PPIL2
VAMP2
GLT8D1
WDR41
C12orf49
HS3ST1
PPP3R1
WDR19
GRHPR
YIPF3
CALR
HTN1
PSD4
ZCCHC24
GTSE1
ZNF593
CAMK2B
HYI
PUM1
ZFP36L2
HELLS
CAMK4
INADL
RAB30
ZMYND10
HJURP
CASC1
ITM2C
RAB6B
ZNF22
KCMF1
CCDC176
ITPR1
RAI2
ZNF506
KDM5A
CCDC25
IVD
RALGAPA1
ZNF778
KIF14
CD1E
KIAA0930
RAPGEF3
ZSCAN32
MRPL18
CNTRL
KIAA1549L
RCAN1
ZZEF1
MRPL9
CPSF7
LAP3
RPS6KA6
ACOT13
MRPS17
CROCC
ME3
SERHL2
NFATC3
CTDSPL
ABAT
RECQL5
HEATR3
ABCB1
RTN1
AHNAK
RUNX1
KIF18A
ACAN
TENC1
ALPK1
SCUBE2
KIF23
AMN
TGFB3
BCAT2
SF3B1
KPNA2
COL4A6
TGFBR3
BMP8A
SF3B2
PAPOLA
CSF1
ADAM9
BTRC
SLC27A2
RAD51AP1
DDX11
ADM
CACNA1G
SLC6A2
RFC4
FGFR1
CALB2
CALCOCO1
SMARCC2
RPN1
FGFR2
CTSV
CBX7
SNRNP70
SEC61G
GSTM1
DBNDD1
COL14A1
SRSF7
SF3B3
GUSB
FAM96B
DCLRE1C
SSX3
SMAD5
IGF1
IGF1R
ESR1
SYMPK
SMYD2
LRRN3
KIF11
FBXO4
SYNC
SPAG5
MAP3K12
KIF210A
FMO5
TMC5
SRPK1
MST1
LAPTM4B
GART
USP19
SUB1
MYB
MMP15
H6PD
USP4
TAF11
NTRK2
RAB2A
JADE2
WSB1
TAF2
RBM5
SERPINH1
KIRG1
ACTR3
TCEB1
RLN1
TCEB2
KMT2A
AQP9
USP10
MAFG
ARPC4
VPS28
MAPRE2
ATAD2
WWTR1
MYOF
AURKA
XPOT
NOVA1
CA9
NSMCE4A
CDK7
POLE2
CEP55
PTGDS
CFDP1
PTGER3
DSN1
ACAA1
MTMR3
RPS28
EIF6
SLC25A5
ABHD14A
RPS4XP2
ACKR1
MTMR7
RPS4X
EPRS
SLC52A2
C1orf21
RPS4XP3
ACSL6
MXD4
RPS6
ETFA
SPIN1
C3orf18
SLC35D2
ADRA2A
MYOZ3
SAMD4A
EXOSC4
SQLE
C4A
SLC38A7
AGTR2
MYT1
SIRPA
EXOSC7
STAU1
CCDC30
SPATA6L
AUNIP
NME5
SLC16A5
GNB2L1
SYNCRIP
CFAP69
SSX7
C2CD2
NMT1
SLC4A7
GPR56
TKT
CLUL1
TNXA
CCDC170
NPY1R
SLC7A6
GTPBP4
TMEM194A
FCGR3B
TPSAB1
CELSR2
NPY5R
SORBS1
ILF2
TUBA1B
GUSBP11
TPSB2
CHAD
OSGEPL1
SQSTM1
KARS
UBE2V1
IGHD
UGT1A8
CREBL2
P2RY4
SRPK3
LAMA3
YWHAZ
IGHJ3
WDR78
CSDE1
P2RY6
THEMIS2
LRPPRC
IGHV3-20
ZNF710
CX3CR1
PAPPA
TTLL1
NDUFC1
IGHV3-23
ZNRD1-
CYR61
PDCD2
ZNF395
NELFE
IGLJ3
AS1
DDX3X
PDCD4
ABHD5
NOP56
KIAA0040
BOLA2
DHTKD1
PER3
ADRBK2
QARS
KIR2DL1
MRPL23
EGOT
PNPLA4
AIMP1
RACGAP1
KIR2DL3
EIF1
PTCD3
ALG3
RAD21
LINC01260
EML2
PTPN1
BRIX1
RAD23B
LOC389906
EPHX2
PTPRO
CDKN3
RC3H2
LRRC48
FAM134A
PTPRT
CHAF1A
RPL14
NBPF8
FRS3
PURA
EIF3A
RPL15
NSUN7
ICA1
RAMP2
EIF3B
RPL29
PGAP2
LAMA2
RGS5
EIF3K
RPS9
PGPEP1
LPAR2
RHBDD3
EIF4B
RPSA
RBMY1J
LZTS1
RPL10
EIF4E
SFPQ
RBMY2MP
MAOA
RPL22
EIF4G1
SHCBP1
RGPD6
Genes whose overexpression is associated with poorer survival are in bold and those whose underexpression is associated with poorer survival are underlined
Genes whose overexpression is associated with poorer survival are in bold and those whose underexpression is associated with poorer survival are underlined
The preceding example identified 133 genes, associated with 12 oncogenic functions, the expression of which is strongly associated with cancer aggressiveness and clinical outcome (Table 22). The expression of genes from this list was investigated for association with survival in (i) follicular lymphoma patients before receiving pidilizurnab in combination with rituximab (Westin et al. Lancet Oncol, 2014, vol 15(1)) (ii) colorectal cancer patients treated with cetuximab (GSE5851); (iii) triple negative breast cancer patients treated with cetuximab and cisplatin (GSE23428); (iv) lung cancer patients treated with. erlotinib (GSE33072): and (v) lung cancer patients treated with sorafenib (GSE33072). This analysis identified new sets of genes, with partial overlap to the iBCR signature, the expression of which was highly associated with survival in the different treatment groups (Table 23). Scores for each patient group, which were calculated based on these gene signatures were shown to be highly predictive of survival in these patient groups (pidilizumab+rituximab:
Genes whose underexpression is associated with a response to treatment are in bold and those whose overexpression is associated with a response to treatment are underlined
| Number | Date | Country | Kind |
|---|---|---|---|
| 2014900813 | Mar 2014 | AU | national |
| 2014901212 | Apr 2014 | AU | national |
| 2014904716 | Nov 2014 | AU | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/AU2015/050096 | 3/11/2015 | WO | 00 |
