Patent Application
20110014191
The present invention relates to a method for analyzing cancer comprising detection of differential expression of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes encoding serine/threonine kinases listed in Table 1, or of said 16 genes.
It finds many applications in particular in the development of prognosis or diagnostic of cancer or for monitoring the treatment of a patient with a cancer.
In the description which follows, the references between brackets [ ] refer to the attached reference list.
All the documents cited herein in the reference list are incorporated by reference in the texte below.
Breast cancer (BC) is a heterogeneous disease whose clinical outcome is difficult to predict and treatment is not as adapted as it should be. BC can be defined at the clinical, histological, cellular and molecular levels. Efforts to integrate all these definitions improve our understanding of the disease and its management (Charafe-Jauffret E, Ginestier C, Monville F, et al. How to best classify breast cancer: conventional and novel classifications (review). Int J Oncol 2005; 27:1307-13 [1]). Initial studies using DNA microarrays have identified five major BC molecular subtypes (luminal A and B, basal, ERBB2-overexpressing and normal-like) (Perou C M, Sorlie T, Eisen M B, et al. Molecular portraits of human breast tumours. Nature 2000; 406:747-52; Sorlie T, Perou C M, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98:10869-74; Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 2003; 100:8418-23; Bertucci F, Finetti P, Rougemont J, et al. Gene expression profiling identifies molecular subtypes of inflammatory breast cancer. Cancer Res 2005; 65:2170-8 [2-5]). These subtypes, which are defined by the specific expression of an intrinsic set of almost 500 genes, are variably associated with different histological types and with different prognosis. Luminal A BCs, which express hormone receptors, have an overall good prognosis and can be treated by hormone therapy. ERBB2-overexpressing BCs, which overexpress the ERBB2 tyrosine kinase receptor, have a poor prognosis and can be treated by targeted therapy using trastuzumab or lapatinib (Geyer C E, Forster J, Lindquist D, et al. Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N Engl J Med 2006; 355:2733-43; Hudis C A. Trastuzumab—mechanism of action and use in clinical practice. N Engl J Med 2007; 357:39-51 [6,7]). No specific therapy is available against the other subtypes although the prognosis of basal and luminal B tumors is poor. This biologically relevant taxonomy remains imperfect since clinical outcome may be variable within each subtype, suggesting the existence of unrecognized subgroups.
Progress can be made in several directions. First, it is necessary to identify among good prognosis tumors such as luminal A BCs the ones that will relapse and metastasize. Second, a better definition of poor prognosis BCs and associated target genes will allow the development of new drugs that will in turn allow a better management of these cancers.
The human kinome constitutes about 1.7% of all human genes (Manning G, Whyte D B, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science 2002; 298:1912-34 [8]), and represents a great part of genes whose alteration contributes to oncogenesis (Futreal P A, Coin L, Marshall M, et al. A census of human cancer genes. Nat Rev Cancer 2004; 4:177-83 [9]). Protein kinases mediate most signal transduction pathways in human cells and play a role in most key cell processes. Some kinases are activated or overexpressed in cancers, and constitute targets for successful therapies (Krause D S, Van Etten R A. Tyrosine kinases as targets for cancer therapy. N Engl J Med 2005; 353:172-87 [10]). In parallel to ongoing systematic sequencing projects (Stephens P, Edkins S, Davies H, et al. A screen of the complete protein kinase gene family identifies diverse patterns of somatic mutations in human breast cancer. Nat Genet 2005; 37:590-2 [11]), analysis of differential expression of kinases in cancers may identify new oncogenic activation pathways. As such, kinases represent an attractive focus for expression profiling in two important subtypes of BC.
So, evolution remains difficult to predict within some subtypes such as luminal A BC, and treatment is not as adapted as it should be. Refinement of prognostic classification and identification of new therapeutical targets are needed.
The authors of the present invention have now discovered, entirely unexpectedly, that the expression of genes encoding certain serine/threonine kinases involved in mitosis, allows distinguishing subgroups of cancers, e.g. two subgroups of breast cancer, more particularly luminal A breast cancer: luminal Aa, of good prognosis, and luminal Ab, of poor prognosis.
Surprisingly, the authors also found that this set of genes is sufficient to distinguish basal from luminal A tumors, e.g., cancers.
So, in a first aspect, the invention relates to a method of analyzing cancer, advantageously breast cancer, comprising detecting differential expression of at least one of the 16 genes encoding serine/threonine kinases listed in Table 1.
In other words the present invention relates to a method for analyzing cancer, advantageously breast cancer, comprising detection of differential expression of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes encoding serine/threonine kinases listed in Table 1, or of said 16 genes.
Table 1 indicates the name of each gene with its gene symbol, the kinase activity, and for each gene the relevant sequence(s) defining the gene (identification numbers: SEQ ID NO.). The present invention defines the nucleotide sequences by the different genes but it may cover also a definition of the polynucleotide sequences by the name of the gene or fragments thereof.
In a particular embodiment, the invention relates to a method for analyzing breast cancer comprising detection of differential expression of the 16 genes encoding serine/threonine kinases listed in Table 1.
In other words, the method of the invention is a method for analyzing a breast cancer based on the analysis of the over or under expression of genes in a breast tissue sample, said analysis comprising the detection of at least one of the 16 genes mentioned above.
By “genes”, in the sense of the present invention, is meant a polynucleotide sequence, e.g., isolated, such as deoxyribonucleic acid (DNA), and, where appropriate, ribonucleic acid (RNA). The sequence of the genes may be the sequences SEQ ID NO. 17-32, or any complement sequence. This sequence may be the complete sequence of the gene, or a subsequence of the gene which would be also suitable to perform the method of the analysis according to the invention. A person skilled in the art may choose the position and length of the gene by applying routine experiments. The term should also be understood to include, as equivalents, analogs of RNA or DNA made from nucleotide analogs, and, as applicable to the embodiment being described, single (sense or antisense) and double-stranded polynucleotides. ESTs, chromosomes, cDNAs, mRNAs, and rRNAs are representative examples of molecules that may be referred to as nucleic acids. DNA may be obtained from said nucleic acids sample and RNA may be obtained by transcription of said DNA. In addition, mRNA may be isolated from said nucleic acids sample and cDNA may be obtained by reverse transcription of said mRNA.
By <<differential expression>>, in the sense of the present invention, is meant the difference between the level of expression of a gene in a normal tissue, i.e. a breast tissue free of cancer, and the level of expression of the same gene in the sample analysed.
Thus, the detection of differential expression of genes is the analysis of over or underexpression of polynucleotide sequences on a biological sample. Advantageously, this analysis comprises the detection of the overexpression and underexpression of at least one or more genes as described above.
By <<overexpression>>, in the sense of the present invention, is meant a level of expression that is higher than the level of a reference sample, for example a sample of breast tissue free of breast cancer.
By <<underexpression>>, in the sense of the present invention, is meant a level of expression that is lesser than the level of a reference sample, for example a sample of breast tissue free of breast cancer.
The over or under expression may be determined by any known method of the prior art. It may comprise the detection of difference in the expression level of the polynucleotide sequences according to the present invention in relation to at least one reference. Said reference comprises for example polynucleotide sequence(s) from sample of the same patient or from a pool of patients afflicted with luminal breast cancer, or from a pool of sample as described in Finetti et al. (Finetti P., Cervera N, Charafe-Jauffret E., Chabannon C., Charpin C, Chaffanet M., Jacquemier J., Viens P., Birnbaum D., Bertucci F. Sixteen kinase gene expression identifies luminal breast cancers with poor prognosis. Cancer Res. 2008; 68: (3); 1-10 [27]), or selected among reference sequence(s) which may be already known to be over or under expressed. The expression level of said reference can be an average or an absolute value of reference polynucleotide sequences. These values may be processed in order to accentuate the difference relative to the expression of the polynucleotide of the invention.
The analysis of the over or underexpression of polynucleotide sequences can be carried out on sample such as biological material derived from any mammalian cells, including cell lines, xenografts, human tissues preferably breast tissue, etc. The method according to the invention may be performed on sample from a patient or an animal.
Advantageously, the overepxression of at least one sequence is detected simultaneously to the underexpression of others sequences. “Simultaneously” means concurrent with or within a biologic or functionally relevant period of time during which the over expression of a sequence may be followed by the under expression of another sequence, or conversely, e.g., because both expressions are directly or indirectly correlated.
The number of sequences according to the various embodiments of the invention may vary in the range of from 1 to the total number of sequences described therein, e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 sequences.
In a particular embodiment of the invention, the differential gene expression separates basal and luminal A breast cancer.
By <<basal breast cancer>>, in the sense of the present invention, is meant a Basal-phenotype or basal-like breast cancers, characterized by specific molecular profile based on a gene list defined in Sorlie et al. [3], incorporated herein by reference. The specific molecular profile may be high expression of keratins 5 and 17, and fatty acid binding protein 7.
By <<luminal A breast cancer>>, in the sense of the present invention, is meant a breast cancer characterized by molecular profile on a specific gene list defined in Sorlie et al. [3], incorporated herein by reference. The specific molecular profile may be high expression of the ERα gene GATA binding protein 3, X-box binding protein 1, trefoil factor 3, hepatocyte nuclear factor 3, and estrogen-regulated LIV-1.
Advantageously, the differential gene expression distinguishes subgroups of luminal A tumors of good or poor prognosis.
By <<subgroups>>, in the sense of the present invention, is meant groups of patients afflicted with luminal A breast cancer of good prognosis and groups of patients afflicted with luminal A breast cancer of poor prognosis.
By <<good prognosis>>, in the sense of the present invention, is meant luminal A tumors (Aa cases) characterized by low mitotic activity as compared to other luminal A tumors (Ab cases). Good prognosis may also refer to the scoring defined below and according to Finetti el al. ([27]), i.e. a negative kinase-score. A good prognosis may also indicate that the patient afflicted with luminal A breast cancer is expected to have no distant metastases within 5 years of initial diagnosis of cancer (i.e. relapse-free survival (RFS) superior to 5 years).
By <<low mitotic activity>>, in the sense of the present invention, is meant kinase-score value below 0 ([27]), i.e. a negative kinase-score. By <<poor prognosis>>, in the sense of the present invention, is meant luminal A tumors (Ab cases) characterized by high mitotic activity as compared to other luminal A tumors (Aa cases). Poor prognosis may also refer to the scoring defined below and according to Finetti el al. ([27]), i.e. a positive kinase-score. A poor prognosis may also indicate that the patient afflicted with luminal A breast cancer is expected to have some distant metastases within 5 years of initial diagnosis of cancer (i.e. relapse-free survival (RFS) superior to 5 years).
By <<high mitotic activity>>, in the sense of the present invention, is meant kinase-score value above 0 ([27]), i.e. a positive kinase-score.
In this embodiment of the invention, the subgroup of luminal A tumors of poor prognosis presents a higher mitotic activity compared with other luminal A tumors.
Advantageously, the method may comprise the determination of the expression level or overexpression level of AURKA and/or AURKB and/or PLK genes. The overexpression of these genes may be associated with a poor clinical outcome.
The method may comprise the determination of the expression level of AURKA gene, or AURKB gene, or PLK gene.
The method of the invention may comprise the determination of AURKA and PLK genes, or the determination of the expression level of AURKB and PLK genes, or the determination of the expression level of AURKA and AURKB genes, or the determination of the expression level of AURKA and AURKB and PLK genes.
In a particular embodiment of the invention, the detection is performed on nucleic acids from a tissue sample.
By <<tissue sample>>, in the sense of the present invention, is meant a sample of tissue, preferably breast tissue or a cell. If the tissue sample is breast tissue, it may come from invasive adenocarcinoma.
In another embodiment of the invention, the detection is performed on nucleic acids from a tumor cell line.
By <<tumor cell line>>, in the sense of the present invention, is meant cell line derived from a cancer cell obtained from a patient.
In a particular embodiment of the invention, the dermination of the expression level of the gene(s) disclosed herein may be performed by various methods well-known in the art, e.g., real-time PCR (polymerase chain reaction), including 5′nuclease TaqMan® (Roche), Scorpions® (DxS Genotyping) (Whitcombe, D., Theaker J., Guy, S. P., Brown, T., Little, S. (1999)—Detection of PCR products using self-probing amplicons and flourescence. Nature Biotech 17, 804-807 [35]) or Molecular Beacons™ (Abravaya K, Huff J, Marshall R, Merchant B, Mullen C, Schneider G, and Robinson J (2003) Molecular beacons as diagnostic tools: technology and applications. Clin Chem Lab Med 41, 468-474 [36]).
In another embodiment of the invention, the detection is performed on DNA microarrays.
By <<DNA microarrays>>, in the sense of the present invention, is meant an arrayed series of thousands of microscopic spots of DNA oligonucleotides, each containing picomoles of a specific DNA sequence chosen among the genes of the invention. This DNA oligonucleotide is used as probes to hybridize a cDNA or cRNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by fluorescence-based detection of fluorophore-labeled targets to determine relative abundance of nucleic acid sequences in the target.
In standard microarrays, the probes are attached to a solid surface by a covalent bond to a chemical matrix (via epoxy-silane, amino-silane, lysine, polyacrylamide or others).
The cDNA oligonucleotide probes (also called “probeset”) that may be used to hybridyze a DNA or RNA sample corresponding to one or more of the 16 genes encoding serine/threonine kinases as defined above are defined in Table 2.
The cDNA oligonucleotide probesets that may be used to hybridyze a DNA or RNA sample corresponding to one or more of the 16 genes encoding serine/threonine kinases, can be any sequence between 3′ and 5′ end of the polynucleotide sequence(s) of the corresponding SET as defined in Table 2, allowing a complete detection of the implicated genes.
In order to detect the expression of a determined gene described above, at least one probeset sequence or subsequence of the corresponding SET may be used.
By “cDNA subsequence of the gene”, in the sense of the invention, is meant a sequence of nucleic acids of cDNA total sequence of the gene that allows a specific hybridization under stringent conditions, as an example more than 10 nucleotides, preferably more than 15 nucleotides, and most preferably more than 25 nucleotides, as an example more than 50 nucleotides or more than 100 nucleotides.
In other words, the method of the invention may comprise the detection of at least one, or at least two or three polynucleotide sequence(s) or subsequence(s), or a complement thereof, selected in the SETS defined in Table 2.
Another aspect of the invention is to provide a polynucleotide library that molecularly characterizes cancer comprising or corresponding to at least one of the 16 genes encoding serine/threonine kinases listed in Table 1.
The polynucleotide library of the invention may comprise, or may consist of, at least one polynucleotide sequence allowing the detection of a corresponding at least one gene of the 16 genes encoding serine/threonine kinases listed in Table 1.
In other words, an aspect of the invention relates to a polynucleotide library that molecularly characterizes a cancer, comprising or corresponding to polynucleotide sequence(s) allowing the detection of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes encoding serine/threonine kinases listed in Table 1, or to said 16 genes.
The polynucleotide library of the invention may comprise, or may consist of at least one, or at least 2 or 3, polynucleotide sequence(s) or subsequence(s), or complement(s) thereof, selected in at least one SET of Table 2, allowing the detection of a corresponding at least one gene of the 16 genes encoding serine/threonine kinases listed in Table 1. In a particular aspect of the invention, the invention relates to polynucleotide library that molecularly characterizes a cancer comprising or corresponding to the 16 genes encoding serine/threonine kinases listed in Table 1. In this embodiment, the polynucleotide library of the invention may comprise, or may consist of, polynucleotide sequences allowing the detection of the 16 genes encoding serine/threonine kinases listed in Table 1.
For example, in this case, the polynucleotide library of the invention may comprise, or may consist of at least one, or at least 2 or 3, polynucleotide sequence(s) or subsequence(s), or complement(s) thereof, selected in each SET of Table 2.
By <<corresponding to>>, in the sense of the present invention, is meant a polynucleotide library that consists of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes encoding serine/threonine kinases listed in Table 1, or of said 16 genes.
In a particular embodiment of the invention, the library is immobilized on a solid support.
Such a solid support may be selected from the group comprising at least one of nylon membrane, nitrocellulose membrane, glass slide, glass beads, membranes on glass support or silicon chip, plastic support.
Another aspect of the invention is to provide a method of prognosis or diagnostic of breast cancer or for monitoring the treatment of a patient with a breast cancer comprising the implementation of the method of analyzing breast cancer as described above on nucleic acids from a patient.
Such a method is the use of a method for analyzing breast cancer as described above for prognosis or diagnostic of breast cancer or for monitoring the treatment of a patient with a breast cancer comprising the implementation of the method of analyzing breast cancer as described above on nucleic acids from a patient.
Another aspect of the invention is to provide a method for analysing differential gene expression associated with breast cancer disease, comprising:
a) obtaining a polynucleotide sample from a patient,
b) reacting said polynucleotide sample obtained in step (a) with a polynucleotide library as defined above, and
c) detecting the reaction product of step (b).
In other words, the invention provides a method for analysing differential gene expression associated with breast cancer disease, comprising:
a) reacting a polynucleotide sample from the patient with the polynucleotide library as defined above, and
b) detecting a reaction product of step (b).
A differential gene expression “associated with” breast cancer refers to an underexpression or a overexpression of a nucleic acid caused by, or contributed to by, or causative of a breast cancer.
By “reacting a polynucleotide sample with the polynucleotide library”, in the sense of the invention, is meant contacting the nucleic acids of the sample with polynucleotide sequences in conditions allowing the hybridization of cDNA or mRNA total sequence of the gene or of cDNA or mRNA subsequences or of primers of the gene with polynucleotide sequences of the library.
By “reaction product” in the sense of the present invention, is meant the product resulting of the hybridization between the polynucleotide sample from the patient with the polynucleotide library as defined above.
The detection of the reaction product of step (b) may be quantitative, related to the transcript expression level.
In a particular embodiment of the invention, the method for analysing differential gene expression associated with breast cancer disease further comprises:
a) obtaining a reference polynucleotide sample,
b) reacting said reference sample with said polynucleotide library, for example by hybridising the polynucleotide sample with the polynucleotide library as defined above,
c) detecting a control sample reaction product, and
d) comparing the amount of said polynucleotide sample reaction product to the amount of said control sample reaction product.
By <<reference polynucleotide sample>>, in the sense of the present invention, is meant one or more biological samples from a cell, a tissue sample or a biopsy from breast. Said reference may be obtained from the same female mammal than the one to be tested or from another female mammal, preferably from the same specie, or from a population of females mammal, preferably from the same specie, that may be the same or different from the test female mammal or subject. Said control may correspond to a biological sample from a cell, a cell line, a tissue sample or a biopsy from breast.
The step d) of comparison of the amount of said polynucleotide sample reaction product to the amount of said reference sample reaction product may be performed by any method well-known in the art.
For example, the method may comprise the following steps:
a) comparing molecular profile from breast cancer samples (e.g. 50, 100 or more, e.g., 138 breast cancers samples) based on polynucleotide library associated to kinome according to the gene list defined as covering all the kinase family according, e.g., to Manning et al. [8],
b) identifying a specific polynucleotides cluster (e.g. with 5, 10 or 16 kinase genes) by unsupervised Quality Threshold cluster analyses as described in Finetti et al. [27], where gene expression were observed differential among the luminal A breast cancers,
c) computing a score using mean of the kinase genes combined with normalisation parameters, to assess the classification of luminal A breast cancers.
By “kinome” is meant the ensemble of kinases proteins that are expressed in a particular cell or tissue or present in the genome of an organism.
Another aspect of the invention is a method for classifying a patient, e.g., a female patient, afflicted with a breast cancer as having a luminal A breast cancer with relapse-free survival (RFS) superior to 5 years (luminal Aa breast cancer) or as having a luminal A breast cancer with RFS inferior to 5 years (luminal Ab breast cancer), comprising the steps of:
a) calculating the kinase score (KS) based on the expression of at least one gene, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 kinases, or on said 16 kinases listed in Table 1 or their expression product, of the sample of said patient, distinguishing the subgroups luminal Aa and luminal Ab, and,
b) classifying said patient as having luminal Aa breast cancer when the kinase score is negative, or classifying patient as having luminal Ab when the kinase score is positive.
By “Kinase Score (KS)”, in the sens of the invention, is meant a score which is based on the expression level of 16 kinase genes. It was defined as:
where A and B represent normalization parameters, which make the KS comparable across the different datasets, n the number of available kinase genes (7 to 16), and xi the logarithmic gene expression level in tumor i. Using a cut-off value of 0, each tumor was assigned a low score (KS<0, i.e. with overall low expression of 16 kinase genes) or a high score (KS>0, i.e. with overall strong expression of 16 kinase genes). In the present invention, the number of available kinase genes, i.e. n, is from 1 to 16.
The method of the invention allows the prediction of the clinical outcome of patient afflicted with luminal A, by classifying these patients in luminal Aa or luminal Ab patients.
Another aspect of the invention is to provide a method for screening molecule for treating luminal A cases of poor prognosis comprising the analysis of the action of said molecule on at least one the 16 kinases listed in table 1 or their expression.
In other words, the invention relates to a method for screening molecule for treating luminal A cases of poor prognosis comprising the analysis of the action of said molecule on at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 kinases listed in table 1 or their expression, or on said 16 kinases.
In a particular aspect of the invention, the invention relates to a method for screening molecule for treating luminal A cases of poor prognosis comprising the analysis of the action of said molecule on at least one, or at least two, or at least three, or more, e.g., all of the 16 kinases listed in table 1 or their expression product.
By <<the action of said molecule>>, in the sense of the present invention, is meant the positive effect of the molecule on the survival of the patient, or on the RFS of the patient, the reduction of size of the tumor, or the diminution of the expression of the kinase.
Another aspect of the invention is to provide a kit comprising the polynucleotide library as described above, for carrying out a method of the invention, i.e. a method for analyzing breast cancer, a method for analysing differential gene expression associated with breast cancer, or a method for screening molecule for treating luminal A cases of poor prognosis.
A kit of the invention may contain sets of polynucleotide sequences of the library as well as control samples. The kit may also contain test reagents necessary to perform the pre-hybridization, hybridization, washing steps and hybridization detection.
Another aspect of the invention is a method for treating a patient with a breast cancer. This method comprises i) implementing a method of analysing of differential gene expression profile according to the present invention on a sample from said patient, and ii) determining a treatment for this patient based on the analysis of differential gene expression profile obtained with said method. “Treating” encompasses treating as well as ameliorating at least one symptom of the condition or disease.
Another aspect of the invention is a method for predicting clinical outcome for a patient diagnosed with cancer, comprising determining the expression level of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes listed in Table 1, or all of the 16 genes of Table1, or their expression products, in a cancer tissue obtained from the patient, normalized against a reference gene or genes, and compared to the amount found in a reference cancer tissue set, wherein overexpression of the group of genes predicts a poor clinical outcome.
By “clinical outcome” in the sens of the invention, is meant the survival, the partial remission, the total remission, the time to progression of the disease or the relapse of the disease. By “clinical outcome”, it may be also meant the evolution of luminal A breast cancer to luminal Aa or luminal Ab breast cancer.
The poor clinical outcome may be measured in terms of relapse-free survival (RFS). A poor clinical outcome may indicate that the patient afflicted by luminal A breast cancer is expected to have some distant metastases within 5 years of initial diagnosis of cancer.
This method may be used to predict clinical outcome of patient diagnosed with a breast cancer, or a colon cancer, or a lung cancer, or a prostate cancer, or a hepatocellular cancer, or a gastric cancer, or a pancreatic cancer, or a cervical cancer, or a ovarian cancer, or a liver cancer, or a bladder cancer, or a cancer of the urinary tract, or a thyroid cancer, or a renal cancer, or a carcinoma, or a melanoma, or a brain cancer.
Preferably, all of the methods of the invention may be applicable to the cancers listed above.
In a particular embodiment, the method may be used to predict clinical outcome of a patient diagnosed with breast cancer.
Advantageously, the method may comprise the determination of the expression level or overexpression level of AURKA and/or AURKB and/or PLK genes. The overexpression of these genes may be associated with a poor clinical outcome.
The method may comprise the determination of the expression level of AURKA gene, or AURKB gene, or PLK gene.
The method of the invention may comprise the determination of AURKA and PLK genes, or the determination of the expression level of AURKB and PLK genes, or the determination of the expression level of AURKA and AURKB genes, or the determination of the expression level of AURKA and AURKB and PLK genes.
Advantageously, the expression level of the genes may be determined using RNA obtained from a frozen or fresh tissue sample.
The expression level may be determined by reverse phase polymerase chain reaction (RT-PCR).
Another object of the invention is a method of predicting the likelihood of the recurrence of cancer following treatment in a cancer patient, comprising determining the expression level of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes listed in Table 1, or all of the 16 genes of Table1, or their expression products, in a cancer tissue obtained from the patient, normalized against a control gene or genes, and compared to the amount found in a reference cancer tissue set, wherein overexpression of the group of genes indicates increased risk of recurrence following treatment.
The cancer analyzed by the method of the invention may be breast cancer, or colon cancer, or lung cancer, or prostate cancer, or hepatocellular cancer, or gastric cancer, or pancreatic cancer, or cervical cancer, or ovarian cancer, or liver cancer, or bladder cancer, or cancer of the urinary tract, or thyroid cancer, or renal cancer, or carcinoma, melanoma, or brain cancer.
Advantageously, the cancer may be breast cancer.
The expression level may be determined before any surgical removal of tumor, or may be determined following surgical removal of tumor, i.e. removal of cancer.
The expression level may be determined using RNA obtained from a fresh or frozen sample.
The expression level may be determined by reverse phase polymerase chain reaction (RT-PCR).
The method of predicting the likelihood of the recurrence of cancer may follow the treatment of the cancer with one or more kinase inhibitor drugs, e.g., serine and/or threonine kinase inhibitor drugs, e.g., the following drugs: MK0457, PHA-739358, MLN8054, AZD1152, ON01910, BI2536, flavopiridol, USN-01, ZM447-439 (AstraZeneca, MK0457 (Merck), AZD1152 (AstraZeneca), PHA-680632, MLN8054 (Millenium Pharmaceutical), PHA739358 (Nerviano Sciences), scytonemin, BI2536, ON01910 as described in Carvajal D., Tse Archie, Schwartz G. Aueora kinases: new targets for cancer therapy. Clin. Cancer Res 2006; 12(23) ([33]) and Strebhardt K., Ullrich A. Targeting polo-like kinase 1 for cancer therapy. Nature 2006, Vol. 6, 321-330 ([34]), the content of which is incorporated herein by reference.
Another object of the invention is a kit comprising one or more of (1) extraction buffer/reagents and protocol; (2) reverse transcription buffer/reagents and protocol; and (3) quantitative PCR buffer/reagents and protocol suitable for performing a method of the invention.
Advantageously, the kit may comprise a data retrieval and analysis software.
Advantageously, the kit may comprise pre-designed primers.
Advantageously, the kit may comprise pre-designed PCR probes and primers.
Another object of the invention is a method for predicting, for example in vitro, the therapeutic success of a given mode of treatment in a subject having cancer, comprising
(i) determining the pattern of expression levels of at least one, or at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15 of the 16 genes encoding serine/threonine kinases listed in Table 1, or of said 16 genes,
(ii) comparing the pattern of expression levels determined in (i) with one or several reference pattern(s) of expression levels,
(iii) predicting therapeutic success for said given mode of treatment in said subject from the outcome of the comparison in step (ii).
Advantageously, the cancer may be selected from the group consisting of breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, and brain cancer.
Advantageously, the cancer may be breast cancer.
The given mode of treatment (i) may act on cell proliferation, and/or (ii) may act on cell survival, and/or (iii) may act on cell motility; and/or (iv) may comprise administration of a chemotherapeutic agent.
The given mode of treatment may be E7070, PHA-533533, hymenialdisine, NU2058 & NU6027, AZ703, BMS-387032, CYC202 (R-roscovitine), CDKi277, NU6140, PNU-252808, RO-3306, CVT-313, SU9516, Olomoucine, ZK-CDK (ZK304709), JNJ-7706621, PD0332991, PD0183812, Fascplysin, CA224, CINK4, caffeine, pentoxifylline, wortmannin, LY294002, UCN-01, debromohymenialdisine, Go6976, SB-218078, ICP-1, CEP-3891, TAT-S216A, CEP-6367, XL844, PD0166285, BI2536, ON01910, Scytonemin, wortmannin, HMN-214, cyclapolin-1, hesperadin, JNJ-7706621, PHA-680632, VX-680 (MK-0457), ZM447-439, MLN8054, R763, AZD1152, CYC116, SNS-314, MKC-1693, AT9283, quinazoline derivatives, MP235, MP529, cincreasin, SP600125 (de Carcer et al. Targeting cell cycle kinases for cancer therapy, Current Medicinal Chemistry, 2007, Vol. 14, No. 1; 1-17 [29], Malumbres et al. Current Opinion in genetics & Development 2007, 17:60-65 [30], Malumbres et al. Therapeutic opportunities to control tumor cell cycles, Clin. Transl. Oncol. 2006; 8(6):1-000 [31], Iressa (gefitnib, ZD1839, anti-EGFR, PDGFR, c-kit, Astra-Zeneca); ABX-EGFR (anti-EGFR, Abgenix/Amgen); Zamestra (FTI, J & J/Ortho-Biotech); Herceptin (anti-HER2/neu, Genentech); Avastin (bevancizumab, anti-VEGF antibody, Genentech); Tarceva (ertolinib, OSI-774, RTK inhibitor, Genentech-Roche); ZD66474 (anti-VEGFR, Astra-Zeneca); Erbitux (IMC-225, cetuximab, anti-EGFR, Imclone/BMS); Oncolar (anti-GRH, Novartis); PD-183805 (RTK inhibitor, Pfizer); EMD72000, (anti-EGFRNEGF ab, MerckKgaA); CI-1033 (HER2/neu & EGF-R dual inhibitor, Pfizer); EGF10004; Herzyme (anti-HER2 ab, Medizyme Pharmaceuticals); Corixa (Microsphere delivery of HER2/neu vaccine, Medarex), and the drugs listed in Awada et al., The Pipeline of new anticancer agents for breast cancer treatment in 2003, Critical Reviews in Oncology/Hematology 48 (2003), 45-63 ([32]), ZM447-439 (AstraZeneca, MK0457 (Merck), AZD1152 (AstraZeneca), PHA-680632, MLN8054 (Millenium Pharmaceutical), PHA739358 (Nerviano Sciences), scytonemin, BI2536, ON01910 ([33] and [34]).
The method of the invention may use a predictive algorithm.
Another object of the invention is a method of treatment of a neoplastic disease in a subject, comprising the steps of:
a) predicting therapeutic success for a given mode of treatment in a subject having cancer, e.g., breast cancer by any method of the invention,
b) treating said neoplastic disease in said patient by said mode of treatment, if said mode of treatment is predicted to be successful.
Another object of the invention is a method of selecting a therapy modality for a subject afflicted with a neoplastic disease, comprising
(i) obtaining a biological sample from said subject,
(ii) predicting from said sample, by any method of the invention, therapeutic success in a subject having cancer, e.g., breast cancer, for a plurality of individual modes of treatment,
(iii) selecting a mode of treatment which is predicted to be successful in step (ii).
Advantageously, the expression level may be determined:
(i) with a hybridization based method, or
(ii) with a hybridization based method utilizing arrayed probes, or
(iii) with a hybridization based method utilizing individually labeled probes, or
(iv) by real time PCR, or
(v) by assessing the expression of polypeptides, proteins or derivatives thereof, or (vi) by assessing the amount of polypeptides, proteins or derivatives thereof.
Other advantages may also appear to one skilled in the art from the non-limitative examples given below, and illustrated by the enclosed figures.
Breast cancer (BC) is a heterogeneous disease made of various molecular subtypes with different prognosis. However, evolution remains difficult to predict within some subtypes such as luminal A, and treatment is not as adapted as it should be. Refinement of prognostic classification and identification of new therapeutical targets are needed. Using oligonucleotide microarrays, we profiled 227 BCs. We focused our analysis on two major BC subtypes with opposite prognosis, luminal A (n=80) and basal (n=58), and on genes encoding protein kinases. Whole-kinome expression separated luminal A and basal tumors. The expression (measured by a Kinase Score KS) of 16 genes encoding serine/threonine kinases involved in mitosis distinguished two subgroups of luminal A tumors: Aa, of good prognosis, and Ab, of poor prognosis. This classification and its prognostic impact were validated in 276 luminal A cases from three independent series profiled across different microarray platforms. The classification outperformed the current prognostic factors in univariate and multivariate analyses in both training and validation sets. The luminal Ab subgroup, characterized by high mitotic activity as compared to luminal Aa tumors, displayed clinical characteristics and a KS intermediate between the luminal Aa subgroup and the luminal B subtype, suggesting a continuum in luminal tumors. Some of the mitotic kinases of the signature represent therapeutical targets under investigation. The identification of luminal A cases of poor prognosis should help select appropriate treatment, while the identification of a relevant kinase set provides potential targets.
Our study focused on the kinome of luminal A and bc cancers, whose relevance to cancer biology and therapeutics is well established (Manning G, Whyte D B, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science 2002; 298:1912-34 [8]). To our knowledge, this is the first study of profiling and exclusive and comprehensive analysis of kinase genes in bc.
As an exploratory step, we applied hierarchical clustering to 435 kinase genes. We found that luminal A and basal tumors had different global kinome expression patterns, with some degree of transcriptional heterogeneity within luminal A tumors. This observation suggests differential expression of many kinases, and consequently different phosphorylation programs between the two subtypes. Global clustering revealed broad coherent kinase clusters corresponding to cell processes (proliferation, differentiation) or to cell type (immune response), with overxepression of the proliferation cluster in basal samples and of the differentiation cluster in luminal A samples.
Interestingly, a Kinase Score (KS) based on their expression distinguished two subgroups of luminal A tumors (Aa and Ab) with different survival. Identified in our tumor series, this classification and its prognostic impact were validated in 276 luminal A cases from three independent series profiled across different microarray platforms. Importantly, the KS outperformed the current prognostic factors in uni- and multivariate analyses in both training and validation sets.
Analysis of molecular function and biological processes revealed that the prognostic value of this kinase signature is mainly related to proliferation. Indeed, the 16 genes encode kinases involved in G2 and M phases of the cell cycle. Aurora-A and -B are two major kinases regulating mitosis and cytokinesis, respectively. BUB1 (budding inhibited by benzimidazole), BUB1B, CHEK1 (checkpoint kinase 1), PLK1 (polo-like kinase 1), NEK2 (never in mitosis kinase 2) and TTK/MPS1 play key roles in the various cell division checkpoints. PLK4 (polo-like kinase 4) is involved in centriole duplication. CDC2/CDK1 is a major component of the cell cycle machinery in association with mitotic cyclins. CDC7, MELK (maternal embryonic leucine zipper kinase) and VRK1 (vaccinia-related kinase 1) are regulators of the S/G2 and G2/M transitions. SRPK1 regulates splicing. Not much is known about MASTL and PBK kinases.
Prognostic gene expression signatures related to grade (Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006; 98:262-72; Ivshina A V, George J, Senko O, et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 2006; 66:10292-301 [18, 19]) or proliferation (Dai H, van't Veer L, Lamb J, et al. A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res 2005; 65:4059-66 [20]) have been reported. We found respectively 8 and 10 of our 16 kinase genes in the lists of genes differentially expressed in grade I vs grade III BCs reported by Sotiriou et al (97 genes) and Ivshina et al (264 genes). Three kinase genes, AURKA, AURKB, and BUB1, are included in a prognostic set of 50 cell cycle-related genes [20], and AURKB is one of the 5 proliferation genes included in the Recurrence Score defined by Paik et al (Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351:2817-26 [21]). Furthermore, proliferation appears to be the most prominent predictor of outcome in many other published prognostic gene expression signatures (Desmedt C, Sotiriou C. Proliferation: the most prominent predictor of clinical outcome in breast cancer. Cell Cycle 2006; 5:2198-202 [22]). This link of our signature with proliferation also explains the correlation of our luminal A subgrouping with histological grade, which is in part based on a mitotic index. But interestingly, comparison with Ki67 and grade showed that our mitotic kinase signature performed better in identifying these tumors and predicting the survival of patients.
Targeting cell proliferation is a main objective of anticancer therapeutic strategies. Kinases have proven to be successful targets for therapies. Mitotic kinases have stimulated intense work focused on identifying novel antimitotic drugs. Some of them included in our signature represent targets under investigation (Miglarese M R, Carlson R O. Development of new cancer therapeutic agents targeting mitosis. Expert Opin Investig Drugs 2006; 15:1411-25 [23]). For example, targeting of Aurora kinases is a promising way of treating tumors (Carvajal R D, Tse A, Schwartz G K. Aurora kinases: new targets for cancer therapy. Clin Cancer Res 2006; 12:6869-75 [24]). Clinical trials of four Aurora kinase inhibitors are ongoing in the United States and Europe: MK0457 and PHA-739358 inhibit Aurora-A and Aurora-B, MLN8054 selectively inhibits Aurora-A, and AZD1152 selectively inhibits Aurora-B. Similarly, small-molecule inhibitors of PLK1 such as ON01910 and BI2536 are being tested (Strebhardt K, Ullrich A. Targeting polo-like kinase 1 for cancer therapy. Nat Rev Cancer 2006; 6:321-30 [25]), as well as flavopiridol (inhibitor of the cyclin-dependant kinase CDC2), and UCN-01 (inhibitor of CHEK1). Other less studied but potential therapeutic targets include TTK, BUB and NEK proteins (de Carcer G, de Castro I P, Malumbres M. Targeting cell cycle kinases for cancer therapy. Curr Med Chem 2007; 14:969-85 [26]).
Despite their relatively good prognosis as compared to luminal B tumors, luminal A tumors display a heterogeneous clinical outcome after treatment, which generally includes hormone therapy. It is important to define the cases that may evolve unfavorably, all the more so that different types of hormone therapy, chemotherapy, and targeted molecular therapy are available. Our poor prognosis subgroup of luminal A tumors (Ab cases) is characterized by high mitotic activity as compared to other luminal A tumors (Aa cases). Any error in the key steps in division regulated by these kinases—centrosome duplication, spindle checkpoint, microtubule-kinetochore attachment, chromosome condensation and segregation, cytokinesis—may lead to aneuploïdy and progressive chromosomal instability. This may in part explain the high grade and poor prognosis of these tumors.
In fact, the luminal Ab subgroup displayed clinical characteristics and a KS intermediate between the luminal Aa subgroup and the luminal B subtype. These subgroups were not previously recognized by the Sorlie's intrinsic gene set. We interpret this finding as follows. The use of intrinsic set distinguishes a large proportion of luminal B cancers but is unable to pick all proliferative cases. A small proportion of cases is left to cluster with the luminal A cases, and are therefore labeled luminal A. An explanation for the poor efficacy of Sorlie's set to define all proliferative luminal cases may be the low number of genes involved in proliferation, including a very low number of kinases. Our mitotic kinase signature makes possible to identify all proliferative luminal cases, and reveals a continuum of luminal cases from the more proliferative (luminal B) to the less proliferative (luminal Aa). Reciprocally, there may be a gradient of luminal differentiation giving a continuum of luminal BCs, including, from poorly-differentiated to highly-differentiated, luminal B, Ab and Aa (
A total of 227 pre-treatment early breast cancer samples were available for RNA profiling on Affymetrix microarrays. They were collected from 226 patients with invasive adenocarcinoma who underwent initial surgery at the Institut Paoli-Calmettes and Hôpital Nord (Marseille) between 1992 and 2004. Samples were macrodissected by pathologists, and frozen within 30 min of removal in liquid nitrogen. All profiled specimens contained more than 60% of tumor cells. Characteristics of samples and treatment are listed in Supplementary Table 1.
In addition, we profiled RNA extracted from 8 cell lines that provided models for cell types encountered in mammary tissues: 3 luminal epithelial cell lines (HCC1500, MDA-MB-134, ZR-75-30), 3 basal epithelial cell lines (HME-1, HMEC-derived 184B5, MDA-MB-231), and 2 lymphocytic B and T cell lines (Daudi and Jurkatt, respectively). All cell lines were obtained from ATCC (Rockville, Md.—http://www.atcc.org/) and were grown as recommended
Gene Expression Profiling with DNA Microarrays
Gene expression analyses were done with Affymetrix U133 Plus 2.0 human oligonucleotide microarrays containing over 47,000 transcripts and variants, including 38,500 well-characterized human genes. Preparation of cRNA from 3 μg total RNA, hybridizations, washes and detection were done as recommended by the supplier (Affymetrix). Scanning was done with Affymetrix GeneArray scanner and quantification with Affymetrix GCOS software. Hybridization images were inspected for artifacts.
Expression data were analyzed by the RMA (Robust Multichip Average) method in R software (Brian D. Ripley. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network., 1(1):23-25, February 2001 [28] and http://www.r-project.org/doc/bib/R-other_bib.html#R:Ripley:2001 using Bioconductor and associated packages (Irizarry R A, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003; 4:249-64 [12]). Before analysis, a filtering process removed from the dataset the genes with low and poorly measured expression as defined by expression value inferior to 100 units in all 227 breast cancer tissue samples, retaining 31189 genes/ESTs.
Before unsupervised hierarchical clustering, a second filter excluded genes showing low expression variation across the 227 samples, as defined by standard deviation (SD) inferior to 0.5 log 2 units (only for calculation of SD, values were floored to 100 since discrimination of expression variation in this low range can not be done with confidence), retaining 14486 genes/ESTs. Data was then log 2-transformed and submitted to the Cluster program (Eisen M B, Spellman P T, Brown P O, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95:14863-8 [13]) using data median-centered on genes, Pearson correlation as similarity metric and centroid linkage clustering. Results were displayed using TreeView program [13]. Quality Threshold (QT) clustering identifies sets of genes with highly correlated expression patterns among the hierarchical clustering. It was applied to the kinase probe sets and basal and luminal A tumors using TreeView program [13]. The cut-offs for minimal cluster size and minimal correlation were 15 and 0.7, respectively. The gene clusters were interrogated using Ingenuity software (Redwood City, Calif., USA) to assess significant representation of biological pathways and functions.
The kinome database established by Manning et al [8] was used as reference to extract the kinase-encoding genes from the Affymetrix Genechip U133 Plus 2.0. First, because annotation of the HUGO (Human Genome Organisation) symbols did not correspond necessarily between the genes represented on the Affymetrix chip and the kinome, we used the mRNA accession number as cross-reference. cDNA sequences of the kinome were compared with the representative mRNA sequences of the Unigene database using BLASTn, and alignements between these sequences were obtained. All mRNAs with exact match were retained, and their accession number compared with those of the 31,189 selected probe sets given by Affymetrix. Second, some kinase genes were represented by several probe sets on the Affymetyrix chip. This may introduce bias in the weight of the groups of genes for analysis by QT-clustering. In these cases, probe sets with an extension <<_at>>, next <<s_at>> and followed by all other extensions were preferentially kept. When several probe sets with the best extension were available, the one with the highest median value was retained. From the initial list of 518 kinases, we finally retained 435 probe sets representing 435 kinase genes.
To test the performance of our multigene signature in other BC samples, we analyzed three major publicly available data sets: van de Vijver et al (van de Vijver M J, He Y D, van't Veer U, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009 [14]), Wang et al Wang Y, Klijn J G, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005; 365:671-9 (Wang Y, Klijn J G, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005; 365:671-9 [15]) collected from NCBI/Genbank GEO database (series entry GSE2034), and Loi et al (Loi S, Haibe-Kains B, Desmedt C, et al. Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 2007; 25:1239-46 [16]) collected from NCBI/Genbank GEO database (series entry GSE6532). Analysis of each data set was done in several successive steps: identification of molecular subtypes based on the common intrinsic gene set, identification of the kinase gene set common with ours, followed by computing of the Kinase Score (see below) for the luminal A samples. Clinical data of luminal A samples from our series and public series used for analyses are detailed in Supplementary Table 3.
We defined a score, called the Kinase Score (KS), which was based on the expression level of 16 kinase genes. It was defined as:
where A and B represent normalization parameters, which make the KS comparable across the different datasets, n the number of available kinase genes (7 to 16), and xi the logarithmic gene expression level in tumor i. Using a cut-off value of 0, each tumor was assigned a low score (KS<0, i.e. with overall low expression of 16 kinase genes) or a high score (KS>0, i.e. with overall strong expression of 16 kinase genes). In the present invention, the number of available kinase genes, i.e. n, is from 1 to 16.
The samples included in the statistical analysis (luminal A subtype) were ER and/or PR-positive as defined using immunohistochemistry (IHC). We introduced two qualitative variables based on the mRNA expression level of ER and PR (ESR1 estrogen receptor 1 probe set 205225_at and PGR progesterone receptor probe set 208305_at): the cut-off for defining ESR1 or PGR-rich or -poor was the median expression level of the corresponding probe set. The two probe sets were chosen by using the same above-cited criteria.
Correlations between sample groups and histoclinical factors were calculated with the Fisher's exact test for qualitative variables with discrete categories, and the Wilcoxon test for continuous variables. Follow-up was measured from the date of diagnosis to the date of last news for patients without relapse. Relapse-free survival (RFS) was calculated from the date of diagnosis until date of first relapse whatever its location (local, regional or distant) using the Kaplan-Meier method and compared between groups with the log-rank test. The univariate and multivariate analyses were done using Cox regression analysis. The p-values were based on log-rank test, and patients with one or more missing data were excluded. All statistical tests were two-sided at the 5% level of significance. Statistical analysis was done using the survival package (version 2.30), in the R software (version 2.4.1—www.cran.r-project.org).
A total of 227 samples were profiled using whole-genome DNA microarrays. Hierarchical clustering was applied to the 14,486 genes/ESTs with significant variation in expression level across all samples (Supplementary
We wanted to identify kinase genes whose differential expression is associated with clinical outcome. We focused our analysis on two major subtypes of BC with opposite prognosis, the basal and the luminal A subtypes. From our subtyping, we selected a series of 138 BC samples with available full histoclinical annotations, including 80 luminal A and 58 basal BCs. We identified a total of 435 unique Affymetrix probe sets for 435 kinases as satisfying simultaneously presence, quality and reliability (Supplementary Table 4).
A hierarchical clustering analysis was applied to these probe sets and 138 BCs and 8 cell lines (
These results suggest that kinase gene expression is highly different between basal and luminal A BCs.
As shown in
This subdivision of luminal A tumors led us to define for each of them the Kinase Score (KS) based on expression level of these 16 genes. A cut-off of 0 identified two tumor groups: a group containing the luminal A BCs with negative score (hereafter designated Aa) and a group containing the luminal A BCs with positive score (hereafter designated Ab;
Proteins encoded by the 16 genes overexpressed in luminal Ab BCs (Table 1) are all serine/threonine kinases (except SRPK1, which is a serine/arginine kinase) involved in the regulation of the late phases of the cell cycle, suggesting that luminal Ab tumors show a transcriptional program associated with mitosis.
The histoclinical characteristics of the two luminal A subgroups are listed in Table 3. Strikingly, they shared most features but were different according to SBR grade with more grade III in the Ab subgroup and more grade I-II in the Aa subgroup. Ki67 expression did not distinguish Ab from Aa cases but three-fourths of luminal Ab were Ki67-positive. In conclusion, no factor but grade could distinguish Aa from Ab BCs.
We compared the survival of three groups of patients, i.e. patients with basal, luminal Aa and luminal Ab BCs. We excluded from analysis the basal medullary breast cancers known to harbor good prognosis. With a median follow-up of 55 months after diagnosis, 5-year relapse-free survival (RFS;
We then compared the prognostic ability of our KS-based classifier with other histoclinical factors (age, pathological tumor size, SBR grade, and axillary lymph node status, IHC P53 (1%) and Ki67 (20%) status, ESR1 and PGR mRNA levels) in our 80 luminal A samples (Table 4A). In univariate and multivariate Cox analyses, the only factor that correlated with RFS was the KS-based classifier. The hazard ratio (HR) for relapse was 7.77 for luminal Ab tumors compared to luminal Aa tumors ([95% CI 1.97-30.66], p=0.003).
As a validation step, we analyzed three sets of published gene expression data to identify and compare the two subgroups of luminal A BCs identified by the KS. We first defined as above the molecular subtypes of tumors. Before assigning a subtype, each centroid was evaluated by its concordance with those defined by Sorlie et al [4], and none was under 90% in the three data sets. The distribution of the subtypes is shown in Supplementary Table 5.
A total of 276 samples were identified as luminal A. The number of genes in the KG set represented in each dataset ranged from 7 to 16 (Supplementary Table 5). We computed the KS for each tumor. The same cut-off as in our series led to the identification of Aa (190 samples) and Ab (86 samples) subgroups in each set (
Samples form the three studies were pooled before prognostic analyses. Histoclinical correlations of the two subgroups were similar to those found in our series (Supplementary Table 6).
We then compared RFS of the two luminal A subgroups in the 276 samples. With a median follow-up of 104 months after diagnosis, luminal Ab tumors were associated with a worse prognosis than luminal Aa tumors, with respective 5-year RFS of 90% and 73% (p=6.3E-6, log-rank test;
We also performed univariate and multivariate survival analyses (Table 4B). Wang et al's series (79 Luminal A samples) was analyzed separately due to the lack of available histoclinical data. In univariate analysis, the HR for relapse was 4.84 for luminal Ab tumors compared to luminal Aa tumors ([95% CI 2.13-11.00], p=1.7E-04). The two other series were merged for analyses (197 Luminal A samples). Three variables, including pathological tumor size, PGR mRNA expression level and KS-based subgrouping, were significantly associated to RFS in univariate analysis. In multivariate analysis, only the KS-based classifier retained significant prognostic value, confirming the prominence of the KS over the SBR grade and other variables. The HR for relapse was 2.48 for luminal Ab tumors compared to luminal Aa tumors ([95% CI 1.37-4.50], p=0.002)
We then studied the association of the KS with the intrinsic molecular subtypes. We merged all data sets, including our 227 tumors, the 295 van de Vijver et al's tumors, the 414 Loi et al's tumors, and the 286 Wang et al's tumors, resulting in a total of 1222 tumors. The KS and molecular subtypes were determined for all tumors: 367 tumors were luminal A, 99 luminal B, 172 ERBB2-overexpressing, 214 basal, 161 normal-like and 209 unassigned. We computed and compared the distribution of the KS in each subtype. As shown in
The five molecular subtypes displayed different KS. However, because the range of KS was rather large in each subtype, we studied whether the KS had any prognostic value in other subtypes than luminal A by comparing survival (log-rank test) between KS-negative and KS-positive tumors (
The luminal Ab tumors displayed an intermediate KS pattern between luminal Aa tumors and luminal B tumors (
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/IB2008/003622 | 12/24/2008 | WO | 00 | 9/29/2010 |
| Number | Date | Country | |
|---|---|---|---|
| 61009395 | Dec 2007 | US |
