Patent Application
20100087330
The present invention relates to a method for prognosing or classifying breast cancer subtypes in a subject with breast cancer. More specifically, the invention relates to set of biomarkers useful for prognosing or classifying breast cancer subtypes in a subject with breast cancer.
Tumor-associated antigens (TAAs) can help diagnose various tumors and sometimes determine the response to therapy or recurrence. An ideal tumor marker would be released only from tumor tissue, be specific for a given tumor type, be detectable at low levels of tumor cell burden, have a direct relationship to the tumor cell burden, and be present in all subjects with the tumor. However, although most tumors release detectable antigenic macromolecules into the circulation, no tumor marker has all the requisite characteristics to provide enough specificity or sensitivity to be used in early diagnosis or mass cancer screening programs.
Carcinoembryonic antigen (CEA) is a protein-polysaccharide complex present in colon carcinomas and in normal fetal intestine, pancreas, and Blood levels are elevated in subjects with colon carcinoma, but the specificity is relatively low because positive results also occur in heavy cigarette smokers and in subjects with cirrhosis, ulcerative colitis, and other cancers (eg, breast, pancreas, bladder, ovary, cervix). Monitoring CEA levels may be useful for detecting cancer recurrence after tumor excision if the subject initially had an elevated CEA.
CA 15-3 is elevated in 54 to 80% of subjects with metastatic breast cancer. It may also be elevated in other benign (eg, chronic hepatitis, cirrhosis, TB, sarcoidosis, SLE) and malignant (eg, lung, ovarian, endometrial, GI, and bladder carcinomas) conditions. This marker is primarily used to monitor the response to therapy.
Chromogranin A is used as a marker for carcinoid and other neuroendocrine tumors. Abnormal levels are seen in ⅓ of subjects with localized disease and in ⅔ of those with metastatic cancer. Levels can be elevated in other cancers, such as lung and prostate.
TA-90 is a highly immunogenic subunit of a urinary tumor-associated antigen that is present in 70% of melanomas, soft-tissue sarcomas, and carcinomas of the breast, colon, and lung. Some studies have shown that TA-90 levels can accurately predict survival and the presence of subclinical disease after surgery for melanoma.
Proteomic analyses of early stages cancers represent a new diagnostic tool for early detection of the disease. This technique evaluates the presence of various biomarkers in readily accessible body fluids such as serum, urine or saliva that are particular of specific changes in gene expression only occurring in cancer cells. Protein-based assays, such as the ELISA system, are used to evaluate the presence of biomarkers, therefore allowing detection and monitoring of cancer. The search for always more reliable cancer-related biomarkers is oriented towards proteins that are overexpressed, as a consequence of the disease process, and subsequently shed into body fluid. Novel proteomics methods and technologies are being used to discover new biomarkers for early-stage disease. Those methods comprise, besides the ELISA system, other antibody arrays, protein-based microarray technologies and multiplexed on-chip technologies. Despite their utility, there are several inherent disadvantages to these methods, such as the fact that they are often limited by the requirements for highly specific, high-affinity antibodies, two-site approaches and/or sensitive detection and signal amplification systems. Moreover, the development of proteomic pattern diagnostics is intricate since the specificity between physiologic biomarkers and the various types of cancer is hard to establish.
Accordingly, novel methods of prognosis or classifying breast cancer subtypes are highly desirable.
In accordance with the present invention there is provided a method for prognosing or classifying a subject or a tumor from a subject for breast cancer subtypes.
The inventors have identified the 512 genes listed in Table 1 that are particularly useful for classifying breast cancer tumor subtypes. Also listed are probes that can be used in accordance with one embodiment of the present invention.
The inventors have also identified a selection of 73 of the 512 genes listed in Table 2 that are biomarkers useful for classifying breast cancer tumor subtypes based on their ESR1-PGR-ERBB2 makeup. The inventors have further identified MPP7 as a useful breast cancer biomarker.
Accordingly, one aspect of the invention is a method of prognosing or classifying breast cancer subtypes of a subject, comprising the steps of:
a) determining the expression of a biomarker in a test sample from the subject, wherein the biomarker comprises one or more biomarkers as shown in Table 1; and
b) comparing the expression of the biomarker with a control representative of a cancer subtype, wherein a difference in the expression of the biomarker between the control and the test sample is used to prognose or classify the breast cancer subtype.
The prognosis and classifying methods of the invention can be used to select treatment. For example, the methods can be used to select or identify subjects who might (or might not) benefit from particular forms of chemotherapy. More specifically differences in the expression or regulation pattern of the biomarkers in Table 2 can be used to determine a certain cancer treatment.
Another aspect of the invention is to use the 512-gene custom breast cancer panel to potentially identify genes and biomarkers in the genome that can be used prognostically to predict outcome (recurrence, survival) and to predict sensitivity or resistance to various breast cancer therapies.
The invention also provides for kits for the prognosis or classification of breast cancer subtype of subject with breast cancer into groups based on their ESR1-PGR-ERBB2 makeup that includes at least one detection agent that can detect the expression products of the biomarkers.
Other features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.
This invention relates to a method for diagnosing, prognosing or classifying breast cancer subtypes in a subject, which comprises determining the expression of at least one gene chosen from the list of 512 genes of Table 1 in a subject sample.
The term “biomarker” as used herein refers to a gene that is differentially expressed in individuals with breast cancer and is predictive of different tumor types, tumor responsiveness or survival outcomes. In a preferred embodiment, the biomarkers are predictive of the ESR1, PGR and/or ERBB2 status of a sample taken from an individual with breast cancer. The term “biomarker” includes one or more of the genes listed in any of Tables 1 to 7.
Accordingly, one aspect of the invention is a method of prognosing or classifying breast cancer subtypes in a subject, comprising the steps of determining the expression of a biomarker in a test sample from the subject, wherein the biomarker comprises one or more biomarkers as shown in any of Tables 1 to 7 and comparing the expression of the biomarker with a control representative of various cancer subtypes, wherein a difference in the expression of the biomarker between the control and the test sample is used to prognose or classify the subject with a breast cancer subtype.
The phrase “prognosing or classifying” as used herein refers to a method or process of determining whether a subject has a specific tumor subtype based on biomarker expression profiles. In a preferred embodiment, the method is used to prognose or classify a tumor sample based on its ESR1, PGR or ERBB2 status.
The term “test sample” as used herein refers to any fluid, cell or tissue sample from a subject which can be assayed for biomarker expression products, particularly genes differentially expressed in subjects with different forms of breast cancer subtypes. In one embodiment, the test sample is a cell, cells or tissue from a tumor biopsy from the subject.
The preferred test sample to test using the cancer panel consists in obtaining FFPE tumor blocks in 5×5 μm sections by subject, each section being incorporated in a sterile 1.5 ml Eppendorf tube. Moreover, one 5 μm section of tumor sample on a slide by subject may be used for haematoxylin and eosin (H&E) staining.
As used herein, the term “control” refers to a specific value that one can use to prognose or classify the value obtained from the test sample. In one embodiment, a dataset may be obtained from samples from a group of subjects known to have a particular breast cancer type or subtype. In a preferred embodiment, the control dataset consists of tumor or subject samples for which the status of ESR1, PGR and/or ERBB2 has been determined. The expression data of the biomarkers in the dataset can be used to create a control value that is used in testing samples from new subjects. In such an embodiment, the “control” is a predetermined value for each biomarker or set of biomarkers obtained from subjects with breast cancer subjects whose biomarker expression values and tumor types are known.
In another embodiment, the control can be an actual sample from a subject with a known ESR1, PGR and/or ERBB2 breast cancer subtype.
The term “differentially expressed” or “differential expression” as used herein refers to a difference in the level of expression of the biomarkers that can be assayed by measuring the level of expression of the products of the biomarkers, such as the difference in level of messenger RNA transcript expressed or proteins expressed of the biomarkers. In a preferred embodiment, the difference is statistically significant. The term “difference in the level of expression” refers to an increase or decrease in the measurable expression level of a given biomarker as measured by the amount of messenger RNA transcript and/or the amount of protein in a sample as compared with the measurable expression level of a given biomarker in a control. In one embodiment, the differential expression can be compared using the ratio of the level of expression of a given biomarker or biomarkers as compared with the expression level of the given biomarker or biomarkers of a control, wherein the ratio is not equal to 1.0. For example, an RNA or protein is differentially expressed if the ratio of the level of expression in a first sample as compared with a second sample is greater than or less than 1.0. For example, a ratio of greater than 1, 1.2, 1.5, 1.7, 2, 3, 3, 5, 10, 15, 20 or more, or a ratio less than 1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.05, 0.001 or less. In another embodiment the differential expression is measured using p-value. For instance, when using p-value, a biomarker is identified as being differentially expressed as between a first sample and a second sample when the p-value is less than 0.1, preferably less than 0.05, more preferably less than 0.01, even more preferably less than 0.005, the most preferably less than 0.001.
In another embodiment, expression data from multiple biomarkers is analyzed using cluster techniques. In one embodiment, clustering is based on correlation of average normalized signal intensities. In one embodiment, the biomarkers comprise the 512-gene custom breast cancer panel. In another embodiment, the biomarkers comprise the 73 biomarkers listed in Table 2. In another embodiment, the biomarkers comprise the ones listed in Tables 5, 6 and 7.
The phrase “determining the expression of biomarkers” as used herein refers to determining or quantifying RNA or proteins expressed by the biomarkers. The term “RNA” includes mRNA transcripts, and/or specific spliced variants of mRNA. The term “RNA product of the biomarker” as used herein refers to RNA transcripts transcribed from the biomarkers and/or specific spliced variants. In the case of “protein”, it refers to proteins translated from the RNA transcripts transcribed from the biomarkers. The term “protein product of the biomarker” refers to proteins translated from RNA products of the biomarkers.
A person skilled in the art will appreciate that a number of methods can be used to detect or quantify the level of RNA products of the biomarkers within a sample, including microarrays, RT-PCR (including quantitative RT-PCR), nuclease protection assays and Northern blot analyses. In one embodiment, the assay used is a DASL assay as shown in Example 1 which uses a bead-array format.
In addition, a person skilled in the art will appreciate that a number of methods can be used to determine the amount of a protein product of a biomarker of the invention, including immunoassays such as Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE and immunocytochemistry.
Conventional techniques of molecular biology, microbiology and recombinant DNA techniques, are within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Sambrook, Fritsch & Maniatis, 1989, Molecular Cloning: A Laboratory Manual, Second Edition; Oligonucleotide Synthesis (M. J. Gait, ed., 1984); Nucleic Acid Hybridization (B. D. Harnes & S. J. Higgins, eds., 1984); A Practical Guide to Molecular Cloning (B. Perbal, 1984); and a series, Methods in Enzymology (Academic Press, Inc.); Short Protocols In Molecular Biology, (Ausubel et al., ed., 1995).
A person skilled in the art will appreciate that a number of detection agents can be used to determine the expression of the biomarkers. For example, to detect RNA products of the biomarkers, probes, primers, complementary nucleotide sequences or nucleotide sequences that hybridize to the RNA products can be used. To detect protein products of the biomarkers, ligands or antibodies that specifically bind to the protein products can be used.
The term “nucleic acid” includes DNA and RNA and can be either double stranded or single stranded.
The term “hybridize” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid. In a preferred embodiment, the hybridization is under high stringency conditions. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0× sodium chloride/sodium citrate (SSC) at about 45° C., followed by a wash of 2.0×SSC at 50° C. may be employed.
The term “primer” as used herein refers to a nucleic acid sequence, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of synthesis of when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand is induced (e.g. in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH). The primer must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon factors, including temperature, sequences of the primer and the methods used. A primer typically contains 15-25 or more nucleotides, although it can contain less. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art. The term “primer” as used herein refers a set of primers which can produce a double stranded nucleic acid product complementary to a portion of the RNA products of the biomarker or sequences complementary thereof.
The term “probe” as used herein refers to a nucleic acid sequence that will hybridize to a nucleic acid target sequence. In one example, the probe hybridizes to an RNA product of the biomarker or a nucleic acid sequence complementary thereof. The length of probe depends on the hybridize conditions and the sequences of the probe and nucleic acid target sequence. In one embodiment, the probe is at least 8, 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length. In a preferred embodiment, the assay used is a DASL assay and the probes used are those identified in Table 1. The probe sequences are the oligo sequence on the 5′ and 3′ end which is then extended and ligated to form the “probe” sequence.
The term “antibody” as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic animals. The term “antibody fragment” as used herein is intended to include Fab, Fab′, F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, and multimers thereof and bispecific antibody fragments. Antibodies can be fragmented using conventional techniques. For example, F(ab′)2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab′)2 fragment can be treated to reduce disulfide bridges to produce Fab′ fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab′ and F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.
Antibodies having specificity for a specific protein, such as the protein product of a biomarker, may be prepared by conventional methods. A mammal, (e.g. a mouse, hamster, or rabbit) can be immunized with an immunogenic form of the peptide which elicits an antibody response in the mammal. Techniques for conferring immunogenicity on a peptide include conjugation to carriers or other techniques well known in the art. For example, the peptide can be administered in the presence of adjuvant. The progress of immunization can be monitored by detection of antibody titers in plasma or serum. Standard ELISA or other immunoassay procedures can be used with the immunogen as antigen to assess the levels of antibodies. Following immunization, antisera can be obtained and, if desired, polyclonal antibodies isolated from the sera.
To produce monoclonal antibodies, antibody producing cells (lymphocytes) can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells. Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495-497 (1975)) as well as other techniques such as the human B-cell hybridoma technique (Kozbor et al. Immunol. Today 4:72 (1983)), the EBV-hybridoma technique to produce human monoclonal antibodies (Cole at al., Methods Enzymol, 121:140-67 (1986)), and screening of combinatorial antibody libraries (Huse et al., Science 246:1275 (1989)). Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with the peptide and the monoclonal antibodies can be isolated.
A person skilled in the art will appreciate that the detection agents can be labeled.
The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as 3H, 14C, 32P, 35S, 123I, 125I, 131I; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.
Accordingly, the invention includes a kit for prognosing or classifying cancer subtypes in a subject with breast cancer, comprising at least one detection agent that can detect the expression products of biomarkers, wherein the biomarkers comprise at least one biomarker as shown in Table 1.
The kit can also include a control or reference standard and/or instructions for use thereof. In addition, the kit can include ancillary agents such as vessels for storing or transporting the detection agents and/or buffers or stabilizers.
The term “subject” as used herein refers to any member of the animal kingdom, preferably a human being that has breast cancer.
The above disclosure generally describes the present invention. A more complete understanding can be obtained by reference to the following specific examples. These examples are described solely for the purpose of illustration and are not intended to limit the scope of the invention. Changes in form and substitution of equivalents are contemplated as circumstances might suggest or render expedient. Although specific terms have been employed herein, such terms are intended in a descriptive sense and not for purposes of limitation.
The following non-limiting examples are illustrative of the present invention:
The DASL Assay process allows expression profiling of biomarkers in a subject tissue sample. It involves random priming with biotinylated 9mers to generate cDNA. Transcripts are probed in solution using oligo probe sets. The DASL assay probe sets incorporates target specific sequences, universal primers and a short universal address sequence for use in reading out the assay products on Sentrix® universal arrays. In a preferred embodiment, three assays are used for each gene of interest, allowing a total of up to 512 genes to be profiled for each sample or replicate.
The cDNA-mediated annealing, selection, extension and ligation (DASL) assay has been specifically designed as a gene expression profiling system to generate reproducible data from degraded RNAs such as those derived from Frozen Fixed Paraffin Embedded (FFPE) tumor samples as old as 24 years. The assay is amenable to high-throughput screening of subject samples which can be accommodated on one of two array platforms that allow for either 16 or 96 samples to be processed on one slide or plate, respectively. In one embodiment of the invention, the DASL assay is used in conjunction with the 73-gene custom breast cancer panel as detailed above.
The DASL assay protocol only requires around 200 ng of total RNA that is converted to cDNA and processed in the DASL assay. In one embodiment, oligonucleotides targeting biomarkers are used at a density of three non-overlapping probes per gene. This results in a multiplex measurement for each sample. Using this number of probes per gene lends the assay the necessary sensitivity and reproducibility for quantitative detection of differential expression using RNA from FFPE tissues. In one embodiment, random priming is used for cDNA synthesis and, therefore, probes are designed such that they can target any unique region of the gene without limiting the selection of the optimal probe to the 3′ end of transcripts. In addition, due to the small size of the targeted gene sequence (50 nucleotides), along with the use of random primers in the cDNA synthesis, this allows for detection of RNAs that are otherwise too degraded for conventional microarray analysis.
The 5′-oligonucleotides consists of two parts: the gene specific sequence and a universal PCR primer sequence. The 3′-oligonucleotides consist of three parts: the gene specific sequence, a unique address sequence which is complementary to one of the capture sequences on the array and a universal PCR primer sequence at the 3′ end. A single address sequence is uniquely associated with a single target site. This address sequence allows the PCR-amplified products to hybridize to a universal microarray bearing the complementary address sequences.
Breast Cancer FFPE blocks were obtained from St. Mary's Hospital, Montreal, Quebec, and three 5 μm sections per block, placed into a 1.5 mL sterile microfuge tube were taken for each RNA isolation. The commercially available RNA High Pure Kit (Roche, Mannheim, Germany) was used for RNA extraction from FFPE tissues that were used in this experiment. The manufacturer's instructions were followed for each kit, with two exceptions. First, an additional ethanol wash was added to all kits after the deparaffinization step to ensure that all the xylene was completely removed. Second, Proteinase K digestion times were changed slightly to an overnight Proteinase K digestion.
Concentration and Å260/Å280 ratio were determined using the NanoDrop spectrophotometer (NanoDrop, Wilmington, Del.). RNA quality was initially tested using the 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany). In addition, TaqMan (Applied Biosystems, Foster City, Calif.) assays were performed on the RPL13a gene in triplicate using 20 ng of cDNA to determine how many copies of usable RNA molecules were available in each sample. The quantitative RT-PCR reactions were run on the HT7900 real-time PCR instrument (Applied Biosystems, Foster City, Calif.).
The DASL assay was performed using a maximum of 200 ng of input RNA on the custom 512-gene human breast cancer panel of the present invention and a standard IIlumina 502-gene Human Standard Cancer panel. In cases where RNA concentrations were below 40 ng/μL but not less than 20 ng/μL, the maximum allowable volume of RNA (5 μl) was used. The manufacturer's instructions were followed without any changes. The hybridized Sentrix Array Matrix (SAM) was scanned using the BeadStation 500 Instrument (Illumina, Inc., San Diego, Calif.). The data was analyzed using the BeadStudio v3.0 software package (Illumina, Inc., San Diego, Calif.) and Spotfire DecisionSite 9.0 for Functional Genomics (Spotfire Inc, Somerville, Mass.).
Data from Illumina DASL experiments were scanned and interpreted using IIlumina's BeadStudio. Prior to analysis, samples which failed (criteria being a detection p-value <0.05 in less than 40% of the samples) were removed from the data sets. Reference RNA, and samples with no immunohistochemical data (i.e. ESR1, PGR, ERBB2 status) were also removed. Therefore, with removal of these samples from further analysis, we performed DASL analysis on 175 samples from 87 subjects in six major breast cancer subtypes. Non-normalized signal intensity data was exported from BeadStudio and analyzed for correlations in Microsoft Excel and Access.
DASL assay data was plotted for expression of ESR, PGR, and ERBB2 receptors according to receptor subtype as determined by Immunohistochemistry (i.e. ESR1+PGR+ERBB2+, ESR1+PGR−ERBB2+, ESR1−PGR−ERBB2−, ESR1+PGR−ERBB2−, ESR1+PGR+ERBB2−, ESR1−PGR−ERBB2+) on 87 subjects. An excellent correlation between DASL data and IHC data was observed as shown in
To validate the relevance of the genes selected for our custom cancer panel, we conducted an unsupervised clustering of all non-failed samples. Clustering is based on correlation of average normalized signal intensities. Typically, data protocols call for normalizations to be done per experiment (e.g. one SAM normalized within itself, and compared to another SAM normalized within itself), but because the data is being looked at as a whole, average normalization was conducted on both custom cancer panel SAMs (1842787020 & 1892661005) together.
To further elucidate genes significantly up-regulated and down-regulated in both the custom and standard cancer panels, a significance analysis of microarrays was conducted using Stanford's significance analysis of microarrays software. To summarize the number of genes detected, we first looked at raw counts of significant genes detected at no minimum fold change, a minimum of 1.5-fold change, and a minimum of 2-fold change.
To further investigate the sets of genes detected by each panel, data was loaded in Microsoft Access and queries were run against the data sets to generate data for a Venn diagram. Differentially up-regulated genes in NNN samples, with at least 1.5 fold change, are shown in
Thus, we identified a subset of 73 genes (46 up-regulated 1.5-fold; 27 down-regulated 1.5-fold) from our custom 512-gene panel that were significantly different between the NNN subtype and other breast cancer subtypes. Among the significantly decreased genes were ESR1, PGR, ERBB2, and among the increased genes were EGFR, MMP7, FZD7, and MYC. The four aforementioned up-regulated genes, EGFR, MMP7, FZD7, and MYC, are all components or targets of the Wnt signaling pathway as identified by Ingenuity Pathway Analysis (IPA). Correlation of expression of EGFR, MMP7, and MYC with FZD7 expression across the 175 samples was highly significant (Table 3), suggesting a functional link with Wnt signaling, and, therefore, these genes may play an important role in the tumorigenic process.
IPA identified several other significantly altered pathways as expected including estrogen signaling (p=1E-7), neuregulin signaling (p=1E-5), p53 signaling (p=1E-4), and cell cycle checkpoints (p=1E-3).
The 73 biomarkers identified in Example 7 and listed in Table 2 were used to perform a hierarchical clustering of all samples. As shown in
MMP7 was identified as a biomarker of breast cancer in general and of hormone-negative, ERBB2-positive and NNN breast cancer in particular and especially NNN breast cancer. As shown in
In further analysis of gene expression signatures in breast cancer tissue samples, each IHC class was examined separately and compared to all other samples not in the specified IHC class (e.g. ESR1+PGR+ERBB2+ compared with all none ESR1+PGR+ERBB2+samples).
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indicates data missing or illegible when filed
The panel of the present invention detected the most significantly up and down regulated genes in NNN (ESR1−PGR−ERBB2) cancer tissue. A complete listing of all up and down regulated genes in each IHC category (compared independently to all other categories) can be found in Tables 5 and 6, respectively.
To visually explicate results, heat maps of IHC tumor tissue types have been analyzed to inspect the unsupervised clustering of samples. IHC similarly categorized tissues tend to group together in unsupervised clustering indicating the panel has at least the ability to differentiate steroid receptor type in FFPE cancer tissue.
To comprehensively analyze the multiple IHC subtypes a multiclass analysis has been conducted using Stanford's (©Trustees of Leland Standford University) software. This further supports the design of the DASL custom cancer panel by validating 286 of 512 genes on the panel are statistically significant in identifying receptor type (i.e. ESR1, PGR, ERBB2) defined by IHC results (see Table 7 for a list of genes). Of these 286 genes 220 have a local false discovery rate of less than 1%. A z-score normalized heatmap showed significantly regulated genes between all receptor subtypes. This heatmap provided evidence of the z-score normalized gene expression signatures across the 286 genes defined as statistically significant and differentially regulated between receptor subtypes.
While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth, and as follows in the scope of the appended claims.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/CA08/00166 | 1/25/2008 | WO | 00 | 11/17/2009 |
| Number | Date | Country | |
|---|---|---|---|
| 60886772 | Jan 2007 | US |
