WO2006119593A1 - Pronostic algorithmique du cancer base sur les genes gene-based algorithmic cancer prognosis - Google Patents

Pronostic algorithmique du cancer base sur les genes gene-based algorithmic cancer prognosis Download PDF

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WO2006119593A1
WO2006119593A1 PCT/BE2006/000051 BE2006000051W WO2006119593A1 WO 2006119593 A1 WO2006119593 A1 WO 2006119593A1 BE 2006000051 W BE2006000051 W BE 2006000051W WO 2006119593 A1 WO2006119593 A1 WO 2006119593A1
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cancer
gene
tumor sample
genes
grade
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PCT/BE2006/000051
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English (en)
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Christos Sotiriou
Mauro Delorenzi
Martine Piccart
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Universite Libre De Bruxelles
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Priority to EP06752698A priority Critical patent/EP1880335A1/fr
Priority to JP2008510367A priority patent/JP2008539737A/ja
Priority to AU2006246241A priority patent/AU2006246241A1/en
Priority to CN2006800164096A priority patent/CN101356532B/zh
Priority to CA002608643A priority patent/CA2608643A1/fr
Publication of WO2006119593A1 publication Critical patent/WO2006119593A1/fr
Priority to US11/929,043 priority patent/US20080275652A1/en
Priority to IL187241A priority patent/IL187241A0/en
Priority to NO20076444A priority patent/NO20076444L/no
Priority to US13/306,590 priority patent/US20120071346A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P43/00—Drugs for specific purposes, not provided for in groups A61P1/00-A61P41/00
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention is related to new method and tools for improving cancer prognosis.
  • New prognostic tools developed using microarray technology show potential in allowing us to facilitate tailored treatment of breast cancer patients (Paik et al , New England Journal of Medicine 351:27(2004); Van de Vijver et al, New England Journal of Medicine 347:199(2002); Wang et al, Lancet 365: 671 (2005)). These genomic tools may be a much needed improvement over currently used clinical methods .
  • a more accurate grading system would allow for better prognostication and improved selection of women for further breast cancer treatment.
  • Tamoxifen is the most common anti-estrogen agent prescribed today in the adjuvant treatment of these patients. Yet up to 40% of these patients will relapse when given tamoxifen in this setting.
  • the present invention aims to provide new methods and tools for improving cancer prognosis that do not present the drawbacks of the methods of the state of the art .
  • One embodiment of the invention provides a method, comprising the steps of
  • the tumor sample may be from tissue afflicted by a cancer 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, or brain cancer.
  • a cancer 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, or brain cancer.
  • the tumor sample is a histological grade HG2 breast tumor sample .
  • This embodiment may further comprise designating the tumor sample as low risk (GGl) or high risk (GG3) based on the gene expression grade index (GGI) .
  • This embodiment may further comprise providing a breast cancer treatment regimen for a patient consistent with the low risk or high risk designation of the breast tumor sample submitted to the analysis.
  • the gene expression grade index GGI may include cutoff and scale values chosen so that the mean GGI of the HGl cases is about -1 and the mean GGI of the HG3 cases is about +1. The cutoff value is required for calibration of the data obtained from different platforms applying different scales:
  • the G 1 gene set may comprise at least one gene selected from the genes in Table 3 designated as "Up- regulated in grade 1 tumors" .
  • the Gi gene set comprises at least 4 of those genes, and may include the entire set.
  • the G 3 gene set may comprise at least one gene selected from the genes in Table 3 designated as "Up- regulated in grade 3 tumors.”
  • the G 3 gene set comprises at least 4 those genes, and may include the entire set.
  • the method according to the invention comprises the steps of
  • G is a gene set that is associated with distant recurrence of cancer
  • Pi is the probe or probe set
  • i identifies the specific cluster or group of genes
  • Wi is the weight of the cluster i
  • j is the specific probe set value
  • Xij is the intensity of the probe set j in cluster i
  • n* is the number of probe sets in cluster i.
  • This embodiment may further comprises the step of classifying the said tumor sample based on the relapse score as low risk or high risk for cancer relapse.
  • the cutoff for distinguishing low risk from high risk may be a relapse score (RS) of from -100 to +100 or a relapse score
  • the relapse may be relapse after treatment with . tamoxifen or other chemotherapy, endocrine therapy, antibody therapy or any other treatment method used by the person skilled in the art.
  • the relapse is after treatment with tamoxifen.
  • the tumor sample may be from tissue afflicted by a cancer 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, or brain cancer.
  • a cancer 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, or brain cancer.
  • the tumor sample is a breast tumor sample.
  • the patient's treatment regimen may be adjusted based on the tumor sample's cancer relapse risk status. For example (a) if the patient is classified as low risk, treating the low risk patient sequentially with tamoxifen and sequential aromatase inhibitors (AIs) , or
  • the patient's treatment regimen may be adjusted to chemotherapy treatment or specific molecularly targeted anti-cancer therapies.
  • the gene set may be generated from an estrogen receptor (or another marker specific of the cancer tissue sample) positive population.
  • the gene set may be generated by a variety of methods and the component genes may vary depending on the patient population and the specific disorder.
  • a computerized system or diagnostic device comprising: (a) a bioassay module, preferably a bioarray, configured for detecting gene expression for a tumor sample based on a gene set; and (b) a processor module configured to calculate GGI or RS of the tumor sample based on the gene expression and to generate a risk assessment for the breast tumor sample.
  • the bioassay module may include at least one gene chip (microarray) comprising the gene set .
  • the gene set may include at least one gene, preferably at least 4 genes, selected from the genes in Table 3 designated as "Up-regulated in grade 1 tumors" or may include the entire set .
  • the gene may include at least 4 genes selected from the genes in Table 3 designated as "Up- regulated in grade 3 tumors" or may include the entire set .
  • Figure 1 is representing heatmaps showing the pattern of gene expression in the training (panel a) and the validation sets (panel b) .
  • the horizontal axis corresponds to the tumors sorted first by HG and then by GGI as the secondary criterion.
  • the vertical axis corresponds to the genes .
  • the GGI values of each tumor and the relapse free survival are indicated underneath. Two groups of genes are found: those that are highly expressed in grade 1 (16 probe sets; highlighted in red) and, reciprocally, those highly expressed in grade 3 (112 probe sets) .
  • the GGI values for HG2 tumors cover the range of values for HGl and HG3 , and those with high GGI tend to relapse earlier (red dots) .
  • Figure 2 shows Kaplan-Meier RFS analysis based on the HG (panel a) and the GG (panel b) for data pooled from the validation datasets 2-5 (table 11) .
  • HG2 and HG3 can be split further into low and high risk subsets by GG, indicating that GG is an improvement over HG
  • FIG. 3 shows Kaplan-Meier RFS analysis based on the NPI (a) and the NPI-GG (b) classification.
  • NPI-GG improves the prognostic discrimination in both low
  • Figure 4 shows a Forest plot for hazard ratios for HG2 patients split into GGl and GG3 , showing consistent results in different datasets
  • Hazard ratios were estimated with Cox proportional hazard regressions, horizontal lines are 95% confidence intervals for the hazard ratio. P values were determined by the log rank test.
  • Figure 5 shows distant metastasis free survival (DMFS) analysis based on the 70 -gene expression signature (left row, panels a, c and e) and on GGI (right row, panels b, d and f) for data from the Van de Vijver et al . validation study, a) and b) are all patients, c) and d) are node-negative, and e) and f) are node-positive patients. Note that the node-negative subset includes patients used to derive the 70-gene signature.
  • Figure 6 represents a genomic grade applied to previously reported molecular subtypes.
  • Figure 7 represents Kaplan Meyer survival curves for distant metastasis free survival for GGI (high vs . low) .
  • microarray refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate (an insoluble solid support) .
  • differentially expressed gene refers to a gene whose expression is activated to a higher or lower level in a subject suffering from a disease, specifically cancer, such as breast cancer, relative to its expression in a normal ox control subject.
  • the terms also include genes whose expression, is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product.
  • Differential gene expression may include a comparison of expression between two or more genes or theix gene products, or a comparison of the ratios of the expression, between two or more genes or their gene products, or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease, specifically cancer, or between various stages of the same disease.
  • Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages.
  • Gene expression profiling includes all methods of quantification of raRNA and/or protein levels in a biological sample.
  • prognosis is used herein to refer to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as breast cancer.
  • prediction is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal or the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence.
  • the predictive methods of the present invention are valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as, chemotherapy with a given drug or drug combination, and/or radiation therapy, or whether long-term survival of the patient, following surgery and/or termination of chemotherapy or other treatment modalities is likely.
  • high risk means the patient is expected to have a distant relapse in less than 5 years, preferably in less than 3 years.
  • low risk means the patient is expected to have a distant relapse after 5 years, preferably in less than 3 years.
  • tumor refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
  • cancer and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth.
  • examples of cancer include but are not limited to, 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.
  • Raw “GGI” Gene expression grade index
  • GGI Gene expression grade index
  • x is the gene expression level of mRNA
  • Gi and G 3 are sets of genes up-regulated in HGl and HG3 , respectively, and j refers to a probe or probe set .
  • GGI may include cutoff and scale values chosen so that the mean GGI of the HGl cases is about -1 and the mean GGI of the HG3 cases is about +1 :
  • the cutoff in GGI is 0 and corresponds to the mean of means .
  • GGI ranges in value from -4 to +4.
  • GGI grade index
  • RNA extraction, amplification, hybridization and scanning were done according to standard Affymetrix protocols.
  • Affymetrix U133A Genechips (Affymetrix, Santa Clara, CA) .
  • Gene expression values from the CEL files were normalized using RMA (12) .
  • NKI/NKI2 The data set NKI (van't Veer et al . , 2002) and NKI2 (van de Vijver et al . , 2002) were downloaded from Rosetta website www.rii.com. The log ratio was used without further transformation. For NKI2, flagged expression values were considered missing. Age, tumor size, and histological grade were not available for NKI2.
  • GGI gene-expression grade index
  • G 1 and G 3 are the sets of genes up-regulated in HG3 and HGl, respectively. These sets differed across platforms. For convenience, the cutoff and the scale were chosen so that the mean GGI of the HGl cases was -1 and that of the HG3 cases was +1. This rescaling was done separately for each data source .
  • NPI The Nottingham Prognostic Index
  • NPl/GG An index called NPl/GG was defined, where HG was replaced by GG. Cases with NPI ⁇ 3.4 to be high risk in both NPI and NPI/GG were considered. Survival data were visualized using Kaplan-Meier plot. The hazard ratios (HR) were estimated using Cox regression, stratified by the data source. Assumption-free comparisons were done using the stratified log rank test. Heat maps
  • the survival package for R was used by Terry Therneau and a custom program for the KaplanMeier plots, which was checked against the output of the survival package for correctness.
  • probesets which contain oligos that were ambiguously mapped to more than one Unigene id were excluded.
  • the GGI (which essentially summarizes the differences in the GEP of the reporting genes by averaging their expression levels) was defined. As shown under the heat maps in Fig. 1, the GGI distribution of HG2 covered the range of the GGI values of HGl and HG3 , confirming the visual impression. A similar observation was made on the three previously published datasets, despite differences in the clinical populations and microarray platforms (see figures 6a, b, and c) . Histological grade, gene-expression grade (GG) and prognosis
  • GG based on the GGI score was defined. Patients were classified as GGl (low grade) if their GGI value was negative or as GG3 (high grade) otherwise. Note that the
  • GGI score of zero corresponds to the midpoint between the average GGI values of HGl and HG3 (see methods) . This choice might not be clinically optimal and could be improved based on the trade-off between the cost of treatment and risk, but it would be sufficient for evaluating the prognostic value of GGI .
  • breast cancer samples derived from a pool of our own validation population were derived from a pool of our own validation population.
  • FIG. 11 the association between histological grade and relapse-free survival (RFS) was examined.
  • RFS histological grade and relapse-free survival
  • HG3 tumor s had significantly worse RFS than HGl, while HG2 tumor s had an intermediate risk and constituted 38% of the population.
  • GGl and GG3 subgroups showed distinct RFS, similar to the RFS of HGl and HG3 tumor s, respectively.
  • GG was split for each of the histological categories (figure 2c, 2d and 2e) .
  • ER status also had prognostic power in HG2 tumor s (Fig, 2f) , although the hazard ratio was less than that of GG (Fig. 2d) .
  • the ER-positive group showed similar RFS as the total population.
  • NPI/GG Nottingham Prognostic Index
  • Figure 5 shows the comparisons between the NKI prognostic signature and the GGI on distant- ⁇ netastases-free survival for the overall population (fig.5a, b) , as well as for the node negative (fig.5c, d) and positive subgroups (fig.5e, f) .
  • the results were strikingly close. Similar results were found when considering overall survival (see figures 9) . Data were unavailable to compare relapse-free survival.
  • This gene selection process was not meant to define a specific set of genes to be used as a prognostic "signature” .
  • the present invention aims to build a comprehensive "catalogue” where different sets of signatures could be chosen from. This was illustrated by the cross-platform applicability of the catalogue. Although the actual sets of probes used in various platforms differed in numbers and gene compositions, the results were still reproducible. It is remarkable to obtain good prognostic discrimination in very different datasets with a linear classifier where the weights of the genes were simply +1 or -1, based on their association with grade on a training set of 64 patients.
  • the "grade signal” identified was not bound to a particular set of genes nor to any special combination of their expression levels, since the genes were highly correlated and the GGI effectively behaves as a single prognostic factor. It is still beneficial to use many genes, if only to provide redundancy against noise. The consequence for the development of practical diagnostic systems is that arbitrary subsets of the "grade gene catalogue" of the invention might be used, constrained only by technical considerations .
  • Jenssen and Hovig (19) recently discussed two issues regarding the use of gene-expression signatures for prognosis. These were 1) the lack of agreement between genes included in different signatures and 2) the difficulty in understanding the biological basis of the correlation between the signatures and survival.
  • the present gene catalogue is rich in genes with likely roles in cell cycle progression and proliferation.
  • gene-expression based grading could significantly improve current grading systems for the prognostic assessment of cancer, in particular breast cancer.
  • RNA was extracted at the Karolinska Institute and hybridized at the Genome Institute of Singapore in Singapore. The quality of the RNA obtained from each tumour sample was assessed via the RNA profile generated by the Agilent bioanalyzer. RNA extraction, amplification, hybridization, and scanning were done according to standard Affymetrix protocols . Gene expression values from the CEL were normalized by use of RMA 12 . Each population was normalised separately. Each hospital's institutional ethics board approved the use of the tissue material and written informed consent was obtained. The raw data for the tarn- treated dataset are available at the Gene Expression Omnibus repository database
  • accession code GSE XXX accession code
  • Estrogen (ER) and Progesterone receptor (PgR) level [0077] Patients were initially selected at their institutions according to a positive ER status which was determined by protein ligand-binding assay. The inventors subsequently confirmed a positive ER level by using the microarray data. The ER level was measured by probe set (a 30-mer oligonucleotide) on our human AffymetrixTM GeneChip ® U133 A&B microarray. The inventors have used the probe set "205225_at” for ER. PgR was represented by the probe set "208305_at” . The immunohistochemical measurement of ER is known to correlate with mRNA levels of ER 4 . Tumours with any positive expression level of ER and PgR were considered.
  • GGI gene expression grade index
  • GGI Gene expression grade index
  • Cluster program was used to perform average linkage hierarchical cluster analysis 28 after median centering of each gene using an uncentered Pearson correlation as similarity measurement.
  • the cluster results were viewed using "TreeView” .
  • Expression data was downloaded and extracted from datasets Sorlie et al . xx and Sotiriou et al . 10 .
  • the samples were ordered according to subtype as in the original publications 10/ 1X to investigate the relation between the expression of the genes in the GGI and the subtypes .
  • GGI gene expression grade index
  • Figure 6 shows the results of this analysis.
  • the ER-negative subtypes the basal and the erbB2 subtypes, had high expression of GGI, or were of high grade.
  • the ER-positive subtypes showed a diverse range of GGI levels, particularly the luminal C or 3 subtype both highly expressing these proliferation- associated genes, whereas luminal A or 1 , and the normal- like were mostly negative for the expression of the GGI, or low grade. This confirmed the hypothesis that there are varying degrees of contribution of cell cycle genes to the biological makeup of ER-positive tumours, whereas ER- negative tumours seem to consistently have over-expression of these genes. It is interesting to note the similarity in expression profiles of the GGI genes between the high grade ER-positive subtype and the ER-negative subtypes.
  • Genomic grade could distinguish clinically subtypes within the ER-positive tumours and the prognostic value of these genomic grade defined subtypes were an improvement over current traditional methods, such as that based on quantitative levels of estrogen and progesterone receptor levels.
  • a Kaplan-Meier survival analysis was performed comparing classes of ER-positive tumours according to GGI score (high vs. low grade) and expression levels of estrogen and progesterone receptor (rich vs. poor expression) with respect to time to distant metastasis
  • TDM breast cancer specific survival
  • results shown were combined from multiple datasets involving 417 ER-positive samples hybridized using two popular commercially available oligonucleotide microarray platforms- AffymetrixTM and AgilentTM (see methods) .
  • tumours Two subtypes of tumours can be distinguished within patients whose breast cancers express at least some level of estrogen receptor.
  • patients whose tumours express a high level of the genes that comprise the GGI, i.e. corresponding to high genomic grade their disease outcome was clearly different, with a higher incidence of relapses compared with tumours of low genomic grade.
  • their worse disease outcome seemed unchanged even when given adjuvant tamoxifen, suggesting that this group of women do not seem to benefit from adjuvant tamoxifen despite their positive estrogen receptor values.
  • the genes present in the GGI are associated with cell cycle progression and proliferation: among the top 20 overexpressed genes were UBE2C, KPNA2 , TPX2 , FOXMl, STK6, CCNA2, BIRC5, and MYBL2 ; see Supplemental Table 14) .
  • genomic grade was associated with differing relapse-free survival, but for ER-negative tumours, as almost all are associated with high genomic grade, the GGI had no prognostic value. Therefore, cell- cycle related genes seem to have prognostic value only in breast cancer patients with positive expression of ER. Within this group, the incidence of distant metastases seems to be predominantly driven by this set of proliferation and grade-derived genes.
  • Proliferation-related genes appear to be an important—if not the most important—component of many existing prognostic gene signatures for breast cancer that are based on gene-expression profiles.
  • 11 genes in common between the GGI and a 70-gene prognostic gene classifier for women with early stage breast cancer under the age of 55 4 similar survival curves to the validation publication 5 were obtained, suggesting that grade-related genes constitute a significant amount of the prognostic power of this signature.
  • the subgroups achieved by these prognostic signatures and that obtained by the classification of ER-positive tumours by genomic grade overlap significantly because of a strong dependence on cell-cycle genes to drive metastasis and relapse.
  • cyclin Dl a critical controller of the cell cycle, has been associated with tamoxifen resistance and can reverse the growth-inhibitory effect of antiestrogens in estrogen receptor-positive breast cancer cells 32 . Further investigation into the oncogenic pathways that drive the cell cycle machinery will be beneficial in developing new agents to treat the high grade subgroup.
  • genomic grade can distinguish two subtypes with ER-positive breast cancers in a reproducible manner across multiple datasets and microarray platforms. This is validated ept in over 650 ER- positive breast cancer samples. These subgroups have statistically distinct clinical outcome in both systemically untreated and tamoxifen-only treated populations. Stratification by subtype in clinical trials may provide important information on the potentially diverse effect of endocrine therapies, chemotherapies and biological agents on these subgroups . A focussed biological investigation into these distinct phenotypes may result in identification of separate and different therapeutic targets .
  • the genes identified herein may be used to generate a model capable of predicting the breast cancer grade of an unknown breast cell sample based on the expression of the identified genes in the sample .
  • a model may be generated by any of the algorithms described herein or otherwise known in the art as well as those recognized as equivalent in the art using gene(s) (and subsets thereof) disclosed herein for the identification of whether an unknown or suspicious breast cancer sample is normal or is in one or more stages and/or grades of breast cancer.
  • the model provides a means for comparing expression profiles of gene(s) of the subset from the sample against the profiles of reference data used to build the model .
  • the model can compare the sample profile against each of the reference profiles or against model defining delineations made based upon the reference profiles.
  • breast cell samples identified as normal and non-normal and/or atypical from the same subject may be analyzed for their expression profiles of the genes used to generate the model .
  • This provides an advantageous means of identifying the stage of the abnormal sample based on relative differences from the expression profile of the normal sample. These differences can then be used in comparison to differences between normal and individual abnormal reference data which was also used to generate the model .
  • the detection of gene expression from the samples may be by use of a single microarray able to assay gene expression. One method of analyzing such data would be from all pairwise comparisons disclosed herein for convenience and accuracy.
  • Other uses of the present invention include providing the ability to identify breast cancer cell samples as being those of a particular stage and/or grade of cancer for further research or study. This provides a particular advantage in many contexts requiring the identification of breast cancer stage and/or grade based on objective genetic or molecular criteria rather than cytological observation. It is of particular utility to distinguish different grades of a particular breast cancer stage for further study, research or characterization.
  • kits comprising agents for the detection of expression of the disclosed genes for identifying breast cancer stage.
  • kits optionally comprising the agent with an identifying description or label or instructions relating to their use in the methods of the present invention, is provided.
  • kit may comprise containers, each with one or more of the various reagents (typically in concentrated form) utilized in the methods, including, for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP) , reverse transcriptase, DNA polymerase,
  • nucleotide triphosphates e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP
  • reverse transcriptase DNA polymerase
  • RNA polymerase and one or more primer complexes of the present invention (e.g., appropriate length poly (T) or random primers linked to a promoter reactive with the RNA polymerase) .
  • a set of instructions will also typically be included.
  • An exemplary system for implementing the overall system or portions of the invention might include a general purpose computing device in the form of a computer, including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit.
  • the system memory may include read only memory (ROM) and random access memory (RAM) .
  • the computer may also include a magnetic hard disk drive for reading from and writing to a magnetic hard disk, a magnetic disk drive for reading from or writing to a removable magnetic disk, and an optical disk drive for reading from or writing to a removable optical disk such as a CD ROM or other optical media.
  • the drives and their associated machine-readable media provide nonvolatile storage of machine-executable instructions, data structures, program modules and other data for the computer.
  • Embodiments of the present invention may be practiced in a networked environment using logical connections to one or more remote computers having processors.
  • Logical connections may include a local area network (LAN) and a wide area network (WAN) that are presented here by way of example and not limitation.
  • LAN local area network
  • WAN wide area network
  • Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet and may use a wide variety of different communication protocols.
  • Those skilled in the art will appreciate that such network computing environments will typically encompass many types of computer system configurations, including personal computers, hand-held devices, multi -processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like.
  • McQuary P, et al Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci U S A
  • Winer EP Hudis C, Burstein HJ, Wolff AC, Pritchard KI, Ingle JN, Chlebowski RT, Gelber R, Edge SB, Gralow J, Cobleigh MA, Mamounas EP, Goldstein LJ, Whelan TJ, Powles TJ, Bryant J, Perkins C, Perot ti J, Braun S, Langer AS, Browman GP, Somerfield MR. J Clin Oncol. 2005 ; 23 : 619-29.

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Abstract

La présente invention concerne des méthodes et des systèmes qui permettent de déterminer un pronostic de prélèvements de tumeur. Les méthodes consistent à mesurer l'expression génique dans un prélèvement de tumeur et à appliquer un indice-qualité de l'expression génique (GGI) ou un indice de rechute (RS) pour obtenir un indice chiffré du risque.
PCT/BE2006/000051 2005-05-13 2006-05-15 Pronostic algorithmique du cancer base sur les genes gene-based algorithmic cancer prognosis WO2006119593A1 (fr)

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AU2006246241A AU2006246241A1 (en) 2005-05-13 2006-05-15 Gene-based algorithmic cancer prognosis
CN2006800164096A CN101356532B (zh) 2005-05-13 2006-05-15 基于基因的算法型癌症预后
CA002608643A CA2608643A1 (fr) 2005-05-13 2006-05-15 Pronostic algorithmique du cancer base sur les genes gene-based algorithmic cancer prognosis
US11/929,043 US20080275652A1 (en) 2005-05-13 2007-10-30 Gene-based algorithmic cancer prognosis
IL187241A IL187241A0 (en) 2005-05-13 2007-11-08 Gene-based algorithmic cancer prognosis
NO20076444A NO20076444L (no) 2005-05-13 2007-12-13 Genbasert algoritmebasert kreftprognose
US13/306,590 US20120071346A1 (en) 2005-05-13 2011-11-29 Gene-based algorithmic cancer prognosis

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US11530448B2 (en) 2015-11-13 2022-12-20 Biotheranostics, Inc. Integration of tumor characteristics with breast cancer index
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US11459617B2 (en) 2016-04-29 2022-10-04 Board Of Regents, The University Of Texas System Targeted measure of transcriptional activity related to hormone receptors
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CA2608643A1 (fr) 2006-11-16
AU2006246241A1 (en) 2006-11-16

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