WO2009095319A1 - Pronostic de cancer par vote majoritaire - Google Patents

Pronostic de cancer par vote majoritaire Download PDF

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Publication number
WO2009095319A1
WO2009095319A1 PCT/EP2009/050478 EP2009050478W WO2009095319A1 WO 2009095319 A1 WO2009095319 A1 WO 2009095319A1 EP 2009050478 W EP2009050478 W EP 2009050478W WO 2009095319 A1 WO2009095319 A1 WO 2009095319A1
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expression
predetermined
threshold level
atomic
classifier
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PCT/EP2009/050478
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German (de)
English (en)
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Mathias Gehrmann
Christian VON TÖRNE
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Siemens Healthcare Diagnostics Gmbh
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    • 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
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • 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
    • 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
    • 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
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis

Definitions

  • the present invention relates to methods, kits and systems for the prognosis of cancer in untreated patients, preferably breast cancer patients. More specifically, the present invention relates to the prognosis of cancer based on measurements of the expression levels of marker genes in tumor samples. Marker genes are disclosed which allow for an accurate classification of cancer patients using a majority voting scheme, comprising multiple "atomic" classifiers.
  • Breast cancer is one of the leading causes of cancer death in women in western countries. Breast cancer claims the lives of approximately 40,000 women and is diagnosed in approximately 200,000 women annually in the United States alone. Over the last few decades, adjuvant systemic therapy has led to markedly improved survival in early breast cancer. This clinical experience has led to consensus recommendations offering adjuvant systemic therapy for the vast majority of breast cancer patients. In breast cancer a multitude of treatment op- tions are available which can be applied in addition to the routinely performed surgical removal of the tumor and subsequent radiation of the tumor bed.
  • a possible outcome of cancer in particular, breast cancer, in untreated patients.
  • This is also referred to the "progno- sis" of breast cancer in a patient (as opposed to e.g. the "prediction” of the possible outcome of a cancer therapy) .
  • Determination of the risk for an unfavorable outcome of a disease is a valuable basis for deciding on the best possible treatment strategy for a cancer patient.
  • Van ' t Veer et al identified a prognostic signature consisting of 70 and 231 genes in a finding cohort of 78 sporadic breast cancers of node negative women younger than 53 years of age (Van't Veer et al . , 2002. Gene expres- sion profiling predicts clinical outcome of breast cancer. Nature 415: 530-536; Van de Vijver et al . , 2002. A gene- expression signature as a predictor of survival in breast cancer. N Eng J Med 347 : 1999-2009) .
  • Majority voting is a known method for robust classification based on multivariate data (e.g. James, G. (1998) "Majority Vote Classifiers: Theory and Applications", Stanford Univer- sity Doctoral Thesis, available online at http: //www- rcf.usc.edu/ ⁇ gareth/research/thesis.ps). This document does not disclose the application of majority voting schemes to the prediction of cancer from expression profiling data.
  • Majority voting classification has been applied to the classification of breast cancer patients in non-published Patent Applications PCT/EP2006/005717 claiming priority of GB0512299 (16 June 2005), and EP 06020209.0 of 27 September 2006.
  • the specific classification scheme of the present invention (using multiple multivariate atomic classifiers), however, is not disclosed.
  • the present invention fulfills the need for advanced methods for the prognosis of breast cancer on the basis of readily accessible experimental data.
  • the present invention is based on the surprising finding that the outcome of cancer, preferably breast cancer in patients which do not receive chemotherapy can be accurately predicted from the expression levels of a small number of marker genes, using a majority voting classification scheme. Accordingly, the present invention relates to prognostic methods for the determination of the outcome of breast cancer in non-treated breast cancer patients, using information on the expression of a small number of highly informative marker genes. Methods of the invention use simple "atomic" classifiers, each based on a small number of marker genes, which can classify a tumor sample into 2 or multiple prognostic groups, such as a "high risk” group and a "low risk group”. The results of multiple atomic classifiers are combined by majority voting to an accurate and robust prognosis.
  • prognosis within the meaning of the invention, shall be understood to be the prediction of the outcome of a disease under conditions where no systemic chemotherapy is applied in the adjuvant setting.
  • prognosis is an estimation of the likelihood of metastasis fee survival of said patient over a predetermined period of time, e.g. over a period of 5 years.
  • said prognosis is an estimation of the likelihood of death of disease of said patient over a predetermined period of time, e.g. over a period of 5 years.
  • Tumor sample shall be understood to be any sample taken from a tumor of the respective patient.
  • Tumor samples can be taken by biopsy, needle biopsy, by surgery or any other way of obtaining samples of a tumor of a cancer patient.
  • Formalin-fixed paraffin embedded (FFPE) samples are preferred. Fresh-frozen samples can also be used.
  • a “majority vote”, within the meaning of the invention shall be understood to be any algorithm combining votes of multiple classifiers in terms of a majority decision.
  • the term “majority vote” takes thus the ordinary meaning of this term in the art of statistics.
  • a "weighted majority vote” is understood to be a majority vote in which the votes of the individual classifiers are weighted by multiplication with a suitable scaling factor. In a weighted majority vote at least some of the scaling factors are different from 1.
  • a "classifier”, within the meaning of the invention, shall be understood to be any algorithm or scheme for classifying objects into one of multiple classes, based on properties of said object.
  • Preferred objects of this invention are cancer patients, and preferred properties of said patients, within this invention, are expression levels of marker genes.
  • a "marker gene”, within the meaning of the invention, shall be understood as being any gene of a patient, the expression level of which provides information useful in the prognosis of cancer in said patient.
  • Preferred marker genes of the invention are those mentioned in the claims and examples section .
  • a “multivariate classifier”, within the meaning of the invention, shall be understood to be a classifier which depends on more than one variable, i.e., depends on the expression level of more than one marker gene.
  • a "risk class”, within the meaning of the invention, shall be understood to relate to a multitude of patients which are binned in a group of patients according to their risk of unfavorable disease outcome.
  • Unfavorable disease outcome in this regard, can be death of disease within a certain period of time, e.g. within 5 years.
  • "Risk classes” of the current invention can be "high risk” classes, "low risk” classes and optionally “intermediate risk” classes. Any other definition of "risk classes”, however, can be assumed as well.
  • an "expression level”, within the present invention, shall be understood to be any measure for the strength of expression of a gene in a tissue sample. In preferred embodiments of the invention, the average expression of a gene in a tissue sample is measured.
  • a "system for the prognosis of cancer”, within the meaning of the invention, shall be understood any collection of equipment, hardware and software that is capable of performing a prognosis of cancer, e.g. in accordance to the present inven- tion.
  • the system can be built from separate pieces of hardware (and software), but can also be integral, e.g. combined in a single piece of hardware, having all necessary features combined within a housing.
  • the present invention relate to a method of prognosis of cancer in a patient from a tumor sample of said patient comprising the steps of: determining the expression level of a first, a second and a third marker gene of multiple atomic classifiers, said atomic classifiers being multivariate classifiers; performing a classification of said sample into one of multiple risk classes, for each of said multiple atomic classifiers; performing a majority vote using the outcome of said multiple classifications.
  • said majority vote is a weighted majority vote. This leads to improved sensitivity and specificity of the method.
  • the method in- volves two consecutive majority votes. This can further improve the sensitivity and specificity of the methods of the invention .
  • said cancer is breast cancer or ovarian cancer.
  • said determination of expression levels is in a formalin-fixed paraffin embedded sample.
  • Formalin-fixed paraffin embedded samples are rou- tinely prepared, when tissue samples are taken from tumors of cancer patients.
  • said determination of expression levels is in a fresh-frozen sample.
  • the prognosis is a classification of said patient into one of two distinct classes, said classes being a "high risk” class and a "low risk” class.
  • said prognosis is a classification of the patient into one of three classes, said three classes corresponding to a "high risk” class, an "intermedi- ate risk” class and a "low risk” class.
  • said risk is a risk of death of said patient within a predetermined period of time, e.g. within 5 years from the taking of the sample.
  • said multiple atomic classifiers are distinct atomic classifiers selected from the group consisting of:
  • an atomic classifier predicting "high risk” if PGR expression is below a predetermined first threshold level and ESRl expression is below a predetermined second threshold level; said atomic classifier predicting "low risk” if PGR expression is below said predetermined first threshold level and ESRl expression is above said a predetermined second threshold level; said atomic classifier predicting "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and said atomic classifier predicting "high risk” if
  • PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level;
  • an atomic classifier predicting "high risk” if PGR expression is below a predetermined first threshold level and ILlRl expression is below a predetermined second threshold level; said atomic classifier predicting "low risk” if PGR expression is below said predetermined first threshold level and ILlRl expression is above said a predetermined second threshold level; said atomic classifier predicting "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and said atomic classifier predicting "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level;
  • PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level;
  • an atomic classifier predicting "low risk” if PGR expression is below a predetermined first threshold level and TOP2A expression is below a predetermined second threshold level; said atomic classifier predicting "high risk” if PGR expression is below said predetermined first threshold level and TOP2A a is above said a predetermined second threshold level; said atomic classifier predicting "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and said atomic classifier predicting "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level; (v) an atomic classifier predicting "low risk” if PGR expression is below a predetermined first threshold level and UBE2C expression is below a predetermined second threshold level; said atomic classifier predicting "high risk” if PGR expression is below said predetermined first threshold level and UBE2C a is above said a predetermined second threshold level; said atomic classifier
  • PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level;
  • atomic classifier (vi) and atomic classifier (vii) are used in the ma- jority voting step.
  • all atomic classifiers of said group of atomic classifiers are used for majority voting.
  • only a single atomic classifiers of said group of atomic classifiers are used for ma- jority voting.
  • an expression level of a marker gene is substituted with the expression level of a substitute gene, said substitute gene being co-regulated with said marker gene.
  • the threshold for PGR expression is about 3.8
  • the threshold for ESRl expression is about 6.2
  • the threshold for MLPH expression is about 11.4
  • the threshold for ILlRl expression is about 7.2
  • the threshold for TOP2A expression is about 9.8
  • the threshold for UBE2C expression is about 10.0
  • the threshold for TOP2A expression is about 9.8 in ato- mic classifier (iv)
  • the threshold for TOP2A expression is about 8.5 in atomic classifier
  • the threshold for TOP2A expression is about 8.1 in atomic classifier
  • the threshold for GREMl expression is about 9.0
  • the present invention also relates to a system for the prog- nosis of cancer in a patient from samples taken from said patient, said system comprising means for determining the expression level of a first, a second and a third marker gene of multiple atomic classifiers; means for performing multiple classifications of said sample into one of multiple risk classes, with each of said multiple atomic classifiers; means for performing a majority vote using the outcome of said multiple classifications.
  • said means for determining the expression level is a gene chip system, a real time PCR system.
  • said means for performing multiple classifications is a separate personal computer, or a computer integral with the remaining components of the system.
  • the computer receives expression level measurements directly from said means for determining the expression level. This reduces labor needed to perform the methods of the invention on said system.
  • a user can choose between at least two majority voting schemes. This allows to compare the outcome of two or more distinct majority voting schemes, which is helpful to increase the confidence in the obtained results.
  • the present invention further relates to methods of prognosis, wherein an expression level of a marker gene is substituted with the expression level of a substitute gene, said substitute gene being co-regulated with said marker gene.
  • a first gene is considered to be co-regulated with a second gene, if the expression level of said first gene is increased if the expression level of the second gene is increased, and if the expression level of said first gene is decreased if the expression level of said second gene in decreased.
  • substitute genes are used as marker genes.
  • the substitute genes are normally those genes, which are co-regulated with the original marker genes, thus carry the same information content as the original marker genes. Examples :
  • Tissue samples were collected from a large number of test objects afflicted with breast cancer. Clinical data on all test objects was available. In particular, data on the survival of the test objects over a period of 5 years was available for all test objects. Tissue samples were formalin fi- xed paraffin embedded (FFPE) samples of tumors of the test objects. These samples are routinely prepared for cancer patients .
  • FFPE formalin fi- xed paraffin embedded
  • Methods of the present invention can also be applied to fresh frozen samples, but it is envisioned that the present methods are optimized for FFPE samples.
  • Test data was collected by determining the gene expression of a large number of potential marker genes in multiple test objects.
  • Test objects were divided into two groups, namely a "case” group (i.e. objects that deceased of cancer within 5 years after biopsy) and a “control” group (i.e. objects with >5 years survival after biopsy) .
  • Atomic classifiers which showed a sensitivity of larger than 95% (i.e. more than 95% of the test objects of the "case” group of the training set were classified "high risk"), and specificity of >50% (i.e. [[... add definition of specificity ...]] were selected.
  • Suitable cutoff values for the expression levels of the marker genes were determined from the bimodal distribution of the expression level measurement. [[... add more detailed description of determination of cutoff values here ...]].
  • a first atomic classifier is shown in Fig. 1. It predicts
  • Threshold levels are preferably 3.8 for PGR expression, 6.2 for ESRl, and 11.4 for MLPH.
  • a second atomic classifier is shown in Fig. 2. It predicts "high risk” if PGR expression is below a predetermined first threshold level and ILlRl expression is below a predetermined second threshold level; it predicts "low risk” if PGR expression is below said predetermined first threshold level and ILlRl expression is above said a predetermined second threshold level; it predicts "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; or it predicts "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level.
  • Threshold levels are preferably 3.8 for PGR, 7.2 for ILlRl, 11.4 for MLPH.
  • a 3rd atomic classifier is shown in Fig. 3. Said classifier predicts "high risk” if PGR expression is below a predetermined first threshold level; it predicts "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and it predicts "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level. Threshold levels are preferably 3.8 for PGR and 11.4 for MLPH.
  • a 4th atomic classifier is shown in Fig. 4.
  • Said classifier predicts "low risk” if PGR expression is below a predetermined first threshold level and TOP2A expression is below a predetermined second threshold level; it predicts "high risk” if PGR expression is below said predetermined first threshold level and TOP2A expression is above said a predetermined second threshold level; it predicts "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and it predicts "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level.
  • Preferred thresholds are 3.8 for PGR expression, 9.8 for TOP2A, 11.4 for MLPH.
  • a 5th atomic classifier is shown in Fig. 5.
  • This classifier predicts "low risk” if PGR expression is below a predeter- mined first threshold level and UBE2C expression is below a predetermined second threshold level; it predicts "high risk” if PGR expression is below said predetermined first threshold level and UBE2C a is above said a predetermined second threshold level; it predicts "low risk” if PGR expression is above said predetermined first threshold level and MLPH expression is below a predetermined third threshold level; and it predicts "high risk” if PGR expression is above said predetermined first threshold level and MLPH expression is above a predetermined third threshold level.
  • Preferred thresholds for this classifier are 3.8 for PGR, 10.0 for UBE2C and 11.4 for MLPH.
  • the 1st to 5th atomic classifiers depend on the expression of the MLPH gene. They are referred to as the classifiers of the "MLPH cluster”.
  • a 6th atomic classifier is shown in Fig. 6. This classifier predicts "high risk” if TOP2A expression is below a predetermined first threshold level and GREMl expression is below a predetermined second threshold level; it predicts "low risk” if TOP2A expression is below said predetermined first threshold level and GREMl a is above said a predetermined second threshold level; and it predicts "high risk” if TOP2A expression is above said predetermined first threshold level. Pre- ferred thresholds for this classifier are 8.5 for TOP2A and 9.0 for GREMl.
  • a 7th atomic classifier is shown in Fig. 7.
  • This classifier predicts "low risk” if PLAU expression is below a predetermined first threshold level and MYBL2 expression is below a predetermined second threshold level; it predicts "high risk” if PLAU expression is below said predetermined first threshold level and MYBL2 a is above said a predetermined second threshold level; it predicts "low risk” if PLAU expression is above said predetermined first threshold level and TOP2A expression is below a predetermined third threshold level; and it predicts "high risk” if PLAU expression is above said predetermined first threshold level and TOP2A expression is above a predetermined third threshold level.
  • Preferred threshold levels are 8.6 for PLAU, 7.4 for MYBL2, and 8.1 for TOP2A.
  • the 6th and the 7th atomic classifiers are referred to as the "Cluster 5".
  • a first majority voting scheme is shown in Fig. 8. Seven atomic classifiers are used, each of them providing one vote (-1 corresponding to "low risk” and +1 corresponding to "high risk”) .
  • the average vote of atomic classifiers 1 to 5 i.e. the "MLPH cluster” is determined, and compared to a predetermined threshold. A preferred threshold is zero.
  • the average vote of atomic classifiers 6 and 7 is calculated and compared to a predetermined threshold.
  • a preferred threshold is zero.
  • the result of the two comparisons is combined by comparing the average of the two average votes to a predetermined threshold. Again, a preferred threshold is zero.
  • An average vote of the two average votes above the threshold results in a final classification in the "high risk” group.
  • a second majority voting scheme is shown in Figure 9. It uses the same 7 atomic classifiers as were used in the previous scheme.
  • the second majority voting scheme however, a comparison step of the average votes from the "MLPH Cluster", and from the "Cluster 5" is not performed. Instead a weighted vote is performed, in which the weighted average is com- puted according to the formula: (2 * MLPH + 5 * Cluster ⁇ ) / 7, wherein MLPH is the average vote of the MLPH Cluster, and Custer ⁇ is the average vote of the Cluster 5.
  • the weighted average so computed is compared to a predetermined threshold, preferably to a zero threshold.
  • a third majority voting scheme uses only the two atomic classifiers of the Cluster 5 for the computation of an average vote.
  • the average vote of atomic classifiers (vi) and (vii) is then compared to a suitable threshold, preferably to a ze- ro threshold.
  • Majority voting schemes can also be designed to classify a sample into one of multiple classes, e.g. three classes. These classes can be a "high risk” class, an "intermediate risk” class and a "low risk” class. In a majority voting scheme this can be achieved if not one, but multiple, e.g. two, thresholds are used for the final classification. For example, an average vote a can be compared to two threshold values tl and t2, with tl ⁇ t2, and a sample can then be classified "low risk", if a ⁇ tl; it can be classified “intermediate risk”, if tl ⁇ a ⁇ t; and it can be classified "high risk", if t2 ⁇ a.
  • the average vote of the MPLH cluster and the Cluster 5 can only be -1, 0 or 1.
  • "low risk” is classified, if the average vote is -1, "intermediate risk” is classified if the average vote is 0, and "high risk” is classified if the average vote is 1.
  • U-plasminogen activator Contains Urokinase-type plasmino- gen activator long chain A Urokinase-type plasminogen acti- vator short chain A

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Abstract

L'invention concerne des procédés, des trousses et des systèmes qui permettent d'établir un pronostic de cancer chez des patients non traités. De préférence, l'invention permet d'établir par vote majoritaire un pronostic de cancer du sein sur la base de mesures des niveaux d'expression de gènes marqueurs dans des échantillons de tumeurs issus de patients souffrant d'un cancer du sein. L'invention se rapporte à des gènes marqueurs qui permettent d'établir un pronostic de cancer précis chez des patients cancéreux.
PCT/EP2009/050478 2008-01-28 2009-01-16 Pronostic de cancer par vote majoritaire WO2009095319A1 (fr)

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WO2010003771A1 (fr) * 2008-06-16 2010-01-14 Siemens Healthcare Diagnostics Gmbh Marqueurs moléculaires permettant de poser un pronostic en matière de cancer
US10301685B2 (en) 2013-02-01 2019-05-28 Sividon Diagnostics Gmbh Method for predicting the benefit from inclusion of taxane in a chemotherapy regimen in patients with breast cancer
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US10577661B2 (en) 2010-03-31 2020-03-03 Myriad International Gmbh Method for breast cancer recurrence prediction under endocrine treatment
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