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WO2010002367A1 - Marqueurs de diagnostic d'un traitement et d'une progression du cancer du sein et procédés d'utilisation de ceux-ci - Google Patents

Marqueurs de diagnostic d'un traitement et d'une progression du cancer du sein et procédés d'utilisation de ceux-ci Download PDF

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Publication number
WO2010002367A1
WO2010002367A1 PCT/US2008/008271 US2008008271W WO2010002367A1 WO 2010002367 A1 WO2010002367 A1 WO 2010002367A1 US 2008008271 W US2008008271 W US 2008008271W WO 2010002367 A1 WO2010002367 A1 WO 2010002367A1
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Prior art keywords
algorithms
breast cancer
markers
marker
algorithm
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PCT/US2008/008271
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English (en)
Inventor
Steven P. Linke
Troy M. Bremer
Cornelius Allen Diamond
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Prediction Sciences Llc
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Priority to PCT/US2008/008271 priority Critical patent/WO2010002367A1/fr
Publication of WO2010002367A1 publication Critical patent/WO2010002367A1/fr

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    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6842—Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the present invention generally pertains to the prediction of the outcome of adjuvant therapy in the treatment of breast cancer based on the presence and quantities of certain protein molecular markers, called biomarkers, present in the treated patients.
  • the present invention also pertains to the prediction of progression of breast cancer, e.g. whether or not the patient's tumour is likely to metastasize, based upon cancer based on the presence and quantities of certain protein molecular markers.
  • the present invention specifically concerns the identification of groups, or "panels", of biomarkers particularly useful in combination for enhanced predictive accuracy of patient response to breast cancer therapy with endocrine therapy, chemotherapy, targeted therapy, surgical-only resection with no drug therapy and/or radiation treatment, or a combination of these treatments.
  • Binding of estrogen to ER causes its phosphorylation and dimerization, followed by movement into the nucleus and transcription of a variety of genes, including secreted growth and angiogenic factors (See for instance Osborne CK, Shou J, Massarweh S, et al: Crosstalk between estrogen receptor and growth factor receptor pathways as a cause for endocrine therapy resistance in breast cancer. Clin Cancer Res 11:865s-870s, 2005 (suppl)), in a process called nuclear-initiated steroid signalling.
  • tamoxifen can produce a weak agonist effect by stimulating the membrane-initiated signalling pathway when the relevant growth factors (e.g., EGFR and/or ERBB2) are overexpressed and/or by stimulating the nuclear- initiated pathway in the presence of overexpressed coactivators (e.g., NCOA1 and/or NCOA3) (See for instance Smith CL, Nawaz Z, O'Malley BW: Coactivator and corepressor regulation of the agonist/antagonist activity of the mixed antiestrogen, 4-hydroxytamoxifen.
  • coactivators e.g., NCOA1 and/or NCOA3
  • tamoxifen can have a growth stimulatory effect on tissues such as the endometrium, leading to increased risk of endometrial hyperplasia and cancer.
  • Other side effects include deep venous thrombosis and pulmonary emboli, development of benign ovarian cysts, vaginal discharge or irritation and hot flashes, and vision problems.
  • PDQ Breast cancer
  • patent application 10/376,538, filed March 1, 2003 specifies a ratio of the isoforms of the signaling protein She, e.g. PY-Shc to p66- Shc, differentiates between aggressive and non-aggressive breast cancer tumors. Ratios like these, as also demonstrated in U.S. patent application 10/727,100, generally do not separate patients any better than using just clinicopathological variable such as stage, grade, and ER status, and do not work in lymph-node negative patients (see for instance Jansen M et ai, HOXB13-to- IL17BR expression ratio is related with tumor aggressiveness and response to tamoxifen of recurrent breast cancer, J Clin Oncol.
  • U.S. patent application 11/061 ,067 details several multi-marker panels that define patient outcome based upon "...assessing the patient's likely prognosis based upon binding of the panel to the tumor sample.” This method is equivalent with a 'voting scheme' in which just the presence or absence of the binding of an antibody is enough to give a prognostic indication.
  • the scheme detailed in U.S. patent application 11/061,067 is not enough to produce a diagnostic of sufficient sensitivity and specificity, as shown by the fact that the method described does not beat current prognostic indicators such as the Nottingham Prognostic Index or Adjuvant!
  • gene expression assays can only measure transcript levels, which do not always correlate with functional protein levels, and they cannot detect protein mislocalization.
  • the assays are relatively complicated and costly, often requiring sophisticated and/or proprietary technology and multiple steps, including methods to try to reduce the contribution of adjacent non-tumor tissue and to account for RNA degradation.
  • the present invention will be seen to concern the development of a multi-molecular marker diagnostic with significant contributions by ER, PGR, BCL2, ERBB2, CDKN 1 B, c-MYC, TP-53, and others, in addition to standard clinicopathological factors, all interpolated by an algorithm that can deliver superior prognostic ability as compared to individual protein markers or gene expression techniques.
  • a method of providing a prognosis of disease-free survival in a cancer patient comprising the steps of obtaining a sample from the patient; and determining various polypeptide levels (e.g. molecular markers) in the sample, wherein change in various polypeptide levels as compared to a control sample indicates the good prognosis of a prolonged disease-free survival.
  • various polypeptide levels e.g. molecular markers
  • the present invention contemplates a multiple molecular marker diagnostic, the values of each assayed marker collectively interpolated by a non-linear algorithm, to (1) predict the outcomes of endocrine, particularly tamoxifen, therapy for breast cancer in consideration of multiple molecular makers, called biomarkers, of a patient's; and (2) identify whether or not a tumour from a patient is likely to be more aggressive, or malignant, than another and thus requiring neoadjuvant chemotherapy in addition to surgical and radiological treatment.
  • the model was built by multivariate mathematical analysis of (1) many more multiple molecular markers, called biomarkers, than ultimately proved to be significant in combination for forecasting treatment outcomes, in consideration of (2) real-world, clinical, outcomes of real patients who possessed these biomarkers.
  • the diagnostic is subject to updating, or revision, as any of (1) new biomarkers are considered, (2) new patient data (including as may come from patients who had their own treatment outcomes predicted) becomes available, and/or (3) new (drug) therapies are administered, all without destroying the validity of the instant invention and of discoveries made during the building, and the exercise, thereof, as hereinafter discussed.
  • a number of different insights are derived from the (1) building the (2) the exercise of the diagnostic.
  • a primary insight may be considered to be the identification of a number, or "palette", of biomarkers that are in combination of superior, and even greatly superior, accuracy for predicting the outcomes of tamoxifen therapy for breast cancer than would be any one, or even two, markers taken alone or in a ratio. This combination's predictive power over that of a simple voting panel response is increased by use of an algorithm that interpolates the linear and non-linear collective contributions of said collection to predict the clinical outcome of interest.
  • biomarkers are or increased predictive accuracy of, in particular, percentage disease-specific survival at 30+ months from onset of treatment when these biomarkers taken in pairs. This does not mean that these biomarker pairs are of overall predictive accuracy to the palette of predictive biomarkers. It only means that, when considered in pairs, certain biomarkers provide useful subordinate predictions.
  • exercise of the diagnostic primarily serves to (1) identify and quantify a palette of biomarkers interpolated by a non-linear algorithm having superior predictive capability for prognosis of outcomes in endocrine therapy of breast cancer; and (2) determine which patients would benefit from adjuvant chemotherapy and/or targeted therapy.
  • the instant invention is embodied in methods for choosing one or more marker(s) for diagnosis, prognosis, or therapeutic treatment of breast cancer in a patient that together, and as a group, have maximal sensitivity, specificity, and predictive power.
  • Said maximal sensitivity, specificity, and predictive power is in particular realized by choosing one or more markers as constitute a group by a process of plotting receiver operator characteristic (ROC) curves for (1) the sensitivity of a particular combination of markers versus (2) specificity for said combination at various cutoff threshold levels.
  • ROC receiver operator characteristic
  • the instant invention further discloses methods to interpolate the nonlinear correlative effects of one or more markers chosen by any methodology to such that the interaction between markers of said combination of one or more markers promotes maximal sensitivity, specificity, and predictive accuracy in the diagnosis, prognosis, or therapeutic treatment of breast cancer.
  • the present invention relates to (1) materials and procedures for identifying markers that are associated with the diagnosis, prognosis, or differentiation of breast cancer in a patient; (2) using such markers in diagnosing and treating a patient and/or monitoring the course of a treatment regimen; (3) using such markers to identify subjects at risk for one or more adverse outcomes related to breast cancer; and (4) using at one of such markers an outcome marker for screening compounds and pharmaceutical compositions that might provide a benefit in treating or preventing such conditions.
  • the preferred predictive palette was derived from a multivariate mathematical model where over 56 biomarkers were taken into consideration, and where seven (7) such biomarkers were found to be of improved prognostic significance taken in combination.
  • the most preferred palette of biomarkers predictive of outcome in adjuvant therapy for breast cancer include the protein expression of ER, PGR, BCL2, ERBB2, TP-53, CDKN 1 B, and c-MYC gene amplification.
  • a method of providing a treatment decision for a cancer patient receiving endocrine, chemo- and/or targeted therapy comprising obtaining a sample from the patient; and determining various molecular marker levels of interest in the sample, inputting such values into an algorithm which has previously correlated in a machine-learning fashion relationships between said molecular marker levels and clinical outcome, wherein output from such an algorithm indicates that that the cancer is a type of cancer that would be treatable with the selected treatment regime.
  • a plurality of markers and clinicopathological factors are combined using an algorithm to increase the predictive value of the analysis in comparison to that obtained from the markers taken individually or in smaller groups.
  • one or more markers for adhesion, angiogenesis, apoptosis, catenin, catenin/cadherin proliferation/differentiation, cell cycle, cell-cell interactions, cell-cell movement, cell- cell recognition, cell-cell signalling, cell surface, centrosomal, cytoskeletal, ERBB2 interaction, growth factors, growth factor receptors, invasion, metastasis, membrane/integrin, oncogenes, proliferation, tumour suppression, signal transduction, surface antigen, transcription factors and specific and non-specific markers of breast cancer are combined in a single assay to enhance the predictive value of the described methods.
  • This assay is usefully predictive of multiple outcomes, for instance: diagnosis of breast cancer, then predicting breast cancer prognosis, then further predicting response to treatment outcome.
  • different marker combinations in the assay may be used for different indications.
  • different algorithms interpret the marker levels as indicated on the same assay for different indications.
  • particular thresholds for one or more molecular markers in a panel are not relied upon to determine if a profile of marker levels obtained from a subject are indicative of a particular diagnosis/prognosis. Rather, in accordance with the present invention, an evaluation of the entire profile is made by (1) first training an algorithm with marker information from samples from a test population and a disease population to which the clinical outcome of interest has occurred to determine weighting factors for each marker, and (2) then evaluating that result on a previously unseen population. Certain persons skilled in bioinformatics will recognise this procedure to be tantamount to the construction, and to the training, of a neural network.
  • the evaluation is determined by maximising the numerical area under the ROC curve for the sensitivity of a particular panel of markers versus specificity for said panel at various individual marker levels. From this number, the skilled artisan can then predict a probability that a subject's current marker levels in said combination is indicative of the clinical marker of interest.
  • the test population might consist solely of samples from a group of subjects who have survived over 10 years after treatment of their breast cancer with adjuvant chemotherapy and no other comorbid disease conditions
  • the disease population might consist solely of samples from a group of subjects who have had breast cancer and treated such cancer with endocrine therapy only, and have no other comorbid disease conditions.
  • a third, "normal" population might also be used to establish baseline levels of markers as well in a non-diseased population.
  • the aforementioned weighting factors are multiplicative of marker levels in a non-linear fashion.
  • Each weighting factor is a function of other marker levels in the panel combination, and consists of terms that relate individual contributions, or independent and correlative, or dependent, terms. In the case of a marker having no interaction with other markers in regards to then clinical outcome of interest, then the specific value of the dependent terms would be zero.
  • the response to therapy is a complete pathological response.
  • the subject is a human patient.
  • the tumor is breast cancer, it can, for example, be invasive breast cancer, or stage Il or stage III breast cancer.
  • the patient is not receiving an endocrine therapy, a chemotherapy, a targeted therapy or another hormonal therapy.
  • the patient is concurrently receiving an endocrine therapy, chemotherapy or a hormonal therapy.
  • the endocrine therapy comprises tamoxifen, raloxifene, megestrol, or toremifene.
  • the targeted therapy comprises lapitinab, bevacizumab, trastuzumab, cetuximab, or panitumumab.
  • another hormonal therapy is an aromatase inhibitor such as anastrozole, letrozole, or exemestane, or pure anti-estrogens such fulvestrant, or surgical or medical means (goserelin, leuprolide) for reducing ovarian function.
  • the cancer comprises an estrogen receptor-positive cancer or a progesterone receptor-positive cancer.
  • the chemotherapy is adjuvant or neoadjuvant chemotherapy.
  • the neoadjuvant chemotherapy may, for example, comprise the administration of a taxane derivative, such as docetaxel and/or paclitaxel, and/or other anti-cancer agents, such as, members of the anthracycline class of anticancer agents, doxorubicin, topoisomerase inhibitors, etc.
  • a taxane derivative such as docetaxel and/or paclitaxel
  • other anti-cancer agents such as, members of the anthracycline class of anticancer agents, doxorubicin, topoisomerase inhibitors, etc.
  • the method may involve determination of the expression levels of at least two, or at least three, or at least four, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 15, or at least 20 of the prognostic proteins listed within this specification, listed above, or their associative protein expression products, as well as one or more gene amplifications of the markers listed within this specification.
  • the biological sample may be e.g. a tissue sample comprising cancer cells, where the tissue can be fixed, paraffin-embedded, or fresh, or frozen.
  • the tissue is from fine needle, core, or other types of biopsy.
  • the tissue sample is obtained by fine needle aspiration, bronchial lavage, or transbronchial biopsy.
  • the expression level of said prognostic protein levels or associated protein levels can be determined, for example, by immunohistochemistry or a western blot, or other proteomics techniques, or any other methods known in the art, or their combination.
  • the assay for the measurement of said prognostic proteins or their associated expression products is provided is provided in the form of a kit or kits for staining of individual proteins upon sections of tumor tissue.
  • kits are designed to work on an automated platform for analysis of cells and tissues such as described in U.S. Patent Application 10/062,308 entitled 'Systems and methods for automated analysis of cells and tissues'.
  • An embodiment of the invention is a method of screening for a compound that improves the effectiveness of a said adjuvant therapy in a patient comprising the steps of: introducing to a cell a test agent, wherein the cell comprises polynucleotide(s) mentioned in the instant invention encoding polypeptide(s) under control of a promoter operable in the cell; and measuring said polypeptide level(s), wherein when the level(s) are decreased following the introduction, the test agent is the compound that improves effectiveness of said adjuvant therapy in the patient.
  • such an agent will prevent the development of said adjuvant therapy resistance in a patient receiving such a therapy.
  • the patient is said adjuvant therapy-resistant.
  • the test agent is a ribozyme, an antisense nucleotide, a receptor blocking antibody, a small molecule inhibitor, or a promoter inhibitor.
  • An embodiment of the invention is a method of screening for a compound that improves the effectiveness of a said adjuvant therapy in a patient comprising the steps of: contacting a test agent with polypeptide(s) mentioned in the instant invention, wherein said polypeptide(s) or the ER polypeptide is linked to a marker; and determining the ability of the test agent to interfere with the binding of said polypeptide(s), wherein when the marker level(s) are decreased following the contacting, the test agent is the compound that improves effectiveness of the adjuvant therapy in the patient.
  • the patient is adjuvant therapy-resistant for said adjuvant therapy.
  • One embodiment of the invention is a method of treating a cancer patient comprising administering to the patient a therapeutically effective amount of an antagonist of polypeptide(s) mentioned in the instant invention and an said adjuvant therapy.
  • the patient is said adjuvant therapy-resistant.
  • the antagonist interferes with translation of the polypeptide(s) mentioned in the instant invention.
  • the antagonist interferes with an interaction between the polypeptide(s) mentioned in the instant invention and an estrogen receptor polypeptide.
  • the antagonist interferes with phosphorylation or any other posttranslational modification of the said polypeptide(s) in yet another specific embodiment of the invention.
  • the antagonist inhibits the function of a polypeptide encoding a kinase that specifically phosphorylates said polypeptide(s).
  • the antagonist is administered before, together with, or after the said adjuvant therapy.
  • the antagonist and the said adjuvant therapy are administered at the same time in another embodiment.
  • An embodiment of the invention is method of improving the effectiveness of a said adjuvant therapy in a cancer patient comprising administering a therapeutically effective amount of an antagonist of polypeptide level (s) mentioned in the instant invention to the patient to provide a therapeutic benefit to the patient.
  • the administering is systemic, regional, local or direct with respect to the cancer.
  • An embodiment of the invention is a method of determining whether a premenopausal breast cancer patient should have ovariectomy as a treatment option (also goserulin, leupitine, letrozole, exesmestane, anastrozole, fulvestrant). Elevated levels of polypeptide(s) mentioned in the instant invention in a tumor sample are indicative of ovariectomy as a possible treatment option.
  • An embodiment of the invention is a method of determining whether a cancer patient has de novo endocrine therapy resistance comprising the steps of: obtaining a sample from the patient; and determining polypeptide(s) mentioned in the instant invention in the sample and a HER-2 polypeptide level in the sample, wherein elevated polypeptide(s) mentioned in the instant invention as compared to a control sample indicate de novo endocrine therapy resistance.
  • Figure 1 is Table 1 of patient characteristics of the Tristar and IPC dataset and Table 2 of numbers and outcomes of the patients studied in the IPC dataset.
  • Figure 2 is Table 3 of molecular markers studied.
  • Figure 3 is Table 4 of IHC marker utility analysis for several additional markers in assay models described in various examples and Table 5 of Multivariate Cox Proportional hazard analysis of a model described in the examples and its relevance in certain patient subsets whose prognosis is determined by current clinical guidelines.
  • Figure 4 is comprised of graphs showing the previously published model in the IPC dataset.
  • Figure 4a shows a Kaplan-Meier survival curve for overall survival.
  • Figure 4b shows the effect of chemotherapy on the good prognosis group as defined by the previously published model.
  • Figure 5 is comprised of graphs showing Kaplan-Meier survival curves in the IPC dataset.
  • Figure 5a shows the effect of chemotherapy on the good prognosis group as defined by the previously published model.
  • Figure 5b shows the effect of chemotherapy on all HR positive patients who received hormone therapy.
  • Figure 6 is comprised of graphs showing Kaplan-Meier survival curves of model 1 D in the IPC dataset.
  • Figure 6a is model 1D applied to HR positive untreated patients.
  • Figure 6b is model 1 D applied to HR-positive patients treated with chemotherapy only.
  • Figure 7 is comprised of graphs showing Kaplan-Meier survival curves of model 1D in the IPC dataset.
  • Figure 7a is model 1 D applied to HR-positive patients treated with endocrine therapy only.
  • Figure 7b is model 1D applied to HR-positive patients treated with chemotherapy and endocrine therapy.
  • Figure 8 is comprised of graphs showing Kaplan-Meier survival curves of model 1 D in the IPC dataset.
  • Figure 8a shows the chemotherapy benefit in the poor prognosis group of HR-positive patients.
  • Figure 8b shows model 1 D separating patients not treated with hormone therapy in the NPI-bad (NPI>3.4) category.
  • Figure 9 is comprised of graphs showing Kaplan-Meier survival curves of model 1D in the IPC dataset.
  • Figure 9a shows model 1 D separating patients treated with hormone therapy in the NPI-bad (NPI>3.4) category.
  • Figure 9b shows model 1 D the chemotherapy benefit in patients treated with hormone therapy in the NPI-bad (NPI>3.4) category.
  • Figure 10 is comprised of graphs showing Kaplan-Meier survival curves of model 1 D in the IPC dataset.
  • Figure 10a shows model 1 D separating patients not treated with hormone therapy in the St. Gallen's intermediate and high-risk categories.
  • Figure 10b shows model 1D separating patients treated with hormone therapy in the St. Gallen's intermediate and high-risk categories.
  • Figure 11 is comprised of graphs showing Kaplan-Meier survival curves of model 1 D in the IPC dataset.
  • Figure 11a shows the chemotherapy benefit of model 1D in the St. Gallen's intermediate and high-risk categories.
  • Figure 11b shows continuous decrease in risk of death based upon a risk score given by model 1D.
  • Figure 12 is a look-up table to which the interpolative algorithm behind model 1D can be reduced to.
  • Figure 13 is a look-up table of the adjusted risk using Adjuvant! Online. As noted in these tables, an adjustment for pN>0 still remains in the biomarker risk score as in the original model.
  • adjuvant refers to a pharmacological agent that is provided to a patient as an additional therapy to the primary treatment of a disease or condition.
  • control sample indicates a sample that is compared to a patient sample.
  • a control sample may be obtained from the same tissue that the patient sample is taken from. However, a noncancerous area may be chosen to reflect the individual polypeptide levels in normal cells for a particular patient.
  • a control may be a cell line, such as MCF-7, in which serial dilutions are undertaken to determine the exact concentration of elevated polypeptide levels. Such levels are compared with a patient sample.
  • a "control sample” may comprise a theoretical patient with an elevated polypeptide level of a certain molecule that is calculated to be the cutoff point for elevated polypeptide levels of said certain molecule.
  • a patient sample that has polypeptide levels equal to or greater than such a control sample is said to have elevated polypeptide levels.
  • all survival is defined to be survival after first diagnosis and death. For instance, long-term overall survival is for at least 5 years, more preferably for at least 8 years, most preferably for at least 10 years following surgery or other treatment.
  • disease-free survival is defined as a time between the first diagnosis and/or first surgery to treat a cancer patient and a first reoccurrence.
  • a disease-free survival is "low” if the cancer patient has a first reoccurrence within five years after tumor resection, and more specifically, if the cancer patient has less than about 55% disease-free survival over 5 years.
  • a high disease-free survival refers to at least about 55% disease-free survival over 5 years.
  • endocrine therapy is defined as a treatment of or pertaining to any of the ducts or endocrine glands characterized by secreting internally and into the bloodstream from the cells of the gland.
  • the treatment may remove the gland, block hormone synthesis, or prevent the hormone from binding to its receptor.
  • adjuvant therapy-resistant patient is defined as a patient receiving an endocrine therapy and lacks demonstration of a desired physiological effect, such as a therapeutic benefit, from the administration of an adjuvant therapy.
  • estrogen-receptor positive refers to cancers that do have estrogen receptors while those breast cancers that do not possess estrogen receptors are “estrogen receptor-negative.”
  • polypeptide as used herein is used interchangeably with the term “protein”, and is defined as a molecule which comprises more than one amino acid subunits.
  • the polypeptide may be an entire protein or it may be a fragment of a protein, such as a peptide or an oligopeptide.
  • the polypeptide may also comprise alterations to the amino acid subunits, such as methylation or acetylation.
  • molecular marker is also used interchangeably with the terms protein and polypeptide, though the two latter terms are subclasses of the former.
  • 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 surgical intervention, 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.
  • prognosis as used herein are defined as a prediction of a probable course and/or outcome of a disease.
  • prognostic model for determination of survival outcome in a cancer patient.
  • proteome is defined as the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time.
  • Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as "expression proteomics").
  • Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics.
  • Proteomics methods are valuable supplements to other methods of gene expression profiling, and can be used, alone or in combination with other methods, to detect the products of the prognostic markers of the present invention.
  • therapeutic benefit refers to anything that promotes or enhances the well-being of the subject with respect to the medical treatment of his condition, which includes treatment of pre-cancer, cancer, and hyperproliferative diseases.
  • a list of nonexhaustive examples of this includes extension of the subject's life by any period of time, decrease or delay in the neoplastic development of the disease, decrease in hyperproliferation, reduction in tumor growth, delay of metastases, reduction in cancer cell or tumor cell proliferation rate, and a decrease in pain to the subject that can be attributed to the subject's condition.
  • a therapeutic benefit refers to reversing de novo adjuvant therapy-resistance or preventing the patient from acquiring an adjuvant therapy-resistance.
  • the term "therapeutically effective amount" as used herein is defined as the amount of a molecule or a compound required to improve a symptom associated with a disease.
  • a molecule or a compound which decreases, prevents, delays or arrests any symptom of the breast cancer is therapeutically effective.
  • a therapeutically effective amount of a molecule or a compound is not required to cure a disease but will provide a treatment for a disease.
  • a molecule or a compound is to be administered in a therapeutically effective amount if the amount administered is physiologically significant.
  • a molecule or a compound is physiologically significant if its presence results in technical change in the physiology of a recipient organism.
  • treatment is defined as the management of a patient through medical or surgical means.
  • the treatment improves or alleviates at least one symptom of a medical condition or disease and is not required to provide a cure.
  • treatment outcome is the physical effect upon the patient of the treatment.
  • sample indicates a patient sample containing at least one tumor cell. Tissue or cell samples can be removed from almost any part of the body. The most appropriate method for obtaining a sample depends on the type of cancer that is suspected or diagnosed. Biopsy methods include needle, endoscopic, and excisional. The treatment of the tumor sample after removal from the body depends on the type of detection method that will be employed for determining individual protein levels.
  • TriStar dataset included 324 patients treated adjuvantly with HT (>98% tamoxifen) with or without locoregional radiotherapy (RT), but not cytotoxic chemotherapy (CT). It also included an additional 103 patients who received both HT and CT on which no models have been trained.
  • the preliminary published model included six of the molecular markers plus age>85 years and pathological lymph node status (pN).
  • the molecular markers were ER 1 PGR, ERBB2, BCL2, and TP53 assessed by immunohistochemistry (IHC), and MYC assessed by fluorescence in situ hybridization (FISH). Thresholds for each of the selected features were chosen from the available continuous or categorical data.
  • This preliminary published model, as well as all of the models described herein, generate a risk score for each patient that is directly associated with risk of death.
  • a risk score cut-off of -0.31 was used to classify individual patients into good and poor prognosis categories, which accurately predicted their outcome (Linke et al., A multi-marker model to predict outcome in tamoxifen-treated breast cancer patients. Clin Cancer Res 12:1175-1183, 2006).
  • the instant invention provides an expanded panel model that incorporates the marker CDKN1B (hereafter CDKN1 B) and additional markers, is determined by an algorithm whose weights for individual marker interactions relating to outcome can be reduced to a look-up table, and is shown to be beneficial in predicting likely survival for various time-periods when chemotherapy is given alone or in concert with an endocrine therapy.
  • CDKN1 B markers that are associated with CDKN1B
  • additional markers is determined by an algorithm whose weights for individual marker interactions relating to outcome can be reduced to a look-up table, and is shown to be beneficial in predicting likely survival for various time-periods when chemotherapy is given alone or in concert with an endocrine therapy.
  • markers and clinicopathological information form a set of examples of clinical inputs and their corresponding outputs, the outputs being the clinical outcome of interest, for instance breast cancer prognosis and/or breast cancer therapeutic treatment outcome.
  • This process is also known as feature selection.
  • the minimum number of relevant clinical inputs e.g. features, that are needed to fully differentiate and/or predict disease prognosis, diagnosis, or detection with the highest sensitivity and specificity are selected for each time period.
  • the feature selection is done with an algorithm that selects markers that differentiate between patient disease groups, say those likely to have recurrence versus those likely to no recurrence.
  • the relevant clinical input combinations might change at different time periods, and might be different for different clinical outcomes of interest.
  • the classifier is trained, it is ready for use by a clinician.
  • the clinician enters the same classifier inputs used during training of the network by assaying the selected markers and collecting relevant clinical information for a new patient, and the trained classifier outputs a maximum likelihood estimator for the value of the output given the inputs for the current patient.
  • the clinician or patient can then act on this value.
  • data may be obtained from a group of subjects.
  • the subjects may be patients who have been tested for the presence or level of certain polypeptides and/or clinicopathological variables (hereafter 'markers' or 'biomarkers').
  • 'markers' or 'biomarkers' Such markers and methods of patient extraction are well known to those skilled in the art.
  • a particular set of markers may be relevant to a particular condition or disease. The method is not dependent on the actual markers.
  • the markers discussed in this document are included only for illustration and are not intended to limit the scope of the invention. Examples of such markers and panels of markers are described in U.S. patent application Ser. No. 11/407,169 and the incorporated references.
  • a preferred embodiment of the instant invention is that the samples come from two or more different sets of patients, one a disease group of interest and the other(s) a control group, which may be healthy or diseased in a different indication than the disease group of interest. For instance, one might want to look at the difference in markers between patients who have had endocrine therapy and had a recurrence of cancer within a certain time period and those who had endocrine therapy and did not have recurrence of cancer within the same time period to differentiate between the two populations.
  • the samples represent or reflect characteristics of a population of patients or samples. It may also be useful to handle and process the samples under conditions and according to techniques common to clinical laboratories.
  • the present invention is not intended to be limited to the strategies used for processing tumor samples, we note that, in the field of pathology, it is often common to fix samples in buffered formalin, and then to dehydrate them by immersion in increasing concentrations of ethanol followed by xylene. Samples are then embedded into paraffin, which is then molded into a "paraffin block" that is a standard intermediate in histologic processing of tissue samples.
  • the present inventors have found that many useful antibodies to biomarkers discussed herein display comparable binding regardless of the method of preparation of tumor samples; those of ordinary skill in the art can readily adjust observations to account for differences in preparation procedure.
  • tissue arrays are prepared. Tissue arrays may be constructed according to a variety of techniques. According to one procedure, a commercially-available mechanical device (e.g., the manual tissue arrayer MTA1 from Beecher Instruments of Sun Prairie, Wis.) is used to remove an 0.6-micron-diameter, full thickness "core" from a paraffin block (the donor block) prepared from each patient, and to insert the core into a separate paraffin block (the recipient block) in a designated location on a grid.
  • a commercially-available mechanical device e.g., the manual tissue arrayer MTA1 from Beecher Instruments of Sun Prairie, Wis.
  • cores from as many as about 400 patients can be inserted into a single recipient block; preferably, core-to-core spacing is approximately 1 mm.
  • the resulting tissue array may be processed into thin sections for staining with interaction partners according to standard methods applicable to paraffin embedded material.
  • a single tissue array can yield about 50-150 slides containing >75% relevant tumor material for assessment with interaction partners. Construction of two or more parallel tissue arrays of cores from the same cohort of patient samples can provide relevant tumor material from the same set of patients in duplicate or more. Of course, in some cases, additional samples will be present in one array and not another.
  • the tumor test samples are assayed by one or more techniques, well- known for those versed in ordinary skill in the art for various polypeptide levels. Briefly, assays are conducted by binding a certain substance with a detectable label to the antibody of the protein in question to be assayed and bringing such in contact with the tumor sample to be assayed. Any available technique may be used to detect binding between an interaction partner and a tumour sample. One powerful and commonly used technique is to have a detectable label associated (directly or indirectly) with the antibody. For example, commonly-used labels that often are associated with antibodies used in binding studies include fluorochromes, enzymes, gold, iodine, etc.
  • Tissue staining by bound interaction partners is then assessed, preferably by a trained pathologist or cytotechnologist.
  • a scoring system may be utilised to designate whether the antibody to the polypeptide does or does not bind to (e.g., stain) the sample, whether it stains the sample strongly or weakly and/or whether useful information could not be obtained (e.g., because the sample was lost, there was no tumor in the sample or the result was otherwise ambiguous).
  • staining may be assessed qualitatively or quantitatively; more or less subtle gradations of staining may be defined; etc.
  • the present invention is not limited to using antibodies or antibody fragments as interaction partners of inventive tumour markers.
  • the present invention also encompasses the use of synthetic interaction partners that mimic the functions of antibodies.
  • synthetic interaction partners that mimic the functions of antibodies.
  • Several approaches to designing and/or identifying antibody mimics have been proposed and demonstrated (e.g., see the reviews by Hsieh-Wilson et al., Ace. Chem. Res. 29:164, 2000 and Peczuh and Hamilton, Chem. Rev. 100:2479, 2000).
  • small molecules that bind protein surfaces in a fashion similar to that of natural proteins have been identified by screening synthetic libraries of small molecules or natural product isolates (e.g., see Gallop et al., J. Med. Chem.
  • the peptide loop performs the binding function in these mimics (e.g., see Smythe et al., J. Am. Chem. Soc. 116:2725, 1994).
  • a synthetic antibody mimic comprising multiple peptide loops built around a calixarene unit has also been described (e.g., see U.S. Pat. No. 5,770,380 to Hamilton et al.).
  • association can be detected by adding a detectable label to the antibody.
  • association can be detected by using a labeled secondary antibody that associates specifically with the antibody, e.g., as is well known in the art of antigen/antibody detection.
  • the detectable label may be directly detectable or indirectly detectable, e.g., through combined action with one or more additional members of a signal producing system. Examples of directly detectable labels include radioactive, paramagnetic, fluorescent, light scattering, absorptive and calorimetric labels. Examples of indirectly detectable include chemiluminescent labels, e.g., enzymes that are capable of converting a substrate to a chromogenic product such as alkaline phosphatase, horseradish peroxidase and the like.
  • the complex may be visualized or detected in a variety of ways, with the particular manner of detection being chosen based on the particular detectable label, where representative detection means include, e.g., scintillation counting, autoradiography, measurement of paramagnetism, fluorescence measurement, light absorption measurement, measurement of light scattering and the like.
  • detection means include, e.g., scintillation counting, autoradiography, measurement of paramagnetism, fluorescence measurement, light absorption measurement, measurement of light scattering and the like.
  • association between an antibody and its polypeptide molecular marker may be assayed by contacting the antibody with a tumor sample that includes the marker.
  • appropriate methods include, but are not limited to, immunohistochemistry (IHC), radioimmunoassay, ELISA, immunoblotting and fluorescence activates cell sorting (FACS).
  • IHC immunohistochemistry
  • ELISA ELISA
  • FACS fluorescence activates cell sorting
  • 1 HC is a particularly appropriate detection method. Techniques for obtaining tissue and cell samples and performing IHC and FACS are well known in the art.
  • the results of such an assay can be presented in any of a variety of formats.
  • test report may indicate only whether or not a particular protein biomarker was detected, perhaps also with an indication of the limits of detection. Additionally the test report may indicate the subcellular location of binding, e.g., nuclear versus cytoplasmic and/or the relative levels of binding in these different subcellular locations.
  • the results may be presented in a semi-quantitative fashion. For example, various ranges may be defined and the ranges may be assigned a score (e.g., 0 to 5) that provides a certain degree of quantitative information. Such a score may reflect various factors, e.g., the number of cells in which the tumor marker is detected, the intensity of the signal (which may indicate the level of expression of the tumor marker), etc.
  • results may be presented in a quantitative fashion, e.g., as a percentage of cells in which the tumor marker is detected, as a concentration, etc.
  • the type of output provided by a test will vary depending upon the technical limitations of the test and the biological significance associated with detection of the protein biomarker. For example, in the case of certain protein biomarkers a purely qualitative output (e.g., whether or not the protein is detected at a certain detection level) provides significant information. In other cases a more quantitative output (e.g., a ratio of the level of expression of the protein in two samples) is necessary.
  • the resulting set of values are put into a database, along with outcome, also called phenotype, information detailing the treatment type, for instance tamoxifen plus chemotherapy, once this is known. Additional patient or tumour test sample details such as patient nodal status, histological grade, cancer stage, the sum total called patient clinicopathological information, are put into the database.
  • the database can be simple as a spreadsheet, i.e. a two-dimensional table of values, with rows being patients and columns being filled with patient marker and other characteristic values.
  • a computerized algorithm can first perform preprocessing of the data values. This involves normalisation of the values across the dataset and/or transformation into a different representation for further processing. The dataset is then analysed for missing values. Missing values are either replaced using an imputation algorithm, in a preferred embodiment using KNN or MVC algorithms, or the patient attached to the missing value is excised from the database. If greater than 50% of the other patients have the same missing value then value can be ignored.
  • the dataset is split up into three parts: a training set comprising 33-80% of the patients and their associated values, a testing set comprising 10-50% of the patients and their associated values, and a validation set comprising 1-50% of the patients and their associated values.
  • a feature selection algorithm is applied to the training dataset. This feature selection algorithm selects the most relevant marker values and/or patient characteristics. Preferred feature selection algorithms include, but are not limited to, Forward or Backward Floating, SVMs, Markov Blankets, Tree Based Methods with node discarding, Genetic Algorithms, Regression-based methods, kernel-based methods, and filter-based methods.
  • Cross-validation is one of several approaches to estimating how well the features selected from some training data is going to perform on future as-yet- unseen data and is well-known to the skilled artisan.
  • Cross validation is a model evaluation method that is better than residuals. The problem with residual evaluations is that they do not give an indication of how well the learner will do when it is asked to make new predictions for data it has not already seen.
  • One way to overcome this problem is to not use the entire data set when training a learner. Some of the data is removed before training begins. Then when training is done, the data that was removed can be used to test the performance of the learned model on " new" data.
  • the algorithm can optimize these selected markers by applying a classifier to the training dataset to predict clinical outcome.
  • a cost function that the classifier optimizes is specified according to outcome desired, for instance an area under receiver-operator curve maximising the product of sensitivity and specificity of the selected markers, or positive or negative predictive accuracy.
  • Testing of the classifier is done on the testing dataset in a cross-validated fashion, preferably na ⁇ ve or k-fold cross- validation. Further detail is given in U.S. patent application 09/611,220, incorporated by reference. Classifiers map input variables, in this case patient marker values, to outcomes of interest, for instance, prediction of stroke sub-type.
  • Preferred classifiers include, but are not limited to, neural networks, Decision Trees, genetic algorithms, SVMs, Regression Trees, Cascade Correlation, Group Method Data Handling (GMDH), Multivariate Adaptive Regression Splines (MARS), Multilinear Interpolation, Radial Basis Functions, Robust Regression, Cascade Correlation + Projection Pursuit, linear regression, Non-linear regression, Polynomial Regression, Regression Trees, Multilinear Interpolation, MARS, Bayes classifiers and networks, and Markov Models, and Kernel Methods.
  • neural networks Decision Trees, genetic algorithms, SVMs, Regression Trees, Cascade Correlation, Group Method Data Handling (GMDH), Multivariate Adaptive Regression Splines (MARS), Multilinear Interpolation, Radial Basis Functions, Robust Regression, Cascade Correlation + Projection Pursuit, linear regression, Non-linear regression, Polynomial Regression, Regression Trees, Multilinear Interpolation
  • the classification model is then optimised by for instance combining the model with other models in an ensemble fashion.
  • Preferred methods for classifier optimization include, but are not limited to, boosting, bagging, entropy-based, and voting networks.
  • This classifier is now known as the final predictive model.
  • the predictive model is tested on the validation data set, not used in either feature selection or classification, to obtain an estimate of performance in a similar population.
  • the predictive model can be translated into a decision tree format for subdividing the patient population and making the decision output of the model easy to understand for the clinician.
  • the marker input values might include a time since symptom onset value and/or a threshold value. Using these marker inputs, the predictive model delivers diagnostic or prognostic output value along with associated error.
  • the predictive model might be further reduced to a look-up table that can be stored in a database that details the outcome for markers in all possible states.
  • the instant invention anticipates a kit comprised of reagents, devices and instructions for performing the assays, and a computer software program comprised of the predictive model that interprets the assay values when entered into the predictive model run on a computer. The predictive model receives the marker values via the computer that it resides upon.
  • a tissue tumor sample is taken from the patient using standard techniques well known to those of ordinary skill in the art and assayed for various tumor markers of cancer by slicing it along its radial axis and placing such slices upon a substrate for molecular analysis by assaying for various molecular markers.
  • Assays can be preformed through immunohistochemistry or through any of the other techniques well known to the skilled artisan.
  • the assay is in a format that permits multiple markers to be tested from one sample, such as the Aqua platform.TM., and/or in a quantitative fashion, defined to within 10% of the actual value and in the most preferred enablement of the instant invention, within 1% of the actual value.
  • the values of the markers in the samples are inputted into the trained, tested, and validated algorithm residing on a computer, which outputs to the user on a display and/or in printed format on paper and/or transmits the information to another display source the result of the algorithm calculations in numerical form, a probability estimate of the clinical diagnosis of the patient.
  • a probability estimate of the clinical diagnosis of the patient There is an error given to the probability estimate, in a preferred embodiment this error level is a confidence level. The medical worker can then use this diagnosis to help guide treatment of the patient.
  • the present invention provides a kit for the analysis of markers.
  • a kit preferably comprises devises and reagents for the analysis of at least one test sample and instructions for performing the assay.
  • the kits may contain one or more means for using information obtained from immunoassays performed for a marker panel to rule in or out certain diagnoses.
  • Marker antibodies or antigens may be incorporated into immunoassay diagnostic kits depending upon which marker autoantibodies or antigens are being measured.
  • a first container may include a composition comprising an antigen or antibody preparation. Both antibody and antigen preparations should preferably be provided in a suitable titrated form, with antigen concentrations and/or antibody titers given for easy reference in quantitative applications.
  • kits may also include an immunodetection reagent or label for the detection of specific immunoreaction between the provided antigen and/or antibody, as the case may be, and the diagnostic sample.
  • Suitable detection reagents are well known in the art as exemplified by radioactive, enzymatic or otherwise chromogenic ligands, which are typically employed in association with the antigen and/or antibody, or in association with a second antibody having specificity for first antibody.
  • the reaction is detected or quantified by means of detecting or quantifying the label.
  • Immunodetection reagents and processes suitable for application in connection with the novel methods of the present invention are generally well known in the art.
  • the reagents may also include ancillary agents such as buffering agents and protein stabilizing agents, e.g., polysaccharides and the like.
  • the diagnostic kit may further include where necessary agents for reducing background interference in a test, agents for increasing signal, software and algorithms for combining and interpolating marker values to produce a prediction of clinical outcome of interest, apparatus for conducting a test, calibration curves and charts, standardization curves and charts, look-up tables, and the like.
  • TriStar dataset included 324 patients treated adjuvantly with HT (>98% tamoxifen) with or without locoregional radiotherapy (RT), but not cytotoxic chemotherapy (CT). It also included an additional 103 patients who received both HT and CT on which no models have been trained.
  • the preliminary published model included six of the molecular markers plus age ⁇ 85 years and pathological lymph node status (pN).
  • the molecular markers were ER, PGR, ERBB2, BCL2, and TP53 assessed by immunohistochemistry (IHC), and MYC assessed by fluorescence in situ hybridization (FISH). Thresholds for each of the selected features were chosen from the available continuous or categorical data.
  • This preliminary published model, as well as all of the models described herein, generate a risk score for each patient that is directly associated with risk of death. A risk score cut-off of -0.31 was used to classify individual patients into good and poor prognosis categories, which accurately predicted their outcome.
  • An independent dataset was obtained from the lnstitut Paoli-Calmette, Marseille, France, (hereafter the IPC dataset) in which we obtained demographic, treatment, outcome, clinicopathologic, and molecular data on an independent set of 547 patients.
  • the IPC dataset included a total of 56 molecular markers selected based on their reported predictive or prognostic role in breast cancer, including the five IHC markers in our preliminary published model.
  • Table 1 ( Figure 1) gives some comparison statistics relevant to the instant invention between the two datasets.
  • Table 2 ( Figure 2) gives numbers and outcomes relevant to the instant invention.
  • Table 3 ( Figure 3) provides a categorical list of the 56 IHC markers in the IPC dataset. The categories are only general, because many of the markers appear to play multiple roles, and some have currently ill-defined roles. Many of the markers center on the ER signaling pathway and the complex cross-talk that occurs with alternative growth factor receptor pathways.
  • markers are involved in proliferation, cell division, differentiation, apoptosis, adhesion, invasion, or angiogenesis pathways, and several are transcription factors, tumor suppressors, or oncogenes.
  • Daidone MG Biomolecular prognostic factors in breast cancer. Curr Opin Obstet Gynecol 16:49- 55, 2004; Esteva FJ, Hortobagyi GN: Prognostic molecular markers in early breast cancer. Breast Cancer Res 6:109-18, 2004; and Ross JS, et ah Breast cancer biomarkers and molecular medicine: part II. Expert Rev MoI Diagn 4:169-88, 2004.
  • the markers may play direct roles, indirect roles (general prognostic indicators), or both.
  • the threshold values used for the five IHC markers in the IPC dataset were equivalent to those used to produce the preliminary published model in the TriStar dataset.
  • the remaining molecular marker, MYC amplification by FISH, was determined to be a poor prognostic indicator that was largely independent of all of the other features in the original TriStar dataset, and it affected a relatively small subset of patients ( ⁇ 11%). Based on these observations, and because reliable MYC FISH data was not available in the IPC dataset, all of the IPC patients were assumed to be negative for MYC amplification.
  • Model 1A was developed using a third-order polynomial kernel partial least squares (KPLS) method. Risk scores were calculated for the patients in the IPC dataset using Model 1A, and they were classified as good or poor prognosis. Identical to the preliminary published model, a risk score threshold of -0.31 was used to classify patients into good and poor prognosis categories (good prognosis, ⁇ -0.31; poor prognosis, >- 0.31).
  • Model 1A provided a strong and highly statistically significant discrimination of the good and poor prognosis groups in the HR-positive, non-CT-treated IPC patients.
  • the good prognosis group had a 5-year overall survival (OS) approximating the expected survival of a similar non-cancer population, as calculated in R using the "survexp" routine based on an age- and sex-matched general mortality rate from 1960-1980 United States census records.
  • OS overall survival
  • FIG. 7 shows that the good prognosis group treated with CT had a survival that was equivalent to the non-CT-treated good prognosis group shown in Figure 6. This indicates that patients in our good prognosis group do not derive a benefit from CT, suggestive of over-treatment.
  • Figure 7 shows that the poor prognosis group treated with CT had a substantial benefit relative to the non-CT-treated poor prognosis group shown in Figure 6.
  • Figure 8 directly shows the benefit of CT in the poor prognosis population.
  • the survival benefit imparted by CT in the overall HR-positive, HT-treated IPC population did not reach statistical significance (Figure 9).
  • these data indicate that Model 1A is effective at identifying patients who are at risk of death without more aggressive adjuvant therapy (poor prognosis), and that the poor prognosis patients have an elevated chance of benefit from CT.
  • an executable program was developed to allow easy application of the outcome model to new datasets.
  • the parameters for the features in the preliminary published model were transferred to the executable after being robustly estimated using repeated application of five-fold cross-validation on the original TriStar dataset.
  • the parameter estimates were stored in a data file compliant with the executable program to enable consistent application of the model (henceforth "Model 1 B").
  • the executable program reads in the raw continuous or categorical data for each of the model features and applies the same thresholds that were used in the original published study.
  • the program also codes any missing data to allow consistent application of the model. As described above for data distribution testing, patients missing MYC FISH data are assumed to be negative for MYC amplification by the program.
  • Nested, stratified cross-validation During feature selection and modeling, the data was broken up into different sets: training/validation and testing.
  • the training/validation sets are the complement to the disjoint testing sets, which are na ⁇ ve to model optimization and feature selection.
  • a model is optimized for each putative feature combination on the training set, and then the feature set/model is scored using the validation component of the training set to allow robust feature selection.
  • the models form nonlinear "maps" of the effects of various factors upon the clinical outcome of interest from the input data.
  • the cross-validation during feature selection avoids overfitting (i.e., memorization of features in a specific dataset that are not applicable in a general manner).
  • SFFS is an excellent wrapper-based, sub-optimal search procedure with lower computational cost than the gold standard "branch and bound" procedure. It is similar to sequential forward search, but it provides for removal of features in an evolving feature set when it improves classifier performance (see Pudil P, Novovicov J, Kittier J: Floating Search Methods in Feature Selection. Pattern Recognition Letters 15:1119- 1125, 1994).
  • the putative features were added and removed in a floating sequential manner to thoroughly explore feature space and to maximize the diagnostic utility of the model.
  • an optimization function for biomarker selection was used to drive model diagnostic utility as measured by area under the receiver-operating characteristic curves (AUC ROC).
  • Machine learning A low-order KPLS ( see Rosipal R, Trejo LJ: Kernel Partial Least Squares Regression in Reproducing Kernel Hubert Space. Journal of Machine Learning Research 2:97-123, 2002) machine learning methodology was used in all modeling rounds to provide a good compromise between rapid computation training time and a reasonably flexible space to be used for modeling, while significantly minimizing the number of parameters to be estimated compared to working directly with higher-order interactions.
  • the initialization phase was conducted using cross-validated training data from the first training fold to identify the cost function leading to the best learning rate and lowest false-positive rates.
  • the false-positive rates were ascertained by appending false surrogate markers to the training set fold.
  • the false surrogate markers were constructed using approximately half of the IHC marker data that was randomly reordered to be unrelated to the labeled outcome, while maintaining the same distributions.
  • a false surrogate call rate was used to provide an indication of an inadequate cost function or insufficient training cross-validation during biomarker selection that was corrected and re-tested during the initialization phase.
  • Model 1A e.g., see the expressions in the multivariate Cox model of the preliminary published model
  • Treatment types and subtypes were treated as coded variables to help produce a model that would be applicable to all patients.
  • Model 1C the form of the extended model (Model 1C) using the clinicopathologic factors and Model 1B in HR-positive patients is a weighting of the clinicopathologic factors (here termed Clin_Base_HR-positive) in conjunction with Model 1B and a scaling factor:
  • Clin_Base_HR-positive dsq * grade*(grade>1) * (.065+.03 * LVI status)+age*.O45) LVI status: 0, LVI-negative; 1 , LVI-positive
  • A1 is a scaling factor that allows normalization and accounts for the relative contributions of the Clin_Base and Model 1 B components.
  • tumor diameter in this case, pTd
  • grade were significant factors in addition to Model 1B.
  • This simple form was similar to that identified in the HR-positive population, except that the magnitude of grade did not appear to further increase the risk of death as it increased from 2 to 3. This may indicate that when a patient is HR-negative, only low grade is relevant, although small dataset effects must be considered. Likewise, the fact that age and LVI were not significant may be due to the risks of tumor diameter and grade superseding them, although this also may be a small dataset phenomenon.
  • Model 1C the form of the extended model (Model 1C) using the clinicopathologic factors and Model 1 B in HR-negative patients is a weighting of the relevant clinicopathologic factors (here termed Clin_Base_HR-negative) in conjunction with Model 1B and a scaling factor:
  • Clin_Base_HR-negative pTd * (grade>1)
  • A2 is a scaling factor that allows normalization and accounts for the relative contributions of the Clin_Base and Model 1B components.
  • the IHC markers identified during feature selection were analyzed using Cox proportional hazards analysis in the HR-positive subpopulation.
  • Model 1C was used as a covariate with each of the IHC markers individually and with stratification by HT and CT.
  • the molecular markers were analyzed as a continuous outcome, column 1 , p(all), or by cut-points defined by their 1 st and 3 rd quartiles.
  • the analysis was completed in the IPC dataset for subjects receiving RT and false discovery rate analysis was applied to account for multiple hypothesis testing.
  • Table 4 indicates that TIMP1 , MTA1 , CDKN1B, and high ER have significant p values ( ⁇ 0.05), and MTA1 and CDKN1B have false discovery rates of less than 10%, indicating that they will likely generalize.
  • Model 1 D f(Model 1C, CDKN1B>120, A3)
  • A3 is a scaling factor that allows normalization and accounts for the relative contributions of Model 1C and CDKN1B.
  • Model 1 D 1 containing all of the new clinicopathologic (age, tumor diameter, grade, and LVI) and IHC marker (CDKN1B) features, was applied to HR-positive patients in each of the different treatment groups in the IPC dataset to demonstrate model utility using a risk score cut-point of 1.0.
  • HT/CT/RT-treated patients from the TriStar dataset were combined with those in the IPC dataset, as they had never been used for model training.
  • Figures 10-13 demonstrate that the model was statistically significant in the untreated, HT-only, and CT-only treatment groups. Similar to our previous findings, the Model 1D poor prognosis group showed a benefit with CT treatment ( Figure 14), whereas the good prognosis group did not (compare to Figures 10-13).
  • Model 1D has diagnostic utility beyond the standard models employed today, it was applied to the subset of the IPC population with an NPI score corresponding to an intermediate or high risk (>3.4).
  • Model 1D was able to separate NPI>3.4 patients into good and poor prognosis groups in the subsets who did not receive HT ( Figure 15) or who received HT ( Figure 11), whether they received CT or not, shown in Table 5 ( Figure 5). Similar to the results shown in Figure 14, the Model 1 D poor prognosis group exhibited significant CT benefit within the NPI>3.4 group ( Figure 17). Likewise, Model 1 D demonstrated significant diagnostic utility within the St. Gallen intermediate and high risk groups ( Figures 18-20; Table 5 ( Figure 5)).
  • Model 1D was assessed in HR-negative patients within the elevated clinical risk (NPI>3.4 or St. Gallen>1) groups.
  • NPI>3.4 the NPI>3.4 analysis
  • St. Gallen>1 the St. Gallen>1 analysis
  • CDKN1B also known as p27 Kip1 , plays an anti-proliferative role carried out through inhibition of G1 cell cycle phase cyclin-dependent kinases. Although not all published reports in breast cancer show a statistically significant role, a preponderance of studies indicate that high levels of nuclear CDKN 1 B are associated with better prognosis and/or response to therapy (extensively reviewed in Alkarain A, Jordan R, Slingerland J: p27 deregulation in breast cancer: prognostic significance and implications for therapy. J Mammary Gland Biol Neoplasia 9:67-80, 2004 and Colozza M 1 et al: Proliferative markers as prognostic and predictive tools in early breast cancer: where are we now?
  • CDKN 1 B was an independent linear factor (i.e., it did not interact to any large extent with the other features, indicating that it is in a distinct pathway, perhaps related to cell cycle).
  • MTA1 or "metastasis-associated 1" was originally identified as being over- expressed in metastatic rat mammary carcinoma cell lines in a differential screen with non-metastatic cells (Toh Y, Pencil SD, Nicolson GL: A novel candidate metastasis-associated gene, mta1, differentially expressed in highly metastatic mammary adenocarcinoma cell lines. cDNA cloning, expression, and protein analyses. J Biol Chem 269:22958-63, 1994).
  • MTA1 is complicated in that it can be found in different isoforms in different cellular compartments and in different cell types, including normal epithelium, tumor cells, and stromal cells (Acconcia F, Kumar R: Signaling regulation of genomic and nongenomic functions of estrogen receptors. Cancer Lett 238:1-14, 2006). More recent reports indicate that it may be indicative of local invasion and lymph node metastasis, but not necessarily distant metastasis, and that tumors with the highest levels of MTA1 rarely undergo distant metastasis (Hofer MD, et ah Comprehensive analysis of the expression of the metastasis-associated gene 1 in human neoplastic tissue. Arch Pathol Lab Med 130:989-96, 2006).
  • MTA1 sensitizes breast tumors to systemic therapies, such as tamoxifen (Martin MD, et ah Breast tumors that overexpress nuclear metastasis-associated 1 (MTA1) protein have high recurrence risks but enhanced responses to systemic therapies.
  • systemic therapies such as tamoxifen (Martin MD, et ah Breast tumors that overexpress nuclear metastasis-associated 1 (MTA1) protein have high recurrence risks but enhanced responses to systemic therapies.
  • MTA1 nuclear metastasis-associated 1
  • PSA prostate specific antigen
  • TIMP1 is a member of a family of tissue inhibitors of matrix metalloproteinases (MMPs).
  • MMPs matrix metalloproteinases
  • ECM extracellular matrix
  • PLAU also known as uPA or urokinase plasminogen activator, is a serine protease that is involved in the degradation of ECM as a member of the plasminogen activation system (PAS) pathway.
  • MMPs matrix metalloproteinases
  • PAS proteins include PLAU's cell surface-associated receptor PLAUR (uPAR) (see for instance Andreasen PA, Kjoller L, Christensen L, Duffy MJ: The urokinase-type plasminogen activator system in cancer metastasis: a review, lnt J Cancer 72:1-22, 1997 and Schmitt M et a ⁇ . Clinical impact of the plasminogen activation system in tumor invasion and metastasis: prognostic relevance and target for therapy. Thromb Haemost 78:285-96, 1997), as well as at least two of its inhibitors, SERPINE1 (PAI-1) and SERPINB2 (PAI-2).
  • Model 1 D results [0179] Given the ability to independently validate the CDKN1 B finding in the TriStar dataset, Model 1 D was created by adding CDKN1B and the new clinicopathologic features to the preliminary published model, and it was the focus of the remaining analyses ( Figures 10-11). Adjuvant! Online predictions of clinical outcome based on the SEER data base for Breast Cancer were used to weight the effect of clinicopathological parameters. Model 1 D was assessed in the NPI intermediate- and high-risk subpopulations, as well as the 2005 St. Gallen intermediate- and high-risk subpopulations. It maintained significant diagnostic utility in these subpopulations, and it was even useful in the HR-positive subpopulation not receiving HT. Thus, the selected molecular markers appear to represent not only those pathways directly related to HT response, but also related to the general aggressiveness of the cancer. This is also supported by the inclusion of CDKN1 B as an independent linear factor, and the identification of markers related to invasion.
  • CCND1 is required to advance in the cell cycle
  • CDKN1B inhibits the cell cycle.
  • CKDN1 B binds to CCND1 and in sufficient concentration inhibits the formation of this complex, halting cell cycle progression. Therefore either negligible CCND1 or high CDKN1B have a similar positive prognostic effect.
  • Molecular markers analysed included BCL2, EGFR, ER, ERBB2, MYC, PGR, TP-53, CDKN 1 B, and over 50 others. The statistical analysis has also investigated assigning a patient to a sub-group(s) based on interdependencies of certain markers.
  • the multivariate model of the present invention predicts outcomes based on statistically significant contributions of clinicopathological features and several molecular markers: ER, PGR, ERBB2, BCL2, TP-53, CDKN 1 B, MTA-1 and c-MYC gene amplification, among others. Analysis of additional molecular markers, such as ER coregulators such as AIB1, may further enhance this model.
  • the present invention will thus be realized to provide at least three separate and different insights, though not limited by such, as claimed below.
  • the primary insight of the invention can be expressed by the statement: "Ms. Patient, the aggressiveness of your breast cancer tumor is derived from considering a set of biomarkers in combination, and these biomarkers are the protein expression values of ER, PGR, BCL2, ERBB2, CDKN1B, CCND1, and TP- 53, and c-MYC gene amplification, interpolated by an algorithm. Your personal probability of survival with adjuvant therapy only or without any therapy at all may be seen on this graph accompanying your test results.”
  • the secondary aspect of the invention can be expressed, by way of example, in the statement: "Ms. Patient, if you chose an adjuvant chemotherapy in addition to a treatment of endocrine therapy, your personal probability of survival may be seen on this graph accompanying your test results.”
  • the tertiary aspect of the invention can be expressed, by way of example, in the statement: "Ms. Patient, given that considering said biomarker panel has given you a low chance of long-term survival from your breast cancer using current treatment, you may want to consider a more aggressive course of treatment, including first-line use of an adjuvant targeted therapy in conjuction with or instead of said current treatment protocol, including investigational therapies.”

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Abstract

Selon l’invention, afin de maximiser à la fois l'espérance de vie et la qualité de vie de patients avec un cancer du sein opérable, il est important de prédire un résultat de traitement par adjuvant et une probabilité de progression avant le traitement. La présente invention concerne l'utilisation d'un procédé selon un apprentissage de machine pour développer un modèle validé de façon croisée pour prédire le résultat d'un traitement par adjuvant, en particulier un résultat de traitement par chimiothérapie, et la probabilité d'une progression avant le traitement. Le modèle comprend des caractéristiques clinico-pathologiques standard, ainsi que des marqueurs moléculaires rassemblés à l'aide d'une immuno-histochimie standard et d'une hybridation in situ par fluorescence. Le modèle surpasse de manière significative les directives du consensus de St Galles et l'index de pronostic de Nottingham et a le potentiel de fournir un pronostic cliniquement utile et rentable pour des patients atteints d'un cancer du sein.
PCT/US2008/008271 2008-07-03 2008-07-03 Marqueurs de diagnostic d'un traitement et d'une progression du cancer du sein et procédés d'utilisation de ceux-ci WO2010002367A1 (fr)

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2471949A1 (fr) 2010-12-31 2012-07-04 Progenika Biopharma, S.A. Procédé pour l'identification par des techniques moléculaires de variantes génétiques qui ne codent pas pour l'antigène D (D-) et l'antigène C modifié (C+W)
WO2012097820A1 (fr) * 2011-01-20 2012-07-26 Syddansk Universitet Procédé et dosage pour la prédiction de l'efficacité a long terme d'un traitement par le tamoxifène chez des patients atteints d'un cancer du sein positif pour le récepteur des œstrogènes
EP2697649A2 (fr) * 2011-04-15 2014-02-19 Nuclea Biotechnologies, Inc. Profil d'expression génique de réponse thérapeutique à des inhibiteurs du vegf
CN106066493A (zh) * 2016-05-24 2016-11-02 中国石油大学(北京) 贝叶斯岩相判别方法及装置
WO2019103714A1 (fr) * 2017-11-27 2019-05-31 Floteks Plastik Sanayi Ve Ticaret Anonim Sirketi Réservoir d'urée et de carburant en plastique combiné en une seule pièce avec des déflecteurs, rotomoulé avec un système de chauffage de moule spécial
KR102293109B1 (ko) * 2020-12-07 2021-08-25 주식회사 온코크로스 인공지능을 이용하여 도출된 유전자 세트를 이용한 유방암 예후 예측방법
WO2022124717A1 (fr) * 2020-12-07 2022-06-16 주식회사 온코크로스 Procédé de prédiction du pronostic du cancer du sein à l'aide d'un ensemble de gènes ribosomiques issus de l'intelligence artificielle
CN115017671A (zh) * 2021-12-31 2022-09-06 昆明理工大学 基于数据流在线聚类分析的工业过程软测量建模方法、系统
CN117607443A (zh) * 2024-01-23 2024-02-27 杭州华得森生物技术有限公司 用于诊断乳腺癌的生物标志物组合
RU2823488C1 (ru) * 2023-11-22 2024-07-23 Федеральное государственное бюджетное научное учреждение "Томский национальный исследовательский медицинский центр" Российской академии наук ("Томский НИМЦ") Способ прогнозирования риска прогрессирования рака молочной железы на фоне тамоксифена с учетом экспрессионных особенностей опухоли

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US20080108091A1 (en) * 2006-08-07 2008-05-08 Hennessy Bryan T Proteomic Patterns of Cancer Prognostic and Predictive Signatures

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US20080108091A1 (en) * 2006-08-07 2008-05-08 Hennessy Bryan T Proteomic Patterns of Cancer Prognostic and Predictive Signatures

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2471949A1 (fr) 2010-12-31 2012-07-04 Progenika Biopharma, S.A. Procédé pour l'identification par des techniques moléculaires de variantes génétiques qui ne codent pas pour l'antigène D (D-) et l'antigène C modifié (C+W)
WO2012097820A1 (fr) * 2011-01-20 2012-07-26 Syddansk Universitet Procédé et dosage pour la prédiction de l'efficacité a long terme d'un traitement par le tamoxifène chez des patients atteints d'un cancer du sein positif pour le récepteur des œstrogènes
EP2697649A2 (fr) * 2011-04-15 2014-02-19 Nuclea Biotechnologies, Inc. Profil d'expression génique de réponse thérapeutique à des inhibiteurs du vegf
US20140080737A1 (en) * 2011-04-15 2014-03-20 Nuclea Biotechnologies, Inc. Gene expression profile for therapeutic response to vegf inhibitors
EP2697649A4 (fr) * 2011-04-15 2015-04-22 Nuclea Biotechnologies Inc Profil d'expression génique de réponse thérapeutique à des inhibiteurs du vegf
CN106066493A (zh) * 2016-05-24 2016-11-02 中国石油大学(北京) 贝叶斯岩相判别方法及装置
WO2019103714A1 (fr) * 2017-11-27 2019-05-31 Floteks Plastik Sanayi Ve Ticaret Anonim Sirketi Réservoir d'urée et de carburant en plastique combiné en une seule pièce avec des déflecteurs, rotomoulé avec un système de chauffage de moule spécial
US11370297B2 (en) 2017-11-27 2022-06-28 Floteks Plastik Sanayi Ve Ticaret Anonim Sirketi One-piece combined plastic urea and fuel tank with baffles, rotomoulded with a special mold heating system
KR102293109B1 (ko) * 2020-12-07 2021-08-25 주식회사 온코크로스 인공지능을 이용하여 도출된 유전자 세트를 이용한 유방암 예후 예측방법
WO2022124717A1 (fr) * 2020-12-07 2022-06-16 주식회사 온코크로스 Procédé de prédiction du pronostic du cancer du sein à l'aide d'un ensemble de gènes ribosomiques issus de l'intelligence artificielle
CN115017671A (zh) * 2021-12-31 2022-09-06 昆明理工大学 基于数据流在线聚类分析的工业过程软测量建模方法、系统
RU2823488C1 (ru) * 2023-11-22 2024-07-23 Федеральное государственное бюджетное научное учреждение "Томский национальный исследовательский медицинский центр" Российской академии наук ("Томский НИМЦ") Способ прогнозирования риска прогрессирования рака молочной железы на фоне тамоксифена с учетом экспрессионных особенностей опухоли
CN117607443A (zh) * 2024-01-23 2024-02-27 杭州华得森生物技术有限公司 用于诊断乳腺癌的生物标志物组合
CN117607443B (zh) * 2024-01-23 2024-04-16 杭州华得森生物技术有限公司 用于诊断乳腺癌的生物标志物组合

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