JP2019032334A - Method for determining breast cancer-related disease state, and array to be used in the method - Google Patents

Abstract

To provide an improved breast cancer classification and a prognosis determination method, using a tissue biomarker.SOLUTION: Provided is a method for determining a breast cancer-related disease state. The method comprises the steps of: a) preparing a sample to be tested; and b) determining a biomarker signature of the test sample by measuring presence and/or an amount of at least one biomarker selected from a separately predefined group in the test sample. The presence and/or the amount of the at least one biomarker selected from the separately predefined group in the test sample indicates a breast cancer-related disease state. The present invention further provides an array and a kit to be used in this method.SELECTED DRAWING: None

Description

  The present invention provides methods for determining breast cancer-related disease states, and arrays and kits for use in such methods.

  Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in women, accounting for 23% of all cancer cases and 14% of cancer-related deaths (Non-Patent Document 1). Traditional clinicopathological parameters such as histologic grading, tumor size, age, lymph node involvement and hormone receptor status are used to determine treatment and estimate prognosis (non- Non-patent document 3; Non-patent document 4; Non-patent document 5; Non-patent document 6). Histological grading, one of the most commonly used prognostic factors, was combined based on microscopic evaluation of tumor cell morphological and cytological characteristics, reflecting tumor malignancy It is a score. This combined score is then used to stratify breast cancer tumors; grade 1-slow growing, well differentiated, grade 2-moderately differentiated, and grade 3-highly proliferative and poorly differentiated (Non-Patent Document 3). However, the clinical value of histologic grade for patient prognosis has been questioned primarily reflecting current challenges associated with tumor grading (Non-Patent Document 7). Non-patent document 8). In addition, 30-60% of tumors are classified as tissue grade 2, which has been shown to show a very heterogeneous patient cohort and has proven to provide less information for making clinical decisions (Non-patent document 9). Clearly, traditional clinical laboratory parameters are still insufficient for proper prognosis and risk group identification and to predict whether a given treatment will be successful. As a result, some patients may be overtreated, undertreated or fairly treated with a therapy that does not provide any benefit. Therefore, a deeper molecular understanding of breast cancer biology and tumor progression is needed to further advance prognostic outcomes and consequently therapeutic outcomes in combination with improved methods for individualizing prognostic and treatment decisions (Non-Patent Document 10).

Jemal et al., 2011 Ciocca and Elledge, 2000 Elston and Ellis, 1991 Hondarmark et al., 2008 Hudis, 2007 Slamon et al., 2001 Frierson et al., 1995 Robbins et al., 1995 Sotiriou et al., 2006 Dowsett et al., 2007

To date, a set of genomic approaches has been used to subgroup breast cancer types (Ivshina et al., 2006; Perou et al., 2000; Sorlie et al., 2001) and for breast cancer prognosis and risk stratification (Paik et al. , 2004; van't Veer et al.
2002; van de Vijver et al., 2002). On the other hand, proteomic findings have been expected to accelerate the transfer of important findings to clinical practice (Hanash, 2003). In this context, classical mass spectrometry (MS) -based proteomics is a breast cancer proteome that primarily targets cell lines and several tissue samples (Bouchal et al., 2009; Geiger et al., 2010; Geiger et al., 2012; Gong et al. 2008; Kang et al., 2010; Strande et al., 2009; Sutton et al., 2010), and more recently, affinity proteomic attempts have been used to diagnose breast cancer and to predict the risk of recurrence. Resulted in the first multiplexed serum portrait (Carlsson et al., 2008; Carlsson et al., 2011). However, in spite of recent technological advances, in either a sensitive and reproducible manner using either classical proteomics techniques (Aebersold and Mann, 2003) or affinity proteomics (Borrebaeck and Wingren, 2011) Generating a detailed protein expression profile of a large cohort of crude proteomes, such as tissue extracts, remains a challenge.

  In order to solve these problems, the inventors recently developed a global proteomic survey (GPS) technology platform (Wingren et al., 2009) that combines the best features of affinity proteomics and MS. . GPS is suitable for discovery attempts and reproducibly decodes the crude proteome in a sensitive and quantitative manner (Olsson et al., 2012; Olsson et al., 2011).

  In this study, we used GPS to delineate a thorough molecular tissue portrait of tissue graded breast cancer tissue that reflects tumor progression. For this purpose, 52 breast cancer tissue proteomes were profiled, which, to the best of our knowledge, represent one of the largest label-free LC-MS / MS based breast cancer tissue studies. This protein expression profile was successfully verified using the orthogonal method. In long-term practice, these tissue biomarker portraits could pave the way for improved classification and prognosis.

Accordingly, a first aspect of the invention provides a method for determining a breast cancer-related disease state comprising the following steps:
a) preparing a sample to be tested; and b) testing by measuring the presence and / or amount of one or more biomarkers selected from the group defined in Table 1 in the test sample. Determining the biomarker signature of the sample;
Including:
The presence and / or amount in the test sample of one or more biomarkers selected from the group defined in Table 1 indicates a breast cancer-related disease state. Thus, in effect, step (b) uses or uses the presence and / or amount of one or more biomarkers selected from the group defined in Table 1 in the test sample. Alternatively, the method further comprises the step of determining a breast cancer-related disease state ((b) (i)) based on the amount.

  By “breast cancer related disease state” we mean the histological grade of breast cancer cells and / or the metastasis-free survival time of an individual comprising breast cancer cells.

  The breast cancer-related disease state can be a histological grade (of breast cancer cells) and / or a metastasis-free survival time (of an individual).

  By “biomarker” we mean a naturally occurring biological molecule, or a component or fragment thereof, whose measurement can provide information useful in the prognosis of breast cancer. For example, a biomarker can be a naturally occurring protein or carbohydrate moiety, or an antigenic component or fragment thereof.

  Preferably, the sample to be tested is provided from a mammal. The mammal can be any domestic animal or farm animal. Preferably, the mammal is a rat, mouse, guinea pig, cat, dog, horse or primate. Most preferably, the mammal is a human. Preferably, the sample is a cell or tissue sample (or derivative thereof) containing or consisting of breast cancer cells, or a protein or nucleic acid derived from a cell or tissue sample containing or consisting of breast cancer cells Are equally preferred. Preferably, the test sample and the control sample are from the same species.

If the breast cancer-related disease state is a histological grade of breast cancer cells or comprises a histological grade of breast cancer cells, the method comprises the following steps:
c) comprising histological grade 1 breast cancer cells, histological grade 2 breast cancer cells and / or histological grade 3 breast cancer cells, or histological grade 1 breast cancer cells, histological grade 2 breast cancer Providing one or more control samples consisting of cells and / or histological grade 3 breast cancer cells; and d) a control sample of one or more biomarkers measured in step (b) Determining the biomarker signature of the control sample (s) by measuring the presence and / or amount in the (s);
May further include:
The following events in which the presence and / or amount of one or more biomarkers in a test sample is measured in step (b):
i) an event corresponding to the presence and / or amount in a control sample comprising or consisting of a first histological grade breast cancer cell (if present) or consisting of a first histological grade breast cancer cell (if present) ;
ii) an event different from the presence and / or amount in a control sample comprising or consisting of a second histological grade breast cancer cell (if present) or consisting of a second histological grade breast cancer cell (if present) And / or iii) the presence and / or amount in a control sample comprising or consisting of a third histological grade breast cancer cell (if present) or consisting of a third histological grade breast cancer cell (if present); Different events,
The presence of breast cancer cells is identified.

  Thus, if the first histological grade was Elston grade 1, the second and third histological grades (if present) are Elston grade 2 and Elston grade 3 (or vice versa). ). If the first histological grade was Elston grade 2, the second and third histological grades (if present) are Elston grade 1 and Elston grade 3 (or vice versa). If the first histological grade was Elston grade 3, the second and third histological grades (if present) are Elston grade 1 and Elston grade 2 (or vice versa).

By “corresponding to the presence and / or amount in a control sample comprising, or consisting of, a first histological grade breast cancer cell,” the present inventors Or the amount is identical to the presence and / or amount of a control sample comprising or consisting of a first histological grade breast cancer cell; or a second histology Control sample comprising or second grade histological grade breast cancer cells and / or comprising a third histological grade breast cancer cell or comprising a third histological grade breast cancer cell Than the presence and / or amount of a control sample consisting of (or a predefined reference value indicative thereof) of the first histological grade Including cancer cells, or the first histological grade consisting of breast cancer cells control sample the presence and / or amount and its proximity means. Preferably, the presence and / or amount comprises at least 60% of the presence and / or amount of a control sample comprising or consisting of a first histological grade breast cancer cell, For example, at least 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80% 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97 %, 98%, 99% or 100%.

  By “differing from the presence and / or amount in a control sample comprising or consisting of a third histological grade breast cancer cell,” the present inventors Or the presence and / or amount of a control sample, wherein the amount comprises a first histological grade breast cancer cell or consists of a first histological grade breast cancer cell, or a second histological grade breast cancer cell The presence and / or amount of a control sample comprising, or comprising a second histological grade breast cancer cell, and / or comprising a third histological grade breast cancer cell, or of a third histological grade Meaning different from the presence and / or amount of a control sample consisting of breast cancer cells (or a predefined reference value indicative thereof). Preferably, the presence and / or amount is the presence and / or amount of a control sample comprising or consisting of a second histological grade breast cancer cell, and / or 40% or less of the presence and / or amount of a control sample comprising 3 histological grade breast cancer cells or consisting of a 3rd histological grade breast cancer cell, eg 39%, 38%, 37%, 36 %, 35%, 34%, 33%, 32%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3% 2%, 1%, or 0% or less.

Preferably, the histological grade control sample comprises a single histological grade of breast cancer cells or consists of a single histological grade of breast cancer cells. Preferably, step (c) comprises the following:
i) preparing one or more control samples comprising histological grade 1 breast cancer cells or consisting of histological grade 1 breast cancer cells; comprising histological grade 2 breast cancer cells or tissue Providing one or more control samples consisting of histological grade 2 breast cancer cells; and one or more comprising histological grade 3 breast cancer cells or consisting of histological grade 3 breast cancer cells Preparing a control sample of;
ii) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells; and comprising histological grade 2 breast cancer cells; or Providing one or more control samples of histological grade 2 breast cancer cells;
iii) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells; and comprising histological grade 3 breast cancer cells; or Providing one or more control samples of histological grade 3 breast cancer cells;
iv) providing one or more control samples comprising or consisting of histological grade 2 breast cancer cells; and comprising histological grade 3 breast cancer cells; or Providing one or more control samples of histological grade 3 breast cancer cells;
v) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells;
vi) providing one or more control samples comprising or consisting of histological grade 2 breast cancer cells; or vii) comprising histological grade 3 breast cancer cells Or comprising or consisting of providing one or more control samples consisting of histological grade 3 breast cancer cells.

If the breast cancer related disease state is or includes an individual's metastasis-free survival time, the method comprises the following steps:
c) one or more first control samples comprising breast cancer cells derived from individuals having a metastasis-free survival of less than 10 years or consisting of breast cancer cells derived from individuals having a metastasis-free survival of less than 10 years; And / or one or more second control samples comprising breast cancer cells from an individual having a metastasis-free survival of 10 years or more, or consisting of breast cancer cells from an individual having a metastasis-free survival of 10 years or more D) of the control sample (s) by measuring the presence and / or amount of one or more biomarkers measured in step (b) in the control sample (s) Determining a biomarker signature;
May further include:
An individual's metastasis-free survival time is the presence and / or amount of one or more biomarkers measured in step (b) corresponding to the presence and / or amount of a first control sample (if present) And / or an event that is different from the presence and / or amount of the second control sample (if present) is identified as less than 10 years;
The presence and / or amount of the one or more biomarkers measured in step (b) is the presence and / or amount of the first control sample (if present) as determined by the individual metastasis-free survival time. In different events and / or events corresponding to the presence and / or amount of a second control sample (if present) are identified as longer than 10 years.

  By “corresponding to the presence and / or amount of one or more first control samples”, we indicate that this presence and / or amount is the presence of one or more first control samples. And / or is identical to the amount; or one or more than to the presence and / or amount of one or more second control samples (or a predefined reference value indicative thereof) It means close to the presence and / or amount of the first control sample. Preferably, the presence and / or amount is at least 60% of the presence and / or amount of the first control sample, eg at least 65%, 66%, 67%, 68%, 69%, 70%, 71% 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88 %, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

By “differing from the presence and / or amount of one or more second control samples”, we determine that this presence and / or amount is the presence and / or amount of the second control sample (or It means that it is different from a predetermined reference value). Preferably, the presence and / or amount is 40% or less of the presence and / or amount of the second control sample, eg 39%, 38%, 37%, 36%, 35%, 34%, 33%, 32%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23
%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or 0% or less.

  Preferably, the one or more first and / or second metastasis-free survival control samples are of the same histological grade as the sample being tested.

  Preferably, the one or more control samples are age and / or gender matched to the individual being tested. In other words, healthy individuals are approximately the same age (eg, within 5 years) and the same gender as the individual being tested.

  Preferably, the presence and / or amount of one or more biomarkers measured in step (b) in the test sample is compared against a predetermined reference value.

  Accordingly, the presence and / or amount of one or more biomarkers measured in step (b) in the test sample is determined by the presence and / or presence of one or more biomarkers measured in step (d). Preferably, it is significantly different (ie statistically different) from the quantity or the predetermined reference value. For example, as discussed in the accompanying examples, a significant difference between the presence and / or amount of a particular biomarker in a test sample and a control sample is when p <0.05 (eg, p <0. 04, p <0.03, p <0.02, or p <0.01).

  Thus, the method of the first aspect of the present invention may comprise determining the histological grade of breast cancer cells and the metastasis-free survival time of the individual (either concomitantly or continuously), or It can consist of making decisions.

  Preferably, step (b) is one or more biomarkers selected from the group defined in Table 1, eg at least 2, 3, 4 selected from the group defined in Table 1 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 , 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78 or at least The presence of 79 biomarkers in the test sample and / or Properly consists in measuring the amount or measured.

Thus, the first aspect of the invention may comprise or consist of a method for determining the histological grade of breast cancer cells (ie staging a breast cancer sample to determine the histological grade). This method consists of the following steps:
a) preparing a sample to be tested;
b) determining the biomarker signature of the test sample by measuring the presence and / or amount in the test sample of one or more biomarkers selected from the group defined in Table 1;
Including
The presence and / or amount in the test sample of one or more biomarkers selected from the group defined in Table 1 indicates the histological grade of breast cancer cells.

By “determining the histological grade of breast cancer cells”, the inventors have described Elston, C., et al., Which is incorporated herein by reference. W. And Elis,
I. O. (1991). Pathological prognostic factors in breast cancer. I. The value of histologic grade in breast cancer: experience from a large study with long-term flow-up. Histological Grade 1, (ie Elston Grade 1), Histological Grade 2 (ie Elston Grade 2) or Histological Grade 3 (ie Elston Grade 3) as defined in Histopathology 19, 403-410. ) And categorized.

  If the method comprises or consists of determining a histological grade of breast cancer cells, step (b) comprises one or more selected from the group defined in Table 1A It may comprise or consist of measuring the presence and / or amount of biomarkers, for example at least two biomarkers selected from the group defined in Table 1A, in the test sample. Preferably, step (b) comprises one or more biomarkers selected from the group defined in Table 1B, such as at least 2, 3, 4, selected from the group defined in Table 1B, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or Comprising or consisting of measuring the presence and / or amount of at least 30 biomarkers in a test sample. Preferably, step (b) comprises one or more biomarkers selected from the group defined in Table 1C, such as at least 2, 3, 4, selected from the group defined in Table 1C, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or at least 28 bio Comprise or consist of measuring the presence and / or amount of a marker in a test sample. Although less preferred, step (b) comprises one or more biomarkers selected from the group defined in Table 1D, eg at least 2, 3 selected from the group defined in Table 1D Comprising or consisting of measuring the presence and / or amount of 4, 5, 6, 7, 8, 9, or at least 10 biomarkers in a test sample. Also less preferred, step (b) comprises one or more biomarkers selected from the group defined in Table 1E, such as at least two selected from the group defined in Table 1E, Comprising or consisting of measuring the presence and / or amount of 3, 4, 5, 6, 7, 8 or at least 9 biomarkers in a test sample. Thus, step (b) may comprise or consist of measuring the presence and / or amount in the test sample of all biomarkers defined in Table 1.

Accordingly, a first aspect of the invention may comprise or consist of a method for determining an individual's metastasis-free survival time, comprising the following steps:
a) preparing a sample to be tested;
b) determining the biomarker signature of the test sample by measuring the presence and / or amount in the test sample of one or more biomarkers selected from the group defined in Table 1;
Including:
The presence and / or amount in the test sample of one or more biomarkers selected from the group defined in Table 1 indicates the individual's metastasis-free survival time.

  By “determining an individual's metastasis-free survival”, the inventors have determined that the individual from whom the test sample is obtained is either metastasis-free survival (less than 10 years or longer than 10 years from the initial diagnosis) Means prognostic to have (distant metastasis survival / DMFS).

If the method comprises or consists of determining the individual's metastasis-free survival time, step (b) comprises one or more bio-materials selected from the group defined in Table 1A. It may comprise or consist of measuring the presence and / or amount of a marker, such as at least two biomarkers selected from the group defined in Table 1A, in the test sample. Preferably, step (b) comprises one or more biomarkers selected from the group defined in Table 1B, such as at least 2, 3, 4, selected from the group defined in Table 1B, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or Comprising or consisting of measuring the presence and / or amount of at least 30 biomarkers in a test sample. Preferably, step (b) comprises one or more biomarkers selected from the group defined in Table 1D, for example at least 2, 3, 4, selected from the group defined in Table 1D Comprise or consist of measuring the presence and / or amount of 5, 6, 7, 8, 9 or at least 10 biomarkers in a test sample. Preferably, step (b) comprises or consists of measuring the presence and / or amount in all test samples of those defined in Table 1A, Table 1B and Table 1D.

  If this method involves or consists of determining the metastasis-free survival time of an individual, although less preferred, step (b) is selected from the group defined in Table 1C 1 One or more biomarkers, eg, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, selected from the group defined in Table 1C 16, or comprising measuring the presence and / or amount of 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or at least 28 biomarkers in a test sample It can consist of Also less preferred, step (b) comprises one or more biomarkers selected from the group defined in Table 1E, such as at least two selected from the group defined in Table 1E, It may comprise or consist of measuring the presence and / or amount of 3, 4, 5, 6, 7, 8 or at least 9 biomarkers in the test sample. Also less preferred, step (b) may comprise or consist of measuring the presence and / or amount of all biomarkers defined in Table 1C and Table 1E in the test sample. obtain.

  Thus, although less preferred, the method of the first aspect of the invention may comprise or consist of determining the metastasis-free survival time of an individual, step (b) is Comprising or consisting of measuring the presence and / or amount in the test sample of all biomarkers defined in 1.

Thus, the method according to the first aspect of the invention may comprise measuring SPON1 expression. The method can include measuring KERA expression. The method can include measuring APCS expression. The method can include measuring ATP6V1G1 expression. The method can include measuring RPS27L expression. The method can include measuring DPYSL3 expression. The method can include measuring ERP44 expression. The method can include measuring RAPGEF1 expression. The method can include measuring ACLY expression. The method can include measuring CMA1 expression. The method can include measuring MCM3 expression. The method can include measuring ANGPTL2 expression. The method can include measuring AEBP1 expression. The method can include measuring UBE2V2 expression. The method can include measuring MIS18BP1 expression. The method can include measuring CLCF1 expression. The method can include measuring ABAT expression. The method can include measuring SLC25A5 expression. The method can include measuring STIP1 expression. The method can include measuring OLFML3 expression. The method can include measuring CD3G expression. The method can include measuring MCM7 expression. The method can include measuring SLC25A11 expression. The method can include measuring NOP56 expression. The method can include measuring RRP8 expression. The method can include measuring SLTM expression. The method can include measuring TSN expression. The method can include measuring ECH1 expression. The method can include measuring PRELP expression. The method can include measuring SARS expression. The method can include measuring RPS25 expression. The method can include measuring ESYT1 expression. The method can include measuring PODN expression. The method can include measuring RPRD1B expression. The method can include measuring RPLP0P6 expression. The method can include measuring CD300LG expression. The method can include measuring SUGT1 expression. The method can include measuring POTEF expression. The method can include measuring KARS expression. The method can include measuring NDUFS2 expression. The method can include measuring HNRNPH2 expression. The method can include measuring CALU expression. The method can include measuring EIF3B expression. The method can include measuring SLC4A1AP expression. The method can include measuring RPS5 expression. The method can include measuring PLXDC2 expression. The method can include measuring KIAA1324 expression. The method can include measuring MRC1 expression. The method can include measuring RPRD1A expression. The method can include measuring SHMT2 expression. The method can include measuring CCT4 expression. The method can include measuring TSSC1 expression. The method can include measuring IKZF3 expression. The method can include measuring UBE2Q1 expression. The method can include measuring PSMD9 expression. The method can include measuring SNRNP70 expression. The method can include measuring RALB expression. The method can include measuring ACO2 expression. The method can include measuring MYO18A expression. The method can include measuring QARS expression. The method can include measuring PABPC4 expression. The method can include measuring SCGB1D2 expression. The method can include measuring PFKP expression. The method can include measuring SLC3A2 expression. The method can include measuring ASPN expression. The method can include measuring CD38 expression. The method can include measuring MXRA5 expression. The method can include measuring CDK1 expression. The method can include measuring STC2 expression. The method can include measuring CTSC expression. The method can include measuring NOP58 expression. The method can include measuring PGK1 expression. The method can include measuring FKBP3 expression. The method can include measuring GSTM3 expression. The method can include measuring CALML5 expression. The method can include measuring PML expression. The method can include measuring ADAMTS4 expression. The method can include measuring THBS1 expression. The method can include measuring FN1 expression.

  In one preferred embodiment, step (b) comprises the presence in the test sample of one or more biomarkers selected from the group consisting of MCM7, NOP56, MCM3, PABPC4, MXRA5, STC2, SCGB1D2 and ANGPTL2. Or comprises or consists of measuring a quantity. For example, step (b) may comprise or consist of measuring the presence and / or amount in 2, 3, 4, 5, 6, 7 or 8 test samples of these biomarkers. obtain. Preferably, in this embodiment, the breast cancer-related disease state is histological grade; however, less preferably, the breast cancer-related disease state is metastasis-free or metastasis-free survival time. Including.

  In another preferred embodiment, step (b) comprises measuring the presence and / or amount in the test sample of one or more biomarkers selected from the group consisting of OLFML3, SPON1, PODN and ASPN. Or consist of measuring. For example, step (b) may comprise or consist of measuring the presence and / or amount in 2, 3 or 4 test samples of these biomarkers. Preferably, in this embodiment, the breast cancer-related disease state is histological grade; however, less preferably, the breast cancer-related disease state is metastasis-free or metastasis-free survival time. Including.

  By “expression” we mean the level or amount of a gene product such as mRNA or protein.

  Methods for detecting and / or measuring protein and / or nucleic acid concentrations are well known to those skilled in the art, see, for example, Sambrook and Russell, 2001, Cold Spring Harbor Laboratory Press.

  Preferred methods for protein detection and / or measurement include Western blots, Northern-Western blots, immunosorbent assays (ELISAs), antibody microarrays, tissue microarrays, including sandwich assays using monoclonal and / or polyclonal antibodies. (TMA), immunoprecipitation, in situ hybridization and other immunohistochemical techniques, radioimmunoassay (RIA), immunoradioassay (IRMA) and enzyme antibody assay (IEMA). An exemplary sandwich assay is described by David et al. In US Pat. Nos. 4,376,110 and 4,486,530, incorporated herein by reference. Antibody staining of cells on slides can be used in well-known methods in cytological laboratory diagnostic tests, as is well known to those skilled in the art.

  Typically, an ELISA involves the use of an enzyme that produces a colored reaction product, usually in a solid phase assay. Enzymes such as horseradish peroxidase and phosphatase have been widely used. A way to amplify the phosphatase reaction is to use NADP as a substrate to produce NAD that immediately acts as a coenzyme for the second enzyme system. Pyrophosphatase from E. coli provides a good conjugate because this enzyme is not present in the tissue and is stable and produces a good reaction color. Chemiluminescent systems based on enzymes such as luciferase can also be used.

  Conjugation with vitamin biotin is frequently used because it can be easily detected by reaction with enzyme-linked avidin or streptavidin to which biotin binds with high specificity and affinity.

  Preferred methods for detection and / or measurement of nucleic acids (eg, mRNA) include Southern blots, Northern blots, polymerase chain reaction (PCR), reverse transcription PCR (RT-PCR), quantitative real-time PCR (qRT-PCR ), Nanoarrays, microarrays, macroarrays, autoradiography and in situ hybridization.

  In one embodiment of the first aspect of the invention, step (b) comprises measuring the expression of a nucleic acid molecule encoding one or more biomarker (s). The nucleic acid molecule can be a cDNA molecule or an mRNA molecule. Preferably the nucleic acid molecule is an mRNA molecule. Also preferably, the nucleic acid molecule is a cDNA molecule.

  Thus, measuring the expression of one or more biomarker (s) in step (b) includes Southern hybridization, Northern hybridization, polymerase chain reaction (PCR), reverse transcription PCR (RT-PCR) Can be performed using a method selected from the group consisting of quantitative real-time PCR (qRT-PCR), nanoarrays, microarrays, macroarrays, autoradiography and in situ hybridization. Preferably, measuring the expression of one or more biomarker (s) in step (b) is determined using a DNA microarray. Thus, this method uses one or more binding moieties, each capable of selectively binding to a nucleic acid molecule that encodes one of the biomarkers identified in Table 1. And may comprise or consist of measuring the expression of one or more biomarker (s) in step (b).

  Preferably, the one or more binding moieties each comprise or consist of a nucleic acid molecule such as DNA, RNA, PNA, LNA, GNA, TNA or PMO (preferably DNA). Preferably, the one or more binding moieties are 5 to 100 nucleotides in length. More preferably, the one or more nucleic acid molecules are 15-35 nucleotides in length. The binding moiety can include a detectable moiety.

  A suitable binding agent (also referred to as a binding molecule) can be selected or screened from a library based on its ability to bind to a given nucleic acid, protein or amino acid motif, as discussed below.

  In another embodiment of the first aspect of the invention, step (b) comprises measuring the expression of one or more biomarker (s) protein or polypeptide or fragment or derivative thereof. . Preferably, measuring the expression of one or more biomarker (s) in step (b) selectively binds to one of the biomarkers each identified in Table 1. This is done using one or more binding moieties that can be done.

  The one or more binding moieties may comprise an antibody or antigen-binding fragment thereof, or may consist of an antibody or antigen-binding fragment thereof.

  The term “antibody” is produced by any synthetic antibody, recombinant antibody or antibody hybrid, such as, but not limited to, immunoglobulin light and / or heavy chain variable and / or constant region phage display. Single chain antibody molecules, or other immunointeractive molecules capable of binding antigen in an immunoassay format known to those skilled in the art are included. We also include the use of antibody-like binding agents such as affibodies and aptamers.

  A general review of the techniques involved in the synthesis of antibody fragments that retain their specific binding site is found in Winter and Milstein (1991) Nature 349, 293-299.

  Additionally or alternatively, one or more first binding molecules can be aptamers (see Collett et al., 2005, Methods 37: 4-15).

Molecular libraries, such as antibody libraries (Clackson et al., 1991, Nature 352, 624-628; Marks et al., 1991, J Mol Biol 222 (3): 581-97), peptide libraries (Smith, 1985, Science 228 ( 4705): 1315-7), the expressed cDNA library (Sa
nti et al. (2000) J Mol Biol 296 (2): 497-508), libraries on scaffolds other than antibody frameworks such as Affibody (Gunnerison et al. 1999, Appl Environ Microbiol 65 (9): 4134-40). Or an aptamer-based library (Kenan et al., 1999, Methods Mol Biol 118, 217-31) from which a binding molecule specific for a given motif is selected for use in the methods of the invention Can be used as

  This molecular library is either prokaryotic cells (Clackson et al., 1991, supra; Marks et al., 1991, supra) or eukaryotic cells (Kieke et al., 1999, Proc Natl Acad Sci USA, 96 (10): 5651-6) or in vitro without cellular involvement (Hanes and Pluckthun, 1997, Proc Natl Acad Sci USA 94 (10): 4937-42; He and Taussig, 1997). Nucleic Acids Res 25 (24): 5132-4; Nemoto et al., 1997, FEBS Lett, 414 (2): 405-8).

  When a protein-based library is used, the gene encoding the library of potential binding molecules is often packaged in the virus, and the potential binding molecule is displayed on the surface of the virus ( Clackson et al., 1991, supra; Marks et al., 1991, supra; Smith, 1985, supra).

  Perhaps the most commonly used display system is a filamentous bacteriophage that displays antibody fragments on its surface, which are expressed as a fusion with the minor coat protein of this bacteriophage (Clackson et al. 1991, supra; Marks et al., 1991 supra). However, other suitable systems for display are other viruses (EP 39578), bacteria (Gunnerisson et al., 1999, supra; Daugherty et al., 1998, Protein Eng 11 (9): 825-32; Daugherty et al., 1999. , Protein Eng 12 (7): 613-21) and yeast (Shusta et al., 1999, J Mol Biol 292 (5): 949-56).

  Furthermore, the display system can be a linkage of the polypeptide product to its coding mRNA in a so-called ribosome display system (Hanes and Pluckthun, 1997, supra; He and Tausig, 1997, supra; Nemoto et al., 1997 supra), or alternatively, It has been developed utilizing the linkage of polypeptide products to coding DNA (see US Pat. No. 5,856,090 and WO 98/37186).

The variable heavy (V _H ) and variable light (V _L ) domains of an antibody are involved in antigen recognition, a fact that was first recognized by early protease digestion experiments. Further confirmation was found by “humanization” of rodent antibodies. The variable domain of rodent origin can be fused to a constant domain of human origin such that the resulting antibody retains the antigen specificity of the rodent parent antibody (Morrison et al. (1984) Proc. Natl. Acad. Sci. USA 81, 6851-6855).

It is known from experiments involving bacterial expression of antibody fragments that all contain one or more variable domains, where antigen specificity is conferred by the variable domains and is independent of the constant domains. These molecules include Fab-like molecules (Better et al. (1988) Science 240, 1041); Fv molecules (Skerra et al. (1988) Science 240, 1038); V _H partner domains and V _L via flexible oligopeptides. Single chain Fv (ScFv) molecules linked to partner domains (Bird et al. (1988) Science 242, 423; Huston et al. (1988) Proc. Natl. Acad. Sci. USA 85, 5879) and isolated V domains Single domain antibody (dAb) containing (Ward et al. (1989) Nature 341, 544). A general review of the techniques involved in the synthesis of antibody fragments that retain their specific binding site is found in Winter and Milstein (1991) Nature 349, 293-299.

The antibody or antigen-binding fragment can be an intact antibody, an Fv fragment (eg, a single chain Fv and a disulfide-linked Fv), a Fab-like fragment (eg, a Fab fragment, a Fab ′ fragment and a F (ab) ₂ fragment), a single fragment. It can be selected from the group consisting of one variable domain (eg, V _H domain and _VL domain) and domain antibody (including dAb, single format and dual format [ie dAb-linker-dAb]). Preferably, the antibody or antigen-binding fragment is a single chain Fv (scFv).

  One or more binding moieties may alternatively comprise an antibody-like binding agent such as an affibody or aptamer, or may consist of an antibody-like binding agent such as an affibody or aptamer.

By “scFv molecule” we mean a molecule in which a _VH partner domain and a _VL partner domain are linked via a flexible oligopeptide.

  The advantages of using antibody fragments rather than whole antibodies are several times. Smaller size fragments can lead to improved pharmacological properties, such as better penetration of solid tissue. Effector functions throughout the antibody, such as complement binding, are eliminated. Fab, Fv, ScFv and dAb antibody fragments can all be expressed in and secreted from E. coli, thus allowing easy production of large quantities of said fragments.

Whole antibodies and F (ab ′) ₂ fragments are “bivalent”. By “bivalent” we mean that the antibody and F (ab ′) ₂ fragment have two antigen binding sites. In contrast, Fab, Fv, ScFv and dAb fragments are monovalent and have only one antigen binding site.

  The antibody can be monoclonal or polyclonal. Suitable monoclonal antibodies are described in “Monoclonal Antibodies: A manual of techniques”, H Zola (CRC Press, 1988) and “Monoclonal Hybridoma Antibodies: Technology”. 1982) and can be prepared by known techniques.

When potential binding molecules are selected from a library, one or more selector peptides with defined motifs are usually used. Amino acid residues that provide a structure that reduces the flexibility in peptides, charged or polar or hydrophobic side chains that allow interaction with the binding molecule can be used in the design of motifs for selector peptides. For example:
(I) Proline can stabilize the peptide structure when its side chain is attached to both the alpha carbon and nitrogen;
(Ii) Phenylalanine, tyrosine and tryptophan have aromatic side chains and are highly hydrophobic, while leucine and isoleucine have aliphatic side chains, which are also hydrophobic;
(Iii) Lysine, arginine and histidine have basic side chains and are positively charged at neutral pH, while aspartic acid and glutamic acid have acidic side chains and are negatively charged at neutral pH Do;
(Iv) Asparagine and glutamine contain amide groups that are neutral at neutral pH but can participate in hydrogen bonding;
(V) Serine, threonine and tyrosine side chains contain hydroxyl groups that can participate in hydrogen bonding.

  Typically, the selection of binding molecules can involve the use of array technology and systems to analyze binding to spots that correspond to the type of binding molecule.

Thus, preferably, this antibody or fragment thereof is a monoclonal antibody or fragment thereof. Preferably, the antibody or antigen-binding fragment is an intact antibody, Fv fragment (eg, single chain Fv and disulfide-linked Fv), Fab-like fragment (eg, Fab fragment, Fab ′ fragment and F (ab) ₂ fragment) ), Single variable domains (eg, _VH and _VL domains) and domain antibodies (including dAbs, single and dual formats [ie, dAb-linker-dAb]). Thus, the antibody or antigen-binding fragment can be a single chain Fv (scFv). Alternatively, the one or more binding moieties comprise or consist of an antibody-like binding agent such as an affibody or aptamer. The one or more binding moieties include a detectable moiety.

  By “detectable moiety” we can determine its presence and / or relative quantity and / or position (eg, position on the array) either directly or indirectly. Including a part that enables

  Suitable detectable moieties are well known in the art.

  For example, the detectable moiety can be a fluorescent and / or luminescent and / or chemiluminescent moiety that can be detected when exposed to certain conditions. Such fluorescent moieties may need to be exposed to irradiation at a specific wavelength and intensity (ie, light) to cause excitation of the fluorescent moiety, thereby producing detectable fluorescence at a specific wavelength that can be detected. You can start.

  Alternatively, the detectable moiety can be an enzyme capable of converting (preferably undetectable) a substrate into a detectable product that can be visualized and / or detected. Examples of suitable enzymes are discussed in more detail below, for example with respect to ELISA assays.

  Thus, the detectable moiety can be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety (eg, a radioactive atom); or an enzyme moiety. Preferably, the detectable moiety comprises or consists of a radioactive atom. This radioactive atom is technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulfur-35, heavy It may be selected from the group consisting of hydrogen, tritium, rhenium-186, rhenium-188 and yttrium-90.

Clearly, the agent to be detected (eg, for use in detecting one or more biomarkers and / or selected proteins in the test and / or control samples described herein) Antibody molecules, etc.) must have adequate atomic isotopes in order for the detectable moiety to be readily detectable.

  In an alternative preferred embodiment, the detectable moiety of the binding moiety is a fluorescent moiety.

A radioactive label or other label can be incorporated in a known manner into the biomarker present in the sample of the method of the invention and / or the binding moiety of the invention. For example, if the binding agent is a polypeptide, the binding agent can be biosynthesized or synthesized by chemical amino acid synthesis using, for example, a suitable amino acid precursor containing fluorine-19 instead of hydrogen. Labels such as ^99m Tc, ¹²³ I, ¹⁸⁶ Rh, ¹⁸⁸ Rh and ¹¹¹ In can be attached, for example, via a cysteine residue in the binding moiety. Yttrium-90 can be attached via a lysine residue. The IODOGEN method (Fraker et al. (1978) Biochem. Biophys. Res. Comm. 80, 49-57) can be used to incorporate ¹²³ I. References (“Monoclonal Antibodies in Immunoscintigraphy”, J-F Chattal, CRC Press, 1989) describe other methods in detail. Methods for conjugating other detectable moieties (eg, enzyme moieties, fluorescent moieties, luminescent moieties, chemiluminescent moieties, or radioactive moieties) to proteins are well known in the art.

  It will be appreciated by those skilled in the art that the biomarker in the sample (s) to be tested can be labeled with a moiety that indirectly assists in determining the presence, amount and / or location of the protein. Therefore, this part may constitute one component of the multi-component detectable part. For example, the biomarker in the sample (s) to be tested can be labeled with biotin that allows subsequent detection using streptavidin fused or otherwise linked to a detectable label.

  The detectable moiety can be selected from the group consisting of a fluorescent moiety, a luminescent moiety, a chemiluminescent moiety, a radioactive moiety, and an enzyme moiety.

  Thus, the detectable moiety can comprise or consist of a radioactive atom. This radioactive atom is technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulfur-35, heavy It may be selected from the group consisting of hydrogen, tritium, rhenium-186, rhenium-188 and yttrium-90.

  Alternatively, the detectable moiety of the binding moiety can be a fluorescent moiety.

  In the method according to the first aspect of the present invention, the sample prepared in step (a) and / or step (c) is prepared in such a way that any biomarker present in the sample can be labeled with biotin. processed before b) and / or step (d). Step (b) and / or step (d) can be performed using a detection agent comprising streptavidin and a detectable moiety (eg, a fluorescent moiety).

  Thus, the protein of interest in the sample to be tested can be first isolated and / or immobilized using the first binding agent (s), and then the presence and / or relative presence of the biomarker The amount can be determined using the second binder (s).

  In one embodiment, the second binding agent is an antibody or antigen-binding fragment thereof; typically a recombinant antibody or fragment thereof. Conveniently, the antibody or fragment thereof is selected from the group consisting of: scFv; Fab; binding domain of an immunoglobulin molecule. Suitable antibodies and fragments and methods for making them are described in detail above.

  Alternatively, the second binding agent can be an antibody-like binding agent, such as an affibody or aptamer.

  Alternatively, if the detectable moiety on the protein in the sample being tested comprises or consists of a member of a specific binding pair (eg, biotin), the second binding agent can be of a specific binding pair Complementary members (eg, streptavidin) can be included, or can consist of such members.

  When a detection assay is used, the detectable moiety is preferably selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; Examples of detectable moieties suitable for use in the methods of the invention are described above.

  Preferred assays for detecting serum or plasma proteins include enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), immunoradioassay (IRMA), including sandwich assays using monoclonal and / or polyclonal antibodies. And enzyme antibody assays (IEMA). An exemplary sandwich assay is described by David et al. In US Pat. Nos. 4,376,110 and 4,486,530, incorporated herein by reference. Antibody staining of cells on slides can be used in well-known methods in cytological laboratory diagnostic tests, as is well known to those skilled in the art.

  Thus, in one embodiment, the assay is an ELISA (enzyme linked immunosorbent assay), which typically involves the use of an enzyme that produces a colored reaction product, usually in a solid phase assay. Enzymes such as horseradish peroxidase and phosphatase have been widely used. A way to amplify the phosphatase reaction is to use NADP as a substrate to produce NAD that immediately acts as a coenzyme for the second enzyme system. Pyrophosphatase from E. coli provides a good conjugate because this enzyme is not present in the tissue and is stable and produces a good reaction color. Chemiluminescent systems based on enzymes such as luciferase can also be used.

  Conjugation with vitamin biotin is frequently used because it can be easily detected by reaction with enzyme-linked avidin or streptavidin to which biotin binds with high specificity and affinity.

  In an alternative embodiment, the assay used for protein detection is conveniently a fluorometric assay. Thus, the detectable moiety of the second binding agent can be a fluorescent moiety such as an Alexa fluorophore (eg, Alexa-647).

  Preferably, the predicative accuracy of the method as determined by the ROC AUC value is at least 0.50, such as at least 0.55, 0.60, 0.65, 0.70, 0.75, 0 .80, 0.85, 0.90, 0.95, 0.96, 0.97, 0.98 or at least 0.99. More preferably, the accuracy of this method determined by the ROC AUC value is at least 0.80 (most preferably 1).

  In the method of the first aspect of the invention, step (b) may be performed using an array, such as a bead-based array or a surface-based array. Preferably, the array is selected from the group consisting of: macroarray; microarray; nanoarray.

  Methods for determining breast cancer-related disease states can be found at http: // cran. r-project. org / web / packages / e1071 / index. It can be performed using a support vector machine (SVM), such as that available from html (eg e1071 1.5-24). However, any other suitable means can also be used. The SVM also includes a ROC AUC for a biomarker signature comprising one or more of the biomarkers of Table 1 as defined herein or consisting of one or more of the biomarkers of Table 1. Can be used.

  A support vector machine (SVM) is a set of related supervised learning methods used for classification and regression. Considering the set of training cases that are each shown to belong to one of the two categories, the SVM training algorithm creates a model that predicts whether a new case will fall into one category or the other. Intuitively, the SVM model is a representation of cases as points in space, mapped so that different categories of cases are separated by the widest clear gap possible. The new case is then mapped into the same space and predicted to belong to one category based on which side of the gap the new case is located.

More formally, support vector machines construct hyperplanes or sets of hyperplanes in high-dimensional space or infinite-dimensional space, which can be used for classification, regression or other tasks. Intuitively, good separation is achieved by the hyperplane with the greatest distance (so-called functional margin) to the nearest training data point of any class, which is generally classified as the margin increases. This is because the generalization error of the child is reduced. For more information on SVMs see, for example, Burges, 1998, Data Mining and
See Knowledge Discovery, 2: 121-167.

  In one embodiment of the invention, the SVM uses a biomarker profile of a known drug (ie, a known histological grade breast cancer cell or a breast cancer cell from a breast cancer patient with known distant metastasis survival). Thus, it is “trained” before performing the method of the present invention. By performing such training samples, the SVM can learn which biomarker profile is associated with a particular feature. Once the training process is complete, an SVM is then possible as to whether the biomarker sample being tested is from a specific breast cancer sample type (ie, a specific breast cancer-related disease state).

  However, this training procedure can be bypassed by pre-programming the SVM with the necessary training parameters. For example, cells belonging to a particular breast cancer-related disease state can be determined based on the biomarker measurements listed in Table 1 using the values and / or adjustment patterns detailed herein. It can be identified according to known SVM parameters using the SVM algorithm detailed above.

  Appropriate SVM parameters are used with appropriate selection of data (ie biomarker measurements from known histological grade cells and / or cells from individuals with known metastasis-free survival time). It will be appreciated by those skilled in the art that for any combination of the biomarkers listed in Table 1 can be determined.

Alternatively, the data in Table 1 may be obtained from any other suitable statistical method known in the art, such as principal component analysis (PCA) and other multivariate statistical analysis (eg, variableward stepwise Can be used to determine a particular breast cancer-related disease state according to a logistic regression model). For a review of multivariate statistical analyses, see, for example, Scherish, Mark J. et al., Incorporated herein by reference. (November 1987). “A Review of Multivariate Analysis”. Statist
Ical Science 2 (4): 396-413.

  Preferably, the method of the present invention has an accuracy of at least 65%, such as 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93% 94%, 95%, 96%, 97%, 98%, 99% or 100% accuracy.

  Preferably, the method of the present invention has a sensitivity of at least 65%, such as 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93% , 94%, 95%, 96%, 97%, 98%, 99% or 100%.

  Preferably, the method of the present invention has a specificity of at least 65%, such as 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76% 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93 %, 94%, 95%, 96%, 97%, 98%, 99% or 100% specificity.

  By “accuracy” we mean the exact outcome percentage of the method, and by “sensitivity” we mean the percentage of all positive chemicals that are correctly classified as positive. By “specificity” we mean the proportion of all negative chemicals that are correctly classified as negative.

The method of the first aspect of the present invention comprises the following steps:
e) treating the individual being tested based on the breast cancer-related disease state determined in the preceding step;
May further be included.

  Thus, the method includes treating the patient according to the histological grade of breast cancer and / or according to the predicted metastasis-free survival time. For example, more aggressive treatment is for higher grade breast cancer and / or metastasis-free survival time is relatively low (eg, less than 10 years) vs. relatively high (eg, longer than 10 years) ) Can be provided. Appropriate therapeutic approaches can be determined by those skilled in the art according to prevailing guidance at the time, for example, NICE Clinical Guideline 80 “Early and locally advanced breast cancer: Diagnostics and treatment”, which is incorporated herein by reference. (Available at: http://www.nice.org.uk/nicemedia/pdf/CG80NICEGuideline.pdf).

  Accordingly, the present invention includes an antineoplastic agent for use in the treatment of breast cancer, wherein the dosage regimen is determined based on the results of the method of the first aspect of the present invention.

  The invention includes the use of an anti-neoplastic agent in the treatment of breast cancer, wherein the dosing regimen is determined based on the results of the method of the first aspect of the invention.

  The invention includes the use of an anti-neoplastic agent in the manufacture of a medicament for treating breast cancer, wherein the dosing regimen is determined based on the results of the method of the first aspect of the invention.

  The invention includes a method of treating breast cancer, comprising providing a sufficient amount of an anti-neoplastic agent, wherein the amount of anti-neoplastic agent sufficient to treat breast cancer is the first of the invention. It is determined based on the result of the method of one aspect.

  In one embodiment, the antineoplastic agent is an alkylating agent (ATC code L01a), an antimetabolite (ATC code L01b), a plant alkaloid or other natural product (ATC code L01c), a cytotoxic antibiotic or related (ATC code L01d) or other anti-neoplastic agents (ATC code L01x).

Thus, in one embodiment, the antineoplastic agent is a nitrogen mustard analog (eg, cyclophosphamide, chlorambucil, melphalan, chlormethine, ifosfamide, trophosphamide, prednisomine or bendamustine), an alkyl sulfonate (eg, busulfan, Threosulfan or mannosulfan) , ethyleneimine (eg, thiotepa, triadicon or carbocon), nitrosourea (eg, carmustine, lomustine, semustine, streptozocin, hotemustine, nimustine or ranimustine), epoxide (eg, ethogluside) From another alkylating agent (ATC code L01ax, eg mitoblonitol, pipobroman, temozolomide or dacarbazine) An alkylating agent selected from the group consisting of:

  In another embodiment, the antineoplastic agent is a folate analog (eg, methotrexate, raltitrexed, pemetrexed or pralatrexate), a purine analog (eg, mercaptopurine, thioguanine, cladribine, fludarabine, clofarabine or nelarabine) or a pyrimidine analog (For example, an antimetabolite selected from the group consisting of cytarabine, fluorouracil, tegafur, carmofur, gemcitabine, capecitabine, azacitidine, or decitabine).

  In still further embodiments, the antineoplastic agent is a vinca alkaloid or vinca alkaloid analog (eg, vinblastine, vincristine, vindesine, vinorelbine or vinflunine), a podophyllotoxin derivative (eg, etoposide or teniposide), a colchicine derivative (eg, , Demecoltin), a taxane (eg, paclitaxel, docetaxel or paclitaxel polygourax) or another plant alkaloid or natural product (ATC code L01cx, eg trabectedin) or a plant alkaloid or other natural product .

  In one embodiment, the antineoplastic agent is an actinomycin (eg, dactinomycin), an anthracycline or related substance (eg, doxorubicin, daunorubicin, epirubicin, aclarubicin, zorubicin, idarubicin, mitoxantrone, pirarubicin, valrubicin , Amrubicin or pixantrone) or another (ATC code L01dc, eg, bleomycin, priomycin, mitomycin or ixabepilone) a cytotoxic antibiotic or related substance.

In further embodiments, the anti-neoplastic agent is a platinum compound (eg, cisplatin, carboplatin, oxaliplatin, satraplatin or polyplatylene), methylhydrazine (eg, procarbazine), monoclonal antibody (eg, edrecolomab, rituximab, trastuzumab) , Alemtuzumab, gemtuzumab, cetuximab, bevacizumab, panitumumab, kazumaxomab or ofatumumab), sensitizers used in photodynamic / radiotherapy (eg, porfimer sodium, methyl aminolevulinate, aminolevulinic acid, temoporfin or efaproxiral) Or protein kinase inhibitors (eg imatinib, gefitinib, erlotinib, sunitinib, sola Enibu, dasatinib, lapatinib, nilotinib, temsirolimus, everolimus, pazopanib, vandetanib, AFATINIB is another antineoplastic agent selected from the group consisting of Masuchinibu or Toseranibu).

  In still further embodiments, the anti-neoplastic agent is amsacrine, asparaginase, artretamine, hydroxycarbamide, lonidamine, pentostatin, miltefosine, masoprocol, estramustine, tretinoin, mitoguazone, topotecan, thiazofurin, irinotecan, alitretinoin, mitotane, Selected from the group consisting of pegaspargase, bexarotene, arsenous acid, denileukin diftitox, bortezomib, celecoxib, anagrelide, oblimersen, stigmaine seradenovec, vorinostat, romidepsin, omecetaxin mepesuccinate or eribulin Other anti-neoplastic agents.

  Accordingly, a second aspect of the present invention provides an array for use in a method according to the first aspect of the present invention, the array defined above in connection with the first aspect of the present invention. One or more first binders are included.

  The array binding agent collectively binds to one or more biomarkers selected from the group defined in Table 1A, eg, at least two biomarkers selected from the group defined in Table 1A Can comprise or consist of such a binder. Preferably, the array binding agent is one or more biomarkers selected from the group defined in Table 1B, such as at least 2, 3, 4, selected from the group defined in Table 1B, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or It may comprise or consist of a binding agent capable of collectively binding to at least 30 biomarkers. Preferably, the array binding agent is one or more biomarkers selected from the group defined in Table 1C, for example at least 2, 3, 4, selected from the group defined in Table 1C, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or at least 28 bio It may comprise or consist of a binding agent capable of collectively binding to the marker. Preferably, the array binding agent is one or more biomarkers selected from the group defined in Table 1D, for example at least 2, 3, 4, selected from the group defined in Table 1D, It may comprise or consist of a binding agent capable of collectively binding to 5, 6, 7, 8, 9 or at least 10 biomarkers. Preferably, the array binding agent is one or more biomarkers selected from the group defined in Table 1E, for example at least 2, 3, 4, selected from the group defined in Table 1E It may comprise or consist of a binding agent capable of collectively binding to 5, 6, 7, 8 or at least 9 biomarkers.

Thus, the array binding agent may comprise or consist of a binding agent capable of collectively binding to all the biomarkers defined in Table 1A. The array binding agent may comprise or consist of a binding agent capable of collectively binding to all the biomarkers defined in Table 1B. The array binding agent may comprise or consist of a binding agent capable of collectively binding to all biomarkers defined in Table 1C. The array binding agent may comprise or consist of a binding agent capable of collectively binding to all biomarkers defined in Table 1D. The array binding agent may comprise or consist of a binding agent capable of collectively binding to all biomarkers defined in Table 1E. Preferably, the array binding agent comprises or consists of a binding agent capable of collectively binding to all the biomarkers defined in Table 1.

  The first binding agent of the array can be immobilized.

  The array itself is well known in the art. Typically, the array is a linear structure or two, formed on the surface of a solid support, each having a finite area, with spaced (ie, separate) regions (“spots”). Form a dimensional structure. An array can also be a bead structure where each bead can be identified by a molecular or color code or can be identified in a continuous flow. The analysis can also be performed continuously as the sample passes through a series of spots, each adsorbing a class of molecules from solution. This solid support is typically glass or a polymer, and the most commonly used polymers are cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. This solid support can be a tube, bead, disk, silicon chip, microplate, polyvinylidene fluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (eg, plastic, among others) Suitable for immobilizing proteins, polynucleotides and other suitable molecules and / or for performing immunoassays, polymers, perspex, silicon), multiple polymeric pins or multiple microtiter wells It can be in the form of any other surface. The conjugation process is well known in the art and becomes common from cross-linking, covalently bonding or physically adsorbing protein molecules, polynucleotides, etc. to a solid support. Alternatively, affinity coupling of probes via affinity tags or similar constructs can be used. By using well-known techniques such as contact printing or non-contact printing, masking or photolithography, the position of each spot can be defined. For a review, see Jenkins, R .; E. Pennington, S .; R. (2001, Proteomics, 2, 13-29) and Lal et al. (2002, Drug Discover Today 15; 7 (Appendix 18): S143-9).

Typically this array is a microarray. By “microarray” we include the meaning of an array of regions having discrete regions with a density of at least about 100 / cm ² , preferably at least about 1000 / cm ² . The regions in the microarray have typical dimensions, eg, diameters, ranging between about 10-250 μm and are about the same distance away from other regions in the array. This array can alternatively be a macroarray or a nanoarray.

  Once the appropriate binding molecule (discussed above) has been identified and isolated, one skilled in the art can produce the array using methods well known in the field of molecular biology; see the examples below. .

  A third aspect of the invention provides one or more selected from the group defined in Table 1A, Table 1B, Table 1C, Table 1D and / or Table 1E for determining a breast cancer-related disease state Provide the use of the above biomarkers.

  In one embodiment, all biomarkers defined in Tables 1A, 1B, 1C, 1D and 1E are collectively used to determine breast cancer-related disease states.

A fourth aspect of the invention provides an analytical kit for use in the method according to the first aspect of the invention, the analytical kit comprising:
A) an array according to the second aspect of the invention or as defined in the first aspect of the invention; and B) instructions for carrying out the method as defined in the first aspect of the invention (optional) ,
including.

  This assay kit may comprise one or more control samples as defined in the first aspect of the invention.

  Preferred non-limiting examples embodying particular aspects of the invention are described herein with reference to the following drawings:

FIG. 6 shows peptide and protein statistics. (A) Total number of unique peptide sequences identified per sample (using FDR 0.01, Mascot + X! Tandem). (B) Total number of identified assembled protein groups per sample (FDR 0.01, set at protein level, using Mascot + X! Tandem). (C) Number of unique peptides per protein group (FDR 0.01, set at protein level, using Mascot + X! Tandem) that yields total protein coverage of 2140 protein groups throughout the study (replication, pooling and Data based on all samples and practices, including samples lacking clinical parameters). (D) Evaluation of quantified peptides against PeptideAtlas (version 2011-08 Ens62, human) (Progenesis LC-MS software, limited to Mascot scored peptides, using FDR 0.01). In addition, a second comparison was performed to assess whether the corresponding protein has been reported for peptides that are not present in PeptideAtlas. In the case of multiple protein accessions, all were evaluated. (E) Comparison of peptide lengths. (F) Peptide frequency observed with PeptideAtlas. Continuation of FIG. 1-1. Continuation of FIG. 1-2. Continuation of FIG. 1-3. Diagram showing the reproducibility of the entire GPS setup (ie acquisition + LC-MS / MS) for both the combined data and individual mixtures for a representative sample (sample 7267) and a reference (pool) sample. is there. In order to include (plot) data points, the protein had to be quantified in all triplicates (normalized abundance> 0). This requirement was used for all data plotted in panels AE. (A) Shown for all combined data (based on 1264 proteins). (B) shown for CIMS-mix 1 (based on 315 proteins) (C) shown for CIMS-mix 2 (based on 661 proteins) (D) shown for CIMS-mix 3 (based on 452 proteins) ) (E) shown for CIMS-mix 4 (based on 370 proteins). Continuation of FIG. Continuation of FIG. FIG. 5 shows significantly differentially expressed proteins based on tissue grade, estrogen receptor status and HER2 status. Differentially expressed analytes are shown in the heat map (red-upregulated, green-downregulated). (A) PCA plot and associated heat map of tissue grade 1, tissue grade 2 and tissue grade 3 samples (data filtered with variance 0.2, p value <0.01, q value <0.25). Furthermore, the results from a leave-one out cross-validation approach using SVM are shown in ROC area values. (B) PCA plots and associated heat maps for ER positive and ER negative samples (data filtered with variance 0.2, p value <0.01, q value <0.32). In addition, the results from a single cross validation approach are shown as ROC curves. (C) PCA plot and associated heat map for HER2 positive and HER2 negative samples (data filtered with a variance of 0.2, p value <0.01, q value <0.9). Results from a single cross validation approach are shown as ROC curves. Continuation of FIG. 3-1. Continuation of FIG. Continuation of FIG. Continuation of FIG. Continuation of FIG. Continuation of FIG. Continuation of FIG. FIG. 5 shows the biological relevance of analytes that were differentially expressed between three tissue graded tumor types using IPA. (A) 49 proteins identified as significantly differentially expressed proteins among the three tumor cohorts mapped to their cellular localization. Colored log ₂ ratio (median grade 3 / median grade 1), with red indicating upward adjustment and green indicating downward adjustment. Proteins with known associations with tumor development are shown. FIG. 5 shows the biological relevance of analytes that were differentially expressed between three tissue graded tumor types using IPA. (B) The first reported network found to be associated with DNA replication, recombination, cell cycle and free radical scavenging. FIG. 5 shows the biological relevance of analytes that were differentially expressed between three tissue graded tumor types using IPA. (C) Second reported network found to be associated with gene expression, infectious disease and cancer. FIG. 6 shows validation of protein expression profile using orthogonal method. For this purpose, mRNA expression profiles based on data from 1411 histologically graded tumor samples were used. Of the 49 proteins differentially expressed between tissue grades 1, 2 and 3, 42 were successfully mapped into the GOBO database (using Gene Entrez ID). (A) Fifteen (out of a total of 16) analytes were found to exhibit reduced protein expression (median ratio compared to tissue grade 1) in tissue grade 3 tumors that could be mapped with the GOBO tool. MRNA expression profile for proteins. In addition, the correlation of 15 genes to different gene set module expression patterns is shown. Gray points indicate actual correlation values. FIG. 6 shows validation of protein expression profile using orthogonal method. For this purpose, mRNA expression profiles based on data from 1411 histologically graded tumor samples were used. Of the 49 proteins differentially expressed between tissue grades 1, 2 and 3, 42 were successfully mapped into the GOBO database (using Gene Entrez ID). (B) mRNA expression profile for proteins found to show increased expression in tissue grade 3 tumors (compared to tissue grade 1), 27 (out of a total of 33) could be mapped with the GOBO tool. Furthermore, the correlation of 27 genes to different gene set module expression patterns is shown. Gray points indicate actual correlation values. FIG. 2 shows a kaplan meier analysis of exemplary biomarker signatures of varying lengths and combinations of the biomarkers of Table 1. It is a figure which shows the schematic overview of the workflow used by this research. (A) Tumor sample preparation and (B) Peptide capture using CIMS-antibody, LC-MS / MS implementation schedule and data analysis. Analysis of all eluates from one CIMS-binder mix was terminated before proceeding to the eluate from the next CIMS-binder mix. As a result, all CIMS-binder mix analyzes began with analysis of eluates from the pooled samples and continued with half of the individual samples in a random sequence according to histological grade. In the middle of the mix, another pool sample was injected, then the remaining samples were injected, and finally finished with the third pool sample. After completion, analysis of the eluate from the next CIMS-binder mix was started. Two injections of blank beads were performed between Binder Mix 2 and Binder Mix 3. Since blank beads do not contain attached antibody, only background peptides that bind to the magnetic beads should elute. Data was analyzed on Proteios SE and Progenis to obtain peptide and protein identification and quantification. FIG. 6 shows the identification reproducibility of the entire GPS setup (ie acquisition + LC-MS / MS), shown as a Venn diagram. (A) Peptide overlap between replicate capture runs for sample 7267 (all unique sequences). Statistics are shown for the total coverage of sample 7267 (top figure) and individual mixes (four smaller Venn figures). Data generated from Proteios SE (ie Mascot and X! Tandem scored peptides). (B) Peptide overlap between replicate capture runs for pool samples (all unique sequences). Statistics are shown for both the gross (top figure) and individual mixes. Data generated from Proteios SE (ie Mascot and X! Tandem scored peptides). It is a figure which shows distribution of log2 MS intensity | strength of the protein quantified. (A) Median normalized abundance plotted for 1364 proteins (based on 50 samples with clinical records) (24 proteins with a median log2 intensity value of 0 were excluded). The bars are colored according to MS intensity, ranging from pale yellow (low MS intensity) to dark red (high MS intensity). It is a figure which shows distribution of log2 MS intensity | strength of the protein quantified. (B) Distribution of log2 MS intensity values based on GO biological processes for selected protein categories. Analytes were grouped by major biological processes using the Generic Gene Ontology (GO) Term Mapper tool (http://go.princeton.edu/cgi-bin/GOTermMapper). FIG. 7 shows individual intensity box plots for eight of the proteins differentially expressed between the three histological grades that show the highest expression in histological grade 3 tumors. Continuation of FIG. FIG. 7 shows individual intensity box plots for 8 of the differentially expressed proteins between the 3 histological grades that show the highest expression in histological grade 1 tumors. Continuation of FIG. FIG. 5 shows an expanded comparison between histological grades. (A) Between tissue grade 2 (H2) and tissue grade 1 (H1), between tissue grade 3 (H3) and tissue grade 1 (H1), and between tissue grade 3 (H3) and tissue grade 2 (H2) ) Log2 times change. The top 49 indicated analytes are protein signatures identified as being differentially expressed between the three grades. Therefore, all comparisons are calculated and shown. The lower 47 analytes are derived from SVM calculations between two of these grades, but the third grade is excluded. This calculation was done for all three comparisons and the list of significant analytes was compiled as a result. Matrix color graphics were generated using Matrix2 png (Pavlidis and Noble, 2003). FIG. 5 shows an expanded comparison between histological grades. (B) ROC AUC value derived from SVM calculation from two-group comparison. Both ROC AUC values from unfiltered data (entire data set) and filtered data (variance 0.2 and p-value <0.01) are listed. FIG. 5 shows an expanded comparison between histological grades. (C) Histological grade 1 and histological grade 3 heat maps (data filtered with variance 0.2, p value <0.01, q value <0.25). Differentially expressed analytes are shown in the heat map, with red indicating upregulation and green indicating downregulation. (D) Histological grade 1, histological grade 2 and histology using 50 proteins (FIG. S8C) differentially expressed between histological grade 1 and histological grade 3. Grade 3 PCA plot. FIG. 5 shows an expanded comparison between histological grades. (D) Histological grade 1, histological grade 2 and histology using 50 proteins (FIG. S8C) differentially expressed between histological grade 1 and histological grade 3. Grade 3 PCA plot. FIG. 6 shows individual intensity box plots shown for a subset of proteins identified as differentially expressed in an ER status comparison or HER2 / neu status comparison. (A) Analyte differentially expressed between ER positive and ER negative tumors. FIG. 6 shows individual intensity box plots shown for a subset of proteins identified as differentially expressed in an ER status comparison or HER2 / neu status comparison. (B) Analyte differentially expressed between HER2-negative and HER2-positive tumors. FIG. 6 shows the evaluation of Ki67 positive (25% cut-off) and Ki67 negative staged tumors. Differentially expressed analytes are shown in the heat map, with red indicating upregulation and green indicating downregulation. (A) PCA plot of Ki67 positive and Ki67 negative staged tumors. Corresponding analyte and sample heat maps (data filtered with variance 0.2, p-value <0.01, q-value <0.27). Continuation of FIG. S8A-1. FIG. 6 shows the evaluation of Ki67 positive (25% cut-off) and Ki67 negative staged tumors. Differentially expressed analytes are shown in the heat map, with red indicating upregulation and green indicating downregulation. (B) Results from a single cross validation approach using SVM are shown as ROC curves. FIG. 6 shows transcription factor-related network analysis using IPA for differentially expressed analytes reflecting histological grade or ER status. Lines connecting molecules indicate molecular relevance, and arrow style indicates specific molecular relevance and direction of interaction. (A) 49 proteins identified by multigroup histological grade comparison were used as input. Median Log ₂ ratio for histological grade 3 to histological grade 1 used to color-encode the measured analyte. Red indicates upward adjustment. Green indicates down adjustment. FIG. 6 shows transcription factor-related network analysis using IPA for differentially expressed analytes reflecting histological grade or ER status. Lines connecting molecules indicate molecular relevance, and arrow style indicates specific molecular relevance and direction of interaction. (B) 39 proteins identified by ER status comparison were used as input. Median Log ₂ ratio used to color code the measured analyte. In ER negative samples, red indicates upregulation and green indicates downregulation. Diagram showing individual mRNA expression profiles based on data from 1411 histologically graded tumor samples, exemplified for a subset of analytes found to show significantly different protein expression between tissue grades It is. (AE) mRNA expression levels for five proteins found to show increased expression in tissue grade 3 tumors. (FJ) mRNA expression levels for five proteins found to show reduced expression in tissue grade 3 tumors. Continuation of FIG. Continuation of FIG. Continuation of FIG. Continuation of FIG. FIG. 14 shows an mRNA expression profile based on data from a breast tumor sample with defined ER status of 1620. Of the 39 differentially expressed proteins, 32 were successfully mapped into the GOBO database using the gene entrez ID. (A) mRNA expression profile for 10 proteins found to show increased protein expression in ER positive tumors. Furthermore, the correlation of 10 genes to different gene set module expression patterns is shown. Gray points indicate actual correlation values. (B) mRNA expression profile for proteins found to show decreased expression in ER positive tumors. In addition, the correlation of 22 genes to different gene set module expression patterns is shown. Gray points indicate actual correlation values. (CD) Individual mRNA expression profiles exemplified for two proteins found to show increased expression in ER positive tumors. (EF) Individual mRNA expression profiles exemplified for two proteins found to show reduced expression in ER positive tumors. Continuation of FIG. Continuation of FIG. Continuation of FIG. FIG. 6 shows individual mRNA expression profiles mapped using the GOBO database tool, exemplified for three analytes that were found to show significantly differential protein expression in a HER2 / neu comparison. Data based on 1881 available tumor samples. (A) HER2 / neu, (B) S100A9 (C) GRB7. In addition to the three analytes, a fourth protein (Accession P22392) was tested and mapped. However, data is missing due to error messages from the GOBO database tool using either Gene Entrez ID 4831 or 654364. Continuation of FIG. FIG. 6 shows Kaplan-Meier analysis using DMFS as a 10-year endpoint. Of the 49 proteins that differentiated histological grade, 42 were successfully mapped to the gene expression database using Entrez Gene ID (after conversion to Swiss plot ID). These analytes were divided into two (based on up-regulation or down-regulation using the ratio between histological grade 3 and histological grade 1 samples for observed protein expression levels). Dividing into groups resulted in 15 down-regulated analytes and 27 up-regulated analytes. These two groups were then used to assess the potential risk of distant metastasis survival (DMFS) using gene expression datasets. Kaplan-Meier analysis using DMFS as a 10-year endpoint for histologically graded tumors (n = 1379) was performed by stratifying gene expression data into three quantiles. In addition, four individual Kaplan-Meier analyzes were generated using DMFS as a 10-year endpoint based on a single gene (two down-regulated and two up-regulated), using the GOBO tool And shown in a similar manner. Continuation of FIG. FIG. 6 shows that a breast cancer tissue sample comprising 6 grade 1 samples, 9 grade 2 samples and 6 grade 9 samples was selected from the same original cohort of 52 samples. These samples were digested in solution (trypsinization) and analyzed using a selective monitoring reaction (SRM) setup (established mass spectrometry based approach). Nine peptides corresponding to eight proteins from the indicated list of biomarkers were targeted and quantified. Samples were run in triplicate. Data was analyzed using Anubis, followed by P-value filtering (p <0.01) and q-value filtering (q <0.11). This data shows that breast cancer tissue samples could be differentiated according to grade using a shortened list of markers. FIG. 5 shows that breast cancer tissue samples, including 47 samples (due to technical replication) spanning grades 1, 2 and 3, were selected from the same original cohort of 52 samples. These samples were digested in gel (trypsinization) and analyzed using a selective monitoring reaction (SRM) setup (established mass spectrometry based approach). Eight peptides corresponding to four proteins from the indicated list of biomarkers were targeted and quantified. Samples were run in triplicate. Data was analyzed using Anubis, followed by P-value filtering (p <0.01) and q-value filtering (q <0.009). This data indicated that breast cancer tissue samples could be differentiated according to grade using a shortened list of markers. Continuation of FIG.

Introduction Tumor progression and prognosis in breast cancer patients is difficult to assess using current clinical and laboratory parameters and there is no candidate multi-tissue biomarker signature. In an attempt to solve this unmet clinical need, we applied a recently developed proteomic discovery tool called the Global Proteome Survey. Thus, by combining affinity proteomics based on 9 antibodies only and unlabeled LC-MS / MS, we profiled 52 breast cancer tissue samples representing one of the largest breast cancer tissue proteomics studies, 1388 A detailed quantified proteomic map showing the proteins of was successfully generated. This result indicated that the present inventors have deciphered a thorough molecular portrait of tissue graded breast cancer tumors that reflect tumor progression. More specifically, a tissue biomarker signature with 49 components (if p <0.01) and a tissue biomarker with 79 components that distinguish tissue grade 1-3 breast cancer tumors with high accuracy. A signature was defined (if p <0.02). Highly biologically relevant proteins were identified and differentially expressed proteins supported the current hypothesis on reconstructing the tumor microenvironment for tumor progression. In addition, the use of markers to estimate the risk of survival without distant metastases has also been demonstrated. In addition, breast cancer-related biomarker signatures reflecting ER status, HER2 status and Ki67 status, respectively, were depicted. These biomarker signatures were supported using independent methods (mRNA profiling) and patient cohorts. Taken together, these molecular portraits provide improved classification and prognosis of breast cancer.

Experimental Procedure Clinical Sample This study was approved by the local ethics review board in Lund, Sweden. 52 breast cancer patients were recruited from Department of Oncology (SUS, Lund). All clinical records were accessible for 50 of the tissue samples. These samples were subdivided based on tissue grade 1 (n = 9), tissue grade 2 (n = 17) and tissue grade 3 (n = 24).

Preparation of Trypsin Digested Human Breast Cancer Tissue Samples Proteins were extracted from 52 breast cancer tissue pieces, subsequently reduced, alkylated, trypsin digested, and finally stored at −80 ° C. until further use. In addition, pooled samples used as reference samples were generated by combining 5 μl aliquots from all digested samples and stored at −80 ° C. until further use. Details on sample preparation are provided in the supplemental experimental procedure.

Production of CIMS-scFv antibodies and coupling to magnetic beads Nine CIMS scFv antibodies to six short C-terminal amino acid peptide motifs (
Table S2) was produced in E. coli cultures and purified using Ni ²⁺ -NTA affinity chromatography. The purified antibody was then coupled to magnetic beads. Details about scFv production and coupling are provided in the supplemental experimental procedure.

Unlabeled quantitative GPS experiments Four different pools of antibody-conjugated beads (referred to as CIMS-binder mix 1-4) were created by mixing equal amounts of two or three different binders (Table S2). These antibody mixes were exposed to trypsin samples, washed and finally incubated with acetic acid to elute the captured peptides. This eluate was then used directly for MS analysis without further cleaning. The complete study was conducted using 26 days of MS instrument usage divided into 6.5 days of 4 blocks (1 CIMS-binder mix / block). All samples were analyzed individually once per CIMS-binder mix. In addition, triplicate capture of selected samples was performed within each block as a continuous LC-MS / MS implementation. Reference samples were repeatedly analyzed over time within and between 4 blocks (FIG. S1). A total of 238 LC-MS / MS runs were performed and all details about peptide capture and associated mass spectrometric analysis are provided in the supplemental experimental procedure.

Protein identification and quantification The data generated were analyzed by two software packages Proteios SE (Hakkinen et al., 2009) and Progenesis LC-MS (Nonlinear Dynamics, UK). Search forward and reverse combined databases (Homo Sapiens Swiss-Prot, 2011) with an estimated false discovery rate (FDR) of 0.01 based on the number of identified reverse hits to generate peptide identifications. In August, resulting in a total of 71324 database entries). Progenesis-LC-MS software (v4.0) was used for feature alignment, identification (Mascot) and generation of quantitative values. Details regarding search parameters and data processing are provided in the supplemental experimental procedure.

Statistical and bioinformatics analysis Qlucore Omics Explorer v (2.2) (Qlucore AB, Lund, Sweden) was significantly up- or down-regulated using one-way ANOVA (p <0. 01) was used to identify. q values were generated based on the method of Benjamini and Hochberg (Benjamini and Hochberg, 1995). Principal component analysis (PCA) plots and heat maps were generated with Qlucore. Support Vector Machine (SVM) is a learning method that was used to classify samples using a single cross-validation procedure (Cortes and Vapnik, 1995), analyzing the unfiltered data and p-value Run on both filtered data. Receiver operating characteristic (ROC) curves (Lasko et al., 2005) constructed using SVM determined values and area under the curve (AUC) were used as a measure of classifier performance. In addition, Ingenuity Systems Pathway Analysis (IPA) (v 11904312, www.ingenuity.com) can be used to extract information such as protein localization, potential network interactions, transcription factor association, and association with tumor development. Used for significantly differentially expressed proteins. A GOBO search tool for experimentally derived protein signatures against a large cohort of published gene expression data for breast cancer tissue with clinical parameters such as tissue grade 1, 2 and 3, ER status or HER2 status (Ringner et al., 2011) was used for final verification at the mRNA level.

Supplemental Experimental Procedures Preparation of Trypsin Digested Human Breast Cancer Tissue Samples Proteins were extracted from breast cancer tissue pieces and stored at −80 ° C. until use. Briefly, a piece of tissue (approximately 50 mg / sample) was fixed in a shaker for 2 × 30 seconds using rapid cooling in liquid nitrogen between two shaking rounds. Homogenized in a Teflon vessel pre-cooled with liquid nitrogen. Homogenized tissue powder was collected in lysis buffer (pH 8.5) containing 8M urea, 30 mM Tris, 5 mM magnesium acetate and 4% (w / v) CHAPS (2 mg tissue / 30 μl buffer). The tube was vortexed briefly and the sample was vortexed briefly every 5 minutes and incubated on ice for 40 minutes. After incubation, the sample was centrifuged at 13000 rpm and the supernatant was transferred to a new tube, followed by a second centrifugation. The buffer was exchanged for 0.15 M HEPES, 0.5 M urea (pH 8.0) using a Zeba desalting spin column (Pierce, Rockford, IL, USA), after which the protein concentration was adjusted to the Total Protein Kit, Determined using Micro Lowry (Sigma, St. Louis, MO, USA). Finally, the sample was aliquoted and stored at −80 ° C. until further use. The protein extract was thawed, reduced, alkylated and trypsin digested. First, SDS and TCEP-HCl (Thermo Scientific, Rockford, IL, USA) were added to 0.02% (w / v) and 5 mM, respectively, and the sample was reduced at 56 ° C. for 60 minutes. The sample was cooled to room temperature, after which iodoacetamide was added to 10 mM and then alkylated at room temperature for 30 minutes. Sequencing grade modified trypsin (Promega, Madison, Wisconsin, USA) was then added at 20 μg / mg protein at 37 ° C. for 16 hours. To ensure complete digestion, a second aliquot of trypsin (10 μg / mg protein) was added and the tubes were incubated at 37 ° C. for an additional 3 hours. Finally, the digested sample was aliquoted and stored at −80 ° C. until further use. In addition, separate pooled samples produced by combining samples containing 5 μl aliquots from all digested samples were prepared and stored at −80 ° C. until further use. In order to increase the potential provisional proteome coverage, two samples with limited clinical data in hand in Table S1 were included in the individual and pooled samples for further analysis.

Production of CIMS-scFv antibodies and coupling to magnetic beads Nine CIMS scFv antibodies (6 short C-terminal amino acid peptide motifs (M-1, M-15, M-31, M-32, M-33 and M-34) Clones 1-B03, 15-A06, 17-C08, 17-E02, 31-001-D01, 32-3A-G03, 33-3C-A09, 33-3D-F06 and 34-3A- D10) was selected from the n-CoDeR (Soderlind et al., 2000) library, which was kindly provided by BioInventive International AB, Lund, Sweden (Table S2). Specificity and dissociation constants (low μM range) for 6 of these CIMS antibodies have recently been determined (Olsson et al., 2011). These antibodies were produced in 100 ml E. coli cultures and purified using affinity chromatography on Ni2 ⁺ -NTA agarose (Qiagen, Hilden, Germany). Bound molecules were eluted with 250 mM imidazole, dialyzed against PBS (pH 7.4) for 72 hours and stored at + 4 ° C. until use. Protein concentration was determined by measuring absorbance at 280 nm. The integrity and purity of the scFv antibody was confirmed by running a Protein 80 chip on an Agilent Bioanalyzer (Agilent, Waldbrunn, Germany). Purified scFv was purified using magnetic beads (M-270 carboxylic acid activated form, Invitrogen D) as previously described (Olsson et al., 2011).
ynal, Oslo). Briefly, a batch of 180-250 μg of purified scFv was covalently coupled to about 9 mg (300 μl) of magnetic beads (EDC-NHS chemistry) and 0.005% (v / V) in Tween-20 at 4 ° C. In addition, batches of blank beads (ie, beads generated using the coupling protocol but without adding scFv) were generated.

Unlabeled quantitative GPS experiments Four different pools of conjugated beads (designated CIMS-Binder Mix 1-4) were created by mixing equal amounts of two or three different binders according to the following: Mix 1 (CIMS-33-3D-F06 and CIMS-33-3C-A09), Mix 2 (CIMS-17-C08 and CIMS-17-E02), Mix 3 (CIMS-15-A06 and CIMS-34-3A) -D10) and mix 4 (CIMS-1-B03, CIMS-32-3A-G03 and CIMS-31-001-D01) (Table S2). For each capture, 50 μl of pooled bead solution was used and scFv-beads were never reused. These beads were pre-washed with 350 μl PBS, followed by trypsin sample digestion in a final volume of 35 μl (diluted with PBS and added phenylmethylsulfonyl fluoride (PMSF) to a final concentration of 1 mM). It was exposed and then incubated with the beads for 20 minutes with gentle mixing. The tube was then placed on a magnet, the supernatant was removed, and the beads were washed with 100 μl and 90 μl PBS, respectively (the beads were transferred to a new tube between each wash step, for a total wash time of 5 Minutes). Finally, the beads were incubated with 9.5 μl of 5% (v / v) acetic acid solution for 2 minutes to elute the captured peptide. This eluate was then used directly for mass spectrometry without further cleaning.

An ESI-LTQ-Orbitrap XL mass spectrometer (Thermo Electron, Bremen, Germany) used in conjunction with the Ekgent nanoLC 2DTM plus HPLC system (Eksigent technologies, Dublin, CA, USA). An automatic sampler injected 6 μl of GPS generated eluate. A blank LC-MS / MS run was used between each analyzed sample. Peptides were loaded onto a precolumn (PepMap 100, C18, 5 μm, 5 mm × 0.3 mm, LC Packings, Amsterdam, Netherlands) at a constant flow rate of 15 μl / min. Subsequently, these peptides were transformed into 10 μm fused quartz emitters packed in-house with Reprosil-Pur C18-AQ resin (3 μm Dr. Maisch, GmbH, Germany), 75 μm × 16 cm (PicoTip ™ Emitter, New Objective, Inc. MA, USA). Peptides were prepared in a 35 minute linear gradient of 3% (v / v) to 35% (v / v) acetonitrile in water containing 0.1% (v / v) formic acid using a flow rate of 300 nl / min. Eluted. LTQ-Orbitrap was operated in a data dependent manner to automatically switch between Orbitrap-MS (m / z 400-2000) and LTQ-MS / MS acquisition. Four MS / MS spectra were acquired at 60,000 FWHM nominal resolution setting using the lock mass option (m / z 445.10025) for internal calibration per FT-MS scan. Acquired in a linear ion trap. The dynamic exclusion list was limited to 500 entries using two iteration counts with a 20 second iteration duration and a 120 second maximum retention period. Precursor ion charge state screening was able to select for ions with at least two charges and reject ions with undetermined charge states. Normalized collision energy was set at 35% and one microscan was acquired for each spectrum. All samples
Individual analysis was performed once per CIMS-binder mix. In addition, triplicate capture of pooled samples (based on all samples in this study) was performed for each CIMS-binder mix and partitioned for longer time MS analysis (per binder mix, (At the start, middle and end of the LC-MS sequence execution sequence) (FIG. S1). This was possible for CIMS-Binder Mix 1 and 4. However, in more than half of the sequence runs for both CIMS-Binder-Mix 2 and 3, the analytical LC column needs to be replaced (twice) and the last pool scheduled on the newly replaced column Deciding to perform the run resulted in some (9 samples of 11 each) being analyzed after the pool run. In addition, triple capture was performed on the samples (7267, 8613) for each CIMS-binder mix. Blank beads, ie beads without any conjugated antibody, were exposed to the pooled digest to evaluate potential bead background binding peptides. Based on the low number of background bound peptides identified from the “capture” of the two blank beads, all generated data was left unfiltered unless otherwise noted.

Protein Identification and Quantification Generated data can be used with Mascot and X! First analyzed using Proteios SE to generate identification using both Tandem. Briefly, all files are processed and converted to mzML and mgf formats using the Proteios (v 2.17) platform, and the following search parameters are changed to Mascot and X! Used for Tandem: Enzyme: Trypsin; Missed Cleavage 1; Fixed modification: Carbamidomethyl (C); Variable modification: Methionine oxidation (O). Furthermore, variable N-acetyl is X! Allowed searches to be performed in Tandem (www.thegpm.org/tandem/). Using 3 ppm peptide mass tolerance and 0.5 Da fragment mass tolerance, the search was performed against forward and reverse combined databases (Homo Sapiens Swiss-Prot, August 2011, resulting in a total of 71324 database entries). And executed. Mascot and X! An automatic database search (using a false discovery rate (FDR) of 0.01) in both Tandem as well as the resulting combination was used to generate peptide identification (estimated based on the number of reverse hits identified). . When Proteios SE was used to generate protein identification for each sample, an FDR of 0.01 was applied to the protein level. All raw data is stored in the Proteios SE.

At the time of analysis, Proteios SE did not provide a quantitative unlabeled plug-in analysis module (in development), so Progenesis-LC-MS software (v4.0) generated all quantitative values Used to do. Briefly, raw data files were converted to mzXML using the ProteoWizard software package before using the Progenesis-LC-MS software. A built-in feature discovery tool, Mascot search tool and combined fractions tool (CIMS-Binder-Mix 1, 2, 3 and 4) were used with default settings and minimum inputs. For optimal feature alignment, the first injection run of the pooled sample for each CIMS-Binder mix (Figure S1), except for the CIMS-Mix 3 run, where the intermediate pool run is used as the reference alignment file, Used as a reference alignment file. Aligned and detected features for retention times 10-50 minutes for CIMS-Binder Mix 1 and 2 and for 10-49 minutes for CIMS-Binder Mix 3 and 4 were included for quantification. . Generated normalized abundance values were extracted and used for statistical and bioinformatics analyses. Due to the limitations of the Progenesis software, identification is limited to Mascot searches only, which is X! From Proteios SE! This means that the Tandem generated identification was not included in downstream quantitative analysis. A cut-off FDR value of 0.01 was applied using the same database (Homo Sapiens Swiss-Prot, August 2011, forward and reverse combined databases) and search parameters as above.

Results In this study, the semi-global protein expression profiles (identification and quantification) of 52 crude breast cancer tissue extracts were decoded using GPS. Tissue biomarker signatures reflecting tissue grade and other important clinical laboratory parameters such as estrogen receptor (ER), HER2 and Ki-67 are depicted. The overall workflow outlining the experimental design is shown in FIG.

Protein Coverage, Dynamic Range and Assay Performance A total of 2,140 protein groups were identified using GPS (FIGS. 1A-C). This identification reproducibility was high, resulting in 54.7% peptide duplication (FIG. S2A). In comparison, the reference sample analyzed repeatedly throughout the project showed a 43.9% peptide identification overlap (Figure S2B). Of the identified proteins, a total of 1388 was successfully quantified (Figure S3) and was subsequently used in searches for disease-related markers. The total median CV value for quantification for the 7267 sample was found to be 10.8% (FIG. 2A), while the corresponding total median CV value for the reference sample was 22.8%. (FIG. 2A). Of note, about 38% of the quantified peptides (833 peptides), corresponding to 61 proteins, have not been previously reported in PeptideAtals (FIG. 1D), indicating a substantial new coverage. Indicates. This was further emphasized by the fact that the prominent part of the detected peptide is a median length of 9 versus 11 amino acids, shorter than previously reported (FIG. 1E).

Evaluation of the measured log 2 _-MS intensity normalized abundance distribution for all quantified protein, a dynamic range of about ¹⁰⁶ was shown (Fig. S3A). The fact that peptides ranging from those frequently reported at PeptideAtlas to those rarely reported were easily detected (FIG. 1F) further demonstrated the thorough coverage produced by the GPS assay. . The detected proteins were then grouped by major biological processes and found to be distributed in several groups (Figure S3B). Interestingly, proteins grouped in processes such as translation (eg, 60S ribosomal proteins) have a higher overall presence than other proteins involved in mitosis etc. (eg, CDK1), as can be predicted It was found to show an amount. Taken together, this data demonstrated the ability of GPS to provide new and deep coverage in a reproducible manner.

Protein Expression Profile Reflecting Tissue Grade Initially, we tested whether a tissue biomarker signature reflecting tissue grade could be decoded. 49 proteins differentially expressed significantly (p <0.01, q value <0.25) between grade 1, grade 2 and grade 3 cohorts using multivariate analysis (group 3 comparison) Was identified. Based on this signature, PCA plots showed that tissue grade 1 and tissue grade 3 tumors were well separated, but tissue grade 2 tumors appeared to be more heterogeneous and spread to both other groups (FIG. 3A). A pattern of both up- and down-regulated analytes with increasing tissue grade could be observed. For example, cyclin-dependent kinase 1 (CDK1), minichromosome maintenance complex component 3 (MCM3), DNA replication licensing factor MCM7, ATP
Citrate synthase (ACLY), polyadenylate binding protein 4 (PABPC4) and 6-phosphofructokinase type C (PFKP) were among the up-regulated tissue markers (FIGS. 3A and S4). In contrast, analytes such as keratocan (KERA), sponge (SPON1), asporin (ASPB), adipocyte enhancer binding protein 1 (AEBP1), chymase (CMA1) and olfactedin-like protein 3 (OLFML3) Among the regulated analytes, ie, those that showed higher expression levels in tissue grade 1 tumors (FIGS. 3A and S5).

  The inventors then tested whether a list of 49 p-value filtered (p <0.01) biomarkers could be used to classify tissues based on tissue grade. For this purpose, we performed a single cross-validation using SVM and collected decision values for all samples. This predicted value was then used to construct a ROC curve and calculate the AUC value (FIG. 3A). These results indicated that the tissue-grade tumor subgroup was well separable (AUC = 0.75-0.93), but Grade 2 again appeared more heterogeneous.

  Next, we investigated the impact of using a two-group comparison instead of a multivariate approach to define differentially expressed markers (Figure S6). As can be expected, this data showed that the classification of individual organizational subgroups was improved as judged by AUC values (AUC = 0.91 to 0.92). Focused on tissue grade 1 vs. tissue grade 3, depicting 50 significantly (p <0.01) differentially expressed analytes, 31 of which overlapped the previous 49 biomarker signatures (See FIG. 3 and FIG. S6C). When tissue grade 2 was mapped onto a grade 1 vs. grade 3 frozen 50 biomarker comparison, it again showed heterogeneous features and spread across both cohorts (see FIGS. 3A and S6D). ).

Impact of ER status Since 14 of 24 tissue grade 3 tumors were classified as ER negative and 14 of 17 ER negative samples were actually grade 3 tumors, we The direct effect of ER status was investigated. To test this hypothesis, tumors were retested using only ER positive samples (n = 33). When using a multivariate approach, the results accurately show 18 significantly differentially expressed proteins (p <0.01, q value <0.51), tissue grade 1 tissue vs tissue grade 3 Were successfully classified (0.9 AUC value, data not shown). Remarkably, 16 out of 18 analytes (eg, ASPN, SPON1, KERA, ACLY, APCS and PABPC4) were found to overlap with the originally decoded 49 biomarker signatures (FIG. 3A). . Therefore, this data further supported the observation that 49 biomarker signatures reflect tissue grade.

  In addition, we also tested whether the ER associated tissue biomarker signature could be unresolved. The results show that ER positive and ER negative breast cancer tissues could be well classified (AUC = 0.82) (FIG. 3B) and 39 differentially expressed analytes (p <0.01, q value) <0.32) was identified (eg, GREB1, FIG. 3B and FIG. S7A). Therefore, this data indicated that an ER related tissue biomarker signature was detected.

Protein Expression Profiles Reflecting HER2 / neu and Ki67 Status When comparing 52 breast cancer tissue extracts based on HER2 / neu status using single cross validation, the data was able to distinguish two cohorts (AUC = 0.98) and that 5 differentially expressed markers (p <0.01, q value <0.9) were identified (FIG. 3C). Most importantly, the receptor tyrosine protein kinase erbB-2 (HER2) was found among the up-regulated proteins (FIGS. 3C and S7B).

  Furthermore, in a similar manner, tissue protein signatures reflecting Ki67 status (when 25% of Ki67 positive cancer nuclei were used as cut-offs) could also be deciphered. A total of 45 proteins were found to be differentially expressed (p <0.01, q value <0.27) (FIG. S8A). This data demonstrated that Ki67 positive versus Ki67 negative tumors could be separated (AUC = 0.84) (FIG. S8B). Therefore, this result indicated that the protein expression profile that reflected both the HER2 / neu state and the Ki67 state was accurately shown.

Biological Relevance The biological relevance of 49 tissue biomarker signatures that differentiate tissue grades 1-3 were then tested. For this purpose, the cellular localization of each individual protein was mapped using IPA software (FIG. 4A) to investigate network-related functions and potential associations (FIGS. 4B-4C). A pattern of predominantly down-regulated proteins (extracellular matrix (ECM)) and up-regulated analytes (cell membrane, cytoplasm and nucleus) reflecting cell localization was revealed. More importantly, higher-ranking networks are found to be associated with DNA replication, recombination and repair, cell cycle and free radical scavenging, and the second highest-ranking network is gene expression, Associated with infectious diseases and cancer. Of note, several proteins in the top 1 network were directly or indirectly associated with NF-kB and VEGF (FIG. 4B). Furthermore, the majority of ECM proteins are shown correctly in the second network, with some proteins being directly or indirectly associated with transforming growth factor-β (TGFβ1) (FIG. 4C). . Therefore, this result indicated that biologically highly relevant tissue biomarkers that reflect tissue grade have been identified.

  Furthermore, the association between 49 tissue biomarker signatures and transcription factor networks was also assessed using IPA (FIG. S9). Of note, Rb and E2F2 were found among the top related transcriptional regulators (FIG. S9A). In comparison, estrogen receptor 2 (ESR2) and progesterone receptor (PGR) were found among the top related regulators when the tissue biomarker signature differentiated between ER positive and ER negative tumors (FIG. S9B).

Validation of Candidate Breast Cancer Progression Signatures In an attempt to validate 49 tissue biomarker signatures that distinguish tissue grades 1-3, the data were compared to a publicly available orthogonal breast cancer mRNA profiling dataset. A validation cohort was created from 1,881 samples, of which 1,411 were assigned tissue grades including grade 1 (n = 239), grade 2 (n = 677) and grade 3 (n = 495). Forty-two of the 49 tissue biomarkers could be mapped to a gene expression database using gene entrez ID, which was subsequently used in validation studies.

The 42 tissue markers were then divided into two groups based on the down-regulated (15 analytes) or up-regulated (27 analytes) protein expression profiles observed for grade 3 versus grade 1. Compared with the corresponding mRNA expression profile (FIG. 5). Downregulated proteins (eg, SPON1 and KERA) (FIGS. 5A, S5, S10I and S11J) and upregulated proteins (eg, CDK1 and MCM3) (FIGS. 5B, S4, S10A and S10B) ) Was found to support both protein expression profiles well at the mRNA expression level. Interestingly, up-regulated markers were found to show mRNA profiles with a high correlation to checkpoint and M-phase gene modules (FIG. 5A), and the group of down-regulated markers was stromal genes An mRNA profile with high correlation to the set module was shown (FIG. 5B).

Validation of ER-related and HER2-related tissue biomarker signatures Then, in a similar manner, using the same publicly available orthogonal breast cancer mRNA profiling dataset as above, ER status (FIG. 3B) and HER2 status (FIG. 3C An attempt was made to verify a tissue biomarker signature that reflects).

  In the case of ER, a validation set was created with 1,620 samples assigned ER status, including 395 ER negative samples and 1225 ER negative samples. Of the 39 tissue biomarkers, 32 could be mapped to the gene expression database and subsequently used in the validation. These 32 markers were then divided into two groups (10 up-regulated and 22 down-regulated) based on the observed protein expression profiles and compared to the corresponding mRNA expression profiles ( FIG. S11). With some exceptions (eg, complement C3, FIG. S11F and FIG. S7A), the observed protein expression profile was well supported by the corresponding mRNA expression profile (see FIG. 3B, FIG. S7A and FIG. S11). ) In this context, a group of up-regulated proteins in ER-positive tumors was found to exhibit a highly correlated mRNA profile for the steroid response gene module, and a group of down-regulated proteins was found to be immune response and basal It was interesting to note that it was found to show a highly correlated mRNA profile for the gene set module (FIGS. S11A-S11B).

  Validation sets for HER2 are HER2 positive (n = 152), basal (n = 357), lumen-A (n = 483), lumen-B (n = 289), normal-like (n = 257) and Created from 1,881 samples divided into unclassified (n = 344). Three of the five tissue markers could be mapped to the validation data set and used in subsequent evaluations (Figure S12). This result showed that the protein expression profile correlated well with the gene expression profile (see FIG. 3C, FIG. S7B, and FIG. S12), and the observation was further validated.

Assessing the distance-free metastasis survival Finally, we have found that the 49 tissue biomarker signatures that reflect the tissue grade are again using the same publicly available gene expression data set. It was tested whether it could also be used to assess the risk of (DMFS). Of the 49 tissue biomarkers, 42 could be mapped to 1379 samples with 10-year endpoint survival data. These markers were divided into two groups reflecting down-regulated (n = 15) and up-regulated (n = 27) markers in grade 3 vs. grade 1, and then Kaplan-Meier analysis was Performed with DMFS at the 10-year endpoint by stratifying gene expression data into three quantiles (low, medium and high) based on the expression levels of these analytes (Figure S13). . This data specifically indicated that a cohort of down-regulated analytes (mainly ECM related analytes) predicted DMFS risk. In fact, this could be achieved by targeting a single down-regulated (eg, KERA and OLFM3) or up-regulated (eg, CDK1) biomarker.

DISCUSSION In this study, we decoded the first thorough multiplexed tissue biomarker signature that reflects tumor progression in breast cancer, taking the next step towards tailored medicine in breast cancer. This outcome was achieved using our recent in-house developed GPS technology (Olsson et al., 2012; Olsson et al., 2011; Wingren et al., 2009). Thus, by combining affinity proteomics based on 9 antibodies only and unlabeled LC-MS / MS, we profiled 52 breast cancer tissue samples representing one of the largest breast cancer tissue proteome studies, 1388 We successfully generated a detailed quantified proteomic map reflecting the proteins of.

  More specifically, a tissue biomarker signature with the first 49 components that differentiates tissue grade 1-3 breast cancer tumors with high specificity and sensitivity was depicted. This list can be expanded to 79 differentially expressed markers by setting the p-value criterion to p <0.02, but here for the top 49 analytes (p <0.01) Concentrated the discussion. Molecular profiles or protein fingerprints supported the current view that grade 1 and grade 3 tumors are more distinct but grade 2 tumors are more heterogeneous (Sotriou et al., 2006). When investigating signatures, we identified a priori known markers and novel candidate biomarkers known to be associated with breast cancer. From a technical point of view, this new coverage was reflected by the fact that the majority of quantified peptides (about 38%) have not been previously reported in the PeptideAtlas database (Deutschsch et al., 2008). This new coverage provided by the GPS setup was also revealed when searching these 49 analytes against the Human Protein Atlas project (Uhlen et al., 2010). The Human Protein Atlas project currently covers more than 50% of non-overlapping human proteomes, but no antibody or histology staining has been reported for 13 of 49 differentially expressed proteins. .

  ER status alone has been shown to affect expression of more than 10% of genes in breast tumors and is generally considered to have an impact on survival. Since ER-negative breast cancer is generally more aggressive and anti-estrogen-based therapies are ineffective, further targeted therapies are urgently needed (Rochefort et al., 2003). We have identified 39 protein signatures that can differentiate ER positive and ER negative tumors with appropriate specificity and sensitivity. Interestingly, 11 of the 39 markers have not yet been covered by the Human Protein Atlas project and review the new coverage provided by GPS technology again (Uhlen et al., 2010). GREB1, one of 39 markers, has been suggested as a candidate clinical marker and potential therapeutic target for response to endocrine therapy (Hnatyszyn et al., 2010; Rae et al., 2005). GREB1 is an estrogen-regulated gene that mediates estrogen-stimulated cell proliferation and has recently been reported to be expressed in ER positive breast cancer cells and normal breast tissue but not in ER negative samples. Outlines its potential as a surrogate marker (Hnatyszyn et al., 2010). The protein profile generated by GPS further supported this opinion (Figure S7A).

In addition, five protein signatures capable of distinguishing clinically defined HER2 positive and HER2 negative samples were decoded (FIG. 3C). In fact, the low abundance receptor tyrosine protein kinase erbB-2 (HER2 protein) was identified and quantified and found to be one of the differentially expressed markers. Thus, the potential of measuring HER2 using GPS in a clinical setting can be envisioned as a complement to currently used classical immunohistochemistry or fluorescence in situ hybridization (FISH) based detection systems . Recent studies have shown that one of five HER2-based tests can produce inaccurate results (Philli
ps et al., 2009). Furthermore, S100-A9 and growth factor receptor binding protein 7 (GRB7) were also found to show increased expression in the majority of defined HER2-positive samples (FIG. S7B). High GRB7 expression has recently been reported to be associated with high HER2 expression and was used to define a subset of breast cancer patients with reduced survival (Nadler et al., 2010). The S100 gene family encodes low molecular weight calcium binding proteins, and certain S100 members are associated with cancer progression, metastasis and have potential as predictors of drug resistance in patients with breast cancer (McKiernan et al. 2011; Yang et al. 2011).

  Most importantly, not only biomarker signatures that reflect tissue grade, but also biomarker signatures that reflect ER and HER2 status, use the GOBO tool to perform independent data sets and orthogonal methods (mRNA expression levels). Used and verified (Ringner et al., 2011). A group of up-regulated and down-regulated proteins was evaluated based on correlations to known gene set modules, which is a functional process that is captured by gene signatures, not important individual genes. This is because there are many cases (Wirapati et al., 2008). The significant correlation to the gene set module for stroma, checkpoint and steroid response was particularly noteworthy (Figure 5 and Figure S11). Furthermore, when using histologically derived protein analytes and assessing DMFS as an endpoint, the data clearly showed worse clinical outcomes, especially when using down-regulated ECM proteins. . Thus, independent mRNA validation added strong support for the reported candidate biomarker signature and its potential in future breast tissue tumor classification.

  Taken together, we have demonstrated the applicability of our recently developed GPS technology platform to clinical proteomic discovery profiling attempts. Tissue biomarker signatures reflecting tissue grade, ie tumor progression, and other important clinical laboratory parameters such as ER status, HER2 status and Ki67 status have been reported in this study; these new tissue biomarker ports The rate allows for improved breast cancer classification and prognosis.

References
Aebersold, R., and Mann, M. (2003) .Mass spectrometry-based proteomics.Nature 422, 198-207.
Benjamini, Y., and Hochberg, Y. (1995) .Controlling the false discovery rate: a practical and powerful approach to multiple testing.Journal of the Royal Statistical Society Series B 57, 289-300.
Bergamaschi, A., Tagliabue, E., Sorlie, T., Naume, B., Triulzi, T., Orlandi, R.,
Russnes, HG, Nesland, JM, Tammi, R., Auvinen, P., et al. (2008). Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome.The Journal of pathology 214, 357-367.
Bierie, B., and Moses, HL (2006). Tumour microenvironment: TGFbeta: the molecular Jekyll and Hyde of cancer.Nature reviews Cancer 6, 506-520.
Borrebaeck, CA, and Wingren, C. (2011) .Recombinant antibodies for the generation of antibody arrays.Methods Mol Biol 785, 247-262.
Bouchal, P., Roumeliotis, T., Hrstka, R., Nenutil, R., Vojtesek, B., and Garbis,
SD (2009). Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS / MS) based quantitative proteomic analysis.Journal of proteome research 8, 362- 373.
Carlsson, A., Wingren, C., Ingvarsson, J., Ellmark, P., Baldertorp, B., Ferno, M
., Olsson, H., and Borrebaeck, CA (2008). Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays. Eur J Cancer 44, 472-480.
Carlsson, A., Wingren, C., Kristensson, M., Rose, C., Ferno, M., Olsson, H., Jernstrom, H., Ek, S., Gustavsson, E., Ingvar, C., et al. (2011) .Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases.Proceedings of the National Academy of Sciences of the United States of America 108, 14252-14257.
Ciocca, DR, and Elledge, R. (2000). Molecular markers for predicting response
to tamoxifen in breast cancer patients.Endocrine 13, 1-10.
Cortes, C., and Vapnik, V. (1995). Support-Vector Networks. Machine Learning 20,
273-297.
Cortez, D., Glick, G., and Elledge, SJ (2004) .Minichromosome maintenance proteins are direct targets of the ATM and ATR checkpoint kinases.Proceedings of the National Academy of Sciences of the United States of America 101, 10078-10083 .

Desmedt, C., Haibe-Kains, B., Wirapati, P., Buyse, M., Larsimont, D., Bontempi, G., Delorenzi, M., Piccart, M., and Sotiriou, C. (2008) Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes.Clinical cancer research: an official journal of the American Association for Cancer Research 14, 5158-5165.
Deutsch, EW, Lam, H., and Aebersold, R. (2008). PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows.EMBO reports 9, 429-434.
Dowsett, M., Goldhirsch, A., Hayes, DF, Senn, HJ, Wood, W., and Viale, G. (2007). International Web-based consultation on priorities for translational breast cancer research.Breast cancer research: BCR 9, R81.
Elston, CW, and Ellis, IO (1991) .Pathological prognostic factors in breast cancer.I. The value of histological grade in breast cancer: experience from a
large study with long-term follow-up.Histopathology 19, 403-410.
Fata, JE, Werb, Z., and Bissell, MJ (2004) .Regulation of mammary gland branching morphogenesis by the extracellular matrix and its remodeling enzymes.Breast cancer research: BCR 6, 1-11.
Frierson, HF, Jr., Wolber, RA, Berean, KW, Franquemont, DW, Gaffey, MJ, Boyd, JC, and Wilbur, DC (1995) .Interobserver reproducibility of the Nottingham modification of the Bloom and Richardson histologic grading scheme
for infiltrating ductal carcinoma.American journal of clinical pathology 103, 195-198.
Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, JR, and Mann, M. (2010). Super-SILAC mix for quantitative proteomics of human tumor tissue.Nature methods 7, 383-385.
Geiger, T., Madden, SF, Gallagher, WM, Cox, J., and Mann, M. (2012). Proteomic portrait of human breast cancer progression identifies novel prognostic markers. Cancer research.
Gong, Y., Wang, N., Wu, F., Cass, CE, Damaraju, S., Mackey, JR, and Li, L.
(2008) .Proteome profile of human breast cancer tissue generated by LC-ESI-MS / MS combined with sequential protein precipitation and solubilization.Journal of proteome research 7, 3583-3590.
Ha, SA, Shin, SM, Namkoong, H., Lee, H., Cho, GW, Hur, SY, Kim, T. E
., and Kim, JW (2004) .Cancer-associated expression of minichromosome maintenance 3 gene in several human cancers and its involvement in tumorigenesis.Clinical cancer research: an official journal of the American Association for Cancer
Research 10, 8386-8395.

Hakkinen, J., Vincic, G., Mansson, O., Warell, K., and Levander, F. (2009) .The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data.Journal of proteome research 8, 3037-3043.
Hanahan, D., and Weinberg, RA (2000) .The hallmarks of cancer.Cell 100, 57-70.
Hanahan, D., and Weinberg, RA (2011) .Hallmarks of cancer: the next generation.Cell 144, 646-674.
Hanash, S. (2003). Disease proteomics. Nature 422, 226-232.
Hnatyszyn, HJ, Liu, M., Hilger, A., Herbert, L., Gomez-Fernandez, CR, Jorda, M., Thomas, D., Rae, JM, El-Ashry, D., and Lippman, ME (2010). Correlation of GREB1 mRNA with protein expression in breast cancer: validation of a novel GREB1 monoclonal antibody.Breast cancer research and treatment 122, 371-380.
Hondermarck, H., Tastet, C., El Yazidi-Belkoura, I., Toillon, RA, and Le Bourhis, X. (2008) .Proteomics of breast cancer: the quest for markers and therapeutic targets. Journal of proteome research 7 , 1403-1411.
Hudis, CA (2007) .Trastuzumab--mechanism of action and use in clinical practice.The New England journal of medicine 357, 39-51.
Ivshina, AV, George, J., Senko, O., Mow, B., Putti, TC, Smeds, J., Lindahl, T., Pawitan, Y., Hall, P., Nordgren, H., et al (2006). Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.
research 66, 10292-10301.
Jemal, A., Bray, F., Center, MM, Ferlay, J., Ward, E., and Forman, D. (2011).
Global cancer statistics.CA: a cancer journal for clinicians 61, 69-90.
Johnson, N., Li, YC, Walton, ZE, Cheng, KA, Li, D., Rodig, SJ, Moreau, LA, Unitt, C., Bronson, RT, Thomas, HD, et al. (2011). Compromised CDK1 activity sensitizes BRCA-proficient cancers to PARP inhibition.Nature medicine 17, 875-882.
Kang, S., Kim, MJ, An, H., Kim, BG, Choi, YP, Kang, KS, Gao, MQ, Park, H., Na, HJ, Kim, HK, et al. (2010). Proteomic molecular portrait of interface zone in breast cancer.Journal of proteome research 9, 5638-5645.
Kuhn, U., and Wahle, E. (2004) .Structure and function of poly (A) binding proteins.Biochimica et biophysica acta 1678, 67-84.
Lasko, TA, Bhagwat, JG, Zou, KH, and Ohno-Machado, L. (2005) .The use of receiver operating characteristic curves in biomedical informatics.
biomedical informatics 38, 404-415.
Malumbres, M., and Barbacid, M. (2009) .Cell cycle, CDKs and cancer: a changing paradigm.Nature reviews Cancer 9, 153-166.

Mangus, DA, Evans, MC, and Jacobson, A. (2003) .Poly (A) -binding proteins: multifunctional scaffolds for the post-transcriptional control of gene expression.Genome biology 4, 223.
McKiernan, E., McDermott, EW, Evoy, D., Crown, J., and Duffy, MJ (2011) .The role of S100 genes in breast cancer progression. Tumour biology: the journal
of the International Society for Oncodevelopmental Biology and Medicine 32, 441-450.
Moon, JS, Kim, HE, Koh, E., Park, SH, Jin, WJ, Park, BW, Park, S.
W., and Kim, KS (2011) .Kruppel-like factor 4 (KLF4) activates the transcription of the gene for the platelet isoform of phosphofructokinase (PFKP) in breast cancer.The Journal of biological chemistry 286, 23808-23816.
Nadler, Y., Gonzalez, AM, Camp, RL, Rimm, DL, Kluger, HM, and Kluger, Y. (2010) .Growth factor receptor-bound protein-7 (Grb7) as a prognostic marker and therapeutic target in breast cancer. Annals of oncology: official journal
of the European Society for Medical Oncology / ESMO 21, 466-473.
Olsson, N., James, P., Borrebaeck, CA, and Wingren, C. (2012) .Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry. Submitted.
Olsson, N., Wingren, C., Mattsson, M., James, P., D, OC, Nilsson, F., Cahill,
DJ, and Borrebaeck, CA (2011) .Proteomic analysis and discovery using affinity proteomics and mass spectrometry.Molecular & cellular proteomics: MCP 10, M110 003962.
Olsson, N., Wingren, C., Mattsson, M., James, P., D, OC, Nilsson, F., Cahill,
DJ, and Borrebaeck, CA (2011) .Proteomic analysis and discovery using affinity proteomics and mass spectrometry.Molecular & cellular proteomics: MCP 10, M110 003962.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, FL, Walker, MG, Watson, D., Park, T., et al (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England journal of medicine 351, 2817-2826.
Pavlidis, P., and Noble, WS (2003) .Matrix2png: a utility for visualizing matrix data.Bioinformatics 19, 295-296.
Pei, DS, Qian, GW, Tian, H., Mou, J., Li, W., and Zheng, JN (2012). Analysis of human Ki-67 gene promoter and identification of the Sp1 binding sites for Ki-67 transcription. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine 33, 257-266.
Perou, CM, Sorlie, T., Eisen, MB, van de Rijn, M., Jeffrey, SS, Rees, CA, Pollack, JR, Ross, DT, Johnsen, H., Akslen, LA, et al. (2000 ). Molecular portraits of human breast tumours.Nature 406, 747-752.

Phillips, KA, Marshall, DA, Haas, JS, Elkin, EB, Liang, SY, Hassett, MJ, Ferrusi, I., Brock, JE, and Van Bebber, SL (2009). Clinical practice patterns and cost effectiveness of human epidermal growth receptor 2 testing strategies in breast cancer patients.Cancer 115, 5166-5174.
Place, AE, Jin Huh, S., and Polyak, K. (2011) .The microenvironment in breast
cancer progression: biology and implications for treatment.Breast cancer research: BCR 13, 227.
Rae, JM, Johnson, MD, Scheys, JO, Cordero, KE, Larios, JM, and Lippman, ME (2005) .GREB 1 is a critical regulator of hormone dependent breast cancer growth.Breast cancer research and treatment 92, 141- 149.
Ringner, M., Fredlund, E., Hakkinen, J., Borg, A., and Staaf, J. (2011). GOBO: gene expression-based outcome for breast cancer online.PloS one 6, e17911.
Robbins, P., Pinder, S., de Klerk, N., Dawkins, H., Harvey, J., Sterrett, G., Ellis, I., and Elston, C. (1995). Histological grading of breast carcinomas : a study of interobserver agreement. Human pathology 26, 873-879.
Rochefort, H., Glondu, M., Sahla, ME, Platet, N., and Garcia, M. (2003). How to target estrogen receptor-negative breast cancer? Endocrine-related cancer 10,
261-266.
Slamon, DJ, Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A.,
Fleming, T., Eiermann, W., Wolter, J., Pegram, M., et al. (2001) .Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that
overexpresses HER2. The New England journal of medicine 344, 783-792.
Soderlind, E., Strandberg, L., Jirholt, P., Kobayashi, N., Alexeiva, V., Aberg, AM, Nilsson, A., Jansson, B., Ohlin, M., Wingren, C., et al. (2000) .Recombining germline-derived CDR sequences for creating diverse single-framework antibody libraries.Nature biotechnology 18, 852-856.
Sorlie, T., Perou, CM, Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, MB, van de Rijn, M., Jeffrey, SS, et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.Proceedings of the National Academy of Sciences of the United States of America 98, 10869-10874.
Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H., Farmer, P., Praz, V., Haibe-Kains, B ., et al. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute 98, 262-272.
Strande, V., Canelle, L., Tastet, C., Burlet-Schiltz, O., Monsarrat, B., and Hondermarck, H. (2009) .The proteome of the human breast cancer cell line MDA-MB-231 : Analysis by LTQ-Orbitrap mass spectrometry.Proteomics Clinical applications 3, 41-50.

Sutton, CW, Rustogi, N., Gurkan, C., Scally, A., Loizidou, MA, Hadjisavvas, A., and Kyriacou, K. (2010) .Quantitative proteomic profiling of matched normal and tumor breast tissues. proteome research 9, 3891-3902.
Turashvili, G., Bouchal, J., Baumforth, K., Wei, W., Dziechciarkova, M., Ehrmann, J., Klein, J., Fridman, E., Skarda, J., Srovnal, J., et al. (2007). Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis.BMC cancer 7, 55.
Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M.,
Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010). Towards a knowledge-based Human Protein Atlas. Nature biotechnology 28, 1248-1250.
van de Vijver, MJ, He, YD, van't Veer, LJ, Dai, H., Hart, AA, Voskuil, DW, Schreiber, GJ, Peterse, JL, Roberts, C., Marton, MJ, et al. (2002). A gene-expression signature as a predictor of survival in breast cancer.The New England journal of medicine 347, 1999-2009.
van 't Veer, LJ, Dai, H., van de Vijver, MJ, He, YD, Hart, AA, Mao, M., Peterse, HL, van der Kooy, K., Marton, MJ, Witteveen, AT, et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer.Nature 415, 530-536.
Wang, TH, Chao, A., Tsai, CL, Chang, CL, Chen, SH, Lee, YS, Chen,
JK, Lin, YJ, Chang, PY, Wang, CJ, et al. (2010) .Stress-induced phosphoprotein 1 as a secreted biomarker for human ovarian cancer promotes cancer cell proliferation.Molecular & cellular proteomics: MCP 9, 1873-1884 .
Wingren, C., James, P., and Borrebaeck, CA (2009). Strategy for surveying the
proteome using affinity proteomics and mass spectrometry.Proteomics 9, 1511-1517.
Wirapati, P., Sotiriou, C., Kunkel, S., Farmer, P., Pradervand, S., Haibe-Kains,
B., Desmedt, C., Ignatiadis, M., Sengstag, T., Schutz, F., et al. (2008). Meta-
analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures.Breast cancer research: BCR 10, R65.
Yang, WS, Moon, HG, Kim, HS, Choi, EJ, Yu, MH, Noh, DY, and Lee, C. (2011) .Proteomic Approach Reveals FKBP4 and S100A9 as Potential Prediction Markers of Therapeutic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer. Journal of proteome research.
Zhang, L., Reidy, SP, Bogachev, O., Hall, BK, Majdalawieh, A., and Ro, HS (2011) .Lactation defect with impaired secretory activation in AEBP1-null mice.PloS one 6, e27795.

Claims (68)

The following process:
a) preparing a sample to be tested; and b) the presence in the test sample of one or more biomarkers selected from the group defined in Table 1A, Table 1B and / or Table 1C and / or Determining the biomarker signature of the test sample by measuring the amount;
Including:
The presence and / or amount in the test sample of one or more biomarkers selected from the groups defined in Table 1A, Table 1B and / or Table 1C is indicative of a breast cancer-related disease state;
A method of determining a disease state associated with breast cancer.   2. The method of claim 1, wherein the breast cancer-related disease state is histological grade and / or metastasis-free survival time.   The method according to claim 1 or 2, wherein the breast cancer-related disease state is a histological grade of breast cancer cells. The following process:
c) comprising histological grade 1 breast cancer cells, histological grade 2 breast cancer cells and / or histological grade 3 breast cancer cells, or histological grade 1 breast cancer cells, histological grade 2 breast cancer Providing one or more control samples comprising cells and / or histological grade 3 breast cancer cells; and d) a control of one or more biomarkers measured in step (b) Determining the biomarker signature of the control sample (s) by measuring the presence and / or amount in the sample (s);
Further includes:
The following events in which the presence and / or amount of one or more biomarkers in a test sample is measured in step (b):
i) an event corresponding to the presence and / or amount in a control sample comprising or consisting of a first histological grade breast cancer cell (if present) or consisting of a first histological grade breast cancer cell (if present) ;
ii) an event different from the presence and / or amount in a control sample comprising or consisting of a second histological grade breast cancer cell (if present) or consisting of a second histological grade breast cancer cell (if present) And / or iii) the presence and / or amount in a control sample comprising or consisting of a third histological grade breast cancer cell (if present) or consisting of a third histological grade breast cancer cell (if present); Different events,
4. The method of claim 3, wherein the presence of breast cancer cells is identified.   5. The method of claim 4, wherein each control sample comprises a single histological grade of breast cancer cells or consists of a single histological grade of breast cancer cells. Step (c) is:
i) preparing one or more control samples comprising histological grade 1 breast cancer cells or consisting of histological grade 1 breast cancer cells; comprising histological grade 2 breast cancer cells or tissue Providing one or more control samples consisting of histological grade 2 breast cancer cells; and one or more comprising histological grade 3 breast cancer cells or consisting of histological grade 3 breast cancer cells Preparing a control sample of;
ii) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells; and comprising histological grade 2 breast cancer cells; or Providing one or more control samples of histological grade 2 breast cancer cells;
iii) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells; and comprising histological grade 3 breast cancer cells; or Providing one or more control samples of histological grade 3 breast cancer cells;
iv) providing one or more control samples comprising or consisting of histological grade 2 breast cancer cells; and comprising histological grade 3 breast cancer cells; or Providing one or more control samples of histological grade 3 breast cancer cells;
v) providing one or more control samples comprising or consisting of histological grade 1 breast cancer cells;
vi) providing one or more control samples comprising or consisting of histological grade 2 breast cancer cells; or vii) comprising histological grade 3 breast cancer cells Or providing one or more control samples of histological grade 3 breast cancer cells,
The method according to claim 4 or 5, comprising or consisting of:   The method according to claim 1 or 2, wherein the breast cancer-related disease state is the individual's metastatic survival time. The following process:
c) one or more first control samples comprising breast cancer cells derived from individuals having a metastasis-free survival of less than 10 years or consisting of breast cancer cells derived from individuals having a metastasis-free survival of less than 10 years; And / or one or more second control samples comprising breast cancer cells from an individual having a metastasis-free survival of 10 years or more, or consisting of breast cancer cells from an individual having a metastasis-free survival of 10 years or more D) of the control sample (s) by measuring the presence and / or amount of one or more biomarkers measured in step (b) in the control sample (s) Determining a biomarker signature;
Further includes:
An individual's metastasis-free survival time is the presence and / or amount of one or more biomarkers measured in step (b) corresponding to the presence and / or amount of a first control sample (if present) And / or an event that is different from the presence and / or amount of the second control sample (if present) is identified as less than 10 years;
The presence and / or amount of the one or more biomarkers measured in step (b) is the presence and / or amount of the first control sample (if present) as determined by the individual metastasis-free survival time. 8. The method of claim 7, wherein the event is identified as longer than 10 years in a different event and / or an event corresponding to the presence and / or amount of a second control sample (if present).   9. The method of claim 8, wherein the one or more first control samples and / or second control samples are of the same histological grade as the sample being tested. A test sample of one or more biomarkers selected from the group defined in Table 1A, such that step (b) is at least two biomarkers selected from the group defined in Table 1A 7. A method according to any one of claims 3 to 6, comprising or consisting of measuring the presence and / or amount in   Step (b) is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 selected from the group defined in Table 1B , 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or one or more selected from the group defined in Table 1B, such as at least 30 biomarkers 11. The method of any one of claims 3-6 and 10, comprising or consisting of measuring the presence and / or amount of the biomarker in a test sample.   Step (b) is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 selected from the group defined in Table 1C One or more biomarkers selected from the group defined in Table 1C, such as 18, 19, 19, 20, 21, 22, 23, 24, 25, 26, 27 or at least 28 biomarkers 12. A method according to any one of claims 3-6, 10 and 11, comprising or consisting of measuring the presence and / or amount in a test sample.   Step (b) is defined in Table 1D, such as at least 2, 3, 4, 5, 6, 7, 8, 9 or at least 10 biomarkers selected from the group defined in Table 1D. A method according to any one of claims 3 to 6 and 10 to 12, comprising or consisting in measuring the presence and / or amount in a test sample of one or more biomarkers selected from the group The method according to item.   A group as defined in Table 1E, wherein step (b) is at least 2, 3, 4, 5, 6, 7, 8 or at least 9 biomarkers selected from the group defined in Table 1E 14. In any one of claims 3-6 and 10-13 comprising or consisting of measuring the presence and / or amount in a test sample of one or more biomarkers selected from The method described.   15. The method of claims 3-6 and 10-14, wherein step (b) comprises or consists of measuring the presence and / or amount of all biomarkers defined in Table 1 in a test sample The method according to any one of the above.   A test sample of one or more biomarkers selected from the group defined in Table 1A, such that step (b) is at least two biomarkers selected from the group defined in Table 1A 10. A method according to any one of claims 7 to 9, comprising or consisting of measuring the presence and / or amount in   Step (b) is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 selected from the group defined in Table 1B , 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or one or more selected from the group defined in Table 1B, such as at least 30 biomarkers 17. The method of any one of claims 7-9 and 16, comprising or consisting of measuring the presence and / or amount of the biomarker in a test sample.   Step (b) is defined in Table 1D, such as at least 2, 3, 4, 5, 6, 7, 8, 9 or at least 10 biomarkers selected from the group defined in Table 1D. 18. One of one or more biomarkers selected from the group comprising, or measuring, comprising or consisting of measuring the presence and / or amount in a test sample The method according to item.   Steps (b) comprise or consist of measuring the presence and / or amount in all test samples of those defined in Table 1A, Table 1B and Table 1D, The method of any one of 16-18.   Step (b) is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 selected from the group defined in Table 1C One or more biomarkers selected from the group defined in Table 1C, such as 18, 19, 19, 20, 21, 22, 23, 24, 25, 26, 27 or at least 28 biomarkers 20. A method according to any one of claims 7-9, 16-19, comprising or consisting of measuring the presence and / or amount of said in a test sample.   A group as defined in Table 1E, wherein step (b) is at least 2, 3, 4, 5, 6, 7, 8 or at least 9 biomarkers selected from the group defined in Table 1E 21. In any one of claims 7-9, 16-20, comprising or consisting of measuring the presence and / or amount in a test sample of one or more biomarkers selected from The method described.   17. Step (b) comprises or consists of measuring the presence and / or amount of all biomarkers defined in Table 1C and Table 1E in a test sample. The method of any one of -21.   The method of claim 7-9, 16-22, wherein step (b) comprises or consists of measuring the presence and / or amount of all biomarkers defined in Table 1 in the test sample The method according to any one of the above.   24. The method of any one of claims 1-23, wherein step (b) comprises measuring the expression of a nucleic acid molecule that encodes one or more biomarker (s).   25. The method of claim 24, wherein the nucleic acid molecule is a cDNA molecule or an mRNA molecule.   26. The method of claim 25, wherein the nucleic acid molecule is an mRNA molecule.   Measuring the expression of one or more biomarker (s) in step (b) can be Southern hybridization, Northern hybridization, polymerase chain reaction (PCR), reverse transcription PCR (RT-PCR), quantification 27. The method of claim 25 or 26, wherein the method is performed using a method selected from the group consisting of dynamic real-time PCR (qRT-PCR), nanoarrays, microarrays, macroarrays, autoradiography, and in situ hybridization.   28. The method of any one of claims 25-27, wherein measuring the expression of one or more biomarker (s) in step (b) is determined using a DNA microarray.   Measuring the expression of one or more biomarker (s) in step (b) is selective for nucleic acid molecules each encoding one of the biomarkers identified in Table 1. 29. The method of any one of claims 1-28, wherein the method is performed using one or more binding moieties capable of binding to.   30. The method of claim 29, wherein the one or more binding moieties each comprise or consist of a nucleic acid molecule.   31. The one or more binding moieties each comprise DNA, RNA, PNA, LNA, GNA, TNA or PMO, or consist of DNA, RNA, PNA, LNA, GNA, TNA or PMO. the method of.   32. A method according to claim 30 or 31, wherein the one or more binding moieties each comprise or consist of DNA.   33. A method according to any one of claims 30 to 32, wherein the one or more binding moieties are 5 to 100 nucleotides in length.   34. A method according to any one of claims 30 to 33, wherein the one or more nucleic acid molecules are 15 to 35 nucleotides in length.   35. A method according to any one of claims 30 to 34, wherein the binding moiety comprises a detectable moiety.   24. The method of any one of claims 1-23, wherein step (b) comprises measuring the expression of one or more biomarker (s) protein or polypeptide.   Measuring the expression of one or more biomarker (s) in step (b) may selectively bind to one of the biomarkers each identified in Table 1. 38. The method of claim 36, wherein the method is performed using one or more possible binding moieties.   38. The method of claim 37, wherein the one or more binding moieties comprise or consist of an antibody or antigen-binding fragment thereof.   40. The method of claim 38, wherein the antibody or fragment thereof is a monoclonal antibody or fragment thereof. The antibody or antigen-binding fragment may be an intact antibody, an Fv fragment (eg, a single chain Fv and a disulfide-linked Fv), a Fab-like fragment (eg, a Fab fragment, a Fab ′ fragment and a F (ab) ₂ fragment), a single 39. or 38 selected from the group consisting of variable domains (eg, _VH and _VL domains) and domain antibodies (including dAbs, single format and dual format [ie dAb-linker-dAb]) 40. The method according to 39.   41. The method of claim 40, wherein the antibody or antigen-binding fragment is a single chain Fv (scFv).   42. The method of claim 41, wherein the one or more binding moieties comprise or consist of an antibody-like binding agent such as an affibody or aptamer.   43. The method of any one of claims 37 to 42, wherein the one or more binding moieties comprise a detectable moiety.   44. The method of claim 35 or 43, wherein the detectable moiety is selected from the group consisting of a fluorescent moiety, a luminescent moiety, a chemiluminescent moiety, a radioactive moiety and an enzyme moiety.   45. The method of claim 44, wherein the detectable moiety comprises or consists of a radioactive atom.   The radioactive atoms are technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulfur-35, deuterium. 46. The method of claim 45, selected from the group consisting of: tritium, rhenium-186, rhenium-188, and yttrium-90.   46. The method of claim 45, wherein the detectable moiety of the binding moiety is a fluorescent moiety.   Prior to step (b) and / or step (d), respectively, so that the sample prepared in step (a) and / or step (c) is labeled with biotin in any biomarkers present in the sample. 48. The method of any one of claims 1-47, wherein step (b) and / or step (d) is performed using a detection agent comprising a fluorescence detectable moiety and streptavidin. Method.   The predicative accuracy of the method determined by the ROC AUC value is at least 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 49. The method of any one of claims 1-48, wherein the method is at least 0.50, such as 0.95, 0.96, 0.97, 0.98 or at least 0.99.   50. The method of claim 49, wherein the assertion accuracy of the method determined by the ROC AUC value is at least 0.80.   51. A method according to any one of claims 1 to 50, wherein step (b) is performed using an array.   52. The method of claim 51, wherein the array is a bead-based array.   52. The method of claim 51, wherein the array is a surface based array.   54. The method according to any one of claims 51 to 53, wherein the array is selected from the group consisting of a macroarray; a microarray; a nanoarray.   55. For use in a method according to any one of claims 1 to 54, comprising one or more first binders according to any one of claims 29 to 35 and 37 to 48. ,array.   56. The array of claim 55, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1A.   57. An array according to claim 55 or 56, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1B.   58. The array of any one of claims 55 to 57, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1C.   59. The array of any one of claims 55 to 58, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1D. 60. The array of any one of claims 55 to 59, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1E.   61. The array of any one of claims 55-60, comprising a binding agent capable of collectively binding to all biomarkers defined in Table 1.   62. The array according to any one of claims 55 to 61, wherein the first binder is immobilized.   Use of one or more biomarkers selected from the group defined in Table 1A, Table 1B and / or Table 1C for determining breast cancer-related disease states.   64. Use according to claim 63, wherein all biomarkers defined in Table 1A, Table 1B, Table 1C, Table 1D and Table 1E are used collectively to determine breast cancer-related disease states. C) Array according to any one of claims 55 to 62; and D) Instructions (optional) for performing the method according to any one of claims 1 to 54.
55. An analytical kit for use in the method of any one of claims 1 to 54, comprising:   66. The analysis kit of claim 65, further comprising one or more control samples.   A method or use substantially as herein described.   An array or kit substantially as described herein.