METHODS FOR DETERMINING A BREAST CANCER-ASSOCIATED DISEASE STATE AND ARRAYS FOR USE IN THE METHODS

Abstract

The present invention provides a method for determining a breast cancer-associated disease state comprising the steps of: a) providing a sample to be tested; and b) determining a biomarker signature of the test sample by measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1; wherein the presence and/or amount in the test sample of the one or more biomarker selected from the group defined in Table 1 is indicative of the breast cancer-associated disease state. The invention further provides arrays and kits fir use in the same.

Claims

1. A method for determining a breast cancer-associated disease state comprising the steps of: a) providing a sample to be tested; and b) determining a biomarker signature of the test sample by measuring the presence and/or amount in the test sample of one or more biomarker selected from the group defined in Table 1A, Table 1B and/or Table 1C; wherein the presence and/or amount in the test sample of the one or more biomarker selected from the group defined in Table 1A, Table 1B and/or Table 1C is indicative of the breast cancer-associated disease state.

2. The method according to claim 1 wherein the breast cancer associated disease state is the histological grade and/or the metastasis-free survival time.

3. The method according to claim 1 wherein the breast cancer-associated disease state is the histological grade of breast cancer cells.

4. The method according to claim 3 wherein the method further comprises the steps of: c) providing one or more control sample comprising or consisting of histological grade 1 breast cancer cells, histological grade 2 breast cancer cells and/or histological grade 3 breast cancer cells; and d) determining a biomarker signature of the control sample(s) by measuring the presence and/or amount in the control sample(s) of the one or more biomarker measured in step (b); wherein the presence of breast cancer cells is identified in the event that the presence and/or amount in the test sample of the one or more biomarker measured in step (b): i) corresponds to the presence and/or amount in a control sample comprising or consisting breast cancer cells of a first histological grade (where present); ii) is different to the presence and/or amount in a control sample comprising or consisting breast cancer cells of a second histological grade (where present); and/or iii) is different to the presence and/or amount in a control sample comprising or consisting breast cancer cells of a third histological grade (where present).

5. The method according to claim 4 wherein each control sample comprises or consists of a single histological grade of breast cancer cells.

6. The method according to claim 4 wherein step (c) comprises or consists of: i) providing one or more control sample comprising or consisting of histological grade 1 breast cancer cells; providing one or more control sample comprising or consisting of histological grade 2 breast cancer cells; and providing one or more control sample comprising or consisting of histological grade 3 breast cancer cells; ii) providing one or more control sample comprising or consisting of histological grade 1 breast cancer cells; and providing one or more control sample comprising or consisting of histological grade 2 breast cancer cells; iii) providing one or more control sample comprising or consisting of histological grade 1 breast cancer cells; and providing one or more control sample comprising or consisting of histological grade 3 breast cancer cells; iv) providing one or more control sample comprising or consisting of histological grade 2 breast cancer cells; and providing one or more control sample comprising or consisting of histological grade 3 breast cancer cells; v) providing one or more control sample comprising or consisting of histological grade 1 breast cancer cells; vi) providing one or more control sample comprising or consisting of histological grade 2 breast cancer cells; or vii) providing one or more control sample comprising or consisting of histological grade 3 breast cancer cells.

7. The method according to claim 1 wherein the breast cancer-associated disease state is the metastasis-free survival time of an individual.

8. The method according to claim 7 wherein the method further comprises the steps of: c) providing one or more first control sample comprising or consisting of breast cancer cells from an individual with less than 10 years metastasis-free survival; and/or one or more second control sample comprising or consisting of breast cancer cells from an individual with 10 or more years metastasis free survival; and d) determining a biomarker signature of the control sample(s) by measuring the presence and/or amount in the control sample(s) of the one or more biomarker measured in step (b); wherein the metastasis-free survival time of an individual is identified as less than 10 years in the event that the presence and/or amount of the one or more biomarker measured in step (b) corresponds to the presence and/or amount of the first control sample (where present) and/or is different to the presence and/or amount of the second control sample (where present); and wherein the metastasis-free survival time of an individual is identified as more than 10 years in the event that the presence and/or amount of the one or more biomarker measured in step (b) is different to the presence and/or amount of the first control sample (where present) and/or corresponds to the presence and/or amount of the second control sample (where present).

9. The method according to claim 8 wherein the one or more first and/or second control sample is of the same histological grade as the sample to be tested.

10. A method according to claim 3 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of: one or more biomarkers selected from the group defined in Table 1A, for example at least 2, biomarkers selected from the group defined in Table 1A; one or more biomarkers selected from the group defined in Table 1B, for example at least 2, 3, 4, 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 at least 30 biomarkers selected from the group defined in Table 1B; one or more biomarkers selected from the group defined in Table 1C, for example at least 2, 3, 4, 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 biomarkers selected from the group defined in Table 1C; one or more biomarkers selected from the group defined in Table 1D, for example at least 2, 3, 4, 5, 6, 7, 8, 9 or at least 10 biomarkers selected from the group defined in Table 1D; and/or one or more biomarkers selected from the group defined in Table 1E, for example at least 2, 3, 4, 5, 6, 7, 8 or at least 9 biomarkers selected from the group defined in Table 1E.

11-14. (canceled)

15. A method according to claim 3 wherein step (b) comprises or consists of measuring the presence and/or amount in the test sample of all of the biomarkers defined in Table 1.

16-23. (canceled)

24. The method according to claim 1 wherein step (b) comprises measuring the expression of a nucleic acid molecule encoding the one or more biomarker(s).

25-28. (canceled)

29. The method according to claim 1 wherein measuring the expression of the one or more biomarker(s) in step (b) is performed using one or more binding moieties, each capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in Table 1.

30-35. (canceled)

36. The method according to claim 1 wherein step (b) comprises measuring the expression of the protein or polypeptide of the one or more biomarker(s).

37. The method according to claim 36 wherein measuring the expression of the one or more biomarker(s) in step (b) is performed using one or more binding moieties each capable of binding selectively to one of the biomarkers identified in Table 1.

38-48. (canceled)

49. The method according to claim 1 wherein the predicative accuracy of the method, as determined by an ROC AUC value, is at least 0.50, for example 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.

50. (canceled)

51. The method according to claim 1 wherein step (b) is performed using an array.

52-54. (canceled)

55. An array for determining a breast cancer-associated disease state, the array comprising one or more first binding agents as defined in claim 29.

56-62. (canceled)

63. Use of one or more biomarkers selected from the group defined in Table 1A, Table 1B and/or Table 1C for determining a breast cancer-associated disease state.

64. (canceled)

65. An analytical kit for determining a breast cancer-associated disease state comprising: A) an array according to claim 55; and B) instructions for performing the method (optional).

66-68. (canceled)

Description

[0160] Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following figures:

[0161] FIGS. 1A-1F. Peptide and protein statistics. (FIG. 1A) Total number of unique peptide sequences identified per sample (FDR 0.01, using Mascot+X!Tandem). (FIG. 1B) Total number of assembled protein groups identified per sample (FDR 0.01, set at protein level, using Mascot+X!Tandem). (FIG. 10) Number of unique peptides per protein group (FDR 0.01, set at protein level, using Mascot+X!Tandem) resulting in a total protein coverage of 2140 protein groups in the entire study. (Data based on all samples and runs, including replicates, pool runs and samples with missing clinical parameters). (FIG. 1D) Evaluation of quantified peptides (Progenesis LC-MS software, limited to Mascot scored peptides using FDR 0.01) against the PeptideAtlas (version 2011-08 Ens62, human). In addition, for peptides not present in the PeptideAtlas, a second comparison was performed in order to evaluate if the corresponding protein had been reported. In cases of multiple protein accessions, all were assessed. (FIG. 1E) Comparison of peptide length. (FIG. 1F) Observed peptide frequency in PeptideAtlas.

[0162] FIGS. 2A-2E. Reproducibility of the entire GPS setup (i.e. capture+LC-MS/MS) illustrated both for combined data and individual mixtures for a representative sample (sample 7267) and the reference (pool) sample. In order to include (plot) a data point, the protein had to be quantified (normalized abundance >0) in all triplicate runs. Such requirement was used for all data plotted in FIGS. 2A-2E. (FIG. 2A) Illustrated for all data combined (based on 1264 proteins). (FIG. 2B) Illustrated for CIMS-mix 1 (based on 315 proteins) (FIG. 2C) Illustrated for CIMS-mix 2 (based on 661 proteins) (FIG. 2D) Illustrated for CIMS-mix 3 (based on 452 proteins) (FIG. 2E) Illustrated for CIMS-mix 4 (based on 370 proteins).

[0163] FIGS. 3A-3C. Significantly differentially expressed proteins based on histologic grade, estrogen receptor status, and HER2 status. Differentially expressed analytes are shown in heatmaps (redup-regulated, greendown-regulated). (FIG. 3A) PCA-plot and associated heatmap of histologic grade 1, grade 2 and grade 3 samples (data filtered on variance 0.2, p-value <0.01, q-value <0.25). In addition results from a leave-one out cross validation approach with a SVM demonstrated with ROC-area values. (FIG. 3B) PCA-plot and associated heatmap of ER-positive and ER-negative samples (data filtered on variance 0.2, p-value <0.01, q-value <0.32). In addition result from a leave-one out cross validation approach illustrated with a ROC-curve. (FIG. 3C) PCA-plot and associated heatmap of HER2-positive and negative samples. (data filtered on variance 0.2, p-value <0.01, q-value <0.9). Result from a leave-one out cross validation approach illustrated with a ROC-curve.

[0164] FIGS. 4A-4C. Biological relevance of differentially expressed analytes between the three histologic graded tumor types using IPA. (FIG. 4A) The 49 proteins identified as significantly differentially expressed proteins between the three tumor cohorts mapped to their cellular localization. Colored log.sub.2-ratio (median grade 3/median grade 1) where red color illustrates up-regulation and green color illustrates down-regulation. Proteins with known association to tumorigenesis have been indicated. (FIG. 4B) The top reported network found to be associated with DNA replication, recombination, cell cycle, and free radical scavenging. (FIG. 4C) The second reported network found to be associated with gene expression, infectious disease, and cancer.

[0165] FIGS. 5A-5B. Validation of protein expression profiles using an orthogonal method. To this end, mRNA expression profiles based on data from 1411 histological graded tumor samples was used. 42 of 49 differentially expressed proteins among histologic grade 1, 2 and 3 were successfully mapped (using Gene Entrez ID) into the GOBO-database. (FIG. 5A) mRNA expression profiles for proteins found to display a decreased protein expression in histologic grade 3 tumors (median ratio compared to histologic grade 1) whereof 15 (of total 16) analytes could be mapped with the GOBO-tool. In addition, correlation of the 15 genes to different gene set module expression pattern is indicated. Grey dots indicate actual correlation values. (FIG. 5B) mRNA expression profiles for proteins found to display an increased expression in histologic grade 3 tumors (compared to histologic grade 1) whereof 27 (of total 33) could be mapped with the GOBO-tool. In addition, correlation of the 27 genes to different gene set module expression pattern is indicated. Grey dots indicate actual correlation values.

[0166] FIG. 6. kaplan meier analyses of exemplary biomarker signatures of varying lengths and combinations of Table 1 biomarkers.

[0167] FIGS. S1A-S1B. Schematic overview of the workflow used in the study. (FIG. S1A) Tumor sample preparations and (FIG. S1B) peptide capture using CIMS-antibodies, run schedule on the LC-MS/MS and data analysis. The analysis of all eluates derived from one CIMS-binder mix were finalised prior to move on to the next CIMS-binder mix derived eluates. Consequently, all CIMS-binder mix analysis started with an analysis of an eluate from the pooled sample, continuing with half of the individual samples in a random sequence according to histological grade. Halfway through the mix, another pool sample was injected, then the remaining samples and at the end finishing with the third pool sample. After completion, the analysis of the eluates from the next CIMS-binder mix was started. Between binder mix 2 and binder mix 3, two injections of blank beads were run. Blank beads contained no antibodies attached, so only background peptides that bind to the magnetic beads should elute. Data was analyzed in Proteios SE and Progenisis to obtain identification and quantification of peptides and proteins.

[0168] FIGS. S2A-S2B. Identification reproducibility of the entire GPS setup (i.e. capture +LC-MS/MS) illustrated as Venn diagrams. (FIG. S2A) Overlap of peptides (all unique sequences) between replicate capture runs for sample 7267. Statistics for total coverage of sample 7267 (top diagram) and individual mixes (the smaller four Venn diagrams) are shown. Data generated from Proteios SE (i.e. Mascot and X!Tandem scored peptides). (FIG. S2B) Overlap of peptides (all unique sequences) between replicate capture runs for the pool sample. Statistics for both total (top diagram) and individual mixes are shown. Data generated from Proteios SE (i.e. Mascot and X!Tandem scored peptides).

[0169] FIGS. S3A-S3B. Distribution of log 2 MS intensity for quantified proteins. (FIG. S3A) Median normalized abundance (based on 50 samples with clinical records) plotted for 1364 proteins (24 proteins with a median log 2 intensity value of 0 were excluded). Bars are colored according to MS intensity, ranging from light yellow (low MS intensity) to dark red (high MS intensity). (FIG. S3B) The distribution of log 2 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).

[0170] FIG. S4. Individual intensity boxplots for 8 of the differentially expressed proteins between the three histological grades, demonstrating highest expression in histological grade 3 tumors.

[0171] FIG. S5. Individual intensity boxplots for 8 of the differentially expressed proteins between the three histological grades, demonstrating highest expression in histological grade 1 tumors.

[0172] FIGS. S6A-S6D. Extended comparisons between histological grades (FIG. S6A) log 2-fold change between histologic grade 2 (H2) and histologic grade 1 (H1), between histologic grade 3 (H3) and histologic grade 1 (H1), and between histologic grade 3 (H3) and histologic grade 2 (H2). The top 49 illustrated analytes are the protein signature identified as 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 the grades while the third grade is left out. This calculation was done for all three comparisons, and the list of significant analytes was consequently compiled. The matrix color figure was generated using Matrix2png (Pavlidis and Noble, 2003). (FIG. S6B) The ROC AUC values derived from the SVM calculations from a two group comparison. Listed are both ROC AUC values from unfiltered, (entire dataset) as well as filtered data (variance 0.2 and p-value <0.01). (FIG. S6C) Heatmap of histological grade 1 and grade 3 (data filtered on variance 0.2, p-value <0.01, q-value <0.25). Differentially expressed analytes are shown in heatmaps, where red color illustrates up-regulation and green color illustrates down-regulation. (FIG. S6D) PCA-plot of histological grade 1, grade 2 and grade 3 using the 50 differentially expressed proteins between grade 1 and grade 3.

[0173] FIGS. S7A-S7B. Individual intensity boxplots exemplified for a subset of proteins identified as differentially expressed in the ER-status comparison or the HER2/neu-status comparison. (FIG. S7A) Differentially expressed analytes between ER-positive and ER-negative tumors. (FIG. S7B) Differentially expressed analytes between HER2-negative and HER2-positive tumors.

[0174] FIGS. S8A-S8B. Evaluation of Ki67-positive (25% cut off) and Ki67-negative staged tumors. Differentially expressed analytes are shown in heatmaps, where red color illustrates up-regulation and green color illustrates down-regulation. (FIG. S8A) PCA-plot of Ki67-positive and Ki67-negative staged tumors. Heatmap of corresponding analytes and samples. (data filtered on variance 0.2, p-value <0.01, q-value <0.27). (FIG. S8B) Result from a leave-one out cross validation approach with a SVM illustrated with a ROC-curve.

[0175] FIGS. S9A-S9B. Transcription factor association network analysis using IPA for the differentially expressed analytes reflecting histological grade or ER-status. Lines connecting molecules indicate molecular relationships and the style of the arrows indicate specific molecular relationships and the directionality of the interaction. (FIG. S9A) The 49 proteins identified in the multi-group histological grad comparison were used as input. Log.sub.2 ratio of the median value for histological grade 3 vs histological grade 1 used in order to color code measured analytes. Red color illustrates up regulation. Green color illustrates down-regulation. (FIG. S9B) The 39 proteins identified in the ER-status comparison were used as input. Log.sub.2 ratio of the median value used in order to colour code measured analytes. Red colour illustrates up regulation and green colour illustrates down-regulation in the ER-negative samples.

[0176] FIGS. S10A-S10J. Individual mRNA expression profiles based on data from 1411 histological graded tumor samples exemplified for a subset of the analytes found to display significant differential protein expression between histologic grades. (FIGS. S10A-S10E) mRNA expression levels for five proteins found to display increased expression in histologic grade 3 tumors. (FIGS. S10E-S10J) mRNA expression levels for five proteins found to display decreased expression in histologic grade 3 tumors.

[0177] FIGS. S11A-S11F. mRNA expression profiles based on data from 1620 ER-status defined breast tumor samples. 32 of the 39 differentially expressed proteins were successfully mapped into the GOBO-database using gene entrez ID. (FIG. S11A) mRNA expression profiles for 10 proteins found to display an increased protein expression in ER-positive tumors. In addition correlation of the 10 genes to different gene set module expression pattern is illustrated. Grey dots indicate actual correlation values. (FIG. S11B) mRNA expression profiles for proteins found to display an decreased expression in ER-positive tumors. In addition correlation of the 22 genes to different gene set module expression pattern can be seen. Grey dots indicate actual correlation values. (FIGS. S11C-S11D) Individual mRNA expression profiles exemplified for two proteins found to display increased expression in ER-positive tumors. (FIGS. S11E-S11F) Individual mRNA expression profiles exemplified for two proteins found to display decreased expression in ER-positive tumors.

[0178] FIGS. S12A-S12C. Individual mRNA expression profiles mapped, using the GOBO-database tool, exemplified for three analytes found to display significant differential protein expression in the HER2/neu comparison. Data based on 1881 available tumor samples. (FIG. S12A) HER2/neu, (FIG. S12B) S100A9 (FIG. S12C) GRB7. In addition to above three analytes a fourth protein (accession P22392) was tested to be mapped. However, due to error message from the GOBO-database tool using either Gene Entrez ID 4831 or 654364 the data is missing.

[0179] FIG. S13. Kaplan-Meier analysis, using DMFS as 10-year endpoint. 42 of the 49 proteins differentiating the histological grades were successfully mapped to the gene expression database using Entrez Gene ID (after converting swissprot ID). The analytes were divided into two groups (based on up- or down-regulation, using a ratio between histological grade 3 and grade 1 samples for the observed protein expression level) resulting in 15 down-regulated analytes and 27 up-regulated analytes. These two groups were then used to assess potential risk of distant metastasis free survival (DMFS) using the gene expression dataset. Kaplan-Meier analysis, using DMFS as 10-year endpoint for histological graded tumors (n=1379) was performed by stratifying the gene expression data into three quantiles. In addition, four individual Kaplan-Meier analysis using DMFS as 10-year endpoint based on single genes (2 down-regulated and two up-regulated) were generated and displayed in a similar manner using the GOBO-tool.

[0180] FIG. 7. Breast cancer tissue samples were selected from the same original cohort of 52 samples, here including 6 grade 1 samples, 9 grade 2 samples and 6 grade 9 samples. The samples were digested (trypsinated) in solution and analysed using Selective Monitoring Reaction (SRM) set-up (an established mass spectrometry based approach). 9 peptides corresponding to 8 proteins from the stated list of biomarkers were targeted and quantified. Samples were run in triplicate. Data was analysed using Anubis Followed by P-value filtering (p<0.01) and q-value filtering (q<0.11). The data shows that the breast cancer tissues samples could be differentiated according to grade using a truncated list of markers.

[0181] FIG. 8. Breast cancer tissue samples were selected from the same original cohort of 52 samples, here including 47 samples (with technical replicates) spread among grade 1, 2 and 3. The samples were digested (trypsinated) in gel and analysed using Selective Monitoring Reaction (SRM) set-up (an established mass spectrometry based approach). 8 peptides corresponding to 4 proteins from the stated list of biomarkers were targeted and quantified. Samples were run in duplicate. Data was analysed using Anubis followed by P-value filtering (p<0.01) and q-value filtering (q<0.009). The data showed that the breast cancer tissues samples could be differentiated according to grade, using a truncated list of markers.

EXAMPLES

[0182] Introduction

[0183] Tumor progression and prognosis in breast cancer patients is difficult to assess using current clinical and laboratory parameters, and no candidate multiplex tissue biomarker signature exist. In an attempt to resolve this clinical unmet need, we applied a recently developed proteomic discovery tool, denoted global proteome survey. Thus, by combining affinity proteomics, based on 9 antibodies only, and label-free LC-MS/MS, we profiled 52 breast cancer tissue samples, representing one of the largest breast cancer tissue proteomic studies, and successfully generated detailed quantified proteomic maps representing 1388 proteins. The results showed that we have deciphered in-depth molecular portraits of histologic graded breast cancer tumors reflecting tumor progression. In more detail, a 49-plex tissue biomarker signature (where p<0.01) and a 79-plex tissue biomarker (where p<0.02) signature discriminating histologic grade 1 to 3 breast cancer tumors with high accuracy were defined. Highly biologically relevant proteins were identified, and the differentially expressed proteins supported the current hypothesis regarding the remodeling of the tumor microenvironment for tumor progression. In addition, using the markers to estimate the risk of distant metastasis free survival was also demonstrated. Furthermore, breast cancer associated biomarker signatures reflecting ER-, HER2-, and Ki67-statues were delineated, respectively. The biomarkers signatures were corroborated using an independent method (mRNA profiling) and patient cohort, respectively. Taken together, these molecular portraits provide improved classification and prognosis of breast cancer.

[0184] Experimental Procedures

[0185] Clinical Samples

[0186] This study was approved by the regional ethics review board in Lund, Sweden. Fifty-two breast cancer patients were recruited from the Department of Oncology (SUS, Lund). Full clinical records were accessible for 50 of the tissue samples. The samples were subdivided based on histologic grade 1 (n=9), grade 2 (n=17), and grade 3 (n=24).

[0187] Preparation of Trypsin-Digested Human Breast Cancer Tissue Samples

[0188] Proteins were extracted from 52 breast cancer tissue pieces and subsequently reduced, alkylated, trypsin digested, and finally stored at 80 C. until further use. In addition, a pooled sample, used as reference sample, was generated by combining 5 l aliquots from all digested samples, and stored at 80 C. until further use. Details on sample preparation are provided in Supplemental Experimental Procedures.

[0189] Production and Coupling of CIMS-scFv Antibodies to Magnetic Beads

[0190] Nine CIMS scFv antibodies (Table S2) directed against six short C-terminal amino acid peptide motifs were produced in E. coli cultures, and purified using Ni.sup.2+-NTA affinity chromatography. Next, the purified antibodies were coupled to magnetic beads. Details on scFv production and coupling are provided in Supplemental Experimental Procedures.

[0191] Label-Free Quantitative GPS Experiments

[0192] Four different pools (denoted CIMS-binder mix 1 to 4) of antibody-conjugated beads were made by mixing equal amounts of two or three different binders (Table S2). The antibody mixes were exposed to a tryptic sample, washed, and finally incubated with acetic acid in order to elute captured peptides. The eluate was then used directly for MS-analysis without any additional clean up. The complete study was run using 26 days of MS-instrumentation time, divided into four blocks of 6.5 days (one CIMS-binder mix/block). All samples were individually analyzed one time per CIMS-binder mix. In addition, triplicate captures of selected samples were performed within each block as back-to-back LC-MS/MS runs. The reference sample was repeatedly analysed over time within and between the 4 blocks (FIG. S1). A total of 238 LC-MS/MS runs were performed and all details on the peptide capture and associated mass spectrometry analysis are provided in Supplemental Experimental Procedures.

[0193] Protein Identification and Quantification

[0194] The generated data was analyzed by two software packages, Proteios SE (Hakkinen et al., 2009) and Progenesis LC-MS (Nonlinear Dynamics, UK). Searches were performed against a forward and a reverse combined database (Homo Sapiens Swiss-Prot, August-2011, resulting in a total of 71324 database entries) with a false discovery rate (FDR) of 0.01 estimated on the basis of the number of identified reverse hits for generating peptide identifications. The Progenesis-LC-MS software (v 4.0) was used for aligning features, identification (Mascot), and generating quantitative values. Details regarding search parameters and data processing are provided in Supplemental Experimental Procedures.

[0195] Statistical and Bioinformatical Analysis

[0196] Qlucore Omics Explorer v (2.2) (Qlucore AB, Lund, Sweden) was used for identifying significantly up- or down-regulated proteins (p<0.01) using a one-way ANOVA. The q-values were generated based on the Benjamini and Hochberg method (Benjamini and Hochberg, 1995). Principal component analysis (PCA) plots and heatmaps were generated in Qlucore. The support vector machine (SVM) is a learning method (Cortes and Vapnik, 1995) that was used to classify the samples using a leave-one-out cross-validation procedure and the analyses were performed on both unfiltered and p-value filtered data. A receiver operating characteristics (ROC) curve (Lasko et al., 2005), constructed using the SVM decision values and the area under the curve (AUC), was used as a measurement of the performance of the classifier. Furthermore, the Ingenuity Systems Pathway Analysis (IPA) (v 11904312, www.ingenuity.com) was used for the significantly differentially expressed proteins in order for extracting information, such as protein localisation, potential network interactions, transcription factor associations, and association with tumorigenesis.

[0197] The experimentally derived protein signatures were finally validated at the mRNA level using the GOBO search tool (Ringner et al., 2011) against large cohorts of published gene expression data for breast cancer tissues with clinical parameters such as histologic grades 1, 2 and 3, ER-status or HER2-status.

[0198] Supplementary Experimental Procedures

[0199] Preparation of Trypsin-Digested Human Breast Cancer Tissue Samples

[0200] Protein was extracted from the breast cancer tissue pieces, and stored at 80 C. until use. Briefly, tissue pieces (about 50 mg/sample) were homogenized in Teflon containers, pre-cooled in liquid nitrogen, by fixating the bomb in a shaker for 230 seconds with quick cooling in liquid nitrogen in between the two shaking rounds. The homogenized tissue powder was collected in lysis buffer (2 mg tissue/30 l buffer) containing 8 M urea, 30 mM Tris, 5 mM magnesium acetate and 4% (w/v) CHAPS (pH 8.5). The tubes were briefly vortexed and incubated on ice for 40 min, with brief vortex of the sample every 5 minutes. After incubation, the samples were centrifuged at 13000 rpm, and the supernatant was transferred to new tubes followed by a second centrifugation. The buffer was exchanged to 0.15 M HEPES, 0.5 M Urea (pH 8.0) using Zeba desalting spin columns (Pierce, Rockford, Ill., USA) before the protein concentration was determined using Total Protein Kit, Micro Lowry (Sigma, St. Louis, Mo., USA). Finally, the samples were aliquoted and stored at 80 C. until further use. The protein extracts were thawed, reduced, alkylated and trypsin digested. First, SDS and TCEP-HCl (Thermo Scientific, Rockford, Ill., USA) were added to 0.02% (w/v) and 5 mM, respectively, and the samples were reduced for 60 minutes at 56 C. The samples were cooled down to room temperature before iodoacetamide was added to 10 mM and then alkylated for 30 minutes at room temperature. Next, sequencing-grade modified trypsin (Promega, Madison, Wis., USA) was added at 20 g per mg of protein for 16 hours at 37 C. In order to ensure complete digestion, a second aliquot of trypsin (10 g per mg protein) was added and the tubes were incubated for an additional 3 hours at 37 C. Finally, the digested samples were aliquoted and stored at 80 C. until further use. In addition, a separate pooled sample, generated by combining 5 l aliquots from all digested samples, was prepared and stored at 80 C. until further use. In order to increase the potential tentative proteome coverage, the two samples for which limited clinical data were at hand Table 51, were still analyzed individually as well as included in the pooled sample.

[0201] Production and Coupling of CIMS-scFv Antibodies to Magnetic Beads

[0202] Nine CIMS scFv antibodies (clones 1-B03, 15-A06, 17-008, 17-E02, 31-001-D01, 32-3A-G03, 33-3C-A09, 33-3D-F06 and 34-3A-D10 directed against six short C-terminal amino acid peptide motifs (denoted M-1, M-15, M-31, M-32, M-33, and M-34), were selected from the n-CoDeR (Soderlind et al., 2000) library, and kindly provided by BioInvent International AB, Lund, Sweden (Table S2). The specificity and dissociation constant (low M range) for six of the CIMS antibodies have recently been determined (Olsson et al., 2011). The antibodies were produced in 100 ml E. coli cultures and purified using affinity chromatography on Ni.sup.2+-NTA agarose (Qiagen, Hilden, Germany). Bound molecules were eluted with 250 mM imidazole, dialyzed against PBS (pH 7.4) for 72 hours and then stored at +4 C. until use. The protein concentration was determined by measuring the absorbance at 280 nm. The integrity and purity of the scFv antibodies was confirmed by running Protein 80 chips on Agilent Bioanalyzer (Agilent, Waldbronn, Germany). The purified scFvs were individually coupled to magnetic beads (M-270 carboxylic acid-activated, Invitrogen Dynal, Oslo) as previously described (Olsson et al., 2011). Briefly, batches of 180-250 g purified scFv was covalently coupled (EDC-NHS chemistry) to 9 mg (300 l) of magnetic beads, and stored in 0.005% (v/v) Tween-20 in PBS at 4 C. until further use. In addition was a batch of blank beads generated (i.e. beads generated with the coupling protocol but without adding scFv).

[0203] Label-Free Quantitative GPS Experiments

[0204] Four different pools (denoted CIMS-binder mix 1 to 4) of conjugated beads were made 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-17-008 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 l of the pooled bead solution was used and the scFv-beads were never reused. The beads were prewashed with 350 l PBS prior to being exposed to a tryptic sample digest in a final volume of 35 l (diluted with PBS and addition of phenylmethylsulfonyl fluoride (PMSF) to a final concentration of 1 mM) and then incubated with the beads for 20 min with gentle mixing. Next, the tubes were placed on a magnet, the supernatant removed, and the beads were washed with 100 and 90 l PBS, respectively (the beads were transferred to new tubes in between each washing step and the total washing time was 5 min). Finally, the beads were incubated with 9.5 l of a 5% (v/v) acetic acid solution for 2 min in order to elute captured peptides. The eluate was then used directly for mass spectrometry analysis without any additional clean up.

[0205] An ESI-LTQ-Orbitrap XL mass spectrometer (Thermo Electron, Bremen, Germany) interfaced with an Eksigent nanoLC 2DTM plus HPLC system (Eksigent technologies, Dublin, Calif., USA) was used for all samples. The auto-sampler injected 6 l of the GPS-generated eluates. A blank LC-MS/MS run was used between each analyzed sample. Peptides were loaded with a constant flow rate of 15 l/min onto a pre-column (PepMap 100, C18, 5 m, 5 mm0.3 mm, LC Packings, Amsterdam, Netherlands). The peptides were subsequently separated on a 10 m fused silica emitter, 75 m16 cm (PicoTip Emitter, New Objective, Inc. Woburn, Mass., USA), packed in-house with Reprosil-Pur C18-AQ resin (3 m Dr. Maisch, GmbH, Germany). Peptides were eluted with a 35 minutes linear gradient of 3 to 35% (v/v) acetonitrile in water, containing 0.1% (v/v) formic acid, with a flow rate of 300 nl/min. The LTQ-Orbitrap was operated in data-dependent mode to automatically switch between Orbitrap-MS (from m/z 400 to 2000) and LTQ-MS/MS acquisition. Four MS/MS spectra were acquired in the linear ion trap per each FT-MS scan, which was acquired at 60,000 FWHM nominal resolution settings using the lock mass option (m/z 445.120025) for internal calibration. The dynamic exclusion list was restricted to 500 entries using a repeat count of two with a repeat duration of 20 seconds and with a maximum retention period of 120 seconds. Precursor ion charge state screening was enabled to select for ions with at least two charges and rejecting ions with undetermined charge state. The normalized collision energy was set to 35%, and one micro scan was acquired for each spectrum. All samples were analyzed individually one time per CIMS-binder mix. In addition, a triplicate capture of the pooled sample (based on all samples in the study) was performed for each CIMS-binder mix and distributed for MS-analysis over a longer time period (start, middle and the end of the LC-MS sequence run order per binder mix) (FIG. S1). This was possible for CIMS-binder mix 1 and 4. However, more than halfway in the sequence runs for both CIMS-binder-mix 2 and 3 the analytical LC-column needed to be replaced (twice) and it was decided to run the scheduled last pool runs directly on the new replaced columns resulting in that a few (11 respectively 9 samples) were analyzed after the pool runs. Furthermore, triplicate captures were performed on samples (7267, 8613) for each CIMS-binder mix. Blank beads, i.e. beads without any conjugated antibody, were exposed to the pooled digest, in order to evaluate potential bead background binding peptides. Based on the low number of identified background binding peptides from two blank bead captures, all generated data was left unfiltered unless noted.

[0206] Protein Identification and Quantification

[0207] The generated data was first analyzed using the Proteios SE for generating identifications using both Mascot and X!Tandem. Briefly, all files were processed and converted into mzML and mgf format using the Proteios (v 2.17) platform and the following search parameters were used for Mascot and X!Tandem: enzyme: trypsin; missed cleavages 1; fixed modification: carbamidomethyl (C); variable modification: methionine oxidation (O). In addition, a variable N-acetyl was allowed for searches performed in X!Tandem (www.thegpm.org/tandem/). A peptide mass tolerance of 3 ppm and fragment mass tolerance of 0.5 Da was used and searches were performed against a forward and a reverse combined database (Homo Sapiens Swiss-Prot, August-2011, resulting in a total of 71324 database entries). The automated database searches in both Mascot and X!Tandem and consequently combination (with a false discovery rate (FDR) of 0.01) was used (estimated on the basis of the number of identified reverse hits) for generating peptide identifications. When generating protein identifications for each sample using the Proteios SE, a FDR of 0.01 on the protein level was applied. All raw data is stored within the Proteios SE.

[0208] Since the Proteios SE at the time of analysis offered no quantitative label-free plug-in analyzing modules (development in progress), the Progenesis-LC-MS software (v 4.0) was used for generating all quantitative values. Briefly, the raw data files were converted to mzXML using the ProteoWizard software package prior to using the Progenesis-LC-MS software. The built-in feature finding tool, Mascot search tool and combined fractions tool (CIMS-binder-mix 1, 2, 3 and 4) with default settings and minimal input was used. In order for optimal feature alignment, the first injection run of the pooled sample, for respectively CIMS-binder mix (FIG. S1), was used as reference alignment file, except for CIMS-mix 3 runs, where the halfway pool run was used as the reference alignment file. Features aligned and detected, between retention times 10-50 min for CIMS-binder mix 1 and 2 and between 10-49 min for CIMS-binder mix 3 and 4, were included for quantification. The generated normalized abundance values were extracted and used for statistical and bioinformatics analysis. Due to limitations with the Progenesis software, the identifications was limited to only Mascot searches, meaning that no X!Tandem generated identifications from Proteios SE were included for downstream quantitative analysis. The same database (Homo Sapiens Swiss-Prot, August-2011, a forward and a reverse combined database) and search parameters as mentioned above was used, and a cut-off FDR value of 0.01 was applied.

[0209] Results

[0210] In this study, semi-global protein expression profiles (identification and quantification) of 52 crude breast cancer tissue extracts were deciphered using GPS. Tissue biomarker signatures reflecting histologic grade, as well as other key clinical laboratory parameters, such as estrogen receptor (ER), HER2, and Ki-67 were delineated. An overall workflow outlining the experimental design is shown in Figure S1.

[0211] Protein Coverage, Dynamic Range, and Assay Performance

[0212] Using GPS, a total of 2,140 protein groups were identified (FIG. 1A-C). The identification reproducibility was high, resulting in a 54.7% peptide overlap (Figure S2A). In comparison, the reference sample, which was repeatedly analysed throughout the entire project, showed a 43.9% peptide identification overlap (Figure S2B). Of the identified proteins, a total of 1388 were successfully quantified (Figure S3), and subsequently used in the search for disease-associated 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). Notably, about 38% (833 peptides) of the quantified peptides, corresponding to 61 proteins had not previously been reported in the PeptideAtals (FIG. 1D), indicating on a substantial novel coverage. This was further highlighted by the fact that a significant portion of the detected peptides were shorter, with a median length of 9 versus 11 amino acids (FIG. 1E), than those previously reported.

[0213] The distribution of measured log.sub.2-MS intensity normalized abundances for all quantified proteins was assessed and indicated a dynamic range of 10.sup.6 (Figure S3A). The in-depth coverage generated by the GPS assay was further illustrated by the fact that peptides, ranging from frequently reported in the PeptideAtlas to rarely reported, readily were detected (FIG. 1F). The detected proteins were then grouped by major biological processes, and found to be distributed among several groups (FIG. S3B). Interestingly, proteins grouped with processes, such as translation (e.g. 60S ribosomal protein), were, as might be expected, found to display a higher overall abundance than other proteins involved in e.g. mitosis (e.g. CDK1). Taken together, the data showed the capability of GPS to provide a novel and deep coverage in a reproducible manner.

[0214] Protein Expression Profiles Reflecting Histologic Grade

[0215] First, we examined whether a tissue biomarker signature reflecting histologic grade could be deciphered. Using a multivariate analysis (3 group comparison), 49 significantly (p<0.01, q-value <0.25) differentially expressed proteins were identified between the grade 1, grade 2, and grade 3 cohorts. Based on this signature, PCA-plots showed that histologic grade 1 and grade 3 tumors could be well separated, while histologic grade 2 tumors appeared to be more heterogeneous and were spread among both of the other groups (FIG. 3A). A pattern of both up- and down-regulated analytes with increasing histologic grade could be observed. As 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 (FIG. 3A and FIG. S4). In contrast, analytes such as keratocan (KERA), spondin (SPON1), asporin (ASPB), adipocyte enhancer-binding protein 1 (AEBP1), chymase (CMA1), and olfactomedin-like protein 3 (OLFML3) were among the down-regulated analytes, i.e. displayed higher expression levels in histologic grade 1 tumors (FIG. 3A and FIG. S5).

[0216] We then examined whether the 49 p-value filtered (p<0.01) biomarker list could be used to classify the tissues based on histologic grade. To this end, we ran a leave-one-out cross-validated with SVM and collected the decision values for all samples. The prediction values were then used to construct a ROC curve, and the AUC values were calculated (FIG. 3A). The results showed that the histologic grade tumor subgroups could be well separated (AUC=0.75-0.93), although grade 2 again appeared to be more heterogeneous.

[0217] Next, we investigated the impact of using a two-group comparison instead of a multivariate approach to define differentially expressed markers (FIG. S6). As might be expected, the data showed that the classification of the individual histologic subgroups improved as judged by the AUC-values (AUC=0.91-0.92). Focusing on histologic grade 1 versus grade 3, 50 significantly (p<0.01) differentially expressed analytes were delineated, of which 31 overlapped with the previous 49-biomarker signature (cfs. FIGS. 3 and S6C). When histologic grade 2 was mapped onto the frozen 50 biomarker comparison of grade 1 versus grade 1, it again displayed a heterogeneous feature and was spread among both cohorts (cfs. FIGS. 3A and S6D).

[0218] Impact of ER-Status

[0219] Since 14 of 24 histologic grade 3 tumors were classified as ER-negative and 14 of 17 ER-negative samples were in fact grade 3 tumors, we investigated the direct impact of ER-status on the expression profile. To test this hypothesis, the tumors were re-examined using the ER-positive samples (n=33) only. Adopting a multivariate approach, the results showed that 18 significantly differentially expressed proteins (p<0.01, q-value <0.51) were pin-pointed and histologic grade 1 versus grade 3 tissues could be well classified (AUC-value of 0.9, data not shown). Notably, 16 of 18 analytes (e.g. ASPN, SPON1, KERA, ACLY, APCS and PABPC4) were found to overlap with the originally deciphered 49 biomarker signatures (FIG. 3A). Hence, the data further supported the observation that the 49 biomarker signature reflected histologic grade.

[0220] In addition, we also examined whether an ER-associated tissue biomarker signature could be unraveled. The results showed that ER-positive and ER-negative breast cancer tissues could be well classified (AUC=0.82) (FIG. 3B), and that 39 differentially expressed analytes (p<0.01, q-value <0.32) were identified (e.g. GREB1) FIGS. 3B and 57A). Hence, the data showed that an ER-associated tissue biomarker signature had been detected.

[0221] Protein Expression Profiles Reflecting HER2/Neu-Status and Ki67-Status

[0222] When comparing the 52 breast cancer tissue extracts based on HER2/neu-status using a leave-one-out cross-validation, the data showed that the 2 cohorts could be discriminated (AUC=0.98) and that five 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 to be among the up-regulated proteins (FIG. 3C and FIG. S7B).

[0223] Furthermore, in a similar manner, a tissue protein signature reflecting Ki67-status (where 25% of Ki67-positive cancer nuclei was used as cut-off) could also be deciphered. In total, 45 proteins were found to be differentially expressed (p<0.01, q-value <0.27) (FIG. S8A). The data demonstrated that Ki67-positive versus Ki67-negative tumors could be separated (AUC=0.84) (FIG. S8B). Hence, the results showed that protein expression profiles reflecting both HER2/neu status and Ki67-status had been pin-pointed.

[0224] Biological Relevance

[0225] The biological relevance of the 49 tissue biomarker signature differentiating histologic grade 1 to 3 was then examined. To this end, the cellular localization of each individual protein was mapped using the IPA software (FIG. 4A), and network associated functions and potential relationships were investigated (FIG. 4B-4C). A pattern of mainly down-regulated proteins (extra cellular matric (ECM)) and up-regulated analytes (plasma membrane, cytoplasm, and nucleus) reflecting cellular localization was revealed. More importantly, the top ranked network was found to be associated with DNA replication, recombination, and repair, cell cycle and free radical scavenging, while the second highest ranked network was associated with gene expression, infectious disease and cancer. Noteworthy, several of the proteins within the top 1 network were directly or indirectly associated with NF-kB and VEGF (FIG. 4B). Furthermore, a majority of the ECM proteins were pin-pointed within the second network, and several were directly or indirectly associated with transforming growth factor- (TGF1) (FIG. 4C). Hence, the results showed that biologically highly relevant tissue biomarkers reflecting histologic grade had been identified

[0226] In addition, the relationship between the 49 tissue biomarker signature and transcription factor network was also assessed using IPA (FIG. S9). Of note, Rb and E2F2 were found to be among the top associated transcription regulators (Figure S9A). In comparison, the estrogen receptor 2 (ESR2) and progesterone receptor (PGR) were found to be among the top associated regulators when the tissue biomarker signature differentiating ER-positive versus ER-negative tumors (Figure S9B).

[0227] Validation of Candidate Breast Cancer Progression Signature

[0228] In an attempt to validate the 49 tissue biomarker signature discriminating histologic grade 1 to 3, the data was compared to publicly available orthogonal breast cancer mRNA profiling data set. The validation cohort was composed of 1,881 samples, of which 1,411 with assigned histologic grade, including grade 1 (n=239), grade 2 (n=677), and grade 3 (n=495). Forty-two of 49 tissue biomarkers could be mapped to the gene expression data base using gene entrez ID, and were subsequently used in the validation test.

[0229] The 42 tissue markers were then split into two groups, based on the observed down-(15 analytes) or up-regulated (27 analytes) protein expression profile for grade 3 versus grade 1, and compared to the corresponding mRNA expression profiles (FIG. 5). The protein expression profiles of both down-regulated (e.g. SPON1 and KERA) (FIGS. 5A, S5, S10I, and S11J) and up-regulated proteins (e.g. CDK1 and MCM3) (FIGS. 5B, S4, S10A, and S10B) were found to corroborate well with the mRNA expression levels. Interestingly, the up-regulated markers were found to display mRNA profiles with a high correlation to checkpoint and M-phase gene modules (FIG. 5A), while the group of down-regulated markers displayed mRNA profiles with high correlation to the stroma gene set module (FIG. 5B).

[0230] Validation of ER- and HER2-Associated Tissue Biomarker Signatures

[0231] In a similar manner, attempts were then made to validate the tissue biomarker signatures reflecting ER-status (FIG. 3B), and HER2-status (FIG. 3C), using the same publicly available orthogonal breast cancer mRNA profiling data set as above.

[0232] In case of ER, the validation set was composed of 1,620 samples with assigned ER-status, including 395 ER-negative and 1225 ER-negative samples. Thirty-two of 39 tissue biomarkers could be mapped to the gene expression data base, and were subsequently used in the validation. The 32 markers were then split into two groups (10 up-regulated and 22 down-regulated) based on the observed protein expression profile, and compared to the corresponding mRNA expression profiles (FIG. S11). With a few exceptions (e.g. complement C3 (FIG. S11F and FIG. S7A), the observed protein expression profiles corroborated well with the corresponding mRNA expression profiles (cfs. FIGS. 3B, S7A, and S11). In this context, it was of interest to note that the group of up-regulated proteins in ER-positive tumors were found to display mRNA profiles with high correlation to the steroid response gene module, while the group of down-regulated proteins were found to display mRNA profiles with high correlation to the immune-response and basal gene set modules (FIG. S11A-S11B).

[0233] The validation set for HER2 was composed of 1,881 samples, split into HER2-positive (n=152), basal (n=357), luminal-A (n=483), luminal-B (n=289), normal like (n=257), and unclassified (n=344). Three of 5 tissue markers could be mapped to the validation data set, and was used in the subsequent evaluation (FIG. S12). The results showed that the protein expression profiles and gene expression profiles correlated well (cfs. FIGS. 3C, S7B and S12), further validating the observations.

[0234] Assessing Distant Metastasis Free Survival

[0235] Finally, we examined whether the 49 tissue biomarker signature reflecting histologic grade also could be used to assess the risk of distant metastasis free survival (DMFS) again using the same publicly available gene expression data set. Forty-two of 49 tissue biomarkers could be mapped to 1379 samples with 10-year endpoint survival data. The markers were split in two groups, reflecting down-regulated (n=15)) and up-regulated (n=27) markers in grade 3 versus grade 1, and Kaplan-Meier analysis were then performed with DMFS with a 10-year endpoint by stratifying the gene expression data into three quantiles (low, intermediate, and high) based on the expression levels of these analytes (FIG. S13). The data showed that in particular the cohort of down-regulated analytes (mainly ECM-associated analytes) predicted the risk of DMFS. In fact, this could be accomplished by targeting single down-regulated (e.g. KERA and OLFM3) or up-regulated (e.g. CDK1) biomarkers.

DISCUSSION

[0236] In this study we have deciphered the first in-depth, multiplexed tissue biomarker signature reflecting tumor progression in breast cancer, taking the next step towards personalized medicine in breast cancer. This achievement was accomplished using our recently in-house developed GPS technology (Olsson et al., 2012; Olsson et al., 2011; Wingren et al., 2009). Hence, by combining affinity proteomics, based on 9 antibodies only, and label-free LC-MS/MS, we profiled 52 breast cancer tissue samples, representing one of the largest breast cancer tissue proteomic studies, and successfully generated detailed quantified proteomic maps reflecting 1388 proteins.

[0237] In more detail, the first 49-plex tissue biomarker signature differentiating histologic grade 1 to 3 breast cancer tumors with high specificity and sensitivity was delineated. This list can be extended to 79 differentially expressed markers setting the p-value criteria to p<0.02, but here the discussions focussed towards the top 49 analytes (p<0.01). The molecular profile, or protein fingerprints, supported the current view that grade 1 and grade 3 tumors were more distinct, while grade 2 tumors were more heterogeneous (Sotiriou et al., 2006). When dissecting the signature a priori known markers, known to be associated with breast cancer, as well as novel candidate biomarkers were identified. From a technical point of view, this novel coverage was reflected by the fact that a large portion (38%) of the quantified peptides had not be previously been reported in the PeptideAtlas database (Deutsch et al., 2008). This novel coverage provided by the GPS set-up also became evident when searching for these 49 analytes against the Human Protein Atlas project (Uhlen et al., 2010). Although the Human Protein Atlas project currently covers more than 50% of the non-redundant human proteome, had neither any antibodies nor any histology staining reported for 13 of 49 differentially expressed proteins.

[0238] ER-status alone has been shown to affect the expression of more than 10% of the genes in breast tumors, and is generally thought to have an impact on survival. Since ER-negative breast cancers generally are more aggressive and anti-estrogen based therapy is inefficient, additional targeted therapies are urgently needed (Rochefort et al., 2003). We identified a 39 protein signature capable of differentiating ER-positive and ER-negative tumors with adequate specificity and sensitivity. Noteworthy, 11 of 39 markers have not yet been covered by the Human Protein Atlas project, again outlining the novel coverage provided by the GPS technology (Uhlen et al., 2010). One of the 39 markers, GREB1, has been suggested as a candidate clinical marker for response to endocrine therapy as well as a potential therapeutic target (Hnatyszyn et al., 2010; Rae et al., 2005). GREB1 is an estrogen-regulated gene that mediates estrogen-stimulated cell proliferation and was recently reported to be expressed in ER-positive breast cancer cells and normal breast tissue, but not in ER-negative samples outlining its potential as surrogate marker for ER (Hnatyszyn et al., 2010). The protein profile generated with GPS further supported this notion (Figure S7A).

[0239] Furthermore, a 5 protein signature capable of discriminating the clinically defined HER2-positive and HER2-negative samples was deciphered (FIG. 3C). In fact, the low abundant receptor tyrosine-protein kinase erbB-2 (HER2-protein) was identified, quantified and found to be one of the differentially expressed markers. Hence, the potential of measuring HER2 using GPS in clinical settings could be envisioned as a complement to currently used classical immunohistochemistry or fluorescence in situ hybridization (FISH) based detection systems. A recent study indicated that one in five HER2-based tests might generate incorrect results (Phillips et al., 2009). In addition, S100-A9 and the growth factor receptor-bound protein 7 (GRB7) were also found to display an increased expression in a majority of HER2-positive defined samples (FIG. S7B). High GRB7 expression was recently reported to be associated with high HER2-expression, and used to define a subset of breast cancer patients with decreased survival (Nadler et al., 2010). The S100 gene family encode for low molecular weight calcium-binding proteins, and specific S100 members have been associated with cancer progression, metastasis, and to have a potential as a prediction marker of drug resistance in patients with breast cancer (McKiernan et al., 2011; Yang et al., 2011).

[0240] Most importantly, not only the biomarker signature reflecting histologic grade, but also those reflecting ER-status and HER2-status, were validated using an independent data set and an orthogonal method (mRNA expression levels) using the GOBO-tool (Ringner et al., 2011). Groups of up- and down-regulated proteins were evaluated based on correlation to known gene set modules, since it often is the functional processes captured by a gene signature, and not the individual genes that are important (Wirapati et al., 2008). The significant correlation to the gene-set modules for stroma, checkpoint, and steroid responses were in particular noteworthy (FIG. 5 and FIG. S11). Furthermore, when assessing the DMFS as endpoint, using the histologic derived protein analytes, the data clearly indicated worse clinical outcome, in particular when using the down-regulated ECM proteins. Hence, the independent mRNA validations, added strong support for reported candidate biomarker signatures and their potential in future breast tissue tumor classifications.

[0241] Taken together, we have demonstrated the applicability of our recently developed GPS technology platform for clinical proteomic discovery profiling efforts. Tissue biomarker signatures reflecting histologic grade, i.e. tumor progression, as well as other key clinical laboratory parameters, such as ER-, HER2-, and Ki67-status have been reported in this study; these novel tissue biomarker portraits allow for improved classification and prognosis of breast cancer.

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TABLE-US-00001 TABLE 1 BIOMARKERS FOR DETERMINING A BREAST CANCER-ASSOCIATED DISEASE STATE A) Core biomarkers (H-grade and DMSF) Up- or down regulation in individual comparisons Prot acc. p-Value q-Value F-statistic Name H-grade DMSF H1 vs H2 H1 vs H3 H2 vs H3 DMSF* 1 O60938 3.54E06 0.001483776 16.58840179 KERA yes yes Down H2 Down H3 Down H3 Down H3 2 Q9HCB6 3.27E06 0.001483776 16.72493744 SPON1 yes yes Down H2 Down H3 Down H3 Down H3 B) Preferred biomarkers (H-grade and DMSF) Up- or down regulation in individual comparisons Prot acc. p-Value q-Value F-statistic Name H-grade DMSF H1 vs H2 H1 vs H3 H2 vs H3 DMFS 3 P02743 1.74E06 0.001483776 17.82355118 APCS yes yes Down H2 Down H3 Down H3 Down H3 4 O75348 0.000799949 0.123381185 8.331157684 ATP6V1G1 yes yes Down H2 Up H3 Up H3 Down H3 5 Q71UM5 0.00098264 0.123381185 8.053757668 RPS27L yes yes Up H2 Up H3 Up H3 Up H3 6 Q14195 0.001159707 0.123381185 7.832079411 DPYSL3 yes yes Down H2 Down H3 Down H3 Down H3 7 Q9BS26 0.001177863 0.123381185 7.811373711 ERP44 yes yes Down H2 Up H3 Up H3 Up H3 8 Q13905 0.001910644 0.184744553 7.173429966 RAPGEF1 yes yes Down H2 Up H3 Up H3 Up H3 9 P53396 0.002351787 0.194665471 6.903478146 ACLY yes yes Up H2 Up H3 Up H3 Up H3 10 P23946 0.002436705 0.194665471 6.857621193 CMA1 yes yes Down H2 Down H3 Down H3 Down H3 11 P25205 0.002640618 0.194665471 6.75398016 MCM3 yes yes Up H2 Up H3 Up H3 Up H3 12 Q9UKU9 0.002787572 0.194665471 6.684337139 ANGPTL2 yes yes Down H2 Down H3 Down H3 Down H3 13 Q8IUX7 0.00329872 0.210392612 6.468857288 AEBP1 yes yes Down H2 Down H3 Down H3 Down H3 14 Q15819 0.003347536 0.210392612 6.450129509 UBE2V2 yes yes Up H2 Up H3 Down H3 Up H3 15 Q6P0N0 0.003670423 0.215540903 6.333002567 MIS18BP1 yes yes Up H2 Up H3 Up H3 Up H3 16 Q9UBD9 0.003821762 0.215540903 6.281753063 CLCF1 yes yes Up H2 Up H3 Up H3 Up H3 17 P80404 0.004283228 0.220097415 6.137635708 ABAT yes yes Up H2 Down H3 Down H3 Down H3 18 P05141 0.004800725 0.220097415 5.994134426 SLC25A5 yes yes Up H2 Up H3 Up H3 Up H3 19 P31948 0.005012118 0.220097415 5.940101147 STIP1 yes yes Up H2 Up H3 Up H3 Up H3 20 Q9NRN5 0.00549968 0.220097415 5.824034214 OLFML3 yes yes Down H2 Down H3 Down H3 Down H3 21 P09693 0.006353439 0.220097415 5.644515991 CD3G yes yes Up H2 Up H3 Up H3 Up H3 22 P33993 0.006506666 0.220097415 5.614975929 MCM7 yes yes Up H2 Up H3 Up H3 Up H3 23 Q02978 0.006755395 0.220097415 5.568535328 SLC25A11 yes yes Down H2 Up H3 Up H3 Up H3 24 O00567 0.006943766 0.220097415 5.534535408 NOP56 yes yes Up H2 Up H3 Up H3 Up H3 25 O43159 0.006985712 0.220097415 5.527095318 RRP8 yes yes Up H2 Up H3 Down H3 Up H3 26 Q9NWH9 0.007683607 0.220097415 5.409715176 SLTM yes yes Up H2 Up H3 Up H3 Up H3 27 Q15631 0.007749403 0.220097415 5.399227619 TSN yes yes Up H2 Up H3 Up H3 Up H3 28 Q13011 0.007879382 0.220097415 5.378779411 ECH1 yes yes Down H2 Up H3 Up H3 Up H3 29 P51888 0.008461666 0.229086405 5.291296959 PRELP yes yes Down H2 Down H3 Down H3 Down H3 30 P49591 0.008565681 0.229086405 5.276332378 SARS yes yes Up H2 Up H3 Down H3 Up H3 31 P62851 0.009544854 0.249955868 5.144096375 RPS25 yes yes Up H2 Up H3 Up H3 Up H3 32 Q9BSJ8 0.009871082 0.253223467 5.103161812 ESYT1 yes yes Down H2 Up H3 Up H3 Up H3 C) Preferred biomarkers (H-grade) Up- or down regulation in individual comparisons Prot acc. p-Value q-Value F-statistic Name H-grade DMSF H1 vs H2 H1 vs H3 H2 vs H3 DMSF* 33 Q7Z5L7 0.000518761 0.123381185 8.92324543 PODN yes T.B.D. Up H2 Down H3 Down H3 T.B.D. 34 Q9NQG5 0.00488782 0.220097415 5.971577644 RPRD1B yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 35 Q8NHW5 0.005050767 0.220097415 5.930479527 RPLP0P6 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 36 Q6UXG3 0.005269477 0.220097415 5.877438545 CD300LG yes T.B.D. Down H2 Up H3 Up H3 T.B.D. 37 Q9Y2Z0 0.005865416 0.220097415 5.743804455 SUGT1 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 38 A5A3E0 0.00721476 0.220097415 5.487272739 POTEF yes T.B.D. Up H2 Up H3 Down H3 T.B.D. 39 Q15046 0.010250654 0.257701434 5.057272911 KARS yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 40 O75306 0.010613548 0.261592736 5.015027523 NDUFS2 yes T.B.D. Up H2 Up H3 Down H3 T.B.D. 41 P55795 0.01129597 0.267906306 4.939514637 HNRNPH2 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 42 O43852-2 0.01160955 0.26933089 4.906396389 CALU yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 43 P55884 0.012088996 0.26933089 4.857522011 EIF3B yes T.B.D. Down H2 Up H3 Up H3 T.B.D. 44 Q9BWU0 0.012345209 0.26933089 4.8322258 SLC4A1AP yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 45 P46782 0.01242736 0.26933089 4.824230671 RPS5 yes T.B.D. Up H2 Up H3 Down H3 T.B.D. 46 Q6UX71 0.012772194 0.272112683 4.791261196 PLXDC2 yes T.B.D. Up H2 Down H3 Down H3 T.B.D. 47 Q6UXG2 0.01324416 0.277465145 4.747610569 KIAA1324 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 48 P22897 0.014702935 0.299546134 4.622289181 MRC1 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 49 Q96P16 0.014796831 0.299546134 4.614672184 RPRD1A yes T.B.D. Down H2 Up H3 Up H3 T.B.D. 50 P34897 0.015248769 0.299546134 4.578701496 SHMT2 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 51 P50991 0.015384493 0.299546134 4.568115711 CCT4 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 52 Q53HC9 0.016831151 0.299546134 4.460979462 TSSC1 yes T.B.D. Down H2 Up H3 Up H3 T.B.D. 53 Q9UKT9 0.016953656 0.299546134 4.452352047 IKZF3 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 54 Q7Z7E8 0.017060978 0.299546134 4.444847107 UBE2Q1 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 55 O00233 0.017963635 0.305783473 4.383607388 PSMD9 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 56 P08621 0.018244837 0.305783473 4.365183353 SNRNP70 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 57 P11234 0.018597743 0.307596876 4.342475891 RALB yes T.B.D. Up H2 Up H3 Down H3 T.B.D. 58 Q99798 0.018888468 0.307987276 4.324104309 ACO2 yes T.B.D. Down H2 Up H3 Up H3 T.B.D. 59 Q92614 0.019111382 0.307987276 4.310216427 MYO18A yes T.B.D. Up H2 Up H3 Down H3 T.B.D. 60 P47897 0.019857326 0.308754485 4.264941692 QARS yes T.B.D. Up H2 Up H3 Up H3 T.B.D. D) Optional biomarkers (H-grade and DMFS) Up- or down-regulation in individual comparisons Prot acc. p-Value q-Value F-statistic Name H-grade DMSF H1 vs H2 H1 vs H3 H2 vs H3 DMFS 61 Q13310 0.000177815 0.055878409 10.43467236 PABPC4 yes yes Up H2 Up H3 Down H3 Up H3 62 O95969 0.000669988 0.123381185 8.572206497 SCGB1D2 yes yes Down H2 Down H3 Down H3 Down H3 63 Q01813 0.00080514 0.123381185 8.322399139 PFKP yes yes Up H2 Up H3 Up H3 Up H3 64 P08195 0.001042488 0.123381185 7.974472523 SLC3A2 yes yes Up H2 Up H3 Up H3 Up H3 65 Q9BXN1 0.002542201 0.194665471 6.802918911 ASPN yes yes Down H2 Down H3 Down H3 Down H3 66 P28907 0.003943867 0.215540903 6.241922855 CD38 yes yes Up H2 Up H3 Up H3 Up H3 67 Q9NR99 0.00573962 0.220097415 5.770796299 MXRA5 yes yes Down H2 Down H3 Down H3 Down H3 68 P06493 0.006362452 0.220097415 5.642757893 CDK1 yes yes Up H2 Up H3 Up H3 Up H3 69 O76061 0.006825774 0.220097415 5.555717945 STC2 yes yes Down H2 Down H3 Down H3 Down H3 70 P53634 0.007255011 0.220097415 5.480411053 CTSC yes yes Up H2 Up H3 Down H3 Up H3 E) Optional biomarkers (H-grade) Up- or down regulation in individual comparisons Prot acc. p-Value q-Value F-statistic Name H-grade DMSF H1 vs H2 H1 vs H3 H2 vs H3 DMSF* 71 Q9Y2X3 0.0078144 0.220097415 5.388957977 NOP58 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 72 P00558 0.011192822 0.267906306 4.950618267 PGK1 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 73 Q00688 0.011943688 0.26933089 4.872117996 FKBP3 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 74 P21266 0.016301906 0.299546134 4.499019623 GSTM3 yes T.B.D. Down H2 Down H3 Down H3 T.B.D. 75 Q9NZT1 0.016391 0.299546134 4.492526531 CALML5 yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 76 P29590 0.016807432 0.299546134 4.462657452 PML yes T.B.D. Up H2 Up H3 Up H3 T.B.D. 77 O75173 0.016811191 0.299546134 4.462391376 ADAMTS4 yes T.B.D. Down H2 Down H3 Down H3 T.B.D. 78 P07996 0.017157774 0.299546134 4.438120365 THBS1 yes T.B.D. Down H2 Down H3 Down H3 T.B.D. 79 P02751 0.018241382 0.305783473 4.365407944 FN1 yes T.B.D. Down H2 Down H3 Down H3 T.B.D. *based on median H1/H3 ratio T.B.D. = to be determined

TABLE-US-00002 TABLE 2 RECOMMENDED NAMES OF BIOMARKERS FOR DETERMINING A BREAST CANCER-ASSOCIATED DISEASE STATE Prot acc. Name Recommended name Q9HCB6 SPON1 Spondin-1 O60938 KERA Keratocan P02743 APCS Serum amyloid P-component Q7Z5L7 PODN Podocan O75348 ATP6V1G1 V-type proton ATPase subunit G 1 Q71UM5 RPS27L 40S ribosomal protein S27-like Q14195 DPYSL3 Dihydropyrimidinase-related protein 3 Q9BS26 ERP44 Endoplasmic reticulum resident protein 44 Q13905 RAPGEF1 Rap guanine nucleotide exchange factor 1 P53396 ACLY ATP-citrate synthase P23946 CMA1 Chymase P25205 MCM3 DNA replication licensing factor MCM3 Q9UKU9 ANGPTL2 Angiopoietin-related protein 2 Q8IUX7 AEBP1 Adipocyte enhancer-binding protein 1 Q15819 UBE2V2 Ubiquitin-conjugating enzyme E2 variant 2 Q6P0N0 MIS18BP1 Mis18-binding protein 1 Q9UBD9 CLCF1 Cardiotrophin-like cytokine factor 1 P80404 ABAT 4-aminobutyrate aminotransferase, mitochondrial P05141 SLC25A5 ADP/ATP translocase 2 Q9NQG5 RPRD1B Regulation of nuclear pre-mRNA domain- containing protein 1B P31948 STIP1 Stress-induced-phosphoprotein 1 Q8NHW5 RPLP0P6 60S acidic ribosomal protein P0-like Q6UXG3 CD300LG CMRF35-like molecule 9 Q9NRN5 OLFML3 Olfactomedin-like protein 3 Q9Y2Z0 SUGT1 Suppressor of G2 allele of SKP1 homolog P09693 CD3G T-cell surface glycoprotein CD3 gamma chain P33993 MCM7 DNA replication licensing factor MCM7 Q02978 SLC25A11 Mitochondrial 2-oxoglutarate/malate carrier protein O00567 NOP56 Nucleolar protein 56 O43159 RRP8 Ribosomal RNA-processing protein 8 A5A3E0 POTEF POTE ankyrin domain family member F Q9NWH9 SLTM SAFB-like transcription modulator Q15631 TSN Translin Q13011 ECH1 Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial P51888 PRELP Prolargin P49591 SARS Serine--tRNA ligase, cytoplasmic P62851 RPS25 40S ribosomal protein S25 Q9BSJ8 ESYT1 Extended synaptotagmin-1 Q15046 KARS Lysine--tRNA ligase O75306 NDUFS2 NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitochondrial P55795 HNRNPH2 Heterogeneous nuclear ribonucleoprotein H2 O43852-2 CALU Calumenin P55884 EIF3B Eukaryotic translation initiation factor 3 subunit B Q9BWU0 SLC4A1AP Kanadaptin P46782 RPS5 40S ribosomal protein S5 Q6UX71 PLXDC2 Plexin domain-containing protein 2 Q6UXG2 KIAA1324 UPF0577 protein KIAA1324 P22897 MRC1 Macrophage mannose receptor 1 Q96P16 RPRD1A Regulation of nuclear pre-mRNA domain- containing protein 1A P34897 SHMT2 Serine hydroxymethyltransferase, mitochondrial P50991 CCT4 T-complex protein 1 subunit delta Q53HC9 TSSC1 Protein TSSC1 Q9UKT9 IKZF3 Zinc finger protein Aiolos Q7Z7E8 UBE2Q1 Ubiquitin-conjugating enzyme E2 Q1 O00233 PSMD9 26S proteasome non-ATPase regulatory subunit 9 P08621 SNRNP70 U1 small nuclear ribonucleoprotein 70 kDa P11234 RALB Ras-related protein Ral-B Q99798 ACO2 Aconitate hydratase, mitochondrial Q92614 MYO18A Unconventional myosin-XVIIIa P47897 QARS Glutamine--tRNA ligase Q13310 PABPC4 Polyadenylate-binding protein 4 O95969 SCGB1D2 Secretoglobin family 1D member 2 Q01813 PFKP 6-phosphofructokinase type C P08195 SLC3A2 4F2 cell-surface antigen heavy chain Q9BXN1 ASPN Asporin P28907 CD38 ADP-ribosyl cyclase 1 Q9NR99 MXRA5 Matrix-remodeling-associated protein 5 P06493 CDK1 Cyclin-dependent kinase 1 O76061 STC2 Stanniocalcin-2 P53634 CTSC Dipeptidyl peptidase 1 Q9Y2X3 NOP58 Nucleolar protein 58 P00558 PGK1 Phosphoglycerate kinase 1 Q00688 FKBP3 Peptidyl-prolyl cis-trans isomerase FKBP3 P21266 GSTM3 Glutathione S-transferase Mu 3 Q9NZT1 CALML5 Calmodulin-like protein 5 P29590 PML Protein PML O75173 ADAMTS4 A disintegrin and metalloproteinase with thrombospondin motifs 4 P07996 THBS1 Thrombospondin-1 P02751 FN1 Fibronectin

TABLE-US-00003 TABLE 3 ROC AUC VALUES FOR EXEMPLARY BIOMARKER COMBINATIONS ROC AUC value Biomarker signature H1 vs H3 H2 vs H3 H1 vs H2 Core-1 0.94 0.82 0.69 Core-2 0.82 0.82 0.56 Core-1 + core-2 0.94 0.83 0.50 Core-1 + core-2 + (marker 3-12) 0.90 0.85 0.69 Core-1 + core-2 + (marker 3-22) 0.88 0.76 0.88 Core-1 + core-2 + (marker 3-32) 0.81 0.73 0.94 Core-1 + core-2 + (marker 3-42) 0.81 0.74 0.87 Core-1 + core-2 + (marker 3-52) 0.80 0.77 0.76 Core-1 + core-2 + (marker 3-60) 0.80 0.77 0.76 Core-1 + core-2 + (marker 3-62) 0.93 0.86 0.76 Core-1 + core-2 + (marker 3-72) 0.94 0.83 0.82 Core-1 + core-2 + (marker 3-79) 0.94 0.79 0.86

TABLE-US-00004 TABLE 4 HISTOLOGICAL GRADE SVM SCRIPT filnamn<Input. txt # 1.1 Change FILNAME to datafile ------------------ --- # Lser in och logaritmerar datan rawfile < read.delim(filnamn) samplenames < as.character(rawfile[,1]) Diagnosis < rawfile[,2] Morphology< rawfile[,3] Treatment<rawfile[,4] data < t(rawfile[,c(1:4)]) ProteinNames < read.delim(filnamn,header=FALSE) ProteinNames < as.character(as.matrix(ProteinNames)[1,]) ProteinNames < ProteinNames[(1:4)] rownames(data) < ProteinNames colnames(data) < samplenames logdata < log(data)/log(2) # Tar reda p{dot over (a)} vilka gruppjmfrelser som ska gras PairWiseGroups < as.matrix(read.delim(Comparison_to_do.txt,header=FALSE)) # 1.2 Change filname and use criteria file ----- # Definierar Wilcoxontestet wilcoxtest < function(prot,subset1,subset2){ res < wilcox.test(prot[subset1],prot[subset2]) res$p.value } # Definierar foldchange foldchange < function(prot,subset1,subset2) { 2{circumflex over ()}(mean(prot[subset1]) mean(prot[subset2])) } # Definierar q-vrdesberkningen BenjaminiHochberg < function(pvalues){ # This function takes a vector of p-values as input and outputs # their q-values. No reordering of the values is performed NAindices < is.na(pvalues) Aindices < !NAindices Apvalues < pvalues[Aindices] N < length(Apvalues) orderedindices < order(Apvalues) OrdValues < Apvalues[orderedindices] CorrectedValues < OrdValues * N /(1:N) MinValues < CorrectedValues for (i in 1:N) {MinValues[i] < min(CorrectedValues[i:N])} Aqvalues < numeric(N) Aqvalues[orderedindices] < MinValues Qvalues < pvalues Qvalues[Aindices] < Aqvalues return(Qvalues) } # Laddar in tv{dot over (a)} bibliotek library(MASS) library(gplots) # Definierar farger till heatmapen redgreen < function(n) { c( hsv(h=0/6, v=c( rep( seq(1,0.3,length=5) , c(13,10,8,6,4) ) , 0 ) ) , hsv(h=2/6, v=c( 0 , rep( seq(0.3,1,length=5) , c(3,5,7,9,11) ) ) ) ) } pal < rev(redgreen(100)); #Laddar in fler bibliotek och funktioner library(e1071) source(NaiveBayesian) #Definierar SVM med Leave One Out svmLOOvalues < function(data , fac){ n1 < sum(fac==levels(fac)[1]) n2 < sum(fac==levels(fac)[2]) nsamples < n1+n2 ngenes < nrow(data) SampleInformation < paste(levels(fac)[1], ,n1, , ,levels(fac)[2], ,n2,sep=) res < numeric(nsamples) sign < numeric(nsamples) for (i in 1:nsamples){ svmtrain < svm(t(data[,i]) , fac[i] , kernel=linear ) pred < predict(svmtrain , t(data[,i]) , decision.values=TRUE) res[i] < as.numeric(attributes(pred)$decision.values) facnames < colnames(attributes(pred)$decision.values)[1] if (facnames == paste(levels(fac)[1],/,levels(fac)[2],sep=)) {sign[i] < 1} if (facnames == paste(levels(fac)[2],/,levels(fac)[1],sep=)){sign[i] < 1} } if (length(unique(sign)) >1){print(error)} res < sign * res names < colnames(data , do.NULL=FALSE) orden < order(res , decreasing=TRUE) Samples < data.frame(names[orden],res[orden],fac[orden]) ROCdata < myROC(res,fac) SenSpe < SensitivitySpecificity(res,fac) return(list(SampleInformation=SampleInformation,ROCarea=ROCdata[1],p. value=ROCdata[2],SenSpe < SenSpe, samples=Samples)) } # Definierar hur analysen ska kras om man INTE ANVNDER apriorianalyter Analysera< function (group1 ,group2){ outputfiletxt < paste(group1, versus ,group2,.txt ,sep=) outputfilepdf < paste (group1, versus , group2,.pdf ,sep=) #outputfilejpeg < paste(group1, versus ,group2,.jpg ,sep=) subset1 < is.element (Diagnosis , strsplit (group1,,)[[1]]) subset2 < is.element(Diagnosis , strsplit(group2,,)[[1]]) wilcoxpvalues < apply(logdata , 1 , wilcoxtest , subset1 , subset2) foldchange < apply(logdata , 1 , foldchange , subset1 , subset2) QvaluesAll < BenjaminiHochberg(wilcoxpvalues) HugeTable < cbind(ProteinNames,foldchange,wilcoxpvalues,QvaluesAll) write.table(HugeTable, file=outputfiletxt , quote=FALSE, sep=\t,row.names=FALSE) color < rep(black , length(subset1)) color[subset1] < red color[subset2] < blue pdf(outputfilepdf) #jpeg(outputfilejpeg, quality=100, width=600, height=600) Sam < sammon(dist(t(logdata[,subset1|subset2])) , k=2) plot(Sam$points , type=n , xlab = NA , ylab=NA, main=All proteins ,asp=1) text(Sam$point , labels = colnames(logdata[,subset1|subset2]), col=color[subset1|subset2]) heatmap.2(logdata[,subset1|subset2] , labRow = row.names(logdata) , trace=none , labCol = , ColSideColors= color[subset1|subset2],col=pal , na.color= grey, key=FALSE , symkey =FALSE , tracecol = black , main = , dendrogram= both , scale =row ,cexRow=0.2) svmfac < factor(rep(rest,ncol(logdata)),levels=c(group1,group2,rest)) svmfac[subset1] < group1 svmfac[subset2] < group2 svmResAll < svmLOOvalues(logdata[,subset1|subset2] , factor(as.character(svmfac[subset1|subset2]), levels=c(group1, group2)) ) ROCplot(svmResAll , sensspecnumber=4) # N < length(ProteinNames) # par(mfrow=c(1,2)) # for (k in 1:N){ # boxplot(data[k,subset1],data[k,subset2], names=c(group1, group2), main=c(ProteinNames[k], test)) # } write( , file=outputfiletxt , append=TRUE) write(All proteins , file=outputfiletxt , append=TRUE) write( , file=outputfiletxt , append=TRUE) for (i in 1:5){write.table(svmResAll[[i]], file=outputfiletxt , append=TRUE, sep=\t , quote=FALSE) write( , file=outputfiletxt , append=TRUE) } dev.off( ) } Analysera(X,Y) # 1.3 Select comparisons to do--------------- ------

TABLE-US-00005 Supplemental Table 1 Tumor ER/PgR/ Sample Hist. size HER2/ Nr of ID Grade Age (mm) ki67_gt_25 Lymph_pos Lymph pos. 6616 1 37, 62 22 +/+// yes 1 6617 1 66, 30 20 +/+//na yes 5 7149 1 74, 49 31 +/+// no 0 7454 1 47, 82 22 +/+// yes 5 7940 1 53, 94 30 +/+// no 0 8415 1 66, 61 31 +/+// yes 4 9317 1 47, 42 18 +/+// no 0 9795 1 43, 26 15 +/+// yes 2 10524 1 64, 34 30 +/+// no 0 4404 2 49, 92 25 +/+//+ yes 1 5614 2 45, 48 37 +/+// yes 8 5096 2 37, 35 6 +/+//+ yes 8 5572 2 43, 55 18 /// yes 2 6096 2 36, 92 12 +/+// yes 1 6627 2 43, 63 15 +/+// yes 2 7015 2 46, 77 22 +/+//na yes 1 7267 2 48, 39 22 +/// yes 1 7296 2 46, 38 14 +/+//na yes 4 8173 2 47, 03 25 +/+//na yes 10 9257 2 43, 78 7 +/+//+ no 0 9340 2 52, 10 29 +/+// no 0 5402 2 44, 26 50 /+// yes 5 6514 2 49, 18 30 //na/na no 0 7424 2 47, 98 25 +///+ yes 1 8278 2 47, 54 10 +/+// yes 1 8504 2 49, 66 25 +/+// yes 1 5706 3 41, 19 50 ///+ yes 5 4239 3 40, 66 33 ///+ no 0 5744 3 44, 04 21 +/+/na/ no 0 5811 3 49, 75 45 ///+ yes 1 5997 3 46, 37 20 ///na no 0 6009 3 49, 57 20 +/+// no 0 6029 3 42, 80 25 //na/+ yes 4 6158 3 55, 81 20 +/+/+/+ yes 2 6191 3 45, 04 25 /// no 0 6276 3 52, 30 32 //+/+ yes 6 4723 3 48, 89 40 //+/+ yes 3 5198 3 46, 66 32 /+/+/na yes 4 5203 3 33, 22 30 /// yes 1 5634 3 44, 33 25 +/+//na yes 2 5996 3 50, 94 22 //+/ yes 2 6013 3 41, 60 50 +/+//na yes 3 6176 3 50, 62 35 +/+//+ no 0 6503 3 43, 39 28 ///+ no 0 6877 3 34, 39 27 +/+//+ yes 8 7694 3 47, 66 18 ///+ yes 1 7722 3 46, 61 27 +/+/na/ no 0 8613 3 44, 04 35 +/+// yes 6 9322 3 50, 33 30 ///+ no 0 9460 3 49, 01 17 +/+//+ no 0 5784 na na na na/na/na/na na na 4917 na na na na/na/na/na na na na = not available

TABLE-US-00006 SUPPLEMENTAL TABLE 2 Affinity (K.sub.D) CIMS antibody* Selection peptide (M) Mix CIMS-33-3C-A09 Biotin-SGSGLSADHR 1.6 1 CIMS-33-3D-F06 Biotin-SGSGLSADHR 5.1 1 CIMS-17-C08 Biotin-SGSGSSAYSR 0.2 2 CIMS-17-E02 Biotin-SGSGSSAYSR 0.4 2 CIMS-15-A06 Biotin-SGSGLTEFAK 2.2 3 CIMS-34-3A-D10 Biotin-SGSGSEAHLR 2.5 3 CIMS-1-B03 Biotin-SGSGEDFR 3.5 4 CIMS-31-001-D01 Biotin-SGSGLNVWGK NA 4 CIMS-32-3A-G03 Biotin-SGSGQEASFK 11.5 4 *For details regarding binder characteristics see Olsson et al. (2011) MCP M110.003962.