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Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis

Srikanth Nagalla1, Jeff W Chou2, Mark C Willingham3, Jimmy Ruiz1, James P Vaughn4, Purnima Dubey3, Timothy L Lash56, Stephen J Hamilton-Dutoit7, Jonas Bergh89, Christos Sotiriou10, Michael A Black11 and Lance D Miller4*

Author Affiliations

1 Section of Hematology & Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

2 Department of Biostatistical Sciences, Wake Forest Public Health Sciences, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

3 Department of Pathology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

4 Department of Cancer Biology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

5 Department of Epidemiology & Prevention, Wake Forest University Health Sciences, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

6 Department of Clinical Epidemiology, Aarhus University Hospital, Noerrebrogade 44, DK-8000 Aarhus C, Denmark

7 Institute of Pathology, Aarhus University Hospital, Noerrebrogade 44, DK-8000 Aarhus C, Denmark

8 Department of Oncology-Pathology, Cancer Center Karolinska, Radiumhemmet, Karolinska Institutet and University Hospital, S-171 76, Stockholm, Sweden

9 University of Manchester, Paterson Institute for Cancer Research, Wilmslow Road, Manchester, M20 4BX, UK

10 Université Libre de Bruxelles, Institut Jules Bordet, 121 Boulevard de Waterloo, 1000 Brussels, Belgium

11 Department of Biochemistry, Otago School of Medical Sciences, University of Otago, 710 Cumberland Street, Dunedin, 9016, New Zealand

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Genome Biology 2013, 14:R34  doi:10.1186/gb-2013-14-4-r34

Published: 29 April 2013

Additional files

Additional file 1:

Table S1 - Data table of patient populations comprising the breast cancer microarray database. Reference data for each breast cancer population is provided.

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Additional file 2:

Spreadsheet S1 - Distant metastasis-free survival-associated genes selected from patient groups 977A and 977B. Selected probe sets and their corresponding Cox regression coefficient, hazard ratio, confidence interval and FDR q-value are shown for 977A (1st tab) and 977B (2nd tab).

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Additional file 3:

Figure S1 - Hierarchical clustering of distant metastasis-free survival-associated genes in group 977B. The heatmap (far left) shows the hierarchical clustering of the 3,304 genes (probe sets) associated with distant metastasis-free survival. A zoomed in view of the proliferation and immune gene clusters are shown with gene dendrograms (right). Clustered genes having average correlations of approximately 0.6 are indicated by colored branches. Heatmap coloring: mean gene expression (signal intensity) is colored black, red indicates above-mean expression, green denotes below-mean expression and the degree of color saturation reflects the magnitude of expression relative to the mean.

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Additional file 4:

Figure S2 - The proliferation metagene score is highly correlated with tumor cell proliferation rate. Two hundred and thirty-two primary breast tumors from the Uppsala population [3] were annotated for markers of proliferation including Ki-67 staining levels (by immunohistochemistry, MIB1 monoclonal antibody) and mitotic index. Shown is the correlation between the (A) proliferation metagene and mitotic index and (B) Ki-67 staining. The metagene is depicted in (C), and tumor samples are ordered (in all figures) from left to right in ascending order, according to the proliferation metagene score (average log intensity of the proliferation genes). The Pearson product-moment correlation coefficient (r) and P-value are shown (box insert, A, B).

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Additional file 5:

Table S2 - Ontology analysis and gene components of the immune gene cluster. Table A: Gene Ontology analysis of 161 gene probe sets comprising the large immune gene cluster demarcated in Figure 1. Table B: Probe sets and their corresponding gene names that comprise the immune gene cluster.

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Additional file 6:

Spreadsheet S2 - Table of Affymetrix probe sets and corresponding genes that comprise the proliferation and immune metagenes.

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Additional file 7:

Figure S3 - Concordance among gene clusters derived from patient groups 977A and 977B. (A) Expression patterns of probes comprising the proliferation (P) and immune clusters (IC) were compared between 977A and 977B. All selected probes (n = 210) and tumors (n = 1,954) were hierarchically clustered, then the tumors were partitioned (in cluster order) by patient group. Genes comprising the proliferation and immune clusters are distinguished by color according to the key shown. (B) Proliferation and immune cluster metagene values (ie, averaged log2 signal intensities; see Methods), derived from 977A and 977B, were compared to one another by Pearson correlation. Pearson coefficients (r) are represented by heatmap and described by the color key. r values corresponding to the cognate clusters are shown in white font. Biological titles equated with the immune clusters elsewhere in the manuscript are shown for continuity.

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Additional file 8:

Figure S4 - Breast cancer immune and proliferation gene clusters differentiate specific leukocyte cell types. This figure is derived from Figure 2 of the main text, but includes original experimental annotations for each array sample (as labeled in [26]) and includes the genes of the proliferation metagene cluster. Dendrograms are omitted for space.

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Additional file 9:

Figure S5 - Magnitude of immune metagene expression correlates with abundance of immune cell infiltrate. Histological characterization of immune cell abundance was previously conducted for 35 tumors (22 ER+, 13 ER-) from Guy's Hospital, London [29], for which corresponding tumor material was profiled on expression microarrays and included in our multi-study microarray database [97]. (A) Distributions of mean-centered metagene values (977A) are shown as box and whisker plots for each measure of immune cell abundance (L = low, I = intermediate, H = high). Shaded rectangles define the interquartile ranges. The midline of each rectangle marks the median value. T-bars extending from the interquartile range mark the 5th and 95th percentiles, and outliers are indicated by open circles. P-values for differential distributions were generated by Kruskal-Wallis one-way analysis of variance by ranks (Sigma Plot 11.0). (B-D) Genes representative of the T/NK and B/P metagenes were prospectively analyzed for expression in a panel of 28 ER+ breast tumors using the Panomics QuantiGene Plex 2.0 assay system (Affymetrix; see paper Methods). H&E-stained, FFPE breast tumor samples exhibiting (B) high or (C) low levels of infiltrating immune cells are shown. Red arrows indicate small, darkly staining nuclei of leukocytes; blue arrows mark tumor cell nuclei. (D) Distributions of mean-centered metagene values (based on three representative genes, per metagene) are shown as a function of immune cell abundance (L = low; I/L = intermediate-low; I/H = intermediate-high; H = high). Box and whisker plot parameters and statistical method are the same as for (A).

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Additional file 10:

Figure S6 - The immune metagenes are prognostic of outcome in the aggressive intrinsic subtypes. (A) Intrinsic subtype distributions are shown (colored vertical bars) relative to the proliferation metagene, whereby tumors are ranked by the proliferation metagene from left to right in ascending order. (B) The percentage of each tumor subtype comprising the three proliferation tertiles is shown. (C) Kaplan-Meier plots show the PH HER2-enriched (left), luminal B (middle) and Basal-like (right) populations stratified by the B/P metagene.

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Additional file 11:

Table S3 - Metagene value thresholds defined by tertile cut-points in the training set and subsequently applied to the test set.

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