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An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

Andrew E Teschendorff1*, Ahmad Miremadi2, Sarah E Pinder2, Ian O Ellis3 and Carlos Caldas12*

Author Affiliations

1 Breast Cancer Functional Genomics Laboratory, Cancer Research UK Cambridge Research Institute and Department of Oncology, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK

2 Cambridge Breast Unit, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, CB2 0QQ, UK

3 Histopathology, Nottingham City Hospital NHS Trust and Department of Pathology, University of Nottingham, Nottingham NG5 1PB, UK

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Genome Biology 2007, 8:R157  doi:10.1186/gb-2007-8-8-r157

Published: 2 August 2007

Additional files

Additional data file 1:

Columns label the gene, the negative kurtosis of its expression profile over 186 ER- samples, the number of clusters predicted by PAC and Fisher's test P value testing for an association between outcome and the two clusters.

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Additional data file 2:

Columns label the gene, the negative kurtosis of its expression profile over 527 ER+ samples, the number of clusters predicted by PAC and Fisher's test P value testing for an association between outcome and the two clusters.

Format: XLS Size: 29KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional data file 3:

Hierarchical clustering over 186 ER- breast cancers and 813 negative kurtosis profile genes selected using the PAK algorithm, as explained in the text. Five main clusters were identified and characterized in terms of over-expression of genes related to cell cycle (CC), immune response (IR), extracellular matrix (ECM), and steroid hormone response (SR) functions. Red denotes relative over-expression and green relative under-expression.

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Additional data file 4:

(A) Hierarchical clustering dendrogram with the different ER- subtypes as defined by the clustering in Figure 2a. (B) The distribution of lymphocytic infiltration scores (LI) and histologic grade. Color codes: black = high LI and high grade; gray = low LI; blue = intermediate grade; and sky blue = low grade. (C) Expression profiles of validated basal markers from [27] across ER- subtypes. (D) Expression profiles of genes in the ERBB2 amplicon. Color codes: green = relative under-expression; red = relative over-expression.

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Additional data file 5:

Table gives the gene expression centroids over the five identified ER- subclasses. Centroids were defined over the 813 genes with negative kurtosis expression profiles.

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Additional data file 6:

Expression profiles (on a log2 scale) of immune response module genes in the validation ER- cohort UPP. Black indicates good outcome samples and red poor outcome samples. Clusters were inferred using the pam algorithm. Inferred clusters are indicated by different shapes (triangles and diamonds).

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Open Data

Additional data file 7:

Expression profiles (on a log2 scale) of immune response module genes in the validation ER- cohort UPP. Black indicates good outcome samples and red poor outcome samples. Clusters were inferred using the pam algorithm. Inferred clusters are indicated by different shapes (triangles and diamonds).

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Additional data file 8:

Heatmap of gene expression of the 11-gene humoral IR module in the ER+ samples of the (A) UPP and (B) JRH-2 cohorts. Shown are the clusters over-expressing (purple) and underexpressing (yellow) the humoral IR module as predicted by the pam algorithm. Good outcome samples are presented in gray and poor outcome samples in black. Green indicates relative under-expression, and red relative over-expression. (C) Kaplan-Meier survival curves over combined external cohorts (for UPP the end-point was disease-specific survival, and for JRH-2 it was recurrence-free survival), with the number of events and samples in each of the two predicted groups.

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