Open Access Highly Accessed Research article

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

Manfred Beleut15*, Philip Zimmermann2, Michael Baudis3, Nicole Bruni4, Peter Bühlmann4, Oliver Laule2, Van-Duc Luu1, Wilhelm Gruissem2, Peter Schraml1* and Holger Moch1

Author Affiliations

1 Institute of Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland

2 Department of Biology, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland

3 Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

4 Seminar for Statistics, ETH Zurich, Rämistrasse 101, 8092, Zurich, Switzerland

5 PAREQ Research AG, Wagistrasse 14, 8952, Schlieren, Switzerland

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BMC Cancer 2012, 12:310  doi:10.1186/1471-2407-12-310

Published: 23 July 2012

Additional files

Additional file 1 Table S1:

List of samples used in expression array, 55 of them were also used for SNP experiment.

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Additional file 2 Figure S1:

The strategy to find group-specific expression signatures in RCC. Hierarchical clustering of HG-U133A microarray probe sets representing genes from the Angiogenesis (A), Inflammation (B), Integrin (C), and Wnt (D) “pathways” as annotated by PANTHER, across a set of 146 microarrays from our RCC experiment. For each “pathway”, up to four probe set clusters (red boxes) were selected and combined for subsequent re-clustering. (E) Another PANTHER “pathway” (Apoptosis) and one RCC-relevant “pathway” (HIF). Note the presence of less genes in these matrices compared to A-D and the absence of clear probe set clusters (except for cell lines in “Apoptosis”, indicated by the green bottom line), visually subdividing the matrix.

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Additional file 3 Table S2:

List of clusters and containing genes, picked from separate "pathway clusterings" to be combined into one matrix.

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Additional file 4 Figure S2:

The three RCC gene expression signatures spread genome-wide. Hierarchical clustering of 5 times arbitrarily chosen probe sets (each composed of ca. 660 genes) against group affiliated tumors (individual group-sample is labeled as A_, B_ or C_) (A-E). Note the tumor-group forming coincidence within the 5 independent analyses and the similarity with that shown in Figure 2A.

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Additional file 5 Table S3:

List of top 48 genes with expression values, specific for RCC tumors of group B, relative to A and C.

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Additional file 6 Table S4:

List of top 23 genes with expression value, distinguishing RCC tumors of group A from group C.

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Additional file 7 Figure S3:

The landscape of CNAs in RCC does not correlate with novel molecular subgroups. (A) Regional genomic CNAs in RCC shown as percentage of analyzed cases (genomic gains: yellow, up; losses: blue, down). Top: depiction of the overall CNAs in the 45 study cases; Down: published chromosomal and array CGH RCC data accessible through the Progenetix database (568 cases). Copy number variants (CNVs) were not filtered from the study case data besides application of a 100 kb size limit. Note the similar profiles. (B) Case specific regional copy number imbalances in 36 RCC study cases with regional genomic gain or loss status matched to 811 cytogenetic regions. The genomic profiles are randomly arranged within their subtypes. White areas indicate concurrent gain and loss in this cytoband. Note the appearance of known subtype-specific genomic alterations (3p deletions, 5q gains identifying clear cell RCC – asterisk and arrow/left side; gains of chromosomes 7, 17 and 20 identifying papillary RCC - arrows right side).

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Additional file 8 Table S5:

List of 36 RCC tumors considered on expression- and SNP array, and their affiliation to a specific group according to gene expression array.

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Additional file 9 Table S6:

List of tumor-specific regions (0–5 Mb) and involved genes, identified by SNP experiment.

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Additional file 10 Table S7:

The Test Tissue Microarray to establish antibody combinations for tumor/group affiliations.

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Additional file 11 Figure S4:

Examples of immunostained RCC group-specific markers CD34, DEK and MSH6. ccRCC with CD34-stained vascular microvessels (A, B); ccRCC with strong nuclear DEK (C) and MSH6 (D) positivity.

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Additional file 12 Table S8:

Cox proportional hazard regression analysis for survival.

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