Abstract
Renal cell carcinoma (RCC) subtypes are characterized by distinct molecular profiles. Using RNA expression profiles from 1,009 RCC samples, we constructed a condition-annotated gene coexpression network (GCN). The RCC GCN contains binary gene coexpression relationships (edges) specific to conditions including RCC subtype and tumor stage. As an application of this resource, we discovered RCC GCN edges and modules that were associated with genetic lesions in known RCC driver genes, including VHL, a common initiating clear cell RCC (ccRCC) genetic lesion, and PBRM1 and BAP1 which are early genetic lesions in the Braided Cancer River Model (BCRM). Since ccRCC tumors with PBRM1 mutations respond to targeted therapy differently than tumors with BAP1 mutations, we focused on ccRCC-specific edges associated with tumors that exhibit alternate mutation profiles: VHL-PBRM1 or VHL-BAP1. We found specific blends molecular functions associated with these two mutation paths. Despite these mutation-associated edges having unique genes, they were enriched for the same immunological functions suggesting a convergent functional role for alternate gene sets consistent with the BCRM. The condition annotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic biomarkers and their relationships to RCC tumors with specific molecular and mutational profiles.
Highlights
In the case of the most common Renal cell carcinoma (RCC) subtype, clear cell RCC (ccRCC), several biomarkers have been discovered with variable prevalence between individual tumors
We constructed a condition-annotated RCC gene coexpression network (GCN) and detected edges that are specific to cancer subtype, tissue type, tumor stage, and unique mutation profile
Knowledge Independent Network Construction (KINC) software allowed us to construct a GCN from diverse kidney cancer samples and identify GCN edges that are specific to only a subset of the input samples
Summary
In the case of the most common RCC subtype, ccRCC, several biomarkers have been discovered with variable prevalence between individual tumors. Other common ccRCC mutations include a histone methyltransferase – SETD2 – and the mTOR kinase which plays key roles in cell growth[9] These biomarkers are clearly relevant to understanding ccRCC biology, but aberrations in these genes are not always consistent between tumors and probably do not fully explain ccRCC tumor progression. Each sample cluster in each pairwise gene comparison is tested for correlation This procedure reduces extrinsic noise due to sample variation, and since the samples are tracked it is possible to test each edge for overrepresentation of an attribute or condition (e.g. sex, tumor subtype, tumor stage). We searched for GCN edges specific to tumors with co-occurring mutations in known genes relevant to ccRCC. We assigned GCN edges to ccRCC tumor subsets that have accumulated specific sets of known driver mutations
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