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A pan-cancer proteomic perspective on The Cancer Genome Atlas.

ABSTRACT: Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

SUBMITTER: Akbani R

PROVIDER: S-EPMC4109726 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Akbani Rehan R Ng Patrick Kwok Shing PK Werner Henrica M J HM Shahmoradgoli Maria M Zhang Fan F Ju Zhenlin Z Liu Wenbin W Yang Ji-Yeon JY Yoshihara Kosuke K Li Jun J Ling Shiyun S Seviour Elena G EG Ram Prahlad T PT Minna John D JD Diao Lixia L Tong Pan P Heymach John V JV Hill Steven M SM Dondelinger Frank F Städler Nicolas N Byers Lauren A LA Meric-Bernstam Funda F Weinstein John N JN Broom Bradley M BM Verhaak Roeland G W RG Liang Han H Mukherjee Sach S Lu Yiling Y Mills Gordon B GB

Nature communications 20140529

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total p ...[more]

PMID: 24871328

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Project description:BackgroundThe Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles. We aim to identify a set of genes whose expression patterns can distinguish diverse tumor types. Those features may serve as biomarkers for tumor diagnosis and drug development.MethodsUsing RNA-seq expression data, we undertook a pan-cancer classification of 9,096 TCGA tumor samples representing 31 tumor types. We randomly assigned 75% of samples into training and 25% into testing, proportionally allocating samples from each tumor type.ResultsWe could correctly classify more than 90% of the test set samples. Accuracies were high for all but three of the 31 tumor types, in particular, for READ (rectum adenocarcinoma) which was largely indistinguishable from COAD (colon adenocarcinoma). We also carried out pan-cancer classification, separately for males and females, on 23 sex non-specific tumor types (those unrelated to reproductive organs). Results from these gender-specific analyses largely recapitulated results when gender was ignored. Remarkably, more than 80% of the 100 most discriminative genes selected from each gender separately overlapped. Genes that were differentially expressed between genders included BNC1, FAT2, FOXA1, and HOXA11. FOXA1 has been shown to play a role for sexual dimorphism in liver cancer. The differentially discriminative genes we identified might be important for the gender differences in tumor incidence and survival.ConclusionsWe were able to identify many sets of 20 genes that could correctly classify more than 90% of the samples from 31 different tumor types using TCGA RNA-seq data. This accuracy is remarkable given the number of the tumor types and the total number of samples involved. We achieved similar results when we analyzed 23 non-sex-specific tumor types separately for males and females. We regard the frequency with which a gene appeared in those sets as measuring its importance for tumor classification. One third of the 50 most frequently appearing genes were pseudogenes; the degree of enrichment may be indicative of their importance in tumor classification. Lastly, we identified a few genes that might play a role in sexual dimorphism in certain cancers.

Dataset Information

A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Publications

A pan-cancer proteomic perspective on The Cancer Genome Atlas.

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