Connecting Cancer Genomics to Cancer Biology using Proteomics

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Presentation transcript:

Connecting Cancer Genomics to Cancer Biology using Proteomics Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center

NCI: CPTAC Phase II Clinical Proteomic Tumor Analysis Consortium (CPTAC): 2011-2016 Comprehensive study of genomically characterized (TCGA) cancer biospecimens by bottom-up mass-spectrometry-based proteomics workflows Follows Clinical Proteomics Technology Assessment Consortium (CPTAC Phase I) CPTAC 3.0 just starting…

NCI: CPTAC Phase II

CPTAC-II Data Coordinating Center (DCC) Partnership between ESAC Inc. and Georgetown ICBI & UIS Subha Madhavan (ICBI), Peter McGarvey (ICBI), Nathan Edwards (BMCB) ESAC: Karen Ketchum (PM), Mauricio Oberti, Ratna Thangudu, Shuang Cai, many others… Recently awarded - CPTAC 3.0 DCC Simina Boca (ICBI), Shaojun Tang (ICBI)

CPTAC Data Portal All data is publicly released… …subject to responsible use guidelines Consortium has 15 months to publish first global analysis Data available in the meantime. http://grg.tn/cptac Edwards et al., J. Proteome Res., 2015, 14 (6).

CPTAC Data Portal

Three CPTAC-TCGA Studies: Big (Proteomics) Data Per study: ~ 100 samples, ~ 1 TB of data, ~ 10M-50M spectra Breast, Ovarian, Colorectal Cancer Tumor samples only! Global protein abundance, plus phospho- and glyco- sub-proteomes From TCGA: Exome, RNA-Seq, copy-number Same tumors! Connect genomics to proteomics!

Big Question for CPTAC-II Henry Rodrigez, Office of Cancer Clinical Proteomics Research, NCI (10/12/2016) Despite the investment in TCGA (34 cancer types, 12,000 individuals), “missing biology” Can additional (cancer) biology be elucidated from deep proteomic analysis? YES! New molecular cancer subtypes, new drug targets, new biological insight. CPTAC/TCGA Colorectal cancer study Zhang, et al. , Nature 513, 382 (2014).

Non-synonymous Germline & Somatic Variants 800 single amino-acid variants in 86 tumor samples. Mostly germline mutations, relatively few somatic mutations. Proteins with somatic variants are less abundant. Some variants are germline in some tumors, somatic in others. Sometimes, variant peptides are observed without accompanying RNA-Seq evidence – tumor heterogeneity or sampling depth for low abundance mRNA? Zhang, et al. , Nature 513, 382 (2014).

Protein abundance and mRNA expression are poorly correlated The correlation of a gene’s mRNA expression (RNA-Seq) and protein abundance across samples is not strong. Few genes have strong correlation. Unfortunate, as we have been using mRNA expression as a surrogate for protein abundance for years. mRNA expression is better thought of as the /rate/ of protein production. Zhang, et al. , Nature 513, 382 (2014).

…except for proteins in metabolic pathways? Metabolic pathways are enriched with genes that have strong mRNA-protein correlation, suggesting that the proteins are made on demand when the relevant metabolite is available, and the proteins discarded quickly once they are not needed. Housekeeping genes where a constant amount of protein is required only make proteins when needed to “top-up”, so highly uncorrelated. Zhang, et al. , Nature 513, 382 (2014).

Protein abundance is mostly unaffected by copy-number var. Copy number variation has a much stronger cis and trans effect on mRNA expression. Copy number variation effect on protein abundance is buffered by feedback mechanisms. Gross changes such as at 20q still produce an effect in both mRNA and proteins, especially a trans effect, but affect different genes. Zhang, et al. , Nature 513, 382 (2014).

New colorectal cancer subtypes Proteomic subtypes, defined using consensus clustering, from protein abundance data Note the relative lack of concordance with mRNA based sub-types. Some interesting structure to the proteomics subtypes – lack of TP53 and 18q loss genomic features in subtype B, etc. Notice too, that the “black” TCGA-subtype is isolated in subtypes B and C and partitioned by the proteins that distinguish them. Zhang, et al. , Nature 513, 382 (2014).

CPTAC 3.0 6 new cancer types, 200 tumors / study, matched normals. Proteogenomics is the next big thing: Cancer Moonshot (Biden) APOLLO Program (VA) 7 international collaborations with NCI Should be fun!