Abstract 4262: A pan-cancer proteomic analysis of The Cancer Genome Atlas (TCGA) project

Protein levels and function are predicted poorly by genomic and transcriptomic analysis of patient tumors. Direct proteomic study can provide a wealth of information that complements those analysis in The Cancer Genome Atlas (TCGA) projects. We used reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 phospho-proteins. The resultant proteomic data were used for identifying commonalities, differences, emergent pathway properties, and novel network biology within and across tumor lineages. In general, tumor type and subtype were the dominant determinants of protein levels. However, we were able to identify several potentially targetable markers. E.g. luminal breast cancers demonstrated selective elevation of AR, BCL2, FASN, and pACC. SRC was activated in all but the hormone-responsive and bladder cancers, offering another potential therapeutic opportunity. Similarly, HER3 suggested itself as a potential target in renal cancer. EGFR activity, in general, paralleled SRC activity, but in GBM it was associated with NOTCH1 and HER3 activation, suggesting an opportunity for combination therapy. pSRC was highly expressed in a subset of head and neck tumors, suggesting that those may be more sensitive to EGFR targeting strategies. HER2 levels were elevated in a subset of endometrial, bladder, breast, and colorectal cancers. MYC was selectively amplified and expressed in high-grade serous ovarian cancers. We implemented a computational approach, MC, to decrease the effect of tissue-specific protein expression. MC allowed us to identify processes that drive cell behavior across tumor type and made it possible to find new therapeutic opportunities. We found 7 cross-tumor clusters, each driven by different markers and pathways. We found pan-cancer clusters with elevated HER2 and EGFR, elevated hormone signaling pathways, enriched MAPK and PI3K pathway activity, elevated EMT signatures, and cell cycle signatures. We also saw strong links between MYH11 and Rictor and between ETS1 and pPEA15 across tumor types. Those findings can provide useful clues for developing targeted, cross-tumor therapies. Pathway analysis of the data revealed several expected cross-tumor type associations, including pMEK with pERK, beta-catenin with E-cadherin and pPKCdelta with pPKCalpha and pPKCbeta. The findings support the ability of RPPA analysis to yield high-quality information from TCGA samples. A number of other links such as MYH11 with Rictor, cyclinB1 with FOXM1, and pACC with FASN were not expected and warrant further exploration, as does a negative link between p85 and claudin7 in lung squamous. Analysis of key nodes (e.g., CDK1) revealed other unexpected links to a wide range of protein pathways. Overall, those findings demonstrate the power of pan-cancer proteomic analysis, identifying several novel single-tumor and cross-tumor targets and pathways. Citation Format: Rehan Akbani, Kwok-Shing Ng, Henrica M. Werner, Fan Zhang, Zhenlin Ju, Wenbin Liu, Ji-Yeon Yang, Yiling Lu, John N. Weinstein, Gordon B. Mills. A pan-cancer proteomic analysis of The Cancer Genome Atlas (TCGA) project. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4262. doi:10.1158/1538-7445.AM2014-4262