Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Proteogenomic analysis of cetuximab-resistant clonal populations derived from colorectal cancer cells (#938)

SEOJIN YANG 1 , Hyeryeon Jung 1 , Yu Ri Seo 1 , Ye-Lim Park 1 2 , Hwang-Phill Kim 1 2 , Eugene C Yi* 1
  1. Department of Molecular Medicine& Biophamaceutical Science, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
  2. Cancer Research Institute, Seoul National University, Seoul, Korea

Acquired resistance to cetuximab in wild-type KRAS colorectal cancer (CRC) has become a major clinical challenge, while mutation events in KRAS are well recognized as frequent drivers of acquired resistance to cetuximab in CRC. Hence, elucidating accurate molecular mechanisms conferring resistance to cetuximab in wild-type KRAS CRC would provide new treatment options in CRC patients. In-vitro cell model system can mask molecular signatures of individual cells with intracellular heterogeneity, which limits defining distinct molecular mechanisms. Hence, an approach at the relatively-homogeneous cellular level would allow study of specific aspect of resistance mechanisms in detail. Here we established five CRC homogenous clonal populations through single-cell cloning and expansion of cetuximab-resistant human colorectal cancer cells (NCI-H508, mix cell line), which were generated by prolonged incubation with cetuximab. Utilizing Tandem Mass Tag (TMT) isobaric labeling, we performed a quantitative global and phosphoproteome analysis of the five different resistant clonal populations including the parental and the mix cell lines by a serial proteome sample enrichment process. We then integrated proteomic data with previously acquired transcriptomic data and systematically identified functional proteins along with key biological pathways of each resistant clonal population. In this work, we present wild-type KRAS-centric specific biological pathways associated with cetuximab-resistant CRC. The integrative approach combining multiple datasets collected from the homogenous cell populations enables us to accurately capture key biological pathways related to anti-cancer drug resistance.