Abstract B26: Accelerating prediction of tumor vulnerabilities using next-generation cancer models

Abstract

Abstract The development of new cancer therapeutics requires sufficient genetic and phenotypic diversity of cancer models. Current collections of human cancer cell lines are limited and for many rare cancer types, zero models exist that are broadly available. Here, we report results from the pilot phase of the Cancer Cell Line Factory (CCLF) project that aims to overcome this obstacle by systematically creating next-generation in vitro cancer models from adult and pediatric cancer patients' specimens and making these models broadly available. We first developed a workflow of laboratory, genomics and informatics tools that make it possible to systematically compare published ex vivo culture conditions for each individual tumor to enable the scientific community to iterate towards disease-specific culture recipes. Based on sample volume and rarity, 4-100 conditions were applied to each sample and all data was captured in a custom Laboratory Information Management System to enhance subsequent predictions. We developed a $150, 5-day turnaround genomics panel to validate cultures based on genomics. Importantly, we show that tumor genomics can be retained in such patient-derived models and tumor genomics are generally stable across 20 passages. Since the inception of this project, we have processed over 650 patient cancer specimens from 450 patients across 16 tumor types and report the successful generation of over 100 genomically characterized adult and pediatric cancer and normal models. We next hypothesized that novel patient-derived cultures could be used to enhance dependency predictions. To do so, we tested 65 cell lines against the “informer” set of 440 compounds developed by the Broad Cancer Target Discovery and Development (CTD2) Center. We show that generating cell lines and testing their sensitivities within 3 months is feasible and the drug responses are reproducible. Moreover, to strengthen relationships between drug sensitivities and cellular features, we compared results with recently published data on the identical compounds tested against 860 existing cell lines. With this approach, we are able to identify many known drug dependencies in these novel models and exhibit the consistency sensitivities compared to existing cell lines. We are also evaluating drug sensitivity predictors for novel dependencies. Overall, our proof-of-concept framework demonstrates initial feasibility of rapidly generating cancer models and assessing drug sensitivities at scale. Citation Format: Yuen-Yi Tseng, Paula Keskula, Andrew L. Hong, Shubhroz Gill, Jaime H. Cheah, Gregory V. Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Coyin Oh, Anson Peng, Abeer Sayeed, Rebecca Deasy, Peter Ronning, Philip Kantoff, Levi Garraway, Mark A. Rubin, Calvin Kuo, Sidharth Puram, Adi Gazdar, Filemon S. Dela Cruz, Jr., Adam Bass, Jr., Nikhil Wagle, Keith L. Ligon, Katherine Janeway, David Root, Stuart L. Schreiber, Paul A. Clemons, Aly Shamji, William C. Hahn, Todd R. Golub, Jesse S. Boehm. Accelerating prediction of tumor vulnerabilities using next-generation cancer models. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr B26.