Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C. Our studies identify a synthetic lethal relationship between SMARCB1-deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors.

23 24 48 identify a synthetic lethal relationship that may serve as a therapeutic approach for patients with 49 SMARCB1 deficient cancers. 50 51 52

Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. Despite successes in multiple common cancers, such prediction remains challenging for the majority of rare and understudied tumors given the absence of laboratory model systems in which to discover and/or validate therapeutic hypotheses. Here, we describe our efforts to address this challenge systematically with the ultimate goal of making it possible to learn how to predict ex vivo growth requirements for cancer samples based on technical, clinical and genomic properties of the starting tumor material. Over the last 5 years, we have processed nearly 2,000 tumor biospecimens and created over 375 genomically-confirmed patient-derived cell lines, organoids and neurosphere cultures, with >10% of these representing rare cancers. To make this possible, we have implemented three key workflows including (1) direct-to-patient sample sourcing, (2) a tissue cryopreservation and genomic credentialing system to ensure quality prior to model creation, and (3) a systematic empirical approach to screening rich medias and variations on organoid technologies ex vivo (HYBRID). We have begun performing genome-wide CRISPR viability screens in these cultures as part of our larger activities to generate a systematic laboratory-based functional map of cancer dependencies (a ‘Cancer Dependency Map'). The novel organoid, spheroid and cell line models created as part of this effort are being made publically available to the scientific community. Looking ahead, as the barriers to culturing rare tumors are overcome, we expect that preclinical functional genomics data will be useful for difficult-to-treat tumors without existing molecularly guided standard-of-care regimens. Citation Format: Yuen-Yi Tseng, Mushriq AI-Jazrawe, Rebecca Deasy, Paula Keskula, Grace Johnson, Andrew Hong, Priya Chatterji, Francisca Vasquez, Adam Bass, Barbara Van Hare, David Sandak, Keith Ligon, Jesse Boehm. Cancer Cell Line Factory: A systematic approach to create next-generation cancer model at scale [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3453.

Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. Despite successes in multiple common cancers, such prediction remains challenging for the majority of rare and understudied tumors, given the absence of laboratory model systems in which to discover and/or validate therapeutic hypotheses. Crucially, we lack a comprehensive knowledge of ex vivo growth requirements given the tumor’s molecular and cellular makeup. To address this challenge, we developed a low-input multiplexed sequencing protocol allowing the systematic tracking of changes to tumor cell fraction across hundreds of growth conditions. We coupled this approach with a patient-partnered pipeline for fresh sample sourcing to tackle the challenge of model generation in rare diseases including desmoid tumors, a rare soft-tissue tumor driven by activating beta-catenin mutations. We show that non-malignant cell outgrowth contributes to the failure of long-term model generation in over 70% of cases when a traditional single-media approach is used. By utilizing our systematic media screening strategy, we were able to identify several conditions that preserved the tumor component over at least 3 passages, in triplicate. Notably, there was a sample-to-sample variability in which media conditions preserved tumor composition, supporting our hypothesis that empirical screening of media conditions increases model generation success rate. We also aim to understand the relationship between tumor cell preservation in culture and their molecular makeup. However, while classic tissue markers or copy-number variation can be used to identify the tumor and/or stromal components in common epithelial cancers, no such reference exists for rare sarcomas with relatively quiet genomes. To create a reference of transcriptional patterns for these diseases, we are adapting Seq-Well, a low-cost single-cell RNA sequencing platform, to annotate gene expression with allelic information. In a proof-of-concept, we sequenced 552 cells from an admixed sample and we successfully resolved the genotype of 331 (60%) cells. Identification of differentially expressed genes (DEGs) between genotypes using the single cell data showed agreement with DEGs identified via bulk sequencing methods, demonstrating the feasibility of our approach. Looking ahead, we aim to predict ex vivo growth requirements for rare sarcomas based on technical, clinical, and genomic properties of the starting tumor material. We also aim to utilize our strategy to identify genetic or drug perturbations that specifically give the tumor cells a growth disadvantage, enabling the validation of putative targets in early patient samples. Moreover, we are making all expandable, long-term cell lines generated from our strategy publicly available to the scientific community. Citation Format: Kathryn Cebula, Grace Johnson, Mushriq Al-Jazrawe, Irene Lernman, Barbara Van Hare, Carmen Rios, Moony Tseng, Jesse Boehm. Partnering with patients to create a rare soft tissue sarcoma target discovery platform as a community resource [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 697.