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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. |
