Treatment advances for soft-tissue sarcomas have been slow. This is, in part, due to their rarity (accounting for 0.7% of all cancers) and heterogeneity (over 50 different diseases fall under this category). Moreover, preclinical models are scarce, often exhibiting slow growth kinetics, which limits their study by large genetic and pharmacological libraries. Here, we present an update on our efforts to harness the power of patient-partnered research to create a platform for rare cancer drug target discovery as a broadly available community resource. We developed a patient-partnered tissue donation pipeline to enable patients anywhere in the United States to participate and piloted our approach for CTNNB1-driven desmoid tumors. To overcome challenges in tissue heterogeneity during ex vivo culture, we optimized a multiplexed sequencing protocol to quantitatively track changes in tumor cell fraction across hundreds of media formulations. Following this strategy, we were able to verify and expand three cell lines that preserve the CTNNB1 mutations at high purity. To identify potential therapeutics, we completed a 6,750-drug repurposing screen, at 2.5uM in duplicate, in two verified cell line models. After extensive quality control assessments and data integration steps to leverage the power of other large scale drug screens, we selected 263 compounds for follow-up based on potency, selectivity, and association with molecular features associated with desmoid tumors. Approximately 70% of selected compounds were validated by an 8-point, 2-fold dilution, dose-response format with a top concentration of 10uM. Of the confirmed active compounds, 80 showed a strong pattern of selectivity, 20 are FDA approved drugs and 13 investigational compounds show a statistical association with CTNNB1 hotspot mutation status or transcriptomic features associated with desmoid tumors. To prioritize potential therapeutic targets, we tested an efficient CRISPR/Cas9 all-in-one library design. The reduction of the CRISPR/Cas9 library size was achieved via multiple gene- and guide-level strategies, which enables statistically powered gene essentiality interrogation in slow-growing patient-derived models. We tested several plating and infection parameters and developed an optimized pipeline for the rapid introduction of this library into early patient-derived samples. Established cell lines of mesenchymal and non-mesenchymal origin, which have previously been tested by genome-wide libraries, were used to control for library and lineage effects. We are developing a biologist-friendly web portal that will enable the research community to easily interact with models and data produced by this effort. Our study provides evidence that a systematic patient-powered approach can facilitate discovery of therapeutic hypotheses for these understudied diseases. Citation Format: Mushriq Al-Jazrawe, Kathryn Cebula, Elisabeth A. Abeyta, Haley S. Curtis, Jane K. McIninch, Jaime H. Cheah, James Berstler, Lisa Miller, James Neiswender, Lisa Brenan, Mike Burger, Francisca Vazquez, Jesse S. Boehm. Drug repurposing and genetic screening strategies for effective treatment discovery in soft-tissue sarcomas. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5324.
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.
To successfully map cancer dependencies, it is essential to conduct genetic and pharmacological screenings in a diversity of cell models. However, existing model development approaches require long periods of culture time and it is difficult to create long-term models of many cancers, greatly limiting the share of patient samples that can be studied. To enable high-throughput perturbation screens in primary cells without an intermediate model generation step, we are developing a label-free imaging-based platform for early living tissue perturbation. Here, we present our ex vivo system for the preservation and morphological characterization of malignant ascites from patients with gastroesophageal cancer, whose prognosis remains poor and there is an urgent need for rapid evidence-based therapeutic discovery. First, we established a workflow to acquire and perturb cells within 1 day of sample collection. We found that mixing ascites fluid with organoid media improves the preservation of cellular composition and viability of the samples. Next, we hypothesized that label-free microscopy can be a potential alternative for fluorescence-based biomarkers of which signal fades over time in live-cell imaging. To test this, we designed a systematic approach for data generation to assess the reproducibility of measures, and we built a dataset consisting of over 1.0M cells from 14 samples (10 unique patients). For training input, we extracted morphological features of each identified cell by generating several projections from z-stacks of label-free bright-field microscopy images. For training labels, we used fluorescence labels to annotate cell type and viability during the imaging screens. Then, using solely bright-field morphological data as input, we trained models to infer cell identity and viability, we found that the accuracy of predictions was 92% and 82%, respectively. Strong correlations were found between tumor fractions determined by flow cytometry and the prediction of tumor cell fraction from label-free morphology only. Based on single-cell RNA sequencing data, we designed a candidate panel of 28 compounds that are anticipated to exhibit antitumor activity via different mechanisms that are of relevance to our study cohort. We observed that label-free inference of compound activity showed a strong correlation (R2 > 0.8) with fluorescent-based predictions. We are now expanding the scale of our rapid screens by finding the minimal number of necessary z-stacks of bright-field and fluorescent channels maintaining the prediction accuracy of cell identity and viability. By finding the appropriate minimum setting for imaging setup, the throughput of the system can be increased both in the compound and sample number. Our approach couples the timing of the perturbation with the preservation of subcellular heterogeneity, it will serve as a strong foundation for preclinical studies. Citation Format: Mushriq Al-Jazrawe, Csaba Molnar, Elisabeth Abeyta, Steven Blum, Niklas Rindtorff, Kathryn Cebula, Sean Misek, Maria Alimova, William Colgan, Carmen Rios, Moony Tseng, James McFarland, Aviv Regev, Beth Cimini, Anne Carpenter, Adam Bass, Samuel Klempner, Jesse Boehm. Rapid label-free imaging-based evaluation of cancer dependencies in zero-passage primary cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1117.
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