Pancreatic cancer (PDAC) is a highly aggressive malignancy for which the identification of novel therapies is urgently needed. Here, we establish a human PDAC organoid biobank from 31 genetically distinct lines, covering a representative range of tumor subtypes, and demonstrate that these reflect the molecular and phenotypic heterogeneity of primary PDAC tissue. We use CRISPR-Cas9 genome editing and drug screening to characterize drug-gene interactions with ARID1A and BRCA2. We find that missense- but not frameshift mutations in the PDAC driver gene ARID1A are associated with increased sensitivity to the kinase inhibitors dasatinib (p < 0.0001) and VE-821 (p < 0.0001). We conduct an automated drug-repurposing screen with 1,172 FDA-approved compounds, identifying 26 compounds that effectively kill PDAC organoids, including 19 chemotherapy drugs currently approved for other cancer types. We validate the activity of these compounds in vitro and in vivo. The in vivo validated hits include emetine and ouabain, compounds which are approved for non-cancer indications and which perturb the ability of PDAC organoids to respond to hypoxia. Our study provides proof-of-concept for advancing precision oncology and identifying candidates for drug repurposing via genome editing and drug screening in tumor organoid biobanks.
Understanding molecular mechanisms of response and resistance to anticancer therapies requires prospective patient follow-up and clinical and functional validation of both common and low-frequency mutations. We describe a whole-exome sequencing (WES) precision medicine trial focused on patients with advanced cancer.
Objective
To understand how WES data affect therapeutic decision making in patients with advanced cancer and to identify novel biomarkers of response.
Design, Setting, and Patients
Patients with metastatic and treatment-resistant cancer were prospectively enrolled at a single academic center for paired metastatic tumor and normal tissue WES during a 19-month period (February 2013 through September 2014). A comprehensive computational pipeline was used to detect point mutations, indels, and copy number alterations. Mutations were categorized as category 1, 2, or 3 on the basis of actionability; clinical reports were generated and discussed in precision tumor board. Patients were observed for 7 to 25 months for correlation of molecular information with clinical response.
Main Outcomes and Measures
Feasibility, use of WES for decision making, and identification of novel biomarkers.
Results
A total of 154 tumor-normal pairs from 97 patients with a range of metastatic cancers were sequenced, with a mean coverage of 95X and 16 somatic alterations detected per patient. In total, 16 mutations were category 1 (targeted therapy available), 98 were category 2 (biologically relevant), and 1474 were category 3 (unknown significance). Overall, WES provided informative results in 91 cases (94%), including alterations for which there is an approved drug, there are therapies in clinical or preclinical development, or they are considered drivers and potentially actionable (category 1-2); however, treatment was guided in only 5 patients (5%) on the basis of these recommendations because of access to clinical trials and/or off-label use of drugs. Among unexpected findings, a patient with prostate cancer with exceptional response to treatment was identified who harbored a somatic hemizygous deletion of the DNA repair geneFANCAand putative partial loss of function of the second allele through germline missense variant. Follow-up experiments established that loss of FANCA function was associated with platinum hypersensitivity both in vitro and in patient-derived xenografts, thus providing biologic rationale and functional evidence for his extreme clinical response.
Conclusions and Relevance
The majority of advanced, treatment-resistant tumors across tumor types harbor biologically informative alterations. The establishment of a clinical trial for WES of metastatic tumors with prospective follow-up of patients can help identify candidate predictive biomarkers of response.
Responses to therapy often cannot be exclusively predicted by molecular markers, thus evidencing a critical need to develop tools for better patient selection based on relations between tumor phenotype and genotype. Patient-derived cell models could help to better refine patient stratification procedures and lead to improved clinical management. So far, such ex vivo cell models have been used for addressing basic research questions and in preclinical studies. As they now enter the era of functional precision oncology, it is of utmost importance that they meet quality standards to fully represent the molecular and phenotypical architecture of patients' tumors. Well-characterized ex vivo models are imperative for rare cancer types with high patient heterogeneity and unknown driver mutations. Soft tissue sarcomas account for a very rare, heterogeneous group of malignancies that are challenging from a diagnostic standpoint and difficult to treat in a metastatic setting because of chemotherapy resistance and a lack of targeted treatment options. Functional drug screening in patient-derived cancer cell models is a more recent approach for discovering novel therapeutic candidate drugs. However, because of the rarity and heterogeneity of soft tissue sarcomas, the number of well-established and characterized sarcoma cell models is extremely limited. Within our hospital-based platform we establish high-fidelity patient-derived ex vivo cancer models from solid tumors for enabling functional precision oncology and addressing research questions to overcome this problem. We here present 5 novel, well-characterized, complex-karyotype ex vivo soft tissue sarcosphere models, which are effective tools to study molecular pathogenesis and identify the novel drug sensitivities of these genetically complex diseases. We addressed the quality standards that should be generally considered for the characterization of such ex vivo models. More broadly, we suggest a scalable platform to provide high-fidelity ex vivo models to the scientific community and enable functional precision oncology.
Abstract Single-cell RNA-sequencing is advancing our understanding of synovial pathobiology in inflammatory arthritis. Here, we optimized the protocol for the dissociation of fresh synovial biopsies and created a reference single-cell map of fresh human synovium in inflammatory arthritis. We utilized the published method for dissociating cryopreserved synovium and optimized it for dissociating small fresh synovial biopsies. The optimized protocol enabled the isolation of a good yield of consistently highly viable cells, minimizing the dropout rate of prospectively collected biopsies. Our reference synovium map comprised over 100’000 unsorted single-cell profiles from 25 synovial tissues of patients with inflammatory arthritis. Synovial cells formed 11 lymphoid, 15 myeloid and 16 stromal cell clusters, including IFITM2+ synovial neutrophils. Using this reference map, we successfully annotated published synovial scRNA-seq datasets. Our dataset uncovered endothelial cell diversity and identified SOD2 high SAA1+SAA2+ and SERPINE1+COL5A3+ fibroblast clusters, expressing genes linked to cartilage breakdown (SDC4) and extracellular matrix remodelling (LOXL2, TGFBI, TGFB1), respectively. We broadened the characterization of tissue resident FOLR2+COLEC12 high and LYVE1+SLC40A1+ macrophages, inferring their extracellular matrix sensing and iron recycling activities. Our research brings an efficient synovium dissociation protocol and a reference annotation resource of fresh human synovium, while expanding the knowledge about synovial cell diversity in inflammatory arthritis.
