Multiplexed mosaic tumor models reveal natural phenotypic variations in drug response within and between populations [scRNA-seq]
Heterogeneity between patients poses a significant challenge in cancer drug development. Although success of a drug candidate in the clinic depends on efficacy across large numbers of diverse patients, only a few in vivo preclinical models are used for assaying a preclinical drug candidate due to the lack of scalability of traditional xenograft models. To address this limitation, we developed GENEVA, a scalable platform that enables the measurement of molecular and phenotypic responses to drug perturbations at single-cell resolution, both in 3D and in vivo. GENEVA models the genetic diversity of cancer by combining multiple patient-derived cell lines and cancer cell lines into pooled 3D cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. This platform integrates high-resolution transcriptomics with multiplexed single-cell profiling, providing a unique ability to uncover the interplay between cancer genotypes and drug responses. In this study, we apply GENEVA to investigate KRAS G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition. Additionally, we identify epithelial to mesenchymal transition (EMT) as a prominent resistance mechanism to KRAS G12C inhibition using in vivo GENEVA mice. These findings highlight the utility of GENEVA in identifying novel therapeutic targets and optimizing combination therapies, while underscoring its potential to bridge the gap between preclinical cancer models and patient outcomes by modeling the complexity of tumor heterogeneity in a scalable and robust assay. Overall design: Lung cancer patient-derived organoids from four genetic backgrounds (KRAS G12C, KRAS G12D, EML4-ALK fusion, and EGFR L858R) were pooled and grown in 3D Matrigel culture. These pooled PDOs were exposed to an ARS-1620 dosage series. After treatment, cells were dissociated and processed using 10x Genomics Chromium Single Cell 3' v3.1 chemistry. Libraries were demultiplexed using SNP-based assignment from scRNA-seq reads and hashed sample tags (MULTI-seq). Single-cell transcriptomes were quantified using Cell Ranger and integrated into downstream analyses to assess differential drug responses.