Functional characterization of genetic regulators driving melanoma drug resistance via single-cell high-content CRISPR screening
Drug resistance remains a major challenge in cancer therapy, often arising from genetic and epigenetic alterations that enable tumor cells to evade targeted treatments. While pooled CRISPR screens have identified sets of genes implicated in drug resistance, the mechanisms by which these distinct perturbations converge to drive relapse remain largely unclear. Here, we introduce PerturbFate, a high-throughput combinatorial-indexing platform that simultaneously profiles chromatin accessibility along with nascent, pre-existing, or steady-state transcriptomes at the single-cell level in response to multiplexed genetic perturbations. Applying PerturbFate to a BRAFV600E melanoma model, we generated single-cell multi-omic profiles from 318,032 cells following comprehensive perturbation of 143 candidate genes previously implicated in Vemurafenib (Vem) resistance. We revealed converged cell behavior-concordant melanoma phenotypic states and coordinated epigenetic and gene expression dynamics underlying perturbation-driven cell state transitions. Our integrative analyses identified synergistic transcription factor (TF)-regulated gene modules, represented by AP-1, TEADs, KLF5, RREB1, and SMAD3, that promote an undifferentiated Vemurafenib-resistant melanoma phenotype. Combined TF inhibition effectively mitigated resistance across a broad spectrum of perturbations. We further delineated heterogeneous responses to perturbations in Mediator Complex components, linking sub-module-specific biochemical properties to YAP-mediated melanoma dedifferentiation and drug resistance via distinct mechanisms. These findings underscore the capability of PerturbFate to systematically dissect both convergent and divergent molecular drivers of therapeutic resistance at scale, establishing it as a robust framework for mapping molecular programs to physiological and pathological outcomes.