Lineage plasticity and tumor heterogeneity limit the effectiveness of targeted therapies, yet the functional dependencies used to nominate therapeutic targets are often derived from homogeneous systems that fail to capture this complexity. Here, we establish a framework to resolve state-specific genetic vulnerabilities by integrating single-cell multiomics (RNA and ATAC) with pooled CRISPR-Cas9 screening across a large panel of patient-derived organoids (PDOs) from castrate-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). We generate a single-cell multiome atlas spanning >190,000 cells across 22 PDOs, defining seven lineage states-including intermediate and plastic populations not resolved by bulk profiling-and demonstrate that these lineage programs robustly classify independent transcriptomic datasets from prostate cancer patient tumors. By systematically coupling this atlas to subtype-resolved CRISPR screens, we construct a functional dependency map linking cell state in heterogeneous 3D human tumor models. We show that intratumoral heterogeneity fundamentally reshapes the interpretation of gene essentiality, whereby gene-level depletion reflects the composite behavior of co-existing subpopulations, and identify a general principle in which resistant "limiting" populations disproportionately determine aggregate fitness effects. This framework reveals both canonical and previously unrecognized lineage-restricted dependencies within highly plastic tumor and NEPC states, including a therapeutically targetable dependency on the aryl hydrocarbon receptor (AHR) in a novel hybrid stem-like/ASCL1 population. Together, these data establish an extensive multi-dimensional prostate cancer resource, identify novel lineage-resolved biology, and provide a generalizable strategy for interpreting functional genomics in heterogeneous human tumors.
CRISPR Gene Editing