Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale
Abstract
UniPert-G2CP (Li et al., Cell, 2026) bridges genetic and chemical screens from molecular representation to phenotype modeling across five cancer cell lines [1]. Here we extend this architecture to 162 cell lines, 32,039 compounds, and genome-wide output (12,328 genes), and find that the resulting prediction platform reveals a striking performance divide: normal and primary cell lines achieve substantially higher prediction fidelity (mean per-cell-line Pearson Correlation Coefficient (PCC) = 0.480, median 0.502) than cancer lines (0.296, median 0.324; Mann-Whitney p = 3.93e-11), identifying cancer cell line heterogeneity as a primary bottleneck for virtual perturbation screening at scale. To understand the sources of this divide, we analyzed per-cell-line performance, genome-wide directional accuracy, mechanism-clustering (SMD), and compound-protein interaction (CPI) enrichment. Directional accuracy on top-5% effect-size genes reaches 73.8% (genome-wide 60.8%), with pathway-dependent recovery: of four literature-supported perturbation-gene pairs queried across three cell lines, two were recapitulated (dexamethasone-TSC22D3/NFKBIA/FKBP5; bortezomib-BAG3/DNAJB1/HSPA1A), one was absent (CD36 depletion-PPARG/CEBPA in ASC), and one was partially recapitulated (metformin-SLC7A5 in HEPG2). Mechanism-clustering SMD of the learned embedding reached 1.636 (vs. original 1.85; 88.5% retention at 32x cell-line coverage), exceeding the ECFP4 fingerprint baseline (1.613), while…
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