A cell geometry framework to monitor cancer cell state diversity and plasticity and mitigate therapy resistance

Melanoma is known for its heterogeneity and plasticity, yet the origins and extent of its cell state diversity are unclear. It remains uncertain whether melanoma growth, metastasis, and therapy resistance arise from overlapping or distinct subpopulations. By utilizing mouse genetics, lineage tracing, and single-cell spatial transcriptomics, we have mapped the diversity and trajectories of cancer cell states within the melanoma ecosystem. Our findings indicate that distinct pools of melanoma cells support growth, metastasis, and drug resistance, and that these capabilities can be dynamically acquired through specific extrinsic niche signals.

To explore therapeutic strategies that disrupt this reprogramming, comprehensive methods for monitoring intratumor heterogeneity (ITH) and cell state dynamics at the single-cell level are essential. Although single-cell transcriptomics is the standard, it has limitations such as high costs and long analysis times. We discovered that distinct morphological features characterize canonical melanoma transcriptional states, leading to the development of SHAPE, a simple, cost-effective FACS-based method for quantifying morphometric parameters.

Leveraging SHAPE, we analyzed escape trajectories from therapies and identified a drug combination that effectively targets melanoma cells regardless of genetic background, enhancing sensitivity to standard treatments in preclinical models. This approach is also applicable to other tumor types. Overall, these findings create a geometry-based framework for monitoring ITH and propose a universal combination strategy that addresses genetic diversity and non-genetic adaptive resistance.

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