Quantifying local malignant adaptation in tissue-specific evolutionary trajectories by harnessing cancer's repeatability at the genetic level

Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics

Despite advances in single-cell and molecular techniques, it is still unclear how to best quantify phenotypic heterogeneity in cancer cells that evolved beyond normal, known classifications. We present an approach to phenotypically characterize cells based on their activities rather than static classifications. We validated the detectability of specific activities (epithelial-mesenchymal transition, glycolysis) in single cells, using targeted RT-qPCR analyses and in vitro inductions. We analyzed 50 established activity signatures as a basis for phenotypic description in public data and computed cell-cell distances in 28,513 cells from 85 patients and 8 public datasets. Despite not relying on any classification, our measure correlated with standard diversity indices in populations of known structure. We identified bottlenecks as phenotypic diversity reduced upon colorectal cancer initiation. This suggests that focusing on what cancer cells do rather than what they are can quantify phenotypic diversity in universal fashion, to better understand and predict intra-tumor heterogeneity dynamics.

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Cells categorized as having the same identity can perform different activities

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Single-cell expression data can be used to infer the activities cells take part in

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Activity profiles provide a basis to measure phenotypic cell-cell divergence

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Cell activity can quantify intra-tumor heterogeneity more fully than identity

Separating the Local and Malignant Dimensions of Cancer Adaptation

The repeatability observed across cancers arising in the same tissue can help understand the evolutionary process of tumour initiation. We recently developed a framework to quantify the local malignant adaptation of genetic clones in tissue-specific environments. In this Commentary, we argue that such a 1-dimensional model can be improved by separating its 2 components to obtain a dual scale: local adaptation, dictating proliferation rates in the local environment, and malignant adaptation, influencing the likelihood that a clone becomes cancerous and invasive. Such a change could strengthen our understanding of the population dynamics underlying cancer initiation and assess different evolutionary scenarios.