Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

This article addresses research on drug resistance, which continues to be a major limiting factor across diverse anti-cancer therapies.

The research was carried out by Prof. Nataly Kravchenko-Balalsha together with her colleagues from the Hebrew University of Jerusalem and The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev.

The results have just been published in the leading research journal Genome Medicine that publishes outstanding research in the application of genetics, genomics and multi-omics to understand, diagnose and treat disease..

Drug resistance and cancer cell plasticity are principal contributors to therapeutic failure. Discovering a strategy with the ability to transform the potential evolution of certain intra-tumor subpopulations within treated/irradiated tumors into a therapeutic advantage, is an unmet need in cancer research and clinical practice.

This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.  An information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach, that computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy.

The study reveals a novel approach to resolving in-depth intra-tumor heterogeneity at the single-cell level, providing an essential step towards the accurate design of targeted drug combinations for evolving tumor resistance. Importantly, this strategy can be universally applied to any cancer type and any treatment strategy.

Read more about this exciting  and promising approach to optimizing cancer therapy in the Genome Medicine  Journal

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