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

Heba Alkhatib, Ariel M. Rubinstein, Swetha Vasudevan, Efrat Flashner-Abramson, Shira Stefansky, Sangita Roy Chowdhury, Solomon Oguche, Tamar Peretz-Yablonsky, Avital Granit, Zvi Granot, Ittai Ben-Porath, Kim Sheva, Jon Feldman, Noa E. Cohen, Amichay Meirovitz*, Nataly Kravchenko-Balasha*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. 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. Methods: In this research, 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. Briefly, this single-cell quantification strategy 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. Results: 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 strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). Conclusions: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Original languageAmerican English
Article number120
JournalGenome Medicine
Volume14
Issue number1
DOIs
StatePublished - 20 Oct 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Cancer resistance
  • Individualized targeted therapy
  • Information-theoretic single-cell analysis
  • Intra-tumor heterogeneity
  • Radiation oncology
  • Triple-negative breast cancer
  • Tumor plasticity

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