Abstract

Abstract Imaging in monitoring metastasis in mouse models have low sensitivity and is not quantitative. Cell DNA barcoding, demonstrating high sensitivity and resolution, allows monitoring effects of drugs on the number of tumor and metastatic clones. However, this technology is not suitable for comparison of sizes of metastatic clones in different animals, e.g. drug treated and untreated, due to high biological and technical variability upon tumor and metastatic growth and isolation of barcodes from tissue DNA. Yet, both numbers of clones and their sizes are critical parameters for analysis of drug effects. Here we developed a modification of the barcoding approach for monitoring drug effects on tumors and metastasis that is quantitative, highly sensitive and highly reproducible. This novel cell double barcoding system allows simultaneously following the fate of two or more cell variants or cell lines in xenograph models in vivo, and also following the fates of individual clones within each of these populations. This system allows comparing effects of drugs on different cell populations, and thus normalizing drug effects by drug-resistant lines, which corrects for both biological and technical variabilities and significantly increases the reproducibility of results. Using this barcoding system, we uncovered that effects of a novel DYRK1B kinase inhibitor FX9847 on primary tumors and metastasis is clone-dependent, while a distinct drug osimertinib demonstrated clone-independent effects on cancer cell populations. Overall cell double barcoding approach can significantly enrich our understanding of drug effects in basic research and preclinical studies. Citation Format: Arkadi Hesin, Santosh Kumar, Valid Gahramanov, Maria Becker, Maria Vilenchik, Ilya Alexandrov, Julia Yaglom, Michael Sherman. A cell double barcoding system for quantitative evaluation of primary tumors & metastasis in animals: Uncovers clonal-specific anti-cancer drug effects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6269.

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