Although mechanisms of acquired resistance of EGFR mutant non-small cell lung cancers to EGFR inhibitors have been identified, little is known about how resistant clones evolve during drug therapy. Here, we observe that acquired resistance caused by the T790M gatekeeper mutation can occur either by selection of pre-existing T790M clones or via genetic evolution of initially T790M-negative drug tolerant cells. The path to resistance impacts the biology of the resistant clone, as those that evolved from drug tolerant cells had a diminished apoptotic response to third generation EGFR inhibitors that target T790M EGFR; treatment with navitoclax, an inhibitor of BCL-XL and BCL-2 restored sensitivity. We corroborated these findings using cultures derived directly from EGFR inhibitor-resistant patient tumors. These findings provide evidence that clinically relevant drug resistant cancer cells can both pre-exist and evolve from drug tolerant cells, and point to therapeutic opportunities to prevent or overcome resistance in the clinic.
Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.
PISCES eases processing of large mRNA-seq experiments by encouraging capture of metadata using simple textual file formats, processing samples on either a single machine or in parallel on a high performance computing cluster (HPC), validating sample identity using genetic fingerprinting, and summarizing all outputs in analysis-ready data matrices. PISCES consists of two modules: 1) compute cluster-aware analysis of individual mRNA-seq libraries including species detection, SNP genotyping, library geometry detection, and quantitation using salmon, and 2) gene-level transcript aggregation, transcriptional and read-based QC, TMM normalization and differential expression analysis of multiple libraries to produce data ready for visualization and further analysis. PISCES is implemented as a python3 package and is bundled with all necessary dependencies to enable reproducible analysis and easy deployment. JSON configuration files are used to build and identify transcriptome indices, and CSV files are used to supply sample metadata and to define comparison groups for differential expression analysis using DEseq2. PISCES builds on many existing open-source tools, and releases of PISCES are available on GitHub or the python package index (PyPI).
Targeted therapies, such as erlotinib and imatinib, lead to dramatic clinical responses, but the emergence of resistance presents a significant challenge. Recent studies have revealed intratumoral heterogeneity as a potential source for the emergence of therapeutic resistance. However, it is still unclear if relapse/resistance is driven predominantly by pre-existing or de novo acquired alterations. To address this question, we developed a high-complexity barcode library, ClonTracer, which contains over 27 million unique DNA barcodes and thus enables the high resolution tracking of cancer cells under drug treatment. Using this library in two clinically relevant resistance models, we demonstrate that the majority of resistant clones pre-exist as rare subpopulations that become selected in response to therapeutic challenge. Furthermore, our data provide direct evidence that both genetic and non-genetic resistance mechanisms pre-exist in cancer cell populations. The ClonTracer barcoding strategy, together with mathematical modeling, enabled us to quantitatively dissect the frequency of drug-resistant subpopulations and evaluate the impact of combination treatments on the clonal complexity of these cancer models. Hence, monitoring of clonal diversity in drug-resistant cell populations by the ClonTracer barcoding strategy described here may provide a valuable tool to optimize therapeutic regimens towards the goal of curative cancer therapies. Citation Format: Hyo-eun C. Bhang, David A. Ruddy, Viveksagar Krishnamurthy Radhakrishna, Rui Zhao, Iris Kao, Daniel Rakiec, Pamela Shaw, Marissa Balak, Justina X. Caushi, Elizabeth Ackley, Nicholas Keen, Michael R. Schlabach, Michael Palmer, William R. Sellers, Franziska Michor, Vesselina G. Cooke, Joshua M. Korn, Frank Stegmeier. High complexity barcoding to study clonal dynamics in response to cancer therapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2847. doi:10.1158/1538-7445.AM2015-2847
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