Single-molecule sequencing reveals patterns of preexisting drug resistance that suggest treatment strategies in Philadelphia-positive leukemias

Michael W. Schmitt, Justin R. Pritchard, Scott M. Leighow, Bella I. Aminov, Lan Beppu, Daniel S. Kim, J. Graeme Hodgson, Victor M. Rivera, Lawrence A. Loeb, Jerald P. Radich

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Purpose: Sequential treatment with targeted therapies can result in complex combinations of resistance mutations in drug targets. This mutational complexity has spurred the development of pan-target inhibitors, i.e., therapies for which no single target mutation can cause resistance. Because the propensity for on- versus off-target resistance varies across cancer types, a deeper understanding of the mutational burden in drug targets could rationalize treatment outcomes and prioritize pan-target inhibitors for indications where on-target mutations are most likely. Experimental Design: To measure and model the mutational landscape of a drug target at high resolution, we integrated single-molecule Duplex Sequencing of the ABL1 gene in Philadelphia-positive (Ph+) leukemias with computational simulations. Results: A combination of drug target mutational burden and tumor-initiating cell fraction is sufficient to predict that most patients with chronic myeloid leukemia are unlikely to harbor ABL1 resistance mutations at the time of diagnosis, rationalizing the exceptional success of targeted therapy in this setting. In contrast, our analysis predicts that many patients with Ph+ acute lymphoblastic leukemia (Ph+ ALL) harbor multiple preexisting resistant cells with single mutants. The emergence of compound mutations can be traced to initial use of an ABL1 inhibitor that is susceptible to resistance from single point mutations. Conclusions: These results argue that early use of therapies that achieve pan-inhibition of ABL1 resistance mutants might improve outcomes in Ph+ ALL. Our findings show how a deep understanding of the mutational burden in drug targets can be quantitatively coupled to phenotypic heterogeneity to rationalize clinical phenomena.

Original languageEnglish (US)
Pages (from-to)5321-5334
Number of pages14
JournalClinical Cancer Research
Volume24
Issue number21
DOIs
StatePublished - Nov 1 2018

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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