Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer

Nat Genet. 2020 Apr;52(4):408-417. doi: 10.1038/s41588-020-0590-9. Epub 2020 Mar 16.

Abstract

Local adaptation directs populations towards environment-specific fitness maxima through acquisition of positively selected traits. However, rapid environmental changes can identify hidden fitness trade-offs that turn adaptation into maladaptation, resulting in evolutionary traps. Cancer, a disease that is prone to drug resistance, is in principle susceptible to such traps. We therefore performed pooled CRISPR-Cas9 knockout screens in acute myeloid leukemia (AML) cells treated with various chemotherapies to map the drug-dependent genetic basis of fitness trade-offs, a concept known as antagonistic pleiotropy (AP). We identified a PRC2-NSD2/3-mediated MYC regulatory axis as a drug-induced AP pathway whose ability to confer resistance to bromodomain inhibition and sensitivity to BCL-2 inhibition templates an evolutionary trap. Across diverse AML cell-line and patient-derived xenograft models, we find that acquisition of resistance to bromodomain inhibition through this pathway exposes coincident hypersensitivity to BCL-2 inhibition. Thus, drug-induced AP can be leveraged to design evolutionary traps that selectively target drug resistance in cancer.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adaptation, Physiological / genetics
  • Animals
  • Biological Evolution
  • CRISPR-Cas Systems / genetics
  • Cell Line
  • Cell Line, Tumor
  • Drug Resistance, Neoplasm / genetics*
  • Environment
  • Genetic Fitness / genetics
  • Genetic Pleiotropy / genetics*
  • HEK293 Cells
  • HL-60 Cells
  • Humans
  • Mice
  • Neoplasms / genetics*
  • Nuclear Proteins / genetics
  • Phenotype
  • Quantitative Trait Loci / genetics
  • Transcription Factors / genetics

Substances

  • Nuclear Proteins
  • Transcription Factors