Combating subclonal evolution of resistant cancer phenotypes. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Brady, Samuel; McQuerry, Jasmine; Qiao, Yi; Piccolo, Stephen; Shrestha, Gajendra; Jenkins, David; Layer, Ryan; Pedersen, Brent; Miller, Ryan; Esch, Amanda; Selitsky, Sara; Parker, Joel; Anderson, Layla; Dalley, Brian; Factor, Rachel; Reddy, Chakravarthy; Boltax, Jonathan; Li, Dean; Moos, Philip; Gray, Joe; Heiser, Laura; Buys, Saundra; Cohen, Adam; Johnson, W. Evan; Quinlan, Aaron; Marth, Gabor; Werner, Theresa; Bild, Andrea
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 15
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-01174-3
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves. In metastatic breast cancer, subclonal evolution can drive drug resistance. Here, the authors genetically and transcriptionally follow the evolution of four breast cancers over time and treatment, and suggest a phenotype-targeted treatment strategy to adapt to cancer as it evolves.