Evolution of core archetypal phenotypes in progressive high grade serous ovarian cancer

Nat Commun. 2021 May 24;12(1):3039. doi: 10.1038/s41467-021-23171-3.


The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy.

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

  • Cell Line, Tumor
  • Cystadenocarcinoma, Serous / genetics*
  • DNA Repair
  • Drug Resistance, Neoplasm
  • Female
  • Genetic Heterogeneity
  • Humans
  • Mutation
  • Ovarian Neoplasms / genetics*
  • Ovarian Neoplasms / metabolism*
  • Phenotype*
  • Sequence Analysis, RNA
  • Transcriptome