Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma

Research output: Contribution to journalJournal articleResearchpeer-review

  • Alexandra Lahtinen
  • Kari Lavikka
  • Anni Virtanen
  • Yilin Li
  • Sanaz Jamalzadeh
  • Aikaterini Skorda
  • Anna Røssberg Lauridsen
  • Kaiyang Zhang
  • Giovanni Marchi
  • Veli Matti Isoviita
  • Valeria Ariotta
  • Oskari Lehtonen
  • Taru A. Muranen
  • Kaisa Huhtinen
  • Olli Carpén
  • Sakari Hietanen
  • Antti Häkkinen
  • Johanna Hynninen
  • Jaana Oikkonen
  • Sampsa Hautaniemi

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we integrate clonal composition and topology using whole-genome sequencing data from 510 samples of 148 patients with HGSC in the prospective, longitudinal, multiregion DECIDER study. Our results reveal three evolutionary states, which have distinct features in genomics, pathways, and morphological phenotypes, and significant association with treatment response. Nested pathway analysis suggests two evolutionary trajectories between the states. Experiments with five tumor organoids and three PI3K inhibitors support targeting tumors with enriched PI3K/AKT pathway with alpelisib. Heterogeneity analysis of samples from multiple anatomical sites shows that site-of-origin samples have 70% more unique clones than metastatic tumors or ascites. In conclusion, these analysis and visualization methods enable integrative tumor evolution analysis to identify patient subtypes using data from longitudinal, multiregion cohorts.

Original languageEnglish
JournalCancer Cell
Volume41
Issue number6
Pages (from-to)1103-1117.e12
ISSN1535-6108
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

    Research areas

  • Cancer, Evolutionary trajectories, Integrative analysis, Multiregion sampling, Organoid experiments, Ovarian cancer, PI3K/AKT, Prospective cohort, Tumor evolution, Tumor heterogeneity

ID: 357048987