A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä

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




TekijätLahtinen Alexandra, Lavikka Kari, Virtanen Anni, Li Yilin, Jamalzadeh Sanaz, Skorda Aikaterini, Lauridsen Anna Rössberg, Zhang Kaiynag, Marchi Giovanni, Isoviita Veli-Matti, Ariotta Valeria, Lehtonen Oskari, Muranen Taru A., Huhtinen Kaisa, Carpén Olli, Hietanen Sakari, Senkowski Wojciech, Kallunki Tuula, Häkkinen Aantti, Hynninen Johanna, Oikkonen Jaana, Hautaniemi Sampsa

KustantajaCell Press

Julkaisuvuosi2023

JournalCancer Cell

Tietokannassa oleva lehden nimiCancer Cell

Vuosikerta41

Numero6

Aloitussivu1103

DOIhttps://doi.org/10.1016/j.ccell.2023.04.017

Verkko-osoitehttps://doi.org/10.1016/j.ccell.2023.04.017

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/180232993


Tiivistelmä

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.


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