Abstract 3889: Access and selection shapes peritoneal metastasis evolution in colorectal cancer
Whether metastasis in humans can be accomplished by most primary tumor cells or requires the evolution of a specialized trait remains an open question. To evaluate whether metastases are founded by non-random subsets of primary tumor lineages requires extensive, difficult-to-implement sampling. We have realized an unusually dense multi-region sampling scheme in a cohort of 26 colorectal cancer patients with peritoneal metastases, reconstructing the evolutionary history of on average 28.8 tissue samples per patient with a microsatellite-based fingerprinting assay. To assess metastatic randomness, we evaluate inter- and intra-metastatic heterogeneity relative to the primary tumor and find that peritoneal metastases are more heterogeneous than liver metastases but less diverse than locoregional metastases. Metachronous peritoneal metastases exposed to systemic chemotherapy show significantly higher inter-lesion diversity than synchronous, untreated metastases. Projection of peritoneal metastasis origins onto a spatial map of the primary tumor reveals that they often originate at the deep-invading edge, in contrast to liver and lymph node metastases which exhibit no such preference. Furthermore, peritoneal metastases typically do not share a common subclonal origin with distant metastases in more remote organs. Synthesizing these insights into an evolutionary portrait of peritoneal metastases, we conclude that the peritoneal-metastatic process imposes milder selective pressures onto disseminating cancer cells than the liver-metastatic process. Peritoneal metastases’ unique evolutionary features have potential implications for staging and treatment. Citation Format: Emma CE Wassenaar, Alexander N. Gorelick, Wei-Ting Hung, David M. Cheek, Emre Kucukkose, I-Hsiu Lee, Martin Blohmer, Sebastian Degner, Peter Giunta, Rene MJ Wiezer, Mihaela G. Raicu, Inge Ubink, Sjoerd J. Klaasen, Nico Lansu, Emma V. Watson, Ryan B. Corcoran, Genevieve Boland, Gad Getz, Geert JPL Kops, Dejan Juric, Jochen K. Lennerz, Djamila Boerma, Onno Kranenburg, Kamila Naxerova. Access and selection shapes peritoneal metastasis evolution in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3889.
Authors
- Emma V. Watson (ORCID: https://orcid.org/0000-0003-1321-6604)
- Gad A. Getz (ORCID: https://orcid.org/0000-0002-0936-0753)
- David Cheek (ORCID: https://orcid.org/0000-0002-4429-7315)
- Genevieve Marie Boland (ORCID: https://orcid.org/0000-0002-7522-6173)
- Onno Kranenburg (ORCID: https://orcid.org/0000-0002-2112-4390)
- Inge Ubink (ORCID: https://orcid.org/0000-0001-8199-0226)
- Emre Küçükköse (ORCID: https://orcid.org/0000-0002-8983-9464)
- Ryan Bruce Corcoran (ORCID: https://orcid.org/0000-0001-8173-5778)
- Emma C. E. Wassenaar (ORCID: https://orcid.org/0000-0002-7525-9135)
- Sjoerd J. Klaasen (ORCID: https://orcid.org/0000-0002-2915-3217)
- Martin Blohmer (ORCID: https://orcid.org/0000-0002-5911-8420)
- Alexander N. Gorelick (ORCID: https://orcid.org/0000-0001-6736-8246)
- Kamila Naxerova (ORCID: https://orcid.org/0000-0001-7744-5110)
- Peter Giunta (ORCID: https://orcid.org/0009-0002-8906-431X)
- Jochen K. Lennerz (ORCID: https://orcid.org/0000-0003-2434-4978)
- Nico Lansu
- Mihaela G. Raicu
- I‐Hsiu Lee (ORCID: https://orcid.org/0000-0001-7200-7867)
- Sebastian Degner (ORCID: https://orcid.org/0000-0002-6979-7017)
- Geert J.P.L. Kops (ORCID: https://orcid.org/0000-0003-3555-5295)
- Wei‐Ting Hung (ORCID: https://orcid.org/0000-0002-8533-9972)
- Rene MJ Wiezer
- Djamila Boerma
- Dejan Juric
Institutions
- Broad Institute (US)
- Harvard University (US)
- University of Massachusetts Chan Medical School (US)
- University Medical Center Utrecht (NL)
- Massachusetts General Hospital (US)
- St. Antonius Ziekenhuis (NL)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-3889
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00