Both the timing and molecular determinants of metastasis are unknown, hindering treatment and prevention efforts. Here we characterize the evolutionary dynamics of this lethal process by analyzing exome-sequencing data from 118 biopsies from 23 patients with colorectal cancer with metastases to the liver or brain. The data show that the genomic divergence between the primary tumor and metastasis is low and that canonical driver genes were acquired early. Analysis within a spatial tumor growth model and statistical inference framework indicates that early disseminated cells commonly (81%, 17 out of 21 evaluable patients) seed metastases while the carcinoma is clinically undetectable (typically, less than 0.01 cm3). We validated the association between early drivers and metastasis in an independent cohort of 2,751 colorectal cancers, demonstrating their utility as biomarkers of metastasis. This conceptual and analytical framework provides quantitative in vivo evidence that systemic spread can occur early in colorectal cancer and illuminates strategies for patient stratification and therapeutic targeting of the canonical drivers of tumorigenesis.
Bibliographical noteFunding Information:
C. Curtis is supported by the National Institutes of Health through the NIH Director’s Pioneer Award (DP1-CA238296) and NCI Cancer Target Discovery and Development Network (CA217851). This work was funded in part by grants from the American Cancer Society (IRG–58-007-54), the Emerson Collective Cancer Research Fund and a gift from the Wunderglo Foundation to C. Curtis. Z.H. is supported by an Innovative Genomics Initiative (IGI) Postdoctoral Fellowship. The project was supported in part by Cancer Center Support Grants from the National Cancer Institute to the Stanford Cancer Institute (P30CA124435) and the University of Southern California Norris Comprehensive Cancer Center (P30CA014089). We thank J. Caswell-Jin and A. Harpak for critical feedback on the manuscript. This study is dedicated to the memory of G. Borges, a tireless cancer warrior.
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't