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Quantitative evidence for early metastatic seeding in colorectal cancer

Abstract

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.

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Data availability

Data have been deposited at the European Genotype Phenotype Archive (EGA) under accession number EGAS00001003573. Data from previously published studies are available from the DDBJ (accession number JGAS00000000060)21 and the SRA (accession numbers SRP052609, SRP074289 and SRP041725)29,30,31.

Code availability

Code used for genomic data analysis and simulation studies are available at https://github.com/cancersysbio/mCRCs and https://github.com/cancersysbio/SCIMET.

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Competing interests

A.S.B. has received research support from Daiichi Sankyo and honoraria for lectures, consultation or advisory board participation from Roche Bristol-Myers Squibb, Merck and Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. M.P. has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group, CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo and Merck Sharp & Dome. P.B. has received travel support, honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Biocartis, Novartis, Pfizer, Roche and Roche Diagnostics. C. Curtis is a scientific advisor to GRAIL and reports stock options as well as consulting for GRAIL and Genentech.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

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.

Author information

Z.H. implemented the computational and mathematical models, performed simulation studies and statistical analyses. J.D. implemented the genomic data analysis pipeline, analyzed and visualized genomic data and provided statistical advice. Z.M. processed clinical samples and generated the genomic data. Z.H., R.S. and J.A.S. analyzed the genomic data. Z.H., J.D., R.S., J.A.S. and C. Curtis interpreted the data. J.S.S. contributed to simulation studies. C.J.S., A.S.B. and P.B. performed pathology review. A.S.B. and M.P. performed immunohistochemistry experiments. M.P., P.B., F.L., C. Cremolini, A.F. and H.-J.L. contributed clinical samples and expertise. Z.H. and C. Curtis wrote the manuscript, which was reviewed by all authors. C. Curtis conceived and supervised the study.

Competing interests

A.S.B. has received research support from Daiichi Sankyo and honoraria for lectures, consultation or advisory board participation from Roche Bristol-Myers Squibb, Merck and Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. M.P. has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group, CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo and Merck Sharp & Dome. P.B. has received travel support, honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Biocartis, Novartis, Pfizer, Roche and Roche Diagnostics. C. Curtis is a scientific advisor to GRAIL and reports stock options as well as consulting for GRAIL and Genentech.

Correspondence to Christina Curtis.

Supplementary information

Supplementary Information

Supplementary Figures 1–26, Supplementary Tables 1 and 5–7, and Supplementary Note

Reporting Summary

Supplementary Table 2

Somatic SNVs, indels, allele frequencies, cancer cell fraction and LOH status for individual patients and tumor regions

Supplementary Table 3

Colorectal cancer and pan-cancer driver gene lists

Supplementary Table 4

Gene ontology enrichment analyses

Supplementary Table 8

Enrichment of driver gene modules in the MSK-Impact and MSKImpact plus GENIE cohorts

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Fig. 1: Study overview.
Fig. 2: The mutational landscape and patterns of genetic divergence in paired primary CRCs and metastases.
Fig. 3: Within- and between-lesion heterogeneity in paired primary CRCs and metastases.
Fig. 4: Correlation between the Lp, Lm and H and primary carcinoma size at the time of dissemination.
Fig. 5: Patient-specific inference of the timing of metastasis in CRC.
Fig. 6: Enrichment of early driver gene modules in mCRC and clinical implications of early dissemination.