. 2018 Sep 7;361(6406):1033-1037.
Minimal Functional Driver Gene Heterogeneity Among Untreated Metastases
Free PMC article
Item in Clipboard
Minimal Functional Driver Gene Heterogeneity Among Untreated Metastases
Free PMC article
Metastases are responsible for the majority of cancer-related deaths. Although genomic heterogeneity within primary tumors is associated with relapse, heterogeneity among treatment-naïve metastases has not been comprehensively assessed. We analyzed sequencing data for 76 untreated metastases from 20 patients and inferred cancer phylogenies for breast, colorectal, endometrial, gastric, lung, melanoma, pancreatic, and prostate cancers. We found that within individual patients, a large majority of driver gene mutations are common to all metastases. Further analysis revealed that the driver gene mutations that were not shared by all metastases are unlikely to have functional consequences. A mathematical model of tumor evolution and metastasis formation provides an explanation for the observed driver gene homogeneity. Thus, single biopsies capture most of the functionally important mutations in metastases and therefore provide essential information for therapeutic decision-making.
Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Conflict of interest statement
Competing interests: K.W.K. and B.V. are founders of Personal Genome Diagnostics. B.V. and K.W.K. are on the Scientific Advisory Board of Sysmex-Inostics. B.V. is also on the Scientific Advisory Boards of Exelixis GP. These companies and others have licensed technologies from Johns Hopkins, and K.W.K. and B.V. receive equity or royalties from these licenses. The terms of these arrangements are being managed by Johns Hopkins University in accordance with its conflict of interest policies.
Fig. 1.. Three scenarios of heterogeneity of mutations in driver genes.
The original clone (green cells) contains three driver gene mutations (D1, D2, D3). Brown, yellow, and red cells acquired additional driver mutations during the growth of the primary tumor (PT) and may expand to form detectable subpopulations (brown) which can seed metastases. Top panels illustrate seeding subpopulations and biopsies (blue circles) of different regions (R1, R2) of the PT and of distinct metastases (M1, M2). Bottom panels illustrate reconstructed cancer phylogenies from those biopsies. (
A) Original clone seeds all metastases. All metastases share same founding driver mutations. Subclones with additional driver mutations (D4) evolve too late to seed metastases, but might be detectable in the PT. ( B) A single highly metastatic subclone evolves and gives rise to all metastases. All metastases share same founding driver mutations. ( C) A new subclone with an additional driver mutation (D4) evolves and independently seeds metastases. PT regions and metastases exhibit driver mutation heterogeneity.
Fig. 2.. Most mutations in putative driver genes occur on the trunk of metastases.
A) Twenty patients with 76 untreated metastases. Thirteen patients acquired mutations in putative driver genes along the MetBranch (MB) while seven did not. ( B) Inferred phylogeny of a colorectal cancer exhibits inter-metastatic driver mutation heterogeneity. Nonsynonymous mutations in driver genes are denoted in orange. Percentages denote branch confidence. Integers denote number of point mutations per branch. Table shows predicted functional effects of mutations in driver genes. Heterogeneous driver mutations were predicted to have no functional effect or were likely sequencing artifacts (low coverage and low VAF across all sites). MetTrunk (MT) denotes that variant was acquired on the trunk of all metastases. Sample origin: rectum: PT1–5; liver: Met1–6.
Fig. 3.. Predicted functional mutations in putative driver genes are strongly enriched along metastases trunks.
A) Ratio of driver gene mutations to nonsynonymous mutations is enriched by 42-fold along trunks compared to branches. Orange diamond denotes mean, black bar denotes median (two-sided paired t-test P = 0.004). ( B) Fraction of nonsynonymous variants in driver genes along MetTrunk in COSMIC was 38% compared to 16% along MetBranch (two-sided Fisher’s exact test P = 0.025). ( C) Relative occurrence of variants in driver genes along MetTrunk in individual COSMIC samples was 0.32% compared to 0.0016% along MetBranch (two-sided Wilcoxon rank-sum test P = 0.008). ( D) VEP inferred that 30% and 6% of driver gene mutations were of high impact along MetTrunk and MetBranch, respectively (two-sided Fisher’s exact test P = 0.006). ( E-F) FATHMM (value below −0.75 indicates likely driver mutation) and CHASMplus predicted increased functional consequences for variants in driver genes in MetTrunk. Two-sided Wilcoxon rank-sum tests were used. Thick black bars denote 90% confidence interval. No other statistically significant differences were observed. Numbers in brackets denote number of variants in each group. * indicates P < 0.05, ** P < 0.01, *** P < 0.001.
Fig. 4.. Mathematical analysis provides an explanation for inter-metastatic driver gene mutation homogeneity or heterogeneity.
A) Primary tumor expands stochastically from a single advanced cancer cell and seeds metastases. Cells of original clone (green) divide at rate b and die at rate 0 d per day. Additional driver mutations increase the birth rate to b 1 = b 0(1+ s), where s denotes the relative driver advantage ( b 1 ≥ b 0, q = q 1; B- E), or increase the dissemination rate ( q 1 ≥ q 0, b 1 = b 0; F). ( B) Representative model realizations for typical parameter values. Growth rate r 0 = 1.24% per day, s = 0.4%, dissemination rate q 0 = 10 −7 per cell per day. ( C) Distribution of metastases detection times for parameter values in B. Numbers denote mean ± standard deviation. Colored marks show mean detection times of first, second, third, and fourth metastases seeded by the corresponding subclone (SC). ( D-F) Probability of distinct driver mutations among four metastases. Green dashed lines depict bounds separating parameter regions of likely inter-metastatic driver homogeneity from heterogeneity. Orange dotted lines denote s = 0.4%. ( D) Fixed q 0 = 10 −7. ( E) Fixed death-birth rate ratio d/ b 0 = 0.95. ( F) Fixed q 0 = 10 −7. Other parameter values: d = 0.2475, driver mutation rate u = 3.4 10 −5 per cell division.
Genetic Heterogeneity in Therapy-Naïve Synchronous Primary Breast Cancers and Their Metastases.
Clin Cancer Res. 2017 Aug 1;23(15):4402-4415. doi: 10.1158/1078-0432.CCR-16-3115. Epub 2017 Mar 28.
Clin Cancer Res. 2017.
28351929 Free PMC article.
Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer.
Nat Genet. 2017 Mar;49(3):358-366. doi: 10.1038/ng.3764. Epub 2017 Jan 16.
Nat Genet. 2017.
28092682 Free PMC article.
An analysis of genetic heterogeneity in untreated cancers.
Nat Rev Cancer. 2019 Nov;19(11):639-650. doi: 10.1038/s41568-019-0185-x. Epub 2019 Aug 27.
Nat Rev Cancer. 2019.
31455892 Free PMC article.
Evaluation and consequences of heterogeneity in the circulating tumor cell compartment.
Oncotarget. 2016 Jul 26;7(30):48625-48643. doi: 10.18632/oncotarget.8015.
26980749 Free PMC article.
Subclonal Genomic Architectures of Primary and Metastatic Colorectal Cancer Based on Intratumoral Genetic Heterogeneity.
Clin Cancer Res. 2015 Oct 1;21(19):4461-72. doi: 10.1158/1078-0432.CCR-14-2413. Epub 2015 May 15.
Clin Cancer Res. 2015.
Mapping the spreading routes of lymphatic metastases in human colorectal cancer.
Nat Commun. 2020 Apr 24;11(1):1993. doi: 10.1038/s41467-020-15886-6.
Nat Commun. 2020.
32332722 Free PMC article.
Molecular Analysis of Clinically Defined Subsets of High-Grade Serous Ovarian Cancer.
Cell Rep. 2020 Apr 14;31(2):107502. doi: 10.1016/j.celrep.2020.03.066.
Cell Rep. 2020.
32294438 Free PMC article.
Morphologic and Genomic Heterogeneity in the Evolution and Progression of Breast Cancer.
Cancers (Basel). 2020 Mar 31;12(4):848. doi: 10.3390/cancers12040848.
Cancers (Basel). 2020.
32244556 Free PMC article.
Integrated Informatics Analysis of Cancer-Related Variants.
JCO Clin Cancer Inform. 2020 Mar;4:310-317. doi: 10.1200/CCI.19.00132.
JCO Clin Cancer Inform. 2020.
32228266 Free PMC article.
Edgetic perturbation signatures represent known and novel cancer biomarkers.
Sci Rep. 2020 Mar 9;10(1):4350. doi: 10.1038/s41598-020-61422-3.
Sci Rep. 2020.
32152446 Free PMC article.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Neoplasm Metastasis / drug therapy*
Neoplasm Metastasis / genetics*
Neoplasm Metastasis / pathology
Neoplasms / drug therapy*
LinkOut - more resources
Full Text Sources Other Literature Sources Medical