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. 2018 Sep 7;361(6406):1033-1037.
doi: 10.1126/science.aat7171.

Minimal Functional Driver Gene Heterogeneity Among Untreated Metastases

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Free PMC article

Minimal Functional Driver Gene Heterogeneity Among Untreated Metastases

Johannes G Reiter et al. Science. .
Free PMC article

Abstract

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.

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.

Figures

Fig. 1.
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.
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.
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.
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 b0 and die at rate d per day. Additional driver mutations increase the birth rate to b1 = b0(1+s), where s denotes the relative driver advantage (b1b0, q = q1; B-E), or increase the dissemination rate (q1q0, b1 = b0; F). (B) Representative model realizations for typical parameter values. Growth rate r0 = 1.24% per day, s = 0.4%, dissemination rate q0 = 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 q0 = 10−7. (E) Fixed death-birth rate ratio d/b0 = 0.95. (F) Fixed q0 = 10−7. Other parameter values: d = 0.2475, driver mutation rate u = 3.4 10−5 per cell division.

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