Somatic evolutionary timings of driver mutations

Abstract

Background: A unified analysis of DNA sequences from hundreds of tumors concluded that the driver mutations primarily occur in the earliest stages of cancer formation, with relatively few driver mutation events detected in the late-arising subclones. However, emerging evidence from the sequencing of multiple tumors and tumor regions per individual suggests that late-arising subclones with additional driver mutations are underestimated in single-sample analyses.

Methods: To test whether driver mutations generally map to early tumor development, we examined multi-regional tumor sequencing data from 101 individuals reported in 11 published studies. Following previous studies, we annotated mutations as early-arising when all tumors/regions had those mutations (ubiquitous). We then inferred the fraction of mutations occurring early and compared it with late-arising mutations that were found in only single tumors/regions.

Results: While a large fraction of driver mutations in tumors occurred relatively early in cancers, later driver mutations occurred at least as frequently as the early drivers in a substantial number of patients. This result was robust to many different approaches to annotate driver mutations. The relative frequency of early and late driver mutations varied among patients of the same cancer type and in different cancer types. We found that previous reports of the preponderance of early driver mutations were primarily informed by analysis of single tumor variant allele profiles, with which it is challenging to clearly distinguish between early and late drivers.

Conclusions: The origin and preponderance of new driver mutations are not limited to early stages of tumor evolution, with different tumors and regions showing distinct driver mutations and, consequently, distinct characteristics. Therefore, tumors with extensive intratumor heterogeneity appear to have many newly acquired drivers.

Keywords: Driver mutation; Private mutation; Somatic mutation; Ubiquitous mutation.

Fig. 1

Overall timing of driver mutations. The fraction of driver mutations that are early (pink) and late (blue) are shown for each of the driver mutation annotation schemes (I–V, see Methods), a) including CpG sites, and b) after the removal of CpG sites

Fig. 2

Fraction of driver mutations occurring at early and late time. Driver mutations were annotated as those found in cancer-associated genes. a Fraction of all driver mutations that occurred early and late as inferred from multi-sample profiles. Each dot refers to data from one patient from a study, and a bar shows the average. Statistical tests (paired t-test) were performed to test if the fraction of early-driver mutations is significantly different from late-driver mutations for a cancer type. Cancer types that have significantly different fractions (P ≤ 0.05) are shown with asterisks. b The difference in early and late driver mutation fractions for individual patients. Zero difference was found for 15 patients. Three data sets were removed because there were zero driver mutations after removing variants absent from all tumors after the application of Treeomics software (see Methods)

Fig. 3

Fraction of early and late driver mutations in metastatic tumors. a The fraction of driver mutations that are early and late. b Difference between late-and early-driver mutation fraction. Each bar represents a patient: pink marks patients that have a greater fraction of early-driver mutations than late, and blue marks patients that show an opposite trend. Nine patients showed zero difference

Fig. 4

Timing of driver mutations using single and multiple tumor samples. Driver mutations were annotated as those found in driver genes identified in the previous report [12]. a Fraction of driver mutations occurring at early time. For the single sample data set (left), we generated 100 replicates, where we randomly selected a single sector per patient. For each replicate, we pooled driver mutations and computed the fraction of early driver mutations (mean: 66%). For multiple samples (right), all samples available for each data set were used to compute the fraction of early driver mutations (45%). The fraction of early drivers found in 100 replicates of single-tumor sampling was statistically greater than the early driver fraction found using multiple samples by single single-sample t test (P < 10⁻¹⁵). b Difference between late-and early-driver mutation fraction calculated using single-tumor samples (one replicate is shown). Each bar represents a patient: pink marks patients that have a greater fraction of early-driver mutations than late, and blue marks patients that show an opposite trend. Eleven patients contained equal proportions of early and late drivers, and 7 patients were removed as no driver mutations were identified

Fig. 5

The number of early driver mutations when some samples may have wild-type alleles. We annotated mutations as early mutations, when 100% (all), >80, >70, and >60% of samples had mutant alleles. The number of late driver mutations are shown with the blue bar

Fig. 6

Numbers of driver mutations and passenger mutations. The number of mutations were pooled for each study. a and b The fractions of driver and passenger mutations that are (a) early and (b) late. c The fractions of driver mutations over total mutations (driver and passenger mutations) for early (pink) and late (blue)

Fig. 7

Observed mutant frequencies of late mutations. Observed mutant frequencies were computed by dividing the number of mutant read counts by the number of total read counts. a Mutaant frequency distribution where all late mutations were pooled together. b Histogram for one region with the largest number of late driver mutations (163 mutations). The data are from region rec52 from the patient 1402 [25]. c Regional average mutant frequencies of late drivers and late passengers for all Regions with at least 10 late driver mutations. Patient IDs are presented along x-axis, and region IDs are shown within parentheses. The differences of mutant frequencies between driver and passenger were not statistically significant in any region (P > 0.05; t-test). Also, the results of all late mutations pooled from all regions are shown (All; P = 0.01 by t-test, while the difference was only 1%). Error bars are standard errors. Driver and passenger mutations are shown with red and gray bars, respectively