Genome-wide analysis of DNA replication timing in single cells: Yes! We're all individuals

Comprehensive characteristics of somatic mutations in the normal tissues of patients with cancer and existence of somatic mutant clones linked to cancer development

BACKGROUND

Somatic mutations are a major driver of cancer development and many have now been identified in various cancer types, but the comprehensive somatic mutation status of the normal tissues matched to tumours has not been revealed.

METHOD

We analysed the somatic mutations of whole exome sequencing data in 392 patient tumour and normal tissue pairs based on the corresponding blood samples across 10 tumour types.

RESULTS

Many of the mutations involved in oncogenic pathways such as PI3K, NOTCH and TP53, were identified in the normal tissues. The ageing-related mutational signature was the most prominent contributing signature found and the mutations in the normal tissues were frequently in genes involved in late replication time (p<0.0001). Variants were rarely overlapping across tissue types but shared variants between normal and matched tumour tissue were present. These shared variants were frequently pathogenic when compared with non-shared variants (p=0.001) and showed a higher variant-allele-fraction (p<0.0001). Normal tissue-specific mutated genes were frequently non-cancer-associated (p=0.009). PIK3CA mutations were identified in 6 normal tissues and were harboured by all of the matched cancer tissues. Multiple types of PIK3CA mutations were found in normal breast and matched cancer tissues. The PIK3CA mutations exclusively present in normal tissue may indicate clonal expansions unrelated to the tumour. In addition, PIK3CA mutation was appeared that they arose before the occurrence of the allelic imbalance.

CONCLUSION

Our current results suggest that somatic mutant clones exist in normal tissues and that their clonal expansion could be linked to cancer development.

DNA copy-number measurement of genome replication dynamics by high-throughput sequencing: the sort-seq, sync-seq and MFA-seq family

Genome replication follows a defined temporal programme that can change during cellular differentiation and disease onset. DNA replication results in an increase in DNA copy number that can be measured by high-throughput sequencing. Here we present a protocol to determine genome replication dynamics using DNA copy-number measurements. Cell populations can be obtained in three variants of the method. First, sort-seq reveals the average replication dynamics across S phase in an unperturbed cell population; FACS is used to isolate replicating and non-replicating subpopulations from asynchronous cells. Second, sync-seq measures absolute replication time at specific points during S phase using a synchronized cell population. Third, marker frequency analysis can be used to reveal the average replication dynamics using copy-number analysis in any proliferating asynchronous cell culture. These approaches have been used to reveal genome replication dynamics in prokaryotes, archaea and a wide range of eukaryotes, including yeasts and mammalian cells. We have found this approach straightforward to apply to other organisms and highlight example studies from across the three domains of life. Here we present a Saccharomyces cerevisiae version of the protocol that can be performed in 7-10 d. It requires basic molecular and cellular biology skills, as well as a basic understanding of Unix and R.

Spontaneous mutations in the single TTN gene represent high tumor mutation burden

Tumor mutation burden (TMB) is an emerging biomarker, whose calculation requires targeted sequencing of many genes. We investigated if the measurement of mutation counts within a single gene is representative of TMB. Whole-exome sequencing (WES) data from the pan-cancer cohort (n = 10,224) of TCGA, and targeted sequencing (tNGS) and TTN gene sequencing from 24 colorectal cancer samples (AMC cohort) were analyzed. TTN was identified as the most frequently mutated gene within the pan-cancer cohort, and its mutation number best correlated with TMB assessed by WES (rho = 0.917, p < 2.2e-16). Colorectal cancer was one of good candidates for the application of this diagnostic model of TTN-TMB, and the correlation coefficients were 0.936 and 0.92 for TMB by WES and TMB by tNGS, respectively. Higher than expected TTN mutation frequencies observed in other FLAGS (FrequentLy mutAted GeneS) are associated with late replication time. Diagnostic accuracy for high TMB group did not differ between TTN-TMB and TMB assessed by tNGS. Classification modeling by machine learning using TTN-TMB for MSI-H diagnosis was constructed, and the diagnostic accuracy was 0.873 by area under the curve in external validation. TTN mutation was enriched in samples possessing high immunostimulatory signatures. We suggest that the mutation load within TTN represents high TMB status.