mitoSomatic: a tool for accurate identification of mitochondrial DNA somatic mutations without paired controls

Abstract

Mitochondrial DNA (mtDNA) somatic mutations play important roles in the initiation and progression of cancer. Although next‐generation sequencing (NGS) of paired tumor and control samples has become a common practice to identify tumor‐specific mtDNA mutations, the unique nature of mtDNA and NGS‐associated sequencing bias could cause false‐positive/‐negative somatic mutation calling. Additionally, there are clinical scenarios where matched control tissues are unavailable for comparison. Therefore, a novel approach for accurately identifying somatic mtDNA variants is greatly needed, particularly in the absence of matched controls. In this study, the ground truth mtDNA variants orthogonally validated by triple‐paired tumor, adjacent nontumor, and blood samples were used to develop mitoSomatic, a random forest‐based machine learning tool. We demonstrated that mitoSomatic achieved area under the curve (AUC) values over 0.99 for identifying somatic mtDNA variants without paired control in three tumor types. In addition, mitoSomatic was also applicable in nontumor tissues such as adjacent nontumor and blood samples, suggesting the flexibility of mitoSomatic's classification capability. Furthermore, analysis of triple‐paired samples identified a small group of variants with uncertain somatic/germline origin, whereas application of mitoSomatic significantly facilitated the prediction of their possible source. Finally, a control‐free evaluation of the public pan‐cancer NGS dataset with mitoSomatic revealed a substantial number of variants that were probably misclassified by conventional tumor‐control comparison, further emphasizing the usefulness of mitoSomatic in application. Taken together, our study demonstrates that mitoSomatic is valuable for accurately identifying somatic mtDNA variants in mtDNA NGS data without paired controls, applicable for both tumor and nontumor tissues.

Mutational profiling of mitochondrial DNA reveals an epithelial ovarian cancer‐specific evolutionary pattern contributing to high oxidative metabolism

“…Our previous publications have provided somatic mtDNA mutation data from 110 patients with hepatocellular carcinoma (HCC) (private HCC cohort) and 432 patients with CRC (public CRC cohort). These datasets were compared to explore the mutational patterns of mtDNA among different types of cancer 22,33 . To further validate the findings of the private mtDNA mutation datasets, we acquired a public mtDNA mutation dataset from whole‐genome sequencing (WGS) of tumour tissues from 118 EOC, 344 HCC and 62 CRC patients.…”

“…These datasets were compared to explore the mutational patterns of mtDNA among different types of cancer. 22 , 33 To further validate the findings of the private mtDNA mutation datasets, we acquired a public mtDNA mutation dataset from whole‐genome sequencing (WGS) of tumour tissues from 118 EOC, 344 HCC and 62 CRC patients. The public dataset was obtained from the TCMA database ( http://bioinformatics.mdanderson.org/main/TCMA:Overview ).…”

“…Public somatic mtDNA mutation data are available within three publications 14 , 22 , 33 and our private mtDNA mutation data are available within the Additional File 4 . The sequencing data have been uploaded to the Genome Sequence Archive for Human (GSA‐Human) under accession PRJCA006830 ( http://bigd.big.ac.cn/gsa‐human ).…”

Circulating cell-free mtDNA as a new biomarker for cancer detection and management

“…In addition, we have developed a package to identify tumorspecific mtDNA mutations in mtDNA NGS data, applicable to both tumor and nontumor tissues without paired controls 24 . Moreover, we have found that the copy number of mtDNA in the plasma of patients with HCC is significantly lower than that in tissue, and the cf-mtDNA fragments are shorter than those of cf-nDNA.…”