Comparison of capture-based mtDNA sequencing performance between MGI and illumina sequencing platforms in various sample types.

BACKGROUND: Mitochondrial genome abnormalities can lead to mitochondrial dysfunction, which in turn affects cellular biology and is closely associated with the development of various diseases. The demand for mitochondrial DNA (mtDNA) sequencing has been increasing, and Illumina and MGI are two commonly used sequencing platforms for capture-based mtDNA sequencing. However, there is currently no systematic comparison of mtDNA sequencing performance between these two platforms. To address this gap, we compared the performance of capture-based mtDNA sequencing between Illumina's NovaSeq 6000 and MGI's DNBSEQ-T7 using tissue, peripheral blood mononuclear cell (PBMC), formalin-fixed paraffin-embedded (FFPE) tissue, plasma, and urine samples. RESULTS: Our analysis indicated a high degree of consistency between the two platforms in terms of sequencing quality, GC content, and coverage. In terms of data output, DNBSEQ-T7 showed higher rates of clean data and duplication compared to NovaSeq 6000. Conversely, the amount of mtDNA data obtained by per gigabyte sequencing data was significantly lower in DNBSEQ-T7 compared to NovaSeq 6000. In terms of detection mtDNA copy number, both platforms exhibited good consistency in all sample types. When it comes to detection of mtDNA mutations in tissue, FFPE, and PBMC samples, the two platforms also showed good consistency. However, when detecting mtDNA mutations in plasma and urine samples, significant differenceof themutation number detected was observed between the two platforms. For mtDNA sequencing of plasma and urine samples, a wider range of DNA fragment size distribution was found in NovaSeq 6000 when compared to DNBSEQ-T7. Additionally, two platforms exhibited different characteristics of mtDNA fragment end preference. CONCLUSIONS: In summary, the two platforms generally showed good consistency in capture-based mtDNA sequencing. However, it is necessary to consider the data preferences generated by two sequencing platforms when plasma and urine samples were analyzed.

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

BACKGROUND: Epithelial ovarian cancer (EOC) heavily relies on oxidative phosphorylation (OXPHOS) and exhibits distinct mitochondrial metabolic reprogramming. Up to now, the evolutionary pattern of somatic mitochondrial DNA (mtDNA) mutations in EOC tissues and their potential roles in metabolic remodelling have not been systematically elucidated. METHODS: Based on a large somatic mtDNA mutation dataset from private and public EOC cohorts (239 and 118 patients, respectively), we most comprehensively characterised the EOC-specific evolutionary pattern of mtDNA mutations and investigated its biological implication. RESULTS: Mutational profiling revealed that the mitochondrial genome of EOC tissues was highly unstable compared with non-cancerous ovary tissues. Furthermore, our data indicated the delayed heteroplasmy accumulation of mtDNA control region (mtCTR) mutations and near-complete absence of mtCTR non-hypervariable segment (non-HVS) mutations in EOC tissues, which is consistent with stringent negative selection against mtCTR mutation. Additionally, we observed a bidirectional and region-specific evolutionary pattern of mtDNA coding region mutations, manifested as significant negative selection against mutations in complex V (ATP6/ATP8) and tRNA loop regions, and potential positive selection on mutations in complex III (MT-CYB). Meanwhile, EOC tissues showed higher mitochondrial biogenesis compared with non-cancerous ovary tissues. Further analysis revealed the significant association between mtDNA mutations and both mitochondrial biogenesis and overall survival of EOC patients. CONCLUSIONS: Our study presents a comprehensive delineation of EOC-specific evolutionary patterns of mtDNA mutations that aligned well with the specific mitochondrial metabolic remodelling, conferring novel insights into the functional roles of mtDNA mutations in EOC tumourigenesis and progression.

Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population.

BACKGROUND: Mitochondrial DNA's (mtDNA) haplogroups and SNPs were associated with the risk of different cancer. However, there is no evidence that the same haplogroup or mitochondrial SNP (mtSNP) exhibits the pleiotropic effect on multiple cancers. METHODS: We recruited 2,489 participants, including patients with colorectal, hepatocellular, lung, ovarian, bladder, breast, pancreatic, and renal cell carcinoma. In addition, 715 healthy individuals from Northern China served as controls. Next, cross-tumor analysis was performed to determine whether mtDNA variation is associated with multiple cancers. RESULTS: Our results revealed a significant decrease in the occurrence risk of multiple cancers among individuals belonging to haplogroup A [OR = 0.553, 95% confidence interval (CI) = 0.375-0.815, P = 0.003]. Furthermore, we identified 11 mtSNPs associated with multiple cancers and divided the population into high-risk and low-risk groups. Low-risk groups showed a significantly reduced risk of occurrence compared with high-risk groups (OR = 0.614, 95% CI = 0.507-0.744, P < 0.001). Furthermore, using interaction analysis, we identified a special group of individuals belonging to haplogroup A/M7 and the low-risk population, who exhibit a lower risk of multiple cancers compared with other populations (OR = 0.195, 95% CI = 0.106-0.359, P < 0.001). Finally, gene set enrichment analysis confirmed that haplogroup A/M7 patients had lower expression levels of cancer-related pathway genes compared with haplogroup D patients. CONCLUSIONS: We found that specific mtDNA haplogroups and mtSNPs may play a role in predicting multiple cancer predisposition in Chinese populations. IMPACT: This may provide a potential tool for early screening in clinical settings for individuals in the Chinese population.

A High-Efficiency Capture-Based NGS Approach for Comprehensive Analysis of Mitochondrial Transcriptome.

The transcription of the mitochondrial genome is pivotal for maintenance of mitochondrial functions, and the deregulated mitochondrial transcriptome contributes to various pathological changes. Despite substantial progress having been achieved in uncovering the transcriptional complexity of the nuclear transcriptome, many unknowns and controversies remain for the mitochondrial transcriptome, partially owing to the lack of a highly efficient mitochondrial RNA (mtRNA) sequencing and analysis approach. Here, we first comprehensively evaluated the influence of essential experimental protocols, including strand-specific library construction, two RNA enrichment strategies, and optimal rRNA depletion, on accurately profiling mitochondrial transcriptome in whole-transcriptome sequencing (WTS) data. Based on these insights, we developed a highly efficient approach specifically suitable for targeted sequencing of whole mitochondrial transcriptome, termed capture-based mtRNA seq (CAP), in which strand-specific library construction and optimal rRNA depletion were applied. Compared with WTS, CAP has a great decrease of required data volume without affecting the sensitivity and accuracy of detection. In addition, CAP also characterized the unannotated mt-tRNA transcripts whose expression levels are below the detection limits of conventional WTS. As a proof-of-concept characterization of mtRNAs, the transcription initiation sites and mtRNA cleavage ratio were accurately identified in CAP data. Moreover, CAP had very reliable performance in plasma and single-cell samples, highlighting its wide application. Altogether, the present study has established a highly efficient pipeline for targeted sequencing of mtRNAs, which may pave the way toward functional annotation of mtRNAs and mtRNA-based diagnostic and therapeutic strategies in various diseases.

Metastatic pattern of ovarian cancer delineated by tracing the evolution of mitochondrial DNA mutations.

Ovarian cancer (OC) is the most lethal gynecologic tumor and is characterized by a high rate of metastasis. Challenges in accurately delineating the metastatic pattern have greatly restricted the improvement of treatment in OC patients. An increasing number of studies have leveraged mitochondrial DNA (mtDNA) mutations as efficient lineage-tracing markers of tumor clonality. We applied multiregional sampling and high-depth mtDNA sequencing to determine the metastatic patterns in advanced-stage OC patients. Somatic mtDNA mutations were profiled from a total of 195 primary and 200 metastatic tumor tissue samples from 35 OC patients. Our results revealed remarkable sample-level and patient-level heterogeneity. In addition, distinct mtDNA mutational patterns were observed between primary and metastatic OC tissues. Further analysis identified the different mutational spectra between shared and private mutations among primary and metastatic OC tissues. Analysis of the clonality index calculated based on mtDNA mutations supported a monoclonal tumor origin in 14 of 16 patients with bilateral ovarian cancers. Notably, mtDNA-based spatial phylogenetic analysis revealed distinct patterns of OC metastasis, in which a linear metastatic pattern exhibited a low degree of mtDNA mutation heterogeneity and a short evolutionary distance, whereas a parallel metastatic pattern showed the opposite trend. Moreover, a mtDNA-based tumor evolutionary score (MTEs) related to different metastatic patterns was defined. Our data showed that patients with different MTESs responded differently to combined debulking surgery and chemotherapy. Finally, we observed that tumor-derived mtDNA mutations were more likely to be detected in ascitic fluid than in plasma samples. Our study presents an explicit view of the OC metastatic pattern, which sheds light on efficient treatment for OC patients.

Mutational signature of mtDNA confers mechanistic insight into oxidative metabolism remodeling in colorectal cancer.

Rationale: Mitochondrial dysfunction caused by mitochondrial DNA (mtDNA) mutations and subsequent metabolic defects are closely involved in tumorigenesis and progression in a cancer-type specific manner. To date, the mutational pattern of mtDNA somatic mutations in colorectal cancer (CRC) tissues and its clinical implication are still not completely clear. Methods: In the present study, we generated a large mtDNA somatic mutation dataset from three CRC cohorts (432, 1,015, and 845 patients, respectively) and then most comprehensively characterized the CRC-specific evolutionary pattern and its clinical implication. Results: Our results showed that the mtDNA control region (mtCTR) with a high mutation density exhibited a distinct mutation spectrum characterizing a high enrichment of L-strand C > T mutations, which was contrary to the H-strand C > T mutational bias observed in the mtDNA coding region (mtCDR) (P < 0.001). Further analysis clearly confirmed the relaxed evolutionary selection of mtCTR mutations, which was mainly characterized by the similar distribution of hypervariable region (HVS) and non-HVS mutation density. Moreover, significant negative selection was identified in mutations of mtDNA complex V (ATP6/ATP8) and tRNA loop regions. Although our data showed that oxidative metabolism was commonly increased in CRC cells, mtDNA somatic mutations in CRC tissues were not closely associated with mitochondrial biogenesis, oxidative metabolism, and clinical progression, suggesting a cancer-type specific relationship between mtDNA mutations and mitochondrial metabolic functions in CRC cells. Conclusion: Our study identified the CRC-specific evolutionary mode of mtDNA mutations, which is possibly matched to specific mitochondrial metabolic remodeling and confers new mechanic insight into CRC tumorigenesis.

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

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.

RAD51B Harbors Germline Mutations Associated With Pancreatic Ductal Adenocarcinoma.

PURPOSE: Genetic alterations in many components of the homologous recombination, DNA damage response, and repair (HR-DDR) pathway are involved in the hereditary cancer syndromes, including familial pancreatic cancer. HR-DDR genes beyond BRCA1, BRCA2, ATM, and PALB2 may also mutate and confer the HR-DDR deficiency in pancreatic ductal adenocarcinoma (PDAC). METHODS: We conducted a study to examine the genetic alterations using a companion diagnostic 15-gene HR-DDR panel in PDACs. HR-DDR gene mutations were identified and characterized by whole-exome sequencing and whole-genome sequencing. Different HR-DDR gene mutations are associated with variable homologous recombination deficiency (HRD) scores. RESULTS: Eight of 50 PDACs with at least one HR-DDR gene mutation were identified. One tumor with BRCA2 mutations is associated with a high HRD score. However, another tumor with a CHEK2 mutation is associated with a zero HRD score. Notably, four of eight PDACs in this study harbor a RAD51B gene mutation. All four RAD51B gene mutations were germline mutations. However, currently, RAD51B is not the gene panel for germline tests. CONCLUSION: The finding in this study thus supports including RAD51B in the germline test of HR-DDR pathway genes.

Distinct clinical and genetic features of hepatitis B virus-associated follicular lymphoma in Chinese patients.

Hepatitis B virus (HBV) infection has been associated with an increased risk for B-cell lymphomas. We previously showed that 20% of diffuse large B-cell lymphoma (DLBCL) patients from China, an endemic area of HBV infection, have chronic HBV infection (surface antigen-positive, HBsAg+) and are characterized by distinct clinical and genetic features. Here, we showed that 24% of follicular lymphoma (FL) Chinese patients are HBsAg+. Compared with the HBsAg- FL patients, HBsAg+ patients are younger, have a higher histological grade at diagnosis, and have a higher incidence of disease progression within 24 months. Moreover, by sequencing the genomes of 109 FL tumors, we observed enhanced mutagenesis and distinct genetic profile in HBsAg+ FLs, with a unique set of preferentially mutated genes (TNFAIP3, FAS, HIST1H1C, KLF2, TP53, PIM1, TMSB4X, DUSP2, TAGAP, LYN, and SETD2) but lack of the hallmark of HBsAg- FLs (ie, IGH/BCL2 translocations and CREBBP mutations). Transcriptomic analyses further showed that HBsAg+ FLs displayed gene-expression signatures resembling the activated B-cell-like subtype of diffuse large B-cell lymphoma, involving IRF4-targeted genes and NF-κB/MYD88 signaling pathways. Finally, we identified an increased infiltration of CD8+ memory T cells, CD4+ Th1 cells, and M1 macrophages and higher T-cell exhaustion gene signature in HBsAg+ FL samples. Taken together, we present new genetic/epigenetic evidence that links chronic HBV infection to B-cell lymphomagenesis, and HBV-associated FL is likely to have a distinct cell-of-origin and represent as a separate subtype of FL. Targetable genetic/epigenetic alterations identified in tumors and their associated tumor microenvironment may provide potential novel therapeutic approaches for this subgroup of patients.

Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens.

Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of "dark antigens" (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations.

Heterogeneous immunogenomic features and distinct escape mechanisms in multifocal hepatocellular carcinoma.

BACKGROUND & AIMS: The presence of multifocal tumors, developed either from intrahepatic metastasis (IM) or multicentric occurrence (MO), is a distinct feature of hepatocellular carcinoma (HCC). Immunogenomic characterization of multifocal HCC is important for understanding immune escape in different lesions and developing immunotherapy. METHODS: We combined whole-exome/transcriptome sequencing, multiplex immunostaining, immunopeptidomes, T cell receptor (TCR) sequencing and bioinformatic analyses of 47 tumors from 15 patients with HCC and multifocal lesions. RESULTS: IM and MO demonstrated distinct clonal architecture, mutational spectrum and genetic susceptibility. The immune microenvironment also displayed spatiotemporal heterogeneity, such as less T cell and more M2 macrophage infiltration in IM and higher expression of inhibitory immune checkpoints in MO. Similar to mutational profiles, shared neoantigens and TCR repertoires among tumors from the same patients were abundant in IM but scarce in MO. Combining neoantigen prediction and immunopeptidomes identified T cell-specific neoepitopes and achieved a high verification rate in vitro. Immunoediting mainly occurred in MO but not IM, due to the relatively low immune infiltration. Loss of heterozygosity of human leukocyte antigen (HLA) alleles, identified in 17% of multifocal HCC, hampered the ability of major histocompatibility complex to present neoantigens, especially in IM. An integrated analysis of Immunoscore, immunoediting, TCR clonality and HLA loss of heterozygosity in each tumor could stratify patients into 2 groups based on whether they have a high or low risk of recurrence (p = 0.038). CONCLUSION: Our study comprehensively characterized the genetic structure, neoepitope landscape, T cell profile and immunoediting status that collectively shape tumor evolution and could be used to optimize personalized immunotherapies for multifocal HCC. LAY SUMMARY: Immunogenomic features of multifocal hepatocellular carcinoma (HCC) are important for understanding immune-escape mechanisms and developing more effective immunotherapy. Herein, comprehensive immunogenomic characterization showed that diverse genomic structures within multifocal HCC would leave footprints on the immune landscape. Only a few tumors were under the control of immunosurveillance, while others evaded the immune system through multiple mechanisms that led to poor prognosis. Our study revealed heterogeneous immunogenomic landscapes and immune-constrained tumor evolution, the understanding of which could be used to optimize personalized immunotherapies for multifocal HCC.

A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis.

MicroRNA (miRNA) plays an important role in the degradation and inhibition of mRNAs and is a kind of essential drug targets for cancer therapy. To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. The strategy was validated by using the data sets of acute myeloid leukemia (AML), breast invasive carcinoma (BRCA), and kidney renal clear cell carcinoma (KIRC). The results showed that in the top 20 miRNAs ranked by their degrees, 90.0% (18/20), 70.0% (14/20), and 70.0% (14/20) miRNAs were found to be associated with the cancers for AML, BRCA, and KIRC, respectively. The KEGG pathways and GO terms enriched with the genes that were predicted as the targets of the cancer-related miRNAs were significantly associated with the biological processes of cancers. In addition, several genes, which were predicted to be regulated by more than three miRNAs, were identified to be the potential drug targets annotated by using the human protein atlas database. Our results demonstrated that the proposed strategy can be helpful for predicting the miRNA-mRNA interactions in tumorigenesis and identifying the cancer-related miRNAs as the potential drug targets.

Bipartite network analysis reveals metabolic gene expression profiles that are highly associated with the clinical outcomes of acute myeloid leukemia.

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p=0.038) and in two test sets (HR=1.69 and 1.56 and p=0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.