An innovative data analysis strategy for accurate next-generation sequencing detection of tumor mitochondrial DNA mutations

Mitochondrial and Nuclear DNA Variants in Amyotrophic Lateral Sclerosis: Enrichment in the Mitochondrial Control Region and Sirtuin Pathway Genes in Spinal Cord Tissue

Amyotrophic Lateral Sclerosis (ALS) is a progressive disease with prevalent mitochondrial dysfunctions affecting both upper and lower motor neurons in the motor cortex, brainstem, and spinal cord. Despite mitochondria having their own genome (mtDNA), in humans, most mitochondrial genes are encoded by the nuclear genome (nDNA). Our study aimed to simultaneously screen for nDNA and mtDNA genomes to assess for specific variant enrichment in ALS compared to control tissues. Here, we analysed whole exome (WES) and whole genome (WGS) sequencing data from spinal cord tissues, respectively, of 6 and 12 human donors. A total of 31,257 and 301,241 variants in nuclear-encoded mitochondrial genes were identified from WES and WGS, respectively, while mtDNA reads accounted for 73 and 332 variants. Despite technical differences, both datasets consistently revealed a specific enrichment of variants in the mitochondrial Control Region (CR) and in several of these genes directly associated with mitochondrial dynamics or with Sirtuin pathway genes within ALS tissues. Overall, our data support the hypothesis of a variant burden in specific genes, highlighting potential actionable targets for therapeutic interventions in ALS.

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.

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 profiling of mtDNA control region reveals tumor-specific evolutionary selection involved in mitochondrial dysfunction

Background

Mitochondrial DNA (mtDNA) mutations alter mitochondrial function in oxidative metabolism and play an important role in tumorigenesis. A series of studies have demonstrated that the mtDNA control region (mtCTR), which is essential for mtDNA replication and transcription, represents a mutational hotspot in human tumors. However, a comprehensive pan-cancer evolutionary pattern analysis of mtCTR mutations is urgently needed.

Methods

We generated a comprehensive combined dataset containing 10026 mtDNA somatic mutations from 4664 patients, covering 20 tumor types based on public and private next-generation sequencing data.

Findings

Our results demonstrated a significantly higher and much more variable mutation rate in mtCTR than in the coding region across different tumor types. Moreover, our data showed a remarkable distributional bias of tumor somatic mutations between the hypervariable segment (HVS) and non-HVS, with a significantly higher mutation density and average mutation sites in HVS. Importantly, the tumor-specific mutational pattern between mtCTR HVS and non-HVS was identified, which was classified into three evolutionary selection types (relaxed, moderate, and strict constraint types). Analysis of substitution patterns revealed that the prevalence of C_H > T_H in non-HVS greatly contributed to the mutational selection pattern of mtCTR across different tumor types. Furthermore, we found that the mutational pattern of mtCTR in the four tumor types was clearly associated with mitochondrial biogenesis, mitochondrial oxidative metabolism, and the overall survival of patients.

Interpretation

Our results suggest that somatic mutations in mtCTR may be shaped by tumor-specific selective pressure and are involved in tumorigenesis.

Fundings

National Natural Science Foundation of China [grants 82020108023, 81830070, 81872302], and Autonomous Project of State Key Laboratory of Cancer Biology, China [grants CBSKL2019ZZ06, CBSKL2019ZZ27].

mitoDataclean: A machine learning approach for the accurate identification of cross-contamination-derived tumor mitochondrial DNA mutations

Next-generation sequencing (NGS) of mitochondrial DNA (mtDNA) has widespread applications in aging and cancer studies. However, cross-contamination of mtDNA constitutes a major concern. Previous methods for the detection of mtDNA contamination mainly focus on haplogroup-level phylogeny, but neglect haplotype-level differences, leading to limited sensitivity and accuracy. In this study, we present mitoDataclean, a random-forest-based machine learning package for accurate identification of cross-contamination, evaluation of contamination levels and detection of contamination-derived variants in mtDNA NGS data. Comprehensive optimization of mitoDataclean revealed that training simulation with mixtures of small haplogroup distance and low polymorphic difference was critical for optimal modeling. Compared with existing methods, mitoDataclean exhibited significantly improved sensitivity and accuracy for the detection of sample contamination in simulated data. In addition, mitoDataclean achieved area under the curve values of 0.91 and 0.97 for discerning genuine and contamination-derived mtDNA variants in a simulated Western dataset and private sequencing contamination data, respectively, suggesting that this tool may be applicable for different populations and samples with different sources of contamination. Finally, mitoDataclean was further evaluated in several private and public datasets and showed a robust ability for contamination detection. Altogether, our study demonstrates that mitoDataclean may be used for accurate detection of contaminated samples and contamination-derived variants in mtDNA NGS data. This article is protected by copyright. All rights reserved.

Next-Generation Sequencing-Based Analysis of Urine Cell-Free mtDNA Reveals Aberrant Fragmentation and Mutation Profile in Cancer Patients

BACKGROUND

Many studies have demonstrated the high efficacy of cell-free nuclear DNA in cancer diagnostics. Compared to nuclear DNA, mitochondrial DNA (mtDNA) exhibits distinct characteristics, including multiple copies per cell and higher mutation frequency. However, the potential applicability of cell-free mtDNA (cf-mtDNA) in plasma and urine remains poorly investigated.

METHODS

Here, we comprehensively analyzed the fragmentomic and mutational characteristics of cf-mtDNA in urine and plasma samples from controls and cancer patients using next-generation sequencing.

RESULTS

Compared to plasma cf-mtDNA, urine cf-mtDNA exhibited increased copy numbers and wider spread in fragment size distributions. Based on 2 independent animal models, urine cf-mtDNA originated predominantly from local shedding and transrenal excretion. Further analysis indicated an enhanced fragmentation of urine cf-mtDNA in renal cell carcinoma (RCC) and colorectal cancer (CRC) patients. Using the mtDNA sequence of peripheral blood mononuclear cells for reference, the mutant fragments were shorter than wild-type fragments in urine cf-mtDNA. Size selection of short urine cf-mtDNA fragments (<150 bp) significantly enhanced the somatic mutation detection. Our data revealed remarkably different base proportions of fragment ends between urine and plasma cf-mtDNA that also were associated with fragment size. Moreover, both RCC and CRC patients exhibited significantly higher T-end and lower A-end proportions in urine cf-mtDNA than controls. By integrating the fragmentomic and mutational features of urine cf-mtDNA, our nomogram model exhibited a robust efficacy for cancer diagnosis.

CONCLUSIONS

Our proof-of-concept findings revealed aberrant fragmentation and mutation profiles of urine cf-mtDNA in cancer patients that have diagnostic potential.

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.