Detection of mitochondrial DNA variants at low level heteroplasmy in pediatric CNS and extra-CNS solid tumors with three different enrichment methods
Introduction
The etiology of mitochondrial genomic disorders as well as the role of mitochondria in cancer involves both nuclear-encoded genes and maternally inherited mitochondrial DNA (mtDNA) (Ali et al., 2019). Unlike the nuclear genome, there are numerous mitochondria and hundreds to thousands of copies of the mtDNA genome per cell, which contributes to intra- and intercellular heterogeneity and heteroplasmy (the fraction of mutant mtDNA genomes compared to all mtDNA genomes in a tissue or cell) (Aryaman et al., 2018). Mitochondrial DNA (mtDNA) mutations have been found causal to a number of classic mitochondrial disorders (Wallace and Chalkia, 2013, Muraresku et al., 2018) and also contribute to tumorigenesis (Brandon et al., 2006, Wallace, 2012). Mitochondrial metabolism has been linked to cancer since Oto Warburg described the Warburg effect in 1927. (Vander Heiden et al., 2009, Warburg et al., 1927, Porporato et al., 2018) Warburg observed that, even in the presence of oxygen, cancer cells prefer glycolysis over the more energy efficient oxidative phosphorylation in the mitochondria. He hypothesized that the glycolysis pathway generates precursors that are used by cancer cells to up-regulate biosynthesis and cellular proliferation, thus creating a link between dysfunctional mitochondrial metabolism and cancer. Additionally, there are other aspects of mitochondrial biology that are important in cancer including mitochondrial biogenesis and turnover, fission and fusion dynamics, oxidative stress, variation in mtDNA sequence and abundance, and a complex signaling mechanism through the mitochondrial unfolded protein response (UPRmt) which activates transcription of nuclear encoded mitochondrial chaperones that rescue dysfunctional mitochondria and may provide a survival advantage to cancer cells. (Chatterjee et al., 2006, Lu et al., 2009, Vyas et al., 2016, Kenny and Germain, 2017, Qureshi et al., 2017, Melber and Haynes, 2018).
Somatic mtDNA mutations are present in a variety of cancers, as best illustrated by several recent pan-cancer studies of adult and pediatric cancers. (Ju et al., 2014;3., Stewart et al., 2015) Small-scale studies have demonstrated mtDNA mutations in a variety of pediatric malignancies including medulloblastoma (Lueth et al., 2010, Wong et al., 2003), other pediatric brain tumors (Luna et al., 2015), pediatric acute myelogenous leukemia (AML) (Kang et al., 2016), and neuroblastoma (Riehl et al., 2016). Most recently, we described the landscape of germline and somatic mtDNA mutations in a diverse selection of pediatric cancers using whole genome sequencing (WGS) data from 616 primary tumor and matched normal samples (Triska et al., 2019). We analyzed data from various hematologic malignancies, solid tumors (including 14 retinoblastomas and 13 rhabdomyosarcomas), and CNS tumors (142 total). We identified distinct somatic mtDNA profiles of different tumor types, the presence of multiple patients with germline pathogenic variants for classic mitochondrial diseases, four hotspot LoF somatic mtDNA mutations and one hotspot somatic mtDNA tRNA mutation. Our study established 391 mtDNA mutations in 284 tumors including a total of 45 LoF mutations which clustered in MT-COX3, MT-ND4, and MT-ND5, as well as a mutation hotspot in MT-tRNA-MET. We also observed distinct mtDNA mutation profiles in children compared to adults, with children having more variants at low heteroplasmy.
Prior studies have shed light on the high false positive rate of mtDNA mutations in existing publications. (Salas et al., 2005, Yao et al., 2007). In the present study, we explored three different methods for enriching and sequencing the mitochondrial genome that could be used to prospectively analyze primary pediatric tumors for mtDNA variants, which is very important given the ever-increasing recognition of the contributions of mtDNA mutations to tumorigenesis. The methods that we compared include REPLI-g (Marquis et al., 2017), LRPCR (Zhang et al., 2012), and a hybridization based capture method for the mitochondrial genome using custom Agilent SureSelect probes (Falk et al., 2012). REPLI-g requires a very small genomic DNA input of 5 ng, LRPCR requires a 10-fold greater amount of genomic DNA with a mean quantity of 50 ng used in the literature, while SureSelect requires 200 ng of genomic DNA on average. Both LRPCR and REPLI-g work only with fresh frozen tissue samples and formalin-fixed paraffin-embedded (FFPE) is not recommended as the starting material. Although performance is suboptimal with FFPE, such samples can be used for SureSelect based mtDNA enrichment and sequencing. Our laboratory has a strong clinical focus that requires systemic validation with the intent of generating reproducible results. Thus, we compared these three methods to validate our work and to determine the best method to use for future studies.
In this manuscript, we describe the most accurate and efficient method for enriching and sequencing the mtDNA genome in tumor samples. The methods were compared using DNA isolated from blood, orbital fat pad-derived mesenchymal cells, buccal swab or primary tumor fresh frozen tissue from pediatric patients with CNS tumors, retinoblastoma, rhabdoid tumor, and sarcoma. We demonstrate the potential use of LRPCR as well as Agilent SureSelect methods followed by high depth NGS to characterize the mtDNA in primary tumors, as illustrated by results from the primary, first recurrence, and second recurrence tumors of a gliofibroma patient.
Section snippets
REPLI-g followed by NGS
The mtDNA genome was amplified using REPLI-g Mitochondrial DNA Kit (Qiagen, Germantown, MD). (Marquis et al., 2017, Dasgupta et al., 2010). REPLI-g provides enrichment of the mitochondrial DNA up to 40 million-fold through rolling circle amplification. One µl of template genomic DNA (20 ng/µl) was adjusted to 20 µl with RNAse-free water supplied from the kit. Of note, as little as 5 ng of DNA can be used. The amplification mix was prepared using 27 µl REPLI-g mt Reaction Buffer and 2 µl REPLI-g
Detection of known mtDNA mutations using LRPCR and SureSelect methods
To validate our methods, libraries were prepared from clinical samples with known pathogenic mtDNA variants of various heteroplasmy levels. Sequence data generated from these libraries were compared to clinical data previously generated by either Baylor College of Medicine (BCM) or GeneDx, Inc. DNA from the same four clinical samples with known mtDNA variants was used for all three methods. We were able to detect all the variants reported previously with a positive control method with both
Discussion
The primary goal of this study was to identify a sensitive and accurate method for detecting low-level variants in mtDNA in primary tumor tissue. Initially we used REPLI-g because of the small amount of DNA required, ease of setting up the PCR reaction, as well as encouraging results in terms of sensitivity and accuracy based on published studies (Marquis et al., 2017, Dasgupta et al., 2010). However, comparing the data from three methods, we determined that REPLI-g/NGS introduces many
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
The authors thank David Ruble and Yuxia Zhan for their help with exome sequencing.
Support
Supported in part by funding from National Institutes of Health Grant No. 5T32-CA009659-22T32 (K.K) and the Unravel Pediatric Cancer Foundation (JAB).
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