Somatic mitochondrial mutation discovery using ultra-deep sequencing of the mitochondrial genome reveals spatial tumor heterogeneity in head and neck squamous cell carcinoma

Abstract

Mutations in mitochondrial DNA (mtDNA) have been linked to risk, progression, and treatment response of head and neck squamous cell carcinoma (HNSCC). Due to their clonal nature and high copy number, mitochondrial mutations could serve as powerful molecular markers for detection of cancer cells in bodily fluids, surgical margins, biopsies and lymph node (LN) metastasis, especially at sites where tumor involvement is not histologically apparent. Despite a pressing need for high-throughput, cost-effective mtDNA mutation profiling system, current methods for library preparation are still imperfect for detection of low prevalence heteroplasmic mutations. To this end, we have designed an ultra-deep amplicon-based sequencing library preparation approach that covers the entire mitochondrial genome. We sequenced mtDNA in 28 HNSCCs, matched LNs, surgical margins and bodily fluids, and applied multiregional sequencing approach on 14 primary tumors. Our results demonstrate that this quick, sensitive and cost-efficient method allows obtaining a snapshot on the mitochondrial heterogeneity, and can be used for detection of low frequency tumor-associated mtDNA mutations in LNs, sputum and serum specimens. These findings provide the foundation for using mitochondrial sequencing for risk assessment, early detection, and tumor surveillance.

Keywords: Cancer; Head and neck squamous cell carcinoma (HNSCC); Mitochondrial DNA (mtDNA); Mutations; Sequencing.

Figure 1.. Somatic mutations of the mitochondrial genome in primary HNSCC tumors.

A. Schematic presentation of the assay design. Tracks on the circos plot: blue – annotation of the mtDNA genes, red -expected amplicons. B. Table depicting percentage of detected somatic mtDNA mutations with indicated allelic frequency. C. Circos plot showing the landscape of detected somatic mtDNA mutations across the mitochondrial genome. Each tick represents a mutation at a specific genome location. D. Dot plot depicting minor allele frequency across the functional elements of mt genome. E. Number of somatic mutations in functional regions of the mitochondrial genome (grey bars - absolute numbers; red line - mutations per base pair). F. Venn diagram summarizing mtDNA mutations concurrently detected by two independent sequencing runs.

Figure 2.. Intratumor heterogeneity of mtDNA mutations in primary HNSCC lesions.

Eight tumors with multi-regional sequencing (3 or 5 zones per tumor) in which clonal events (mutations present in more than one zone) were detected are shown. Each color quadrant represents somatic mutation detected in a particular gene (see color code on the right side of the panel). Genomic position of each mutation in indicated on axis Y. Axis X - white quadrants indicate tissues collected form superficial zone, up-pointed triangles indicate tissues collected from the tumor mid zone, down-pointed triangles indicate specimens collected from the deep invasive front. Number of mutations detected in each specimen are indicated beneath each zone. Asterisks indicate mutations detected in the same genomic position.

Figure 3.. Spatial distribution of mtDNA abundance in HNSCC.

A. Comparison of mtDNA copy number index calculated using three nuclear-encoded housekeeping genes. All 3 ratios produced comparable (non-significant) distribution of relative mtDNA abundance. B. Relative mtDNA abundance (calculated as average of 3 mtDNA/nDNA ratios) in tumor samples (black circles), matched histologically negative surgical margin specimens (brown up-pointed triangles) and lymphocytes (blue down-pointed triangles). C. mtDNA copy number index in 14 HNSCC tumors with multiregional sequencing. White circles indicate tissues collected form superficial zone, blue circles indicate tissues collected from the tumor mid zone, red circles indicate specimens collected from the deep invasive front.

Figure 4:. Detection of tumor-specific mtDNA alterations in matched LNs, margins and bodily fluids.

A. Figure summarizes cancer-associated mutations among different lesions collected form the same patient (only tumors in which cancer associated mutations were found in LNs, margins or bodily fluids are shown). From top to bottom - rectangular shapes indicate patient’s number, magenta number beneath the rectangular shape indicates a number of unique somatic mutations detected in this HNSCC malignancy, ovals represent different zones collected for this patient, purple numbers beneath the ovals depict the amount of mutations detected in each distinct tumor zone. Small quadrats indicate different specimens collected from the same patients: white – pathologically clear LNs, red – pathologically involved LNs, cyan – serum, yellow – sputum, brown – surgical margins. Numbers beneath the small quadrats indicate the positions of tumor associated mutation detected in this specimens (the color code indicates the zonal origin of this mutation. Black numbers indicate clonal mutations). Asterisk indicates metastatic LNs positive for extracapsular spread. 3% detection cutoff was used for mutations calling in all samples (primary HNSCC tumors, LNs, margins, serum and sputum. B. Same as (A), however 1% cutoff was used for variants calling in LN, margins, serum and sputum.

Figure 5.. Validation of selective mtDNA mutations with ultra-sensitive ddPCR.

A. ddPCR validation of 2 mutations (at positions 2240 and 10569) detected in primary tumor of patient #2 is shown as an example. DNA samples extracted from all specimens collected form patient #2 (3 distinct primary tumor zones, 7 LNs, serum and lymphocytes) were probed with two different ddPCR assays specifically designed for detecting each of the above substitutions. Only specimens were mutation was detected are shown. Blue dot clusters indicate mutation detected by the specific assay. Black dot cluster indicates empty droplets. Green clusters indicate droplets containing wild-type alleles. Brown clusters indicate droplets containing both wild-type and mutant alleles. B. Contingency table depicts the concordance between selected mutation detection by NGS and ddPCR.