Prognostic Impact and Landscape of Mitochondrial DNA Variants in Myelodysplastic Syndromes Undergoing Allogenic Hematopoietic Cell Transplantation

Background: Myelodysplastic syndromes (MDS) are a heterogenous group of clonal hematopoietic cell disorders characterized by blood cytopenias and a tendency to progress to acute myeloid leukemia. Despite treatment advances, allogeneic hematopoietic stem-cell transplantation (allo-HCT) remains the only potentially curative therapy for MDS. However, mortality after allo-HCT is high due to disease relapse and transplant-related complications. Current prognostic tools defining MDS risk groups, such as the International Prognostic Scoring System (IPSS) and the revised IPSS (IPSS-R), are used to predict posttransplant outcomes for clinical decision-making. A major limitation of these scoring systems is a failure to account for variables other than clinical and hematologic parameters. Comprehensive genomic analyses have shown that mutations in specific genes (e.g., TP53, TET2, and DNMT3A) could inform prognostic stratification of MDS undergoing allo-HCT. However, most studies of MDS genome have focused on mutations occurring in the nuclear genome and ignored the mitochondrial genome. The current study aims to characterize mitochondrial genomic landscape of MDS and identify mitochondrial DNA (mtDNA) genetic variants prognostic for post allo-HCT outcomes in MDS.

Methods: We performed whole genome sequencing (WGS) on whole-blood samples obtained before allo-HCT from 494 patients with MDS who were enrolled in the Center for International Blood and Marrow Transplant Research (CIBMTR) between 2014 and 2018. Mitochondrial genome sequences were extracted from the WGS data and aligned to the mtDNA reference genome (Revised Cambridge Reference Sequence, rCRS) by GSNAP. Putative mtDNA variants, including single nucleotide variants (SNV) and indels, were identified by parsing the SAM CIGAR string. Pathogenicity prediction was conducted for mtDNA variants in the protein-coding genes, MT-tRNAs and MT-rRNAs using different databases. We evaluated the impact of mtDNA variants on the overall survival (OS) and relapse with adjustment for MDS- and HCT-related factors. A random survival forest algorithm was applied to evaluate the prognostic performance of models that include mtDNA variants alone and combined with MDS- and HCT-related clinical factors.

Results: The patients in the cohort have a median age of 67 years, 36.4% had poor/very poor Revised International Prognostic Scores (IPSS-R), and 26% received myeloablative conditioning regimens. On average, the sequencing depth of the mitochondrial genome was 14,596× (213× - 17,070×), and the average coverage was 99.90% (99.89% - 100%). A total of 2,666 mtDNA variants are identified, including 411 potential pathogenic variants. Mitochondrial control region (also known as D-Loop) was the most frequently mutated region in our cohort and the most frequently mutated protein-coding gene was MT-ND5 (16%). MDS patients with higher number of mitochondrial mutations (continuous variable) had significantly worse OS (hazard ratio, HR, 1.11; 95% CI, 1.01-1.22; P = 0.029) and higher risk of relapse (HR, 1.13; 95% CI, 1.00-1.27; P = 0.049) than patients with lower number of mutations. Gene-based analysis yielded significant associations with OS for MT-CYB (P = 1.04×10^-3), MT-ND2 (P = 3.06×10^-3) and MT-ND4 (P = 1.41×10^-3) variants after Bonferroni correction (P < 0.05/16=3.13×10^-3) (Table 1). Four mtDNA genes were significantly associated with relapse, including MT-CYB (P = 7.03×10^-4), MT-ND2 (P = 5.92×10^-4), MT-ND5 (P = 1.91×10^-3) and MT-tRNA (P = 1.99×10^-4). (Table 1). To investigate whether mtDNA variants could improve the prognostic stratification of MDS patients receiving allo-HCT, we fitted random survival forest models with and without inclusion of mtDNA variants in the models. The model based only on mtDNA genes had a c-index of 0.58 to predict OS, which was slightly higher than the IPSS-R (c-index = 0.48) and clinical model (IPSS-R plus MDS type and pre-transplantation treatments, c-index = 0.57). Adding mtDNA genes improved the predictive performance of the model, with c-index increased from 0.48 to 0.63 for the IPSS-R model and from 0.57 to 0.66 for the clinical model (Figure 1). Similar results were observed for the models in predicting relapse (Figure 1).

Conclusions: There may be clinical utility of mtDNA variants to predict allo-HCT outcomes in combination with more standard clinical parameters.