2025 Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) : Submission #3

Submission information
Submission Number: 3
Submission ID: 148385
Submission UUID: f114fe5a-8cc4-47ce-95b3-ee716404bff3
Submission URI: /egrp/seqspaceabstracts

Created: Thu, 08/07/2025 - 13:23
Completed: Thu, 08/07/2025 - 13:23
Changed: Thu, 08/07/2025 - 13:23

Remote IP address: 10.208.24.35
Submitted by: Anonymous
Language: English

Is draft: No
First Name Jing
Middle Initial
Last Name Dong
Degree(s) PhD
Position/Title/Career Status Assistant Professor
Organization Medical College of Wisconsin
Email jidong@mcw.edu
Abstract Title Leveraging Mitochondrial Genome to Predict Posttransplant Outcomes in Patients with Myelodysplastic Syndromes
Abstract Summary Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic stem cell disorders characterized by ineffective hematopoiesis and a propensity to progress to acute myeloid leukemia in approximately 30% of patients. Allogeneic hematopoietic cell transplantation (allo-HCT) remains the only curative treatment for MDS. However, post-transplant mortality remains high, primarily due to disease relapse and transplant-related complications. Currently available prognostic models for MDS in the HCT setting (e.g., IPSS-R) rely exclusively on clinical and hematologic parameters, with no incorporation of genetic information. As a result, they are limited in their ability to accurately predict transplant outcomes. Novel predictive biomarkers are urgently needed to identify patients most likely to benefit from allo-HCT and to inform risk-adapted treatment strategies. Mitochondria—the "powerhouses" of the cell—play critical roles in stem cell homeostasis, heme biosynthesis, iron metabolism, and immune regulation. To investigate the prognostic significance of mitochondrial DNA (mtDNA) variation, we performed whole-genome sequencing (WGS) on 494 MDS patients and their matched donors enrolled through the Center for International Blood and Marrow Transplant Research (CIBMTR). Analysis of both recipient and donor mtDNA genomes revealed that a higher burden of mtDNA variants was significantly associated with adverse post-HCT outcomes. Gene-based analyses identified variants in MT-CYB, MT-ND2, MT-ND4, MT-ND4L, MT-ND5, and MT-tRNA as independent predictors of overall survival (OS), relapse-free survival (RFS), relapse, and/or transplant-related mortality (TRM). Furthermore, we applied random survival forest models and demonstrated that incorporating mtDNA variant data significantly improved the predictive performance of the IPSS-R model across all major transplant outcomes (OS, RFS, relapse, and TRM). To explore the potential mechanisms underlying these associations, we conducted computational structural genomics analyses of variants in mitochondrial complex I genes. Preliminary findings suggest that the adverse prognostic effects of these mtDNA variants may result from their deleterious impact on mitochondrial respiration and function.