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PMCID: PMC6326954 DOI: 10.1038/s41375-018-0229-3


Allogeneic haematopoietic stem cell transplantation currently represents the primary potentially curative treatment for cancers of the blood and bone marrow. While relapse occurs in approximately 30% of patients, few risk-modifying genetic variants have been identified. The present study evaluates the predictive potential of patient genetics on relapse risk in a genome-wide manner. We studied 151 graft recipients with HLA-matched sibling donors by sequencing the whole-exome, active immunoregulatory regions, and the full MHC region. To assess the predictive capability and contributions of SNPs and INDELs, we employed machine learning and a feature selection approach in a cross-validation framework to discover the most informative variants while controlling against overfitting. Our results show that germline genetic polymorphisms in patients entail a significant contribution to relapse risk, as judged by the predictive performance of the model (AUC = 0.72 [95% CI: 0.63-0.81]). Furthermore, the top contributing variants were predictive in two independent replication cohorts (n = 258 and n = 125) from the same population. The results can help elucidate relapse mechanisms and suggest novel therapeutic targets. A computational genomic model could provide a step toward individualized prognostic risk assessment, particularly when accompanied by other data modalities.

Journal Title

Leukemia : official journal of the Leukemia Society of America, Leukemia Research Fund, U.K





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MeSH Keywords

Adolescent; Adult; Aged; Biomarkers, Tumor; Child; Child, Preschool; Female; Genomics; Graft vs Host Disease; Hematologic Neoplasms; Hematopoietic Stem Cell Transplantation; Humans; Male; Middle Aged; Neoplasm Recurrence, Local; Polymorphism, Genetic; Predictive Value of Tests; Tissue Donors; Transplantation, Homologous; Young Adult