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Review
. 2015 Mar;10(1):45-58.
doi: 10.1007/s11899-014-0246-x.

Identification and utilization of donor and recipient genetic variants to predict survival after HCT: are we ready for primetime?

Lara E Sucheston-Campbell  1 , Alyssa Clay, Philip L McCarthy, Qianqian Zhu, Leah Preus, Marcelo Pasquini, Kenan Onel, Theresa Hahn
Affiliations
Review

Identification and utilization of donor and recipient genetic variants to predict survival after HCT: are we ready for primetime?

Lara E Sucheston-Campbell et al. Curr Hematol Malig Rep. 2015 Mar.

Abstract

Overall survival following hematopoietic cell transplantation (HCT) has improved over the past two decades through better patient selection and advances in HLA typing, supportive care, and infection prophylaxis. Nonetheless, mortality rates are still unsatisfactory and transplant-related mortality remains a major cause of death after unrelated allogeneic HCT. Since there are no known pre-HCT, non-HLA biologic predictors of survival following transplant, for over a decade, scientists have been investigating the role of non-HLA germline genetic variation in survival and treatment-related mortality after HCT. Variation in single nucleotide polymorphisms (SNPs) has the potential to impact chemotherapy, radiation, and immune responses, leading to different post-HCT survival outcomes. In this paper, we address the current knowledge of the contribution of genetic variation to survival following HCT and discuss study design and methodology for investigating HCT survival on a genomic scale.

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Conflict of interest statement

Dr. Lara E. Sucheston-Campbell, Dr. Alyssa Clay, Dr. Philip L. McCarthy, Dr. Qianqian Zhu, Dr. Leah Preus, Dr. Marcelo Pasquini, Dr. Kenan Onel, and Dr. Theresa Hahn each declare no potential conflicts of interest.

Figures

Fig. 1
Fig. 1
Power to detect associations with DRM, TRM, and TRM subtypes is shown in the Fig. 1. The x-axis, showing the proportion of events, can be used to determine power for a range of survival outcomes following HCT from 10 to 50 % in frequency. The dashed and solid lines reflect minor allele frequencies of 0.10 and 0.40, respectively. Thus, for example, given a survival outcome occurring in 25 % of DISCOVeRY-BMT cohort 1 and a minor allele frequency of 0.40, we have power to detect hazard ratios of approximately 1.5. A lower MAF of 0.10 yields power to detect hazard ratios of 2.0
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