Identification of high-risk amino-acid substitutions in hematopoietic cell transplantation: a challenging task.
S R MarinoS M LeeT A BinkowskiT WangM HaagensonH-L WangMartin J MaiersS SpellmanKoen van BesienS J LeeT KarrisonA ArtzPublished in: Bone marrow transplantation (2016)
Allogeneic hematopoietic cell transplantation (HCT) offers the potential to cure hematologic malignancies. In the absence of an HLA-matched donor, HLA mismatched unrelated donors may be used, although risks of GvHD and treatment-related mortality (TRM) are higher. Identification and avoidance of amino-acid substitution and position types (AASPT) conferring higher risks of TRM and GvHD would potentially improve the success of transplantation from single HLA mismatched unrelated donors. Using random forest and logistic regression analyses, we identified 19 AASPT associated with greater risks for at least one adverse transplant outcome: grade III-IV acute GvHD, TRM, lower disease-free survival or worse overall survival relative to HLA-matched unrelated donors and to other AASPT. When tested in an independent validation cohort of 3530 patients, none of the AASPT from the training set were validated as high risk, however. Review of the literature shows that failure to validate original observations is the rule and not the exception in immunobiology and emphasizes the importance of independent validation before clinical application. Our current data do not support avoiding any specific class I AASPT for unrelated donors. Additional studies should be performed to fully understand the role of AASPT in HCT outcomes.
Keyphrases
- free survival
- amino acid
- human health
- cord blood
- kidney transplantation
- allogeneic hematopoietic stem cell transplantation
- end stage renal disease
- newly diagnosed
- climate change
- stem cell transplantation
- ejection fraction
- liver failure
- chronic kidney disease
- bone marrow
- prognostic factors
- electronic health record
- respiratory failure
- acute myeloid leukemia
- peritoneal dialysis
- risk factors
- acute lymphoblastic leukemia
- big data
- cardiovascular events
- cell death
- type diabetes
- emergency department
- artificial intelligence
- extracorporeal membrane oxygenation
- combination therapy