A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation
Tekijät: J. Ritari, K. Hyvärinen, S. Koskela, M. Itälä-Remes, R. Niittyvuopio, A. Nihtinen, U. Salmenniemi, M. Putkonen, L. Volin, T. Kwan, T. Pastinen, J. Partanen
Kustantaja: Nature Publishing Group
Julkaisuvuosi: 2019
Journal: Leukemia
Tietokannassa oleva lehden nimi: Leukemia
Vuosikerta: 33
Numero: 1
Aloitussivu: 240
Lopetussivu: 248
Sivujen määrä: 9
ISSN: 0887-6924
eISSN: 1476-5551
DOI: https://doi.org/10.1038/s41375-018-0229-3
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/35850934
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.
Ladattava julkaisu This is an electronic reprint of the original article. |