A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä

Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation




TekijätJ. Ritari, K. Hyvärinen, S. Koskela, M. Itälä-Remes, R. Niittyvuopio, A. Nihtinen, U. Salmenniemi, M. Putkonen, L. Volin, T. Kwan, T. Pastinen, J. Partanen

KustantajaNature Publishing Group

Julkaisuvuosi2019

JournalLeukemia

Tietokannassa oleva lehden nimiLeukemia

Vuosikerta33

Numero1

Aloitussivu240

Lopetussivu248

Sivujen määrä9

ISSN0887-6924

eISSN1476-5551

DOIhttps://doi.org/10.1038/s41375-018-0229-3

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/35850934


Tiivistelmä

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.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.





Last updated on 2024-26-11 at 20:08