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Corrigendum Free access | 10.1172/jci.insight.124015

An early-biomarker algorithm predicts lethal graft-versus-host disease and survival

Matthew J. Hartwell, Umut Özbek, Ernst Holler, Anne S. Renteria, Hannah Major-Monfried, Pavan Reddy, Mina Aziz, William J. Hogan, Francis Ayuk, Yvonne A. Efebera, Elizabeth O. Hexner, Udomsak Bunworasate, Muna Qayed, Rainer Ordemann, Matthias Wölfl, Stephan Mielke, Attaphol Pawarode, Yi-Bin Chen, Steven Devine, Andrew C. Harris, Madan Jagasia, Carrie L. Kitko, Mark R. Litzow, Nicolaus Kröger, Franco Locatelli, George Morales, Ryotaro Nakamura, Ran Reshef, Wolf Rösler, Daniela Weber, Kitsada Wudhikarn, Gregory A. Yanik, John E. Levine, and James L.M. Ferrara

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Published August 23, 2018 - More info

Published in Volume 3, Issue 16 on August 23, 2018
JCI Insight. 2018;3(16):e124015. https://doi.org/10.1172/jci.insight.124015.
Copyright © 2018, American Society for Clinical Investigation
Published August 23, 2018 - Version history
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Related article:

An early-biomarker algorithm predicts lethal graft-versus-host disease and survival
Matthew J. Hartwell, … , John E. Levine, James L.M. Ferrara
Matthew J. Hartwell, … , John E. Levine, James L.M. Ferrara
Graft-versus-host-disease biomarkers measured one week after allogeneic transplant can risk stratify patients for lethal graft-versus-host disease and survival.
Clinical Research and Public Health Oncology Transplantation

An early-biomarker algorithm predicts lethal graft-versus-host disease and survival

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Abstract

BACKGROUND. No laboratory test can predict the risk of nonrelapse mortality (NRM) or severe graft-versus-host disease (GVHD) after hematopoietic cellular transplantation (HCT) prior to the onset of GVHD symptoms. METHODS. Patient blood samples on day 7 after HCT were obtained from a multicenter set of 1,287 patients, and 620 samples were assigned to a training set. We measured the concentrations of 4 GVHD biomarkers (ST2, REG3α, TNFR1, and IL-2Rα) and used them to model 6-month NRM using rigorous cross-validation strategies to identify the best algorithm that defined 2 distinct risk groups. We then applied the final algorithm in an independent test set (n = 309) and validation set (n = 358). RESULTS. A 2-biomarker model using ST2 and REG3α concentrations identified patients with a cumulative incidence of 6-month NRM of 28% in the high-risk group and 7% in the low-risk group (P < 0.001). The algorithm performed equally well in the test set (33% vs. 7%, P < 0.001) and the multicenter validation set (26% vs. 10%, P < 0.001). Sixteen percent, 17%, and 20% of patients were at high risk in the training, test, and validation sets, respectively. GVHD-related mortality was greater in high-risk patients (18% vs. 4%, P < 0.001), as was severe gastrointestinal GVHD (17% vs. 8%, P < 0.001). The same algorithm can be successfully adapted to define 3 distinct risk groups at GVHD onset. CONCLUSION. A biomarker algorithm based on a blood sample taken 7 days after HCT can consistently identify a group of patients at high risk for lethal GVHD and NRM. FUNDING. The National Cancer Institute, American Cancer Society, and the Doris Duke Charitable Foundation.

Authors

Matthew J. Hartwell, Umut Özbek, Ernst Holler, Anne S. Renteria, Hannah Major-Monfried, Pavan Reddy, Mina Aziz, William J. Hogan, Francis Ayuk, Yvonne A. Efebera, Elizabeth O. Hexner, Udomsak Bunworasate, Muna Qayed, Rainer Ordemann, Matthias Wölfl, Stephan Mielke, Attaphol Pawarode, Yi-Bin Chen, Steven Devine, Andrew C. Harris, Madan Jagasia, Carrie L. Kitko, Mark R. Litzow, Nicolaus Kröger, Franco Locatelli, George Morales, Ryotaro Nakamura, Ran Reshef, Wolf Rösler, Daniela Weber, Kitsada Wudhikarn, Gregory A. Yanik, John E. Levine, James L.M. Ferrara

×

Original citation: JCI Insight. 2017;2(3):1–9. https://doi.org/10.1172/jci.insight.89798

Citation for this corrigendum: JCI Insight. 2018;3(16):e124015. https://doi.org/10.1172/jci.insight.124015

In the statistics section, the equation to generate a final prediction model from the training set was incorrect. The correct sentence is below.

We then created a training set at random and repeated the entire process to generate a final model: log[–log(1 – p̂)] = –11.263 + 1.844(log10ST2) + 0.577(log10REG3α), where p̂ = predicted probability of 6-month NRM.

The authors regret the error.

Footnotes

See the related article at An early-biomarker algorithm predicts lethal graft-versus-host disease and survival.

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  • Version 1 (August 23, 2018): Electronic publication

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