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Multicenter Study
. 2017 Feb 9;2(3):e89798.
doi: 10.1172/jci.insight.89798.

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

Affiliations
Multicenter Study

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

Matthew J Hartwell et al. JCI Insight. .

Erratum in

  • An early-biomarker algorithm predicts lethal graft-versus-host disease and survival.
    Hartwell MJ, Özbek U, Holler E, Renteria AS, Major-Monfried H, Reddy P, Aziz M, Hogan WJ, Ayuk F, Efebera YA, Hexner EO, Bunworasate U, Qayed M, Ordemann R, Wölfl M, Mielke S, Pawarode A, Chen YB, Devine S, Harris AC, Jagasia M, Kitko CL, Litzow MR, Kröger N, Locatelli F, Morales G, Nakamura R, Reshef R, Rösler W, Weber D, Wudhikarn K, Yanik GA, Levine JE, Ferrara JL. Hartwell MJ, et al. JCI Insight. 2018 Aug 23;3(16):e124015. doi: 10.1172/jci.insight.124015. eCollection 2018 Aug 23. JCI Insight. 2018. PMID: 30135313 Free PMC article. No abstract available.

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.

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Conflict of interest statement

J.E. Levine and J.L.M. Ferrara are coinventors of a patent (number 62/411,230) for GVHD biomarkers.

Figures

Figure 1
Figure 1. Outcomes according to MAGIC risk stratification.
Six-month cumulative incidences of nonrelapse mortality in high risk (HR) and low risk (LR) were defined by the MAGIC algorithm and compared using Gray’s test. Training set (A): HR 28% (95% CI, 20 to 37); LR 7% (95% CI, 5 to 10); test set (B): HR 33% (95% CI, 21 to 46); LR 7% (95% CI, 5 to 11); validation set (C): HR 26% (95% CI, 17 to 37); LR 10% (95% CI, 7 to 14). Six-month relapse rates were as follows: training set (D): HR 20% (95% CI, 13 to 29); LR 20% (95% CI, 17 to 24); test set (E): HR 17% (95% CI, 8 to 28); LR 19% (95% CI, 15 to 24); validation set (F): HR 14% (95% CI, 7 to 23); LR 15% (95% CI, 11 to 19). Six-month overall survival rates were calculated by the Kaplan-Meier method and compared by the log-rank test: training set (G): HR 60% (95% CI, 51 to 70); LR 84% (95% CI, 80 to 87); test set (H): HR 57% (95% CI, 45 to 72); LR 81% (95% CI, 77 to 86); validation set (I): HR 68% (95% CI, 58 to 80); LR 85% (95% CI, 81 to 89).
Figure 2
Figure 2. MAGIC risk groups.
Six-month cumulative incidence of nonrelapse mortality of all patients (n = 1,287) by (A) related donor: high risk (HR) 26% (95% CI, 15 to 37); low risk (LR) 5% (95% CI, 3 to 7); unrelated donor: HR 30% (95% CI, 23 to 37); LR 10% (95% CI, 8 to 13); (B) HLA matched: HR 26% (95% CI, 20 to 33); LR 7% (95% CI, 5 to 9); HLA mismatched: HR 39% (95% CI, 26 to 53); LR 13% (95% CI, 9 to 18); (C) reduced-intensity conditioning: HR 37% (95% CI, 26 to 48); LR 8% (95% CI, 6 to 11); full-intensity conditioning: HR 25% (95% CI, 19 to 32); LR 8% (95% CI, 6 to 10); (D) age ≤ 21: HR 27% (95% CI, 11 to 47); LR 6% (95% CI, 3 to 11); age > 21:HR 29% (95% CI, 23 to 36); LR 8% (95% CI, 7 to 10). Gray’s test was used for statistical comparisons between groups.
Figure 3
Figure 3. Graft-versus-host disease (GVHD)–related outcomes by MAGIC risk stratification and application of algorithm at GVHD onset.
(A) Six-month cumulative incidences of nonrelapse mortality in Ann Arbor (AA) risk groups AA1, AA2, and AA3 were defined by the 2-biomarker-containing MAGIC algorithm applied at GVHD onset (n = 212): AA3 46% (95% CI, 32 to 58); AA2 24% (95% CI, 14 to 36); and AA1 8% (95% CI, 4 to 15). The proportion of patients in each risk group, as represented by the bar graph, were AA3 27% (n = 57), AA2 28% (n = 59), and AA1 45% (n = 96). (B) Six-month cumulative incidences of nonrelapse mortality in AA1, AA2, and AA3 were defined by the 3-biomarker-containing MAGIC algorithm applied at GVHD onset (n = 212): AA3 47% (95% CI, 32 to 61); AA2 19% (95% CI, 12 to 26); and AA1 8% (95% CI, 2 to 20). The proportion of patients in each risk group, as represented by the bar graph, were AA3 21% (n = 45), AA2 62% (n = 131), and AA1 17% (n = 36).
Figure 4
Figure 4. Study scheme of algorithm development and validation.
Clinical data and plasma samples from day 7 after hematopoietic cellular transplantation were available from 1,287 patients transplanted at 11 MAGIC centers. Patient samples from the 2 largest centers, the University of Michigan and the University of Regensburg, were randomly assigned to the training and test sets in a 2:1 proportion. The remaining 358 patients were assigned to the independent multicenter validation set. The training set alone (n = 620) was used to develop the algorithm. All possible combinations of 1 to 4 biomarkers were used to model 6-month nonrelapse mortality (NRM) by competing-risks regression. Rigorous comparison of models through a Monte Carlo cross validation of 75 different, randomly created training sets confirmed that the models using ST2 and REG3α were superior to all other biomarker combinations. We used this model to predict the probability of 6-month NRM in the patients from the training set, rank ordered them from lowest to highest, and chose a threshold to separate risk groups for the final algorithm (see Methods). We then applied the algorithm to the test set in a first validation and to the multicenter validation set in a second validation.

References

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