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Record W3211615499 · doi:10.1182/blood-2021-154302

Prognostic Value of Elafin in Acute Graft-Versus-Host Disease

2021· article· en· W3211615499 on OpenAlexaff
Makda Zewde, George Morales, Isha Gandhi, Umut Özbek, Paibel Aguayo‐Hiraldo, Francis Ayuk, Janna Baez, Chantiya Chanswangphuwana, Hannah Choe, Zachariah DeFilipp, Aaron Etra, Stephan A. Grupp, Elizabeth O. Hexner, William J. Hogan, Rebeka Javorniczky, Stelios Kasikis, Carrie L. Kitko, Steven Kowalyk, Elisabeth Meedt, Pietro Merli, Ryotaro Nakamura, Muna Qayed, Ran Reshef, Wolf Roesler, Tal Schechter‐Finkelstein, Daniela Weber, Matthias Wölfl, Gregory A. Yanik, Rachel Young, John E. Levine, James L.M. Ferrara, Yi‐Bin Chen

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsElafinMedicineBiomarkerGraft-versus-host diseaseHematopoietic stem cell transplantationPopulationInternal medicineImmunologyGastrointestinal tractOncologyTransplantationGastroenterologyBiology

Abstract

fetched live from OpenAlex

Abstract Background: A major cause of mortality in patients receiving hematopoietic stem cell transplantation (HCT) is acute graft-versus-host disease (GVHD), a multiorgan disorder that includes the skin, liver and gastrointestinal tract. We have previously identified elafin, a protease inhibitor overexpressed in inflamed epidermis, as a diagnostic biomarker of GVHD in the skin, the most commonly involved GVHD organ. However, our initial study was limited to a subset of patients with isolated skin GVHD. The main driver of nonrelapse mortality (NRM) in HCT patients is GI GVHD. Two biomarkers, Regenerating islet-derived 3a (REG3α) and Suppressor of tumorigenesis 2 (ST2), have since been validated as biomarkers of GI GVHD that predict long-term outcomes in patients treated for GVHD. We undertook this study to determine the utility of elafin as a prognostic biomarker of acute GVHD in the general population of previously unstudied acute GVHD patients, and to compare it to ST2 and REG3α. Study Design: 526 patients who received systemic corticosteroid treatment for skin GVHD were analyzed from the Mount Sinai Acute GVHD International Consortium (MAGIC), which includes patients from 25 HCT centers. We used ELISA to measure serum concentrations of elafin, ST2 and REG3α. Patients were divided randomly into equal training and validation sets; and we developed a competing risk regression model for 6-month NRM using elafin concentration in the training set. We developed additional models for 6-month NRM using concentrations of ST2 and REG3α, or the combination of all three biomarkers as predictors. We then constructed ROC curves to evaluate the predictive accuracy of each model and to analyze the ability of each model to stratify patients into high- and low-risk groups. We analyzed the cumulative incidence of 6-month NRM and overall survival in each model and compared the accuracy of each model in the validation set. Results: The area under the receiver operating curve (AUROC) for elafin alone was 0.55 whereas it was 0.75 and statistically superior (P = 0.02) for the combination of ST2 and REG3α. The combination of 3 biomarkers produced an AUROC of 0.76 that was not significantly better than the two biomarker model (P = 0.10). Elafin concentrations, either alone or in combination with ST2 and REG3α, did not produce higher hazard ratios of NRM (data not shown). Patients in the low-risk elafin group paradoxically demonstrated a higher incidence of 6-month NRM, although this difference was not statistically significant (17% vs. 11%, P=0.19), and both overall survival at 6 months (68% vs. 68%, P>0.99) and four-week response (78% vs. 78%, P=0.98) were similar in the low- and high-risk elafin groups (Figure 1). As demonstrated in previous data sets, the combination of ST2 and REG3α divided patients into two groups with a nearly five-fold difference in NRM (6.7% vs. 31%, P <0.001). Conclusion: We demonstrated that serum elafin concentrations measured at the initiation of systemic treatment for acute GVHD in a multicenter population of patients treated systemically for acute GVHD do not predict 6-month NRM, overall survival, or treatment response. As seen in previous studies, serum concentrations of the GI GVHD biomarkers ST2 and REG3α were significant predictors of NRM and the addition of elafin levels did not improve their accuracy. These results underscore the importance of GI disease in driving NRM in patients who develop acute GVHD. Figure 1. Cumulative incidence of nonrelapse mortality and overall survival in high and low risk groups Six-month cumulative incidences of nonrelapse mortality (NRM) in high (solid line) and low (dotted line) risk groups defined by optimized biomarker thresholds (upper panels) and six-month overall survival estimated using the Kaplan-Meier method (lower panels). (A) Cumulative incidence of NRM (14%) and overall survival (75%) in the total validation set (N=263). (B) Cumulative incidence of NRM in the low (N=150) and high (N=113) elafin group (17% vs. 11%, P=0.19). Overall survival in the low and high elafin group (68% vs. 68%, P > 0.99). (C) Cumulative incidence of NRM in the low (N=175) and high (N=88) ST2 + REG3a group (6.7 vs. 31%, P < 0.001). Overall survival in the low and high ST2 + REG3a group (77% vs. 51%, P < 0.001). (D) Cumulative incidence of NRM in the low (N=180) and high (N=83) elafin + ST2 + REG3a group (7.0 vs. 30%, P < 0.001). Overall survival in the low and high elafin + ST2 + REG3a group (79% vs. 64%, P < 0.001). Figure 1 Figure 1. Disclosures Ozbek: Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor. DeFilipp: Omeros, Corp.: Consultancy; Incyte Corp.: Research Funding; Regimmune Corp.: Research Funding; Syndax Pharmaceuticals, Inc: Consultancy. Grupp: Novartis, Kite, Vertex, and Servier: Research Funding; Jazz Pharmaceuticals: Consultancy, Other: Steering committee, Research Funding; Novartis, Roche, GSK, Humanigen, CBMG, Eureka, and Janssen/JnJ: Consultancy; Novartis, Adaptimmune, TCR2, Cellectis, Juno, Vertex, Allogene and Cabaletta: Other: Study steering committees or scientific advisory boards. Hexner: Blueprint medicines: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tmunity Therapeutics: Research Funding; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees. Kitko: Co-investigator on two NIH grants as part of the cGVHD consortium: Research Funding; Vanderbilt University Medical Center: Current Employment; PER: Other: PER - CME educational talks about GVHD; Horizon: Membership on an entity's Board of Directors or advisory committees. Qayed: Novartis: Honoraria; Mesoblast: Honoraria; Medexus: Honoraria. Reshef: ilead, BMS, Precision, Immatics, Atara, Takeda, Shire, Pharmacyclics, Incyte: Research Funding; Bayer: Consultancy; Gilead and Novartis: Honoraria; BMS, Regeneron, TScan, Synthekine, Atara, Jasper, Bayer: Consultancy. Levine: Incyte: Consultancy, Research Funding; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; Equillium Bio: Membership on an entity's Board of Directors or advisory committees; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Biogen: Research Funding; Kamada: Research Funding. Ferrara: Eurofins Viracor: Consultancy, Other: Royalties. Chen: Incyte: Consultancy; Gamida: Consultancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.268
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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