Aziz, Mina

Abstract Steroid-refractory (SR) acute graft-versus-host disease (GVHD) remains a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT), but its occurrence is not accurately predicted by pre-HCT clinical risk factors. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm probability (MAP) identifies patients who are at high risk for developing SR GVHD as early as 7 days after HCT based on the extent of intestinal crypt damage as measured by the concentrations of 2 serum biomarkers, suppressor of tumorigenesis 2 and regenerating islet-derived 3α. We conducted a multicenter proof-of-concept “preemptive” treatment trial of α-1-antitrypsin (AAT), a serine protease inhibitor with demonstrated activity against GVHD, in patients at high risk for developing SR GVHD. Patients were eligible if they possessed a high-risk MAP on day 7 after HCT or, if initially low risk, became high risk on repeat testing at day 14. Thirty high-risk patients were treated with twice-weekly infusions of AAT for a total of 16 doses, and their outcomes were compared with 90 high-risk near-contemporaneous MAGIC control patients. AAT treatment was well tolerated with few toxicities, but it did not lower the incidence of SR GVHD compared with controls (20% vs 14%, P = .56). We conclude that real-time biomarker-based risk assignment is feasible early after allogeneic HCT but that this dose and schedule of AAT did not change the incidence of SR acute GVHD. This trial was registered at www.clinicaltrials.gov as #NCT03459040.

Systemic glucocorticoids are the principal treatment for acute graft-versus-host disease (GVHD), which remains the major cause of non-relapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT). However, there are no validated biomarkers that measure a patient's response to glucocorticoid therapy, and thus response is evaluated by the change in clinical symptom severity. A major weakness in the predictive power of clinical responses is that changes to all organs are weighted equally even though the major driver of NRM is irreversible damage to the crypts of the GI tract. Recent studies from the Mount Sinai Acute GVHD International Consortium (MAGIC) have validated an algorithm probability (MAP) that combines serum concentrations of two biomarkers of GVHD (REG3α and ST2) to generate an estimated probability of 6 month NRM for individual patients. The MAP has been considered a "liquid biopsy" that estimates the damage caused by GVHD to crypts throughout the lower GI tract (Hartwell et al., JCI Insight, 2017; Major-Monfried et al., Blood, 2018). We hypothesized that the change in MAP between start of treatment and 28 days later could serve as a response biomarker for GVHD and might compare favorably to the change in clinical symptoms that measures response to GVHD treatment, which is widely used as a surrogate for long term survival and is the primary endpoint in most GVHD treatment trials (Martin et al., BBMT, 2009; MacMillan et al., Blood, 2010). We prospectively collected serum samples and clinical staging from 368 sequential HCT patients who received systemic treatment for acute GVHD in one of 20 MAGIC centers between January 2016 and February 2018. We measured the serum concentrations of REG3α and ST2 before and after systemic therapy for acute GVHD and computed MAPs, the changes in MAPs, and clinical responses for each patient. MAPs of patients who experienced 6 month NRM showed significantly greater increases than MAPs of patients who survived (p=0.0004). In patients whose MAPs at the start of treatment were low (Ann Arbor 1, MAP < 0.141) or intermediate (Ann Arbor 2, 0.141 ≤ MAP ≤ 0.290), 6 month NRM clustered among those who had the greatest increases in MAP after 28 days (Fig 1A,B). In patients with high MAPs at the start of treatment (Ann Arbor 3, MAP > 0.290), those who survived tended to have the largest decreases in MAP (Fig 1C). These changes in MAP suggested crossing a single threshold could predict risk of mortality. We found that patients whose MAPs rose above a threshold MAP of 0.290 (5% of Ann Arbor 1, 27% of Ann Arbor 2) had significantly worse survival compared to those who remained below it, whereas the large number patients with initially high MAPs that remained above the threshold (66% of Ann Arbor 3) had a large increases in mortality (Fig 2). When measured at day 28, the MAP was significantly more accurate in predicting NRM than the gold standard of the clinical response, with areas under the receiver operating characteristic curve (AUC) of 0.86 and 0.70, respectively (p<0.0001). An algorithm that combined clinical response with biomarkers generated the same AUC as the MAP alone (0.83 v 0.86, p = NS). We next tested whether the same MAP threshold of 0.290 could predict risk within clinical response subsets. A significant minority (10%) of clinical responders had high MAPs and experienced three-fold greater NRM than those with low MAPs (40% v 12%, p<0.0001) whereas the majority (57%) of non-responders had low MAPs and experienced almost three-fold lower NRM than those with high MAPs (24% v 65%, p<0.0001) (Fig 3). Thus the MAP provides important prognostic information over and above the change in clinical symptoms, further stratifying both responders and non-responders at four weeks of treatment. The MAP threshold classified patients both with and without significant lower GI symptoms because the MAP is a more specific measure of irreversible cryptic damage in patients with copious diarrhea and more sensitive in patients with less than 0.5 liters of daily diarrhea (Fig 4). We conclude that the MAP is, to our knowledge, the first validated laboratory test to serve as response biomarker for the treatment for acute GVHD and a more accurate predictor of survival than clinical response after four weeks of treatment. The MAP may serve as a novel endpoint and an important complement to changes in clinical symptom severity in future trials of GVHD treatment. Disclosures Srinagesh: National Institutes of Health: Research Funding. Ozbek:Viracor: Patents & Royalties: Biomarker Patent. Ayuk:Novartis: Honoraria, Other: Advisory Board, Research Funding. Aziz:Doris Duke Charitable Foundation: Research Funding. Defilipp:Incyte: Research Funding. Grupp:Novartis: Consultancy, Research Funding; Roche: Consultancy; GSK: Consultancy; CBMG: Consultancy; Novartis: Research Funding; Kite: Research Funding; Servier: Research Funding; Jazz: Other: study steering committees or scientific advisory boards; Adaptimmune: Other: study steering committees or scientific advisory boards; Cure Genetics: Consultancy; Humanigen: Consultancy. Hexner:novartis: Research Funding. Kitko:Mallinckrodt: Honoraria; Novartis: Consultancy, Honoraria. Mielke:EBMT/EHA: Other: Travel support; ISCT: Other: Travel support; Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; IACH: Other: Travel support; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; DGHO: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau. Merli:Sobi: Consultancy; Amgen: Honoraria; Novartis: Honoraria; Bellicum: Consultancy. Pulsipher:Amgen: Other: Lecture; Miltenyi: Research Funding; Bellicum: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Other: Education for employees; CSL Behring: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Medac: Honoraria. Qayed:Bristol-Myers Squibb: Honoraria. Reshef:Pfizer: Consultancy; Magenta: Consultancy; Kite: Consultancy, Research Funding; Atara: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Research Funding; Shire: Research Funding. Levine:Incyte: Consultancy, Research Funding; Biogen: Other: non-financial support; Viracor: Patents & Royalties: biomarker patent; Ironwood: Honoraria; bluebird bio: Consultancy; National Cancer Institute: Research Funding; Novartis: Honoraria; Kamada: Research Funding. Ferrara:National Institutes of Health: Research Funding; ViraCor: Consultancy; Incyte: Consultancy; Kamada: Consultancy; Mallinckrodt: Consultancy; Enlivex: Consultancy; Xenikos: Consultancy; CSL Behring: Consultancy.

Key Points The MAGIC algorithm probability, computed from 2 serum biomarkers, predicts mortality in all GVHD grades after 4 weeks of treatment. Dynamic changes in the MAGIC algorithm probability occur within all biomarker risk groups and can guide therapy.

Abstract Relapse of malignancy and lethal graft versus host disease (GVHD) are the principal causes of failure of allogeneic hematopoietic cell transplant (HCT). Recently we have shown that at seven days after HCT an algorithm using two serum biomarkers (ST2 and REG3α) can predict severe GVHD (Hartwell et al. JCI Insight 2017). We determined whether serial testing (in the first month following HCT) of patients with low probability biomarkers would improve the predictive accuracy of the algorithm and identify patients with different risks of relapse and lethal GVHD. Patients who received an HCT at 18 centers in the Mount Sinai Acute GVHD International Consortium (MAGIC) for hematologic malignancy and who supplied three blood samples were divided into a training set and validation set with equal numbers of lethal GVHD events, which was defined as death from GVHD or infection during treatment for GVHD. Patients in the training set (n=702) underwent HCT from January 1, 2006 until June 30, 2015, whereas patients in the validation set (n=906) underwent HCT from July 1, 2015 to May 1, 2017. Serum samples were analyzed using the previously published algorithm of two biomarkers up to three times (day 7, day 14, day 28 or GVHD onset, if onset occurred within the first 28 days). The algorithm generates a predicted probability of lethal GVHD between 0 and 1 for each patient. Patients were categorized as either low probability (LP) or high probability (HP) for lethal GVHD. HP thresholds of 0.20 and 0.16 were used to classify patients with and without GVHD symptoms, respectively (once categorized as HP, patients remained in that category and were not retested). All results were similar between training and validation sets, and we present here the validation set results. Serial testing identified 28% of patients as HP with a three-fold greater cumulative incidence of lethal GVHD at one year (13% vs 4%, p<0.001, Figure 1). Relapse rates were the same in both probability groups, and thus LP patients experienced significantly better relapse free survival (RFS) (69% vs 53%, p<0.001). As expected, significantly fewer LP patients experienced severe GVHD at onset (as measured by Minnesota risk), maximum grade III/IV GVHD, or steroid resistant GVHD by day 180 after HCT (Figure 2, p<0.001 for all three parameters). To measure the accuracy of prediction, at each timepoint we calculated the area under the curve (AUC) of receiver operating characteristic curves; the AUC increased significantly with each subsequent evaluation, from 0.59 at one timepoint (dotted line) to 0.74 at the third timepoint (solid line) (p<0.001), with a final sensitivity of 65% and specificity of 74% (Figure 3A). Early development of GVHD (by day 28) is a risk factor for lethal GVHD. Therefore, we next plotted RFS (dashed line), relapse (solid line), and lethal GVHD (dotted line) rates in patients who developed GVHD by day 28. 25% of patients with GVHD were categorized as HP and had a cumulative incidence of lethal GVHD more than four times higher (28%) than that of relapse (6%); however the risks were reversed for the 75% of patients who were LP, where relapse (15%) occurred twice as often as lethal GVHD (7%) (Figure 3B). In patients who did not develop GVHD in the first month, this reversal of risks was even more dramatic. Approximately half (53%) of the entire validation cohort did not develop GVHD by day 28 and was LP at all three evaluations. These patients had an exceptionally low risk of lethal GVHD and thus they relapsed (25%) much more often than they died from GVHD (3%). When malignancies were classified according to risk for relapse by the disease risk index (DRI) (Figure 3C), the probability of relapse was three fold higher than lethal GVHD in malignancies with a low DRI (12%), six fold higher for intermediate DRI (20%), and eleven fold higher for high/very high DRI (33%). We conclude that a serial monitoring strategy using GVHD biomarkers for one month after HCT is able to identify two groups of patients with very different risks of lethal GVHD and relapse. For these patients, the intensity of immunosuppression after day 28 could be tailored according to the probabilities of developing lethal GVHD and relapse in the context of clinical trials. Disclosures Aziz: Doris Duke Charitable Foundation: Research Funding. Ayuk:Therakos (Mallinckrodt): Honoraria; Novartis: Honoraria; Celgene: Consultancy; Gilead: Consultancy. Chen:REGiMMUNE: Consultancy; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees; Magenta Therapeutics: Consultancy; Takeda Pharmaceuticals: Consultancy. Merli:Neovii Biotech: Honoraria; AMGEN: Honoraria. Roesler:Sanofi: Other: Travel, Accommodations, Expenses; Amgen: Equity Ownership; Jazz Pharmaceuticals: Other: Travel, Accommodations, Expenses; Immunomedics: Equity Ownership; Biogen: Equity Ownership; Merck: Consultancy; Pfizer: Consultancy. Kitko:Novartis: Consultancy, Honoraria; Mallinckrodt: Honoraria, Other: Travel, Accommodations, Expenses. Qayed:Novartis: Consultancy. Wölfl:Bristol-myers Squibb: Equity Ownership; Novartis: Equity Ownership; Taheda: Equity Ownership; Juno: Equity Ownership; Neovii: Other: Travel, Accommodations, Expenses. Mielke:Celgene: Speakers Bureau; DGHO: Speakers Bureau; EHA: Speakers Bureau; Kiadis Pharma: Speakers Bureau; Miltenyi: Speakers Bureau. Wudhikarn:Takeda Oncology: Other: Travel, Accommodations, Expenses. Nakamura:Celgene: Honoraria; Molmed: Honoraria; Merck: Consultancy; Pharmacyclics: Consultancy; Atara: Consultancy; Jazz Pharmaceuticals: Consultancy. Pulsipher:CSL Behring: Consultancy; Novartis: Consultancy, Honoraria, Speakers Bureau; Adaptive Biotech: Consultancy, Research Funding; Amgen: Honoraria. Reshef:Pfizer: Consultancy; Atara Biotherapeutics: Consultancy; Kite Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; Bristol-Myers Squibb: Consultancy; Incyte: Consultancy. Levine:Therakos: Consultancy; Novartis: Consultancy; Bluebird: Consultancy; Incyte: Consultancy; Kamada: Research Funding; Viracor: Patents & Royalties. Ferrara:Incyte: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Xenikos: Consultancy, Other: Travel, Accommodations, Expenses; Kamada: Consultancy, Research Funding; Viracor: Consultancy, Patents & Royalties.

Abstract No laboratory test can predict non-relapse mortality (NRM) after hematopoietic cellular transplantation (HCT) prior to the onset graft-versus-host disease (GVHD). Recently, we have shown that a signature of three GVHD plasma biomarkers (TNFR1, ST2, and REG3α) can predict response to GVHD therapy and NRM at the onset of clinical GVHD (Levine, Lancet Haem, 2015). Our goal in the current study was to identify a blood biomarker signature that could predict lethal GVHD and six-month NRM well in advance of the onset of GVHD symptoms. Patient samples on day +7 after HCT were obtained from 1,287 patients from 11 HCT centers in the Mount Sinai Acute GVHD International Consortium (MAGIC). Samples from two large centers (n = 929) were combined and randomly assigned to a training set (n = 620) and test set (n = 309). 358 patients from nine others centers constituted an independent validation set. The overall cumulative incidences of 6-month NRM were 11%, 12%, and 13% for the training, test, and validation sets respectively. The incidence of lethal GVHD, defined as death without preceding relapse while under steroid treatment for acute GVHD, were 18%, 24%, and 14% in the same groups, respectively. The median day of GVHD onset was 28 days in the training set and 29 days in the test and validation sets. We measured four GVHD related biomarkers [ST2, REG3α, TNFR1, and IL2Rα] in all samples and used the training set alone to develop competing risks regression models that used all 13 possible combinations of one to four biomarkers to predict 6-month NRM. The best algorithm, which we rigorously confirmed through Monte Carlo cross-validation of 75 different combinations of training sets, included ST2 and REG3α. No combination of one, three, or four biomarkers was superior to the combination of these two biomarkers. The day 7 algorithm identified high risk (HR) and low risk (LR) groups with 6-month NRMs of 28% and 7%, respectively (p<0.001) (Fig 1A). The relapse rates did not differ between risk groups so that overall survival (OS) was 60% for HR and 84% for LR (p<0.001) (Fig 1B). When applied to the test set (Fig 1C/D), the algorithm identified 54/309 (17%) of the patients as HR with an NRM of 33% vs 7% for LR patients (p<0.001) and 6-month OS of 57% and 81% for HR and LR patients, respectively (p<0.001). In the independent validation set (Fig 1 E/F), the algorithm identified 72/358 (20%) of the patients as HR with an NRM of 26% vs 10% for LR patients (p<0.001) and OS of 68% and 85% for HR and LR patients, respectively (p<0.001). High risk patients were three times more likely to die from GVHD than LR patients in each cohort (p<0.001) (Fig 2). The GI tract is the GVHD target organ that is most resistant to treatment and represents a major cause of NRM, and we observed twice as much severe GI GVHD (stage 3 or 4) in HR patients as in LR patients (p<0.001, data not shown). The algorithm successfully separated HR and LR strata for 6 month NRM in several groups with differing risks for GVHD and NRM, including donor type, degree. of HLA-match, age group, and conditioning regimen intensity (Fig 3). In conclusion, we have developed a blood biomarker algorithm that predicts the development of lethal GVHD seven days after HCT, which performed successfully in large multicenter validation sets. The GVH reaction is already in progress by day +7, even though clinical symptoms may not occur until days or weeks later. We speculate that the blood biomarker concentrations at this early time point reflect subclinical GI pathology, a notion that is reinforced by the fact that ST2 and REG3α, the two biomarkers in the algorithm, are closely associated with GI GVHD. The algorithm identified HR and LR strata in several patient groups with different overall risk for lethal GVHD (donor, HLA match, conditioning regimen intensity, age). This day +7 algorithm should prove useful in clinical BMT research by identifying patients at high risk for lethal GVHD who might benefit from aggressive preemptive treatment strategies. Disclosures Chen: Novartis: Research Funding; Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Levine:Viracor: Patents & Royalties: GVHD biomarkers patent. Ferrara:Viracor: Patents & Royalties: GVHD biomarkers patent.

Abstract Background Transcatheter Aortic Valve Implantation (TAVI) is commonly used to deploy new bioprosthetic valves inside degenerated surgically implanted aortic valves in high risk patients. The three scoring systems used to assess risk of postprocedural mortality are: Logistic EuroSCORE (LES), EuroSCORE II (ES II), and Society of Thoracic Surgeons (STS). Objective The purpose of this study is to analyze the accuracy of LES, ES II, and STS in estimating all‐cause mortality after transcatheter aortic valve‐in‐valve (ViV) implantations, which was not assessed before. Methods Using the Valve‐in‐Valve International Data (VIVID) registry, a total of 1,550 patients from 110 centers were included. The study compared the observed 30‐day overall mortality vs. the respective predicted mortalities calculated by risk scores. The accuracy of prediction models was assessed based on calibration and discrimination. Results Observed mortality at 30 days was 5.3%, while average expected mortalities by LES, ES II and STS were 29.49 (± 17.2), 14.59 (± 8.6), and 9.61 (± 8.51), respectively. All three risk scores overestimated 30‐day mortality with ratios of 0.176 (95% CI 0.138–0.214), 0.342 (95% CI 0.264–0.419), and 0.536 (95% CI 0.421–0.651), respectively. 30‐day mortality ROC curves demonstrated that ES II had the largest AUC at 0.722, followed by STS at 0.704, and LES at 0.698. Conclusions All three scores overestimated mortality at 30 days with ES II showing the highest predictability compared to LES and STS; and therefore, should be recommended for ViV procedures. There is a need for a dedicated scoring system for patients undergoing ViV interventions.

Key Points Biomarker scores generated after 1 week of steroid treatment of GVHD are prognostic. Biomarkers reflect prognosis better than early clinical response to GVHD treatment.

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.