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Major-Monfried, Hannah

https://orcid.org/0000-0002-9910-4437

Mayo Clinic

Tisch Cancer Institute

Citations 5

The MAGIC Algorithm Probability (MAP): A Novel Laboratory Biomarker for the Response to Treatment of Acute Graft-Versus-Host Disease
Özbek, Umut , Kapoor, Urvi , Ayuk, Francis , Aziz, Mina, Ben-David, Kaitlyn , Choe, Hannah , DeFilipp, Zachariah , Etra, Aaron , Hartwell, Matthew J. , Kowalyk, Steven, Lin, Jung-Yi, Major-Monfried, Hannah , Mielke, Stephan , Morales, George , Ordemann, Rainer, Reddy, Pavan, Roesler, Wolf, Sandhu, Karamjeet , Schechter‐Finkelstein, Tal, Weber, Daniela , Wölfl, Matthias , Young, Rachel , Ferrara, James L.M. , Mielke, Stephan , Srinagesh, Hrishikesh K. , Grupp, Stephan A. , Hexner, Elizabeth O. , Hogan, William J. , Kitko, Carrie L. , Merli, Pietro , Pulsipher, Michael A. , Qayed, Muna , Reshef, Ran , Shah, Jay, Sigel, Keith Magnus , Wudhikarn, Kitsada , Levine, John E.

Blood | February 14, 2020 | Volume: 134 | Issue: Supplement_1 | Pages: 367-367

10.1182/blood-2019-122011 Get It

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.
The MAGIC algorithm probability is a validated response biomarker of treatment of acute graft-versus-host disease
Srinagesh, Hrishikesh K. , Özbek, Umut , Kapoor, Urvi , Ayuk, Francis , Aziz, Mina, Ben-David, Kaitlyn , Choe, Hannah , DeFilipp, Zachariah , Etra, Aaron , Hartwell, Matthew J. , Karol, Alexander B. , Kasikis, Stelios, Kitko, Carrie L. , Kowalyk, Steven, Lin, Jung-Yi , Major-Monfried, Hannah , Mielke, Stephan , Morales, George , Ordemann, Rainer, Reddy, Pavan , Reshef, Ran , Rösler, Wolf, Sandhu, Karamjeet , Schechter, Tal , Weber, Daniela , Wölfl, Matthias , Wudhikarn, Kitsada , Young, Rachel , Levine, John E. , Ferrara, James L.M. , Reshef, Ran , Lin, Jung-Yi , Levine, John E , Mielke, Stephan , Grupp, Stephan A. , Hexner, Elizabeth O. , Hogan, William J. , Merli, Pietro , Pulsipher, Michael A. , Qayed, Muna , Shah, Jay, Sigel, Keith Magnus

Blood Advances | December 9, 2019 | Volume: 3 | Issue: 23 | Pages: 4034-4042

10.1182/bloodadvances.2019000791 Get It

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.
Biomarkers Predict Graft-Vs-Host Disease Outcomes Better Than Clinical Response after One Week of Treatment
Major-Monfried, Hannah , Özbek, Umut , Renteria, Anne S. , Pawarode, Attaphol , Yanik, Gregory A. , Ayuk, Francis , Holler, Ernst , Efebera, Yvonne A. , Wölfl, Matthias , Ordemann, Rainer, Mielke, Stephan , Bunworasate, Udomsak , Chen, Yi‐Bin , Harris, Andrew C. , Jagasia, Madan , Litzow, Mark R. , Nakamura, Ryotaro , Roesler, Wolf, Weber, Daniela , Ferrara, James L.M. , Hartwell, Matthew J. , Hogan, William J. , Qayed, Muna , Hexner, Elizabeth O. , Wudhikarn, Kitsada , Mielke, Stephan , Devine, Steven M. , Kröger, Nicolaus , Malki, Monzr M. Al , Kitko, Carrie L. , Locatelli, Franco , Reddy, Pavan , Reshef, Ran , Levine, John E.

Blood | October 11, 2019 | Volume: 128 | Issue: 22 | Pages: 510-510

10.1182/blood.v128.22.510.510 Get It

Abstract Graft-versus-host disease (GVHD), the primary cause of non-relapse mortality (NRM) following allogeneic hematopoietic stem cell transplantation, does not always respond to treatment with high dose systemic corticosteroids. We have recently shown that a combination of three biomarkers (TNFR1, ST2, and REG3α) measured at onset of GVHD can predict day 28 response to treatment and 6-month NRM (Levine, Lancet Haem, 2015). Our goal in the current study was to determine if the same biomarker-based Ann Arbor GVHD algorithm can alsopredict treatment response andmortality whenapplied after one week of systemic corticosteroid treatment. The study population consisted of 378 patients (pts) with acute GVHD from 11 centers in the Mount Sinai Acute GVHD International Consortium. All pts were treated with systemic steroids and provided a plasma or serum sample obtained after one week of treatment (±3 days). The median starting dose of systemic steroids for Grade II-IV GVHD was 2.0 mg/kg/day and for Grade I was 1.0 mg/kg/day, after which treatment varied. Patients were divided into test (n=236) and validation (n=142) cohorts. We applied the Ann Arbor GVHD algorithm to concentrations of TNFR1, ST2, and REG3α measured after one week of treatment to generate a predicted probability of 6-month NRM, which we term the treatment score (TS). We employed unsupervised k-medoidclustering to partition TS values from the test cohort into two groups (high and low). This unbiased approach identified a high score group made up of 25% of pts (n=58) in the test cohort. We observed that the day 28 response rate (complete, CR + partial, PR) was significantly lower in pts with high scores compared to low scores in the test cohort (24% vs 65%, p<0.0001) (Fig 1A). Analysis of the validation cohort using the same TS definitions showed similar differences in response rates (22% vs 61%, p<0.0001) (Fig 1B). Further, nearly four times as many pts with high scores in both cohorts died within 6 months from non-relapse causes compared to pts with low scores (test: 57% vs 17%, p<0.0001; validation: 57% vs 14%, p<0.0001) (Fig 1C/D). As expected, the majority of non-relapse deaths in pts treated for GVHD were directly attributable to GVHD (test: 95%; validation: 89%). Relapse rates for high and low score pts were similar (data not shown), and thus pts with a high TS experienced significantly worse overall survival in both cohorts (test: 37% vs 72%, p<0.0001; validation: 38% vs 79%, p<0.0001) (Fig 1E/F). Approximately half of the pts in each cohort (test: 48%; validation: 44%) responded (CR+PR) to the first week of steroids and these ptshad significantly lower 6-month NRM than non-responders (NR) (test: 17% vs 36%, p=0.0002; validation: 13% vs 36%, p=0.0014). Yet the TS continued to stratify mortality risk independently of clinical response. In the test cohort, pts with a high score comprised 16% of all early responders and experienced more than twice the NRM of early responders with a low score (33% vs 13%, p=0.022) (Fig 2A). Conversely, test cohort pts who did not respond by day 7, but had a low score, fared much better than non-responders with a high score (NRM 21% vs 68%, p<0.0001) (Fig 2B). Two thirds of early non-responders comprised this more favorable group. These highly significant results reproduced in the independent validation cohort in similar proportions (CR+PR: 45% vs 6%, p=0.0003; NR: 61% vs 22%, p=0.0001) (Fig 2C/D). Finally, a subset analysis revealed that pts classified as NR after one week of steroids due to isolated, yet persistent, grade I skin GVHD (24/378, 6%) overwhelmingly had low treatment scores (22/24, 92%) and experienced rates of NRM (9%) comparable to responders with low scores, thus forming a distinct, albeit small, subset of pts with non-responsive GVHD that fares particularly well (Fig 3). In conclusion, a treatment score based on three GVHD biomarkers measured after one week of steroids stratifies pts into two groups with distinct risks for treatment failure and 6-month NRM. It is particularly noteworthy that the TS identifies two subsets of pts with steroid refractory (SR) GVHD who have highly different outcomes (Fig 2B/D). The much larger group, approximately two thirds of all SR pts, may not need the same degree of treatment escalation as is traditional for clinical non-response, and thus overtreatment might be avoided. Because the TSis measured at a common decision making time point, it may prove useful to guide risk-adapted therapy. Disclosures Mielke: Novartis: Consultancy; MSD: Consultancy, Other: Travel grants; Celgene: Other: Travel grants, Speakers Bureau; Gilead: Other: Travel grants; JAZZ Pharma: Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Ferrara:Viracor: Patents & Royalties: GVHD biomarker patent. Levine:Viracor: Patents & Royalties: GVHD biomarker patent.
MAGIC biomarkers predict long-term outcomes for steroid-resistant acute GVHD
Major-Monfried, Hannah , Renteria, Anne S. , Pawarode, Attaphol , Reddy, Pavan , Ayuk, Francis , Holler, Ernst , Efebera, Yvonne A. , Wölfl, Matthias , Wudhikarn, Kitsada , Ordemann, Rainer, Young, Rachel , Hartwell, Matthew J. , Chaudhry, Mohammed S., Aziz, Mina, Etra, Aaron , Yanik, Gregory A. , Kröger, Nicolaus , Weber, Daniela , Chen, Yi‐Bin , Nakamura, Ryotaro , Rösler, Wolf, Kitko, Carrie L. , Harris, Andrew C. , Kowalyk, Steven, Morales, George , Torres, I.M., Özbek, Umut , Ferrara, James L.M. , Levine, John E. , Kroger, Nicolaus , Kroger, Nicolaus , Levine, John E , Hogan, William J. , Qayed, Muna , Hexner, Elizabeth O. , Shah, Jay Narayan , Pulsipher, Michael A. , Reshef, Ran , Kröger, Nicolaus

Blood | March 15, 2018 | Volume: 131 | Issue: 25 | Pages: 2846-2855

10.1182/blood-2018-01-822957 Get It

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

JCI Insight | February 8, 2017 | Volume: 2 | Issue: 3 | Pages: No page available

10.1172/jci.insight.89798 Get It

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.