A Day 14 Endpoint for Acute GVHD Clinical Trials

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.

Phase 2 study of natalizumab plus standard corticosteroid treatment for high-risk acute graft-versus-host disease

Graft-versus-host disease (GVHD) of the gastrointestinal (GI) tract is the main cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation. Ann Arbor (AA) scores derived from serum biomarkers at onset of GVHD quantify GI crypt damage; AA2/3 scores correlate with resistance to treatment and higher NRM. We conducted a multicenter, phase 2 study using natalizumab, a humanized monoclonal antibody that blocks T-cell trafficking to the GI tract through the α4 subunit of α4β7 integrin, combined with corticosteroids as primary treatment for patients with new onset AA2/3 GVHD. Seventy-five patients who were evaluable were enrolled and treated; 81% received natalizumab within 2 days of starting corticosteroids. Therapy was well tolerated with no treatment emergent adverse events in >10% of patients. Outcomes for patients treated with natalizumab plus corticosteroids were compared with 150 well-matched controls from the MAGIC database whose primary treatment was corticosteroids alone. There were no significant differences in overall or complete response between patients treated with natalizumab plus corticosteroids and those treated with corticosteroids alone (60% vs 58%; P = .67% and 48% vs 48%; P = 1.0, respectively) including relevant subgroups. There were also no significant differences in NRM or overall survival at 12 months in patients treated with natalizumab plus corticosteroids compared with controls treated with corticosteroids alone (38% vs 39%; P = .80% and 46% vs 54%; P = .48, respectively). In this multicenter biomarker-based phase 2 study, natalizumab combined with corticosteroids failed to improve outcome of patients with newly diagnosed high-risk GVHD. This trial was registered at www.clinicaltrials.gov as # NCT02133924.

Effective treatment of low-risk acute GVHD with itacitinib monotherapy

The standard primary treatment for acute graft-versus-host disease (GVHD) requires prolonged, high-dose systemic corticosteroids (SCSs) that delay reconstitution of the immune system. We used validated clinical and biomarker staging criteria to identify a group of patients with low-risk (LR) GVHD that is very likely to respond to SCS. We hypothesized that itacitinib, a selective JAK1 inhibitor, would effectively treat LR GVHD without SCS. We treated 70 patients with LR GVHD in a multicenter, phase 2 trial (NCT03846479) with 28 days of itacitinib 200 mg/d (responders could receive a second 28-day cycle), and we compared their outcomes to those of 140 contemporaneous, matched control patients treated with SCSs. More patients responded to itacitinib within 7 days (81% vs 66%, P = .02), and response rates at day 28 were very high for both groups (89% vs 86%, P = .67), with few symptomatic flares (11% vs 12%, P = .88). Fewer itacitinib-treated patients developed a serious infection within 90 days (27% vs 42%, P = .04) due to fewer viral and fungal infections. Grade ≥3 cytopenias were similar between groups except for less severe leukopenia with itacitinib (16% vs 31%, P = .02). No other grade ≥3 adverse events occurred in >10% of itacitinib-treated patients. There were no significant differences between groups at 1 year for nonrelapse mortality (4% vs 11%, P = .21), relapse (18% vs 21%, P = .64), chronic GVHD (28% vs 33%, P = .33), or survival (88% vs 80%, P = .11). Itacitinib monotherapy seems to be a safe and effective alternative to SCS treatment for LR GVHD and deserves further investigation.

The success of behavioral economics in improving patient retention within an intensive primary care practice

Background

A minority of the U.S. population comprises a majority of health care expenses. Health system interventions for high-cost populations aim to improve patient outcomes while reducing costly over-utilization. Missed and inconsistent appointments are associated with poor patient outcomes and increased health care utilization. PEAK Health— Mount Sinai’s intensive primary care clinic for high-cost patients— employed a novel behavioral economics-based intervention to reduce the rate of missed appointments at the practice. Behavioral economics has accomplished numerous successes across the health care field; the effect of a clinic-based behavioral economics intervention on reducing missed appointments has yet to be assessed.

Methods

This was a single-arm, pre-post trial conducted over 1 year involving all active patients at PEAK Health. The intervention consisted of: a) clinic signage, and b) appointment reminder cards containing behavioral economics messaging designed to increase the likelihood patients would complete their subsequent visit; appointment cards (t1) were transitioned to an identical EMR template (t2) at 6 months to boost provider utilization. The primary objective, the success of scheduled appointments, was assessed with visit adherence: the proportion of successful over all scheduled appointments, excluding those cancelled or rescheduled. The secondary objective, the consistency of appointments, was assessed with a 2-month visit constancy rate: the percentage of patients with at least one successful visit every 2 months for 1 year. Both metrics were assessed via a χ² analysis and together define patient retention.

Results

The visit adherence rate increased from 74.7% at baseline to 76.5% (p = .22) during t1 and 78.0% (p = .03) during t2. The 2-month visit constancy rate increased from 59.5% at baseline to 74.3% (p = .01) post-intervention.

Conclusions

A low-resource, clinic-based behavioral economics intervention was capable of improving patient retention within a traditionally high-cost population. A renewed focus on patient retention— employing the metrics described here— could bolster chronic care efforts and significantly improve the outcomes of high-cost programs by reducing the deleterious effects of missed and inconsistent appointments.

The Functional Hallmarks of Cancer Predisposition Genes

The canonical model for hereditary cancer predisposition is a cancer predisposition gene (CPG) that drives either one or both of two fundamental hallmarks of cancer, defective genomic integrity and deregulated cell proliferation, ultimately resulting in the accumulation of mutations within cells. Thus, the genes most commonly associated with cancer-predisposing genetic syndromes are tumor suppressor genes that regulate DNA repair (eg, BRCA1, BRCA2, MMR genes) and/or cell cycle (eg, APC, RB1). In recent years, however, the spectrum of high-penetrance CPGs has expanded considerably to include genes in non-canonical pathways such as oncogenic signaling, metabolism, and protein translation. We propose here that, given the variety of pathways that may ultimately affect genome integrity and cell proliferation, the model of cancer genetic predisposition needs to be expanded to account for diverse mechanisms. This synthesis calls for modeling and multi-omic studies applying novel experimental and computational approaches to understand cancer genetic predisposition.

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073-81. ©2017 AACR.