Mutations in epigenetic regulators including SETD2 are gained during relapse in pediatric acute lymphoblastic leukemia

Relapsed pediatric acute lymphoblastic leukemia (ALL) has high rates of treatment failure. Epigenetic regulators have been proposed as modulators of chemoresistance, here we sequence genes encoding epigenetic regulators in matched diagnosis-remission-relapse ALL samples. We find significant enrichment of mutations in epigenetic regulators at relapse with recurrent somatic mutations in SETD2, CREBBP, MSH6, KDM6A and MLL2, mutations in signaling factors are not enriched. Somatic alterations in SETD2, including frameshift and nonsense mutations, are present at 12% in a large de novo ALL patient cohort. We conclude that the enrichment of mutations in epigenetic regulators at relapse is consistent with a role in mediating therapy resistance

The CAR-HEMATOTOX risk-stratifies patients for severe infections and disease progression after CD19 CAR-T in R/R LBCL

BACKGROUND: CD19-directed chimeric antigen receptor T-cell therapy (CAR-T) represents a promising treatment modality for an increasing number of B-cell malignancies. However, prolonged cytopenias and infections substantially contribute to the toxicity burden of CAR-T. The recently developed CAR-HEMATOTOX (HT) score-composed of five pre-lymphodepletion variables (eg, absolute neutrophil count, platelet count, hemoglobin, C-reactive protein, ferritin)-enables risk stratification of hematological toxicity. METHODS: In this multicenter retrospective analysis, we characterized early infection events (days 0-90) and clinical outcomes in 248 patients receiving standard-of-care CD19 CAR-T for relapsed/refractory large B-cell lymphoma. This included a derivation cohort (cohort A, 179 patients) and a second independent validation cohort (cohort B, 69 patients). Cumulative incidence curves were calculated for all-grade, grade ≥3, and specific infection subtypes. Clinical outcomes were studied via Kaplan-Meier estimates. RESULTS: In a multivariate analysis adjusted for other baseline features, the HT score identified patients at high risk for severe infections (adjusted HR 6.4, 95% CI 3.1 to 13.1). HT(high) patients more frequently developed severe infections (40% vs 8%, p<0.0001)-particularly severe bacterial infections (27% vs 0.9%, p<0.0001). Additionally, multivariate analysis of post-CAR-T factors revealed that infection risk was increased by prolonged neutropenia (≥14 days) and corticosteroid use (≥9 days), and decreased with fluoroquinolone prophylaxis. Antibacterial prophylaxis significantly reduced the likelihood of severe bacterial infections in HT(high) (16% vs 46%, p<0.001), but not HT(low) patients (0% vs 2%, p=n.s.). Collectively, HT(high) patients experienced worse median progression-free (3.4 vs 12.6 months) and overall survival (9.1 months vs not-reached), and were hospitalized longer (median 20 vs 16 days). Severe infections represented the most common cause of non-relapse mortality after CAR-T and were associated with poor survival outcomes. A trend toward increased non-relapse mortality in HT(high) patients was observed (8.0% vs 3.7%, p=0.09). CONCLUSIONS: These data demonstrate the utility of the HT score to risk-stratify patients for infectious complications and poor survival outcomes prior to CD19 CAR-T. High-risk patients likely benefit from anti-infective prophylaxis and should be closely monitored for potential infections and relapse

Core outcome set measurement for future clinical trials in acute myeloid leukemia: the HARMONY study protocol using a multi-stakeholder consensus-based Delphi process and a final consensus meeting

Abstract: Background: Acute myeloid leukemia (AML) is the most common acute leukemia in adults and has an unacceptably low cure rate. In recent years, a number of new treatment strategies and compounds were developed for the treatment of AML. There were several randomized controlled clinical trials with the objective to improve patients’ management and patients’ outcome in AML. Unfortunately, these trials are not always directly comparable since they do not measure the same outcomes, and currently there are no core outcome sets that can be used to guide outcome selection and harmonization in this disease area. The HARMONY (Healthcare Alliance for Resourceful Medicine Offensive against Neoplasms in Hematology) Alliance is a public-private European network established in 2017 and currently includes 53 partners and 32 associated members from 22 countries. Amongst many other goals of the HARMONY Alliance, Work Package 2 focuses on defining outcomes that are relevant to each hematological malignancy. Accordingly, this pilot study will be performed to define a core outcome set in AML. Methods: The pilot study will use a three-round Delphi survey and a final consensus meeting to define a core outcome set. Participants will be recruited from different stakeholder groups, including patients, clinicians, regulators and members of the European Federation of Pharmaceutical Industries and Associations. At the pre-Delphi stage, a literature research was conducted followed by several semi-structured interviews of clinical public and private key opinion leaders. Subsequently, the preliminary outcome list was discussed in several multi-stakeholder face-to-face meetings. The Delphi survey will reduce the preliminary outcome list to essential core outcomes. After completion of the last Delphi round, a final face-to-face meeting is planned to achieve consensus about the core outcome set in AML. Discussion: As part of the HARMONY Alliance, the pilot Delphi aims to define a core outcome set in AML on the basis of a multi-stakeholder consensus. Such a core outcome set will help to allow consistent comparison of future clinical trials and real-world evidence research and ensures that appropriate outcomes valued by a range of stakeholders are measured within future trials

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches