PURPOSE: Severe (grade 3-4) acute graft-versus-host disease (AGVHD) is a major cause of death after unrelated-donor (URD) hematopoietic cell transplant (HCT), resulting in particularly high mortality after HLA-mismatched transplantation. There are no approved agents for AGVHD prevention, underscoring the critical unmet need for novel therapeutics. ABA2 was a phase II trial to rigorously assess safety, efficacy, and immunologic effects of adding T-cell costimulation blockade with abatacept to calcineurin inhibitor (CNI)/methotrexate (MTX)-based GVHD prophylaxis, to test whether abatacept could decrease AGVHD.METHODS: ABA2 enrolled adults and children with hematologic malignancies under two strata: a randomized, double-blind, placebo-controlled stratum (8/8-HLA-matched URD), comparing CNI/MTX plus abatacept with CNI/MTX plus placebo, and a single-arm stratum (7/8-HLA-mismatched URD) comparing CNI/MTX plus abatacept versus CNI/MTX CIBMTR controls. The primary end point was day +100 grade 3-4 AGVHD, with day +180 severe-AGVHD-free-survival (SGFS) a key secondary end point. Sample sizes were calculated using a higher type-1 error (0.2) as recommended for phase II trials, and were based on predicting that abatacept would reduce grade 3-4 AGVHD from 20% to 10% (8/8s) and 30% to 10% (7/8s). ABA2 enrolled 142 recipients (8/8s, median follow-up = 716 days) and 43 recipients (7/8s, median follow-up = 708 days).RESULTS: In 8/8s, grade 3-4 AGVHD was 6.8% (abatacept) versus 14.8% (placebo) ( P = .13, hazard ratio = 0.45). SGFS was 93.2% (CNI/MTX plus abatacept) versus 82% (CNI/MTX plus placebo, P = .05). In the smaller 7/8 cohort, grade 3-4 AGVHD was 2.3% (CNI/MTX plus abatacept, intention-to-treat population), which compared favorably with a nonrandomized matched cohort of CNI/MTX (30.2%, P < .001), and the SGFS was better (97.7% v 58.7%, P < .001). Immunologic analysis revealed control of T-cell activation in abatacept-treated patients.CONCLUSION: Adding abatacept to URD HCT was safe, reduced AGVHD, and improved SGFS. These results suggest that abatacept may substantially improve AGVHD-related transplant outcomes, with a particularly beneficial impact on HLA-mismatched HCT.

Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8⁺ T cell–mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.

Background: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. Objective: This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. Methods: We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. Results: The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. Conclusions: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Background Patient-centered care has been shown to improve patient outcomes, satisfaction, and engagement. However, there is a paucity of research on patient-centered care in the inpatient setting, including an understanding of unmet informational needs that may be limiting patient engagement. Pediatric hematopoietic stem cell transplantation (HSCT) represents an ideal patient population for elucidating unmet informational needs, due to the procedure’s complexity and its requirement for caregiver involvement. Methods We conducted field observations and semi-structured interviews of pediatric HSCT caregivers and patients to identify informational challenges in the inpatient hospital setting. Data were analyzed using a thematic grounded theory approach. Results Three stages of the caregiving experience that could potentially be supported by a health information technology system, with the goal of enhancing patient/caregiver engagement, were identified: (1) navigating the health system and learning to communicate effectively with the healthcare team, (2) managing daily challenges of caregiving, and (3) transitioning from inpatient care to long-term outpatient management. Discussion We provide four practical recommendations to meet the informational needs of pediatric HSCT patients and caregivers: (1) provide patients/caregivers with real-time access to electronic health record data, (2) provide information about the clinical trials in which the patient is enrolled, (3) provide information about the patient’s care team, and (4) properly prepare patients and caregivers for hospital discharge. Conclusion Pediatric HSCT caregivers and patients have multiple informational needs that could be met with a health information technology system that integrates data from several sources, including electronic health records. Meeting these needs could reduce patients’ and caregivers’ anxiety surrounding the care process; reduce information asymmetry between caregivers/patients and providers; empower patients/caregivers to participate in the care process; and, ultimately, increase patient/caregiver engagement in the care process.