Muhsen, Ibrahim N

PURPOSE On the basis of the results of the ZUMA-3 trial, brexucabtagene autoleucel (brexu-cel), a CD19-directed chimeric antigen receptor T-cell therapy, gained US Food and Drug Administration approval in October 2021 for adults with relapsed/refractory (R/R) B-cell ALL (B-ALL). We report outcomes of patients treated with brexu-cel as a standard therapy. METHODS We developed a collaboration across 31 US centers to study adults with B-ALL who received brexu-cel outside the context of a clinical trial. Data were collected retrospectively from October 2021 to October 2023. Toxicities were graded per American Society for Transplantation and Cellular Therapy guidelines for cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS). RESULTS At the time of data lock, 204 patients had undergone apheresis and 189 were infused. Median follow-up time was 11.4 months. Forty-two percent of patients received brexu-cel in morphologic remission and would have been ineligible for participation in ZUMA-3. After brexu-cel, 151 achieved complete remission (CR), of which 79% were measurable residual disease (MRD) negative remissions. Median progression-free survival (PFS) was 9.5 months and median overall survival was not reached. Grade 3-4 CRS or ICANS occurred in 11% and 31%, respectively. In multivariable analysis, patients receiving consolidative hematopoietic cell transplantation (HCT; hazard ratio, 0.34 [95% CI, 0.14 to 0.85]) after brexu-cel had superior PFS compared with those who did not receive any consolidation or maintenance therapy. CONCLUSION Similar to ZUMA-3, high rates of MRD-negative CR were observed after brexu-cel treatment for R/R B-ALL. The use of HCT as consolidation after brexu-cel resulted in improved PFS.

Abstract The effect of prior inotuzumab ozogamicin (InO) treatment on brexucabtagene autoleucel (brexu-cel) outcomes remains unclear in adults with acute lymphoblastic leukemia (ALL). We conducted a retrospective multicenter analysis of 189 patients with relapsed/refractory ALL treated with brexu-cel. Over half of the patients received InO before brexu-cel (InO exposed). InO-exposed patients were more heavily pretreated (P = .02) and frequently had active marrow disease before apheresis (P = .03). Response rate and toxicity profile after brexu-cel were comparable for InO-exposed and InO-naïve patients; however, consolidation therapy after brexu-cel response was used at a higher rate in InO-naïve patients (P = .005). With a median follow-up of 11.4 months, InO-exposed patients had inferior progression-free survival (PFS; P = .013) and overall survival (OS; P = .006) in univariate analyses; however, prior InO exposure did not influence PFS (hazard ratio, 1.20; 95% confidence interval, 0.71-2.03) in multivariate models. Within InO-exposed patients, InO responders had superior PFS (P = .002) and OS (P < .0001) relative to InO-refractory patients. The timing of administering InO did not affect brexu-cel outcomes, with comparable PFS (P = .51) and OS (P = .86) for patients receiving InO as bridging therapy or before apheresis. In conclusion, although InO exposure was associated with inferior survival outcomes after brexu-cel in unadjusted analyses, these associations were no longer significant in multivariate analyses, suggesting it is unlikely that InO negatively affects brexu-cel efficacy. Our data instead imply that InO-exposed recipients of brexu-cel tend to be higher-risk patients with intrinsic adverse leukemia biology.

Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, “snapshot” assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.

As cellular therapies gradually become the mainstay of treatment for several nonmalignant diseases, there appears to be varied accessibility to these therapies globally. Despite considerable burden of nonmalignant conditions, such as sickle cell disease, thalassemia, and aplastic anemia in populations of low–middle-income countries, the utilization of cellular therapies remain sparse because of lack of resources. Globally, the frequency of hematopoietic stem cell transplant (HSCT) has increased disproportionately in countries with higher gross national income (GNI) per capita, governmental healthcare expenditures, and a high human development index. This leads to a large subset of international patients seeking care in the United States. This review summarizes the unique set of challenges that often arise when offering sophisticated therapies such as HSCT to international patients constituting of cross-cultural, logistical, financial, and medical challenges and the opportunities that are available to bridge the gap.

The Internet of Things (IoT) has penetrated many aspects of everyday human life. The use of IoT in healthcare has been expanding over the past few years. In this review, we highlighted the current applications of IoT in the medical literature, along with the challenges and opportunities. IoT use mainly involves sensors and wearables, with potential applications in improving the quality of life, personal health monitoring, and diagnosis of diseases. Our literature review highlights that the current main application studied in the literature is physical activity tracking. In addition, we discuss the current technologies that would help IoT-enabled devices achieve safe, quick, and meaningful data transfer. These technologies include machine learning/artificial intelligence, 5G, and blockchain. Data on current IoT-enabled devices are still limited, and future research should address these devices’ effect on patients’ outcomes and the methods by which their integration in healthcare will avoid increasing costs.

Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time‐consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real‐world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.