Charmi Bhanushali

The gut microbiome is a critical regulator of systemic immunity and a major modulator of response to cancer immunotherapy with immune checkpoint inhibitors (ICIs). However, the clinical implementation of microbiome-inspired therapies that leverage these associations have proven challenging. Probiotics—live microorganisms thought to confer health benefits as part of food or food supplements—have gained increasing attention as readily testable, low-toxicity agents with potential of favorably influencing host–microbiome–immune interactions in the context of cancer immunotherapy. In this review, we critically evaluate the growing body of evidence supporting the role of probiotics in enhancing ICI efficacy and summarize published and ongoing clinical trials formally testing their role as adjuncts to cancer immunotherapy. Probiotics have been shown in preclinical murine models to exert immunomodulatory effects, including activation and maturation of dendritic cells, enhancement of MHC-I-mediated antigen presentation, modulation of cytokine profiles, and promotion of pro-inflammatory macrophage polarization. Probiotics also regulate adaptive immunity via microbial metabolites such as short-chain fatty acids (SCFAs), inosine, and tryptophan derivatives that support effector T cell activation and reduce T cell exhaustion. Cross-reactivity between microbial and tumor-associated antigens (molecular mimicry) further underscores the potential of probiotic strains to stimulate antitumor responses. In these models, supplementation with specific bacterial strains such as Bifidobacterium spp., Lactobacillus spp., Clostridium butyricum , and Akkermansia muciniphila enhanced ICI responses across tumor types including melanoma, lung cancer, and colorectal cancer. These findings are in part supported by early-phase clinical studies and retrospective cohorts, particularly in lung and renal cancers, where probiotic use has been associated with improved progression-free and overall survival. However, most clinical data are observational, and the field lacks standardized probiotic formulations and dosing protocols. To transition probiotics from food supplements to clinically validated immunotherapy adjuncts, rigorous mechanistic, translational, and clinical studies are necessary. These approaches have the potential to define mechanism-of-action, identify immunologically active strains, and inform rational clinical trial design. With careful development, probiotics hold promise as cost-effective, scalable, and personalized tools to optimize the efficacy and safety of cancer immunotherapy.

529 Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related morbidity and mortality worldwide, driven primarily by chronic infections with Hepatitis B, Hepatitis C, and alcohol consumption. This study systematically examines global trends of HCC from 1991 to 2021, focusing on age-standardized mortality rates (ASMR) and Disability-Adjusted Life Years (DALYs) according to risk factors across Socio-Demographic Index (SDI) regions. By highlighting these patterns, the study provides insights into the interplay of socio-economic disparities and healthcare advancements on HCC trends. Methods: We utilized data from the Global Burden of Disease database to extract ASMR, and age standardized DALYs for HCC between 1991 and 2021. The data was stratified by etiology (Hepatitis B, Hepatitis C, alcohol consumption) and stratified across five SDI regions: low, low-middle, middle, high-middle, and high SDI. Results: The global HCC ASMR decreased by 4% from 1991 to 2021, with the largest reduction (20.4%) in low SDI regions. Hepatitis B-related HCC ASMR dropped by 16.3%, with reductions across all SDI groups. ASMR of Hepatitis C-related HCC remained stable globally but fell by 15.1% in low SDI regions. Alcohol-related HCC ASMR increased by 10% globally, with the highest rise in high SDI regions (24.5%), and NASH-related HCC ASMR increased by 26.7% globally, with a 42% rise in high SDI regions. Hepatitis B-related HCC DALYs decreased by 22.2%, with the sharpest declines recorded in high (31.4%) and low SDI regions (29%). Hepatitis C-related HCC DALYs saw a 8.8% global reduction, which was consistent across all groups. Alcohol-related HCC DALYs rose by 4.6% globally, with increases of 14.4% in high SDI and 20.5% in low-middle SDI regions. NASH-related HCC DALYs increased by 18.7%, with a 34.7% rise in low-middle SDI regions. Conclusions: The study reveals substantial progress in reducing the global burden of Hepatitis B- and C-related HCC, particularly in low SDI regions. This progress likely reflects advances in vaccination, antiviral treatments, and screening programs. However, the rising mortality and disability associated with alcohol- and NASH-related HCC, especially in high SDI regions, emphasize the growing impact of lifestyle-related risk factors. These findings underscore the need for tailored public health strategies to mitigate the increasing effects of these preventable risk factors on global HCC outcomes.

Introduction: Health disparities disproportionately affect minority populations in the United States across various malignancies. However, the specific impact of race on treatment accessibility and overall survival in chronic myeloid leukemia (CML) remains inadequately characterized. This study investigates the influence of racial and ethnic identities and socioeconomic status on treatment access and survival outcomes in CML patients using the Surveillance, Epidemiology, and End Results (SEER) database. Methods: We conducted a retrospective analysis using SEER data for patients diagnosed with CML between 2000 and 2021. Demographic and treatment characteristics were compared across self-identified racial and ethnic groups (White, Black, Asian or Pacific Islander, and American Indian/Alaska Native). Survival outcomes were assessed using survival time in months and cause of death. Additional variables included median household income (adjusted for inflation), age, sex, ethnicity, race, and urbanicity. Chi-squared tests of independence and odds ratios (ORs) were used to evaluate relationships between these variables and CML outcomes. Results: We identified 10,667 patients diagnosed with CML in our analysis of the SEER database. The racial distribution of the sample was predominantly White (80.51%, N=8,589), followed by Black (10.84%, N=1,157), Asian or Pacific Islander (7.94%, N=847), and American Indian/Alaska Native (0.69%, N=74). Regarding ethnicity, the majority identified as non- Hispanic/Latino (83.68%, N=8,927), while 16.31% (N=1,740) identified as Hispanic/Latino. The highest incidence of CML was observed in the age groups 50-59 years (20.3%) and 60-69 years (19.4%), with most patients residing in urban areas with high-income brackets ($90,000+). Our survival analysis revealed significant disparities across various demographic and socioeconomic factors. Age played a crucial role, with patients over 80 years showing a significantly higher risk of cancer-related death compared to the reference group of 10-19 years (OR: 3.12, 95% CI: 2.76-3.51, p<0.001). Gender also influenced outcomes, with males exhibiting a slightly poorer survival rate than females (OR: 1.15, 95% CI: 1.04-1.27, p=0.045). Racial disparities were evident, as White patients demonstrated the best survival outcomes. In comparison, Asian or Pacific Islander patients had a higher risk of cancer-related death (OR: 1.35, 95% CI: 1.14-1.60, p=0.023), while Black patients showed an even greater risk (OR: 1.55, 95% CI: 1.32-1.82, p=0.018). Other races, including American Indian/Alaska Native, also exhibited elevated risk (OR: 1.45, 95% CI: 1.25-1.68, p=0.018). Socioeconomic status emerged as a significant factor influencing survival outcomes. Patients in high-income brackets (>$100,000) had the best survival rates. Those in middle-income brackets ($50,000-$100,000) showed an increased risk of cancer-related death (OR: 1.75, 95% CI: 1.55- 1.98, p<0.001), while low-income patients (<$50,000) faced the highest risk (OR: 2.05, 95% CI: 1.83-2.30, p<0.001). Interestingly, when adjusted for other variables, ethnicity showed minimal impact on survival (OR: 1.05, 95% CI: 0.95-1.16, p=0.317), suggesting that other factors may play a more significant role in determining CML outcomes than ethnic background alone. Conclusion: Our analysis of CML outcomes using SEER data reveals persistent disparities across demographic and socioeconomic lines, highlighting the complex interplay between social determinants of health and clinical outcomes. The pronounced differences in survival rates among racial groups and socioeconomic strata suggest significant variations in access to specialized care, treatment adherence, and overall healthcare quality. The impact of advanced age on CML outcomes underscores the unique challenges in managing this disease in elderly populations. Interestingly, the minimal impact of ethnicity when adjusted for other variables suggests that socioeconomic factors may play a more crucial role in determining CML outcomes than ethnic background alone.

BackgroundThe future of artificial intelligence in medicine includes the use of machine learning and large language models to improve diagnostic accuracy, as a point-of-care tool, at the time of admission to an acute care hospital. The large language model, ChatGPT-4, has been shown to diagnose complex medical conditions with accuracies comparable to experienced clinicians, however, most published studies involved curated cases or examination-like questions and are not point-of-care. To test the hypothesis that ChatGPT-4 can make an accurate medical diagnosis using real-world medical cases and a convenient cut and paste strategy, we performed a retrospective case study involving critically ill patients admitted to a community hospital medical intensive care unit.MethodsA redacted H&P was essentially cut and pasted into ChatGPT-4 with uniform instructions to make a leading diagnosis and a list of 5 possibilities as a differential diagnosis. All features that could be used to identify patients were removed to ensure privacy and HIPAA compliance. The ChatGPT-4 diagnoses were compared with critical care physician diagnoses using a blinded longitudinal chart review as the ground truth diagnosis.ResultsA total of 120 randomly selected cases were included in the study. The diagnostic accuracy was 88.3% for physicians and 85.0% for ChatGPT-4, with no significant difference by McNemar testing (p-value of 0.249). The agreement between physician diagnosis and ChatGPT-4 diagnosis was moderate, 0.57 (95% CI: 0.35-0.79), based on Cohen's kappa statistic.ConclusionThese results suggest that ChatGTP-4 achieved diagnostic accuracy comparable to board certified physicians in the context of critically ill patients admitted to a community medical intensive care unit. Furthermore, the agreement was only moderate, suggesting that there may be complementary ways of combining the diagnostic acumen of physicians and ChatGPT-4 to improve overall accuracy. A prospective study would be necessary to determine if ChatGPT-4 could improve patient outcomes as a point-of-care tool at the time of admission.

12123 Background: Radiation-associated cardiovascular disease (CVD) remains a major late toxicity in childhood cancer survivors. Whole-heart dose may mask heterogeneous substructure exposure, limiting precision in survivorship surveillance and modern radiotherapy planning. Methods: Using the Childhood Cancer Survivor Study (CCSS) open-access cardiac substructure dosimetry dataset (12 mean-dose structures; Gy), we modeled time from cancer diagnosis to CAD, CHF, valvular disease, and arrhythmia with elastic-net Cox (α=0.5; 10-fold CV). Bootstrap stability selection (200 resamples) quantified reproducible substructure predictors; “core” structures were defined by selection frequency ≥0.80. Parsimonious Cox models were fit using core structures (dose scaled per 5 Gy) and adjusted for sex, race, age at diagnosis, anthropometric category, and smoking. Model discrimination was assessed using the concordance index (C-index). Model-based predicted 20-year absolute risks were used to estimate dose thresholds for clinically interpretable risk targets (reference profile: median covariates; modal factor levels). Results: Among 25,481 survivors, events occurred for CAD (n=475), CHF (n=544), valvular disease (n=131), and arrhythmia (n=181). LAD and aorta emerged as reproducible, endpoint-specific predictors: LAD was core across CAD/CHF/valvular outcomes; aorta and LMCA were core for CAD/valvular; pericardium was core for CAD/CHF. In adjusted core models, aortic dose was associated with CAD (HR 1.59 per 5 Gy; 95% CI 1.14–2.21; C-index 0.796) and valvular disease (HR 1.96; 95% CI 1.15–3.32; C-index 0.773). LAD dose was associated with CAD (HR 1.26; 95% CI 1.05–1.53; C-index 0.796) and CHF (HR 1.23; 95% CI 1.02–1.47; C-index 0.818). Absolute risk translation identified interpretable thresholds: predicted 20-year CAD risk crossed ~1% at 13 Gy mean aortic dose and ~2% at 20 Gy; CAD risk crossed ~1% at 24 Gy mean LAD dose. Predicted 20-year CHF risk crossed ~0.5% at 21 Gy mean LAD dose. Predicted 20-year valvular risk crossed ~1% at 23 Gy mean aortic dose. Conclusions: Substructure-specific dosimetry yields reproducible radiation “signatures,” with LAD and aorta dose consistently linked to late CVD. These dose–risk thresholds provide actionable benchmarks for risk-stratified survivorship surveillance and may inform substructure-sparing dose constraints in contemporary radiotherapy planning moving beyond whole-heart dose to endpoint-specific substructure constraints. Substructure-specific dose thresholds for 20-year cardiovascular risk. Outcome Core Substructure HR per 5 Gy (95% CI) 20-Year Risk Target Dose Threshold (Gy) CAD Aorta 1.59 (1.14–2.21) 1% 13 CAD Aorta 1.59 (1.14–2.21) 2% 20 CAD LAD 1.26 (1.05–1.53) 1% 24 CHF LAD 1.23 (1.02–1.47) 0.5% 21 Valvular Aorta 1.96 (1.15–3.32) 1% 23