Citations

The intestinal epithelium is exposed to diverse combinations of microbiota-derived compounds; however, the mechanisms by which the host integrates these signals remain poorly defined. Studying two highly abundant microbial metabolites, we identified the purine metabolite hypoxanthine as an effector metabolite that directly drives signaling and the short-chain fatty acid butyrate as a regulatory metabolite that conditions host responsiveness. Specifically, hypoxanthine activates the adenosine A1 receptor–TRPC4 axis in enterochromaffin (EC) cells, triggering calcium influx and serotonin release resulting in accelerated gastrointestinal transit locally and increased platelet activation systemically. In contrast, butyrate epigenetically upregulates specific G protein–coupled receptors and ion channels to enhance response to hypoxanthine and the neurotransmitters norepinephrine and dopamine. These findings define a cooperative signaling framework and highlight the role of EC cells as a distinct epithelial signaling hub that senses and integrates microbial metabolite signals to drive physiological responses. Our findings provide a mechanistic foundation for therapeutic strategies that leverage combinatorial microbial signaling.

Abstract Purpose: Inhibitors of the Ataxia telangiectasia and Rad3-related (ATR) protein kinase, a critical component of the DNA damage repair response, have synergistic anti-cancer activity with platinum compounds in preclinical models. We therefore conducted a Phase 2 study of the ATR inhibitor berzosertib+carboplatin vs. docetaxel+carboplatin in metastatic castration-resistant prostate cancer. Patients and Methods: Patients previously treated with at least one androgen receptor pathway inhibitor and taxane underwent mandatory biopsy and were randomized 1:1 to receive Arm A (docetaxel 60 mg/m2+carboplatin AUC4 day 1; or carboplatin AUC5 if not a docetaxel candidate) or Arm B (berzosertib 90 mg/m2 days 2,9+carboplatin AUC5 day 1) every 21 days. The primary endpoint was overall response rate (ORR; ≥50% PSA decline or radiographic response). Results: Of 73 randomized patients, 65 received protocol treatment: 34 on Arm A (26 docetaxel+carboplatin; 8 carboplatin alone) and 31 on Arm B. Thirteen patients (38%) in Arm A and 20 (65%) in Arm B had ≥Grade 3 treatment-related adverse events (TrAEs). ORR was 15% in Arm A (5/34; 5/26[19%] with docetaxel+carboplatin) and 0% in Arm B (0/31). At interim analysis after 65 of planned 130 patients were treated, enrollment was halted due to futility. Homologous recombination repair deficiency status from tissue and blood did not correlate with clinical outcomes, but ATM-deficiency or high levels of phoshpo-KAP1 detected in tissue was associated with clinical benefit across both arms of the trial. Conclusions: Berzosertib+carboplatin demonstrated lower ORR and more frequent ≥grade 3 TrAEs compared to docetaxel+carboplatin in this heavily pre-treated, biomarker-unselected population. NCT03517969

Low protein (LP) diets improve metabolic health in rodents and humans. In rodents, LP diets are typically implemented by replacing protein with carbohydrates like sucrose or cornstarch, keeping diets isocaloric. However, humans can choose from many different types of carbohydrate, and how dietary carbohydrate quality - the precise composition of the dietary sugars - impacts the response to dietary protein remains largely unexplored. Here, mice were fed control (21% protein) or LP (7% protein) diets with four different carbohydrate sources: sucrose, a 1:1 glucose/fructose mixture, glucose, or fructose. While LP diets improved metabolic health across all groups in male mice, carbohydrate quality also significantly altered specific health outcomes, with fructose-fed mice having the lowest body weight and adiposity of all control diets. In female mice, responses to LP diets were influenced by carbohydrate quality, with certain sugars inducing a stronger metabolic response to LP diets than previously seen. Finally, in female APP/PS1 mice, a model of Alzheimer's disease, we find that although LP diets reduce A-beta; plaque burden irrespective of carbohydrate type, dietary sugar type does influence spatial memory. Together, these results demonstrate that while dietary protein is a critical determinant of metabolic and neurological health, carbohydrate quality influences these outcomes in a sex-specific manner.

Obesity is common among critically ill adults, but its association with outcomes during venovenous extracorporeal membrane oxygenation (VV ECMO) remains controversial. We evaluated associations of body mass index (BMI) with mortality and Extracorporeal Life Support Organization (ELSO)–defined complications in adults supported with VV ECMO. We conducted a retrospective multicenter cohort study of adults receiving VV ECMO at four centers (2017–2025). The primary outcome was in-hospital mortality; secondary outcomes were ELSO-defined complications. Multivariable logistic regression was adjusted for age, sex, and hospital site; BMI was evaluated as continuous, dichotomized (≥30 vs . <30 kg/m 2 ), and World Health Organization (WHO) obesity class exposures, with interaction testing by coronavirus disease 2019 (COVID-19) status. Among 420 patients (median BMI, 31 kg/m 2 ), in-hospital mortality was 62%. Higher BMI was associated with mortality in non–COVID-19 acute respiratory distress syndrome (ARDS; odds ratio [OR] = 1.06 per 1 kg/m 2 , 95% confidence interval [CI] = 1.02–1.10, p = 0.003) but not in COVID-19 ARDS (OR = 1.00, 95% CI = 0.94–1.05); there was insufficient evidence of interaction between BMI and COVID-19 ( p = 0.07). Higher BMI was associated with renal complications in both groups and infectious complications in non–COVID-19 ARDS (OR = 1.05, 95% CI = 1.02–1.09, p = 0.003). No significant BMI × COVID-19 interaction was detected for any outcomes. These findings do not support an obesity survival paradox during VV ECMO.

Abstract Triple-Negative Breast Cancer (TNBC) presents a significant clinical challenge due to its heterogeneity and lack of targeted treatment options, with chemotherapy and immunotherapy combinations currently serving as the main therapeutic strategy. Efforts to address TNBC heterogeneity have largely focused on classifying intrinsic cancer subtypes based on differential tumor mRNA expression, a strategy that has proven effective in hormone receptor-positive breast cancers but has yet to yield a clinically useful predictor of survival or treatment response in TNBC. We hypothesize that both the intrinsic characteristics of TNBC and the surrounding immune microenvironment influence treatment outcomes and that immune cell infiltration affects TNBC subtype classification and response variability. To explore this hypothesis, we compared the predictive and prognostic capabilities of cancer subtype-based (TNBC-type) gene signatures and immune cell deconvolution methods (CIBERSORT) within the same TNBC datasets. We found that immune cell abundance outperformed TNBC subtype-signatures and multicellular immune cell aggregates showed the highest performance of all. More specifically, aggregate immune cells associated with tertiary lymphoid structures and tumor associated macrophages/monocytes demonstrated statistically significant predictive value. These findings were confirmed in an independent cohort of 67 TNBC patients treated with neoadjuvant chemotherapy. Further, single-cell RNA sequencing analysis revealed that the predictive power of cancer-subtype could be partially explained by immune- and stromal features. Examination of single-cell resolution spatial transcriptomic data confirmed presence of TLS-like, TAM- and cancer-stromal niches within TNBC biopsy samples that were associated with treatment response. Overall, our results highlight that immune cell aggregates, which capture the spatial organization of the TME, outperform cell-type specific gene signatures in predicting TNBC outcomes. Our novel approach provides a robust framework for interpreting spatial relationships in bulk RNA-seq data, offering a pathway for reconciling past data with current advancements in spatial profiling technologies. This work paves the way for future studies to leverage the multi-cellular complexity of TNBC, enhancing diagnostic precision and facilitating the development of therapies that strategically modulate the tumor microenvironment for improved anti-cancer responses.

Background Systemic corticosteroids (SCS) remain the cornerstone of eosinophilic granulomatosis with polyangiitis (EGPA) and hypereosinophilic syndrome (HES) therapy but are associated with substantial toxicity. Mepolizumab, an anti-interleukin-5 biologic, has demonstrated steroid-sparing effects in addition to EGPA and HES disease control, yet evidence of reductions in SCS-related complications is limited. Methods This retrospective cohort study used US claims data from the Komodo Research Database to quantify SCS-related complications, healthcare resource utilization, and costs in patients with EGPA or HES treated with mepolizumab vs. chronic SCS. Patients were stratified as mepolizumab (300 mg) or chronic SCS users (≥6 months continuous SCS use with &gt;7.5 mg/day for EGPA and &gt;10 mg/day for HES) based on treatment received during a 6-month landmark period post-index (first claim of mepolizumab or SCS). An inverse probability of treatment weighting approach was applied to the cohorts to minimize confounding. Outcomes were assessed up to 12 months post-landmark. Results 578 patients with EGPA (305 mepolizumab users vs. 273 chronic SCS users) and 272 patients with HES (161 vs. 111) were included. In patients with EGPA, rates of any SCS-related complications were 39% lower with mepolizumab compared with chronic SCS (rate ratio [95% confidence interval]: 0.61 [0.49, 0.76], p &lt; 0.001). Mepolizumab users had 57% lower rate of all-cause hospitalizations (0.43 [0.27, 0.69], p &lt; 0.001), and lower outpatient and emergency department (ED) visits vs. chronic SCS. Patients with HES had significantly lower rates of any SCS-related complications with mepolizumab vs. chronic SCS (0.59 [0.42, 0.82], p = 0.002). Mepolizumab users had 66% lower rates of all-cause hospitalizations (0.34 [0.17, 0.65], p = 0.001), and lower outpatient visits relative to chronic SCS users. In EGPA and HES SCS-complication-related hospitalizations and ED visits were lower with mepolizumab compared with chronic SCS. SCS-complication-related medical costs per patient per year were $16,194 ( p = 0.008) and $3,976 ( p = 0.676) lower, in the mepolizumab vs. chronic SCS cohort of patients with EGPA and HES, respectively. Conclusion This first of its kind study suggests that real-world mepolizumab use is associated with reduced SCS-related complications in EGPA and HES, reinforcing its clinical and economic steroid-sparing benefits in reducing healthcare burden among EGPA and HES populations.

Abstract Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are powerful emerging technologies that demonstrate exceptional capabilities in processing vast amounts of unstructured and structured data, including text, voice, images, video and other formats, and adapting to a wide range of specific tasks. Their immense potential to drive meaningful improvements in treatment outcomes is increasingly evident. The advent of these technologies has marked a transformative era in healthcare, including the fields of radiation oncology and medical physics. Specifically, these powerful technologies offer unprecedented opportunities to analyze domain‐specific data, process and synthesize medical images, automate routine tasks, support clinical decision‐making, optimize and streamline clinical workflows, and enhance the quality of clinical trials. While these emerging technologies present new opportunities to revolutionize radiation therapy practice, their implementation also raises important educational, ethical, and regulatory considerations. This scoping review highlights benefits, promises, risks, and challenges, such as interpretability, data privacy, regulatory compliance, reproducibility, hallucination, and integration into existing clinical workflow. Finally, emerging opportunities are outlined to guide future research directions. This review paper provides a timely overview of Gen AI, FMs and LLMs, aiming to inform medical physicists, clinicians, and researchers of the evolving role of these disruptive technologies in shaping the future of radiation therapy.