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Breast cancer brain metastasis (BCBM) typically results in an end-stage diagnosis and is hindered by a lack of brain-penetrant drugs. Tumors in the brain rely on the conversion of acetate to acetyl-CoA by the enzyme acetyl-CoA synthetase 2 (ACSS2), a key regulator of fatty acid synthesis and protein acetylation. Here, we used a computational pipeline to identify novel brain-penetrant ACSS2 inhibitors combining pharmacophore-based shape screen methodology with absorption, distribution, metabolism, and excretion (ADME) property predictions. We identified compounds AD-5584 and AD-8007 that were validated for specific binding affinity to ACSS2. Treatment of BCBM cells with AD-5584 and AD-8007 leads to a significant reduction in colony formation, lipid storage, acetyl-CoA levels and cell survival in vitro. In an ex vivo brain-tumor slice model, treatment with AD-8007 and AD-5584 reduced pre-formed tumors and synergized with irradiation in blocking BCBM tumor growth. Treatment with AD-8007 reduced tumor burden and extended survival in vivo. This study identifies selective brain-penetrant ACSS2 inhibitors with efficacy towards breast cancer brain metastasis. [ABSTRACT FROM AUTHOR]

PURPOSE: Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. METHODS: Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported. RESULTS: From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets. CONCLUSION: This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy. Novel radiomics model predicts cardiac toxicity from 18F-FDG PET/CT scans with high accuracy. [ABSTRACT FROM AUTHOR]

The circadian system, a vital temporal regulator influencing physiological processes, has implications for cancer development and treatment response. Our study assessed circadian timing's impact on whole-brain radiotherapy outcomes in brain metastases for personalized cancer therapy insights. The aim of the study was to evaluate circadian influence on radiation treatment timing and its correlation with clinical outcomes and to identify patient populations benefiting from interventions synchronizing circadian rhythms, considering subgroup differences and potential disparities. An IRB-approved retrospective analysis of 237 patients undergoing whole-brain radiotherapy for brain metastases (2017–2021), receiving over 80% of treatments in the morning or afternoon, was performed. Survival analyses utilized Kaplan–Meier curves. This was a single-institution study involving patients receiving whole-brain radiotherapy. Demographic, disease, and socioeconomic parameters from electronic medical records were collected. Morning treatment (n = 158) showed a trend toward improved overall survival vs. afternoon (n = 79); the median survival was 158 vs. 79 days (p = 0.20, HR = 0.84, CI95% 0.84–0.91). Subgroup benefits for morning treatment in females (p = 0.04) and trends in controlled primary disease (p = 0.11) and breast cancer metastases (p = 0.08) were observed. Black patients exhibited diminished circadian influence. The present study emphasized chronobiological factors' relevance in brain metastases radiation therapy. Morning treatment correlated with improved survival, particularly in specific subgroups. Potential circadian influence disparities were identified, laying a foundation for personalized cancer therapy and interventions synchronizing circadian rhythms for enhanced treatment efficacy. [ABSTRACT FROM AUTHOR]

A meta-analysis of 252 studies conducted between 1997 and 2023 on dietary interventions in cancer patients found that the majority of these studies focused on feasibility, adherence to diet, weight and body composition, or translational endpoints rather than primary endpoints related to dietary interventions and cancer outcomes. Only 20 trials had a primary endpoint designed to detect an improvement in cancer outcomes, and of those, only 8 met their primary endpoint. However, many of the studies that met their endpoint lacked power calculations, and the remainder failed to meet their enrollment criteria. The review did not account for behavioral factors or engagement levels of the interventions, and did not assess the risk of bias. The study suggests that more rigorous research is needed in this area to determine the impact of dietary changes on cancer outcomes. [Extracted from the article]

Understanding metabolic and immune regulation inherent to patient populations is key to improving the radiation response for our patients. To date, radiation therapy regimens are prescribed based on tumor type and stage. Patient populations who are noted to have a poor response to radiation such as those of African American descent, those who have obesity or metabolic syndrome, or senior adult oncology patients, should be considered for concurrent therapies with radiation that will improve response. Here, we explore these populations of breast cancer patients, who frequently display radiation resistance and increased mortality rates, and identify the molecular underpinnings that are, in part, responsible for the radiation response and that result in an immune-suppressive tumor microenvironment. The resulting immune phenotype is discussed to understand how antitumor immunity could be improved. Correcting nutrient deficiencies observed in these populations should be considered as a means to improve the therapeutic index of radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient outcomes., Materials/methods: An international cohort of 117 oligometastatic castration-sensitive prostate cancer (omCSPC) patients, comprising 34 from John Hopkins Hospital (JHH) and 83 from Baskent University (BU), were treated with stereotactic ablative radiation therapy (SABR) MDT with both pre- and post-MDT PSMA-PET/CT scans acquired. PET radiomic features were analyzed from a CT-PET fusion defined gross tumor volume ((GTV) or zone 1), and a 5 mm expansion ring area outside the GTV (zone 2). A total of 1748 PET radiomic features were extracted from these zones. The six most significant features selected using the Chi2 method, along with five clinical parameters (age, Gleason score, number of total lesions, untreated lesions, and pre-MDT prostate-specific antigen (PSA)) were extracted as inputs to the models. Various machine learning models, including Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayesian, were employed for 2-year MFS prediction and tested using leave-one-out and cross-institution validation., Results: Six radiomic features, including Total Energy, Entropy, and Standard Deviation from pre-PSMA-PET zone 1, Total Energy and Contrast from post-PSMA-PET zone 1, and Entropy from pre-PSMA-PET zone 2, along with five clinical parameters were selected for predicting 2-year MFS. In a leave-one-out test with all the patients, random forest achieved an accuracy of 80 % and an AUC of 0.82 in predicting 2-year MFS. In cross-institution validation, the model correctly predicted 2-year MFS events with an accuracy of 75 % and an AUC of 0.77 for patients from JHH, and an accuracy of 78 % and an AUC of 0.80 for BU patients, respectively., Conclusion: Our study demonstrated the promise of using pre- and post-MDT PSMA-PET-based imaging biomarkers for MFS prediction for omCSPC patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)