Citations

Importance The Breast Cancer Index (BCI) previously identified premenopausal patients with tumors in which the ratio of expression of HOXB13 relative to IL17BR (hereafter, BCI [H/I]–low tumors) as likely to derive greatest benefit from ovarian function suppression (OFS)–containing adjuvant therapy in the Suppression of Ovarian Function Trial (SOFT) trial. Objectives To assess BCI as a predictive biomarker of benefit from exemestane plus OFS vs tamoxifen plus OFS and to validate BCI as a prognostic biomarker for premenopausal patients. Design, Setting, and Participants This prognostic study used a prospective-retrospective translational design within the Tamoxifen and Exemestane (TEXT) and SOFT trials (enrolled November 2003 to April 2011). Blinded BCI testing in all available tumor samples was completed in March 2024. Premenopausal women with hormone receptor–positive breast cancer randomized to tamoxifen plus OFS or exemestane plus OFS who had BCI assessed were included. Analysis occurred from March to August 2024. Exposure 5 years of adjuvant tamoxifen plus OFS or exemestane plus OFS. Main Outcomes and Measures The primary outcomes were breast cancer-free interval (BCFI) for predictive analyses and distant recurrence-free interval (DRFI) for prognostic analyses after a median follow-up of 13 years in the TEXT cohort. Secondary objectives examined the predictive performance of BCI (H/I) in the combined TEXT and SOFT cohort overall and in prespecified clinical subgroups. Results Of 1782 patients in the TEXT study, 1034 (58.0%) had BCI (H/I)–low tumors; 915 (51.3%) of patients had N0 disease and 1077 (60.4%) were younger than 45 years. Patients with BCI (H/I)–low tumors had a 6.6% absolute benefit in 12-year BCFI (HR, 0.61; 95% CI, 0.44-0.85) for exemestane plus OFS vs tamoxifen plus OFS, while those with BCI (H/I)–high tumors had a 6.3% absolute benefit (HR, 0.78; 95% CI, 0.57-1.07; P for interaction = .29). Results were consistent in the combined TEXT plus SOFT cohort (2896 patients) and adjusting for clinicopathological variables. Clinical subgroup analyses consistently showed benefit of exemestane plus OFS vs tamoxifen plus OFS for BCI (H/I)–low tumors, and more variable relative treatment effects among BCI (H/I)–high tumors, including by age. Post hoc exploratory time-varying estimates suggested the treatment × BCI associations may differ in years 0 to 5 vs greater than 5 years. BCI and BCI N+ as continuous indices were prognostic for distant recurrence in N0 (HR, 1.27; 95% CI, 1.11-1.44; P < .001) and N1 (HR, 1.58; 95% CI, 1.21-2.05; P < .001) cancers. The 12-year DRFI was 96.3%, 90.3%, and 84.9% for BCI low-, intermediate-, and high-risk N0 cancers, respectively. Conclusions and Relevance In this study of premenopausal women with hormone receptor–positive breast cancer, BCI (H/I) status did not clearly predict greater benefit of adjuvant exemestane plus OFS vs tamoxifen plus OFS for women with BCI (H/I)–low tumors than for those with BCI (H/I)–high tumors; BCI continuous indices were reconfirmed as prognostic for premenopausal women. These findings support prior results of SOFT, which compared tamoxifen-alone vs OFS with either exemestane or tamoxifen, indicating premenopausal patients with BCI (H/I)–low tumors may benefit from more intensive endocrine therapy.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Galactic cosmic ray (GCR) radiation is a major barrier to human space exploration beyond Earth’s magnetic field protection. Mesenchymal stem cells (MSCs) are found in all organs and play a critical role in repair and regeneration of tissue. We engineered bone marrow-derived MSCs and evaluated their response to ionizing radiation exposure. Epidermal growth factor receptor (EGFR) expression by certain types of cancers has been shown to induce radioresistance. In this study, we tested the feasibility of transfecting MSCs to overexpress EGFR (eMSC-EGFR) and their capacity to tolerate and recover from X-ray exposure. Quantitative real-time PCR (qRT-PCR) and immunoblotting results confirmed the efficient transfection of EGFR into MSCs and EGFR protein production. eMSC-EGFR maintained characteristics of human MSCs as outlined by the International Society for Cell & Gene Therapy. Then, engineered MSCs were exposed to various dose rates of X-ray (1–20 Gy) to assess the potential radioprotective role of EGFR overexpression in MSCs. Post-irradiation analysis included evaluation of morphology, cell proliferation, viability, tumorigenic potential, and DNA damage. eMSC-EGFR showed signs of radioresistance compared to naïve MSCs when assessing relative proliferation one week following exposure to 1–8 Gy X-rays, and significantly lower DNA damage content 24 h after exposure to 4 Gy. We establish for the first time the efficient generation of EGFR overexpressing MSCs as a model for enhancing the human body to tolerate and recover from moderate dose radiation injury in long-term manned space travel.

Background: We aimed to evaluate the outcomes of patients with confirmed and possible cardiac sarcoidosis who had an ICD implant and identify predictors of ventricular arrhythmias and mortality in this patient population. Methods: We performed a retrospective study patients with a diagnosis of cardiac sarcoidosis (CS) at Mayo Clinic and had an ICD implanted for primary or secondary prevention indication. Adult patients (≥18 years) with a diagnosis of possible or confirmed (biopsy-proven) CS with an ICD implanted between 1993 and 2022 were included. Primary outcomes included sustained ventricular arrhythmia (VT) resulting in a documented appropriate ICD shock and death from any cause. Secondary outcomes included ICD-related complications. Results: We identified 148 patients, in which 23 patients (15.5%) experienced VT events with a total of 160 treated episodes. 4 patients (2.7%) experienced inappropriate ICD shocks (11 episodes). During a median follow up of 3.1 years, 26 patients deceased, and the overall survival was 98% and 77% at 1 and 5 year. Older age, CAD, CKD, paced rhythm and QRS duration were associated with worse survival (Figure 1 – 4) . Device related complications were rare, except for the higher percentage of CHF in patients with VT after implant, however this was not statistically significant. Patients with ICD therapies were younger (53 vs 58 years, p=0.029), had higher LGE burden (26.9 vs 16.3%, p=0.01), and higher percentage of LV mass (26.9 vs 16.3, p=0.011). Multivariate logistic regression model which showed that patients with biopsy proven sarcoidosis had significantly higher odds of VT events (OR: 3.04, 95% CI: 1.02 to 9.08, p = 0.046). In patients with increased LV mass (every 10 g increase), the odds of developing VT increased by 1.49 (95% CI: 1.02 to 2.19, p = 0.041). LV mass and LGE percentage showed fair ability of predicting the outcome by ROC analysis. Youden’s index showed 26 for LV mass and 16% for LGE percent as the optimal cut-off points for predicting VT outcomes (Figure 5) . The sensitivity, specificity, positive predictive value, negative predictive value were 81.8%, 74.1%, 37.5%, 95.6% and 75.4% respectively for an LV mass of 26 grams and 81.8%, 63.8%, 30.0%, 94.9% and 66.7% respectively for an LGE percentage of 16%. Conclusion: LGE burden continues to be a major predictor for VT events in patients with CS. Myocardial tissue burden and electrical abnormalities from CS predict mortality than myocardial function loss.

ABSTRACT Antimicrobial‐resistant bacteria, a growing worldwide concern, reduce the effectiveness of antibiotics against a wide range of microbial infections. Various bacterial species have quickly developed antibiotic resistance since the first mention of penicillin resistance in 1947. A rise in mortality, more extended hospital stays, more healthcare expenditures, and morbidity are all brought about by these bacteria that are resistant to antibiotics. To develop resistance, bacteria may undergo genetic changes, engage in horizontal gene transfer, produce β‐lactamase, activate efflux pumps, form biofilms, and alter their metabolism to become less susceptible to drugs. Environmental factors and sublethal antibiotic exposure exacerbate resistance, particularly in cases of persistent infections caused by biofilms. This tendency is prompted by the overuse of antibiotics in both human and veterinary medicine, as well as inadequate infection control measures and environmental pollution. This review presents an extensive survey of antimicrobial resistance across bacterial taxa, with a focus on the physiological and genetic processes underlying this phenomenon. It delves into the current therapeutic landscape and showcases cutting‐edge methods—such as artificial intelligence‐driven antibiotic discovery and resistance prediction—to inform the development of next‐generation antibiotics and containment systems.

Importance The prevalence of cardiovascular disease differs substantially across neighborhoods. However, few studies have examined the contribution of neighborhood-level structural racism to inequities in cardiovascular health. Objective To investigate the association between neighborhood-level manifestations of structural racism and the prevalence of cardiovascular clinical and behavioral risk factors and cardiovascular diseases. Design, Setting, and Participants This cross-sectional study consisted of 71 915 US census tracts. The Structural Racism Effect Index (SREI) was appended to the 2020 Centers for Disease Control and Prevention’s PLACES dataset. Race and ethnicity were self-reported and obtained from the 2019 American Community Survey 5-year estimates. Data analysis was conducted from June 2024 to October 2024. Exposure The SREI is a composite score of census tract measures within 9 domains affected by structural racism (built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth). The SREI scores were categorized into distribution-based quintiles, where higher quintiles represented greater neighborhood structural racism. Main Outcomes and Measures Neighborhood-level prevalence of cardiovascular clinical (high blood pressure, high cholesterol, obesity, and diabetes) and behavioral (current cigarette smoking and no leisure-time physical activity) risk factors and cardiovascular disease (coronary heart disease and stroke). Multilevel-linear mixed models, adjusting for confounders, were used to estimate adjusted prevalence rate ratios (aPRR). Results Among 71 915 census tracts, neighborhoods in the highest quintiles of structural racism were more likely than neighborhoods in the lowest quintile to be in the southern region (quintile 1, 19.9% vs quintile 5, 56.8%). The proportion of the population that was Black was largest in neighborhoods in the highest quintile of structural racism (quintile 1, 2.2% vs quintile 5, 23.0%). After adjustment, compared with the lowest quintile of structural racism, the highest quintile had significantly higher prevalence rates of cardiovascular clinical risk factors, including high blood pressure (aPRR, 1.37; 95% CI, 1.37-1.38), obesity (aPRR, 1.40; 95% CI, 1.39-1.40), and diabetes (aPRR, 1.80; 95% CI, 1.79-1.81). Similarly, for cardiovascular behavioral risk factors, the highest quintile of structural racism had significantly higher prevalence rates of current cigarette smoking (aPRR, 1.84; 95% CI, 1.83-1.85) and no leisure-time physical activity (aPRR, 1.88; 95% CI, 1.87-1.89). Additionally, for cardiovascular diseases, the highest quintile of structural racism had higher prevalence rates of coronary heart disease (aPRR, 1.80; 95% CI, 1.79-1.82) and stroke (aPRR, 1.99; 95% CI, 1.98-2.00). Conclusions and Relevance In this cross-sectional study, structural racism was associated with inequities in neighborhood cardiovascular health, highlighting opportunities for place-based prevention efforts.

Abstract Understanding tumor cell dynamics can improve prognosis and treatment but remains limited for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA sequencing data from 684 NS-LUAD cases and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histologic data further characterized the subtypes. “Steady,” marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver gene alterations, is linked to prolonged survival and low immune evasion. “Proliferative” shows high proliferation markers, TP53 mutations, and gene fusions. “Chaotic,” with high epithelial-to-mesenchymal transition markers, has the worst prognosis, even within stage I tumors. Lacking known molecular or histologic characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the classification and predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving early-stage NS-LUAD prognostication in clinical settings. Significance: The transcriptome of 684 NS-LUAD identifies three subtypes with different cellular dynamics and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a potential clinically applicable tool for treatment decision-making in patients with NS-LUAD. See related commentary by Azizi et al., p. 423

Abstract Clinical flow cytometry laboratories are facing rising test volumes, greater assay complexity, and increasing requirements for quality control and assay validation. In response, the International Clinical Cytometry Society (ICCS) conducted a workload survey in early 2023 to gather updated information on assay volumes, complexity, staffing, and technology. Data analysis focused on identifying correlations between length of time to introduce new assays and other factors as a means to gain insight about laboratories that seem to be either adapting or struggling. Flow cytometry assays were categorized into 3 levels of technical/interpretative complexity: high (e.g., measurable/minimal residual disease (MRD assays)), moderate (e.g., leukemia/lymphoma assays (Assays L&L ), excluding MRD assays), and low (e.g., CD4 count). Annual assays per staff member were calculated according to staff involved in case sign‐out (Staff Signout ) or other laboratory operations (Staff LabOps ). Respondents were from 101 laboratories in the United States (69.3%), Canada (4.0%), and other countries (26.7%). Low, moderate, and high technical/interpretative complexity assays were performed in 85.1%, 97.0%, and 47.5% of all laboratories, respectively. Median annual total assays (Assays Total ) per laboratory were 3515 and, based on complexity, were 1518.5 (low), 1808.8 (moderate), and 350 (high). Among all laboratories, the median time (interquartile range) to introduce new Assays L&L was 6 mos. (4–12 mos.), to introduce MRD assays was 11 mos. (5–12 mos.), and to validate/go‐live with new cytometers was 8 mos. (4–12 mos.); these times positively correlated with each other. This study confirmed significantly increased workload since the prior ICCS 2013 workload survey with a concurrent decrease in Staff LabOps . Faster introduction of new assays correlated with other successes, including quicker validation of and going live with new cytometers. Among all laboratories, those that performed myeloid MRD assays versus those that did not were also found to be faster to introduce new assays. The need for sufficient staffing has been emphasized because laboratories with both higher annual volumes of myeloma MRD assays and higher ratios of Assays Total per Staff LabOps were slower to introduce new assays. “Lack of staff and/or time dedicated or protected for assay development” and, more generally, “staff number” were the most commonly identified major barriers for new assay development, with the former specifically linked to slower introduction of new assays among all laboratories.