Cancer Institute of Florida

Colchicine may reduce cardiovascular events in coronary artery disease (CAD) through its anti-inflammatory effects. PubMed, Scopus, and Cochrane Library were searched from inception to May 10, 2025, for randomized controlled trials (RCTs) comparing colchicine with placebo or usual care in patients with CAD. Eligible trials had ≥ 30 days of colchicine use and reported cardiovascular outcomes. The primary outcome was major adverse cardiovascular events (MACE). Secondary outcomes included extended MACE (eMACE), myocardial infarction (MI), stroke, all-cause mortality, cardiovascular mortality, coronary revascularization, gastrointestinal events, infection, myalgia, arrhythmia, cancer, alopecia, and treatment discontinuation. Random-effects models were used to calculate risk ratios with 95% CIs. Heterogeneity was assessed using the I² statistic, and meta-regression analysis was conducted to explore heterogeneity and potential effect modifiers. Eleven studies included 19,618 patients with 9814 and 9804 patients in the colchicine and control groups, respectively. The colchicine group was significantly associated with a lower rate of MACE compared to the control group (RR = 0.73, 95% CI = 0.59-0.92, p = 0.006; I² = 44%). From the secondary outcomes, eMACE (RR = 0.66, 95% CI = 0.52-0.85; p = 0.001; I² = 73%), MI (RR = 0.82, 95% CI = 0.70-0.96, p = 0.01), and coronary revascularization (RR = 0.60, 95% CI = 0.41-0.87, p = 0.007) were found to be significantly lower in the colchicine group. All the other secondary outcomes did not reach statistical significance. Meta-regression analysis for MACE showed a statistically significant association with diabetes (coefficient: -0.0778, p = 0.0013), indicating a potential modifying effect. Other covariates, including mean age, hypertension, smoking, and prior revascularization, did not demonstrate statistically significant associations. Colchicine reduces MACE, MI, and revascularization in CAD patients, supporting its use for secondary prevention.

Evidence suggests that the coagulation factor thrombin plays a pathologic role in liver fibrosis. Studies have proposed that thrombin signaling through protease-activated receptor-1 (PAR1) promotes activation of hepatic stellate cells (HSCs), the primary collagen-producing cells in the fibrotic liver. However, a comprehensive view of the effect of thrombin on HSCs is lacking. This study tests the hypothesis that thrombin induces expression of fibrogenic mediators in HSCs via PAR1 activation. Immortalized human HSCs (LX-2) were cultured for 48 hours, then stimulated with human α-thrombin (0-10 U/mL). In some experiments, cells were pretreated with the PAR1 antagonist, vorapaxar (0.01-10 μM). PAR1 activation and gene and protein expression were assessed. Rapid intracellular calcium mobilization was detected in thrombin-stimulated HSCs (0-10 U/mL) in a dose-dependent manner, which was blocked by vorapaxar pretreatment. Surprisingly, HSCs treated with thrombin for 24 to 48 hours did not affect gene or protein expression of fibrotic mediators. However, thrombin treatment for 4, 24, or 48 hours did provoke distinct transcriptomic changes. Pathway overrepresentation analysis revealed altered pathways associated with cell migration, inflammation, and metabolism, as well as enrichment for published gene signatures associated with HSC activation. Interestingly, several genes from altered pathways were shown to be regulated by PAR1 signaling. These results show that thrombin alters previously unidentified transcriptomic profiles, suggesting a program toward HSC activation. Thrombin activates complex signaling pathways for HSC preinitiation, which may drive HSC activation and hepatic fibrosis.

Reliable tumor segmentation in thoracic computed tomography (CT) remains challenging due to boundary ambiguity, class imbalance, and anatomical variability. We propose an uncertainty-guided, coarse-to-fine segmentation framework that combines full-volume tumor localization with refined region-of-interest (ROI) segmentation, enhanced by anatomically aware post-processing. The first-stage model generates a coarse prediction, followed by anatomically informed filtering based on lung overlap, proximity to lung surfaces, and component size. The resulting ROIs are segmented by a second-stage model trained with uncertainty-aware loss functions to improve accuracy and boundary calibration in ambiguous regions. Experiments on private and public datasets demonstrate improvements in Dice and Hausdorff scores, with fewer false positives and enhanced spatial interpretability. These results highlight the value of combining uncertainty modeling and anatomical priors in cascaded segmentation pipelines for robust and clinically meaningful tumor delineation. On the Orlando dataset, our framework improved Swin UNETR Dice from 0.4690 to 0.6447. Reduction in spurious components was strongly correlated with segmentation gains (HD95: ρ = −0.83, p < 0.0001), underscoring the value of anatomically informed post-processing.

Hyperoxaluria is caused by increased urinary excretion of oxalate leading to the formation of calcium oxalate (CaOx) stones. The lack of effective management strategies for hyperoxaluria prompted us to investigate molecular mimics as stone inhibitors, a strategy that we previously used successfully to discover small molecule inhibitors of l-cystine crystallization for the prevention of l-cystine stone formation in cystinuria. Herein, we report the discovery of l-lysine dioxalate (LH1513), a novel dioxamate derivative, as a more potent inhibitor of CaOx crystallization than citrate and pyruvate. Such inhibition was corroborated by in situ atomic force microscopy (AFM) measurements of crystal growth rates at the microscopic length scale. A triester prodrug of LH1513 was found to have sufficient oral bioavailability for a preliminary in vivo study demonstrating efficacy in preventing urinary CaOx crystal formation in an Agxt-knockout mouse model for hyperoxaluria.

Abstract Background Breast cancer (BC) incidence increases with age and is the leading cause of new cancer diagnosis among women in the United States. Although the median age of diagnosis is 63 years (yrs.), over a third of patients diagnosed, and about half of BC mortality in Western societies are in patients over 70 yrs. old. Overall, outcomes for early-stage BC have improved. Despite the lower incidence of triple negative BC (TNBC) (12%-15%) the 5-year survival is 8% to 16% lower than in hormone receptor-positive BC. With the improved life expectancy in the US and the increased incidence of BC as patients age, it is of vital importance to know how to treat BC in the elderly. Unfortunately, optimal management of BC among the elderly has not been adequately studied due to underrepresentation in clinical trials. Furthermore, there is limited information of the potential toxicity and real benefit of chemotherapy in older patients. Methods This is a retrospective analysis of data collected from the National Cancer Database (NCDB), a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society, during the years 2004 to 2019. All women ≥65yrs with early stage TNBC (stages I-III) were included in the analysis. Patients were categorized into three treatment groups – those who did not receive chemotherapy (No-CT), those who received chemotherapy (CT), and those who received chemotherapy and immunotherapy (CTIO). After adjusting for multiple variables including race, insurance, Charleson-Deyo score, stage at diagnosis, and receipt of loco-regional therapy, using the log rank P value, the age cutoff over which the survival rates were not significantly different between two treatment groups (No-CT and CT/CTIO) was identified. The main outcome of this study was all-cause mortality Results A total of 11,416 women with TNBC were included in the analysis. Of these, 4105 (36.0%) received No-CT, while 7311 (64.0%) received CT/CTIO. Log rank P values showed that above 81 years, there was no survival benefit between No-CT and CT/CTIO. A further analysis categorized patients into two groups – those between 65-80yrs and those ≥81 yrs. old. Cox proportional regression analysis showed that among patients between 65-80 yrs. all-cause mortality was significantly lower among patients in the CT/CTIO group compared to those in the No-CT group (hazard ratio [HR], 0.52; CI: 0.45-0.60). However, among patients ≥81yrs old, there was no significant difference in all-cause mortality between the treatment groups (hazard ratio [HR], 0.84; CI: 0.67-1.05). Conclusions: Among patients who were &amp;gt;81yrs old with early-stage TNBC, those who received treatment with CT/CTIO did not have an overall survival benefit as compared to those who received No-CT. Limitations of this study includes the small number of patients &amp;gt;81yr old who received chemotherapy which could explain why we were not able to identify a statistically significant benefit of CT/CTIO. Another limitation is that we were not able to assess breast cancer specific mortality. However, this analysis highlights the importance of individualizing treatment recommendations in older patients, who may not garner the same benefit of treatment as younger patients. Additional studies are required to clarify contributing factors and to help optimize the management of geriatric patients with TNBC. Table 1. Patient characteristics based on treatment. Citation Format: Yolcar Chamorro, Muni Rubens, Mukesh Roy, Naomi Dempsey, Reshma Mahtani, Manmeet Ahluwalia, Lauren Carcas, Ana Sandoval-Leon. Systemic therapy in geriatric patients with triple negative breast cancer: a National Cancer Database analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS13-07.

Changes in an organism’s environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism’s metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the Long-Term Evolution Experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass-spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.

Changes in an organism’s environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism’s metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the Long-Term Evolution Experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass-spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.