The Northern Ireland Cancer Centre

The Bcl-2 protein family defines cellular competence for mitochondrial outer membrane permeabilization (MOMP) and apoptotic cell death. In proliferating cells, the Bcl-2 family member Mcl-1 accumulates across the cell cycle and confers trans-mitotic resistance to extrinsic apoptosis. We show here that Mcl-1, but not Bcl-xL, additionally undergoes a coordinated redistribution from the cytosol to mitochondria, concomitant with its over-proportional accumulation late in the cell cycle. Live-cell monitoring of Mcl-1 dynamics at single-cell resolution, combined with mathematical modelling, enabled us to quantify that Mcl-1 redistribution substantially contributes to elevating MOMP thresholds. Furthermore, we found that Mcl-1 accumulation and redistribution act concomitantly but independently to increase MOMP thresholds as cells approach mitosis and this elevated resistance is reset in daughter cells after division. Notably, heterogeneities in Mcl-1 abundance and subcellular distribution are pronounced even among isogenic cells within the same cell-cycle phase, and thus contribute to substantial cell-to-cell variability in MOMP susceptibility. Analysis of colorectal cancer tissue samples showed that variability in Mcl-1 expression and distribution is likewise prominent between cells in patient tumors and were predicted to drive intra-tumour heterogeneity in responses to treatments that induce MOMP. Overall, we demonstrate how changes in Mcl-1 amounts and localisation integrate with cell-cycle progression to modulate apoptotic susceptibility, thereby shaping cell-fate outcomes and contributing to cell-to-cell heterogeneities in death decision making.

The Proliferation Saturation Index (PSI) model is a patient-specific mathematical model designed to simulate and predict tumor volume regression (TVR) during radiation therapy (RT) using early treatment response dynamics. This study validates the PSI model in an independent external cohort of patients with non-small cell lung cancer using previously derived model parameters. In a cohort of 71 patients, treated with definitive RT alone (55 Gy/20#) tumor volume measurements were extracted from 6 cone beam computed tomography (CBCT) scans acquired on days 1, 2, and 3 and weekly thereafter. Model predictions of TVR at final CBCT (CBCT6) were made using tumor volume dynamics recorded from CBCT 1-3, CBCT 1-4, and CBCT 1-5. Agreement between predicted and actual volumes were assessed using the coefficient of determination (R2) and Pearson correlation coefficient (PCC). Model predictions showed strong agreement with measured volumes, improving with additional input data: R2 increased from 0.81 (3 inputs) to 0.94 (5 inputs), and PCC rose from 0.90 to 0.97. Prediction using data from approximately day 10 of RT (CBCT4) yielded R2 = 0.91 and PCC = 0.95. The model demonstrated strong performance in identifying poor responders (TVR ≤10%) with sensitivity of 77.7%, specificity of 73.6%, and a negative predictive value of 90.7%. Sensitivity analysis confirmed model stability with under ±20% parameter variation. This external validation confirms the PSI model's reproducibility and robustness in predicting TVR in patients with non-small cell lung cancer treated with definitive RT, supporting its integration into personalized RT planning strategies.

Ventricular tachycardia (VT) is a life-threatening rhythm disturbance of the heart that has been shown to be amenable to treatment with stereotactic body radiation therapy, known as stereotactic arrhythmia radioablation (STAR). The metabolic impact of directly targeting arrhythmogenic regions of scarred left ventricle (LV) is unknown. In this study, the relationship between radiation dose and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography (18FDG-PET) uptake in LV segments following STAR was interrogated. Patients with VT in the ENCORE-VT trial received a 25 Gy single-fraction of STAR to an electrophysiology guided target volume planned on 4-dimensional computed tomography (CT). Patients underwent 18FDG-PET/CT ± magnetic resonance imaging at baseline, day 3, and day 90. Auto-segmentation of the LV into the American Heart Association 17-segment model was performed on planning CTs and PET/CTs, allowing median radiation dose and standardized uptake value (SUV) to be extracted. Change in segment median SUV was correlated with dose and clinical outcomes (implanted cardiac defibrillator shock, death). Nineteen patients received STAR. Median VT episodes were reduced from 119 in the 6 months before versus 3 in the 6 months after STAR (P < .001). Targeted LV segments had lower baseline 18FDG SUV than nontargeted segments (P < .001). Median SUV of targeted segments at baseline, day 3, and day 90 were 1.43, 1.67, and 2.52, respectively (P = .012). At day 3, a ≥20% SUV increase in targeted segments (n = 7/19) was associated with improved survival (hazard ratio [HR], 0.10; P = .005). At day 90, there was a trend toward a weak association between increased median whole LV myocardium SUV and improved LV ejection fraction (R2 = 0.355, P = .158). In logistic regression models, SUV values, together with cardiomyopathy type, VT storm occurrence, and amiodarone use, predicted implanted cardiac defibrillator shocks (P = .027) and death (P = .016). In this small hypothesis-generating study, late SUV increases (day 90) were observed in targeted segments, consistent with VT burden reduction. Further longitudinal studies with 18FDG-PET/CT after STAR will define how SUV dynamics can be optimally incorporated as a biomarker in practice.

Abstract Background Socio-economic inequalities in breast cancer survival persist globally, including in the UK. Area-based deprivation measures may underestimate true socio-economic effects by assigning average levels to all individuals within an area. This study investigated associations between house value (individual-level socio-economic indicator) and area-based deprivation with breast cancer outcomes in Northern Ireland. Methods Women diagnosed with breast cancer 2011 to 2021 were identified using the Northern Ireland Cancer Registry. House value was determined from Valuation and Lands Agency property valuation data, and area-based deprivation was determined from the Northern Ireland Multiple Deprivation Measure. The primary outcome was breast cancer-specific mortality. Secondary outcomes included stage at diagnosis. Cox regression models calculated adjusted hazard ratios (HR) and (95%CIs) for cancer-specific mortality by house value category and separately for deprivation, adjusting for confounders. Results Among 12,766 women with breast cancer, associations were much more pronounced for house value than area-based deprivation. Women in the lowest house value category, compared to the highest value category, had a 60% increase in mortality (adjusted HR=1.60 95%CI 1.34, 1.92) and were more likely to be diagnosed with stage 4 disease (7.5% versus 4.1%; P&lt;0.001). Women living in the most versus least deprived areas had a 26% increase in mortality (adjusted HR=1.26 95%CI 1.08, 1.47) and were more likely to be diagnosed with stage 4 disease (5.9% vs 5.0%; P =0.157). Conclusion House value demonstrated stronger associations with breast cancer outcomes than area-level deprivation, suggesting it may serve as a more sensitive indicator for monitoring health inequalities in cancer. What is already known Lower socioeconomic status is associated with worse breast cancer survival in the UK and internationally. Area-based measures of deprivation are widely used in epidemiological research but have recognised limitations. What this study adds We observed socio-economic gradients for both late-stage at diagnosis and breast cancer survival for both house value and area-deprivation. However, associations were stronger for house value and effects persisted after adjustment for area deprivation. How this might affect research, policy and practice Our findings establish that publicly available property valuations provide a feasible and sensitive measure for monitoring socioeconomic health inequalities at the population level

This cohort study examines the incidence and prevalence of metastatic breast cancer in Northern Ireland using population-based cancer registry data and health records.

ObjectiveThe Cancer and Aging Research Group (CARG) score was developed to predict severe chemotherapy-induced toxicity risk in older adults; validation study results have varied. The Tolerance of Anti-cancer Systemic Therapy in the Elderly study sought to evaluate the CARG score prospectively in a chemotherapy-naïve UK population.Methods and analysisThis multicentre, prospective, observational study recruited patients aged ≥65 years commencing first-line chemotherapy for any solid organ malignancy or setting. Baseline demographics and established frailty measures were recorded. Follow-up data including toxicity and hospital admissions were collected retrospectively. Baseline CARG score predictive ability was assessed.Results339 patients were recruited from 19 centres; median age 73 years (range 65–92), 51.9% male and 54.9% gastrointestinal primary. At baseline, 85% of patients were of Eastern Cooperative Oncology Group Performance Status (ECOG PS) 0–1, with median Rockwood Clinical Frailty Scale (CFS) 3 (range 0–8).314 (92.6%) patients had follow-up data; 69 (22.3%) patients experienced Common Terminology for Cancer Adverse Events grade ≥3 toxicity and 84 (27%) required hospital admission during treatment.Increasing CARG risk groups had increased grade ≥3 toxicity (low 19.6%, medium 22.2%, high 28.2%); however, this was non-significant with no evidence of robust predictive performance. Predictive performance of CFS and ECOG PS was superior to CARG. Importantly, patient and clinician perceptions of toxicity risk differed significantly.ConclusionsIn older UK patients with cancer commencing chemotherapy, baseline frailty was prevalent. CARG score did not robustly discriminate or predict high-grade toxicity risk. ECOG and CFS showed superior, although limited, ability to predict and discriminate. This study highlights the need for the development of tools that better predict toxicity in this population.