BackgroundPolitical determinants of cancer risk are largely unexplored, conceptually and empirically.MethodsObservational analysis of associations present in 2017-2021 between 5 state-level political metrics and 4 age-standardized cancer outcomes (regional and distant stage at diagnosis for breast, cervical, and colorectal cancer among screening-age adults and premature cancer mortality), overall and in standardized linear regression models adjusting for state-level poverty and medical uninsurance.ResultsIn fully adjusted models (adjusted for state-level poverty and state-level medical uninsurance variables: % working age adults [age 35-64] without medical insurance; number of years of state Medicaid expansion), each 1 SD shift toward a more liberal political ideology (measured by voting record) among elected officials in the US House of Representatives was associated with decreased risk of diagnosis with regional and distant breast and colorectal cancer (respectively: −0.76, 95% confidence interval [CI] = −1.26 to −0.25; −0.75; 95% CI = −1.5 to 0). Risk of premature cancer mortality likewise was lower, in the fully adjusted models, with each 1 SD shift toward more liberal scores for the state electoral college vote (−2.01, 95% CI = −3.68 to −0.33), the state liberalism policy index (−2.51, 95% CI = −4.48 to −0.54), and political ideology of elected officials in the US Senate (−1.93, 95% CI = −3.71 to −0.14).ConclusionOur state-level analyses suggest that political metrics are associated with preventable cancer outcomes. Efforts to reduce population burdens of cancer and inequities in these burdens could benefit from analyses of sociopolitical drivers of cancer risk across the cancer continuum.

Socioeconomically disadvantaged counties exhibit higher cervical cancer incidence and poorer survival. However, the specific impact of the magnitude of persistent poverty on these outcomes remains largely unexamined. Using national cancer registry data, we observed that women living in persistent poverty counties who have experienced extreme poverty (≥40% poverty) have more than 1.5 times higher cervical cancer incidence and twice the mortality rate as women who lived in nonpersistent poverty counties. Furthermore, stage-specific incidence was consistently higher in persistent poverty counties across localized, regional, and distant diagnoses. Five-year mortality for localized cervical cancer diagnoses was nearly twice as high in extreme poverty counties (11% vs 6%, 2-sided P = .03). These findings highlight substantial disparities in cervical cancer outcomes associated with increasing magnitude of persistent poverty and underscore the need for targeted interventions in economically vulnerable communities to reduce disparities and achieve cervical cancer elimination goals.

Numerous exercise oncology trials have been completed, greatly informing exercise recommendations for patients with cancer. Exercise medicine can be administered in various types, doses, and schedules at various time points. Advancing precision exercise medicine requires understanding of how the effects of different exercise interventions vary by characteristics of individual patients. The Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study provides an international infrastructure and shared database to perform pooled analyses of individual patient data (IPD) from multiple randomized controlled trials. This commentary aims to highlight the value of pooled IPD analyses, summarize key findings from published pooled IPD analyses on the effects of physical exercise on various outcomes, and provide guidance to advance precision exercise medicine for patients with cancer. POLARIS currently includes IPD from 52 exercise trials. Findings to date indicate that exercise interventions in patients with cancer have beneficial effects on physical fitness, fatigue, health-related quality of life, self-reported cognition (posttreatment), sleep disturbances, and symptoms of anxiety and depression. Additionally, it was determined that the exercise effects varied by characteristics of the patients, including the initial value of the outcome, age, marital status, and education level, and by characteristics of the intervention, including exercise supervision and specificity. Future research opportunities to advance precision exercise medicine for patients with cancer include pooling of trial data from understudied populations, data on clinical outcomes, and biomarkers, as well as applying machine learning models for identifying combinations of covariables that modify intervention effects and predictions of individual treatment effects.

The INTEGRATE phase 3 trial in advanced gastric and esophagogastric junction cancer involved pooling overall survival data with its preceding phase 2 trial, raising concerns about misalignment due to treatment switching in phase 2, or the "winner's curse." We evaluated phase 2 results, adjusted for these opposing effects, against phase 3 according to the prespecified statistical analysis plan. Overall survival estimates were adjusted for treatment switching using the rank-preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) method. A novel shrinkage approach mitigated overestimation from the winner's curse, and Bayesian prediction methods predicted phase 3 outcomes from phase 2 estimates. A simulation study modeled 10 000 seamless phase 2/3 trials to quantify bias in the pooled estimate. The observed phase 3 hazard ratio (HR = 0.71, 95% CI = 0.54 to 0.93) for overall survival was more conservative than the adjusted phase 2 estimates (RPSFTM and novel shrinkage approach: HR = 0.61, 95% CI = 0.29 to 1.29; RPSFTM and Bayesian prediction: HR = 0.59, 95% CI = 0.48 to 0.73; IPCW and novel shrinkage approach: HR = 0.55, 95% CI = 0.31 to 0.99; IPCW and Bayesian prediction: HR = 0.58, 95% CI = 0.46 to 0.72). Simulations indicated negligible bias in the pooled log hazard ratio of ‒0.011 and 0.005 under the null and alternative hypotheses, respectively. Adjusting phase 2 estimates for both treatment switching and the winner's curse produced point estimates similar to the unadjusted phase 3 results. A prospective plan to pool trial data under a closed testing procedure may be a reasonable strategy when a recruitment shortfall in phase 3 is anticipated, provided that potential sources of misalignment are thoroughly assessed. ACTRN12612000239864 (INTEGRATE I)NCT02773524 (INTEGRATE IIA).

Although the relative proportion of triple-negative breast cancer decreases with age, its prevalence is rising with an aging population. This study examined real-world treatment practices, whether age in older women with triple-negative breast cancer affects therapy and outcomes, focusing on the potentially curable nature of early-stage triple-negative breast cancer. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses, PRISMA-compliant search using population, intervention, comparison, outcomes criteria identified literature from 2014 to 2023 across 5 databases (MEDLINE, Embase, PubMed, Web of Science, and Scopus), focusing on women aged 65 years and older with early-stage triple-negative breast cancer. From 7171 records, 37 studies were included. Older women with triple-negative breast cancer exhibited less aggressive features, including lower Ki67, higher androgen receptor, and higher Bcl2 expression. Breast-conserving surgery with radiation therapy (RT) was associated with improved overall survival and breast cancer-specific survival, with fewer recurrences compared with mastectomy with or without RT. Older women with triple-negative breast cancer were more likely to receive RT than systemic therapy, and the lack of RT correlated with worse outcomes. Multivariate analyses showed that systemic treatment improved 5-year overall survival and breast cancer-specific survival. Overall, outcomes did not show significant differences between women aged 70 years and older and women younger than 70 years at a median follow-up of 46 months. The lack of overall outcome improvements for older women with triple-negative breast cancer following treatment may not solely be due to absent targetable receptors because the intrinsic biology in older patients may be relatively favorable. Instead, treatment selection biases against active treatment due to age-related factors may contribute substantially. Treatment decisions should be biology based and guided by a multidisciplinary, holistic, and patient-centered approach that carefully considers comorbidities, functional status, social support, and patient preferences.

Benign breast disease is an important risk factor for breast cancer development. In this study, we analyzedhematoxylin and eosin-stained whole-slide images from diagnostic benign breast disease biopsies using different deep learning approaches to predict which individuals would subsequently developed breast cancer (cases) or would not (controls). We randomly divided cases and controls from a nested case-control study of 946 women with benign breast disease into training (331 cases, 331 control individuals) and test (142 cases, 142 control individuals) groups. We employed customized VGG-16 and AutoML machine learning models for image-only classification using whole-slide images, logistic regression for classification using only clinicopathological characteristics, and a multimodal network combining whole-slide images and clinicopathological characteristics for classification. Both image-only (area under the receiver operating characteristic curve [AUROC] = 0.83 [SE = 0.001] and 0.78 [SE = 0.001] for customized VGG-16 and AutoML models, respectively) and multimodal (AUROC = 0.89 [SE = 0.03]) networks had high discriminatory accuracy for breast cancer. The clinicopathological-characteristics-only model had the lowest AUROC (0.54 [SE = 0.03]). In addition, compared with the customized VGG-16 model, which performed better than the AutoML model, the multimodal network had improved accuracy (AUROC = 0.89 [SE = 0.03] vs 0.83 [SE = 0.02]), sensitivity (AUROC = 0.93 [SE = 0.04] vs 0.83 [SE = 0.003]), and specificity (AUROC = 0.86 [SE = 0.03] vs 0.84 [SE = 0.003]). This study opens promising avenues for breast cancer risk assessment in women with benign breast disease. Integrating whole-slide images and clinicopathological characteristics through a multimodal approach substantially improved predictive model performance. Future research will explore deep learning techniques to understand benign breast disease progression to invasive breast cancer.

Early phase clinical trials continue to have difficulty with enrolling real-world populations with many minorities being underrepresented. Reasons for this include patient or clinician perception as well as cultural, linguistic, or social barriers. In Australia, there is currently no prospective data in the early phase clinical trial space regarding recruitment of priority populations. Patient Diversity in Early Phase Clinical Trials was a multicenter, prospective, cohort study involving 2 major early phase clinical trial centers in Sydney, Australia. All participants who were consented to an early phase clinical trial between August 2023 and August 2024 were enrolled. Participants completed a baseline demographic survey, which included cultural and linguistic status, sexual orientation, socioeconomic status, and regional diversity. A total of 114 participants were recruited. Median age was 63 years (range = 25-83 years) with predominance for female participants (52%). No participant reported a nonbinary gender. All participants reported their sexuality as heterosexual, with no LGBTQIA+ participants recruited. A total of 34 (30%) participants were identified as culturally diverse, while 28 (25%) were linguistically diverse. One patient identified as Indigenous Australian. Of the participants, 26% were born overseas, with 44% having at least 1 parent born overseas. The majority were living in households with family members, with 8% of participants living alone. Patient Diversity in Early Phase Clinical Trials is the first prospective study that provides granular description of social, cultural, linguistic, economic, and sexual diversity among early phase clinical trial participants. Certain subgroups are underrepresented, including those with sexual diversity, gender diversity, and Indigenous backgrounds. Ongoing efforts to monitor and promote inclusion of diverse populations in clinical trials are vital.

BackgroundData on prospective associations of accelerometer-measured physical activity, sedentary behavior, and mortality among cancer survivors are lacking. Our study examined accelerometer-measured daily physical activity (including light, moderate to vigorous, total, and steps), sedentary behavior (sitting time and mean bout duration), and mortality among cancer survivors in the Women’s Health Accelerometry Collaboration.MethodsPostmenopausal women in the Collaboration who reported a cancer diagnosis at least 1 year prior to wearing an ActiGraph GT3X+ device on the hip for at least 4 of 7 days from 2011 to 2015 were included. Outcomes included all-cause, cancer-related, and cardiovascular disease (CVD)–related mortality. Covariate-adjusted Cox regression estimated hazard ratios (HRs) and 95% CIs for each physical activity and sedentary behavior measure in association with mortality.ResultsOverall, 2479 cancer survivors (mean [SD] age, 74.2 [6.7] years) were followed up for 8.3 years. For all-cause mortality (n = 594 cases), every 78.1 minutes per day in light physical activity, 96.5 minutes per day in total physical activity, 102.2 minutes per day in sitting time, and 4.8 minutes in a sitting bout duration had hazard ratios of 0.92 (95% CI = 0.84 to 1.01), 0.89 (95% CI = 0.80 to 0.98), 1.12 (95% CI = 1.02 to 1.24), and 1.04 (95% CI = 0.96 to 1.12), respectively. Linear associations for cancer mortality (n = 168) and CVD mortality (n = 109) were not statistically significant, except for steps (hazard ratio per 2469 steps/d = 0.66, 95% CI = 0.45 to 0.96) and sitting time (hazard ratio = 1.30, 95% CI = 1.02 to 1.67) for CVD mortality. Nonlinear associations showed benefits of moderate to vigorous physical activity (for all-cause and CVD mortality) and steps (all-cause mortality only) maximized at approximately 60 minutes per day and 5000–6000 steps per day, respectively.ConclusionsAmong postmenopausal cancer survivors, higher physical activity and lower sedentary behavior was associated with reduced hazards of all-cause and CVD mortality.

BackgroundTo determine the performance of a multi-gene copy number variation (MG-CNV) risk score in metastatic tissue and plasma biospecimens from treatment-naïve metastatic castration-resistant prostate cancer (mCRPC) patients for prediction of clinical outcomes.MethodsThe mCRPC tissue and plasma cell-free DNA (cfDNA) biospecimen sequencing results obtained from publicly accessed cohorts in dbGaP, cBioPortal, and an institutional mCRPC cohort were used to develop a MG-CNV risk score derived from gains in AR, MYC, COL22A1, PIK3CA, PIK3CB, NOTCH1 and losses in TMPRSS2, NCOR1, ZBTB16, TP53, NKX3-1 in independent cohorts for determining overall survival (OS), progression-free survival (PFS) to first-line androgen receptor pathway inhibitors (ARPIs). The range of the risk scores for each cohort was dichotomized into “high-risk” and “low-risk” groups and association with OS/PFS determined. Univariate and multivariable Cox proportional hazards regressions were applied for survival analyses (P < .05 for statistical significance).ResultsOf 1137 metastatic tissue-plasma biospecimens across all cohorts, 699/1137 were treatment-naive mCRPC (235/699 metastatic tissue; 464/699 plasma-cfDNA), and 311/1137 were matched tissue-cfDNA pairs. In multivariable analysis, the MG-CNV risk score derived from metastatic tissue or in cfDNA was statistically significantly associated with OS with high score associated with short survival (hazard ratio = 2.65, confidence interval = 1.99 to 3.51; P = 1.35−11) and shorter PFS to ARPIs (median PFS of 7.8 months) compared with 14 months in patients with low-risk score.ConclusionsA molecular risk score in treatment-naïve mCRPC state obtained either in metastatic tissue or cfDNA predicts clinical survival outcomes and offers a tumor biology-based tool to design biomarker-based enrichment clinical trials.

It is unknown if a period of active surveillance before prostatectomy for prostate cancer (PCa) worsens functional outcomes. The aim of this study was to compare functional outcomes after primary vs delayed robot-assisted radical prostatectomy. We included men registered in the National Prostate Cancer Register of Sweden with low and favorable intermediate-risk PCa who underwent robot-assisted prostatectomy in 2018-2020 and had filled a questionnaire on patient-reported outcome measures. Multivariable logistic regression analysis was used to compare the functional outcomes of primary and delayed prostatectomy. 2571 men underwent primary, and 921 men underwent delayed prostatectomy. Delayed prostatectomy was not associated with reduced overall quality of life (adjusted Odds Ratio [OR] 1.04; 95% confidence interval [CI] 0.71-1.55) or erectile dysfunction (adjusted OR 0.90, 95% CI 0.69-1.22). Urinary incontinence was slightly more common after delayed prostatectomy (15% vs 11%; adjusted OR 1.38, 95% CI 0.91-2.01). There were weak associations between time to prostatectomy and urinary symptoms and bother, with a 3% annual increase in the risk for urinary incontinence (adjusted OR 1.03; 95% CI 0.94-1.13). These results suggest that a period on active surveillance before robot-assisted radical prostatectomy has little detrimental effect on functional outcomes. Since only around half of men on active surveillance will transit to prostatectomy, these outcomes represent a worst-case scenario for men who start active surveillance. These results support the use of active surveillance for men with low-risk and favorable intermediate-risk PCa.