Background Renal cell carcinoma (RCC) incidence and mortality trends in California that reflect contemporary patterns of incidental diagnoses and new treatment advances have not been published, and RCC burden across sociodemographic groups remains unclear. We sought to investigate the incidence and mortality rates of first RCC in individuals aged 20 and older in California from 1988 through 2019, including overall trends and by sociodemographic factors. Methods Data were obtained from the California Cancer Registry. We calculated age-adjusted incidence and mortality rates and rate ratios (where relevant) of RCC overall and by sex, age, race and ethnicity, and neighborhood socioeconomic status (nSES). Joinpoint regression was used to estimate annual percent rate changes in incidence and mortality rates across time periods. Findings From 1988 through 2019, we identified 90,659 incident RCC cases, of which 62.9% were localized at diagnosis, and 37,069 RCC-related deaths. Incidence was higher in males than females, among American Indian/Alaska Native, Hispanic, and non-Hispanic Black individuals, and in lower-nSES areas. Incidence rose over time, with steeper increases among Hispanic and non-Hispanic Black populations and in the lowest nSES groups. Although mortality declined overall, disparities persisted, with Hispanic and non-Hispanic Black individuals experiencing higher mortality than non-Hispanic White individuals. Interpretation RCC remains a significant public health concern in California, with disparities by race, ethnicity, and nSES widening over time. This underscores the relevance of RCC as a health equity issue and the need for targeted public health action and equity-focused research. Funding This study utilized publicly accessible data from infrastructure supported by the California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention.

Objective:Timely care is a key quality indicator. Resection delayed beyond 8 weeks for early-stage non-small cell lung cancer (NSCLC) negatively impacts prognosis, yet diagnosis-to-treatment time has steadily increased. We sought to identify drivers of delayed surgical resection and generate evidence for timeliness metrics for optimal outcomes. Methods:We evaluated patient-level factors associated with delayed versus timely surgery (>8 vs ≤8 weeks after diagnosis) for clinical stage I-II NSCLC between 2009 and 2019. Factors were identified by estimating adjusted relative risks (aRRs) and 95% CIs using modified Poisson regression. Time to process-level steps from diagnosis, along with the number and combination of these steps, were examined by timeliness of surgery. Results:Among 2567 patients, 46.0% received timely surgery. Factors associated with delayed surgery included Black (aRR, 1.14; 95% CI, 1.02-1.27) and Asian (aRR, 1.14; 95% CI, 1.00-1.29) race, distance to surgical facility of >50 miles (aRR, 1.23; 95% CI, 1.08-1.40), and greater health care use (≥25 visits, aRR, 1.72; 95% CI, 1.53-1.93). More preoperative steps increased time to surgery, but no individual step drove delays. When performed, preoperative steps occurred within the following intervals in 75% of timely surgery recipients: positron emission tomography-computed tomography: 21 days, biopsy: 20 days, pulmonary function tests: 28 days, and thoracic surgery consult: 30 days. Conclusions:As health system and oncologic care processes grow in complexity, timely treatment warrants greater attention. Our results from an integrated health system indicate several patient- and process-level factors contribute to delays that can be mitigated and offer preliminary benchmarks to promote timely NSCLC management.

Abstract Background: Mammographic density (MD) phenotypes are highly heritable and strongly associated with breast cancer risk. Genetic variants identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability, and the responsible genes remain largely unknown. Transcriptome-wide association studies (TWAS) can improve power and identify genes associated with MD through their genetically regulated gene expression (GReX) levels. However, cell type heterogeneity in bulk tissue samples can obscure disease associations. Here, we conduct TWAS of MD phenotypes using standard approaches and a new cell-type-aware framework. Methods: The study population included 24,158 women of European ancestry who underwent screening with Hologic (n=20,282) or GE (n=3,876) digital mammography and participated in Kaiser’s Research Program on Genes Environment and Health. Dense area (DA), nondense area (NDA), and percent density (PD) were measured centrally using Cumulus6. Tissue-level gene expression was estimated using standard elastic-net models. Cell-type-specific expression in mammary epithelial, fibroblast, and adipocyte cells was estimated using MiXcan. Linear regression was used to assess associations of GReX levels with MD phenotypes, adjusted for age, BMI, and other covariates. Significance was determined by controlling the false-discovery rate at 0.05. Results: A total of 20 unique genes at 16 loci were significantly associated with MD phenotypes, including 10 genes at 7 loci for DA and 8 different genes at 7 loci for NDA. Of the 7 genes for PD, 2 also were associated with DA and 3 with NDA. Standard TWAS methods identified 8 genes whose tissue-level expression was significantly associated with MD phenotypes. In contrast, cell-type-aware analyses using MiXcan identified 10, 12, and 7 genes, respectively, using epithelial, fibroblast, or adipocyte models. Among the 12 genes identified by MiXcan but not standard methods, 2 showed opposite directions of association between different cell types. Conclusion: This TWAS identified novel genes for MD phenotypes, and prioritized genes at known GWAS loci that are likely to be causally associated through their expression levels in mammary epithelial, fibroblast, or adipocyte cells. Disentangling the distinct effects of gene expression in different mammary cell types through cell-type-aware analysis can yield new gene discoveries and insights into the biological basis of dense vs. nondense breast tissue. Citation Format: Joseph H. Rothstein, Adriana Sistig, Sinan Zhu, Tejomay Gadgil, Li Shen, Stacey E. Alexeeff, Ninah Achacoso, Lori C. Sakoda, Vignesh A. Arasu, Laurie R. Margolies, Robert J. Klein, Laurel A. Habel, Xiaoyu Song, Pei Wang, Weiva Sieh. Cell-type aware transcriptome-wide association study of mammographic density phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3611.

Background Many patients with lung cancer receive guideline-discordant nodal staging. However, existing studies lack clinical details—such as imaging indications for biopsy—that obscure attempts to characterize guideline-discordant nodal staging. Research question What are the frequency and types of guideline-discordant nodal staging, and what factors are associated with discordance? Study design and methods We conducted a cohort study of patients diagnosed with non-metastatic non-small cell lung cancer (2010-2021) staged by computed tomography and positron emission tomography four months prior to initiating treatment. We linked data from administrative, cancer, and vital status registries to electronic health records from two health systems. Guideline-discordant nodal staging was defined as no biopsy when one was indicated (e.g. tumor >3cm, central tumor, or lymphadenopathy on imaging), or a non-diagnostic, non-thorough, or negative needle biopsy despite high suspicion for nodal disease. We used generalized estimating equations to investigate imaging factors associated with guideline-discordant nodal staging. We performed a sensitivity analysis to evaluate the robustness of our findings across two national practice guidelines. Results Among 5,582 patients, 3,580 (64%) had an indication for biopsy, of which 2,798 (78%) experienced guideline-discordant nodal staging. Types of guideline-discordant nodal staging included no biopsy despite an indication for one (82%), negative needle biopsy despite a high suspicion of nodal disease (9%), a non-thorough procedure (7%), a non-diagnostic biopsy (1%), and other (1%). These findings were robust across sensitivity analyses—the only factor consistently associated with a higher risk of guideline-discordant nodal staging was higher standardized uptake values for the primary tumor. Interpretation When estimated among patients with an indication for biopsy, rates of guideline-discordant nodal staging were in the upper range of prior reports. The predominant type of guideline-discordant staging was no biopsy despite an indication. These findings further motivate the need for improved adoption and performance of nodal staging.

BACKGROUND:We compared performance across 3 breast cancer risk domains-clinical, polygenic, and mammography artificial intelligence-alone and in combination over a 10-year time horizon among women with a negative screening mammogram within a Kaiser Permanente Research Bank (KPRB) prospective cohort. METHODS:The study included 82 957 women (61 962 non-Hispanic White, 7256 Asian, 3414 Black, and 5466 Latina) who enrolled in KPRB between 2003 to 2020. Women with a prior history of breast cancer or high/moderate-penetrant gene mutation were excluded. The negative screening mammogram (no clinically visible cancer) closest to enrollment was used to generate the Mirai mammography AI risk score. KPRB survey and electronic health record data were used to generate the Breast Cancer Surveillance Consortium version 3 (BCSCv3) clinical risk score. Genome-wide genotypes were used to compute the 313-SNP polygenic risk score, adjusted for genetic ancestry (PRS313adj). Risks of breast cancer (invasive or ductal carcinoma in situ) at 0 to 10 years after the mammogram were estimated using Cox models, with 5-fold cross-validation used to estimate the C-index. RESULTS:During 10 years of follow-up, 2471 women developed breast cancer. The C-index (95% CI) for the combined model with all 3 risk scores (0.70; 95% CI = 0.69 to 0.71) was significantly higher than for univariate models with only the BCSCv3 (0.62; 95% CI = 0.61 to 0.63), PRS313adj (0.61; 95% CI = 0.60 to 0.62), or Mirai (0.66; 95% CI = 0.65 to 0.67) risk score. CONCLUSIONS:Integrating mammographic AI and polygenic risk scores with clinical risk models significantly improved breast cancer risk discrimination, supporting use of combined models for personalized screening and prevention.

Physical activity is an established protective factor for colorectal cancer (CRC), but it is unclear if genetic variants modify this effect. To investigate this possibility, we conducted a genome-wide gene–physical activity interaction analysis. Using logistic regression (1-d.f), two-step screening and testing method (EDGE), and joint tests (3-d.f), we analyzed interactions between common genetic variants across the genome and physical activity in relation to CRC risk. Self-reported physical activity levels were categorized as active (≥ 8.75 MET-h/wk) vs. inactive (< 8.75 MET-h/wk; 39,992 participants) and as study- and sex-specific quartiles of activity (42,602 participants). Physical activity was inversely associated with CRC risk overall (OR [active vs. inactive] = 0.85; 95

Abstract Introduction: Lung cancer rates differ across rural and urban areas, but few studies have evaluated rural-urban differences across the risk-screening continuum. We examined smoking, cessation interventions, and lung cancer screening patterns across the rural-urban continuum in a geographically defined area over 10 years. Materials and Methods: A retrospective cohort study (2014-2023) was conducted using clinical data from the Rochester Epidemiology Project, derived from healthcare encounters in a 27-county region of the midwestern United States. Patients ages 40-80 were included. We used Rural-Urban Commuting Area codes to assign residence as urban, rural, or highly rural. We examined yearly smoking prevalence, cessation intervention (pharmacotherapy, counseling), and low-dose computed tomography (LDCT) lung cancer screening. Results: Over the 10-year study period, the sample size ranged from 305,530 to 340,411 people annually with 36-38% urban, 56-57% rural, and 6-7% highly rural. Current smoking prevalence declined from 14 to 12% over the 10-year period (range=12-16%; p=0.06) and was consistently lower in urban areas (range=10-14%) than rural (range=12-17.0%) and highly rural areas (range=12-18%; p=<0.001). Among individuals who currently smoked with no history of lung cancer, yearly smoking cessation intervention ranged from 16% to 23%, increasing over the 10-year period (p=<0.001). Cessation medication (varenicline, bupropion, or nicotine replacement) prescription consistently increased overall over time but remained higher in urban than rural and highly rural areas (p=<0.001). Cessation counseling rates also increased overall over time and were similar between urban and rural areas and lower in highly rural areas (p=<0.001). Among people who had ever-smoked aged 50-80 years with no diagnosis of lung cancer, LDCT screening increased from 0.0% to 3.1% over the study period. Increases were higher in urban areas (0.0-3.6%) and similar in rural and highly rural areas (0.0-2.8% and 0.0-2.9%, respectively; p=<0.001). Among people who currently smoked, LDCT screening also increased over the study period (0.0-6.4%), with higher increases in urban (0.1-8.0%) than rural (0.0-5.7%) or highly rural (0.1-6.1%) areas (p=<0.001). After the new screening guidelines in 2021, rates in all areas increased year-on-year. Conclusion: In this cohort, we found that smoking prevalence, cessation interventions, and lung cancer screening improved from 2014-2023 across the rural-urban continuum. Unfortunately, smoking prevalence remained higher and cessation interventions and screening rates lower in rural compared to urban areas. The persistent patterns underscore the need for strategies to overcome barriers for rural residents and improve lung cancer prevention and early detection in all areas. Citation Format: Brianna Tranby, Paul A. Decker, Jiang Ruoxiang, David Midthun, Lori C. Sakoda, Melinda C. Aldrich, Debra Friedman, Adoma Manful, Oindrila Bhattacharyya, Christi Patten, Chyke A. Doubeni. Patterns of tobacco use, cessation interventions, and lung cancer screening in rural vs. urban areas over time; a 10-year retrospective cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5045.

PURPOSE:Cancer care is highly interdependent, but optimal timing and sequencing is challenging. Teamwork is a promising approach to optimize care timing and sequencing. We previously showed that the 4R Oncology model fostered a high-functioning team and enabled interdependent care optimizations in lung and breast cancers in a community health system. Herein, we evaluated whether the optimizations and 4R clinic implementation resulted in actual timing/sequencing improvements. METHODS:We activated optimizations and implemented Care Sequences in practice. Care Sequences are 4R tools for patients and clinicians who guide optimal roadmap and timing/sequencing of care. In each cancer, we compared metrics for 11 types of interdependent care using chi-square analyses between the intervention cohort (lung cancer n = 138, breast cancer n = 208), diagnosed post-4R, and the historical cohort (lung cancer n = 173, breast cancer n = 268), diagnosed pre-4R. We used multiple regression analysis to determine factors influencing a composite Optimization Index, a patient-level measure of overall interdependent care optimization. RESULTS:Intervention and historical cohorts were comparable in patient characteristics and care received. In each cancer, timing/sequencing for all care types improved, six of them significantly, including the timing of lung surgery (88% v 72%, P = .02), lung biomarker results (81% v 63%, P = .04), breast gene expression results (70% v 34%, P < .001), and endocrine therapy start (89% v 78%, P = .03). Optimization Index was significantly higher in the intervention than historical cohorts (P < .001 in each cancer, mean = 0.82 v 0.68 in lung cancer; 0.81 v 0.68 in breast cancer). 4R contributed to this increase twice as much as all patient characteristics and care received combined. CONCLUSION:4R is effective in improving timing and sequencing of interdependent care via establishing high-functioning teams and facilitating relevant optimizations. Our study provides a roadmap for other institutions developing high-functioning teams and optimizing timeliness of care.

A new generation of cancer screening tests, Multi-Cancer Detection (MCD) tests, aims to advance cancer early detection. MCD tests combine blood-based assays of biological components and bioinformatics algorithms to simultaneously detect multiple types of cancer. If shown to improve health outcomes, these new technologies would be simple to implement, improving accessibility and uptake. Importantly, MCD tests offer early detection opportunities for cancers with no established screening modalities. Despite their promise, evidence supporting public health benefits of MCD tests for early detection benefits is lacking. No consensus yet exists for determining the level of screening performance or the effects on cancer outcomes needed to recommend adoption. Further, the potential harms of screening (e.g., unnecessary procedures, increased anxiety and cancer worry) are often overlooked. To address the need for objective evaluation of novel screening methods, the National Cancer Institute created the Cancer Screening Research Network (CSRN). The CSRN mission is to implement rigorous clinical trials to evaluate a broad range of technologies and approaches for cancer screening. The CSRN Vanguard Study is the first US randomized trial of MCDs. The study will develop and determine the feasibility of critical protocol components of a subsequent full-scale trial to evaluate MCD tests. The study will enroll 18,000-24,000 participants aged 45-75 years without known cancer and randomize them into one of 3 arms: two separate intervention arms each evaluating one MCD test, or a control arm. Information gained will inform full-scale trial design and implementation features. ClinicalTrials.gov Identifier: NCT06995898; https://clinicaltrials.gov/study/NCT06995898; registered May 29, 2025.

Supplementary Table S4 lists the 140 colorectal-cancer-associated loci and associations with colorectal cancer in European-ancestry population.

Supplementary Figure S6 shows the calibration on relative risk of PRS stratified by PRS with 7 bins in groups of different ancestry in the GERA cohort.

This table includes the overall sample description stratified by colorectal cancer (CRC) status and smoking status.

The increase in chest CT volumes affords radiologists the opportunity to systematically assess imaging biomarkers, including coronary and thoracic arterial calcification, emphysema, airway dysanapsis, adipose tissue in various compartments, skeletal muscle (in terms of both quantity and quality), and vertebral body bone attenuation (as a measure of bone mineral density), extending from the T1 through T12 vertebral body levels. These biomarkers represent a spectrum of disease-induced changes or increases in the risk of developing disease. This Special Series Review provides an overview of these established and emerging imaging biomarkers on chest CT scans, aiming to serve as a reference for practicing radiologists. We discuss the importance of imaging biomarkers for patient care; highlight recent developments; present approaches for interpretation and integration into clinical workflows, with attention given to the role of reference values; consider challenges in serial assessment resulting from variations in technical parameters; describe the incorporation of the biomarkers into societal guidelines; and summarize FDA-approved artificial intelligence tools to aid evaluation.

Lung cancer is a leading cause of cancer mortality for most ethnic groups of Asian American women, including Chinese, Korean, Japanese, and Vietnamese Americans, a striking pattern given the exceedingly low prevalence of smoking among Asian American women in the general population. Recent research demonstrates that among Asian American women with a lung cancer diagnosis, the vast majority of patients have never smoked, a rate as high as 80% among Chinese and Asian Indian American women. Despite declining rates in lung cancer overall in the United States, rates among Asian American women who have never smoked appear to be increasing. This commentary articulates extant knowledge, based on studies in Asia, of a range of risk factors, such as a family history of lung cancer; a history of lung diseases, including tuberculosis and chronic obstructive pulmonary disease; exposure to cooking fumes and second-hand smoke; and various putative risk factors. Unique mutational profiles at the tumor level, including a higher prevalence of EGFR variations among Asian populations, highlight the importance of tumor genomic testing of newly diagnosed patients. Additional research is essential, given the high burden of disease among Asian American women who have never smoked and the limited knowledge regarding contributing risk factors specific to Asian American women, because the risk factors identified in Asian people living in Asia may not apply.

This file includes the expression imputation statistics and included SNPs from the elastin net models.

Supplementary Figure from Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region

This file details the two-step interaction tests, and the gene-based aggregate test.