JNCI: Journal of the National Cancer Institute

Background: Our group previously reported that lung cancer (LC) screening history results and subsequent timing of diagnosis are associated with significant differences in survival outcomes. As a follow-up study, we sought to develop novel personalized risk models that considered screening history for incidence cancers, interval LCs, and prevalence LCs. Methods: Using data from the CT-arm of the NLST, four independent case-control analyses were conducted to develop parsimonious risk models. Controls (n=26,038) were those never diagnosed with LC. The four LC case groups were 270 prevalence LCs, 44 interval LCs, 206 screen-detected LCs (SDLCs) that had a baseline positive screen, and 164 SDLCs that had a baseline negative screen. For each case-control analysis, univariable analyses identified statistically significant covariates from 48 variables and then significant covariates were included into a stepwise backward selection approach to identify a model with the most informative covariates. Results: For prevalence LCs, the model (AUC=0.711) included age, pack-years smoked, BMI, smoking status, smoking onset age, personal history of cancer, family history of LC, alcohol consumption, and milling occupation. For interval LCs, the model (AUC=0.734) included age, smoking status, smoking onset age, cigar smoking, marital status, and asbestos occupation. For baseline positive SDLCs, the model (AUC=0.685) included age, pack-years smoked, BMI, emphysema, chemicals/plastics exposure, and milling occupation. For baseline negative SDLCs, the model (AUC=0.701) included age, pack-years smoked, BMI, smoking status, emphysema, sarcoidosis, and sandblasting occupation. Conclusions: Besides smoking and age, which are inclusion criteria for screening, these models identified other important risk factors which could be used to provide personalized LC risk assessment and screening management.

BackgroundAccelerometer-derived behavioral phenotype captures multidimensional aspects of human behavior extending well beyond physical activity, encompassing light exposure, step counts, physical activity patterns, sleep, and circadian rhythms. Whether these five domains constitute a unified behavioral architecture underlying cancer risk and whether circadian organization and light exposure confer incremental predictive value beyond movement volume alone remains to be comprehensively established. MethodsWe conducted an accelerometer-wide association study (AWAS) encompassing the complete accelerometer-derived behavioral exposome across five behavioral domains in UK Biobank participants with valid wrist accelerometry data. Incident solid cancers were designated as the primary endpoint, with prespecified site-specific solid cancers and hematological malignancy as secondary outcomes. Cox proportional hazards models with age as the timescale were used. The minimal covariate set served as the primary reporting tier, followed by sensitivity analyses additionally adjusting for adiposity/metabolic factors, independent activity patterns, shift work history, and accelerometry measurement quality. Nominal statistical significance was defined as two-sided P < 0.05 ResultsAmong 89,080 participants, 6,598 incident solid cancer events were observed over a median follow-up of 8.39 years. In the minimally adjusted model, the pan-solid-tumor association atlas was dominated by signals from activity volume, inactivity fragmentation, and circadian rhythm. Higher overall acceleration (HR per SD: 0.91, 95% CI: 0.89-0.94) and higher daily step counts (HR: 0.93, 95% CI: 0.90-0.95) were independently associated with reduced solid cancer risk, while inactivity fragmentation metrics were consistently linked to higher risk. Notably, circadian rhythms, most prominently cosinor mesor (Midline Estimating Statistic of Rhythm under cosinor model), emerged as leading inverse risk signals, underscoring the independent contribution of circadian behavioral architecture. Site-specific analyses revealed pronounced heterogeneity across tumor sites. Lung cancer exhibited a robust inverse activity-risk gradient, while breast cancer showed reproducible associations with MVPA. Most strikingly, nocturnal light exposure demonstrated a tumor-site-specific association confined to pancreatic cancer, a signal absent across all other sites examined. Associations for uterine cancer were predominantly inactivity-related and substantially attenuated following adjustment for adiposity and metabolic factors. ConclusionsAcross five accelerometer-derived behavioral domains, solid cancers as a whole were most consistently associated with a high-movement, low-fragmentation, and circadian-coherent behavioral profile. While site-specific heterogeneity exists, the broad cancer risk landscape is dominated by movement volume, inactivity fragmentation, and circadian rhythmicity. Light exposure, although more localized in its contribution, demonstrates a potentially novel and specific association with pancreatic cancer risk. These findings support a five-domain behavioral exposome framework for cancer epidemiology and, importantly, position circadian rhythm integrity and nocturnal light exposure as critically understudied dimensions warranting dedicated mechanistic investigation.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

Excess body weight has been associated with increased cancer risk, but the role of weight change across adulthood remains unclear. We examined body weight trajectories from ages 17 to 60 and their associations with site-specific cancer incidence. Data were based on the ODDS study, a pooled, nationwide cohort study in Sweden, with data on weight spanning 1911 to 2020, and cancer follow-up through 2023. Weight trajectories were estimated with linear mixed effects models in individuals with at least three weight measurements. Cox regressions estimated hazard ratios for associations between weight trajectories and established and potentially obesity-related cancers. Fifth versus first quintile of weight change was associated with many cancers, most strongly with esophageal adenocarcinoma in men (HR 2.25; 95% CI 1.66-3.04), liver cancer in men (HR 2.67; 95% CI 2.15-3.33), endometrial cancer in women (HR 3.78; 95% CI 3.09-4.61), and pituitary tumors in both sexes (men: HR 3.13 [95% CI 2.13-4.61]; women: HR 2.13 [95% CI 1.41-3.22]). Associations varied by sex and age. Heavier weight at age 17 years and earlier obesity onset were also associated with higher cancer incidence. These findings highlight the importance of a life-course approach to weight management and support sex- and age-targeted cancer prevention strategies.

Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. The mutational signatures of mutagens in normal tissues offer opportunities to detect such exposures and survey them at population level. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that normal kidney cell genomes report an extensive repertoire of somatic mutational signatures. Microdissection of kidney structures revealed that proximal tubules exhibit higher mutation rates than other components of the nephron and most normal cell types despite low cell division rates. This is explained by marked enrichment of mutational signatures due to known exogenous carcinogenic mutagens including the plant-derived aristolochic acids, as well as several signatures of unknown causes including an unknown agent prevalent in Japan (SBS12), and signatures of uncertain origins (SBS40b and SBS40c). The results suggest the existence of multiple, common, systemically circulating mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

PURPOSE To evaluate cancer risk, age-specific penetrance, and mortality associated with heterozygous pathogenic or likely pathogenic (P/LP) germline PALB2 variants identified through genomic ascertainment and to assess modification by family history of cancer. PATIENTS AND METHODS We conducted a case-control study in two large population-based adult cohorts: the UK Biobank (n=469,580) and Geisinger MyCode (n=167,050). Individuals with heterozygous PALB2 P/LP variants were identified via exome sequencing and compared with non-carriers. Cancer diagnoses and vital status were obtained from linked registry and electronic health record data. We used multivariable logistic regression to estimate odds ratios (ORs) for cancer outcomes and Cox proportional hazards models to estimate hazard ratios (HRs) for all-cause mortality. Age-specific cumulative incidence (penetrance) was estimated using Kaplan-Meier methods. Models were adjusted for birth year, sex (when applicable), smoking status, and body mass index; stratified analyses assessed modification by family history of cancer. RESULTS PALB2 P/LP variant prevalence was 1:571 in UK Biobank and 1:940 in MyCode, with the higher prevalence in the UK cohort driven by the PALB2 p.Trp1038Ter founder variant. Compared with non-carriers, heterozygotes had significantly increased odds of any cancer, female breast cancer, pancreatic cancer, and cancers of ill-defined or secondary sites in both cohorts (P < 0.01). Adjusted hazard ratios for any cancer and female breast cancer ranged from 1.7 to 3.6. All-cause mortality was increased among PALB2-heterozygotes (HR 1.61-1.67), and survival after cancer diagnosis was reduced. Family history further modified cancer risk. CONCLUSION Genomic ascertainment of PALB2-heterozygotes identifies elevated risk for multiple cancers and increased mortality, although risks were lower than estimates from familial ascertainment. These findings inform risk management for individuals identified through genomic screening.

BackgroundTreatment optimization in HPV-associated oropharyngeal cancer (OPSCC) remains challenging, as recent de-escalation trials have shown limited success. Current patient selection strategies based on smoking history and TNM classification are insufficient, highlighting the need for robust, standardized prognostic biomarkers. We report the first validation of the Immunoscore (IS) for prognostic stratification in HPV-associated OPSCC. Patients and methodsWe analyzed 191 HPV-associated (p16+ and HPV DNA/RNA+) OPSCC patients from an international multicenter cohort (2015-2024), comprising a French monocentric retrospective training cohort (N = 48) and three validation cohorts: French monocentric retrospective (N = 48), French multicenter prospective (N = 50), and US multicenter retrospective (N = 45). IS is a standardized digital pathology assay quantifying CD3lJ and CD8lJ densities in tumor cores and invasive margins, with cut-offs defined in the training cohort and validated across cohorts. Associations with disease-free survival (DFS), time to recurrence (TTR) and overall survival (OS) were assessed, alongside 3RNA-seq and sequential immunofluorescence profiling of immune composition. ResultsMedian age 65; 80% male; 74% smokers; 66% T1-2; 82% N0-1 (AJCC8th). IS-High patients demonstrated superior 3-year DFS in the training and validation cohorts 1-3 (all log-rank P < 0.05). Multivariable analysis identified IS-Low as the strongest independent risk factor for DFS (HR 9.03; 95% CI: 4.02-20.31; P < 0.001). The model combining IS with clinical factors showed higher predictive accuracy for DFS (C-index 0.82) than clinical variables alone (0.7; P < 0.0001). Similar findings were observed for TTR and OS. IS-High tumors showed markedly higher enrichment of lymphoid and myeloid immune cell populations, contrasting with immune-poor signatures in IS-Low tumors. ConclusionsIS is a robust biomarker that outperforms standard clinical variables in both prognostic and predictive accuracy. The enriched cytotoxic immune infiltrate in IS-High tumors explains favorable outcomes and supports their suitability for treatment de-escalation. Prospective validation is warranted.

Introduction Lung cancer is rare before age 45, and its inherited genetic basis remains poorly defined. Methods We performed whole-genome sequencing in 171 predominantly young-onset lung cancer patients and integrated these data with whole-exome sequencing from six major lung cancer consortia, yielding 9,065 patients. After quality control, analyses focused on 6,545 individuals of European ancestry, the largest ancestral group. We compared the prevalence of rare pathogenic and likely pathogenic (P/LP) germline variants between 186 young-onset (age <45 years) and 6,359 older patients at gene and gene-set levels using Fisher's exact test, stratified by histology, sex, and smoking status. Polygenic risk scores (PRS) derived from common variants were also evaluated. Results Young-onset patients carried a higher burden of rare germline P/LP variants in DNA damage response (DDR) genes (including BRIP1, ERCC6, MSH5), and in cilia-related genes, notably GPR161. At the pathway level, DDR genes were significantly enriched (OR=1.66, p=0.007), with the strongest signal in the Fanconi Anemia pathway and among females (OR=1.96, p=0.01). Enrichment was also observed in inborn errors of immunity pathways, with strongest signals in antibody deficiency and the complement system genes. Young-onset patients additionally exhibited higher lung cancer PRS. Conclusion Young-onset lung cancer exhibits a distinct germline genetic architecture, characterized by enrichment of rare P/LP variants in DDR, cilia-related, and immune pathways, and an elevated lung cancer PRS. These findings support a greater role for inherited susceptibility in early-onset disease and have implications for risk stratification, earlier screening, and precision prevention.

Background: Although thin, T1 melanomas have an excellent cure rate with surgery alone, >25% of melanoma deaths originate from thin melanomas (TMs). There is, therefore, an urgent need to improve the identification and management of patients with TMs at high risk of recurrence. Methods: Patients with T1 melanoma and recurrence [&le;] 2 years of diagnosis (T1 rapid group) were compared to patients with T1 melanoma and recurrence [&ge;]10 years after diagnosis (T1 late group). Results: 442 patients from 14 sites were included: 310 and 132 patients in the T1 rapid and late groups, respectively. Median age at primary melanoma diagnosis was 51 years [15-85], 272 (62%) male, 254 (58%) superficial spreading and 101 (23%) head/neck primary. The majority (73%) of recurrences in the T1 rapid group were locoregional. Using univariable logistic regression analysis, age >65 years (p<0.0001), lentigo maligna (LM) melanoma subtype (p=0.025), head/neck primary site (p=0.0065), mitoses [&ge;]1/mm2 (p=0.0181) and ulceration (p=0.0087) were significantly associated with T1 rapid recurrence compared to T1 late recurrence. Using multivariable analysis, age >65 years (p=0.0010), mitoses [&ge;]1/mm2 (p=0.049) and ulceration (p=0.037) remained significant. Conclusions: Rapid recurrence of TM is associated with age >65 years, LM subtype, head/neck primary site, mitoses [&ge;]1/mm2 and ulceration.

Purpose: To evaluate whether chest radiograph-derived age acceleration is associated with incident lung cancer and whether it improves discrimination beyond established lung cancer risk factors. Materials and Methods: This retrospective analysis used prospectively collected data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Baseline digitized chest radiographs from the initial screening year were analyzed using a previously validated deep learning model that estimates chest radiograph-derived age (Xp-age). Age acceleration (AgeAccel) was defined as the residual of Xp-age after calibration to chronological age using a regression model from the development dataset. A 1-year landmark design excluded participants diagnosed with lung cancer or censored within 1 year of baseline. Associations with incident lung cancer were assessed using multivariable Cox proportional-hazards models adjusted for prespecified demographic and clinical predictors, including smoking variables used in the PLCOm2012 risk prediction model. Discrimination was evaluated using the concordance index and 6-year time-dependent area under the receiver-operating-characteristic curve. Results: The analytic cohort included 23,213 participants (mean age, 62.5 years); 790 developed incident lung cancer after the landmark (mean follow-up, 16.7 years). Higher AgeAccel was associated with increased lung cancer incidence (hazard ratio, 1.10 per 1-SD increase; 95% confidence interval: 1.03- 1.17); however, addition of AgeAccel to an established risk factor model resulted in minimal change in discrimination (C-index, 0.840 vs. 0.839; time-dependent AUC at 6 years, 0.852 vs. 0.852). Attribution maps emphasized the aortic arch/mediastinal region with similar spatial patterns across smoking and lung cancer strata. Conclusion: Chest radiograph-derived age acceleration was independently associated with future lung cancer incidence.

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of smoking and pre-existing cardiovascular diseases (CVD). The objectives of this study were to characterize the incidence of CVAEs in this population and evaluate machine learning (ML) models for CVAEs risk stratification and mortality prediction. Methods A retrospective study was conducted among Appalachian patients with lung cancer treated with definitive CRT at a single institution between 2013 and 2025. Baseline clinical variables, including demographics, smoking status, pre-existing CVD, and post-CRT CVAEs were collected. Heart dosimetric parameters were also obtained. ML models [Random Forest (RF), Gradient Boosting (GBM), Support Vector Machine (SVM), Logistic Regression (LR)] were trained using 5 fold cross validation and evaluated using AUC, sensitivity, specificity, and F1 score. Feature importance was assessed using permutation analysis. Wilcoxon and Chi-squared tests were used for descriptive comparisons. Results Eighty-six patients (mean age 66 years, 47% male) were included. At diagnosis, 80% (n=69) had NSCLC and 20% (n=17) had LS-SCLC. CVAEs occurred in 51 patients (59%). The most frequent events were NSTEMI (n=15, 29.4%), pericardial disease (n=15, 29.4%), and arrhythmia (n=8, 15.7%). Mean heart dose was higher in the CVAE group (13.4 vs 9.4 Gy, p=0.27). For CVAE prediction, GBM achieved the highest AUC (0.55, 95% CI 0.44-0.69) and sensitivity (75%), while RF showed the highest sensitivity (80%, 95% CI 69-90%). Key predictors included age and cardiac dosimetrists (Heart V20, V40, V50, and mean heart dose). For mortality prediction, RF achieved the highest discrimination (AUC = 0.63, 95% CI 0.496-0.750). Age, cardiac dosimetry, disease stage, and cardiovascular comorbidity were the most influential predictors. Conclusion High incidence of CVAEs occurred among patients with lung cancer treated with CRT in this Appalachian cohort. While ML models demonstrated modest predictive performance, tree-based approaches demonstrated high sensitivity for identifying patients at risk for CVAEs and mortality. Age and cardiac radiation dose metrics consistently emerged as key predictors, highlighting the importance of cardiac dose optimization and ML-based risk stratification for cardio-oncology surveillance.

Colorectal cancer (CRC) is the second leading cause of cancer death globally and the number one cause of cancer death in people under 50 years old. The reasons for the rise of early-onset CRC are unknown, and while anatomically distinct subtypes of CRC have substantial clinical and molecular associations, the etiology of region-specific disease, such as early-onset CRC's enrichment in the distal colon, remains unclear. Understanding regional mutagenesis may identify risk factors for this public health concern and CRC more broadly. To evaluate mutational dynamics across the premalignant colon, we performed whole-genome sequencing of 125 individual colon crypts taken from six standardized regions biopsied during colonoscopy, collected from 11 donors without polyps and 10 with polyps. We observed mutation spectra and accumulation rates consistent with previous whole-organ studies, with greater subclonal mutation capture enabled by experimental design. T>[A,C,G] mutations, which are associated with colibactin genotoxicity from pks+ Escherichia coli, were significantly enriched in the rectum of donors with and without polyps (adjusted p-values < 0.01). Moreover, when comparing findings to crypts from individuals with CRC and sequenced CRC tumors, we observed consistent enrichment of the colibactin-associated mutational signature "ID18" in the rectum in both normal colon crypts and CRC tumors, without significant difference in colibactin-specific single nucleotide variant or insertion-deletion burden in crypts across the three clinical groups (i.e., no polyp, polyp, and CRC). These findings argue against a causal or prognostic role for colibactin in CRC, instead indicating that the proposed association with early-onset disease reflects anatomic specificity rather than cancer-specific clinical relevance.

Background: The tumor suppressor gene TP53 and the oncogene KRAS are among the most frequently altered core drivers in human malignancies. Although they cooperatively regulate critical biological processes, the prognostic impact of their co alterations remains poorly defined and exhibits striking inconsistency across different cancer types. Methods: We comprehensively analyzed genomic and clinical data from multi-cancer cohorts sourced from the cBioPortal database and The Cancer Genome Atlas (TCGA). Genetic alterations, including sequence variations and copy number alterations (CNAs), were classified for TP53 and KRAS. Patients were stratified into four subgroups based on individual or combined alteration status. Survival analyses were performed using Kaplan-Meier methods. Integrated multi-omics analyses were conducted to assess the relationship between genetic alterations and mRNA/protein expression, and to characterize co-occurring genetic events and their prognostic implications. Results: Patients harboring concurrent TP53 and KRAS alterations exhibited significantly shorter overall survival in pancreatic cancer, colorectal cancer, and ampullary carcinoma, but surprisingly demonstrated the longest survival in gastric cancer. Distinct KRAS mutation subtype distributions were observed across cancer types: G12D/G12V predominated in pancreatic and colorectal cancers, G12C in non small cell lung cancer, and G13D in gastric cancer, with copy number alterations representing a substantial proportion of KRAS alterations in gastric and lung cancers. Multi-omics analysis revealed a lack of concordance between genetic alterations and mRNA/protein expression, indicating that mutation status alone does not reliably reflect downstream molecular changes. Concurrent genetic events displayed striking cancer-type specificity: CDKN2A alterations frequently co-occurred with TP53/KRAS double alterations in pancreatic cancer and were associated with worse prognosis, whereas APC mutations co-occurred in colorectal cancer and correlated with improved survival. Integrated analysis further demonstrated that KRASaltered/TP53altered patients were highly enriched in pancreatic, colorectal, and lung cancers, each exhibiting unique background genomic landscapes. Conclusions: The prognostic significance of TP53 and KRAS alterations is profoundly cancer-type specific, driven by differences in mutation subtype distribution, copy number alteration patterns, co-occurring genetic events, and the discordance between genotype and functional expression. These findings challenge the simplistic view of dual-gene alterations as universal markers of poor prognosis and underscore the necessity of incorporating cancer-specific molecular contexts into prognostic models and precision oncology strategies.

Chimeric antigen receptor (CAR) T-cell therapy has transformed treatment for relapsed /refractory(r/r) lymphoid malignancies. Yet, these cellular immunotherapies are often associated with immune-related adverse events (irAEs), namely cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), that pose significant risks to patient safety and limit broader clinical implementation of CAR T-cell therapies. In the current study, we used proteomics technology to establish circulating protein signatures that would predict severe CRS and ICANS in r/r lymphoma patients that subsequently received CAR T-cell therapy. Initial discovery was performed using plasma samples collected preceding CAR T-cell infusion from 39 r/r lymphoma patients at MD Anderson Cancer Center. A 5-marker and 8-marker protein panel was developed for predicting Grade [&ge;] 2 CRS and ICANS respectively, yielding respective AUCs of 0.85 [95% CI: 0.72-0.98] and 0.91 [95% CI: 0.81-1.00]. Independent testing of the CRS and ICANS panel was performed in a cohort of 59 r/r lymphoma patients from the Moffitt Cancer Center, with resultant AUCs of 0.76 [95% CI: 0.63-0.89] and 0.67 [95% CI: 0.51-0.84] for the CRS and ICANS panel, respectively. Patients were further classified into low-, intermediate-, and high-risk groups based on panel score tertiles. In the combined dataset (MDACC + Moffitt), compared to patients in the low-risk group (reference), patients in the intermediate- and high-risk groups were 3.15 [95% CI: 0.92-12.71] and 13.84 [95% CI: 4.21-56.26] more likely to have Grade [&ge;] 2 CRS, and 1.21 [95% CI: 0.36-4.23] and 8.59 [95% CI: 2.87-29.09] more likely to have Grade [&ge;]2 ICANS. The protein biomarker panels provide a means to risk stratify patients who are at high risk for developing severe CRS and ICANS, to inform on the need for prophylactic interventions and improve patient outcomes.

Widespread genomic sequencing efforts have characterized the molecular foundations of the different cancers. By combining these genomic data in a manner proportional to the population-level abundances of these different cancers, we estimate the overall abundances of each observed missense and nonsense mutation within the U.S. cancer patient population. We find BRAF V600E (5.2%) is the most common mutation in the cancer patient population, TP53 R175H (1.5%) is the most common tumor suppressor mutation, and APC R876X (0.4%) is the most common nonsense mutation. These values differ largely and significantly from what would be found in a typical pan-cancer analysis, where different cancer types are included out of proportion to population level incidence. We present the full ordered lists of population-level abundances for specific missense and nonsense mutations, and we demonstrate the value of these data by further analyzing high priority genes (e.g., TP53, KRAS, BRAF) and pathways (e.g., RTK/RAS, PI3K, and WNT/{beta}-catenin). Overall, this information is a resource that should benefit the basic science, translational, and clinical cancer research communities.

BackgroundTimely cancer diagnosis in children and adolescents is critical to improving outcomes, yet substantial variation in diagnostic intervals persists across cancer types and care settings. We aimed to quantify time to diagnosis and assess variations by patient, demographic, and system-level factors. MethodsWe conducted a retrospective population-based study of children and adolescents aged 0-19 years diagnosed with one of 12 common cancers between 2010 and 2022 in Quebec, Canada. The diagnostic interval was defined as the time from first cancer-related healthcare encounter to diagnosis. We calculated medians and interquartile ranges (IQR) overall and by cancer type and used multivariable quantile regression to identify factors associated with time to diagnosis at the 25th, 50th, and 75th percentiles. ResultsAmong 2,927 individuals with cancer, diagnostic intervals varied by cancer type and age. Median intervals were longest for carcinomas (100 days; IQR 33-192) and shortest for leukemias (8 days; IQR 3-44). Compared with children living in Montreal, living in regional areas and other large urban centres was associated with longer 50th and 75th percentiles of time to diagnosis for hepatic and central nervous system (CNS) tumours. Diagnostic intervals were shorter in the post-pandemic period (2020-2022) across several cancer sites, with CNS tumours showing reductions across all quantiles. InterpretationDiagnostic timeliness differed by cancer type, age, and rurality, but not by sex, material, or social deprivation. The shorter diagnostic intervals observed in the post-pandemic period suggest that pandemic-related changes in care pathways may have expedited diagnosis for some cancers.

BackgroundMulticancer early detection tests could be used for cancer screening, but may lead to harms, including false positive results and overdiagnosis of indolent tumours that would not have become clinically evident during that persons lifetime. We assessed the potential for these screening harms in the context of future population-based screening with a multicancer early detection test. MethodsWe used a microsimulation model to assess potential population-level impacts of screening at ages 50-75 years with a multicancer early detection test in Canada. We assumed high test specificity (97-99.1%) and test sensitivity increasing with cancer stage. The model includes latent indolent cancers that would not be diagnosed within that persons lifetime but can be overdiagnosed through screen-detection. We calculated the yearly and cumulative lifetime probabilities of screening overdiagnosis and false positive test results, assuming a range of preclinical screen-detectable periods (2-5 years). ResultsAn estimated 2.1-6.0% of all yearly screen-detected cancers with a multicancer screening test were predicted to be overdiagnoses across scenarios. The proportion of overdiagnosis varied by site, and strongly increased with age, going from 1% at age 50 to over 10% of screen-detected cancers by age 75. The test positive predictive value ranged from 15.9%-77.6%, meaning that there could be 0.3-5.3 false positives with no underlying cancer for every true cancer case detected by the test. ConclusionPopulation-level multicancer screening with a multicancer early detection test would likely not lead to substantial screen-related overdiagnosis. Healthcare systems should consider how screening false positives may increase their diagnostic service caseload.

Background and aims: Pancreatic ductal adenocarcinoma (PDAC) surveillance is limited to individuals with familial or genetic risk although most future cases arise outside these groups. In a retrospective study, PRISM, an electronic health record (EHR)-based PDAC risk model, identified individuals in the general population at elevated near-term risk of PDAC. We aimed to prospectively evaluate whether PRISM can identify high-risk individuals beyond current surveillance groups across U.S. health systems. Methods: We performed a prospective multicenter cohort study after deployment of PRISM in April 2023 across 44 U.S. health care organizations. Eligible adults aged [&ge;]40 years without prior PDAC received a single baseline risk score and were assigned to prespecified risk tiers. Patients were followed for incident PDAC for 30 months. We estimated tier-specific 30-month cumulative incidence (positive predictive value, PPV), number needed to screen (NNS), standardized incidence ratios (SIRs), and time from deployment and first high-risk flag to diagnosis. Results: Among 6,282,123 adults assigned a PRISM score, 5,058,067 had follow-up; 3,609 developed PDAC. The highest-risk tier had 30-fold higher PDAC incidence than the study population. At the SIR 5 threshold, 30-month cumulative incidence was 0.35% (NNS, 284.2); at SIR 16, 1.14% (NNS, 87.4); and at SIR 30, 2.19% (NNS, 45.7). Median time from deployment to PDAC diagnosis was 9.5 months, and median time from first high-risk flag to diagnosis at SIR 5 was 3.5 years. Shapley additive explanations (SHAP) analyses supported patient- and tier-level interpretability. Conclusions: Prospective deployment of PRISM across multiple U.S. health care organizations identified individuals at elevated near-term risk for PDAC, with substantial risk enrichment and lead time before diagnosis. These findings support the real-world scalability and generalizability of EHRbased risk stratification for risk-adapted early detection. ClinicalTrials.gov identifier NCT05973331

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Radioresistance, partly mediated by glioma stem-like cells, represents a major clinical challenge which could be overcome by the identification of the modulators of radioresistance. Existing CRISPR screens in human GBM models have largely used two-dimensional cultures with short-term viability readouts, failing to capture the long-term clonogenic behaviour underlying tumour recurrence after radiotherapy. Method: We developed ClonoScreen3D-CRISPRi, combining CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays. Two GBM cell lines (G7 and GBML20), differing in MGMT promoter methylation status, were engineered to express the KRAB-dCas9 editor. Nine candidate radiosensitivity modifiers, selected through transcriptomic analysis, pharmacological studies, and literature review, were examined in both lines. Target validation was performed using full radiation dose-response assays and a pharmacological inhibitor. Results: The majority of candidate genes significantly altered survival fraction following irradiation in both cell lines. Knockdown of NFKB2, RELB, and CDK9 produced the most potent radiosensitization, with sensitizer enhancement ratios of 1.39-1.70 in validation studies, exceeding those of established radiosensitizers including PARP and ATM inhibitors. Notably, knockdown of these genes induced no significant cytotoxicity in the absence of radiation. Pharmacological validation using an IKK inhibitor confirmed these findings, implicating non-canonical NF-{kappa}{beta} signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. Conclusions: ClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}{beta} pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation.

Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer survival benefit beyond weight loss remains unresolved. Using a federated electronic health record platform spanning nearly 29 million patients, we evaluated breast cancer survival after semaglutide and tirzepatide initiation in routine care. In 1:1 propensity-matched pooled-comparator analyses, semaglutide was associated with improved overall survival versus metformin, sodium-glucose cotransporter 2 (SGLT2) inhibitor, and dipeptidyl peptidase 4 (DPP4) inhibitor users, with 54 deaths among 2,433 semaglutide users (2.2%) versus 395 deaths among 2,433 comparators (16.2%) over 24 months (log-rank P < 0.001). Tirzepatide showed a favorable survival association relative to pooled anti-diabetic comparators that did not meet statistical significance (P = 0.24), with 3 deaths among 220 users (1.4%) versus 64 deaths among 220 comparators (29.1%). In a head-to-head propensity-score-matched comparison, overall survival did not differ significantly between semaglutide and tirzepatide treated patients with pre-existing breast cancer (2,117 per arm; P = 0.12). In semaglutide-treated patients alive and observable at the 1-year landmark, higher maximum dose achieved was significantly associated with lower post-landmark mortality (P = 0.034), with an event rate of approximately 1.0% in the high-dose group (>=1.7 mg) versus approximately 4.5% in the low-dose group (0.25-1.0 mg). Despite a linear dose weight loss relationship for semaglutide, however, weight loss strata did not separate survival outcomes (global P = 0.22). In tirzepatide-treated patients alive and observable at the same landmark, neither maximum dose achieved nor weight loss strata separated post-landmark survival (P = 0.98 and P = 0.50, respectively). Structured EHR and AI-based clinical note analyses further showed significantly lower frequency of documented metastatic disease in semaglutide-treated patients relative to pooled anti-diabetic comparators, including any metastasis (7.0% versus 15.0%, rate ratio 0.5, P < 0.001), bone metastasis (1.0% versus 5.2%, rate ratio 0.2, P < 0.001), and liver, lung, or brain metastases (all P < 0.001). LLM-derived cause-of-death extraction further showed a 60% lower relative proportion of cancer-associated deaths in semaglutide-treated patients (19% of ascertainable deaths) than in matched pooled anti-diabetic comparators (47% of ascertainable deaths), with comparator deaths more often attributed to cancer progression involving metastatic breast cancer, leptomeningeal carcinomatosis, and cancer-driven organ failure. Overall, this study demonstrates that semaglutide use in patients with pre-existing breast cancer is associated with a dose correlated but weight loss independent improvement in overall survival. These findings motivate prospective trials of GLP-1 receptor agonists in breast cancer across various stages and treatment settings.