Jane C. Figueiredo

Background:Early-onset colorectal cancer (eoCRC), particularly rectal cancer, is increasing. We evaluated the association between high birth weight and eoCRC in a nested case–control study. Methods:We included patients aged 15 to 49 years diagnosed with colorectal adenocarcinoma at Kaiser Permanente Southern California (2009–2021). Cancer-free controls were matched 10:1 on age, sex, and length of health plan membership. Record linkage with the California Department of Public Health’s birth registry was performed. Conditional logistic regression was used to evaluate associations between high birth weight, measured using (i) macrosomia (birth weight >4,000 g) and (ii) large for gestational age (LGA; birth weight above the 90th percentile for gestational age and sex), and overall eoCRC, colon cancer, and rectal cancer. Two-stage model adjustments were performed: first adjusting for prespecified potential confounders, then adjusting for potential intermediates. Results:Of 1,400 eligible cases and 13,608 matched controls, 471 eoCRC cases (mean diagnosis age: 41.1 years) and 1,931 controls with a linked birth certificate were included. The adjusted odds ratio (OR) for eoCRC was 1.32 [95% confidence interval (CI), 0.95–1.84] for macrosomia and 1.38 (0.97–1.98) for LGA. Macrosomia and LGA were associated with a marginal, nonstatistically significant elevated risk of rectal cancer [OR (95% CI), 1.69 (0.97–2.97) and 1.81 (0.96–3.41), respectively], but not colon cancer [OR (95% CI), 1.18 (0.78–1.80) and 1.20 (0.77–1.86) for macrosomia and LGA, respectively]. Conclusions:An elevated risk of early-onset rectal cancer was suggested in individuals with a high birth weight. Impact:The impact of intrauterine conditions on eoCRC risk should be further evaluated.

Supplementary Methods and Materials: Detailed information on linkage to the California Department of Public Health’s (CDPH) birth records and the case-control matching process

Supplementary Figure 1. Population flowchart of early onset colorectal cancer cases and controls. Population flowchart with exclusion criteria for early onset colorectal cancer (eoCRC) cases and matched cancer-free controls included in the analysis.

Supplementary Table 1. Demographic characteristics of early-onset colorectal cancer (eoCRC) cases and cancer-free controls by CDPH birth record linkage status.

Supplementary Table 2. Multivariable (adjusted) association of high birth weight and eoCRC, stratified by age, sex, year of eoCRC diagnosis/index year, and race/ethnicity.

Background: Somatic alterations in the RAS–RAF–MAPK and PI3K pathways are key in colorectal cancer (CRC), yet their distribution, co‐occurrence patterns, and clinical significance remain incompletely defined. We characterized RAS/MAPK‐ and PI3K‐pathway alterations in a large cohort and assessed their prognostic implications. Methods: We analyzed whole‐exome sequencing data from 1243 paired samples from patients with CRC. Somatic variants in KRAS , NRAS , HRAS , BRAF , and PIK3CA were analyzed to quantify frequencies, codon‐level distributions, and co‐occurrence patterns. Descriptive somatic mutation status and demographic and clinical characteristics were evaluated using chi‐square or Fisher exact tests, and associations with overall survival (OS) were evaluated using multivariable Cox proportional hazards models. Results: Any RAS‐family mutation was detected in 507 tumors (40.8%); KRAS , NRAS , and HRAS mutations occurred in 35.3%, 5.4%, and 0.6%, respectively. BRAF V600E was detected in 85 tumors (6.8%), PIK3CA in 221 (17.8%), and KRAS G12C in 38 (3.1%). KRAS codon 12 was the dominant RAS hotspot. KRAS‐PIK3CA co‐mutations occurred in 126 tumors (10.1% overall; 28.7% of KRAS ‐mutant tumors), whereas KRAS‐BRAF and KRAS‐NRAS co‐mutations were uncommon, consistent with MAPK pathway mutual exclusivity. RAS‐mutant tumors were enriched in right‐sided and microsatellite‐stable disease ( p < 0.001 for both). KRAS, BRAF V600E , and PIK3CA mutation frequencies were higher in right‐sided than left‐sided tumors. In unadjusted analyses, Hispanic patients had lower KRAS , BRAF V600E , and PIK3CA mutation frequencies than non‐Hispanic patients. After adjustment for age, sex, tumor location, and MSI status, the associations with KRAS and PIK3CA were attenuated, whereas the lower frequency of BRAF V600E among Hispanic patients persisted ( p < 0.01). Mutation status was not significantly associated with 5‐year overall survival. Conclusions: RAS/MAPK and PI3K pathway alterations in CRC varied primarily by tumor subtype and molecular context, with clinically relevant co‐mutation patterns and a targetable KRAS G12C subset. These findings may support future clinical trials targeting the BRAF–RAS axis.

Background: Currently, prostate-specific antigen (PSA) testing remains the most important tool for prostate cancer (PCa) screening, yet it does not fully inform on personal risk level. Therefore, we collected information on screening behavior, family history, and polygenic risk score (PRS) from unaffected men to assess the association among PCa screening behavior, family history, and PRS. Methods: We collected past screening behavior, family history, and self-estimated risk information using a structured questionnaire, and saliva samples to generate PRS. Genotyping was conducted using the Illumina Oncoarray assay using saliva DNA. We investigated relationships among PCa screening behavior, family history, and PRS. Results: PRS information was generated from 76 unaffected men recruited from various community settings. Among 53 African American (AA) men, 13/53 (24.5%) were in the low-risk category (70 percentile). In comparison, 21/23 (91%) of European American (EA) participants were in the low-risk category, and none of EA participants were in the high-risk category. AA men had a significantly higher PRS for PCa than EAs (p<0.001). Three variables (i.e., age, family history, and PRS) were significantly associated with prior PCa screening behaviors. Discussion: This study suggested differential genetic risk levels between racial groups. Men with a high risk of PCa could benefit from informed discussions around the risks and benefits of available options for PCa screening, especially among AA men. PRS information may assist AA men and clinicians in shared and informed decision-making discussions for routine PSA screening.

Background: Fatigue is a prevalent and debilitating symptom among patients with cancer. Physical activity (PA) may help reduce fatigue and improve quality of life (QoL). This study evaluated longitudinal associations between PA, fatigue, and QoL across the cancer care continuum. Methods: Data were analyzed from 1,718 participants in a prospective multicenter cohort. Self-reported PA was assessed at baseline and up to 24 months post-diagnosis using a questionnaire based on the International PA Questionnaire (IPAQ). Metabolic equivalent task (MET)–minutes per week were calculated for walking, moderate, vigorous, and total physical activity. Fatigue and QoL were measured using the EORTC Quality of Life Questionnaire-Core 30 (QLQ-C30). Mixed-effects models evaluated associations between PA and outcomes. Lagged fluctuation models examined temporal relationships, separating between-person and within-person effects. Results: PA levels increased steadily from diagnosis through 24 months, coinciding with reduced fatigue and improved QoL. Higher PA was consistently linked to better fatigue and QoL outcomes. Walking was associated with lower odds of severe fatigue at 6 months (odds ratio [OR], 0.78; 95% CI, 0.62–0.98; P =.031) and 12 months (OR, 0.74; 95% CI, 0.59–0.94; P =.014), with higher odds of better QoL at all follow-up points. Vigorous activity showed the strongest protective effect against fatigue at 24 months (OR, 0.65; 95% CI, 0.45–0.94; P =.023). In patients with nonmetastatic disease, protective associations were observed across all activity types and time points. Lagged models indicated that higher average vigorous activity predicted lower future fatigue ( P <.001). Conclusions: Greater PA, particularly walking, was associated with lower fatigue and improved QoL, especially in patients with early-stage disease. The posttreatment period (approximately 1 year post-diagnosis) may represent an important window for behavioral intervention. Findings support stage- and recovery phase–specific guidance and highlight walking as a scalable, low-risk target for survivorship care.

Background: The past decade has seen the development of epigenetic models of aging that accurately estimate chronological age and predict disease incidence and mortality. These estimates are modulated by lifestyle and environmental factors linked to carcinogenesis, but to date this has primarily been studied in blood. Methods: We examined epigenetic aging in normal colonic tissue (n = 96), adjacent mucosa (n = 245), and tumors (n = 208), using models trained on age (Horvath, Hannum, Zhang), mortality (PhenoAge, GrimAge), aging rate (DunedinPACE), cellular mitotic history (EpiTOC, epiTOC2, miAGe), and telomere length (DNAmTL). Results: The Horvath model was the most accurate estimator of chronological age in normal colonic mucosa, with high correlation (r > 0.70) between the Horvath, Hannum, Zhang, PhenoAge, and GrimAge models as well as between mitotic clocks (r > 0.94). All models showed similar performance in normal tissue and adjacent mucosa but substantially more variation in estimates in tumors. Statistically significant differences in age acceleration were present between normal and adjacent mucosa by 6 models (Hannum, Zhang, PhenoAge, EpiTOC, epiTOC2, and miAge), while tumors showed highly statistically significant differences by all models. Age acceleration differed by region of the colon, with varying patterns by model type. Physical activity (PhenoAge), smoking history (GrimAge), and alcohol consumption (Horvath, mitotic clocks) were associated with epigenetic aging in adjacent mucosa, while smoking history, smoking intensity, and alcohol consumption were associated with DNAmTL in tumors. Conclusions: Our study reveals an impact of tissue type, region, and lifestyle factors on epigenetic aging and highlights heterogeneity between models and the need for careful consideration within study design.

Background: Early-onset colorectal cancer (eoCRC), particularly rectal cancer, is increasing. We evaluated the association between high birth weight and eoCRC in a nested case–control study. Methods: We included patients aged 15 to 49 years diagnosed with colorectal adenocarcinoma at Kaiser Permanente Southern California (2009–2021). Cancer-free controls were matched 10:1 on age, sex, and length of health plan membership. Record linkage with the California Department of Public Health’s birth registry was performed. Conditional logistic regression was used to evaluate associations between high birth weight, measured using (i) macrosomia (birth weight >4,000 g) and (ii) large for gestational age (LGA; birth weight above the 90th percentile for gestational age and sex), and overall eoCRC, colon cancer, and rectal cancer. Two-stage model adjustments were performed: first adjusting for prespecified potential confounders, then adjusting for potential intermediates. Results: Of 1,400 eligible cases and 13,608 matched controls, 471 eoCRC cases (mean diagnosis age: 41.1 years) and 1,931 controls with a linked birth certificate were included. The adjusted odds ratio (OR) for eoCRC was 1.32 [95% confidence interval (CI), 0.95–1.84] for macrosomia and 1.38 (0.97–1.98) for LGA. Macrosomia and LGA were associated with a marginal, nonstatistically significant elevated risk of rectal cancer [OR (95% CI), 1.69 (0.97–2.97) and 1.81 (0.96–3.41), respectively], but not colon cancer [OR (95% CI), 1.18 (0.78–1.80) and 1.20 (0.77–1.86) for macrosomia and LGA, respectively]. Conclusions: An elevated risk of early-onset rectal cancer was suggested in individuals with a high birth weight. Impact: The impact of intrauterine conditions on eoCRC risk should be further evaluated.