Cancer Medicine

Background: Patient-reported measures (PRMs), including patient-reported outcome and experience measures, capture patients perspectives on their health status and healthcare experiences. In cancer genetics, PRMs have been used to assess genetic knowledge, psychosocial outcomes, and decision-making. However, patients must understand these measures to provide useful information, an ability which is influenced by general and health literacy levels. Readability guidelines recommend that patient-facing materials be written at or below a Grade 6 level. This study evaluated the readability of PRMs used in a cancer genetic testing context. Objective: To assess whether PRMs used in heritable cancer genetic testing meet recommended readability levels using validated indices. Methods: PRMs were identified from a recent systematic review of PRMs used in heritable cancer genetic testing, which reported 83 instruments across eight categories. English-language PRMs containing structured question items and response scales were eligible for extraction and converted into plain text for analysis. Readability was assessed using four validated indices: Flesch Kincaid Grading Level (FKGL), FORd, CAylor, and STicht (FORCAST) formula, Flesch Reading Ease Score (FRES), and Simple Measure of Gobbledygook (SMOG) via an automated readability software. Descriptive analysis and numerical comparison evaluated readability levels across PRM categories and against the recommended Grade 6 reading level. Results: Sixty-five PRMs met the eligibility criteria, with most, including validated instruments, exceeding the recommended Grade 6 reading level. Across the eight categories, genetics-specific PRMs required the highest readability levels, indicating higher readability demands. Conclusions: Most PRMs, particularly those specific to genetics, do not meet readability guidelines. This may limit their accessibility to individuals with limited general and health literacy. Development of PRMs specific to genetics should consider strategies to improve readability, such as plain-language approaches and involvement of individuals with limited general or health literacy. Keywords: readability, patient-reported measures, cancer, genetic testing, health literacy

Background: Oral mucositis is a clinically significant, potentially severe side effect of systemic chemotherapy in children with cancer. Understanding genetic predisposition to this side effect may assist in development of stratified prophylactic and treatment strategies. However, existing literature primarily focuses on children with haematological malignancies. Methods: We performed a candidate gene study of 101 children with solid tumours enrolled in the MARVEL-PIC study at the Royal Children's Hospital, Melbourne. Clinical data were extracted from the electronic medical record, with NCI-CTCAE v6.0 grade >2 oral mucositis defined as the primary outcome. Genetic variants previously associated with oral mucositis were analysed under an additive genetic model to identify significant associations. Exploratory gene-drug interactions were identified based on chemotherapy exposure. Results: 29 patients (28.7%) developed grade >2 oral mucositis. MTHFR A1298C (rs1801131) was associated with lower odds of grade >2 oral mucositis, lower peak mucositis grade, and lower odds of opioid use for oral mucositis. 25 exploratory gene-drug interaction signals were identified, including miR-1206 rs2114358 with methotrexate exposure and ABCB1 rs1045642 with anthracycline exposure. Conclusions: MTHFR A1298C (rs1801131) demonstrated a protective effect against chemotherapy-induced oral mucositis in our cohort of children with solid tumours. Larger, ancestry-informed studies are required to validate our findings.

Purpose: Metastatic pancreatic ductal adenocarcinoma (PDAC) portends a poor prognosis. Prior studies have assessed the association of socioeconomic deprivation (SED) in PDAC often with large geographic areas. This study employed a causal framework to characterize neighborhood SED on treatment receipt and survival in metastatic PDAC. Methods: Using the incidence-based Florida Cancer Data System, metastatic PDAC patients diagnosed from 2007-2015 were identified. The Area Deprivation Index, a composite measure of SED that ranks neighborhoods from 1-100 (higher scores = higher deprivation), was used to assess receipt of systemic therapy and overall survival (OS). Exposures and covariates were assessed using descriptive statistics and a causal inference framework. Results: Overall, 9,574 patients met inclusion criteria. 46.6% of patients received systemic therapy, ranging 39.4% to 54% in the highest and lowest SED quartiles, respectively. After adjustment, the lowest quartile had increased odds of systemic therapy relative to the highest (OR 1.93; 95% CI 1.70-2.18). Median OS was 3.8 months for the lowest quartile and 2.4 months for the highest (p = 0.01). Patients in the highest quartile had an estimated 32% higher hazard of death than the lowest (HR 1.32, 95% bootstrap CI 1.20-1.40). Conclusion: In an incidence-based statewide cohort, most patients did not receive treatment for metastatic PDAC and median OS was poor-2.9 months. Using a causal inference framework, higher SED led to lower rates of systemic therapy receipt and worse overall survival in metastatic PDAC. Future research should focus on the mechanisms that shape these findings.

Objective: The purpose of this study is to determine how family history of cancer, genetic mutations, presenting symptoms, and comorbidity burden collectively influence cancer outcomes in patients with epithelial ovarian cancer. Methods: A retrospective analysis was conducted on all patients with epithelial ovarian cancer treated at the University of Missouri and Ellis Fischel Cancer Center between 2008 and 2024. Patient charts were reviewed for histological subtypes, stage of cancer, status of metastasis, CA-125 values, presenting symptoms, comorbidities, family history of cancer, genetic mutations, and survival outcome. Cox regression and association analyses were performed. Results: In this cohort of patients, comorbidities and genetic mutations did not influence ovarian cancer survival. While histological subtypes, CA-125 levels, and cancer stage remained strongly associated with survival. Significant associations were observed between certain presenting symptoms and cancer histological subtype, a family history of breast cancer, stage of cancer at diagnosis, the status of metastasis, and CA-125 levels. Conclusion: Comorbidities and genetic mutations were not significantly associated with ovarian cancer survival. Presenting symptoms were associated with several clinical and pathological variables linked to ovarian cancer diagnosis.

Background: Population-scale molecular profiling integrated into routine healthcare could accelerate biomarker discovery, validation, and implementation, but the feasibility and sustainability of such an approach have rarely been demonstrated prospectively. The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative was established to integrate prospective molecular profiling with population-based breast cancer care and create an infrastructure for translating molecular discoveries into clinical practice (ClinicalTrials.gov identifier NCT02306096). Methods: We evaluated the first 10 full calendar years of SCAN-B, encompassing patients with primary invasive breast cancer enrolled between August 30, 2010 and December 31, 2020. Enrollment and biospecimen collection were compared with all eligible breast cancer diagnoses in participating hospitals to assess population coverage and representativeness. Clinicopathological characteristics, treatments, recurrence-free survival, overall survival, RNA-sequencing-based molecular subtypes and risk-of-recurrence, and somatic mutations were evaluated. We additionally report the translation of SCAN-B molecular profiling from the research setting into routine clinical diagnostics. Results: Among 16,381 estimated eligible breast cancer diagnoses, 13,940 patients (85.1%) were prospectively enrolled across participating Swedish hospitals. Baseline blood samples were obtained from 98.4% of enrolled patients and tumor specimens from 71.1%; 9,323 tumors (94.0% of submitted tumor specimens) underwent RNA-sequencing. The enrolled cohort was broadly representative of the underlying breast cancer population across major clinicopathological characteristics. Integration of longitudinal clinical data with molecular profiling enabled characterization of real-world treatment patterns, long-term outcomes, molecular subtypes, risk-of-recurrence, and the somatic mutational landscape in this population-based cohort. Building on prospective real-time RNA-sequencing and subsequent development and validation of single-sample molecular subtype and risk-of-recurrence predictors, the SCAN-B workflow was transferred into routine clinical molecular diagnostics in Sk[a]ne and Blekinge in 2021. Through January 2026, more than 3,000 patients had received clinical RNA-sequencing-based molecular subtype and risk-of-recurrence reports, while prospective SCAN-B enrollment and transfer of samples and molecular data into the research infrastructure continued. Patient enrollment continues prospectively, with over 23,000 patients accrued as of January 2026. Conclusions: A prospective, population-based molecular profiling program can be integrated into routine breast cancer care at scale while maintaining high population coverage and representativeness. Over more than a decade, SCAN-B progressed from prospective biosampling and molecular profiling through biomarker development and validation to implementation of RNA sequencing-based testing in routine healthcare. This model establishes a continuous framework linking population-based molecular research, biomarker discovery and validation, and clinical implementation, and provides a strategy for integrating precision oncology research with routine cancer care.

Objective To develop and evaluate an automated large language model (LLM)-based framework for conducting meta-analyses of nutrition-related exposures and the risk of breast, ovarian, and uterine cancers. Design We developed MetaFemina, an automated evidence-synthesis pipeline for women's cancers that integrates keyword-based literature retrieval, LLM-assisted evidence extraction, and random-effects meta-analysis. We evaluated its performance against two recently published peer-reviewed meta-analyses and compared exposure-outcome associations across the three cancer types. Data sources PubMed articles identified through keyword-based searches of titles and abstracts. Methods MetaFemina was developed as a web platform that identifies relevant scientific articles, automatically extracts relevant information using LLMs, and synthesizes extracted evidence using random-effects meta-analysis. Additional analyses included assessment of heterogeneity, publication bias, and leave-one-out sensitivity analyses. The platform also provides sample size calculations based on synthesized effect sizes and generates visual summaries and plain-language interpretations. Results Compared with two recent peer-reviewed meta-analyses of folate and vitamin E intake in relation to breast cancer risk, MetaFemina demonstrated high sensitivity (81.82% and 80%, respectively) in identifying eligible studies and additionally retrieved relevant articles that had been missed by manual screening (27 and 13, respectively). Among 226 exposures considered, lutein and beta-carotene were significantly associated with lower risks of breast, ovarian, and uterine cancers. Vitamin D, antioxidants, and soy were significantly associated with lower risks of both breast and ovarian cancers, whereas calcium and folic acid were significantly associated with lower risks of both breast and uterine cancers. In contrast, iron, red meat, and copper were significantly associated with higher risks of both breast and uterine cancers. omega-6 fatty acids showed contrasting associations, being significantly associated with higher breast cancer risk but lower ovarian cancer risk. After restriction to dietary-intake studies, these cross-cancer significant associations remained statistically significant except for copper, which no longer met the two-study threshold for either breast or uterine cancer. Additionally, calcium became significantly associated with lower ovarian cancer risk, resulting in significant negative associations across all three cancer types, while vitamin E became significantly associated with lower breast cancer risk and remained significantly associated with lower ovarian cancer risk. Conclusions MetaFemina demonstrated high sensitivity for identifying relevant scientific literature, extracts key evidence, and performs statistically rigorous automated meta-analyses. The framework may facilitate more rapid evidence synthesis in nutritional epidemiology and may support researchers in study design, hypothesis generation, and interpretation of emerging evidence.

Background: Patients aged [&le;]50 years with early-stage HR+/HER2- breast cancer are considered to have an aggressive disease biology and are treated with chemotherapy. However, a subset may still experience favourable outcomes without chemotherapy. Commercially available prognostic tests help guide such treatment decisions, but most are developed and validated predominantly in Western populations, with an underrepresentation of Asian patients. In this study, we explore the prognostic value of CanAssist Breast (CAB), a proteomic prognostic test, in optimal treatment management of patients aged [&le;]50 years. Methods: This study includes a previously published retrospective cohort. The performance of CAB was evaluated using Kaplan-Meier analysis, with 5-year Distant recurrence-free interval (DRFI) from diagnosis as the endpoint; the study also used multivariate analysis to evaluate the independent prognostic value of CAB. Results: In the retrospective cohort, CAB identified 70% as low-risk (LR) and 30% as high-risk (HR) with DRFI of 93.1% (P<0.0001); further classification showed 64% LR and 36% HR in the Asian and 75% LR and 25% HR in the Caucasian subgroup. In patients with N0 disease, CAB identified 85% as LR and 15% as HR. In N+ patients, CAB identified 49% as LR. All CAB LR patients have an acceptable DRFI of >90% at 5 years from diagnosis. Conclusions: Based on the results presented, CAB adds prognostic value for patients [&le;]50 years and can be used as a treatment guidance tool for these patients.

Background: Pancreatic ductal adenocarcinoma (PDAC) has remained one of the most serious malignancies and is still a leading cause of cancer-related deaths worldwide. Great attention has been paid to the relationship between diabetes mellitus and PDAC. The aim of our study was to analyse the mutual association of PDAC and diabetes mellitus in the entire population of the Czech Republic within a 5-year period. Methods: The incidence of PDAC in 2018-2022 was estimated based on the individual population data from the Czech National Cancer Registry. Another data source, the National Registry of Reimbursed Health Services that collects data from health insurance companies, was used to identify individuals recently diagnosed with diabetes mellitus. For the purpose of this study, diagnosis of new-onset of diabetes mellitus was defined as the time of the first prescription of any antidiabetic drug or another related health care service. Subsequently, patients diagnosed with PDAC in 2022 were followed retrospectively to see if they had been diagnosed with diabetes mellitus in the last five years before diagnosis of pancreatic cancer. Results: In 2022, 2,189 patients aged 60 years or older were diagnosed with pancreatic cancer. New-onset diabetes was observed in 17.4% within five years prior to diagnosis, with the highest occurrence (12.4%) within the last three years. Among patients aged 60-74 years, the respective proportions were 14.4% within three years and 5.7% four to five years prior to pancreatic cancer diagnosis. Conclusion: The incidence of pancreatic cancer in the Czech Republic is among the highest in Europe. One-fifth of PDAC cases are diagnosed following new-onset diabetes mellitus in patients over sixty. Unintended significant weight loss combined with new-onset diabetes thus must not be overlooked, as these can be early signs of PDAC. An individualised diagnostic work-up should follow without any unnecessary delay.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

Long-term data describing cancer patterns in Iraq remain generally limited. This retrospective observational study was conducted to evaluate the distribution and longitudinal patterns of malignant solid tumors diagnosed over a period of 12 years (2014-2025) at a major tertiary oncology center in the Kurdistan Region of Iraq. Demographic characteristics, cancer types, and temporal trend changes in cancer distribution were analyzed. Comparisons were made between the first (2014-2019) and second (2020-2025) halves of the study period. Descriptive statistics, Chi-square tests, and linear regression analyses were used to evaluate the temporal trends. After excluding records with incomplete data, 11,704 patients were included. Breast cancer was the most frequently diagnosed malignancy, accounting for over one-quarter of all cases, followed by lung, colorectal, prostate, and bladder cancers, in descending order. Women represented the majority of patients, and the mean age at diagnosis increased significantly over time. In general, the relative distribution of colorectal, genitourinary, pancreatic, uterine, and thyroid cancers increased during the study period, whereas breast and lung cancers showed a modest but significant proportional decline despite remaining the most common malignancies. A marked reduction in case numbers was observed in 2020, followed by progressive recovery in subsequent years. To conclude, cancer patterns in the Kurdistan Region of Iraq changed substantially during the 12-year study period, with increasing proportions of colorectal and several other malignancies alongside an older age at diagnosis. These findings likely reflect a combination of demographic changes, evolving lifestyle-related risk factors, and improvements in cancer detection and referral. The results provide contemporary evidence to support regional cancer control strategies, screening programs, resource allocation, and future epidemiological research.

Biomarkers perturbed by exercise-mediated molecular mechanisms, in women with early-stage (stage I-III, non-metastatic) breast cancer are poorly defined, and especially in under-represented Asian cohorts. In this exploratory Breast Cancer Exercise Intervention (BREXINT) pilot study, 15 Asian women were randomized to a combined aerobic and resistance exercise intervention program (n=8) and a control group (n=7). Fasting blood sampling was performed at baseline, 8,16, and 24-week timepoints. Blood parameters were imputed, transformed and screened for intervention-specific variations using IQR-trimmed, paired Wilcoxon tests. Twenty-one blood parameters were found to meet a differential change rule (significance observed in 1 group but not the other). Exercise-associated signatures displayed hematological and cytokine remodeling at 16-weeks. Control-associated signatures include adipokine and renal markers at 16 and 24-weeks. Of note, exercise-driven decrease of IL-10 at 16-weeks (p=0.022) retained significance following linear mixed effects confirmation among screened candidates. IL-10-centred modulation is the most convergent exercise-associated blood derived signature but warrants further validation in larger exercise oncology trials.

Introduction: SkinScan3D (SS3D) is a novel, artificial intelligence-enabled device that provides objective three-dimensional measurements for monitoring Kaposi Sarcoma (KS) lesions. Prior to launching a clinical trial of the device, we obtained end-user perspectives to guide device refinement. Methods: Between April and May 2025, we conducted six focus group discussions and 28 in-depth interviews with patients, healthcare providers, and community representatives in Kenya and Uganda. Participants viewed a demonstration video and handled the SS3D prototype. Data were analyzed using hybrid deductive-inductive thematic analysis informed by the Health Information Technology Usability Evaluation Model and the Consolidated Framework for Implementation Research. Results: Qualitative findings were synthesized into a conceptual framework for SS3D adoption with two interconnected themes: 1) experiences and context, and 2) device perceptions and implementation factors. Participants' receptivity to the device was first shaped by experiences with medical technologies and the broader sociocultural context, including trust in providers, health beliefs, and gender preferences. After interacting with the prototype, participants viewed the SS3D as intuitive, accurate, and potentially capable of improving the objectivity and efficiency of KS lesion monitoring. They identified concerns related to safety, infection prevention, data security, affordability, maintenance, and workflow integration. Successful implementation was perceived to depend on device refinement, supportive organizational factors, including leadership engagement, provider training, maintenance capacity, and patient education to address misconceptions about the device. Participants proposed hardware, software, connectivity, and training refinements to support safe integration into routine clinical care. Conclusion: End users demonstrated overall satisfaction and receptivity to the SS3D, given potential benefits for both patients and providers. We identified targeted refinements to optimize the device's functionality and integration into the oncology environment to improve its fit with the local context.

Purpose Modified FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) are standard first-line treatments for metastatic pancreatic ductal adenocarcinoma (PDAC), but no validated biomarker guides treatment selection. We developed MULTIPL, a multimodal machine learning system, and established the PASS-01 Challenge to benchmark prognostic and predictive biomarkers. Patients and Methods MULTIPL was trained in the COMPASS study (N=268), integrating clinical, digitized histopathology, whole-genome, and RNA-seq data. MULTIPL, PurIST, hENT1 expression, and HRDetect were evaluated in the PASS-01 trial, a randomized phase II trial of FFX versus GNP (N=160), within the Challenge. The primary endpoint was differential treatment benefit measured by concordance-for-benefit for progression-free survival. Results MULTIPL had the highest concordance index for OS among individually evaluated biomarkers (0.595; 95% confidence interval [CI], 0.55-0.65) and separated high- versus low-risk patients (hazard ratio, 1.62; 95% CI, 1.13-2.33; P=0.009). Patients recommended for GNP by MULTIPL had significantly longer OS with GNP than with FFX (hazard ratio, 0.47; 95% CI, 0.28-0.82; P=0.007), whereas patients recommended for FFX had similar OS between treatments. Interpretability analysis of MULTIPL in COMPASS identified KDM6A alterations and SSTR1 expression as prognostic biomarkers, which were validated in PASS-01. However, none of the tested biomarkers significantly predicted differential treatment benefit in the PASS-01 Challenge. Conclusion MULTIPL demonstrated robust prognostic performance in external validation, identified a subgroup enriched for benefit from GNP, and enabled discovery and validation of prognostic biomarkers in metastatic PDAC. However, no biomarker met the primary endpoint for differential treatment benefit, underscoring the value of the PASS-01 Challenge.

IMPORTANCE: A recent meta-analysis showed that chemoimmunotherapy was associated with improved overall survival (OS) compared with immune checkpoint inhibitor (ICI) monotherapy for programmed death-ligand 1 (PD-L1) tumor proportion score (TPS) [&ge;] 50% advanced non-small-cell lung cancer (NSCLC). However, whether this benefit reflects chemotherapy effect or ICI heterogeneity remains unclear. OBJECTIVE: To reassess the survival benefit of adding chemotherapy to ICI monotherapy using agent-stratified comparisons anchored to chemotherapy. DATA SOURCES: The 24 phase 3 randomized clinical trials included in the original meta-analysis (search date, August 3, 2025). DATA EXTRACTION AND SYNTHESIS: Hazard ratios (HRs) for OS and progression-free survival (PFS) were extracted from each trial in the original meta-analysis. Two analytic frameworks were used: within-agent comparisons (same ICI in both chemoimmunotherapy and monotherapy) and across-agent comparisons (ICI in one treatment strategy only). For within-agent comparisons, a two-stage random-effects meta-analysis was conducted. In stage 1, ICI-specific HRs for chemoimmunotherapy and ICI monotherapy versus chemotherapy were pooled and their ratio was calculated (RHR = HRchemoimmuno/HRmono; RHR < 1 favors chemoimmunotherapy). The RHRs were pooled in stage 2. For across-agent comparisons, RHR was derived from pooled HRs by treatment strategy. MAIN OUTCOMES AND MEASURES: Endpoints were OS and PFS. RESULTS: In within-agent comparisons (4 ICIs; 13 trials; N = 3252), pooled RHR was 0.94 (95% CI, 0.78-1.13; P = .48; I2 = 0.0%) for OS and 0.85 (95% CI, 0.68-1.06; P = .14; I2 = 0.0%) for PFS. In across-agent comparisons (7 ICIs; 11 trials; N = 2231), RHR favored chemoimmunotherapy for OS (0.68; 95% CI, 0.50-0.92; P = .01) and PFS (0.46; 95% CI, 0.37-0.58; P < .001). In a sensitivity analysis restricted to trials of NCCN-recommended regimens, pooled RHR was 1.02 (95% CI, 0.81-1.28; P = .87) for OS. CONCLUSIONS AND RELEVANCE: In the within-agent comparisons, adding chemotherapy to ICI monotherapy did not improve OS or PFS in patients with PD-L1 TPS [&ge;] 50% advanced NSCLC. The benefit in the original meta-analysis appears driven by across-ICI heterogeneity. These findings are consistent with ICI monotherapy as a standard first-line option and underscore the need for agent-level stratification in across-trial comparisons.

Background: Late-onset neutropenia (LON) is an infrequently reported, unpredictable side effect of anti-CD20 therapy, with incidence varying by agent, diagnosis, and screening protocol. Objective: The primary objective of this cross-sectional, retrospective study was to estimate the proportion of patients who developed LON over 13 months (April 2023-April 2024). Methods: Consecutive adult patients diagnosed with central nervous system (CNS) autoimmunity who received at least one rituximab(RTX) or ocrelizumab(OCR) infusion between January 2016 and March 2024 were included; patients who switched to another immunotherapy, had no post-treatment blood draw, or had unverifiable infusion records were excluded. LON events were assessed using all post-treatment CBCD blood draws during this period. Results: A total of 171 patients were enrolled: 141 received rituximab and 30 received ocrelizumab. A total of 319 post-treatment blood tests were performed. Sixteen patients (16/171) had neutropenia (9.4%, 95% CI 5.8-14.7): 12 on rituximab (8.5%) and 4 on ocrelizumab (13.3%; p=0.487). LON occurred at a median of 158 days (130-188) since the last infusion. All patients were asymptomatic, mostly had Grade 1 neutropenia (15/16, 93.8%). BMI (22.2 vs. 27.5 kg/m2, p=0.001) and prior natalizumab exposure (37.5% vs. 14.2%, p=0.023) were significantly different between neutropenic and non-neutropenic patients. Conclusion: The proportion of patients with LON in this cohort was higher than most previously reported, with all cases asymptomatic. Lower BMI and prior natalizumab exposure emerged as potential risk factors warranting further investigation. Larger, prospective studies with standardized surveillance are needed to establish the true frequency and risk factors.

Introduction: Tumor mutational burden (TMB) is a biomarker for immune checkpoint inhibitor therapy, traditionally measured in tissue (tTMB). Blood-based TMB (bTMB), derived from circulating tumor DNA, is minimally invasive but shows modest concordance with tTMB. The significance of blood-tissue TMB discordance remains unclear. Methods: We retrospectively analyzed 105 patients with advanced NSCLC who underwent pretreatment blood and tissue next-generation sequencing between October 2020 and September 2024. The blood-to-tissue TMB ratio was defined as ln[(1 + bTMB)/(1 + tTMB)]. Outcomes were overall survival (OS) and progression-free survival (PFS). Survival was assessed using Kaplan-Meier methods and multivariable Cox models. Results: Median follow-up was 10 months. Patients in the lowest ratio tertile had longer OS than those in the upper two tertiles (median, 33 vs 11 months; hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.32-0.97; p = 0.04), whereas PFS did not differ (HR, 0.89; p = 0.62). A higher ratio, analyzed continuously, was independently associated with shorter OS (HR per 1-unit increase, 1.60; 95% CI, 1.10-2.31; p = 0.01), but not PFS. The association persisted after adjustment for metastatic organ count and radiographic tumor burden. The high-bTMB/low-tTMB subgroup had the poorest OS (HR, 3.17 vs low-bTMB/high-tTMB; p = 0.01). Conclusions: A higher blood-to-tissue TMB ratio was independently associated with worse OS in advanced NSCLC. Directional discordance between bTMB and tTMB may reflect tumor heterogeneity and provide prognostic information beyond either measure alone.

Background: Many at-risk patients lack access to genetic services due to a genetic counselor (GC) workforce shortage. Little is known about how digital alternatives impact patients with and without cancer who meet criteria for genetic testing. Methods: eREACH2 is a randomized 4-arm non-inferiority trial where pre-test (visit 1) and/or return of results (visit 2) GC counseling was replaced with a patient-centered digital intervention. Arms include: A (GC/GC), B (GC/digital), C (digital/GC) and D (digital/digital). Primary outcomes were non-inferiority in uptake of genetic services and change in genetic knowledge and general anxiety from baseline to post-disclosure of results (T0-T2). Secondary cognitive and affective outcomes were assessed using non-inferiority ANOVAs and equivalency chi-squared tests in intention-to-treat and per-protocol analyses. Findings: 773 participants were recruited nationwide; 46.6% from rural areas. Mean age was 51 years (range 20-87), 13% male, 12% non-white, 29% had less than a college education, and 33% had a personal history of cancer. 584 (76%) patients completed testing (14% had a positive result, 16% had a VUS). In the primary ITT analyses, we met the non-inferiority for uptake of genetic services and anxiety, but results were inconclusive for knowledge. Secondary outcomes were heterogeneous across arms. Arm C demonstrated consistently favorable effects, while Arms B and D showed less favorable outcomes in select domains (e.g. satisfaction and MICRA). Patients who received positive or VUS results via digital disclosure had significantly higher MICRA scores - indicating greater negative response to testing. Interpretation: In this large, randomized trial of patients with and without cancer, the eREACH intervention was effective for pre-test counseling, but inconclusive for digital disclosure of results. Exploratory analyses suggest that digital delivery could be a reasonable alternative for individuals receiving negative results, while those receiving positive or VUS results may derive some short-term psychosocial benefit from GC disclosure.

Objectives Accurate phenotyping of cases and controls is essential for studying biological and environmental contributors to disease in large biobanks. We aimed to develop a flexible, customizable, and reproducible electronic health record (EHR)-based phenotyping framework for identifying disease cases and generating matched control cohorts for downstream analyses. Here, we developed the Phenotyping Algorithm for Cases and matched Controls using EHR-based Rules (PACER). Materials and Methods Applying PACER to the All of Us Research Program Curated Data Repository v8.0, we identified female breast cancer (BC) cases identified among participants recorded as female at birth using at least two BC-associated diagnostic Observational Medical Outcomes Partnership concept IDs documented at least 30 days apart. A one-to-one matched control cohort was generated by jointly matching on sex, age, genetic ancestry, and state-level residency. Clinical, socioeconomic, and genomic data were integrated for analysis. Results We identified 10,225 BC cases and generated a control cohort of the same size matched for key demographic characteristics. Comparison with a phecodeX-based BC cohort showed 91.03% agreement. Among cases responding to relevant survey items, 80.86% self-reported a personal history of BC, compared to 1.89% of controls. We detected an enrichment of BC-associated GWAS catalog variants, pathogenic mutations in known risk genes, and higher polygenic risk scores in cases compared to controls. Discussion and Conclusion Concordance across a phecodeX-based cohort, self-reported survey responses, and genomic analyses supports the validity of PACER-defined cohorts. PACER is publicly available and readily adaptable to other diseases, supporting future research in risk modeling and precision medicine.

Gene expression-based prognostic models have shown promise for predicting recurrence in colorectal cancer (CRC), but their clinical implementation remains limited. The NanoString nCounter platform provides a practical alternative to RNA sequencing and microarrays through standardized, cost-effective gene expression profiling that is compatible with routine clinical samples. In this study, we evaluated whether NanoString nCounter gene expression data improve prediction of recurrence following curative CRC surgery. Gene expression profiles from the NanoString PanCancer IO 360 panel were analyzed in two independent CRC cohorts (cohort A, n = 189; cohort B, n = 131). Differential gene expression analyses and Cox proportional hazards models were used to assess the prognostic value of gene expression alone and in combination with established clinical risk factors. Model performance was evaluated by five-fold cross-validation and external validation between cohorts using the concordance index (C-index) and Kaplan-Meier risk stratification. The two cohorts differed significantly in recurrence-free survival, and differential expression analysis demonstrated marked cohort-specific transcriptional patterns. Ninety-one recurrence-associated genes were identified in cohort A, whereas no significant genes were detected in cohort B, with poor agreement in gene-level differential expression between cohorts (Pearson r = 0.128). Across all prediction models, external performance was modest, and inclusion of gene expression data did not improve prediction beyond clinical variables. The clinical baseline model, incorporating age, UICC stage, and tumor site, consistently achieved the highest cross-cohort performance, with UICC stage emerging as the strongest predictor of recurrence. Although overall discrimination was moderate, the baseline model successfully stratified patients into significantly different high- and low-risk groups across cohorts. These findings indicate that prognostic gene expression signatures derived from NanoString data showed limited reproducibility across independent cohorts and provided little additional predictive value beyond established clinical factors. The results highlight the importance of external validation and suggest that robust clinical variables remain the most reliable predictors of recurrence risk in this setting.

Background and objective: Colorectal cancer (CRC) is the third leading cause of cancer deaths worldwide. Early identification of high-risk individuals allows targeted prevention and early detection. Design: We constructed a polygenic risk score (PRS) for CRC risk using data from 1,448,354 individuals (103,401 cases). We evaluated its performance for identifying high-risk individuals in 6 major ancestries. Results: The PRS was strongly associated with CRC risk in Europeans (OR per SD =2.13, 95%CI=1.98-2.28), Africans (OR=1.35, 95%CI=1.11-1.64), Hispanics (OR=1.97, 95%CI =1.48-2.61), East Asians (OR=1.98, 95%CI=1.35-2.91), South Asians (OR=1.85, 95%CI=1.44 -2.37), and Middle Easterners (OR=3.10, 95%CI=1.38-6.95). Europeans in the top 10% genetic risk had 14-fold and 5-fold higher CRC risks compared to the bottom 10% (OR=13.50, 95%CI=8.67-21.00), and average (20-70%) risk groups (OR=4.64, 95%CI=3.89-5.52), respectively. The CRC risk in the top 10% individuals was equivalent to having three affected first degree relatives with CRC diagnosed at any age. Genetically high-risk individuals developed CRC up to 15 years earlier than the average. The PRS was strongly associated with early onset CRC risk e.g. in AFR (OR=3.22, 95%CI=1.79-5.81), and improved its prediction e.g. by 9% beyond clinical predictors in EUR. Conclusion: A comprehensive genetic prediction of CRC risk provides insights that could streamline screening and prevention guidelines.