JNCI Cancer Spectrum

Recurrence scores based on a 21-gene assay are clinically useful for predicting prognosis and chemotherapy benefit in postmenopausal node-positive breast cancer patients, but its performance in premenopausal patients is inconsistent. Here, we evaluated Ataraxis Breast RISK (ATX), an AI test that predicts recurrence risk, and compared it with the genomic assay. ATX identified high risk patients misclassified as low risk by the genomic assay and therefore may refine selection of patients for adjuvant chemotherapy.

BackgroundThe management of residual axillary disease after neoadjuvant therapy (NAT) remains controversial, as current recommendations often treat ypN1 breast cancer as a homogeneous entity despite potential prognostic heterogeneity. Evidence supporting uniform axillary surgical strategies across different levels of residual nodal burden is limited. We investigated whether survival associations related to axillary surgical evaluation differ according to residual nodal burden in ypN1 disease, using an adjuvant cohort to validate a SEER-based proxy for surgical extent. MethodsPatients with 1-3 positive lymph nodes were identified in the SEER database (2000-2022) and stratified into neoadjuvant (NAT; n=30,560) and adjuvant (AT; n=197,586) cohorts. Axillary surgical evaluation was categorized as limited (2-3 examined nodes) or extensive ([≥]10 examined nodes). Survival was analyzed using Kaplan-Meier methods and log-logistic accelerated failure-time models, adjusted with inverse probability of treatment weighting. ResultsIn the ypN1 cohort, limited axillary evaluation was not associated with inferior overall survival among patients with a single residual positive node (IPTW-adjusted HR: 1.15, p=0.134; time ratio [TR]: 0.86, p=0.184). In contrast, limited evaluation was associated with worse survival in patients with two positive nodes (HR: 1.70, 95%CI 1.54-1.87; TR: 0.58, 95%CI 0.53-0.64). The findings were similar when using breast cancer-specific survival as the endpoint. ConclusionsSurvival associations related to axillary surgical evaluation after NAT vary according to residual nodal burden. Axillary de-escalation appears feasible in patients with a single residual positive node but cannot be extrapolated to those with multiple residual nodes, underscoring heterogeneity within ypN1 disease.

BackgroundThe genetic architecture of Breast Cancer (BC) in Arab populations remains largely understudied, limiting the precision of current prevention and screening programs. The Emirati Genome Program (EGP), one of the worlds first nation-wide sequencing initiatives, offers an unprecedented opportunity to delineate inherited BC risk across an entire population. MethodsWe analyzed 436,780 EGP individuals, including 229,309 women, integrating whole-genome sequencing (WGS) with electronic health records (EHRs). We quantified the prevalence and penetrance of pathogenic and likely pathogenic (P/LP) variants across 13 NCCN-recommended BC genes, evaluated the performance of established polygenic risk scores (PRS), and reconstructed >48,000 pedigrees to measure familial aggregation. ResultsP/LP variants were identified in 0.84% of women, accounting for 5.2% of BC cases (mean age of 45.9{+/-}11.1 years). Highly penetrant BRCA1 c.4065_4068del (p.Asn1355fs) and BRCA2 c.2808_2811del (p.Ala938Profs) variants showed age-specific cumulative risks of 37.6% and 31% by age 60, respectively, and allele frequencies up to tenfold higher in the Emirati population than in global reference datasets. The European-derived PRS model (PGS000004) demonstrated strong performance, advancing 10-year BC risk onset by a decade for women in the top decile. Family-based PRS discriminated affected from unaffected individuals, revealing higher polygenic risk even within sister pairs. Integration of monogenic, polygenic, and familial data defined a national framework for risk stratification, identifying disease-free women potentially eligible for targeted prevention. ConclusionsNation-scale genome sequencing reveals, for the first time, the comprehensive landscape of inherited BC susceptibility within a Middle Eastern population. The integration of monogenic, polygenic, and familial data establishes a national framework for genomic risk stratification--transforming population genomics into a foundation for precision prevention and early detection in the UAE and beyond.

BackgroundAlthough DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patients risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. MethodsWe trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. ResultsWe found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. ConclusionOur findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.

BackgroundInflammatory breast cancer (IBC) is a rare and highly aggressive form of breast cancer with an increased propensity to metastasize to distant organs including the brain. Higher serum levels of the calcium-binding proteins S100A8/A9, particularly of S100A9, have emerged as a clinically and biologically significant factor in aggressive breast cancers that are associated with poorer prognosis, tumor progression, and resistance to therapy. However, its contribution in IBC specifically remains undefined. Here, we investigated whether serum levels of S100A8/A9 predict outcomes in patients with IBC. MethodsSerum S100A8/A9 levels were measured in a cohort of 304 IBC patients using ELISA assay. S100A8/A9 levels were categorized by their third quartile value (S100A8/A9-low [≤] 3rd quartile; S100A8/A9-high > 3rd quartile). Overall survival (OS) and breast cancer-specific survival (BCSS) were analyzed with Kaplan-Meier curves, log-rank tests, and Cox proportional hazard regression models. The cumulative incidence of any metastases and the cumulative incidence of brain metastases were analyzed using Aalen-Johansen method, Gray test, and Fine-Gray models. ResultsThe median follow-up time was 64 months. Forty-six percent of patients had estrogen receptor (ER)-negative tumors, 61.3% were stage III-IV, 77% high grade, 16.8% received adjuvant chemotherapy and 53.6% received adjuvant radiation. On univariate analysis, S100A8/A9 levels, disease stage, ER status, PR status, HER2 status, adjuvant chemotherapy, and adjuvant radiation therapy were significantly associated with OS and BCSS. Patients with high S100A8/A9 serum levels had poor OS (P=0.01) and BCSS (P=0.007) and had a higher risk of developing brain metastasis (P=0.01) but not other metastasis. On multivariate analysis, high S100A8/A9 serum levels were independently associated with reduced OS (hazard ratio [HR]=1.7, 95% CI 1.1 to 2.6, P=0.01), reduced BCSS (HR=1.8, 95% CI 1.2 to 2.8, P=0.006), and increased cumulative incidence of developing brain metastasis (subdistribution hazard ratio (sHR)=1.8, 95% CI 1.1 to 3.0, P=0.03). ConclusionsIn patients with IBC, high serum levels of S100A8/A9 are an independent prognostic factor for brain metastasis and poor clinical outcomes. These findings support the potential of S100A8/A9 as predictive biomarker for identifying increased risk of brain metastasis and unfavorable prognosis in patients with IBC.

Structured AbstractO_ST_ABSPurpose/ObjectivesC_ST_ABSThe role of postmastectomy radiotherapy (PMRT) in patients with pathologic N1 (pN1) breast cancer, including triple-negative breast cancer (TNBC), remains controversial in the era of modern systemic therapy. We evaluated the association between PMRT and recurrence-free survival (RFS) and overall survival (OS) and identified prognostic factors in a contemporary single-institution pN1 cohort. Materials/MethodsWe retrospectively reviewed female patients with pT1-2N1M0 breast cancer treated with mastectomy between 2016 and 2022. RFS and OS were estimated using Kaplan-Meier methods and compared by PMRT status with log-rank testing. Univariable Cox proportional hazards models assessed associations between clinical factors--including tumor laterality, receptor subtype (TNBC vs non-TNBC), nodal burden, and adjuvant therapies--and survival outcomes, with subgroup analyses by PMRT status and receptor subtype. ResultsFifty-seven patients were included; 22 (38.6%) received PMRT. With a median follow-up of 85 months, PMRT was not associated with improved RFS (median 133 vs 120 months; p=0.256) or OS (not reached vs 195 months; p=0.154). Hormone therapy was significantly associated with improved RFS (HR 0.43; p=0.026) and OS (HR 0.13; p=0.003), while having 2-3 positive lymph nodes predicted worse RFS (HR 2.86; p=0.007). No significant differential benefit from PMRT was observed in patients with TNBC or non-TNBC disease. ConclusionsPMRT was not associated with a survival benefit in this pN1 cohort, including patients with TNBC. Interpretation is limited by modest sample size and statistical power. Outcomes appeared driven by tumor biology, nodal burden, and systemic therapy, supporting individualized PMRT decision-making.

The gut microbiome has been linked to breast cancer, largely through microbial functions involved in estrogen metabolism (the "estrobolome"); however, specific microbial targets remain poorly defined in human studies. Here, we profiled the gut microbiome using whole-metagenome shotgun sequencing, and plasma and stool metabolites were quantified using targeted metabolomics, in a study of 70 postmenopausal female cases with treatment-naive ER-positive breast cancer and 70 controls. Reduced species-level alpha and beta diversity were associated with breast cancer, whereas microbial functional-level diversity was not. Higher levels of DHEA-sulfate, estriol, and isoflavone phytoestrogen metabolites and lower lignan phytoestrogen metabolites were associated with breast cancer, while circulating estrogens and estrogen-related microbial functions were not. Beyond hormone-related pathways, higher levels of conjugated bile acids and carnitines were also associated with breast cancer. Compared to controls, cases exhibited depletion of Blautia obeum, Alistipes shahii, A. finegoldii, A. putredinis, and Anaerotruncus rubiinfantis, along with reduced abundance of functions related to menaquinol-8 biosynthesis, TCA cycle-related energy metabolism, and NAD salvage, indicating depletion of specific metabolic pathways rather than overall functional diversity. These results indicate a potential etiologic role for host-microbe metabolic interactions in breast cancer that extends beyond estrogen-centered mechanisms and warrants validation in independent cohorts.

BackgroundOptimal pre-analytical management of breast tissue specimens, particularly formalin fixation, is essential for accurate immunohistochemical (IHC) biomarker assessment in invasive breast cancer. Although international guidelines suggest using 4% neutral buffered formalin with controlled fixation time, many laboratories in low-resource settings deviate from these standards. This study aimed to determine whether fixative preparation (4% neutral buffered formaldehyde vs. 4% non-buffered formaldehyde) and cold ischemia time impact the preservation and evaluation of tissue biomarkers in invasive breast cancer. MethodsWe conducted an experimental study using fresh mastectomy tissue from a 34-year-old patient with invasive ductal carcinoma (pT4, hormone receptor-positive, HER2-negative, Ki67=40%) who had not received neoadjuvant chemotherapy. Fifty microsamples (5-15 mm in length, 1 mm in width) were obtained and divided into four cohorts: (1) 19 samples fixed in 4% neutral buffered formaldehyde for 0.5 to 144 hours; (2) 19 samples fixed in 4% non-buffered formaldehyde for 0.5 to 144 hours; (3) 6 samples with delayed fixation (0.5 to 8 hours) then fixed in neutral buffered formaldehyde for 10 hours; (4) 6 samples with delayed fixation (0.5 to 8 hours) then fixed in non-buffered formaldehyde for 10 hours. Hormone receptors (estrogen receptor-ER, progesterone receptor-PR) and Ki67 expression were evaluated by IHC using the Allred scoring system and current international recommendations. ResultsFixative preparation had a statistically significant, yet minimal, biological impact on biomarker evaluation. The mean percentage of ER-positive cells was 96.89{+/-}0.74% with neutral buffered formaldehyde compared to 94.32{+/-}1.51% with non-buffered formaldehyde (p=0.011). Similar trends were seen for PR (94.89{+/-}0.95% vs. 92.63{+/-}1.67%, p=0.027) and staining intensity. However, Allred scores remained constant. Cold ischemia time was strongly correlated with decreased biomarker expression regardless of fixative preparation. Hormone receptor expression and Ki67 remained stable with minimal Allred score changes for up to 2 hours of cold ischemia, but significantly decreased after 2 hours, with scores decreasing in proportion to the duration of ischemia (p<0.05). ConclusionsNon-buffered formaldehyde preserves tissue biomarkers almost as effectively as neutral buffered formaldehyde for IHC analysis. Following guidelines, a cold ischemia time of up to 1 hour is still a wise standard to guarantee accurate biomarker assessment. These results are significant for pathology laboratories in resource-limited settings where neutral buffered formalin may not be easily accessible.

Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammography, have recognized limitations in sensitivity, specificity, and performance in dense breast tissue. Circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, while artificial intelligence and machine learning (AI/ML) offer powerful tools for identifying diagnostically relevant multi-marker patterns within complex biomarker datasets. This systematic review and meta-analysis evaluated the diagnostic performance of AI/ML-based circulating miRNA signatures for early breast cancer detection. Methods: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science Core Collection was conducted from database inception to 31 December 2025. Studies were eligible if they were original human investigations evaluating circulating miRNAs using an AI/ML-based diagnostic model for breast cancer detection and reporting extractable diagnostic performance metrics. Study selection followed PRISMA 2020 and PRISMA-DTA guidance. Methodological quality was assessed using QUADAS 2. Pooled sensitivity and specificity were synthesized using a bivariate random-effects model, and overall diagnostic performance was summarized using a hierarchical summary receiver operating characteristic framework. Results: Seven studies met the inclusion criteria for qualitative synthesis, with eligible studies contributing to the quantitative analysis depending on data availability. Across the pooled analysis, AI/ML-based circulating miRNA models demonstrated good overall diagnostic performance, with a pooled AUC of 0.905 (95% CI: 0.890 to 0.921), pooled sensitivity of 81.3% (95% CI: 76.8% to 85.2%), and pooled specificity of 87.0% (95% CI: 82.4% to 90.7%). Heterogeneity was moderate for AUC (I2 = 42.3%) and sensitivity (I2 = 38.7%) and low for specificity (I2 = 28.4%). Risk-of-bias assessment showed overall low-to-moderate methodological concern, with patient selection representing the most variable domain. Deeks funnel plot asymmetry test showed no significant evidence of publication bias (p = 0.34). Conclusions: AI/ML based circulating miRNA signatures show promising diagnostic accuracy for early breast cancer detection and may have value as non invasive adjunctive tools within imaging supported diagnostic pathways. However, the evidence base remains limited by methodological heterogeneity, variable validation rigor, and the predominance of retrospective case control designs. Prospective, standardized, and externally validated studies are needed before routine clinical implementation can be justified.

BackgroundThe tumour microenvironment (TME) influences breast cancer progression and treatment response. We investigated whether TME composition predicts tamoxifen benefit in postmenopausal women with oestrogen receptor-positive, HER2-negative (ER+HER2-) breast cancer. MethodsThis study included 513 patients from the Stockholm Tamoxifen (STO-3) trial, which randomised postmenopausal, lymph node-negative women to tamoxifen or no endocrine therapy. Bulk tumour transcriptomes were deconvoluted with the ConsensusTME algorithm to estimate the relative abundance of 18 immune and stromal cell types. A summary score of combined immune cells was created on a per patient basis and evaluated alongside fibroblast and endothelial stromal compartments. Patients were categorised into immune and stromal tertiles on the basis of these scores. Associations between TME composition and tumour characteristics were evaluated using Spearman correlations and Fishers exact test. Tamoxifen benefit was analysed by univariable Kaplan-Meier (log-rank) and multivariable Cox proportional hazards adjusting for age, tumour size, grade, progesterone receptor, Ki-67, and radiotherapy. Differential expression was assessed with limma and pathway enrichment with fgsea using Hallmark gene sets from MSigDB. ResultsLow immune abundance was significantly associated with higher ER expression (Fishers exact test p < 0.001). Among tamoxifen-treated patients, those with low immune scores showed improved distant recurrence-free interval (DRFI) relative to untreated patients (log-rank p < 0.001). Similarly, intermediate endothelial (p < 0.001) and low/intermediate fibroblast abundances (p = 0.042, p = 0.009) were associated with favourable DRFI. In multivariable models, low immune (aHR = 0.17, 95% CI 0.08-0.40), intermediate endothelial (aHR = 0.21, 95% CI 0.09-0.51), and low/intermediate fibroblast tertiles (aHR = 0.50, 95% CI 0.27-0.93; aHR = 0.36, 95% CI 0.17-0.77) retained significance. Transcriptomic analysis revealed enrichment of oestrogen-response, MYC-target, and oxidative-phosphorylation pathways in low-immune and low-fibroblast tumours, while interferon-{gamma} response and allograft rejection pathways were downregulated. ConclusionsTME composition modulates tamoxifen benefit in postmenopausal ER+HER2-breast cancer. Low immune, intermediate endothelial, and low/intermediate fibroblast abundances are associated with improved benefit from tamoxifen, suggesting that both immune and stromal compartments influence endocrine treatment efficacy.

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

BackgroundLeptomeningeal disease (LMD) is a serious complication of metastatic breast cancer (MBC) with poor survival. This single-institution retrospective study compares overall survival (OS) among MBC patients with LMD based on CSF parameters (glucose, protein, and WBC count) MethodologyMBC patients who were diagnosed with LMD between 2010-2023 at Wilmot Cancer Institute were screened for eligibility. Only those with available data on CSF glucose, protein, and WBC count were included. OS was assessed via the Kaplan-Meier method and compared using the log-rank test. Cox models were used for multivariate analysis. ResultsOut of 69 patients with MBC LMD, 28 had CSF data and were included in the final analysis. The CSF cytology-positive cohort had significantly lower glucose levels vs the CSF cytology-negative cohort [median (IQR) 40 (18-58) vs 64 (53-92) mg/dl, p=0.006]. Median CSF WBC count was significantly higher in the CSF cytology positive cohort vs the CSF cytology negative cohort [median (IQR) 13 (6-44) vs. 2(2-4)cells/mm3, p=0.001]. When stratified by CSF cytology results and CSF glucose levels, the CSF cytology negative, glucose-low group was associated with the worst OS, while the CSF cytology negative, normal/high glucose group was associated with the best OS(p=0.03) in an unadjusted analysis. Multivariate analysis confirmed that low CSF glucose was independently associated with poorer survival [HR 4.64 (1.71, 13.2)]. Neither CSF protein levels nor CSF WBC counts were significantly associated with OS in unadjusted and multivariate analyses. ConclusionLow CSF glucose was associated with worse OS than normal/high CSF glucose. There was insufficient evidence to suggest that CSF protein or CSF WBC counts were associated with OS.

Stromal tumor-infiltrating lymphocytes (sTILs) are promising biomarkers for predicting therapeutic outcomes in triple-negative breast cancer (TNBC), with higher sTIL levels correlating with improved chemotherapy response and survival outcomes. Currently, sTILs are manually evaluated by pathologists, which is prone to inter-reader variability. In this study, we have developed an AI-driven TIL segmentation pipeline to process entire diagnostic hematoxylin-and-eosin-stained whole slide images for reproducible scoring (global TILseg scoring) and reliable prognostication. This pipeline was optimized and tested using two independent TNBC patient cohorts (n = 57 in the discovery cohort, n = 43 in the validation cohort) with clinical outcomes and follow-up data. The global scores generated by TILseg showed moderate to high concordance with expert scoring (Spearman R = 0.84-0.89) and improved patient stratification (p-value = 0.0191) as compared to manual scoring (p-value = 0.0663). Additionally, we investigate how the spatial localization of sTILs (spatial TILseg) impact survival outcomes by identifying TILs in selected stromal subsets (0.02-2 mm from the epithelial clusters). Our findings have shown that TILs up to 50 {micro}m from epithelial regions prove to be most prognostic in predicting recurrence-free survival post-neoadjuvant chemotherapy with higher statistical significance than both manual and global TILseg scoring. Further, spatial TILseg scoring was more significantly associated with pathological complete response status in both patient cohorts. In summary, we present an AI-based digital tool for robust sTIL scoring and spatial mapping to enhance its potential as both a diagnostic and prognostic biomarker, particularly in TNBC patients. SIGNIFICANCEAn automated and spatially resolved AI tool for sTILs scoring enhances patient risk stratification based on both response to treatment and recurrence-free survival, establishing its relevance as an independent prognostic marker.

Nottingham histologic grading is essential for breast cancer prognostication but suffers from inter-observer variability in assessing mitotic activity, nuclear pleomorphism, and tubule formation. We developed MOSAIC (Mammary Oncology Spatial Analysis and Intelligent Classification), an explainable AI framework designed to perform component-wise grading by independently modeling these three histologic features. Model outputs were calibrated using a two-phase pathology study to establish clinically reproducible scoring thresholds and were subsequently evaluated across public datasets and multi-institutional Indian cohorts. MOSAIC demonstrated robust performance, with AI-derived grades providing independent prognostic information (HR >= 1.8 in two datasets, p = < 0.001) and improved survival stratification compared to traditional methods. In pathologist calibration studies, AI-assisted scoring significantly reduced variability, specifically achieving near-perfect agreement in mitotic scoring with a weighted {kappa} up to 0.98. Accuracy and Cohens kappa ({kappa}) analysis further characterized the models technical performance across components: Tubule formation showed the highest agreement (Accuracy >= 0.6607, {kappa} = 0.549), followed by overall Grade (Accuracy = 0.5637, {kappa} = 0.539) and Mitotic activity (Accuracy = 0.4985, {kappa} = 0.4), while Nuclear pleomorphism proved the most challenging (Accuracy = 0.3303, {kappa} = 0.271). Comparative survival models confirmed that AI-derived grades were more significant predictors of risk than manual pathologist-assigned grades, with the AI model yielding a superior global p-value (5.9 x 10-7) and lower AIC (769.61). These results indicate that MOSAIC enables reproducible, interpretable grading by decomposing assessment into pathology-aligned components. By enhancing consistency while preserving prognostic relevance, this framework supports explainable AI as a viable assistive tool for routine breast cancer pathology.

BackgroundHistologic descriptors such as lymphovascular invasion (LVI), visceral pleural invasion (VPI), spread through air spaces (STAS), and grading system have each been associated with adverse outcomes in lung adenocarcinoma (LUAD). However, with the exception of VPI, these features are not formally incorporated into the TNM staging system. We evaluated the prognostic value and incremental contribution of these histologic descriptors within the framework of the 9th edition TNM staging system. MethodsIn total, 1,745 individuals diagnosed with stage I-III invasive non-mucinous lung adenocarcinoma (NM-LUAD) were included in this study, comprising 1139 French-Canadian patients who underwent surgical resection at IUCPQ-Universite Laval (discovery cohort) and 606 patients from the National Cancer Center Hospital in Tokyo, Japan (validation cohort). The objective of this study was to assess the prognostic contribution of histologic descriptors, including STAS, and LVI, as complements to conventional 9th edition TNM staging. ResultsGrade 3 tumors, LVI, and STAS were identified in 880 (50.4%), 809 (46.4%), and 775 (44.4%) of 1745 cases, respectively. Histologic grade and LVI demonstrated the strongest associations, particularly in early-stage disease, while STAS exhibited a stage-dependent effect, being more impactful in stages II-III. VPI showed less consistent prognostic value. Incorporating these histologic descriptors into TNM staging improved prognostic model performance, with the largest gains driven by histologic grade and LVI, while STAS provided additional, complementary prognostic refinement. ConclusionThese findings demonstrate that key histologic descriptors--including grading system, LVI, and STAS--represent robust and reproducible prognostic parameters. Importantly, these descriptors provide complementary, stage-dependent information that may enhance risk stratification and inform refinement of future TNM staging frameworks, including the forthcoming 10th edition.

BackgroundPopulation-based mammographic screening is primarily age-based. However, breast cancer risk is multifactorial, and women may benefit from personalized risk-based screening. This pilot study aimed to explore the use of polygenic risk score (PRS) as a tool for risk stratification in personalized screening. MethodsWe included 80 women aged 40-49 years referred for clinical mammography. Exclusion criteria were prior breast cancer or premalignant breast disease, and previous genetic testing. After DNA collection, PRS was calculated from 2805 Single Nucleotide Polymorphisms (SNPs). Screening recommendations were based on each participants relative 10-year breast cancer risk estimated from PRS and compared with the 10-year risk of an average woman of the same age. Women with a self-reported family history of cancer meeting standard criteria were referred for gene panel testing for pathogenic variants in high-risk genes. A follow up questionnaire regarding participants experiences was distributed 6-9 months after PRS testing. ResultsMean age was 45.2 years (SD 2.8). Mean relative 10-year breast cancer risk was 1.18 (SD 0.57). Based on PRS, 40 participants were recommended standard biennial screening 50-69 years, while 40 were advised to begin biennial screening before age 50. Among these, 7 were recommended annual mammography from when their 10-year risk reached twice that of an average 50-year-old. Twenty-one women underwent gene panel testing; no pathogenic variants in breast cancer genes were identified. Five women were advised annual mammography from 40-60 years due to family history of breast cancer, regardless of PRS. Most respondents viewed breast cancer risk assessment positively and did not report increased anxiety after testing. ConclusionsPolygenic risk score testing may influence current screening recommendations and contribute to more personalized risk-based breast cancer screening strategies.

BackgroundWe previously reported that >5% of the population carries pathogenic or likely pathogenic variants (P/LPVs) in key cancer susceptibility genes. However, gene-specific cancer prevalence, spectrum, burden, lifetime risk, comorbidity, and the risk associated with autosomal recessive (AR) genes among carriers remain incompletely defined. MethodsWe analyzed 72 cancer susceptibility genes in the All of Us dataset (N=633,547), including 287,076 participants with both genomic and electronic health record data. Cancer diagnoses were identified using SNOMED codes and grouped into 35 categories. Associations between P/LPVs and overall and site-specific cancer risk were evaluated using regression models adjusted for age, sex, race, and ethnicity. ResultsAmong genes with [&ge;]10 unique carriers, cancer prevalence was highest for MEN1 (80%), followed by TP53 (57.7%), MLH1 (48.4%), and MSH2 (47.2%). Carriers of P/LPVs in BRCA1, BRCA2, MLH1, APC, NF1, PTEN, and PALB2 had significantly earlier cancer diagnosis compared to non-carriers. Cancer prevalence was markedly higher in BRCA1 and BRCA2 carriers who are also mono-allelic MUTYH carriers (75% and 45.5%, respectively) compared with BRCA1 and BRCA2 alone (43.2% and 36.5%). Adjusted survival analysis showed increased cancer risk for MLH1 (OR=6.08), PTEN (OR=5.80), and MSH2 (OR=5.19). Novel associations included MITF with anal/perianal and prostate cancer; BLM with ovarian and soft tissue/sarcoma; WRN with gynecologic cancer (NOS); and FH with hematologic malignancy. ConclusionsThis population-based analysis defines gene-specific cancer prevalence, spectrum, and risk, including contributions from AR variants, in the U.S. population. These findings support more precise genetic testing, screening, and risk stratification for individuals carrying inherited P/LPVs.

BackgroundTumor-associated macrophages (TAMs) display context-dependent functional polarization, but whether their prognostic impact is consistent across tumor types remains unclear. MethodsWe analyzed RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD; n=648), lung squamous carcinoma (LUSC; n=623), and melanoma (SKCM; n=466). Cox proportional hazards models adjusted for age and AJCC stage evaluated per-standard deviation (SD) expression of TAM markers (FOLR2, TREM2) and T-cell markers (CD8A, CXCL9). Cross-histology interaction terms tested divergence between LUAD and LUSC. ResultsIn melanoma, higher FOLR2 (HR 0.87), TREM2 (HR 0.83), CD8A (HR 0.69), and CXCL9 (HR 0.67) independently predicted improved survival. LUAD showed largely neutral macrophage effects. In contrast, LUSC demonstrated an adverse association for FOLR2 (HR 1.28). Interaction analysis confirmed significant divergence for FOLR2 and TREM2 between LUAD and LUSC. ConclusionsTAM-associated prognostic effects reverse by tumor histology, supporting tumor-context-dependent macrophage polarization and informing macrophage-targeted therapeutic strategies.

This study investigated potential pre-diagnostic proteomic risk markers for 7 solid cancers independent of known risk factors within a multi-center, prospective cohort study. Using the SomaScan(R) 5K assay, we analyzed 4,712 unique plasma proteins (4,955 aptamers) in the Atherosclerosis Risk in Communities (ARIC) study among 9,391 middle-aged and older Black (23%) and White (77%) men and women. Over a maximum follow-up of 25.9 years, incident cases included 136 bladder, 271 colorectal, 96 kidney, 22 liver, 416 lung, 88 pancreatic, and 588 prostate cancers. After false discovery rate (FDR) correction, we identified 144 unique protein-cancer associations in common risk-factor adjusted models, and 41 protein-cancer associations in both common and cancer site-specific risk-factor adjusted models. Associations included several novel circulating proteins related to liver (33 proteins) and lung (4 proteins) cancer risk, and confirmed previously established proteins associated with kidney (HAVCR1 and MMP7) and prostate (KLK3 and ACP3) cancer risk. External validation in the European Prospective Investigation into Cancer and Nutrition cohort (SomaScan 7K) confirmed that the majority of FDR-significant proteins showed consistent effect directions and nominal significance, with the proportion of confirmed proteins varying between 75% and 100% depending on the cancer site. Time-lagged analysis demonstrated that 90% of the identified cancer-associated proteins are markers for long-term cancer risk, with observed associations more than 5 years pre-diagnosis after multiple-testing correction. These findings underscore the potential of circulating proteomic markers beyond known risk factors for elucidating etiologic mechanisms and improving risk stratification across cancers.

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.