JNCI Cancer Spectrum

Objective: To develop and evaluate a deep learning model for five-year breast cancer risk prediction from screening breast ultrasound (BUS) examinations. Methods: This retrospective study included 295,298 breast ultrasound examinations from 122,072 women imaged between 2012 and 2020. Patients were split into training, validation, and test sets; the test set included screening examinations only. BUS-Risk-Net aggregated image features using attention-based multiple instance learning and combined them with age and ultrasound-estimated breast density to predict 2- to 5-year risk. Performance was compared with the full Tyrer-Cuzick model in a matched case-control cohort and with a reduced Tyrer-Cuzick model in the held-out test set. Risk stratification was evaluated within BI-RADS density categories. Results: In the matched case-control cohort (n = 240 women), BUS-Risk-Net achieved a 5-year AUC of 0.632 (95% CI, 0.562-0.702), versus 0.514 for the full Tyrer-Cuzick model (95% CI, 0.440-0.588; p = 0.04). Among 19,548 examinations from 9,015 women eligible for 5-year evaluation in the test set, BUS-Risk-Net achieved an AUC of 0.679 (95% CI, 0.653-0.706), versus 0.594 for the reduced Tyrer-Cuzick model (95% CI, 0.564-0.623; P < .001). Observed 5-year cancer incidence increased across AI-defined risk tiers within each BI-RADS density category, ranging from 0.0% to 5.8% after AI stratification, compared with 2.1% to 3.6% across density categories alone. Discussion: Deep learning models applied to screening breast ultrasound could enable long-term breast cancer risk prediction and stratify risk beyond breast density alone. External and prospective validation is needed before clinical use.

Sentinel lymph node biopsy (SLNB) is increasingly omitted in early-stage breast cancer, often prompting whole-breast irradiation (WBI). We evaluated partial-breast irradiation (PBI) without axillary surgery among 78 clinically node-negative patients (median age 75) treated from 2014 to 2022. After 53-month median follow-up, no ipsilateral, regional, or distant recurrences occurred. These results demonstrate excellent outcomes and suggest PBI is a feasible, safe alternative to WBI when SLNB is omitted.

Background. Breast cancer treatment depends on histopathological features, such as grade and receptor-defined subtype; however, specialist pathologist access is constrained when the workforce is limited. Commercial multimodal large language models (MLLMs) accept hematoxylin and eosin (H&E) image tiles through paid interfaces without local hardware or fine-tuning. However, prior pathology evaluations addressed only coarse tasks. Whether they reach treatment-determining accuracy and whether vendors agree remain unclear. Methods. We aimed to evaluate three vendor-designated flagship MLLMs (Claude Sonnet 4.6, Gemini 2.5 Pro, GPT-5.5) in 427 invasive breast cancer cases. Each case went to all three with identical H&E tiles and prompts, and the subtype was inferred in the second call. The reference was an institutional sign-out report of an immunohistochemistry-derived subtype. We calculated the concordance, sensitivity, specificity, Cohen's kappa, and pairwise McNemar and Bowker tests. Findings. Claude ranked highest by raw histologic-type concordance but lowest by kappa, classifying all 23 lobular and seven micropapillary carcinomas as invasive breast carcinoma of no special type. The models anchored the Nottingham grade to three modal grades. None of the models reliably identified human epidermal growth factor receptor 2-positive disease. The failure direction was vendor-specific: Claude and GPT-5.5 were under-detected, whereas Gemini was over-called. Twelve prompt variants (4,056 calls) did not recover sensitivity. Interpretation. No current commercial MLLM reaches deployment-ready accuracy for any treatment-determining feature of breast pathology. As each vendor fails in its own fixed direction, changing vendors alters the type of error rather than removing it; therefore, the value of these models is assistive rather than autonomous. At USD 0.20-0.50 per case, they may serve as supervised draft generators that leave the diagnosis with the pathologist.

Background Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or treatment decisions. Their relationship to tumor characteristics and patient outcomes remains incompletely characterised. Methods We conducted a retrospective cohort study of 7,603 patients with Stage I-III invasive breast cancer (diagnosed 1991-2022, age <80 years) from the Joint Breast Cancer Registry in Singapore. Natural language processing (NLP) was applied to 9,754 free-text pathology reports to extract co-existing background breast features, with accuracy validated by dual-reviewer assessment of 200 reports. Unsupervised hierarchical clustering grouped extracted features into three categories. Associations with tumor characteristics were assessed by multinomial logistic regression, and ten-year overall survival by Cox proportional hazards models (median follow-up 9.6 years; 620 deaths). Results Here we show that NLP-based extraction of background breast features from routine pathology reports achieves an accuracy of over 90% across features. Lobular neoplasia and benign proliferative changes are associated with less aggressive tumor characteristics, whereas early neoplastic and papillary lesions are more prevalent in HER2-enriched and luminal B tumor subtypes. Benign proliferative changes are associated with better survival in age- and year-adjusted models (hazard ratio 0.91, 95% CI 0.86-0.97), but this association is attenuated after adjustment for stage and subtype. Conclusions NLP-enabled extraction of background breast features from pathology text is feasible at scale. These features reflect tumor biology but do not independently add prognostic information beyond established clinical variables.

Population-based mammography screening improves breast cancer outcomes, but its impact on real-world treatment pathways and quality indicators (QIs) remains incompletely described. We conducted a retrospective nationwide cohort study using linked data from the Czech National Cancer Registry and the National Registry of Reimbursed Health Services. Women aged [&ge;]18 years with a first breast cancer diagnosis between 2017 and 2024 were classified as screen-detected (SCR) or diagnostically-detected (DIG) according to the imaging modality preceding histological verification. Outcomes included stage distribution, untreated cases, first-line treatment, main treatment modality, time to treatment, multidisciplinary team discussion (MDT), centralization to Comprehensive Cancer Centres (COCs), and survival patterns. The verified cohort included 47,648 women: 26,817 SCR cases (56.3 %) and 20,831 DIG cases (43.7 %). In this nationwide analysis, SCR breast cancer was associated with earlier stage at diagnosis and better survival patterns, but also with longer time to treatment and longer time to MDT discussion than DIG-detected disease. Although treatment rates were high and centralization improved over time, substantial regional variation persisted in care pathways, MDT use, and access to COCs. These findings support continued strengthening of screening participation, monitoring of care intervals, and quality assurance of MDT reporting and regional oncology care delivery.

Purpose: Unilateral breast cancer (BC) patients scheduled for mastectomy often choose to undergo contralateral prophylactic mastectomy (CPM), despite substantial declines in contralateral breast cancer (CBC) risk in recent decades. Models predicting absolute risk of future CBC can aid informed decision-making about CPM. CBCRisk is an existing CBC absolute risk prediction model trained on unilateral BC patients regardless of whether they had mastectomy. Here we developed CBCRisk-Mastectomy, tailored specifically to BC patients scheduled for mastectomy and considering CPM. Patients and Methods: We used data on BC patients who underwent mastectomy to treat their first BC from two nationally representative sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER) cancer registry. We imputed missing data in the BCSC sample and used conditional logistic regression models, trained on 2,660 BC patients (665 CBC cases) from BCSC, to identify predictors and estimate relative risks (RRs). These were combined with attributable risks and CBC incidence rates estimated from SEER to obtain absolute risk. Cross-validation was used to internally validate CBCRisk-Mastectomy and compare with CBCRisk. Results: CBCRisk-Mastectomy has nine predictors: first BC type, lobular carcinoma in situ status, estrogen receptor status, tumor stage, breast density, age at BC diagnosis, family history of BC, age at first birth, and body mass index. The areas under the curve and their 95% confidence intervals for 5-year predictions for CBCRisk-Mastectomy and CBCRisk were 0.62 (0.59, 0.65) and 0.58 (0.55, 0.61), respectively. Conclusions: CBCRisk-Mastectomy may aid clinicians in counseling BC patients scheduled for mastectomy, enabling improved decision-making regarding CPM.

PurposeTo develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. MethodsWe developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrells concordance index (C-index) and Kaplan-Meier analyses. ResultsA total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. ConclusionDigital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

PurposeTo test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. MethodsWe conducted a multi-cohort study including CALGB 9344 (anthracycline {+/-} paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrells C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. ResultsA total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. ConclusionHistology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit. HighlightsO_LIHistology-derived H&E signatures consistently predicted recurrence risk across randomized trials and real-world cohorts. C_LIO_LIA single cutoff of a low-risk histology signature predicted taxane benefit and dose-dense chemotherapy benefit. C_LIO_LICombined clinical-histology models identified low-risk groups with 2%-10% risk of distant recurrence. C_LI

Objective Accurate prognostication of recurrence risk in HR+/HER2- early breast cancer is central for therapeutic decision-making, including identifying patients who may safely avoid adjuvant systemic therapy. However, the performance of existing prognostic tools remains insufficient for effective clinical stratification, motivating the development of artificial intelligence (AI)-based methods to improve risk stratification. Methods Ataraxis Breast CTX (ATX) is a multi-modal AI test that integrates H&E-stained whole-slide images with clinicopathologic features to predict risk of recurrence for individual patients. This study aims to validate ATX in an external dataset enriched for clinically low-risk patients from Dordrecht, the Netherlands. ATX scores were generated for 892 women diagnosed with early HR+/HER2- breast cancer. Of the 892 patients, 299 did not receive adjuvant systemic therapy. The discriminative performance of ATX was assessed using C-index and its stratification ability was evaluated by log-rank tests comparing Kaplan-Meier survival curves across risk groups. Results ATX achieved a C-index of 0.71 and a 5-year time-dependent AUC of 0.71, demonstrating strong discrimination in predicting recurrence-free survival (RFS). Among 299 patients who received no adjuvant therapy, ATX achieved a C-index and time-dependent AUC of 0.78 and 0.81 respectively, suggesting ATX retains prognostic information in the absence of systemic therapy. ATX scores were used to stratify patients into risk groups using a pre-specified threshold, where 656 (74%) were classified as ATX low-risk and 236 (26%) were classified as high-risk. Notably, untreated and treated ATX low-risk patients had comparable 5-year RFS (untreated: 5-year RFS = 96%, 95% CI = 92-97%; treated: 5-year RFS = 96%, 95% CI = 93-97%) with near identical 10-year RFS (86%, 95% CI = 83-92% for both), suggesting ATX low-risk status may identify a subgroup with favorable prognosis independent of treatment exposure. Conclusion ATX provides robust prognostic stratification in an external cohort of clinically low-risk HR+/HER2- early breast cancer and identifies a subgroup of patients who did not receive systemic therapy with favorable observed outcomes. These results support prospective validation of ATX as a decision-support tool for adjuvant therapy de-escalation in HR+/HER2- early breast cancer.

Introduction Cervical cancer is the fourth largest cause of cancer deaths in women. HPV self-sampling could increase uptake of cervical screening. This rapid review aimed to determine the accuracy, concordance, uptake and acceptability of self-sampling over clinician-collected samples in high income countries. Method We followed Cochrane Rapid Reviews Methods. Top-up of 4 systematic reviews and meta-analyses was performed. Narrative data synthesis was conducted and meta-analysis where applicable. Databases searched were MEDLINE, EMBASE, CENTRAL and clinical trial registries. Risk of bias was assessed using AMSTAR 2, QUADAS, the Cochrane Risk of Bias (RoB), or the Nudelman and Otto, 2020 tool, depending on the study type. Findings The review included 39 studies for accuracy, 38 studies for concordance, 37 uptake and 48 studies for acceptability. Self-sampling has similar accuracy as clinician-collected samples when PCR-based assays are used. The overall agreement of self-sampling and clinician-collected samples was 87.1%(95%CI;85.6-88.6) with a kappa value of 0.70(95%CI;0.67-0.73). Mail-to-all strategies had higher uptake with participation differences of 11.3%(95%CI:8.4-14.2) in the intention-to-treat analysis and 7.7%(95%CI:4.7-10.8) in the per protocol analysis. Self-sampling is acceptable to non-attendees (91%(95%CI;85.3-94.6). Conclusion and Recommendation Self-sampling shows good performance on the four clinical effectiveness indicators of accuracy, concordance, uptake and acceptability.

Objective: Tumor-infiltrating lymphocytes (TILs) in breast cancer are one of the most important indicators of the immune response within the tumor microenvironment. They play a particularly significant prognostic and predictive role in triple-negative and HER2-positive subtypes. However, substantial inter-observer variability has been reported in TIL scoring among pathologists, which limits its reliability in clinical practice. The aim of this study was to evaluate the agreement between artificial intelligence (AI) models and pathologists in TIL scoring and to compare this agreement using different statistical approaches, thereby assessing the potential of AI integration into pathology practice. Materials and Methods: Digitized histopathological images of breast cancer cases were included in the study. Tumor regions annotated by pathologists were evaluated for both stromal TIL percentage and the proportion of stromal tumor area within each ROI, with assessments performed independently by three pathologists and two AI models. Agreement was assessed among pathologists, between pathologists and AI, and between AI models. Statistical analyses included intraclass correlation coefficient (ICC), Cohen and Fleiss kappa, correlation tests, and Bland-Altman analysis. In addition, categorical agreement was examined using different cut-off values. Results: Inter-pathologist agreement was high, with an ICC of 0.81. In contrast, the global agreement between pathologists and AI models was lower (ICC 0.41). Pairwise comparisons of pathologist-AI agreement yielded substantially lower ICC values (0.12-0.21), although this improved to 0.53 when three pathologists were assessed jointly with a single AI model. The strongest categorical agreement was observed with dichotomized TIL scores ([&le;]10% vs. >10%), whereas multi-category classifications were associated with a marked reduction in kappa values. Spearman correlation coefficients between pathologists and AI models ranged from moderate to good ({rho} = 0.48-0.81). Agreement between the two AI models themselves was moderate, with an ICC of 0.64

Background: In resource-limited settings, a critical bottleneck in cervical cancer prevention is the lack of practical strategies to triage high-risk human papillomavirus (HR-HPV)- positive women. Therefore, this study aimed to develop and internally validate a genotype-specific risk stratification model. Methods: A cross-sectional study enrolled 555 women in Cameroon. Data collection integrated cervical cytology and HPV genotyping using Abbott m2000rt and Sacace multiplex systems. An iterative modeling approach with bootstrap validation was used to develop the model and address model instability. HR-HPV genotypes were transformed into a hierarchical risk variable due to sparsity and integrated with significant predictors. The final model was translated into a scoring system, and the risk gradients and performances were evaluated at two thresholds. Data was analyzed using SPSS 27.0. Results: The mean age was 44.8 years, and the prevalence of HR-HPV was 26.5% (147/555). The final model, incorporating HPV categories, age, and tobacco, demonstrated moderate discriminative ability (AUC=0.702, 0.642-0.762) with a good calibration (Hosmer-Lemeshow {chi}{superscript 2}=4.05, p=0.399). The scoring system assigned women to risk groups based on their total scores which produced a clear monotonic risk gradient; the observed probability of high-grade lesions/cancer ranged from 15% (score 0) to >65% (score [&ge;]4). At a conservative threshold ([&ge;]4 points), 4.7% (26/555) of women were classified as high-risk, concentrating 46% (6/13) of cancers (positive predictive value[PPV]=58%) while a sensitive threshold ([&ge;]3 points) had 16.8% (93/555) high-risk, concentrating 77% (10/13) cancers (PPV=38%). Both thresholds maintained a high negative predictive value (>95%). Conclusion: This bootstrap-validated, risk-stratification tool is a proof-of-concept in resource limited settings that assigns HR-HPV-positive women to distinct management pathways using three variables. After refining through a longitudinal study and external validation, this scoring system can improve the efficiency of cervical cancer screening programs in low-resource settings.

Purpose: There are no clinically validated biomarkers to assess recurrence risk and guide treatment de-escalation in Basal-like and HER2-enriched breast cancer. Taxane-based chemotherapy remains a cornerstone of treatment despite significant toxicity. We evaluated the prognostic and predictive utility of the MHCII Immune Activation Score (IA Score) in these subtypes. Experimental Design: We retrospectively analyzed Basal-like and HER2-enriched breast cancers from the NCIC CTG MA.21 trial, which randomized patients with node-positive or high-risk node-negative disease to adjuvant chemotherapy with or without taxanes. MA.21 predated immune checkpoint inhibitors and routine HER2-targeted therapy. Subtype was previously assigned by PAM50. The 36-gene MHCII-IA assay used RNA from formalin-fixed, paraffin-embedded tissue. Multivariable Cox and Kaplan-Meier analyses evaluated associations between IA Score, clinicopathologic variables, tumor-infiltrating lymphocytes (TILs), relapse-free survival (RFS), and taxane benefit. Results: Among Basal-like (N=317) and HER2-enriched (N=155) tumors, higher IA Score was associated with improved RFS independent of lymph node status and provided stronger prognostic discrimination than TILs. Node-negative patients with high IA Score had excellent outcomes (8-year RFS >90%) versus those with low IA Score (8-year RFS <76%). In node-positive disease, high IA Score increased 8-year RFS by >10% relative to low IA Score. IA Score stratified taxane benefit: node-positive IA-low patients benefited, whereas IA-high tumors had favorable outcomes regardless of regimen. Conclusions: MHCII Immune Activation Score is a prognostic and predictive biomarker in Basal-like and HER2-enriched breast cancer. High IA Score identified patients with excellent outcomes before pembrolizumab, trastuzumab, and taxane-based treatment escalation, providing a rationale for prospective risk-adapted de-escalation strategies.

Endometrial cancer (EC) incidence is closely linked to metabolic and hormonal factors. The TyGFI, a composite indicator integrating the triglyceride-glucose index and frailty index, may capture combined risk dimensions relevant to EC etiology and prediction. This study aimed to evaluate the association between TyGFI and EC prevalence among U.S. women aged 45 years and older, and to explore its predictive utility using machine-learning approaches. Data were drawn from the National Health and Nutrition Examination Survey 2011-2018 cycles. The exposure was TyGFI, and the outcome was EC status ascertained from self-reported cancer history and standardized questionnaires. From an initial 39,156 participants, we excluded males, individuals aged under 45 years, those missing TyGFI components or EC data, and extreme TyGFI values, yielding a final cohort of 2,837 women. We performed exploratory feature selection and built six predictive models using machine-learning algorithms to identify key predictors and evaluate TyGFI's contribution to model performance. Survey-weighted multivariable logistic regression estimated the association between TyGFI and EC prevalence with sequential adjustment for covariates. In a weighted sample representing 30,489,082 U.S. women aged 45 years and older, higher TyGFI was significantly associated with EC prevalence. After full adjustment, each unit increase in TyGFI corresponded to a 57% increase in the odds of EC (odds ratio 1.570; 95% confidence interval 1.033-2.370; p = 0.0322), with a significant dose-response trend across quartiles (p for trend = 0.0257). Among six machine-learning models, CatBoost achieved the highest predictive performance, with an area under the curve of 0.999. SHAP analysis identified TyGFI as the most influential predictor, followed by age and serum albumin. In this nationally representative sample of middle-aged and older U.S. women, TyGFI was significantly associated with EC prevalence and emerged as the dominant predictor in machine-learning models. These findings suggest that TyGFI may enhance risk stratification for EC beyond established reproductive and metabolic factors, though prospective studies are warranted to validate its clinical utility.

Objectives Estimates of breast cancer over-diagnosis related to mammography screening varies substantially. Over-diagnosis is commonly defined as cases that would not have been detected during the persons remaining lifetime in the absence of screening. We here aim to quantify over-diagnosis in the population-based BreastScreen Norway mammography screening program using long-term follow-up and more detailed modeling than previous studies. Setting We applied data on Norwegian screening patterns and breast carcinoma incidence for the period 1987-2019, covering women aged 49-84 years, leveraging the gradual implementation of the organized biennial BreastScreen Norway screening program for women aged 50-69 during 1995-2005. Methods Using an extended age-period-cohort model, we estimated excess lifetime risk of invasive breast cancer and ductal carcinoma in situ in the presence of program screening, as an indicator of over-diagnosis among screen-detected cases. Results Lifetime risk of breast carcinomas was 6.6% (95% confidence interval 2.5% to 10.7%) higher for invited than for non-invited women. This indicates that 18% (95% confidence interval 7.3% to 28.0%) of screen-detected cases may be over-diagnosed, and that approximately one in 86 (95% confidence interval 54 to 210) screened women were over-diagnosed during their screening period. Using effect estimates from previous studies, we estimated that approximately three women are over-diagnosed for every breast cancer death prevented by screening, and that 87% of over-diagnosed tumors might grow extremely slowly. Conclusions Over-diagnosis related to mammography screening is a considerable problem, but its extent may be smaller than reported in some previous studies. Most over-diagnosed tumors likely grow very slowly.

Background: Population-relevant BRCA1/BRCA2 data from Bangladesh are scarce, creating challenges for hereditary breast and ovarian cancer variant interpretation, counseling, and follow-up testing. We examined a clinically referred Bangladeshi cohort to characterize assay-derived BRCA1/BRCA2 short variants, sequencing-depth performance, and copy-number findings in a conservative pilot framework. Methods: Twenty-three de-identified blood-derived DNA samples were assessed using a targeted BRCA1/BRCA2 next-generation sequencing workflow. Downstream analysis used assay-generated short-variant, coverage, and CNV outputs, with coordinates reported on hg19/GRCh37. Short variants were evaluated from high-confidence PASS/VCC-H calls, and CNV review incorporated both target-region and amplicon-level copy-number patterns. Results: After removal of four low-VAF review observations, the primary germline-compatible dataset comprised 304 short-variant observations representing 34 unique variants. Both BRCA1 and BRCA2 contributed comparable variant burdens, while the overall profile was mainly composed of missense and synonymous changes. Six sample-specific heterozygous BRCA1 truncating candidates were observed, including five frameshift variants and one stop-gain variant. Protein-level mapping placed these events across the central-to-C-terminal portion of BRCA1. Sequencing depth was consistently high across the targeted regions, with all 4,255 amplicon-sample measurements exceeding 280x and 99.91% reaching at least 500x. Copy-number analysis highlighted one candidate BRCA1 multi-exon deletion-like event involving exons 15-20 in BCSIR-BRCA-21, with unresolved partial exon 14 involvement. Conclusions: This study provides an initial Bangladesh-focused targeted BRCA1/BRCA2 dataset and identifies candidate short-variant and CNV findings for validation. These findings should be interpreted as analytical candidates only and require confirmatory testing and expert clinical curation before any clinical application. The cohort is referral-enriched and should not be used to infer population prevalence.

Population-associated molecular variation in breast tissue may contribute to differences in tissue biology and disease susceptibility, yet the extent to which such variation is shaped by underlying tissue states remains unclear. Here, we performed RNA-seq and lipidomic profiling of histologically normal breast tissue samples from African American (AA) and Caucasian White (CW) individuals, followed by conceptual integration of the resulting transcriptomic and lipidomic patterns. Unsupervised analysis revealed two distinct baseline transcriptional states (G1 and G2) that defined the primary axis of molecular variation across the cohort and corresponded to epithelial-enriched (G1) and vascular-enriched (G2) tissue contexts as determined by cell-type deconvolution. Global comparisons between AA and CW samples showed minimal transcriptomic differences, with only a single gene reaching significance after multiple testing correction. However, when stratified by baseline tissue state, 191 genes were differentially expressed within G1, with coordinated upregulation of extracellular matrix organization and proliferative/cytoskeletal processes in AA samples. These patterns were consistently supported across multiple enrichment approaches. No comparable population-associated differences were observed within G2. Lipidomic analyses showed partial but non-significant trends consistent with transcriptomic structure, suggesting that lipid variation provides complementary but limited support for baseline molecular differences, likely reflecting constraints of bulk tissue composition. Together, these findings suggest that population-associated molecular differences in normal breast tissue are context-dependent and emerge within specific baseline transcriptional states, where distinct biological programs can coexist and be differentially modulated. These findings highlight the importance of tissue heterogeneity in shaping molecular variation and its potential relevance to disease-associated tissue states.

Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in women worldwide, and the great majority of these deaths are caused by metastatic disease. Whether the immunohistochemical (IHC) phenotype of breast cancer is associated with the anatomical site of metastasis has been characterized mainly in high-income, registry-based populations, while data from ecologically stressed and medically under-served regions such as the Lower Aral Sea basin are lacking. Methods: We retrospectively reviewed 652 women diagnosed with breast cancer at the Khorezm Branch of the Republican Specialized Scientific-Practical Medical Center of Oncology and Radiology (Uzbekistan) between 2020 and 2024, of whom 213 had metastatic disease (306 metastatic foci). Histological type was assessed on hematoxylin-eosin and van Gieson-stained sections; quantitative morphometry was performed in Fiji/ImageJ; and HER2, estrogen receptor (ER), progesterone receptor (PR) and Ki-67 were assessed by IHC. The association between marker expression and metastatic site (liver, lung, lymph node) was tested in 187 foci with adequate tissue using the chi-square test, with significance at p < 0.05. Results: Invasive ductal carcinoma predominated. Metastatic site was significantly associated with the IHC phenotype. Liver metastases showed the highest frequency of HER2 3+ (45.7%), ER-negativity (65.2%), PR-negativity (69.6%) and high proliferation (Ki-67 [&ge;] 60%; 47.8%), whereas lymph-node metastases were more often hormone-receptor-positive (ER+ 58.7%; PR+ 52.4%) with lower HER2 3+ (22.2%); lung metastases were intermediate (all p < 0.05). The combination of HER2 3+ and Ki-67 [&ge;] 60% was associated with multi-organ spread. Morphometry corroborated these patterns: liver lesions had larger atypical cells (up to 132.8 m), a higher nuclear-to-cytoplasmic ratio (0.76 vs 0.51) and more extensive necrosis and microvascularity than lymph-node lesions. A pragmatic 5-criterion morphological score (histological type, Ki-67, HER2, ER/PR status, atypical-cell size) stratified metastatic risk into three tiers. Conclusions: In this regional cohort, the IHC phenotype of breast cancer tracked the anatomical site of metastasis, with an aggressive HER2-driven, hormone-receptor-negative profile concentrated in liver metastases and a hormone-receptor-positive profile in lymph-node metastases. These findings reproduce established organotropism patterns in a previously uncharacterized population and support phenotype-aware, site-specific surveillance together with a low-cost morphological risk score for resource-limited settings.

PurposeHPV-associated carcinomas (HPV+ cancers) account for 5% of all cancers. Circulating tumor HPV DNA (ctHPVDNA) assays for HPV+ cancer surveillance have limited prognostic utility at the time of cancer diagnosis. While HPV integration into the host genome is a proven tissue-based biomarker predicting poor clinical outcomes, existing clinically utilized ctHPVDNA assays cannot classify the viral physical state. MethodsWe previously developed HPV-DeepSeek, a multi-feature HPV whole-genome sequencing liquid biopsy with 99% diagnostic accuracy at the time of HPV+ oropharynx cancer diagnosis. We test the diagnostic accuracy of HPV-DeepSeek in a cohort of 235 HPV+ cancers across nine anatomic sites and employ a novel blood-based computational classifier to infer HPV genome physical state from plasma, termed HPV-SIGNAL, to assess its prognostic potential. ResultsHPV-DeepSeek demonstrated a sensitivity and specificity of 99%. In 181 eligible samples, HPV-SIGNAL identified four viral physical states: episomal-only (N = 69), episomal-rearranged (N = 48), integrated-mixed (N = 55), and integrated-clonal (N = 9), which were confirmed and further elucidated via three orthogonal tissue and blood approaches. Patients harboring integrated viral states in the blood exhibited significantly worse progression-free survival (HR 3.28, 95% CI 1.63-6.58, p = 0.00084) and overall survival (HR 2.98, 95% CI 1.16-7.64, p = 0.023) compared to patients with episomal states. ConclusionHPV whole-genome sequencing liquid biopsy has high diagnostic accuracy across HPV+ cancer types and can be used to identify and classify HPV physical state from blood. Patients with integrated viral states detected in the blood demonstrated worse progression-free and overall survival, suggesting blood-based HPV physical state classification could be used as a prognostic tool at the time of cancer diagnosis. Translational RelevanceCurrent circulating tumor HPV DNA assays for HPV-associated cancer surveillance have limited prognostic utility at the time of cancer diagnosis. While HPV integration into the host genome is a proven tissue-based biomarker predicting poor clinical outcomes, existing circulating tumor HPV DNA assays cannot classify the viral physical state. Here, we show that HPV-SIGNAL, a novel blood-based computational classifier to infer HPV genome physical state from plasma using output from HPV-DeepSeek, an HPV whole genome sequencing liquid biopsy, accurately identifies and classifies HPV physical state from blood and is prognostic of progression-free and overall survival across HPV-associated cancer types.

Rationale and ObjectivesPublished estimates of benign breast disease (BBD) are derived mainly from clinical, surgical, screening-recall, or reduction-mammoplasty series. Forensic autopsy cohorts can reduce referral and symptom-selection bias, although they are not necessarily representative of the whole living population. We describe imaging-detected benign breast findings in the Sisyphus forensic autopsy cohort. Materials and MethodsConsecutive medico-legal autopsies of individuals aged 40 years or older were prospectively evaluated over a multi-year period at a medico-legal autopsy service in Portugal. Bilateral breast specimens obtained by subcutaneous modified radical mastectomy were examined with specimen digital mammography and ultrasonography. Findings were classified according to BI-RADS terminology. Lesions requiring tissue diagnosis in the post-mortem protocol underwent wire-guided or direct excisional biopsy. Female cadavers were analysed as the primary cohort; male cadavers were analysed separately as an exploratory subgroup. Proportions are reported with exact 95% confidence intervals (CIs). ResultsThe cohort included 291 cadavers: 217 women and 74 men. Among female breast specimens, 236/434 were BI-RADS 1 (54.4%; 95% CI, 49.6-59.1), 189/434 were BI-RADS 2 (43.5%; 95% CI, 38.8-48.4), and 8/434 were protocol-sampled suspicious findings (1.8%; 95% CI, 0.8-3.6). At the cadaver level, 99/217 women had at least one benign imaging finding (45.6%; 95% CI, 38.9-52.5). Mammographic benign findings were present in 91/217 women (41.9%; 95% CI, 35.3-48.8), dominated by calcifications; ultrasonographic benign findings were present in 51/217 (23.5%; 95% CI, 18.0-29.7), most often simple cysts and duct ectasia. Plasma cell mastitis-pattern calcifications were observed in 8/217 women (3.7%; 95% CI, 1.6-7.1). Male benign findings were less frequent (9/74, 12.2%; 95% CI, 5.7-21.8) and were dominated by benign lymph-node variants. All nine protocol-sampled lesions were benign at histology. Clinical breast examination identified 5/8 protocol-sampled female lesions (62.5%; 95% CI, 24.5-91.5). ConclusionIn this forensic autopsy cohort unselected for breast symptoms, benign imaging findings were common in women aged 40 years or older and less frequent in men. The results provide descriptive post-mortem imaging reference data, but lesion-specific estimates, especially rare entities, should be interpreted with caution because of small numerators, the older age profile, limited clinical history, and the original cancer-focused design of the Sisyphus study.