JNCI Cancer Spectrum

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.

Purpose: The prognostic role of tumor-infiltrating lymphocytes in luminal breast cancer remains uncertain, partly because density-based metrics do not capture spatial interactions between immune cell subsets. We developed a density-independent spatial metric quantifying macrophage-T cell proximity and assessed its prognostic value. Experimental Design: Using multiplex immunohistochemistry across three breast cancer cohorts (exploratory, n = 17; discovery, n = 687; validation, n = 305), we measured nearest-neighbor distances from T cells to M1-like and M2-like macrophages, benchmarked against a randomly subsampled total macrophage pool. We defined the Macrophage Spatial Polarity Index (MSPI) as the difference between M2-to-T cell and M1-to-T cell affinity scores, where higher values reflect an M2-dominated spatial phenotype. Cox regression was used to assess associations with distant disease-free survival (discovery) and overall survival (validation). Results: M2-like macrophages preferentially localized near T cells, independent of cell density. Higher MSPI was associated with shorter survival in luminal cancers (discovery: HR = 1.45, p < 0.001), with the strongest effect in young women with early-stage disease (HR = 2.16, p < 0.0001). MSPI remained independently prognostic after adjustment for stage, systemic treatment, and diagnosis period (HR = 2.31, 95% CI 1.73-3.09, p < 0.0001) and was non-significant in HER2-positive and triple-negative subtypes. Validation in an independent ER-positive cohort confirmed the finding (HR = 1.30, p = 0.004). Pooled analysis yielded HR = 2.13 (95% CI 1.68-2.70, p = 3.45 x 10-10). Conclusions: MSPI is a robust prognostic biomarker in luminal breast cancer, particularly in young women with early-stage disease, warranting further validation for risk stratification and therapeutic guidance.

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.

BackgroundTriple-negative breast cancer (TNBC) exhibits substantial molecular heterogeneity and lacks targeted receptor therapies. Single-omic approaches inadequately capture its regulatory complexity, necessitating integrated multi-omic frameworks to identify stable prognostic signatures. MethodsMatched transcriptomic and DNA methylation data from the TCGA-BRCA cohort were normalised and mathematically integrated to isolate disease-associated variations. A calibrated machine learning voting ensemble (comprising LightGBM, Random Forest, and Logistic Regression) was trained to predict clinical survival. Model generalisability was tested on an independent microarray cohort (GSE58812) using independent quantile normalisation. SHAP (SHapley Additive exPlanations) values provided biological interpretability. ResultsDifferential and integrative analyses identified a 47-gene master prognostic signature. The ensemble classifier achieved an external validation accuracy of 74.77% (AUC 0.590) on unseen clinical patients. SHAP analysis confirmed the biological directionality of these specific biomarkers in driving mortality. Hypergeometric pathway enrichment highlighted targetable metabolic and signalling networks. ConclusionsThis multi-omic machine learning pipeline identifies a highly prognostic 47-gene signature for TNBC. The model demonstrates strong cross-platform generalisability and offers interpretable clinical utility for stratifying patient risk and guiding future therapeutic target development.

Purpose: Beyond estrogen receptor (ER) positivity, no genomic biomarker reliably identifies ER+ breast cancer patients who derive differential benefit from endocrine therapy (ET). We performed an unbiased genomic screen to discover genes predicting ET response and characterized the top candidate across clinical settings, treatment modalities, and an independent validation cohort. Experimental Design: We screened 240 genes in 1,197 metastatic ET-treated patients from the MSK-CHORD clinical genomics database using Cox proportional hazards regression with false discovery rate (FDR) correction. The top candidate, core-binding factor subunit beta (CBFB), was characterized across four cohorts defined by disease setting (metastatic/adjuvant) and treatment (ET/chemotherapy), with multivariable adjustment, gene-by-treatment interaction testing, left-truncation sensitivity analysis for guarantee-time bias, and external validation in METABRIC (N = 1,499 ER+). Results: CBFB mutations (prevalence, ~5%) were the only gene associated with improved time to progression (TTP). In metastatic ET patients, CBFB-mutated tumors (n = 80) demonstrated significantly longer TTP (hazard ratio [HR], 0.44; 95% CI, 0.29-0.67; P = .0002, FDR q = .010) with no chemotherapy benefit (HR, 1.16; P = .65). The gene-by-treatment interaction was significant (HR, 0.37; P = .009). Effects were robust to multivariable adjustment (HR, 0.46-0.50), independent of histology, and preserved under left-truncated Cox regression (HR, 0.38). In the adjuvant setting, CBFB mutations predicted improved recurrence-free survival (HR, 0.52; 95% CI, 0.31-0.85; P = .010), with no effect under chemotherapy. In METABRIC, CBFB mutations predicted improved ER+ overall survival (HR, 0.52; P = 9.3e-5). Conclusions: CBFB mutations identify ~5% of ER+ breast cancers with exceptional ET benefit. As CBFB is included on all major cancer gene panels, this biomarker requires no additional testing infrastructure for clinical implementation.

BackgroundForensic autopsy cohorts can help estimate the burden of clinically unrecognised cancer that is not captured by routine incidence statistics. We report incidental non-breast malignancies identified as secondary findings in the Sisyphus Study, a prospective forensic autopsy cohort originally established to investigate silent breast cancer prevalence. MethodsThis was a descriptive secondary analysis of 291 consecutive medicolegal autopsies performed in Lisbon, Portugal, between July 2016 and December 2019 (74 male and 217 female decedents). Key exclusions relevant to the present analysis were age below 40 years, major breast-region injury, and known or clinically evident cancer. An incidental cancer was defined as a histologically confirmed malignancy identified at autopsy in an individual without a prior clinical cancer diagnosis. ResultsFifteen incidental non-breast malignancies were identified among 291 decedents, yielding an overall prevalence of 5.15%. Prevalence was 6.76% in males (5/74) and 4.61% in females (10/217). Male findings comprised two colorectal adenocarcinomas, one pancreatic metastatic adenocarcinoma, one gastric adenocarcinoma, and one splenic lymphoma. Female findings comprised six colorectal adenocarcinomas, two lung adenocarcinomas, one perforated gastric adenocarcinoma, and one ovarian metastatic adenocarcinoma. Colorectal malignancies accounted for 8 of 15 cases (53.3%). Metastatic disease was documented in at least five cases, and perforation was present in two gastrointestinal tumours. None of the affected individuals had a prior cancer diagnosis during life. ConclusionsThis cohort demonstrates a measurable burden of clinically silent non-breast cancer, including advanced and potentially fatal disease. Forensic autopsy surveillance may complement conventional cancer surveillance by identifying malignancies that remain invisible to clinical registries. The predominance of colorectal cancer in this series is consistent with missed opportunities for earlier detection, although individual screening histories were unavailable.

Background: The use of immune checkpoint inhibitors (ICIs) in the treatment of cancer has rapidly expanded over the last decade. However, there are several knowledge gaps in understanding how tumor cells evade the immune system. There is paucity of data in HPV negative oral cancer, particularly of the gingivobuccal region. Understanding the mechanism of immune system evasion in this cancer is vital for improving patient outcomes. Methods: We characterized the baseline immune milieu of oral cancer using immunohistochemistry (IHC) on whole tumor sections from 124 cases. Tumors were classified as hot or cold and further stratified into high-risk and low-risk groups. High-risk patients included those with lymph node metastasis at diagnosis/recurrence or distant metastasis within 2 years of treatment completion. Patients without these features were categorized as low risk. Validation by RNA-Seq and Joint Enrichment Analysis of Oncogenic and Immunologic Pathways was carried out in a subset of 46 cases. Results: Hot high-risk tumors (by IHC) were distinguished by elevated PD-L1 expression and reduced NK-cell, PD1, and CTLA-4 expression. There was no difference in the expression levels of CD3+, CD8+, granzyme, or perforin compared to hot low-risk tumors, findings that align with the definition of hot tumors. RNA-Seq revealed a gene signature associated with exhausted T-cells in hot high-risk tumors. Gene and pathway analyses identified differential upregulation of isoform-specific TOX, TCF, CXCR, RUNX, IRF, BRD and BCL6 genes, implicating immune cell exhaustion and tumor aggressiveness. Significantly downregulated genes included PDCD1, HAVCR2, ZAP70, and STAT, indicative of a disabled immune microenvironment. These findings support that a state of immune exhaustion in HHR tumors is driven by progenitor exhausted T-cells and terminally exhausted T-cells; independent of PD1-TIM3. Conclusion: These findings suggest that combining TOX/TCF/BCL6 inhibitors with immune checkpoint inhibitors in the adjuvant setting might benefit patients with hot high-risk tumors. Given the results, testing for a targeted exhaustion-related gene panel at diagnosis is recommended for oral cancers to stratify tumors as high-risk or low-risk. Larger validation studies and clinical trials are now warranted.

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.

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.

Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) affects many people receiving taxane treatment for breast cancer. Symptom trajectories vary, with some recovering, and others experiencing persistent, or delayed worsening (coasting) symptoms. The prevalence and predictors of these trajectories remain unclear. This study identified the prevalence and biopsychosocial predictors of CIPN persistence, improvement, and coasting within three months post-treatment. Methods: This secondary analysis included participants treated with taxanes for stage I-III breast cancer who completed the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity-4 (FACT/GOG-NTX-4) at baseline, post-chemotherapy, and three months later. A minimally important difference (MID) from baseline on the FACT/GOG-NTX-4 defined persistence, improvement, coasting, and no MID-CIPN (below the MID threshold at each assessment) trajectories. Baseline assessments included self-reported pain/well-being, sensory, balance, and lower limb physical functioning measures, and sociodemographic and treatment data were collected. Results: Among 102 participants (51.57{+/-}11.24 years), persistence occurred in 34.3%, improvement in 25.5%, coasting in 6.9%, and no MID-CIPN in 33.3%. Compared to no MID-CIPN, older age (OR=1.120; 95%CI: 1.026-1.222), higher expected pain (OR=1.630; 95%CI: 1.082-2.456), and cold hyperalgesia at the foot (OR=1.130; 95%CI: 1.018-1.254) predicted persistence. Lower fatigue predicted improvement (OR=0.904; 95%CI: 0.845-0.968). No predictors were identified for coasting. Conclusion: CIPN trajectories are heterogeneous. Age and pre-treatment pain expectations, cold hyperalgesia, and fatigue differentiate patients with persistent CIPN and those likely to improve from those with no CIPN. Implications for Cancer Survivors: Early identification of individuals at risk for persistent neurotoxicity may support risk stratification and guide targeted supportive care strategies.

Background Cancer treatment response is highly variable, even among patients with the same tumor type and treatment. Exceptional responders (ERs), who are individuals who experience unusually favorable outcomes, provide critical insights into the biological factors driving treatment success. While prior studies have highlighted the role of somatic changes, the contribution of germline rare variants remains underexplored. This study aimed to uncover the genetic underpinnings of exceptional responses by identifying rare, non-silent and predicted deleterious germline mutations enriched among ERs compared to typical cancer patients. Methods The Network of Enigmatic Exceptional Responders (NEER) project collected clinical and germline whole-genome sequencing (WGS) data from 53 ERs. After quality control procedures and ancestry background checks, 51 ERs were left for final analysis. While non-silent mutations were identified based on allele frequencies and mutation types, multiple pathogenicity predictors were applied for predicted deleterious variants. These were compared to a harmonized and comparable subset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort (n=414) using Fisher's exact tests. Kaplan-Meier survival analysis applied to evaluate prognostic associations in PCAWG patients. Additionally, Fisher's exact tests were conducted stratified by cancer type and treatment regimen to identify potential associations between rare germline variants and therapeutic responses. Results Variants in immune-related genes such as CCL26 and GPRC5D were prevalent, suggesting enhanced immune regulation among ERs. Fourteen genes with non-silent and eight with predicted deleterious mutations showed significantly different frequencies between NEER and PCAWG cohorts (FDR < 0.05). IRX3 emerged as a protective gene enriched in ERs, whereas OR6B2 was associated with poor survival in PCAWG lung cancer patients. Moreover, rare non-silent germline variants in drug target genes were enriched among ERs treated with cisplatin and doxorubicin, implicating altered DNA repair and drug-binding mechanisms in their remarkable outcomes. Conclusions This study reveals a distinctive germline mutation landscape in exceptional cancer responders, marked by immune-related and drug-target-associated variants that may enhance therapy response and prolong survival. The findings highlight potential novel prognostic biomarkers, such as IRX3 and OR6B2, providing a foundation for developing personalized cancer treatments informed by rare genetic variation.

Introduction: With recent approvals of multiple targeted therapies for triple-negative breast cancer (TNBC), including antibody-drug conjugates and immunotherapy in biomarker-selected populations, it is critical to define the temporal evolution of cell-surface target expression from early-stage to metastatic disease, the co-expression patterns across these markers, and optimal quantification methodologies. Here we report biomarker expression profiles measured by multi-omics and pathology-based platforms in patients with TNBC using a large cohort of matched longitudinal tumor samples. Methods: Patients who underwent neoadjuvant chemotherapy (NAC) for stage I-III TNBC or were diagnosed with any stage TNBC and developed metastatic recurrence were retrospectively identified from an institutional database and prospective research metastatic biopsy protocol. Tumor samples from diagnosis (DX), residual disease (RD) post-NAC (if applicable), and metastasis/recurrence (MR) were collected. Quantification of HER2, TROP2, and PD-L1 expression was performed by immunohistochemistry (IHC), whole-exome sequencing, transcriptome sequencing, and targeted mass spectrometry (MS). For HER2, TROP2, and stromal tumor-infiltrating lymphocytes (sTILs), both manual pathologist assessment and computational pathology quantification were obtained. HER2 status was categorized as HER2-0 or HER2-low by local (L-IHC) and central (C-IHC) review, TROP2 status was defined as low (H-score <100), medium (H-score 100-200) or high (H-score >200), and PD-L1 as low (tumor area positivity, TAP <5%) or high (TAP [&ge;]5%). Pathologist-assessed sTILs were classified as low (<10%), medium ([&ge;]10% and <40%) or high ([&ge;]40%). Biomarkers were compared between primary (DX/RD) and MR, and between pre- vs post-NAC (DX-RD) samples. Correlations between markers, quantification methods, inferred PAM50 subtype, and clinical variables of interest were evaluated. Results: A total of 359 samples from 110 patients with TNBC with data available from at least one platform were included in the analysis. HER2-low prevalence at DX, RD, and MR was: 51% (50/98), 40% (21/53), and 27% (16/60); TROP2 high/medium was 90% (47/52), 91% (42/46), and 88% (28/32); PD-L1-high was 51%, 50%, and 38% (9/24); and sTILs-high/medium was 88% (59/67), 80% (40/50), and 49% (17/35), respectively. While TROP2-high/medium vs low remained stable over time, HER2 IHC and sTILs significantly decreased from DX/RD to MR samples, both at the cohort-level (HER2, p=0.0081; sTILs, p=4.6x10e-5) and longitudinal patient-level (HER2, p=0.030; sTILs, p=0.0077), with a similar decreasing trend for PD-L1 that did not reach statistical significance. HER2 concordance (0 vs low) between L-IHC and C-IHC was 78% (91/116). ERBB2, TACSTD2 and CD274 mRNA expression were significantly correlated with IHC protein levels, though only TACSTD2 had limited overlap in distribution of gene expression between high/medium vs low groups. Strong correlation between protein membrane staining intensity from computational pathology, protein expression measured by MS, and pathologist-assed IHC was observed across all biomarkers tested by each method. In comparisons between biomarkers, pathologist-assessed PD-L1 IHC and sTILs were significantly correlated (p=0.0001); 94% (51/54) of PD-L1-high tumors were classified as sTILs high/medium. PAM50 subtype was not significantly correlated with time point or biomarker status, although there was a trend toward more HER2-enriched tumors in HER2-low (20%, 5/25) vs HER2-0 (6%, 3/52) (p=0.086). Across biomarkers and clinical variables, an association between age and sTILs was observed (p=0.038, FDR=0.42) due to a decrease in sTILs high/medium tumors with age, primarily driven by post-treatment (RD/MR) but not DX samples. Conclusions: Multi-platform and multi-omics profiling in this large unique cohort of longitudinal TNBC samples revealed distinct patterns of expression and dynamic changes of key biomarkers of interest for targeted therapies. Given variability with manual IHC scoring, improved methods for quantification of expression may help optimize treatment selection in an individualized manner.

Background Emerging evidence suggests irreversible electroporation (IRE) with standard-of-care (SOC) chemo-therapy may improve survival in patients with Stage 3 pancreatic ductal adenocarcinoma (PDAC) when compared to SOC alone. This study evaluates the overall survival (OS) and progression-free survival (PFS) of Stage 3 PDAC patients treated with SOC plus IRE with the NanoKnife System versus SOC alone. Methods This prospective, multicenter study included two cohorts from the DIRECT registry: an IRE cohort from sites offering IRE as part of clinical care, and a comparator SOC cohort of prospectively enrolled and contemporaneous retrospective patients. Enrollment spanned 08/05/2019 to 02/05/2023, with follow-up through at least 24 months, death, or loss to follow-up. Included were 137 patients (99 IRE; 38 SOC), aged [&ge;]18 years with Stage 3 PDAC and no progression after three months of SOC therapy. Results Median (interquartile range) time from diagnosis to enrollment was 8 (6-10) months for IRE and 4 (3-6) for SOC (p<0.0001). Median OS and PSF from enrollment were 18 (95% confidence interval [CI]: 15-24) months and 9 (95% CI: 7-12) months for IRE, and 10 (95% CI: 8-14) months and 6 (5-8) months for SOC, respectively (p<0.0001 and p=0.009). Adverse events occurred in 80% (79/99) of IRE patients and 95% (36/38) of SOC patients; 29% (29/99) of the IRE cohort experiencing an IRE-related adverse event. Conclusions IRE was associated with improved OS versus SOC alone and may be an effective consolidative treatment for Stage 3 PDAC after three months of induction chemotherapy.

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.

Assessment of pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) remains an unmet clinical need in breast cancer. Fibroblast activation protein inhibitor (FAPI) PET targets the tumor microenvironment and may therefore enhance response evaluation after NAC. This study aimed to compare the performance of [68Ga]Ga-FAPI-04 PET, [18F]FDG PET, and contrast-enhanced MRI for predicting pathologic response after NAC in breast cancer, with separate analyses for primary breast lesions and axillary lymph nodes. MethodsIn this prospective single-center diagnostic accuracy study, women with biopsy-confirmed stage II-III breast cancer underwent baseline and post-therapy [68Ga]Ga-FAPI-04 PET/MRI, [18F]FDG PET/CT, and contrast-enhanced MRI before surgery. Quantitative PET parameters were evaluated for primary tumors and axillary lymph nodes. pCR was defined as ypT0/isN0. Significant variables identified in univariable analyses were further explored using least absolute shrinkage and selection operator (LASSO) analysis, and receiver-operating-characteristic (ROC) analysis was performed to assess diagnostic performance. Fibroblast activation protein expression was also assessed by immunohistochemistry in paired pre- and post-therapy tumor specimens from a subset of patients. ResultsTwenty-four patients completed the study protocol, yielding 25 primary lesions and 44 metastatic lymph nodes across 27 axillary compartments. Overall patient-level pCR was achieved in 13 of 24 patients (54.17%). The lesion-level pCR rate was 60.00% (15/25) for primary breast lesions, and the node-level pCR rate was 72.73% (32/44) for axillary lymph nodes. For primary tumor response, post-therapy [68Ga]Ga-FAPI-04 SUVmax showed the highest diagnostic performance (AUC, 0.84; sensitivity, 80.00%; specificity, 80.00%; accuracy, 80.00%), whereas the optimal [18F]FDG parameter was {Delta} TBR% (AUC, 0.747). For nodal response, post-therapy [68Ga]Ga-FAPI-04 SULmean showed the highest diagnostic performance (AUC, 0.89; sensitivity, 91.67%; specificity, 81.25%; accuracy, 84.09%) and was significantly different from the best [18F]FDG parameter ({Delta} SULmax%, AUC, 0.669) on DeLong testing (P < 0.05). MRI achieved AUCs of 0.733 for primary lesions and 0.770 for lymph nodes. Stromal FAP expression positively correlated with [68Ga]Ga-FAPI-04 SUVmax and was markedly reduced in lesions achieving pCR. ConclusionPost-therapy [68Ga]Ga-FAPI-04 PET may serve as a promising adjunctive imaging biomarker for predicting pathologic response after NAC in breast cancer, particularly for axillary nodal assessment. These findings suggest that FAPI PET may provide clinically relevant information for preoperative evaluation of residual disease burden, potentially contributing to more individualized surgical planning and treatment decision-making.

Background: Immune-oncology has revolutionized cancer treatment, but some patients fail to benefit due to primary resistance and tumour-immune evasion. Extracellular vesicles (EVs) are secreted by both tumour and immune cells and mediate communication between cancer cells and the immune system. Our study used proteomic profiling of circulating EVs collected from NSCLC patients treated with immune checkpoint inhibitors (ICI) to identify predictive biomarkers of response as well as immune evasion mechanisms related to treatment resistance. Methods: EVs were isolated from plasma collected prior to ICI treatment using peptide-affinity purification and high-throughput proteomics was performed using Proximal Extension Assay. Differentially expressed EV proteins between durable (DR) and non-durable responders (NDR) were identified and evaluated using Cox proportional hazards regression, survival analysis, sex-stratified analysis, as well as pathway and network analysis. Results: Proteomics analysis identified 116 differentially expressed EV proteins between DR and NDR. NDR was characterized by enrichment of inflammatory, angiogenic, and immune-suppressive EV proteins, such as IL1RL1, TFRC, IL6ST, galectins, TNF superfamily death receptors, chemokines, and PCSK9. Pathway analysis revealed enrichment of angiogenesis, chemotaxis, ECM remodeling, and neutrophil degranulation associated with poor progression-free survival (PFS). In contrast, DR to ICI treatment was associated with EV proteins related to T- and B-cell activation and adaptive immunity. Sex-related differences in abundance and association with PFS was observed for certain EV proteins, including IL1RL1 and TFRC. A six protein EV model (IL1RL1, TFRC, ERI1, CCN5, IGFBPL1, and TNFRSF13C) demonstrated good prognostic performance for identifying NDR (AUC = 0.907) and stratified patients into three discrete risk groups. Conclusions: High-plex EV proteomics revealed biologically coherent tumour-immune signaling programs that are associated with ICI treatment resistance. Profiling circulating EVs may improve our understanding of EV-mediated immune evasion mechanisms and identify protein signatures that reflect the tumour immune microenvironment and predict response to immune checkpoint blockade.

Lung neuroendocrine tumours (lung NETs) are rare neoplasms comprising approximately 2% of lung cancers. Recent studies have identified distinct molecular groups based on transcriptome and methylome data, but genomic and morphological features remain underexplored due to limited whole-genome and imaging data. We have generated the largest multi-omic dataset of lung NETs to date (201 participants, for a total of n = 294 tumours), including RNA sequencing, EPIC 850K methylation arrays, and whole-genome sequencing. This multiomic dataset also include multi-regional whole-genome sequencing for 41 participants, allowing for the quantification of intra-tumoural heterogeneity. We additionally generated spatial proteomics (64 participants), spatial transcriptomics (4 participants) and whole-slide histopathology images for 212 cases. This dataset enables a comprehensive characterization of lung NET molecular groups and the identification of group-specific morphological features using deep learning algorithms. All quality control analyses, processed data, and scripts are provided to ensure reproducibility. This dataset is available as a basis for further molecular and morphological analysis of lung NETs, and for future research on multi-scale integration.

Background Complex gastrointestinal (GI) oncologic surgeries carry substantial perioperative risk, and nationwide outcomes in low- and middle-income countries (LMICs) are underreported. This study aimed to evaluate national trends in surgical volume, in-hospital mortality, and intensive care unit (ICU) utilization for major GI cancer surgery in Brazils Unified Health System (SUS) over a 14-year period. Methods A population-based analysis was performed using national administrative databases to identify all adult patients undergoing colectomy, gastrectomy, pancreatic resection or esophagectomy for cancer in the SUS from 2010-2023. Annual rates were age-standardized according to the WHO standard population. Temporal trends were assessed using Poisson regression to estimate average annual percent change (AAPC) with 95% confidence intervals (CIs). Results A total of 179,337 hospital admissions were analyzed (median age 63 years; 48% female). Colectomies accounted for 72% of cases, followed by gastrectomies (19%), pancreatic resections (5%), and esophagectomies (3%). Although crude surgical volume increased, population-adjusted rates declined overall (AAPC -2.09%; 95% CI -2.58 to -1.59), mainly due to reductions in gastrectomies and esophagectomies. Median hospital stay decreased from 9 to 7 days (AAPC -1.93%; 95% CI -2.79 to -1.06). Overall in-hospital mortality declined from 8.1% to 5.7% (AAPC -2.88%; 95% CI -4.15 to -1.59). ICU utilization rose from 37% to 43% of admissions (AAPC +1.31%; 95% CI 0.91 to 1.71). Conclusion Over 14 years, in-hospital mortality and length of stay for major gastrointestinal cancer surgery declined within Brazils universal public health system. These temporal trends occurred alongside expansion of accredited oncology services and increased ICU utilization, although causal relationships cannot be established from administrative data. These findings should be interpreted as hypothesis-generating and highlight the need for more granular hospital-level data in LMIC settings.

BackgroundBevacizumab resistance severely limits long-term efficacy in metastatic colorectal cancer (CRC). This study aimed to develop and validate a bevacizumab resistance-associated gene signature for prognosis prediction and immune microenvironment characterization in CRC. MethodsTwo GEO datasets (GSE19862, GSE86582) with bevacizumab response data and TCGA-COAD/READ RNA-seq data were analyzed. Overlapping differentially expressed genes (DEGs) linked to both CRC progression and bevacizumab resistance were identified. An 8-gene signature (AXIN2, PSORS1C1, KRT74, SLC2A3, STIL, IL33, GALNT6, HSD11B2) was constructed via univariate Cox and LASSO-Cox regression. ResultsIn the TCGA cohort, high-risk patients had shorter overall survival (OS; log-rank P < 0.0001). Time-dependent ROC yielded 1-year AUC = 0.638, 3-year AUC = 0.657, and 5-year AUC = 0.757. Multivariate Cox regression confirmed the risk score as an independent prognostic factor. External validation in GSE39582 (optimal cutoff = -1.49) replicated these findings: high-risk patients had inferior OS (P = 0.0016) with acceptable 1/3/5-year AUCs and retained independent prognostic value (HR = 1.634, P = 0.00415). CIBERSORT and ESTIMATE analyses showed that the high-risk group was characterized by increased M2 macrophages and neutrophils, higher immune and stromal scores, and reduced activated memory CD4+ T cells, monocytes, and activated dendritic cells (all P < 0.05). GSEA highlighted enrichment of TNF-/NF-{kappa}B, IL-6/JAK/STAT3, and immune checkpoint pathways in the high-risk group. AXIN2 (HR = 0.829, P = 0.032) was an independent protective factor, while PSORS1C1 (HR = 1.356, P = 0.048) was an independent risk factor. ConclusionThe 8-gene bevacizumab resistance signature robustly predicts prognosis and reflects an immunosuppressive microenvironment closely linked to bevacizumab failure in CRC. These findings provide novel insights into immune-mediated resistance and support clinical risk stratification.

Cancer-related fatigue is the most common and persistent symptom in breast cancer, with fatigue reported up to 10 years post-diagnosis. Unlike many cancers, fatigue in breast cancer often arises during early-stage disease in the absence of cachexia. While many factors contribute to fatigue, the direct contribution of cancer-associated skeletal muscle pathology remains poorly understood. Here we analyzed pectoralis major muscle biopsies from individuals with non-metastatic breast cancer and non-cancer controls. Using single-fiber morphometry and spatial transcriptomics, we identified fiber-type-specific structural alterations and spatially localized transcriptional reprogramming within the muscle microenvironment. Single-fiber morphometry revealed selective atrophy of oxidative type I and type IIa muscle fibers, while glycolytic type IIx fibers were relatively preserved. Concordant spatial transcriptomic profiling revealed suppression of oxidative metabolic programs, evidence of mitochondrial dysfunction, and spatially localized catabolic signaling originating from intramuscular adipocytes. This study introduces an integrated framework for profiling skeletal muscle architecture and spatially localized gene expression in surgically obtained muscle biopsies and represents the first application of spatial transcriptomics to human skeletal muscle from individuals with cancer. These findings demonstrate structural and metabolic remodeling of skeletal muscle in non-metastatic breast cancer and suggest targeting muscle metabolism represents a promising therapeutic strategy for cancer-related fatigue.