The limited efficacy of immunotherapy in clinical trials in high‐grade serous ovarian cancer (HGSOC) may improve by implementing experimental models that are more reflective of human biology into preclinical studies. To address this, we developed and validated a humanized patient‐derived xenograft mouse model of HGSOC. Human hematopoietic stem cells and patient‐derived HGSOC cells were engrafted into immunodeficient mice. The mice were administered durvalumab (anti‐PD‐L1) and/or oleclumab (anti‐CD73) immunotherapy intraperitoneally twice a week for 5 weeks. The treatment showed good tolerability with no observed side effects, though it failed to elicit a measurable antitumor response. Leukocytes in primary tumors were analyzed immunohistochemically, and circulating T cells were characterized using spectral flow cytometry. All tumors exhibited an immune‐excluded immunophenotype. No significant inter‐group differences in disease burden, intratumoral leukocyte density, or circulating T cells were observed. In the durvalumab‐only group, tumor burden significantly positively correlated with intratumoral cytotoxic and regulatory T‐cell densities. This model reflects the immunotherapy resistance of human disease in line with clinical findings, providing a robust platform for studying tumor–immune interactions and immunosuppressive mechanisms in HGSOC. Impact statement Our results address the critical need for representative preclinical models for testing combination immunotherapy in HGSOC by providing a robust preclinical platform that can enhance the reliability of preclinical data and contribute to the improvement of the design and outcomes of future clinical trials.

Abstract The role of nerve innervation in cancer progression remains highly debated. While various in vitro and in vivo models suggest that tumor cells can actively induce neoneurogenesis, tissue-based evidence remains sparse. In this project, we critically examine the hypothesis of tumor-associated nerve innervation by seeking direct, tissue-based evidence of nerve innervation and sprouting in relation to histopathological tumor features.Our study cohort comprises diagnostic FFPE whole-tissue samples from six solid tumor types: breast cancer (Luminal A/B, HER2+, TNBC), non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma), colorectal cancer, pancreatic cancer (PDAC and periampullary adenocarcinoma), prostate cancer, and urinary bladder cancer.Nerve structures are identified via multiplexed immunofluorescence and multispectral imaging, targeting neurofilament light chain (NFL, 70 kDa) and growth-associated protein 43 (GAP43). To delineate the tumor microenvironment, additional markers include CD34 (perineural sheath), CD31 (endothelial cells), CD3 (T-cells), and pan-cytokeratin (tumor cells). We developed both a deep-learning-based algorithm and a thresholding approach to segment nerve fibers and characterize their spatial organization. A custom Python script quantifies nerve density (nerves/tumor area), nerve size (μm2), and the integrity of the perineural sheath via CD34 staining. In parallel, we are constructing 3D nerve reconstructions by aligning and analyzing 30 consecutive 4 μm sections using tailored Python tools.Perineural invasion, as defined as direct contact between tumor cells and nerves, is most frequently observed in tumors from highly innervated organs such as PDAC and prostate cancer, while appearing only sporadically in the other tumor types.In subsequent analyses, nerve features will be systematically correlated with histopathological tumor characteristics, microenvironmental profiles, and clinical data. Ultimately, this study aims to generate a comprehensive atlas of nerve-tumor interactions in human cancer and to delineate both shared and tumor-type-specific patterns of innervation and perineural invasion. Citation Format: Hui Yu, Aglaia Schiza, Victor Ponten, Viktoria Thurfjell, Amanda Lindberg, Julia Sidenius Johansen, Ulrike Segersten, Anca Dragomir, Bengt Glimelius, Artur Mezheyeuski, Astrid Børretzen, Yun-Fan Sun, Lars A. Akslen, Patrick Micke, Carina Strell. Nerve innervation in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 720.

Breast cancers are biologically and clinically diverse. While large-scale gene expression analyses have enabled epithelial-centered molecular classifications, studies of the tumor microenvironment (TME) remain limited, especially at the proteome level using tissue-specific resolution. By laser capture microdissection and mass spectrometry-based proteomics followed by unsupervised clustering of the stromal proteome, we discovered three patient subgroups. The largest cluster revealed the most discrete representation of stromal proteins, including a 35-protein (35P) panel linked to extracellular matrix biology, tumor progression programs, and increased abundance of tumor-associated macrophages (TAM) by single-cell profiling. Clinical validation of 35P, using whole tissue protein and mRNA values from different cohorts of ER+/HER2- breast cancer, including a large randomized controlled trial (STO), identified that more aggressive (or 'high-grade') stromal features were independent of current molecular subtypes. The 35P stromal panel may reflect important clinical information by improving patient stratification beyond current epithelial-based classification of breast cancer.

Mitotic count (MC) and Ki67 expression are well-known prognostic factors in breast cancer. Whereas MC is part of histologic grade but not always reported separately, Ki67 is hampered by lack of methodological standardization. Our aim was to assess how proliferation markers MC, Ki67, and PHH3 perform in prognostication based on different tissue categories (WS, whole sections; CNB, core needle biopsies; TMA, tissue microarrays), and how these markers can stratify hormone receptor positive/HER2 negative breast cancer. We examined a prospective population-based breast cancer series including 534 women (50–69 years) diagnosed during 1996–2003, with a median follow-up time of 13 years. MC, Ki67, and mitotic count by PHH3 were assessed in paired samples of WS (N = 534), CNB (N = 154) and TMA (N = 101). The level of MC in four additional and independent cohorts are presented for comparison (1598 cases). By univariate survival analysis, all proliferation markers showed significant prognostic impact. By multivariate models, MC and Ki67 demonstrated independent prognostic significance. In the luminal/HER2 negative subgroup, which is currently the primary target group for proliferation assessment, MC retained prognostic significance in multivariate analysis, whereas Ki67 and PHH3 did not. In whole sections, a mitotic count of 2.5 per mm2 corresponded to Ki67 of 20

In the growing tumor, hypoxia may lead to tumor necrosis which is associated with more aggressive tumor features and reduced patient survival. However, there are shortcomings in our understanding of biological features and clinical significance linked to tumor necrosis. We therefore analyzed transcriptome mRNA expression profiles from primary breast tumors from TCGA and METABRIC (n = 2289) including TCGA mutational data (n = 409). We employed bioinformatics analysis and independent literature-based signatures for validation to identify alterations unique to necrosis. Necrosis was associated with aggressive tumor features and strongly predicted the basal-like breast cancer phenotype. Increased tumor cell proliferation, hypoxia, stemness, and epithelial-to-mesenchymal transition (EMT) were observed in tumors with necrosis. From gene expression data, we constructed a novel Breast Cancer Necrosis Signature (BCNS) score that was a strong predictor of the basal-like phenotype but also conveyed information relevant to the luminal subtypes of breast cancer, including prognosis. Mutational profiling pointed to enrichment of TP53 and PIK3CA mutations in tumors with and without necrosis, respectively. This study confirms the association of breast cancer necrosis with aggressive tumor features and reduced survival, and points to the BCNS score as a potential biomarker that should be further explored and validated to improve breast cancer diagnosis and management.

Background: Organoid cultures have received much attention in recent years due to the promise of patient-derived organoid cultures for exploration of personalized cancer treatment strategies. Organoid cultures have been established from a variety of malignancies; however, lack of a thorough histopathological analysis has limited the acceptance of organoid models as translational tools. Methods: Here, we aimed to establish patient-derived tumor-organoid (PDTO) models from human non-small-cell lung cancer (NSCLC) resection specimens and provide a thorough histopathological evaluation of the cultures. Results: We show that we were able to establish organoid cultures of lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) successfully, and that the organoid cultures of different subtypes of NSCLC preserved the histoarchitecture and growth pattern of the tumors they derive from. Immunohistochemistry and AB-PAS staining confirmed the subtype-specific protein expression pattern and preserved mucin production in LUAD organoids. The genetic abnormalities of the tumors assessed by immunohistochemistry (IHC-P) were preserved in the organoid cultures. Conclusions: Our thorough study reveals conserved PDTO histopathology, supports further exploration, and encourages using PDTO models in translational research projects. PDTO models hold remarkable promise as patient-specific models and may be applied to predict therapy response in cases where molecular-pathological analyses pose significant management dilemmas, and they also may provide a platform for exploring the molecular mechanisms of therapy resistance in a biologically relevant model system.

IntroductionThe estrogen receptor (ER) is routinely assessed by immunohistochemistry (IHC) in breast cancer to stratify patients into therapeutic and prognostic groups. Pathology laboratories are burdened by an increased number of biopsies, and costly and resource-demanding molecular pathology analyses. Automatic, artificial intelligence-based prediction of biological properties from hematoxylin and eosin (HE)-stained slides could increase efficiency and potentially reduce costs at laboratories. The aim of this study was to develop a model for prediction of ER status from HE-stained tissue microarrays (TMAs). Our methodology can be used as proof-of-concept for the prediction of more complex and costly molecular analyses in cancer.MethodsIn this study, TMAs from more than 2,000 Norwegian breast cancer patients were used to train and predict ER status using the clustering-constrained attention multiple-instance learning (CLAM) framework. Two patch sizes were evaluated, multi-branch and single-branch CLAM configurations were compared, and a comprehensive hyperparameter search with more than 16 000 experiments was performed. The models were evaluated on internal and external test sets.ResultsOn the internal test set, the proposed model achieved a micro accuracy, a macro accuracy, and an area under the curve of 0.91, 0.86, and 0.95, respectively. The corresponding results on the external test set were 0.93, 0.76, and 0.91, respectively. Using larger patch sizes resulted in significantly better classification performance, while no significant differences were observed when changing CLAM configurations.

The prognosis for patients with melanoma loco-regional metastases is very heterogenous. Adjuvant PD-L1-inhibitors have improved clinical outcome for this patient group, but the prognostic impact of tumour PD-L1 expression and number of tumour infiltrating lymphocytes (TILs) is still largely unknown. Here, we investigated the impact on survival for CD3, CD8, FOXP3 and PD-L1 TIL counts and tumour PD-L1 expression in melanoma loco-regional metastases. In a patient series of loco-regional metastases from nodular melanomas (n = 78; n = 26 skin metastases, n = 52 lymph node metastases), expression of PD-L1 in tumour cells and the number of CD3, CD8, FOXP3 and PD-L1 positive TILs were determined by immunohistochemistry on tissue microarray (TMA) slides. Due to limited tumour tissue in the paraffin blocks, 67 of the 78 cases were included for tissue microarrays. Low FOXP3 TIL count and negative tumour PD-L1 expression (cut off 1%) were both significantly associated with reduced survival in lymph node metastases. Low FOXP3 TIL count was significantly associated with low CD8, CD3 and PD-L1 TIL counts. Negative tumour PD-L1 expression was significantly associated with low CD8 and PD-L1 TIL count, large lymph node metastasis tumour size and presence of necrosis in lymph node metastases. Our findings demonstrate for the first time the negative prognostic value of low FOXP3 TIL count and confirm a negative prognostic value of negative tumour PD-L1 expression in melanoma lymph node metastases.

Digital pathology enables automatic analysis of histopathological sections using artificial intelligence. Automatic evaluation could improve diagnostic efficiency and find associations between morphological features and clinical outcome. For development of such prediction models in breast cancer, identifying invasive epithelial cells, and separating these from benign epithelial cells and in situ lesions would be important. In this study, we trained an attention gated U-Net for segmentation of epithelial cells in hematoxylin and eosin stained breast cancer sections. We generated epithelial ground truths by immunohistochemistry, restaining hematoxylin and eosin sections with cytokeratin AE1/AE3, combined with pathologists' annotations. Tissue microarrays from 839 patients, and whole slide images from two patients, were used for training and evaluation of the models. The sections were derived from four breast cancer cohorts. Tissue microarray cores from a fifth cohort of 21 patients was used as a second test set. In quantitative evaluation, mean Dice scores of 0.70, 0.79, and 0.75 were achieved for invasive epithelial cells, benign epithelial cells, and in situ lesions, respectively. In qualitative scoring (0-5) by pathologists, the best results were reached for all epithelium and invasive epithelium, with scores of 4.7 and 4.4, respectively. Scores for benign epithelium and in situ lesions were 3.7 and 2.0, respectively. The proposed model segmented epithelial cells well, but further work is needed for accurate subclassification into benign, in situ, and invasive cells.

Background: Health-related quality of life (HRQoL) of breast cancer survivors has been extensively evaluated. However, HRQoL differences for women diagnosed by organized mammographic screening and women diagnosed due to symptoms have been sparsely described. We aimed to compare self-reported long-term HRQoL and quality adjusted life years (QALYs) between women with screen-detected breast cancer and women with symptomatic breast cancer, adjusting for histopathologic tumor characteristics and treatment. Methods: This study was nested within a cohort of women diagnosed with breast cancer by organized mammographic screening or due to symptoms 2006-2017 who responded a questionnaire measuring HRQoL (VAS, 0-100) and EQ-5D-5L 2019-2020. Responses to EQ-5D-5L were transformed into health utility values using a tariff based on preferences elicited in a national survey. Multivariable linear regression models were used to compare VAS-scores adjusting for tumor characteristics and treatment. QALYs were estimated by summing up the health utility values between the third and the fifth year since breast cancer diagnosis adjusting for breast cancer survival. Results: Mean HRQoL (VAS) was 66.2 (standard deviation, SD: 21.1) for women with screen-detected breast cancer (n = 1141) and 62.5 (SD: 21.2) for women with symptomatic breast cancer (n = 1561). Women with screen-detected breast cancer had 3.8 (95 % confidence interval, CI, 2.3, 5.4) and 3.7 (95 %CI 2.1, 5.2) higher HRQoL VAS-scores compared to women with symptomatic breast cancer in the models adjusted for tumor characteristics and treatment, respectively. Women with screen-detected breast cancer and women with symptomatic breast cancer accrued 2.30 and 2.06 QALYs, respectively. Conclusion: Women with screen-detected breast cancer demonstrated higher estimates of long-term HRQoL and QALYs compared to women with symptomatic cancer. Policy Summary: More favorable long-term quality of life outcomes were shown for women diagnosed with breast cancer by organized mammographic screening compared to women diagnosed due to symptoms.

To retrospectively evaluate the performance of a CE-marked AI system for identifying breast cancer on screening mammograms. Evidence from large retrospective studies is crucial for planning prospective studies and to further ensure safe implementation. We used data from screening examinations performed from 2004 to 2021 at ten breast centers in BreastScreen Norway. In the standard independent double reading setting, each radiologist scored each breast from 1 (negative) to 5 (high probability of cancer). The AI system assigned each examination an NT and an SN score; the NT score aimed to classify examinations as negative with minimal misclassification while the SN score aimed to classify examinations as positive with high confidence. N70 was defined as being among the 70% with the lowest NT score and P3 was defined as being among the 3% with the highest SN score. A total of 1,017,208 screening examinations were included in the study sample. At N70, 1.8% (107/5977) of the screen-detected and 34.5% (625/1812) of the interval cancers were defined as negative. Using P3 to define cases as positive, 81.5% (4871/5977) of the screen-detected and 19.0% (344/1812) of the interval cancers were defined as positive. Among the screen-detected cancers in N70, 11.2% (12/107) had an interpretation score > 2 by both radiologists. The AI system performed well according to identifying negative cases and cancer cases. Thus, the AI system can be used to reduce workload for the radiologists and potentially increase the sensitivity of mammography. Question Results from large mammography screening samples not used in training AI algorithms are important to consider when planning prospective studies and implementation. Findings More than 80% of the screening-detected cancers were classified as positive by AI when considering 3% of the examinations with the highest AI risk score as positive. Clinical relevance A lack of radiologists is a challenge in mammographic screening. Our findings support other studies that suggest the use of AI to reduce screen-reading workload.

Whole slide imaging has transformed the field of pathology by enabling high-resolution digitization of histopathological slides. However, the large image size and variability in morphology, tissue processing, and imaging can pose challenges for robust computational analysis. When working with specific tasks in digital pathology, conventional feature extractors pretrained on general images may not provide features as relevant as those trained on histopathological images. To address this, foundation models pretrained on histopathological images have been developed. Yet, their large size and computational demands might limit widespread adoptions to specific tasks. To facilitate the low-cost adoption of these models, we utilized low-rank adaptation for finetuning the model and developed evolving prototype-based multiple instance learning (EP-MIL). Our method’s capabilities were demonstrated by applying it to the classification of two histological subtypes of lung cancer. The results show that our approach achieves competitive performance when benchmarked against a state-of-the-art technique (CLAM), while offering improvements in efficiency. Specifically, our proposed method requires 8.3 times less training runtime compared with CLAM, uses less than 200.0 MB of memory during training, and enables 73.8 times faster inference runtime. These efficiency gains, combined with competitive performance, suggest that utilizing evolving prototypes with LoRA-tuned foundation models offers a more efficient and practical approach for broader use of foundation models in resource-constrained clinical settings.

The increased workload in pathology laboratories today means automated tools such as artificial intelligence models can be useful, helping pathologists with their tasks. In this paper, we propose a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas and an augmentation method that can improve classification results. The proposed model is a fused combination of truncated pre-trained DenseNet201 and ResNet101V2 as a patch-wise classifier, followed by a lightweight U-Net as a refinement model. Two datasets (Norwegian Lung Cancer Biobank and Haukeland University Lung Cancer cohort) were used to develop the model. The DRU-Net model achieved an average of 0.91 Dice similarity coefficient. The proposed spatial augmentation method (multi-lens distortion) improved the Dice similarity coefficient from 0.88 to 0.91. Our findings show that selecting image patches that specifically include regions of interest leads to better results for the patch-wise classifier compared to other sampling methods. A qualitative analysis by pathology experts showed that the DRU-Net model was generally successful in tumor detection. Results in the test set showed some areas of false-positive and false-negative segmentation in the periphery, particularly in tumors with inflammatory and reactive changes. In summary, the presented DRU-Net model demonstrated the best performance on the segmentation task, and the proposed augmentation technique proved to improve the results.

Introduction:Breast cancer remains a major health challenge due to its molecular heterogeneity and complex interactions with the tumor microenvironment. Adrenergic signaling, mediated by stress hormones such as noradrenaline, has emerged as a potential regulator of cancer progression, influencing cell proliferation, cell adhesion, migration, and invasion. Methods:This study investigates the effects of adrenergic modulation on breast cancer spheroids from basal-like (MDA-MB-231, BT549) and luminal-like (T47D, MCF7) cell lines, using 3D culture systems as a more physiologically relevant model compared to traditional 2D monolayer cultures. The 3D spheroid model better recapitulates the structural complexity of tumors, providing insights into cell-cell and cell-matrix interactions that influence signaling pathways and drug responses. Results:Noradrenaline treatment significantly reduced spheroid size, invasion capacity, and the expression of EMT-related markers and integrins in MDA-MB-231 cells. These effects were partially reversed by propranolol, a non-selective beta-adrenergic receptor antagonist. Luminal-like spheroids, characterized by low ADRB2 abundance, displayed limited responsiveness to adrenergic modulation. Proteomic analysis revealed distinct subtype-specific responses, with basal-like spheroids showing pronounced alterations in pathways related to proliferation, cytoskeletal dynamics, epithelial-mesenchymal transition, and metabolism, whereas luminal-like spheroids exhibited minimal changes. Discussion:Our findings reveal heterogeneity in adrenergic receptor signaling across basal-like and luminal-like breast cancer cell lines, and also within the basal-like subgroup. This diversity underscores the complexity of adrenergic signaling in breast cancer and highlights the advantages of 3D culture systems. These results provide valuable insights into the subtype-specific patterns of response to adrenergic signaling that contribute to tumor progression and may inform future studies including evaluation of therapeutic strategies.

BACKGROUND:Gene expression profiling tests such as the Prosigna-assay are used to aid adjuvant treatment decisions in hormone receptor positive (HR+) HER2 negative (HER2-) early breast cancer (EBC). In this evaluation, the cost-effectiveness of Prosigna against immunohistochemical (IHC) markers including Ki-67, was evaluated from the Norwegian healthcare- and societal perspective. MATERIALS AND METHODS:The treatment decision impact of Prosigna was tested in the prospective, observational EMIT-1 trial. Using individual data collected the first 12 months post-surgery, a decision model was built to project the economic consequences of using the Prosigna compared to IHC-markers for the adjuvant treatment decisions. Health benefits were measured by cost per quality-adjusted life-years (QALYs) and data on income and welfare benefit was obtained from Statistics Norway. RESULTS:Of 2,178 HR+/HER2- pN0 EBC patients in the EMIT-1 trial, 1,985 had available health economic data and 1,850 had complete income and welfare benefit records. Including all pN0 patients in the Prosigna-test strategy, the test was above the cost-effective threshold (€26,000; incremental cost-per QALY gained (ICER) €255,622) in a healthcare sector perspective. Incorporating also productivity costs, Prosigna was cost-saving (ICER €-435,677). Restricting Prosigna-testing to patients assessed as clear/uncertain chemotherapy candidates, the strategy was cost-effective in both the healthcare and societal perspective (ICER €8884 and €-620170, respectively). CONCLUSIONS:Using the Prosigna-assay for all HR+/HER2- pN0 EBC patients was not cost-effective from a healthcare perspective, but from the societal perspective it was cost-saving. Selecting patients who are clear/uncertain candidates for chemotherapy based on IHC-classification, Prosigna is cost-effective from both perspectives.

The presence of cancer stem cells is linked to aggressive disease and higher risk of recurrence, and multiple markers have been proposed to detect cancer stem cells. However, a detailed evaluation of the expression patterns and the prognostic value of markers relevant for endometrial cancer is lacking. As organoid models are suggested to be enriched in cancer stem cells, such models may prove valuable to define tissue-specific cancer stem cells. To address this, imaging mass cytometry and multiplex single-cell analyses were performed on an endometrial cancer patient series including both tumor biopsies and corresponding patient-derived organoids. An antibody panel focused on cancer stem cell markers was used to identify cancer stem cell phenotypes. Over 70% of epithelial cells in the tumor biopsies expressed at least one putative cancer stem cell marker. We identified distinct cancer cell phenotypes with heterogeneous expression within individual patients and between patient samples. Few differences in the distribution of cancer cell phenotypes were observed between tumor biopsies and corresponding organoids. Cells expressing aldehyde dehydrogenase 1 (ALDH1) were more prevalent in high-grade tumors, while expression of CD44 was more prevalent in grade 1 tumors. Spatial analysis revealed significantly less interaction between ALDH1- and CD44-expressing cells. Gene expression data was used to further investigate selected markers. CD44 gene expression was associated with a favorable prognosis and was further validated using immunohistochemistry. High expression of CD44 was significantly associated with better survival. The general high expression of proposed stem cell markers may indicate alternative roles for these in endometrial cancer.

Syndecans are transmembrane proteins involved in inflammation and signaling pathways. Their potential role as pre-diagnostic biomarkers for breast cancer risk remains unexplored. This study aimed to investigate whether pre-diagnostic serum syndecan levels are associated with breast cancer risk in a population-based cohort. We conducted a case-cohort study nested within the Tromsø Study (Norway), including women who participated in the fifth survey (2001). Women with incident breast cancer (cases, n = 158) through 2022 were identified, with a random sub-cohort of 708 women. Serum levels of syndecan-1 (SDC1) and syndecan-4 (SDC4) were measured using ELISA on frozen serum samples obtained in 2001. All participants were stratified into quartiles (Q1–Q4) based on pre-diagnostic levels. Cox proportional hazards regression models assessed associations between serum syndecan levels and breast cancer risk. The median age at diagnosis was 69 years for cases, and 83.3

In breast cancer (BC), the transcription factor GATA3 is linked to estrogen receptor (ER) alpha biology, and its loss is associated with aggressive tumor features. Little is reported about potential roles and implications of GATA3 independent of ER, and possible relationships to the BC tumor microenvironment (TME) have not been much explored. Thus, the discovery of novel biomarkers potentially linked to ER and GATA3 functions and predicting aspects of the TME could significantly improve precision in the management of patient subgroups. We examined GATA3 protein and mRNA expression in a large in-house population-based BC series (n = 837), and in the METABRIC datasets (METABRIC Discovery, n = 997 and METABRIC Validation, n = 995). Associations with primary BC phenotypes, transcriptional programs, TME features, clinical outcomes, and potentially independent roles of GATA3 are reported. We find that low GATA3 expression associates with aggressive features like increased tumor diameter, higher histological grade, triple negative BC, and a basal-like (CK5/6 positive) phenotype. Low GATA3 mRNA expression associated with downregulation of ER-related genes, upregulation of transcriptional signatures reflecting hypoxia, and enrichment of gene sets reflecting tumor cell proliferation, epithelial-mesenchymal transition, and stemness. Low GATA3 protein and mRNA expression both associated with overall reduced BC-specific survival. Notably, low GATA3 expression strongly associated with upregulation of immune checkpoint markers, T-cell activation, and metabolic alterations not previously described in BC. Gene expression patterns underlying GATA3-low tumors, independent of ER status, reflected activation of immunological and metabolic processes. This study suggests that GATA3 might influence the TME independent of ER status. Our results point to metabolic and immunophenotypic alterations in GATA3-low BCs, in particular with T-cell activation and increased expression of immune checkpoints. These findings could be relevant for patient selection in the context of immunotherapies and potential targeting of metabolic pathways.

The incidence of breast cancer in young women (aged under 40) is on the rise and is associated with more aggressive tumor characteristics and lower survival rates. Breast cancer is most frequently diagnosed in the sixth decade, and most research presents results based on data from older patients. By using large-scale clinico-pathologic and transcriptomic data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1932), we aimed to explore age-related differences in treatment, tumor characteristics, and gene expression signatures. Young patients presented more aggressive clinico-pathologic features such as higher histological grade, more frequent lymph node metastasis involvement, and estrogen receptor negativity. Accordingly, age below 40 years was associated with lower mRNA expression of the estrogen- and progesterone receptors, encoded by ESR1 and PGR, a higher proportion of the basal-like subtype, and increased transcription patterns reflecting stemness. Young breast cancer patients showed reduced survival, also within the basal-like subtype. We observed age-related differences in treatment, with more patients receiving chemotherapy among the young. Our results confirm a more challenging disease in young patients with breast cancer despite the more abundant use of chemotherapy. This argues for increased attention to young patients in current management and future research in breast cancer.

Androgen receptor (AR) is reported to be expressed in the majority of breast cancers (BC) with different expression rates across breast cancer subtypes. AR might influence cell proliferation, DNA damage repair, apoptosis, cellular migration and metastasis. The prognostic role of AR varies significantly across breast cancer subtypes. Here, we studied the expression pattern and prognostic relevance of AR in BC, with special focus on molecular stratification. We analyzed a large population-based cohort of breast carcinomas with long and complete follow up. AR expression was evaluated by immuno-histochemistry on tissue microarray slides. Loss of AR was significantly associated with features indicative of poor prognosis (larger tumor diameter, higher histologic grade, estrogen (ER) and progesterone hormone (PR) receptor negativity, elevated proliferation by Ki67, and expression of basal markers). Also, expression of AR was associated with lower proliferation by Ki67 regardless of ER-status. Regarding survival, loss of AR was significantly correlated with decreased breast cancer-specific survival in univariate analysis, both in the overall cohort and within the Luminal A and basal-like (CK5/6+) subgroups. In summary, AR expression emerged as a favorable prognostic factor in breast cancer, demonstrating independent prognostic significance within the basal-like (CK5/6+) subgroup.