Age-Related Clusters and Favorable Immune Phenotypes in Young Breast Cancer Patients.

Breast cancer (BC) patients aged <40 years at diagnosis experience aggressive disease and poorer survival compared with women diagnosed with BC at 40 to 49 years, but the age-related biology is described to little extent. Here, we explored transcriptional alterations in BC to gain better understanding of age-related tumor biology. We studied a subset of the Bergen in-house cohort (n = 127; age range, 26-49 years) and used the NanoString Breast Cancer 360 expression panel on formalin-fixed paraffin-embedded BC tissue, and publicly available global BC messenger RNA expression data (n = 204; age range, 22-49 years), to explore differentially expressed genes between the young (age <40 years) and older (age 40-49 years) patients. Unsupervised hierarchical clustering was applied to identify gene expression-based patient clusters. We applied established computational approaches to define the PAM50 subtypes, risk of recurrence scores (ROR), and risk groups and to infer the proportions of 22 immune cell types from bulk gene expression profiles of patients aged <50 years at BC diagnosis. Differentially expressed genes and gene sets were investigated using OncoEnrichR and g:Profiler to describe functional profiles and pathway enrichment. We identified 4 age-related patient clusters presenting distinct characteristics of PAM50 subtypes and ROR profiles, which demonstrated independent prognostic value when adjusted for traditional clinicopathologic variables and the known molecular subtypes. Our findings showed better survival than expected in the basal-enriched cluster 2 and in triple-negative and basal-like BC. Deconvolution analyses of immunophenotypes indicated higher levels of M0 and M1 macrophages than M2 macrophages in subsets of young BC. Our approach identifies age-based patient clusters with distinct clinicopathologic profiles, to a large extent overlapping with the PAM50 subtypes, although with independent prognostic values in multivariate survival analyses. The patient clusters provided new insight in the immune cell distribution across tumor subtypes, potentially contributing to survival differences between the clusters and the molecular subtypes and indicating age-related mechanisms improving outcome. Our study confirms the applicability of ROR as a valid prognosticator also in a young BC cohort.

Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study.

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.

[Breast cancer in men ­ histopathology and biomarkers]. / Brystkreft hos menn ­ histopatologi og biomarkører.

Background: Breast cancer in men accounts for around 1 % of all cases of the disease. The study aimed to identify histopathological parameters and selected biomarkers in men with breast cancer. Material and method: Retrospective study of archival material from 53 men diagnosed with breast cancer at the department of pathology, Haukeland University Hospital, in the period 1996-2020. The prevalence of the oestrogen receptor (ER), progesterone receptor (PGR) and Human Epidermal Growth Factor (HER2) biomarkers was examined. Results: Median age at time of diagnosis was 72 years. Median tumour diameter was 24 mm. Forty-nine tumours were classified histologically as invasive carcinoma of no special type (NST), 29 tumours were histologic grade 2 and 18 were grade 3. Fifty-two tumours were ER positive, 39 were PGR positive and four were HER2 positive. Twenty-five patients had lymph node metastases. Interpretation: Our findings indicate that men with breast cancer are diagnosed at an older age than women, and that men have a more advanced stage than women at the time of diagnosis. The histopathology and expression of biomarkers of breast cancer differ between men and women.

MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer.

OBJECTIVES: To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC). METHODS: Preoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model. RESULTS: iTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD < 6 mm with OR of 5.8 (p < 0.001) and lower figures for DOI ≥ 5 mm (OR = 2.8, p = 0.01), APD ≥ 17 mm (OR = 2.8, p < 0.001) and iDMI (OR = 1.1, p = 0.82). Patients with iTFD < 6 mm also had significantly reduced progression-free survival with hazard ratio of 2.4 (p < 0.001). CONCLUSION: For predicting pDMI, iTFD yielded best diagnostic performance and iTFD < 6 mm outperformed other cutoff-based imaging markers and conventional subjective assessment of deep myometrial invasion (iDMI) for diagnosing pDMI. Thus, iTFD at MRI represents a promising preoperative imaging biomarker that may aid in predicting pDMI and high-risk disease in EC.

Aneuploidy related transcriptional changes in endometrial cancer link low expression of chromosome 15q genes to poor survival.

Aneuploidy is a widely studied prognostic marker in endometrial cancer (EC), however, not implemented in clinical decision-making. It lacks validation in large prospective patient cohorts adjusted for currently standard applied prognostic markers, including estrogen/progesterone receptor status (ER/PR). Also, little is known about aneuploidy-related transcriptional alterations, relevant for understanding its role in EC biology, and as therapeutic target.We included 825 EC patients with available ploidy status and comprehensive clinicopathologic characterization to analyze ploidy as a prognostic marker. For 144 patients, gene expression data were available to explore aneuploidy-related transcriptional alterations.Aneuploidy was associated with high age, FIGO stage and grade, non-endometrioid histology, ER/PR negativity, and poor survival (p-values<0.001). In patients with ER/PR negative tumors, aneuploidy independently predicted poor survival (p=0.03), lymph node metastasis (p=0.007) and recurrence (p=0.002). A prognostic 'aneuploidy signature', linked to low expression of chromosome 15q genes, was identified and validated in TCGA data.In conclusion, aneuploidy adds prognostic information in ER/PR negative EC, identifying high-risk patients that could benefit from more aggressive therapies. The 'aneuploidy signature' equally identifies these aggressive tumors and suggests a link between aneuploidy and low expression of 15q genes. Integrated analyses point at various dysregulated pathways in aneuploid EC, underlining a complex biology.