Implementation of high-risk human papilloma virus (HPV) screening and the increasing proportion of HPV vaccinated women in the screening program will reduce the percentage of HPV positive women with oncogenic potential. In search of more specific markers to identify women with high risk of cancer development, we used RNA sequencing to compare the transcriptomic immune-profile of 13 lesions with cervical intraepithelial neoplasia grade 3 (CIN3) or adenocarcinoma in situ (AIS) and 14 normal biopsies from women with detected HPV infections. In CIN3/AIS lesions as compared to normal tissue, 27 differential expressed genes were identified. Transcriptomic analysis revealed significantly higher expression of a number of genes related to proliferation, (CDKN2A, MELK, CDK1, MKI67, CCNB2, BUB1, FOXM1, CDKN3), but significantly lower expression of genes related to a favorable immune response (NCAM1, ARG1, CD160, IL18, CX3CL1). Compared to the RNA sequencing results, good correlation was achieved with relative quantitative PCR analysis for NCAM1 and CDKN2A. Quantification of NCAM1 positive cells with immunohistochemistry showed epithelial reduction of NCAM1 in CIN3/AIS lesions. In conclusion, NCAM1 and CDKN2A are two promising candidates to distinguish whether women are at high risk of developing cervical cancer and in need of frequent follow-up.

Background: European treatment guidelines for pTa and pT1 urinary bladder urothelial carcinoma depend highly on stage and WHO-grade. Both the WHO73 and the WHO04 grading systems show some intra- and interobserver variability. The current pilot study investigates which histopathological features are especially sensitive for this undesired lack of reproducibility and the influence on prognostic value. Methods: Thirty-eight cases of primary non-muscle invasive urothelial carcinomas, including thirteen cases with stage progression, were reviewed by three pathologists. Thirteen microscopic features were extracted from pathology textbooks and evaluated separately. Reproducibility was measured using Gwet’s agreement coefficients. Prognostic ability regarding progression was estimated by the area under curve (AUC) of the receiver operating characteristics (ROC) function. Results: The best reproducible features (Gwet’s agreement coefficient above 0.60) were papillary architecture, nuclear polarity, cellular maturation, nuclear enlargement and giant nuclei. Nucleoli was the strongest prognostic feature, and the only feature with an AUC above 0.70 for both grading systems, but reproducibility was not among the strongest. Nuclear polarity also had prognostic value with an AUC of 0.70 and 0.67 for the WHO73 and WHO04, respectively. The other features did not have significant prognostic value. Conclusions: The reproducibility of the histopathological features of the different WHO grading systems varied considerably. Of all the features evaluated, only nuclear polarity was both prognostic and significantly reproducible. Further validation studies are needed on these features to improve grading of urothelial carcinomas.

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Background: The major cause of cervical intraepithelial neoplasia (CIN) is persistent infection with human papillomavirus (HPV). Most CIN grade 2 and 3 lesions are treated with cone excision, although a substantial proportion (6-50%) of CIN2-3 lesions will regresses spontaneously. Predictors for regression of CIN2-3 are desirable in order to reduce this overtreatment. Methods: In this prospective cohort study, 145 consecutive women with first-time onset CIN2-3 in colposcopy-directed biopsies and standardized biopsy-cone excision interval were included. The genotype of the high-risk human papillomaviruses (=hr HPV) and clinical factors including sexual behaviour, parity, contraception and smoking were assessed. Patients were divided into two groups according to lesions containing HPV16 (hr HPV16+) and high-risk non-HPV16 (hr HPV16-) genotypes. Results: Women whose partners consistently used condoms showed a significantly higher regression rate than women using other types of contraception (53% versus 13%, p<0.0001). However, this effect was only seen in hr HPV16- patients (73% regression rate versus 13%, p<0.0001). Hr HPV16+ patients had a significantly higher number of sexual partners and more current smokers compared to hr HPV16- patients. The regression rate was not significantly different in CIN2-3 lesions containing HPV16 (hr HPV16+) versus hr HPV16- genotypes. Conclusions: Heterogeneity among hr HPV genotypes excists. HPV-genotype analyses can identify women who significantly increase their chance of regression by consistent condom use.

Purpose: Prognostic comparison of phosphohistone-H3 (PPH3) with Cytokeratin 5/6 and/or 14 positive (= basal-CK), triple (ER, PR, HER2)-negative (= TNP) and basal-like (= TNP and basal-CK positive) phenotype in invasive breast cancers.Patients and Methods: Classical variables, PPH3, ER, PR, basal-CK and HER2 in 240 T₁–2N₀M₀ patients under 71 years.Results: TNP and basal-like cancers had higher PPH3 expression than the other cancers (mean 48 versus 11, P < 0.001). Fifteen percent of the patients in the whole group, but 32–38% of TNP and basal-like cancers recurred.With multivariate analysis, PPH3 < 13 (n = 156) versus ≥ 13 (n = 84 = 35% of all cases) was the strongest and only prognosticator (10-year survival 96% and 64%, P ≤ 0.001, Hazard ratio = 9.0).Conclusion: PPH3 is the strongest prognosticators in luminal, Triple negative and basal-like T₁–2N₀M₀ invasive breast cancers.

Background/Objectives: Breast cancer (BC) is the most commonly diagnosed cancer in women and the leading cause of cancer-related deaths globally. Molecular subtyping is crucial for prognosis and treatment planning, with immunohistochemistry (IHC) being the most commonly used method. However, IHC has limitations, including observer variability, a lack of standardization, and a lack of reproducibility. Gene expression profiling is considered the ground truth for molecular subtyping; unfortunately, this is expensive and inaccessible to many institutions. This study investigates the potential of an artificial intelligence (AI) model to predict BC molecular subtypes directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). Methods: A pretrained deep learning framework based on multiple-instance learning (MIL) was validated on the Stavanger Breast Cancer (SBC) dataset, consisting of 538 BC cases. Three classification tasks were assessed, including two-class [triple negative BC (TNBC) vs. non-TNBC], three-class (luminal vs. HER2-positive vs. TNBC), and four-class (luminal A vs. luminal B vs. HER2-positive vs. TNBC) groups. Performance metrics were used for the evaluation of the AI model. Results: The AI model demonstrated strong performance in distinguishing TNBC from non-TNBC (AUC = 0.823, accuracy = 0.833, F1-score = 0.824). However, performance declined with an increasing number of classes. Conclusions: The study highlights the potential of AI in BC molecular subtyping from H&E WSIs, offering an easily applicable and standardized method to IHC. Future improvements should focus on optimizing multi-class classification and validating AI-based methods against gene expression analyses for enhanced clinical applicability.