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.

Background: Cervical intraepithelial neoplasia (CIN) grades 2 and 3 are usually grouped and treated in the same way as "high grade", in spite of their different risk to cancer progression and spontaneous regression rates. CIN2-3 is usually diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. This procedure virtually eliminates the availability of water-soluble proteins which could have diagnostic and prognostic value. Aim: To investigate whether a water-soluble protein-saving biopsy processing method followed by a proteomic analysis of supernatant samples using LC-MS/MS (LTQ Orbitrap) can be used to distinguish between CIN2 and CIN3. Methods: Fresh cervical punch biopsies from 20 women were incubated in RPMI1640 medium for 24 hours at 4°C for protein extraction and subsequently subjected to standard FFPE processing. P16 and Ki67-supported histologic consensus review CIN grade (CIN2, n = 10, CIN3, n = 10) was assessed by independent gynecological pathologists. The biopsy supernatants were depleted of 7 high abundance proteins prior to uni-dimensional LC-MS/MS analysis for protein identifications. Results: The age of the patients ranged from 25-40 years (median 29.7), and mean protein concentration was 0.81 mg/ml (range 0.55 - 1.14). After application of multistep identification criteria, 114 proteins were identified, including proteins like vimentin, actin, transthyretin, apolipoprotein A-1, Heat Shock protein beta 1, vitamin D binding protein and different cytokeratins. The identified proteins are annotated to metabolic processes (36%), signal transduction (27%), cell cycle processes (15%) and trafficking/transport (9%). Using binary logistic regression, Cytokeratin 2 was found to have the strongest independent discriminatory power resulting in 90% overall correct classification. Conclusions: 114 proteins were identified in supernatants from fresh cervical biopsies and many differed between CIN2 and 3. Cytokeratin 2 is the strongest discriminator with 90% overall correct classifications.

Purpose: The proliferation marker Ki-67 has been used as a prognostic marker to separate low- and high-risk breast cancer subtypes and guide treatment decisions for adjuvant chemotherapy. The association of Ki-67 with response to tamoxifen therapy is unclear. High-throughput automated scoring of Ki-67 might enable standardization of quantification and definition of clinical cut-off values. We hypothesized that digital image analysis (DIA) of Ki-67 can be used to evaluate proliferation in breast cancer tumors, and that Ki-67 may be associated with tamoxifen resistance in early-stage breast cancer. Patients and Methods: Here, we apply DIA technology from Visiopharm using a custom designed algorithm for quantifying the expression of Ki-67, in a case–control study nested in the Danish Breast Cancer Group clinical database, consisting of stages I, II, or III breast cancer patients of 35– 69 years of age, diagnosed during 1985– 2001, in the Jutland peninsula, Denmark. We assessed DIA-Ki-67 score on tissue microarrays (TMAs) from breast cancer patients in a case–control study including 541 ER-positive and 300 ER-negative recurrent cases and their non-recurrent controls, matched on ER-status, cancer stage, menopausal status, year of diagnosis, and county of residence. We used logistic regression to estimate odds ratios and associated 95% confidence intervals to determine the association of Ki-67 expression with recurrence risk, adjusting for matching factors, chemotherapy, type of surgery, receipt of radiation therapy, age category, and comorbidity. Results: Ki-67 was not associated with increased risk of recurrence in tamoxifen-treated patients (ORadj =0.72, 95% CI 0.54, 0.96) or ER-negative patients (ORadj =0.85, 95% CI 0.54, 1.34). Conclusion: Our findings suggest that Ki-67 digital image analysis in TMAs is not associated with increased risk of recurrence among tamoxifen-treated ER-positive breast cancer or ER-negative breast cancer patients. Overall, our findings do not support an increased risk of recurrence associated with Ki-67 expression.

A subset of triple-negative breast cancer (TNBC) expresses the androgen receptor (AR), but thresholds for AR positivity and its clinical significance vary. We hypothesize that objective assessment outperforms subjective methods, and that high AR negatively impacts prognosis. In a population-based TNBC cohort (n = 198) with long follow-up (4–383 months), AR expression was evaluated via subjective scoring (AR-Manual) and automated digital image analysis (AR-DIA). A 10% cut-off value via AR-DIA was the strongest negative prognostic threshold for distant metastases (p = 0.008). High AR-DIA correlated with lower grade (p = 0.014), and lower proliferation (p = 0.004) but also with larger tumors (p = 0.047), distant metastasis (p = 0.052), and lymph node (LN) positivity (p < 0.001), highlighting its dual roles. Multivariate analysis revealed interaction between LN status and AR-DIA (p < 0.001) as the strongest prognostic factor, followed by fibrotic focus (FF; p = 0.009), mitotic activity index (MAI; p = 0.018), and stromal tumor-infiltrating lymphocytes (sTILs; p = 0.041). AR-DIA had no additional prognostic value in favorable subgroups but was significant in unfavorable subgroups. In high AR-DIA patients with unfavorable characteristics, ACT did not improve survival, and patients may benefit from AR-targeted therapy. Overall, the DIA method provides reproducibility, high AR-DIA (≥10%) shows opposing survival effects in different TNBC subgroups, and AR evaluation is crucial for prognosis and AR-targeted therapies.