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 In colon cancer, the location and density of tumor-infiltrating lymphocytes (TILs) can classify patients into low and high-risk groups for prognostication. While a commercially available ‘Immunoscore®’ exists, the incurred expenses and copyrights may prevent universal use. The aim of this study was to develop a robust and objective quantification method of TILs in colon cancer. Methods A consecutive, unselected series of specimens from patients with colon cancer were available for immunohistochemistry and assessment of TILs by automated digital pathology. CD3 + and CD8 + cells at the invasive margin and in tumor center were assessed on consecutive sections using automated digital pathology and image analysis software (Visiopharm®). An algorithm template for whole slide assessment, generated cell counts per square millimeters (cells/mm2), from which the immune score was calculated using distribution volumes. Furthermore, immune score was compared with clinical and histopathological characteristics to confirm its relevance. Results Based on the quantified TILs numbers by digital image analyses, patients were classified into low (n = 83, 69.7%), intermediate (n = 14, 11.8%) and high (n = 22, 18.5%) immune score groups. High immune score was associated with stage I–II tumors (p = 0.017) and a higher prevalence of microsatellite instable (MSI) tumors (p = 0.030). MSI tumors had a significantly higher numbers of CD3 + TILs in the invasive margin and CD8 + TILs in both tumor center and invasive margin, compared to microsatellite stable (MSS) tumors. Conclusion A digital template to quantify an easy-to-use immune score corresponds with clinicopathological features and MSI in colon cancer.

In urothelial cell type non-muscle invasive urinary bladder carcinoma, TNM stage and WHO grade are widely used to classify patients into low and high-risk groups for prognostic and therapeutic decision-making. However, stage and grade reproducibility and prediction accuracy are wanting. This may lead to suboptimal treatment. We evaluated whether proliferation features, nuclear area of the epithelial cancer cells and the composition of stromal and tumor infiltrating lymphocytes have independent prognostic value. In 183 primary non-muscle invasive bladder cancer patients with long follow-up (median for stage progression cohort: 119 months, range 5-173; median for tumor recurrence cohort: 82, range 3-165) proliferation features Ki67, PPH3 and Mitotic Activity Index (MAI), Mean Nuclear Area (MNA), lymphocyte subsets (CD8+, CD4+, CD25+) and plasma cells (CD138+) were assessed on consecutive sections. Post-resection instillation treatments (none, mitomycin, BCG) were strictly standardized during the intake period. Risk of recurrence was associated with expression of Ki67 (� 39 vs. > 39) and Multifocality (p = 0.01). Patients with low Ki67 had a higher recurrence rate than those with high Ki67. Lymphocyte composition did not predict recurrence. Stage progression was strongly associated with high values for MAI (>15) and CD25+ (>0.2%). In a multivariate analysis the combination of MAI and CD25+ was the single most prognostic feature (p<0.001). Validation of these results in additional, independent studies is warranted.

The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra-and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under-or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low-and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.INDEX TERMS Automated cancer grading, bladder cancer, convolutional neural networks, multiscale classification, urothelial carcinoma, weakly labelled data, whole-slide image.

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are rare epithelial neoplasms. Grading is based on mitotic activity or the percentage of Ki67-positive cells in a hot spot. Routine methods have poor intraobserver and interobserver consistency, and objective measurements are lacking. This study aimed to evaluate digital image analysis (DIA) as an objective assessment of proliferation markers in GEP-NENs. A consecutive cohort of patients with automated DIA measurement of Ki67 (DIA Ki67) and phosphohistone H3 (DIA PHH3) on immunohistochemical slides was analyzed using Visiopharm image analysis software (Hoersholm, Denmark). The results were compared with the Ki67 index from routine pathology reports (pathology Ki67). The study included 159 patients (57% males). The median pathology Ki67 was 2.0% and DIA Ki67 was 4.1%. The interclass correlation coefficient of the DIA Ki67 compared with the pathology Ki67 showed an excellent agreement of 0.96 [95% confidence interval (CI): 0.94-0.96]. The observed kappa value was 0.86 (95% CI: 0.81-0.91) when comparing grades based on the same methods. PHH3 was measured in 145 (91.2%) cases. The observed kappa value was 0.74. (95% CI: 0.65-0.83) when comparing grade based on the DIA PHH3 and the pathology Ki67. The DIA Ki67 shows excellent agreement with the pathology Ki67. The DIA PHH3 measurements were more varied and cannot replace other methods for grading GEP-NENs.