BACKGROUND: The World Health Organization Classification (WHO) of Urinary and Male Genital Tumors has recently been updated to its 5th edition. The new edition presents a comprehensive approach to the classification of urinary and male genital tumors with an incorporation of morphologic, clinical, and genomic data.
OBJECTIVE: This review aims to update the new classification of bladder cancer in the 5th edition and to highlight important changes in nomenclatures, diagnostic criteria, and molecular characterization, as compared to the 4th edition.
METHODS: The pathologic classification of bladder cancer in the 5th edition of WHO Classification of Urinary and Male Genital Tumours was compared to that in the 4th edition. PubMed was searched using key words, including bladder cancer, WHO 1973, WHO 1998, WHO 2004, WHO 2016, histology, pathology, genomics, and molecular classification in the time frame from 1973 to August of 2022. Other relevant papers were also consulted, resulting in the selection of 81 papers as references.
RESULTS: The binary grading of papillary urothelial carcinoma (UC) is practical, but it may be oversimplified and contribute to \"grade migration\" in recent years. An arbitrary cutoff (5%) has been proposed for bladder cancers with mixed grades. The diagnosis of papillary urothelial neoplasm with low malignant potential has been dramatically reduced in recent years because of overlapping morphology and treatment with low-grade papillary UC. An inverted growth pattern should be distinguished from true (or destructive) stromal invasion in papillary UC. Several methods have been proposed for pT1 tumor substaging, but it is often challenging to substage pT1 tumors in small biopsy specimens. Bladder UC shows a high tendency for divergent differentiation, leading to several distinct histologic subtypes associated with an aggressive clinical behavior. Molecular classification based on the genomic analysis may be a useful tool in the stratification of patients for optimal treatment.
CONCLUSIONS: The 5th edition of WHO Classification of Urinary and Male Genital Tumours has made several significant changes in the classification of bladder cancer. It is important to be aware of these changes and to incorporate them into routine clinical practice.

OBJECTIVE: To assess the risk stratification of clinicopathologically and molecularly classified endometrial cancer based on estrogen receptor (ER) and L1 cell adhesion molecule (L1CAM) expression.
METHODS: This was a retrospective study of patients who underwent primary treatment at a single tertiary center. Carcinomas were classified into 5 clinicopathological risk groups, as per European guidelines. Immunohistochemistry and polymerase-ϵ sequencing were conducted for molecular classification and determination of ER and L1CAM expression.
RESULTS: Data from 1044 patients were analyzed. The median follow-up was 67.5 months. In univariable analyses, ER expression correlated with improved disease-specific survival (DSS) in the \"no specific molecular profile\" (NSMP) (P < 0.001) and mismatch repair deficient (MMRd) (P = 0.002) subgroups. Negative L1CAM expression was associated with enhanced DSS in the NSMP subgroup alone (P < 0.001). ER (hazard ratio [HR] 0.18), but not L1CAM, exhibited prognostic significance within NSMP when controlling for parameters available at the time of diagnosis (tumor histotype, grade, age). ER and L1CAM were not independently associated with DSS within NSMP when controlling for parameters available after surgery (clinicopathological risk groups, age, adjuvant therapy). However, in high-risk-advanced-metastatic cases, both ER (HR 0.26) and L1CAM (HR 3.9) independently correlated with DSS. Similarly, within MMRd, ER was associated with improved DSS in high-risk-advanced-metastatic carcinomas (HR 0.42).
CONCLUSIONS: The prognostic significance of ER and L1CAM varies across clinicopathological risk groups and molecular subgroups of endometrial cancer. Notably, risk assessment for high-risk-advanced-metastatic NSMP and MMRd subtype carcinomas can be refined by ER status.

UNASSIGNED: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.
UNASSIGNED: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
UNASSIGNED: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
UNASSIGNED: The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Cholangiocarcinoma (CCA) is widely noted for its high degree of malignancy, rapid progression, and limited therapeutic options. This study was carried out on transcriptome data of 417 CCA samples from different anatomical locations. The effects of lipid metabolism related genes and immune related genes as CCA classifiers were compared. Key genes were derived from MVI subtypes and better molecular subtypes. Pathways such as epithelial mesenchymal transition (EMT) and cell cycle were significantly activated in MVI-positive group. CCA patients were classified into three (four) subtypes based on lipid metabolism (immune) related genes, with better prognosis observed in lipid metabolism-C1, immune-C2, and immune-C4. IPTW analysis found that the prognosis of lipid metabolism-C1 was significantly better than that of lipid metabolism-C2 + C3 before and after correction. KRT16 was finally selected as the key gene. And knockdown of KRT16 inhibited proliferation, migration and invasion of CCA cells.

The study of monoclonal serum proteins has led to the generation of two major theories: one proposing that individuals who had monoclonal proteins without any symptoms or evidence of end-organ damage have a benign condition, the other one suggesting that some individuals with asymptomatic monoclonal proteins may progress to multiple myeloma and thus are affected by a monoclonal gammopathy of undetermined significance (MGUS). Longitudinal studies of subjects with MGUS have supported the second theory. Subsequent studies have characterized and defined the existence of another precursor of multiple myeloma, smoldering multiple myeloma (SMM), intermediate between MGUS and multiple myeloma. Primary molecular events, chromosome translocations, and chromosome number alterations resulting in hyperploidy, required for multiple myeloma development, are already observed in myeloma precursors. MGUS and SMM are heterogeneous conditions with the presence of tumors with distinct pathogenic phenotypes and clinical outcomes. The identification of MGUS and SMM patients with a molecularly defined high risk of progression to MM offers the unique opportunity of early intervention with a therapeutic approach on a low tumor burden.

OBJECTIVE: Molecular classification according to The Cancer Genome Atlas (TCGA) improves endometrial endometrioid carcinoma (EEC) prognostication and has specific treatment implications; however, original data were skewed towards low-grade and low-stage tumours. Herein, we molecularly classify EECs metastatic at the time of diagnosis or with subsequently documented recurrent/metastatic disease to examine correlation with clinical outcomes.
METHODS: TCGA categories include POLE-mutated, microsatellite instability (MSI), p53 abnormal (p53 abnl) and no specific molecular profile (NSMP). POLE targeted sequencing at exons 9, 11, 13 and 14 and immunohistochemistry (IHC) for PMS2, MSH6 and p53 were performed to establish molecular classification.
RESULTS: The distribution in our cohort of 141 EECs was similar to that generally reported in EEC, with nine POLE-mutated (6%), 45 MSI (32%), 16 p53 abnl (11%) and 71 NSMP (50%), with similar distributions between low- and high-stage cohorts. We demonstrate that when stratified by molecular subtype, disease-specific survival from the time of high-stage (stages III-IV) presentation or time of recurrence in low-stage (stages I-II) disease among metastatic and/or recurrent EEC is strongly associated with TCGA classification (high-stage P = 0.02, low-stage P = 0.017). Discordant molecular classification between primary and metastatic/recurrent tumours occurred in four of 105 (3.8%) patients, two related to PMS2/MSH6 IHC and two related to p53 IHC.
CONCLUSIONS: We demonstrate that molecular classification is prognostically relevant not only at the time of diagnosis, but also at the time of recurrence and in the metastatic setting. Rare subclonal alterations occur and suggest a role for confirming TCGA classification in recurrent/metastatic tumours.

Urothelial carcinoma is characterized by the presence of a wide spectrum of histopathologic features and molecular alterations that contribute to its morphologic and genomic heterogeneity. It typically harbors high rates of somatic mutations with considerable genomic and transcriptional complexity and heterogeneity that is reflective of its varied histomorphologic and clinical features. This review provides an update on the recent advances in the molecular characterization and novel molecular taxonomy of urothelial carcinoma and variant histologies.

BACKGROUND: The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines.
METHODS: Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution.
RESULTS: Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %).
CONCLUSIONS: In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

OBJECTIVE: Endometrial cancer (EC) treatment changed substantially with the introduction of molecular classification. Low-middle income (LMIC) countries will face barriers to including molecular classification to guide treatment. This study aims to analyse the value of p53 immunohistochemistry to delineate adjuvant treatment in FIGO stages I and II.
METHODS: Patients with EC treated between 2010 and 2016 were retrospectively evaluated. Patients included in this analysis must have reviewed FIGO stage I/II high-grade histologies (endometrioid grade 3, serous, clear cell, carcinosarcoma, mixed and undifferentiated). Samples were subjected to p53 immunohistochemistry. Recurrence-free and overall survival were analysed using the Kaplan-Meier method and log-rank test. Cox proportional hazards regression was performed for multivariable analysis.
RESULTS: From 2010 to 2016, 265 patients met the inclusion criteria. Patients with aberrant p53 (71.4 %) were associated with older age (59.7 % vs 77.8 % with more than 60 years), relapse (12.5 vs 29.6 %) and death (22.2 vs 46.7 %). The pattern of relapse was not different, with most being at extrapelvic sites (55.5 % vs 62.3 % for p53 wild type and aberrant, respectively). The median overall survival was not reached versus 92.2 months for p53 wild type and aberrant, respectively (p = 0.003). In multivariate analysis, chemotherapy decreased death (p = 0.014) in p53 aberrant tumours, a benefit not seen in the wild-type cohort (p = 0.22).
CONCLUSIONS: This retrospective analysis corroborates the finding of worse outcomes for p53 aberrant tumours in stage I/II EC and the benefit of more aggressive adjuvant treatment (systemic therapy and radiotherapy). Although not ideal as a sole molecular marker, p53 immunohistochemistry could complement the classical anatomopathological features and be part of the decision-making process with patients in LMIC.