Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy.

Clinical benefits of cytokine blockade in ileal Crohn's disease (iCD) are limited to a subset of patients. Here, we applied single-cell technologies to iCD lesions to address whether cellular heterogeneity contributes to treatment resistance. We found that a subset of patients expressed a unique cellular module in inflamed tissues that consisted of IgG plasma cells, inflammatory mononuclear phagocytes, activated T cells, and stromal cells, which we named the GIMATS module. Analysis of ligand-receptor interaction pairs identified a distinct network connectivity that likely drives the GIMATS module. Strikingly, the GIMATS module was also present in a subset of patients in four independent iCD cohorts (n = 441), and its presence at diagnosis correlated with failure to achieve durable corticosteroid-free remission upon anti-TNF therapy. These results emphasize the limitations of current diagnostic assays and the potential for single-cell mapping tools to identify novel biomarkers of treatment response and tailored therapeutic opportunities.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78.

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia.

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1-G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6-G8, showing high expression of HOXA/B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX-primitive (G7), HOX-mixed (G8), and HOX-committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics.

Identification of prognostic biomarkers for cholangiocarcinoma by combined analysis of molecular characteristics of clinical MVI subtypes and molecular subtypes.

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.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data.

BACKGROUND: Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection. RESULTS: FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases. CONCLUSIONS: In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV- HNSCC patients into these four molecular subtypes and characterized the tumor microenvironment using deconvolution method. We determined that atypical and mesenchymal subtypes have greater immune enrichment and exhibit a T-cell exhaustion phenotype, compared to classical and basal subtypes. Further analyses revealed different B cell maturation and antibody isotypes enrichment patterns, and distinct immune microenvironment crosstalk in the atypical and mesenchymal subtypes. Taken together, our study suggests that treatments that enhances B cell activity may benefit patients with HNSCC of the atypical subtypes. The rationale can be utilized in the design of future precision immunotherapy trials based on the molecular subtypes of HPV- HNSCC.

Wnt, glucocorticoid and cellular prion protein cooperate to drive a mesenchymal phenotype with poor prognosis in colon cancer.

BACKGROUND: The mesenchymal subtype of colorectal cancer (CRC), associated with poor prognosis, is characterized by abundant expression of the cellular prion protein PrPC, which represents a candidate therapeutic target. How PrPC is induced in CRC remains elusive. This study aims to elucidate the signaling pathways governing PrPC expression and to shed light on the gene regulatory networks linked to PrPC. METHODS: We performed in silico analyses on diverse datasets of in vitro, ex vivo and in vivo models of mouse CRC and patient cohorts. We mined ChIPseq studies and performed promoter analysis. CRC cell lines were manipulated through genetic and pharmacological approaches. We created mice combining conditional inactivation of Apc in intestinal epithelial cells and overexpression of the human prion protein gene PRNP. Bio-informatic analyses were carried out in two randomized control trials totalizing over 3000 CRC patients. RESULTS: In silico analyses combined with cell-based assays identified the Wnt-ß-catenin and glucocorticoid pathways as upstream regulators of PRNP expression, with subtle differences between mouse and human. We uncover multiple feedback loops between PrPC and these two pathways, which translate into an aggravation of CRC pathogenesis in mouse. In stage III CRC patients, the signature defined by PRNP-CTNNB1-NR3C1, encoding PrPC, ß-catenin and the glucocorticoid receptor respectively, is overrepresented in the poor-prognosis, mesenchymal subtype and associates with reduced time to recurrence. CONCLUSIONS: An unleashed PrPC-dependent vicious circle is pathognomonic of poor prognosis, mesenchymal CRC. Patients from this aggressive subtype of CRC may benefit from therapies targeting the PRNP-CTNNB1-NR3C1 axis.

Gastric cancer molecular classification based on immunohistochemistry and in-situ hybridisation and mortality.

BACKGROUND AND AIMS: Gastric cancers (GC) are divided into subtypes based on molecular profile: Epstein-Barr virus (EBV)-positive, microsatellite instability (MSI), chromosomal instability (CIN) and genomically stable (GS) tumours. The prognostic impact of this classification is unclear. The aim was to evaluate whether the molecular subtypes determined using in-situ hybridisation (ISH) and immunohistochemistry (IHC) are associated with clinicopathological parameters and prognosis. METHODS AND RESULTS: The study included 503 GC patients. Based on ISH (EBV) and IHC (MSI and TP53), tumours were divided into EBV-positive, MSI, CIN (EBVneg/MSS/TP53aberrant) and GS (EBVneg/MSS/TP53wild-type) subgroups. Survival analyses with intestinal- and diffuse-type tumours were examined separately. EBV-positive tumours associated with male sex. Both EBV-positive and MSI tumours associated with intestinal type. CIN tumours associated with intestinal-type and positive lymph node status. GS tumours associated with diffuse-type and negative lymph node status. In the total cohort, no significant differences in the 5-year survival were observed. In intestinal tumours, the 5-year survival was better in EBV-positive tumours compared with GS tumours [hazard ratio (HR) = 0.57, 95% confidence interval (CI) = 0.33-0.99]. In diffuse tumours, the 5-year survival was worse in CIN tumours compared with GS tumours (HR = 1.57, 95% CI = 1.14-2.18). In radically resected diffuse tumours, the 5-year survival was worse in MSI tumours compared with GS tumours (HR = 3.26, 95% CI = 1.20-8.82). CONCLUSIONS: The molecular classification is associated with histological type but not prognosis in GC. As the prognostic effects of molecular subtypes in intestinal- and diffuse-type cancers may differ, combining histological and molecular information is recommended for future studies.

Molecular classification of metastatic and recurrent endometrial endometrioid carcinoma: prognostic relevance among low- and high-stage tumours.

AIMS: 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.

Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort.

OBJECTIVE: The International Federation of Gynecology and Obstetrics (FIGO) 2023 staging system for endometrial cancer (EC) was released with incorporating histology, lympho-vascular space invasion, and molecular classification together. Our objective is to further explore the clinical utility and prognostic significance of the 2023 FIGO staging system in China. METHODS: A retrospective analysis was conducted for patients who received standard surgeries and underwent genetic testing using multigene next-generation sequencing (NGS) panels between December 2018 and December 2023 at Fudan University Shanghai Cancer Center, Shanghai, China. The genomic and clinical data of all patients were analyzed, and stages were determined by both the 2009 and 2023 FIGO staging systems. Kaplan-Meier estimators and Cox proportional hazards models were used for survival analysis. RESULTS: A total of 547 patients were enrolled in the study. After the restaged by the FIGO 2023 staging system, stage shifts occurred in 147/547 (26.9%) patients. In patients with early stages in FIGO 2009 (stage I-II), 63 cases were rearranged to IAmPOLEmut and 53 cases to IICmp53abn due to the molecular classification of POLEmut and p53abn. Altogether 345 cases were in stage I, 107 cases in stage II, 69 cases in stage III, and 26 cases in stage IV according to the FIGO 2023 staging criteria. For stage I diseases, the 3-year PFS rate was 92.7% and 95.3% in 2009 and 2023 FIGO staging systems, respectively. The 3-year PFS of stage II in 2023 FIGO was lower than that of FIGO 2009 (3-year PFS: 85.0% versus 90.9%), especially in substage IIC and IICmp53abn. Three cases (12%) of stage IIIA in FIGO 2009 were shifted to stage IA3 FIGO 2023, with 3-year PFS rates of 90.9% versus 100%, respectively. In NGS analysis, the most prevalent gene alterations were observed in PTEN and PIK3CA. CONCLUSION: The FIGO 2023 staging system was proved to be a good predictor of survival for EC patients with enhanced precision compared to FIGO 2009. Predominant stage shifts were observed in early-stage diseases. Distinct gene alterations of different subtypes may help to explore more accurate target therapies.

Uterine cancer restaging according to the updated 2023 FIGO Guidelines does not uniformly affect prognosis: An institutional retrospective cohort study.

OBJECTIVE: Efforts have been made to better risk stratify patients given the rise in incidence of endometrial cancer (EC). The 2023 FIGO staging now incorporates histologic subtype and molecular classification into determination of EC stage. We sought to elucidate if the new staging system demonstrated prognostic differences compared to the 2009 staging system. METHODS: A retrospective chart review was performed on women treated for EC at our institution from September 2013 to May 2023 and combined with the publicly available TCGA Nature 2013 dataset. Detailed clinical information was captured. Patients were restaged according to the 2023 guidelines. Survival estimates were obtained using Kaplan-Meier method, and the log-rank test was used to compare survival curves for progression-free survival (PFS). RESULTS: 919 patients were included in our analysis. The datasets were comparable regarding histologic grade, stage, and age at diagnosis. 175 (31.5%) of patients in the institution dataset and 115 (31.6%) patients in the TCGA dataset experienced a stage change. Most patients whose stage changed were upstaged (275/290; 94.8%). 3-year PFS estimates for stage IA patients with no stage change versus those upstaged were 92.3% (95% CI: 87.2, 95.4) v. 72.0% (95% CI: 68.4, 84.9), p = 0.002. No significant differences in survival difference were seen in other stage subsets. CONCLUSION: Modest survival differences exist in patients with EC originally staged as IA who underwent upstaging. No significant survival difference is observed in patients who are restaged to stage II or III subsets. Improved risk stratification is needed in assessing prognosis and adjuvant therapy for patients with endometrial cancer.

Molecular classification improves preoperative risk assessment of endometrial cancer.

OBJECTIVE: We aimed to evaluate the performance of endometrial cancer (EC) molecular classification in predicting extrauterine disease after primary surgery alone and in combination with other clinical data available in preoperative setting. METHODS: Retrospective single-center observational study including patients with endometrial adenocarcinoma treated with primary surgery between December 1994 and May 2022. Molecular profiling was performed using immunohistochemistry of p53, MLH1, PMS2, MSH2 and MSH6; and KASP genotyping of the 6 most common mutations of POLE gene. Clinical, pathological and imaging information was reviewed. Logistic regression, regression trees and random forest classification techniques (CART) were performed. RESULTS: We enrolled 658 patients, 47 with POLEmut (7.1%), 234 with MMRd (35.6%), 95 with p53abn (14.4%) and 282 with NSMP (42.8%) tumors. Advanced stage after primary surgery (III-IV FIGO 2009) was diagnosed in 11.7% of patients, p53abn tumors showed increased extrauterine spread (34.1%) and nodal involvement (30.1%) (p < .001). In multivariate analysis, only p53abn subgroup (aOR = 16.0, CI95% = 1.5-165.1) and radiological suspicion of extrauterine disease (aOR = 24.2, CI95% = 12.2-48.2) independently predicted the finding of extrauterine disease after primary surgery. In patients with preoperative uterine-confined disease, deep myometrial and cervical involvement in radiological assessment and p53abn molecular subtype were the best variables to identify patients at-risk of occult extrauterine disease after the staging surgery. CONCLUSION: EC molecular classification is more accurate than histotype or grade in preoperative biopsy to predict advanced disease, and together with imaging tests are the most reliable preoperative information. This work provides an initial framework for using molecular information preoperatively to tailor surgical treatment.

2023 FIGO staging system for endometrial cancer: The evolution of the revolution.

INTRODUCTION: Embracing the complex and diverse nature of the heterogenous group of malignancies that are included under the umbrella of "endometrial cancer" (EC) to better align prognosis with treatment recommendations, requires a more comprehensive staging system. Our goal at the development of the new FIGO staging was to provide 1) high accuracy in the predictive prognosis for a patient with EC, which is the genuine purpose of a staging system, and 2) identification of distinct treatment relevant subgroups. Since the publication of the 2009 staging system by the International Federation of Gynecology and Obstetrics (FIGO) 14  years ago (1, 2), our understanding of the biology and natural history of EC has undergone a radical transformation. The TGCA results in 2013 (3), and the many validation reports published since then (4-9), have taught us that "EC" is composed of at least four distinct molecularly defined diseases. Strong histopathologic markers reflecting tumor biology such as lymph vascular space invasion (LVSI) were identified. Importantly, anatomical borders were shown to lose their prognostic relevance for EC patients in the presence of dominant tumor biology-markers such as molecular subtypes/LVSI (10, 11). This emphasizes the integration of these novel markers into a prognostic staging system that aims to be relevant to patients. The 2023 FIGO staging system for EC harmonizes and integrates old and new knowledge on anatomic, histopathologic, and molecular features (12). It requires a change in our perception of a staging system, from a traditional purely anatomical borders-based system to an integrated staging system integrating anatomical borders and tumor biology as pivotal prognostic factors for EC patients while providing important information for treatment decision making. Therefore, the 2023 FIGO staging system demonstrates the logical next step in the evolution of the revolution in a patient-centric staging approach. Below, we elucidate the rationale for the FIGO 2023 endometrial cancer staging system.

Potential of molecular classification to guide fertility-sparing management among young patients with endometrial cancer.

The traditional histological classification system for endometrial carcinoma falls short in addressing the disease's molecular heterogeneity, prompting the need for alternative stratification methods. Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) has emerged as a clinically efficient tool to categorize endometrial cancers according to mismatch repair deficiency, POLE exonuclease domain mutations, and p53 expression. However, the application of this classification to fertility-sparing treatments remains unexplored, and current guidelines lack specificity in how it should be used. In this review, we summarize the available literature and establish the framework for future investigations focused on molecular profiling-based risk assessment of endometrial cancer, with the goal of utilizing precision medicine to optimally counsel patients seeking fertility-sparing treatment. While the available evidence is limited and of low quality, it does provide insights and frames future perspectives for managing fertility-sparing approaches on the basis of molecular subtypes. Evidence suggests that mismatch repair-deficient tumors are likely to recur despite progestin therapy, emphasizing the need for alternative treatments, with targeted therapies being a new landscape that still needs to be explored. Tumors with POLE mutations exhibit a favorable prognosis, but the safety of hysteroscopic resection alone requires further investigation. p53 abnormal tumors have an unfavorable prognosis, raising questions about their suitability for fertility-sparing treatment. Lastly, the no specific molecular profile (or p53 wild-type) tumors, while having a relatively good prognosis, are heterogeneous and require more precise biomarkers to effectively guide therapy for those with poorer prognoses. Addressing these research gaps will lead to more precise guidelines to ensure optimal selection for fertility-sparing treatment.

Evaluation of prognostic potential of ß-catenin and L1CAM expression according to endometrial cancer risk group.

OBJECTIVE: We investigate the prognostic role of ß-catenin and L1 neuronal cell-adhesion molecule (L1CAM) according to risk groups in endometrial carcinomas (EC). METHODS: A total of 335 EC patients were classified according to the Proactive Molecular Risk Classifier for Endometrial Cancer. We evaluated the expression of ß-catenin and L1CAM using immunohistochemistry, and their association with clinicopathological characteristics and survival. RESULTS: The expressions of ß-catenin and L1CAM were observed in 10.4% of all patients, respectively, and showed mutually exclusive pattern. While ß-catenin expression was associated with endometrioid histology (p = 0.035) and low tumor grade (p = 0.045), L1CAM expression was associated with non-endometrioid histology (p < 0.001), high tumor grade (p < 0.001), lymphovascular space invasion (p = 0.006), and advanced International Federation of Gynecology and Obstetrics (FIGO) stage (p = 0.001). ß-catenin expression was most frequent in the no specific molecular (NSMP) group (26/35, 74.3%), followed by the DNA polymerase-ε-mutated (POLE-mut) (6/35, 17.1%), and mismatch repair-deficiency (dMMR) (3/35, 8.6%). L1CAM expression was most frequent in the p53-abnormal group (22/35, 62.9%), followed by the NSMP (6/35, 17.1%), dMMR (4/35, 11.4%), and POLE-mut (3/35, 8.6%). Although both markers did not show statistical significance in multivariate analysis for both progression-free survival (PFS) and overall survival in entire cohort, ß-catenin positivity was identified as the sole factor associated with worse PFS in the high-intermediate risk subgroup (p = 0.001). CONCLUSION: The expression of nuclear ß-catenin may serve as a potential biomarker for predicting recurrence and guiding therapeutic strategies in high-intermediate risk EC patients.

The impact of estrogen receptor and L1 cell adhesion molecule expression on endometrial cancer outcome correlates with clinicopathological risk group and molecular subgroup.

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). CONCLUSION: 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.

Reviving peritoneal cytology: Exploring its role in endometrial cancer molecular classification.

OBJECTIVE: The prognostic significance of positive peritoneal cytology in endometrial cancer has long been debated. In 2009, the Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) removed cytology as a staging criterion from the endometrial cancer staging system. However, there is still evidence that positive peritoneal cytology may decrease survival among patients with endometrial cancer. The aim of this study was to determine the prognostic significance of positive peritoneal cytology among the different molecular subgroups. METHODS: This study included patients with endometrial cancer who underwent primary surgical treatment between 2004 and 2015 at the Bern University Hospital, Switzerland, with molecular classification of the primary tumor and peritoneal cytology performed. RESULTS: A total, 250 patients with endometrial cancer were enrolled. Peritoneal cytology was assessed in 206 patients, of whom 24% were positive: 25% of the POLEmut, 16% of the MMRd, 41% of the p53abn, and 24% of the NSMP cases. The mean follow-up was 128.7 months. Presence of positive peritoneal cytology was associated with significantly decreased mean recurrence-free and overall survival in patients with p53abn (p = .003 and p = .001) and NSMP (p = .020 and p = .049) endometrial cancer. In multivariable Cox regression analysis, positive peritoneal cytology remained an independent predictor of recurrence (p = .033) and death (p = .008) in p53abn endometrial cancer patients. CONCLUSION: Positive peritoneal cytology is associated with worse oncologic outcomes in NSMP and p53abn endometrial cancer and remains an independent predictor of recurrence and death in patients with p53abn endometrial cancer.

Moving into the modern era of molecular classification for endometrial cancer.

The molecular subtypes of endometrial carcinoma (EC) were first described by The Cancer Genome Atlas (TCGA) a decade ago. Using surrogate approaches, the molecular classification has been demonstrated to be prognostic across EC patients and to have predictive implications. Starting in 2020, the molecular classification has been incorporated into multiple guidelines as part of the risk assessment and most recently into the International Federation of Gynecology and Obstetrics (FIGO) staging. This review article discusses the implementation of the EC molecular classification into clinical practice, the therapeutic implications, and the molecular and clinical heterogeneity of the EC molecular subtypes.

The Lund taxonomy for bladder cancer classification - from gene expression clustering to cancer cell molecular phenotypes, and back again.

Treatment of bladder cancer patients depends on precise diagnosis. Molecular subtyping by gene expression profiling may contribute substantially to subclassification of bladder cancer. Several classification systems have been proposed. Most of these base their classification on whole biopsy features, and molecular subtypes are therefore often defined by a combination of features from the cancer cells as well as infiltrating noncancer cells. This makes the link to what is seen at the cancer cell level unclear. The aim of the Lund taxonomy (LundTax) has been to align gene expression-level classification with immunohistochemical classification to identify cancer cell phenotypes independent of infiltration and proliferation. A systematic approach was used in which gene expression clusters were validated and adjusted by immunohistochemistry using markers expressed only by the cancer cells. This review provides a rationale for defining molecular subtypes and a step-by-step description of the development of the LundTax with motivations for each modification and extension. As the cancer cell phenotype defined by gene expression profiling corresponds with the immunohistochemistry of cancer cells, the LundTax represents a harmonization of the gene expression and immunohistochemical levels. Furthermore, the classification system is independent of pathological stage and is, thus, applicable to all urothelial carcinomas. A unified classification system relevant for both the molecular biologist and pathologist will facilitate systematization of current treatment practices, as well as the development of new treatments. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.