Immunophenotypic and molecular changes during progression of papillary urothelial carcinoma.

PURPOSE: Urothelial carcinoma has various molecular subtypes, each with different tumor characteristics. Although it is known that molecular changes occur during tumor progression, little is known about the specifics of these changes. In this study, we performed transcriptional analysis to understand the molecular changes during tumor progression. MATERIALS AND METHODS: Formalin-fixed, paraffin-embedded tumor tissues were obtained from 12 patients with muscle-invasive bladder cancer (MIBC). The invasive and non-invasive papillary areas were identified in papillary urothelial carcinoma specimens. Immunohistochemistry (IHC) and mRNA sequencing were performed for each tumor area. RESULTS: Patients with CK5/6-negative and CK20-positive non-invasive papillary areas were selected and classified into the IHC switch subgroup (CK5/6-positive and CK20-negative in the invasive area) and the IHC unchanged subgroup (CK5/6-negative and CK20-positive in the invasive area) according to the IHC results of the invasive area. We identified differences in the mRNA expression between the non-invasive papillary and invasive areas of the papillary MIBC tissue samples. In both the non-invasive papillary and invasive areas, the IHC switch subgroup showed basal subtype gene expression, while the IHC unchanged subgroup demonstrated luminal subtype gene expression. CONCLUSIONS: The non-invasive papillary area showed a gene expression pattern similar to that of the invasive area. Therefore, even if the non-invasive papillary area exhibits a luminal phenotype on IHC, it can have a basal subtype gene expression depending on the invasive area.

KRT18 as a Novel Biomarker of Urothelial Papilloma while Evaluating Low-Grade Papillary Urothelial Neoplasms: Bi-Center Analysis.

INTRODUCTION: Although urothelial papilloma (UP) is an indolent papillary neoplasm that can mimic the morphology of low-grade papillary urothelial carcinoma (PUC), there is no immunomarker to differentiate reliably these two entities. In addition, the molecular characteristics of UP are not fully understood. METHODS: We conducted an in-depth proteomic analysis of papillary urothelial lesions (n = 31), including UP and PUC along with normal urothelium. Protein markers distinguishing UP and PUC were selected with machine learning analysis, followed by internal and external validation using immunohistochemistry. RESULTS: In the proteomic analysis, UP and PUC showed overlapping proteomic profiles. We identified EHD4 and KRT18 as candidate diagnostic biomarkers of UP. Through immunohistochemical validation in two independent cohorts (n = 120), KRT18 was suggested as a novel UP diagnostic marker, able to differentiate UP from low-grade PUC. We also found that 3.5% of patients with UP developed urothelial carcinoma in subsequent resections, supporting the malignant potential of UP. KRT18 downregulation was significantly associated with UPs subsequently progressing to urothelial carcinoma, following their initial diagnosis. CONCLUSION: This is the first study that successfully revealed UPs comprehensive proteomic landscape, while it also identified KRT18 as a potential diagnostic biomarker of UP.

The prognostic value of a combined immune score in tumor and immune cells assessed by immunohistochemistry in triple-negative breast cancer.

BACKGROUND: This study aimed to develop a novel combined immune score (CIS)-based model assessing prognosis in triple-negative breast cancer (TNBC). METHODS: The expression of eight immune markers (PD-1, PD-L1, PD-L2, IDO, TIM3, OX40, OX40L, and H7-H2) was assessed with immunohistochemistry on the tumor cells (TCs) and immune cells (ICs) of 227 TNBC cases, respectively, and subsequently associated with selected clinicopathological parameters and survival. Data retrieved from The Cancer Genome Atlas (TCGA) were further examined to validate our findings. RESULTS: All immune markers were often expressed in TCs and ICs, except for PD-1 which was not expressed in TCs. In ICs, the expression of all immune markers was positively correlated between one another, except between PD-L1 and OX40, also TIM3 and OX40. In ICs, PD-1, PD-L1, and OX40L positive expression was associated with a longer progression-free survival (PFS; p = 0.040, p = 0.020, and p = 0.020, respectively). In TCs, OX40 positive expression was associated with a shorter PFS (p = 0.025). Subsequently, the TNBC patients were classified into high and low combined immune score groups (CIS-H and CIS-L), based on the expression levels of a selection of biomarkers in TCs (TCIS-H or TCIS-L) and ICs (ICIS-H or ICIS-L). The TCIS-H group was significantly associated with a longer PFS (p < 0.001). Furthermore, the ICIS-H group was additionally associated with a longer PFS (p < 0.001) and overall survival (OS; p = 0.001), at significant levels. In the multivariate analysis, both TCIS-H and ICIS-H groups were identified as independent predictors of favorable PFS (p = 0.012 and p = 0.001, respectively). ICIS-H was also shown to be an independent predictor of favorable OS (p = 0.003). The analysis of the mRNA expression data from TCGA also validated our findings regarding TNBC. CONCLUSION: Our novel TCIS and ICIS exhibited a significant prognostic value in TNBC. Additional research would be needed to strengthen our findings and identify the most efficient prognostic and predictive biomarkers for TNBC patients.

Plasma Cell-Free DNA in Uterine Cervical Cancer: Therapeutic Monitoring and Prognostic Values after Radical Radiotherapy.

PURPOSE: In the present study, we aimed to establish a liquid biopsy-based monitoring method using peripheral blood cell-free DNA (cfDNA) for patients with cervical cancer who underwent radical radiotherapy (RT). Materials and Methods: Twenty-five patients with cervical cancer were prospectively recruited and treated with external beam RT and brachytherapy. In all patients, except one, chemotherapy was administered concurrently during RT. Whole peripheral blood samples were obtained at least twice from each patient. We performed next-generation sequencing (NGS) for the target-captured libraries (67 oncogenes and human papillomavirus [HPV] type 16/18) using 64 plasma cfDNA samples from the 25 participants. The ratio of HPV cfDNA and the variant allele frequency (VAF) in cfDNA was calculated, and their dynamic changes were monitored. The median follow-up duration was 25.4 months. RESULTS: In total, we identified 21,866 cfDNA variants. ARID1A and frameshift variants occupied the largest portion of altered genes and HIGH-grade variant types, respectively. In most cases, tumor shrinkage was followed by a decrease in the HPV ratio; however, an increase in HPV ratio indicated distant metastasis, despite the reduced tumor size. The initial HPV ratio reflecting the tumor burden was likely associated with treatment outcomes (p = 0.16). We did not determine a role for serial changes in the VAF in cfDNA. CONCLUSION: Our findings suggest that the HPV cfDNA ratio, calculated after targeted NGS, may be valuable for monitoring and predicting treatment responses. Accordingly, further validation of these findings is warranted.

Quantitative proteomics identifies TUBB6 as a biomarker of muscle-invasion and poor prognosis in bladder cancer.

Muscle-invasive urothelial carcinoma (MIUC) of the bladder shows highly aggressive tumor behavior, which has prompted the quest for robust biomarkers predicting invasion. To discover such biomarkers, we first employed high-throughput proteomic method and analyzed tissue biopsy cohorts from patients with bladder urothelial carcinoma (BUC), stratifying them according to their pT stage. Candidate biomarkers were selected through bioinformatic analysis, followed by validation. The latter comprised 2D and 3D invasion and migration assays, also a selection of external public datasets to evaluate mRNA expression and an in-house patient-derived tissue microarray (TMA) cohort to evaluate protein expression with immunohistochemistry (IHC). Our multilayered platform-based analysis identified tubulin beta 6 class V (TUBB6) as a promising prognostic biomarker predicting MIUC of the bladder. The in vitro 2D and 3D migration and invasion assays consistently showed that inhibition of TUBB6 mRNA significantly reduced cell migration and invasion ability in two BUC cell lines with aggressive phenotype (TUBB6 migration, P = .0509 and P < .0001; invasion, P = .0002 and P = .0044; TGFBI migration, P = .0214 and P = .0026; invasion, P < .0001 and P = .0001; T24 and J82, respectively). Validation through multiple public datasets, including The Cancer Genome Atlas (TCGA) and selected GSE (Genomic Spatial Event) databases, confirmed TUBB6 as a potential biomarker predicting MIUC. Further protein-based validation with our TMA cohort revealed concordant results, highlighting the clinical implication of TUBB6 expression in BUC patients (overall survival: P < .001). We propose TUBB6 as a novel IHC biomarker to predict invasion and poor prognosis, also select the optimal treatment in BUC patients.

Proteomic-Based Machine Learning Analysis Reveals PYGB as a Novel Immunohistochemical Biomarker to Distinguish Inverted Urothelial Papilloma From Low-Grade Papillary Urothelial Carcinoma With Inverted Growth.

Background: The molecular biology of inverted urothelial papilloma (IUP) as a precursor disease of urothelial carcinoma is poorly understood. Furthermore, the overlapping histology between IUP and papillary urothelial carcinoma (PUC) with inverted growth is a diagnostic pitfall leading to frequent misdiagnoses. Methods: To identify the oncologic significance of IUP and discover a novel biomarker for its diagnosis, we employed mass spectrometry-based proteomic analysis of IUP, PUC, and normal urothelium (NU). Machine learning analysis shortlisted candidate proteins, while subsequent immunohistochemical validation was performed in an independent sample cohort. Results: From the overall proteomic landscape, we found divergent 'NU-like' (low-risk) and 'PUC-like' (high-risk) signatures in IUP. The latter were characterized by altered metabolism, biosynthesis, and cell-cell interaction functions, indicating oncologic significance. Further machine learning-based analysis revealed SERPINH1, PKP2, and PYGB as potential diagnostic biomarkers discriminating IUP from PUC. The immunohistochemical validation confirmed PYGB as a specific biomarker to distinguish between IUP and PUC with inverted growth. Conclusion: In conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.