Independent Tissue-Based Biomarkers in Endometrioid Endometrial Cancer: Tumor Budding in Microsatellite Instability and WHO Grading in Copy-Number-Low Patients.

The molecular characterization of endometrial endometrioid adenocarcinomas has provided major advances in its prognostic stratification. However, risk assessment of microsatellite instability (MSI) and copy-number (CN)-low cases remains a challenge. Thus, we aimed to identify tissue-based morphologic biomarkers that might help in the prognostic stratification of these cases. Histomorphologic parameters (WHO grading, tumor budding (TB), tumor-stroma ratio (as a quantitative description of stromal desmoplasia), tumor-infiltrating lymphocytes (TIL), "microcystic, elongated, fragmented" (MELF) pattern) were analyzed in resection specimens of the TCGA-UCEC cohort (n = 228). For each quantitative parameter, a two-tiered system was developed utilizing systematically determined cutoffs. Associations with survival outcomes were calculated in univariate and multivariate analysis and validated in two independent cohorts. In MSI tumors, only TB remained an independent prognostic factor. TB (≥3 buds/high-power field) was associated with inferior outcomes and with lymph node metastases. The prognostic significance of TB was confirmed in two validation cohorts. For CN-low tumors, established grading defined by the WHO was independently prognostic with inferior outcomes for high-grade tumors. The evaluation of TB might help in identifying MSI-patients with unfavorable prognosis who, e.g., could benefit from lymphadenectomy. WHO-based grading facilitates independent prognostic stratification of CN-low endometrioid adenocarcinomas. Therefore, we propose the utilization of TB and WHO-based grading, two tissue-based and easy-to-assess biomarkers, in MSI/CN-low endometrial carcinomas for improved clinical management.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping.

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.

Tumour cell budding and spread through air spaces in squamous cell carcinoma of the lung - Determination and validation of optimal prognostic cut-offs.

PURPOSE: Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS. EXPERIMENTAL DESIGN: The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n = 346 patients). Two experienced pathologists probed IOV in the validation cohort. RESULTS: In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥ 0.89 for prognostic parameters. CONCLUSIONS: We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.