Evaluation of alternative prognostic thresholds for SP142 and 22C3 immunohistochemical PD-L1 expression in triple-negative breast cancer: results from a population-based cohort.

Background: Immune checkpoint inhibitors are now a part of the treatment arsenal for triple-negative breast cancer (TNBC) but refinement of PD-L1 as a prognostic and predictive biomarker is a clinical priority. We aimed to evaluate the relevance of novel PD-L1 immunohistochemical (IHC) thresholds in TNBC with regard to PD-L1 gene expression, prognostic value, tumor infiltrating lymphocytes (TILs), and TNBC molecular subtypes.

Material & Methods: PD-L1 was scored in a tissue microarray with the SP142 (immune cell (IC) score) and the 22C3 (combined positive score; CPS) IHC assays and TIL abundance evaluated in whole slides in a population-based cohort of 237 early-stage TNBC patients.

Metabolic Changes After Bronchoscopic Lung Volume Reduction With Endobronchial Valves in COPD Patients.

Objectives: Little is known about the effect of bronchoscopic lung volume reduction using endobronchial valves (BLVR-EBV) on extrapulmonary manifestations like body composition, muscle function or metabolism. Pulmonary rehabilitation (PR) clearly addresses extrapulmonary manifestations of COPD, including physical inactivity and low muscle mass. However, the added impact of BLVR-EBV+PR remains unknown.

The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?

Background: Pathologist-read tumor-infiltrating lymphocytes (TILs) have showcased their predictive and prognostic potential for early and metastatic triple-negative breast cancer (TNBC) but it is still subject to variability. Artificial intelligence (AI) is a promising approach toward eliminating variability and objectively automating TILs assessment. However, demonstrating robust analytical and prognostic validity is the key challenge currently preventing their integration into clinical workflows.