Mesothelioma Risk Score: A New Prognostic Pretreatment, Clinical-Molecular Algorithm for Malignant Pleural Mesothelioma.

ABSTRACT

INTRODUCTION: Prognostic models for malignant pleural mesothelioma have been limited to demographics, symptoms, and laboratory values. We hypothesize higher accuracy using both tumor and patient characteristics. The mesothelioma prognostic test (MPT) and molecular subtype based on claudin-15-to-vimentin expression ratio are molecular signatures associated with survival. Tumor volume (TV) has improved performance compared with clinical staging, whereas neutrophil-to-lymphocyte ratio (NLR) is prognostic for malignant pleural mesothelioma. METHODS: Tumor specimens and clinical data were collected prospectively from patients who underwent extrapleural pneumonectomy (EPP) or pleurectomy and decortication (PD) during 2007 to 2014. MPT and claudin-15-to-vimentin ratio were determined by real-time quantitative polymerase chain reaction, whereas TV was assessed from preoperative scans. Risk groups were derived from combinations of adverse factors on the basis of the Cox model. Predictive accuracy was assessed using Harrell's c-index. RESULTS: MPT, molecular subtype, TV, and NLR were independently prognostic in patients with EPP (N = 191), suggesting equal weighting in a final three-group model (c = 0.644). In the PD cohort (N = 193), MPT poor risk combined with TV greater than 200 cm3 was associated with triple the risk compared with other subgroups (hazard ratio = 2.94, 95% confidence interval 1.70-5.09, p < 0.001) persisting when adjusted for molecular subtype, NLR, performance status, and serum albumin to yield a final three-group model (c = 0.641). The EPP and PD models achieved higher accuracy than published models (c ≤ 0.584, c ≤ 0.575) and pathologic staging (c = 0.554, c = 0.571). CONCLUSIONS: The novel models use pretreatment parameters obtained from minimally invasive biopsy, imaging, and blood tests to evaluate the expected outcome of each type of surgery in newly diagnosed patients and improve stratification on clinical trials.