Salvucci et al. accepted in Clinical Cancer Research

Abstract

Purpose: Apoptosis is essential for chemotherapy responses. In this discovery and validation study, we evaluated the suitability of a mathematical model of apoptosis execution (APOPTO-CELL) as a stand-alone signature and as a constituent of further refined prognostic stratification tools. Experimental Design: Apoptosis competency of primary tumor samples from n=120 stage III colorectal cancer patients was calculated by APOPTO-CELL from measured protein concentrations of Procaspase-3, Procaspase-9, SMAC and XIAP. An enriched APOPTO-CELL signature (APOPTO-CELL-PC3) was synthesized to capture apoptosome-independent effects of Caspase-3. Furthermore, a machine learning Random Forest approach was applied to APOPTO-CELL-PC3 and available molecular and clinicopathological data to identify a further enhanced signature. Association of the signature with prognosis was evaluated in an independent colon adenocarcinoma cohort (TCGA COAD, n=136). Results: We identified three prognostic biomarkers (p=0.04, p=0.006 and p=0.0004 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively) with increasing stratification accuracy for stage III colorectal cancer patients. The APOPTO-CELL-PC3 signature ranked highest among all features. The prognostic value of the signatures was independently validated in stage III TCGA COAD patients (p=0.01, p=0.04 and p=0.02 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively). The signatures provided further stratification for patients of CMS1-3 molecular subtype. Conclusions: The integration of a systems-biology-based biomarker for apoptosis competency with machine learning approaches is an appealing and innovative strategy towards refined patient stratification. The prognostic value of apoptosis competency is independent of other available clinicopathological and molecular factors, with tangible potential of being introduced in the clinical management of stage III colorectal patients.