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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 patients with stage III colorectal cancer (n = 120) 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 clinicopathologic 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 3 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 patients with stage III colorectal cancer.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 with 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 toward refined patient stratification. The prognostic value of apoptosis competency is independent of other available clinicopathologic and molecular factors, with tangible potential of being introduced in the clinical management of patients with stage III colorectal cancer. Clin Cancer Res; 23(5); 1200–12. ©2016 AACR.

Kaplan-Meier estimates for disease-free and overall survival for n=120 stage III patients of the discovery cohort categorized based on TN staging, tumor location and lymphovascular invasion. Additional exploratory analyses investigated the prognostic value of the APOPTO-CELL-PC3 signature within each sub-group identified by the clinical features.

Unadjusted and multivariate Cox proportional hazards analyses to examine the association of APOPTO-CELL and APOPTO-CELL-PC3 signatures with DFS in n=157 stage III patients of the expansion cohort.

Patient clinicopathological and demographic characteristics in the patients of the discovery, expansion and validation cohorts.

Detailed description of the measurements used as inputs for the apoptosis-based signatures in the discovery, expansion and validation cohorts.

Detailed description of the patients data handling and inclusion criteria for downstream analyses of the discovery, expansion and validation cohorts.

Impact of each predictor included in the Random Forest classifier on the probability of recurrence alone and in combination with the others.

Kaplan-Meier estimates for disease-free and overall survival for n=120 stage III patients of the discovery cohort. Patients were grouped based on the protein expression of Procaspase-9, XIAP, SMAC and Procaspase-3. Median expression was used as cut-off value.

Detailed description of the survival analyses performed in both primary and exploratory studies.

Organ transplant recipients (OTRs) have a substantially elevated risk of squamous cell skin carcinoma (SCSC), largely attributed to immunosuppressive medications used to prevent graft rejection, although data to support the role of newer drugs in SCSC risk are sparse. We investigated the association between immunosuppressive medications and SCSC risk among cardiac and renal transplant recipients in the SCOT cohort study. Incident cases were ascertained through medical record review after self-report of skin biopsy (n = 170). Controls without SCSC (n = 324) were matched to cases on sex, age, race, transplant year, hospital, donor type, organ transplanted, and time between transplantation and interview. Conditional logistic regression was used to evaluate the association between specific medications and SCSC. Users of the antimetabolite azathioprine were more than twice as likely to develop SCSC (odds ratio [OR] = 2.67, 95% confidence interval [CI] 1.23-5.76). In contrast, the newer antimetabolite preparations (i.e., mycophenolic acid [MPA]) were associated with lower SCSC risk (OR = 0.45, 95% CI 0.29-0.69). This inverse association between MPA and SCSC persisted among OTRs with no history of azathioprine use, even after adjustment for simultaneous use of the calcineurin inhibitor tacrolimus (OR = 0.52, 95% CI 0.32-0.84). Our data suggest that the increased risk of SCSC historically associated with azathioprine is not seen in OTRs prescribed newer regimens, including MPA and tacrolimus.