A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer

Manuela Salvucci, Maximilian L Würstle, Clare Morgan, Sarah Curry, Mattia Cremona, Andreas U Lindner, Orna Bacon, Alexa J Resler, Áine C Murphy, Robert O'Byrne, Lorna Flanagan, Sonali Dasgupta, Nadege Rice, Camilla Pilati, Elisabeth Zink, Lisa M Schöller, Sinead Toomey, Mark Lawler, Patrick G Johnston, Richard WilsonSophie Camilleri-Broët, Manuel Salto-Tellez, Deborah A McNamara, Elaine W Kay, Pierre Laurent-Puig, Sandra Van Schaeybroeck, Bryan T Hennessy, Daniel B Longley, Markus Rehm, Jochen H M Prehn

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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 clinico pathologic and molecular factors, with tangible potential of being introduced in the clinical management of patients with stage III colorectal cancer. .

Original languageEnglish
Pages (from-to)1200-1212
Number of pages13
JournalClinical Cancer Research
Issue number5
Early online date20 Sep 2016
Publication statusPublished - 01 Mar 2017
Externally publishedYes


  • Journal Article


Dive into the research topics of 'A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer'. Together they form a unique fingerprint.

Cite this