Authors

Manuela Salvucci, Royal College of Surgeons in IrelandFollow
Maximilian L. Würstle, Royal College of Surgeons in IrelandFollow
Clare Morgan, Royal College of Surgeons in IrelandFollow
Sarah Curry, Royal College of Surgeons in IrelandFollow
Mattia Cremona, Royal College of Surgeons in IrelandFollow
Andreas U. Lindner, Royal College of Surgeons in IrelandFollow
Orna Bacon, Royal College of Surgeons in IrelandFollow
Alexa J. Resler, Royal College of Surgeons in IrelandFollow
Áine C. Murphy, Royal College of Surgeons in IrelandFollow
Robert O'Byrne, Royal College of Surgeons in IrelandFollow
Lorna Flanagan, Royal College of Surgeons in IrelandFollow
Sonali Dasgupta, Queen's University Belfast
Nadege Rice, Université Paris Descartes
Camilla Pilati, Université Paris Descartes
Elisabeth Zink, Royal College of Surgeons in IrelandFollow
Lisa M. Schöller, Royal College of Surgeons in IrelandFollow
Sinead Toomey, Royal College of Surgeons in IrelandFollow
Mark Lawler, Queen's University Belfast
Patrick G. Johnston, Queen's University Belfast
Richard Wilson, Queen's University Belfast
Sophie Camilleri-Broët, Université Paris Descartes
Manuel Salto-Tellez, Queen's University Belfast
Deborah A. McNamara, Beaumont Hospital, Dublin
Elaine W. Kay, Royal College of Surgeons in IrelandFollow
Pierre Laurent-Puig, Université Paris Descartes
Sandra Van Schaeybroeck, Queen's University Belfast
Bryan T. Hennessy, Royal College of Surgeons in IrelandFollow
Daniel b. Longley, Queen's University Belfast
Markus Rehm, Royal College of Surgeons in IrelandFollow
Jochen HM Prehn, Royal College of Surgeons in IrelandFollow

Peer Reviewed

1

Document Type

Article

Publication Date

1-3-2017

Keywords

Apoptosis, machine learning, mathematical modelling, prognostic biomarker, systems biology.

Funder/Sponsor

European Union Framework Programme 7. Science Foundation Ireland. Irish Cancer Society. Heath Research Board. ERASMUS.

Comments

The original article is available at http://clincancerres.aacrjournals.org

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 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.

Disciplines

Physics | Physiology

Citation

Salvucci M, Würstle ML, Morgan C, Curry S, Cremona M, Lindner AU, Bacon O, Resler AJ, Murphy ÁC, O'Byrne R, Flanagan L, Dasgupta S, Rice N, Pilati C, Zink E, Schöller LM , Toomey S, Lawler M, Johnston PG, Wilson R, Camilleri-Broët S, Salto-Tellez M, McNamara DA, Kay EW, Laurent-Puig P, Van Schaeybroeck S, Hennessy BT, Longley DB, Rehm M, Prehn JH. A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer. Clinical Cancer Research. 2017;23(5):1200-1212.

PubMed ID

27649552

DOI Link

10.1158/1078-0432.CCR-16-1084

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

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