Type

Other / n/a

Authors

Jochen HM Prehn
Markus Rehm
Daniel b Longley
Bryan T Hennessy
Sandra Van Schaeybroeck
Pierre Laurent-Puig
Elaine W Kay
Deborah A McNamara
Manuel Salto-Tellez
Sophie Camilleri-Broët
and 20 others

Subjects

Biology

Topics
systems biology physiology physics prognostic biomarker colorectal cancer mathematical modelling apoptosis machine learning

A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer. (2017)

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.
Collections Ireland -> Royal College of Surgeons in Ireland -> Physiology and Medical Physics Articles
Ireland -> Royal College of Surgeons in Ireland -> Molecular Medicine Articles
Ireland -> Royal College of Surgeons in Ireland -> Department of Molecular Medicine
Ireland -> Royal College of Surgeons in Ireland -> Department of Physiology and Medical Physics

Full list of authors on original publication

Jochen HM Prehn, Markus Rehm, Daniel b Longley, Bryan T Hennessy, Sandra Van Schaeybroeck, Pierre Laurent-Puig, Elaine W Kay, Deborah A McNamara, Manuel Salto-Tellez, Sophie Camilleri-Broët and 20 others

Experts in our system

1
Jochen H M Prehn
Royal College of Surgeons in Ireland
Total Publications: 206
 
2
Markus Rehm
Royal College of Surgeons in Ireland
Total Publications: 55
 
3
Bryan T Hennessy
Royal College of Surgeons in Ireland
Total Publications: 33
 
4
Elaine W Kay
Royal College of Surgeons in Ireland
Total Publications: 157
 
5
Deborah A McNamara
Royal College of Surgeons in Ireland
Total Publications: 17