Type

Journal Article

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

Mathematics

Topics
learning approaches mathematical model cancer patients caspase 3 machine learning integrated approach colorectal cancer systems biology

A stepwise integrated approach to personalized risk predictions in stage III colorectal cancer. (2016)

Abstract 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. 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). 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. 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.
Collections Ireland -> Royal College of Surgeons in Ireland -> PubMed

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