TY - JOUR
T1 - A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer
AU - Salvucci, Manuela
AU - Würstle, Maximilian L
AU - Morgan, Clare
AU - Curry, Sarah
AU - Cremona, Mattia
AU - Lindner, Andreas U
AU - Bacon, Orna
AU - Resler, Alexa J
AU - Murphy, Áine C
AU - O'Byrne, Robert
AU - Flanagan, Lorna
AU - Dasgupta, Sonali
AU - Rice, Nadege
AU - Pilati, Camilla
AU - Zink, Elisabeth
AU - Schöller, Lisa M
AU - Toomey, Sinead
AU - Lawler, Mark
AU - Johnston, Patrick G
AU - Wilson, Richard
AU - Camilleri-Broët, Sophie
AU - Salto-Tellez, Manuel
AU - McNamara, Deborah A
AU - Kay, Elaine W
AU - Laurent-Puig, Pierre
AU - Van Schaeybroeck, Sandra
AU - Hennessy, Bryan T
AU - Longley, Daniel B
AU - Rehm, Markus
AU - Prehn, Jochen H M
N1 - ©2016 American Association for Cancer Research.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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. .
AB - 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. .
KW - Journal Article
U2 - 10.1158/1078-0432.CCR-16-1084
DO - 10.1158/1078-0432.CCR-16-1084
M3 - Article
C2 - 27649552
SN - 1078-0432
VL - 23
SP - 1200
EP - 1212
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 5
ER -
