%0 Journal Article %1 Salvucci2017 %A Salvucci, Manuela %A Urstle, Maximilian L. %A Morgan, Clare %A Curry, Sarah %A Cremona, Mattia %A Lindner, Andreas U. %A Bacon, Orna %A Resler, Alexa J. %A Murphy, Aine C. %A O'Byrne, Robert %A Flanagan, Lorna %A Dasgupta, Sonali %A Rice, Nadege %A Pilati, Camilla %A Zink, Elisabeth %A Scholler, Lisa M. %A Toomey, Sinead %A Lawler, Mark %A Johnston, Patrick G. %A Wilson, Richard %A Camilleri-Broet, Sophie %A Salto-Tellez, Manuel %A McNamara, Deborah A. %A Kay, Elaine W. %A Laurent-Puig, Pierre %A Van Schaeybroeck, Sandra %A Hennessy, Bryan T. %A Longley, Daniel B. %A Rehm, Markus %A Prehn, Jochen H.M. %D 2017 %J Clinical Cancer Research %K 2017 izi morrison %N 5 %P 1200--1212 %R 10.1158/1078-0432.CCR-16-1084 %T A stepwise integrated approach to personalized risk predictions in stage III colorectal cancer %U http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-16-1084 %V 23 %X urpose: Apoptosis is essential for chemotherapy responses. In this discovery and validation study, 5 we evaluated the suitability of a mathematical model of apoptosis execution (APOPTO-CELL) as a 6 stand-alone signature and as a constituent of further refined prognostic stratification tools. 7 Experimental Design: Apoptosis competency of primary tumor samples from n=120 stage III 8 colorectal cancer patients was calculated by APOPTO-CELL from measured protein concentrations of 9 Procaspase-3, Procaspase-9, SMAC and XIAP. An enriched APOPTO-CELL signature (APOPTO- 10 CELL-PC3) was synthesized to capture apoptosome-independent effects of Caspase-3. Furthermore, a 11 machine learning Random Forest approach was applied to APOPTO-CELL-PC3 and available 12 molecular and clinicopathological data to identify a further enhanced signature. Association of the 13 signature with prognosis was evaluated in an independent colon adenocarcinoma cohort (TCGA 14 COAD, n=136). 15 Results: We identified three prognostic biomarkers (p=0.04, p=0.006 and p=0.0004 for APOPTO- 16 CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively) with increasing 17 stratification accuracy for stage III colorectal cancer patients. 18 The APOPTO-CELL-PC3 signature ranked highest among all features. The prognostic value of the 19 signatures was independently validated in stage III TCGA COAD patients (p=0.01, p=0.04 and 20 p=0.02 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively). The 21 signatures provided further stratification for patients of CMS1-3 molecular subtype Conclusions: The integration of a systems-biology-based biomarker for apoptosis competency with 23 machine learning approaches is an appealing and innovative strategy towards refined patient 24 stratification. The prognostic value of apoptosis competency is independent of other available 25 clinicopathological and molecular factors, with tangible potential of being introduced in the clinical 26 management of stage III colorectal patients.

@article{Salvucci2017, abstract = {urpose: Apoptosis is essential for chemotherapy responses. In this discovery and validation study, 5 we evaluated the suitability of a mathematical model of apoptosis execution (APOPTO-CELL) as a 6 stand-alone signature and as a constituent of further refined prognostic stratification tools. 7 Experimental Design: Apoptosis competency of primary tumor samples from n=120 stage III 8 colorectal cancer patients was calculated by APOPTO-CELL from measured protein concentrations of 9 Procaspase-3, Procaspase-9, SMAC and XIAP. An enriched APOPTO-CELL signature (APOPTO- 10 CELL-PC3) was synthesized to capture apoptosome-independent effects of Caspase-3. Furthermore, a 11 machine learning Random Forest approach was applied to APOPTO-CELL-PC3 and available 12 molecular and clinicopathological data to identify a further enhanced signature. Association of the 13 signature with prognosis was evaluated in an independent colon adenocarcinoma cohort (TCGA 14 COAD, n=136). 15 Results: We identified three prognostic biomarkers (p=0.04, p=0.006 and p=0.0004 for APOPTO- 16 CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively) with increasing 17 stratification accuracy for stage III colorectal cancer patients. 18 The APOPTO-CELL-PC3 signature ranked highest among all features. The prognostic value of the 19 signatures was independently validated in stage III TCGA COAD patients (p=0.01, p=0.04 and 20 p=0.02 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively). The 21 signatures provided further stratification for patients of CMS1-3 molecular subtype Conclusions: The integration of a systems-biology-based biomarker for apoptosis competency with 23 machine learning approaches is an appealing and innovative strategy towards refined patient 24 stratification. The prognostic value of apoptosis competency is independent of other available 25 clinicopathological and molecular factors, with tangible potential of being introduced in the clinical 26 management of stage III colorectal patients.}, added-at = {2018-02-01T14:31:46.000+0100}, author = {Salvucci, Manuela and Urstle, Maximilian L. and Morgan, Clare and Curry, Sarah and Cremona, Mattia and Lindner, Andreas U. and Bacon, Orna and Resler, Alexa J. and Murphy, Aine C. and O'Byrne, Robert and Flanagan, Lorna and Dasgupta, Sonali and Rice, Nadege and Pilati, Camilla and Zink, Elisabeth and Scholler, Lisa M. and Toomey, Sinead and Lawler, Mark and Johnston, Patrick G. and Wilson, Richard and Camilleri-Broet, Sophie and Salto-Tellez, Manuel and McNamara, Deborah A. and Kay, Elaine W. and Laurent-Puig, Pierre and {Van Schaeybroeck}, Sandra and Hennessy, Bryan T. and Longley, Daniel B. and Rehm, Markus and Prehn, Jochen H.M.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26cc230cf4db8167de6821f139da474fb/cristiano}, doi = {10.1158/1078-0432.CCR-16-1084}, interhash = {9835f90c845a38bec38418449d2e4c22}, intrahash = {6cc230cf4db8167de6821f139da474fb}, issn = {15573265}, journal = {Clinical Cancer Research}, keywords = {2017 izi morrison}, month = mar, number = 5, pages = {1200--1212}, pmid = {27649552}, timestamp = {2019-01-17T12:29:29.000+0100}, title = {{A stepwise integrated approach to personalized risk predictions in stage III colorectal cancer}}, url = {http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-16-1084}, volume = 23, year = 2017 }