Document Detail

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy.
MedLine Citation:
PMID:  22082587     Owner:  NLM     Status:  Publisher    
ObjectiveKey to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental systems biology strategy which analyses the susceptibility of cancer cells to undergo caspase activation can be exploited to predict patient responses to 5-fluorouracil-based chemotherapy and to case-specifically identify potential alternative targeted treatments to reactivate apoptosis.DesignWe quantified five essential apoptosis-regulating proteins (Pro-Caspases 3 and 9, APAF-1, SMAC and XIAP) in samples of Stage II (n=13) and III (n=17) tumour and normal colonic (n=8) tissue using absolute quantitative immunoblotting and employed systems simulations of apoptosis signalling to predict the susceptibility of tumour cells to execute apoptosis. Additional systems analyses assessed the efficacy of novel apoptosis-inducing therapeutics such as XIAP antagonists, proteasome inhibitors and Pro-Caspase-3-activating compounds in restoring apoptosis execution in apoptosis-incompetent tumours.ResultsComparisons of caspase activity profiles demonstrated that the likelihood of colorectal tumours to undergo apoptosis decreases with advancing disease stage. Systems-level analysis correctly predicted positive or negative outcome in 85% (p=0.004) of colorectal cancer patients receiving 5-fluorouracil based chemotherapy and significantly outperformed common uni- and multi-variate statistical approaches. Modelling of individual patient responses to novel apoptosis-inducing therapeutics revealed markedly different inter-individual responses.ConclusionsOur study represents the first proof-of-concept example demonstrating the significant clinical potential of systems biology-based approaches for predicting patient outcome and responsiveness to novel targeted treatment paradigms.
Suzanne Hector; Markus Rehm; Jasmin Schmid; Joan Kehoe; Niamh McCawley; Patrick Dicker; Frank Murray; Deborah McNamara; Elaine W Kay; Caoimhin G Concannon; Heinrich J Huber; Jochen H M Prehn
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-11-14
Journal Detail:
Title:  Gut     Volume:  -     ISSN:  1468-3288     ISO Abbreviation:  -     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-11-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  2985108R     Medline TA:  Gut     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Royal College of Surgeons in Ireland, Dublin 2, Ireland.
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