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A new approach for prediction of tumor sensitivity to targeted drugs based on functional data.

Berlow N, Davis LE, Cantor EL, Séguin B, Keller C, Pal R - BMC Bioinformatics (2013)

Bottom Line: We illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs.We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.This framework can be developed as a viable approach for personalized cancer therapy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

ABSTRACT

Background: The success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient's tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.

Results: We illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs. We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.

Conclusions: The proposed framework provides a unique input-output based methodology to model a cancer pathway and predict the effectiveness of targeted anti-cancer drugs. This framework can be developed as a viable approach for personalized cancer therapy.

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Related in: MedlinePlus

Combination therapy design workflow: various steps in the design of combination targeted therapy.
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Related In: Results  -  Collection

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Figure 1: Combination therapy design workflow: various steps in the design of combination targeted therapy.

Mentions: We envision that the overall schematic of the design of personalized pathways and personalized therapy will be similar to the workflow shown in Figure 1.


A new approach for prediction of tumor sensitivity to targeted drugs based on functional data.

Berlow N, Davis LE, Cantor EL, Séguin B, Keller C, Pal R - BMC Bioinformatics (2013)

Combination therapy design workflow: various steps in the design of combination targeted therapy.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750584&req=5

Figure 1: Combination therapy design workflow: various steps in the design of combination targeted therapy.
Mentions: We envision that the overall schematic of the design of personalized pathways and personalized therapy will be similar to the workflow shown in Figure 1.

Bottom Line: We illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs.We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.This framework can be developed as a viable approach for personalized cancer therapy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.

ABSTRACT

Background: The success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient's tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.

Results: We illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs. We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.

Conclusions: The proposed framework provides a unique input-output based methodology to model a cancer pathway and predict the effectiveness of targeted anti-cancer drugs. This framework can be developed as a viable approach for personalized cancer therapy.

Show MeSH
Related in: MedlinePlus