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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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TIM circuit for osteosarcoma primary culture Sy.
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Figure 3: TIM circuit for osteosarcoma primary culture Sy.

Mentions: Among the 60 drugs on the drug screen, 46 drugs have known target inhibition profiles; of these 46 drugs, 2 provide information only on the target mTOR (mammalian target of Rapamycin) and analysis of these drugs are trivial. Thus, the remaining 44 drugs are used to generate the TIMs. These target profiles were extracted from several literature sources ([16,20]) based on experimental quantitative dissociation constants (kd) which are treated as EC50 values (explained in the next section) for each drug across kinase target assays with more than 300 targets. The target profiles of the drugs are shown in Additional file 3. Figures 2 and 3 represent the equivalent TIM circuits generated from experimental data for Bailey and Sy respectively. The TIM circuits for Charley and Cora are included in Additional file 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)

TIM circuit for osteosarcoma primary culture Sy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: TIM circuit for osteosarcoma primary culture Sy.
Mentions: Among the 60 drugs on the drug screen, 46 drugs have known target inhibition profiles; of these 46 drugs, 2 provide information only on the target mTOR (mammalian target of Rapamycin) and analysis of these drugs are trivial. Thus, the remaining 44 drugs are used to generate the TIMs. These target profiles were extracted from several literature sources ([16,20]) based on experimental quantitative dissociation constants (kd) which are treated as EC50 values (explained in the next section) for each drug across kinase target assays with more than 300 targets. The target profiles of the drugs are shown in Additional file 3. Figures 2 and 3 represent the equivalent TIM circuits generated from experimental data for Bailey and Sy respectively. The TIM circuits for Charley and Cora are included in Additional file 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