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Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

Zhang N, Wang H, Fang Y, Wang J, Zheng X, Liu XS - PLoS Comput. Biol. (2015)

Bottom Line: When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model.We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset.Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.

ABSTRACT
The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

No MeSH data available.


Related in: MedlinePlus

Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.
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pcbi.1004498.g006: Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.

Mentions: Although the CGP study used a total of 707 cell lines and 139 drugs, only 653 cell lines had expression profiles and only 124 drugs had chemical information available. Out of the possible 653 × 124 cell line-drug combinations, only 76% have corresponding drug response data. With the cell similarity and drug similarity data, we could use our dual-layer integrated cell line-drug network model to predict the missing activity areas (Fig 6) and IC50 (S9 Fig), with a particular focus on three MEK inhibitors AZD6244, RDEA119, and PD-0325901, where a large number of response values were missing. When grouping the cell lines based on their BRAF mutation profiles, we found that the BRAF-mutated cell lines were significantly more sensitive to MEK inhibitors (Fig 6). These predictions were consistent with those in cell lines where response data were available, and were in agreement with previously published studies. The above findings suggest that our dual-layer integrated cell line-drug network model can be used to optimize the design of cell line screens with new drugs by combining in silico predicted response values from existing screen results and the structure of the new drugs.


Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

Zhang N, Wang H, Fang Y, Wang J, Zheng X, Liu XS - PLoS Comput. Biol. (2015)

Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4587957&req=5

pcbi.1004498.g006: Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.
Mentions: Although the CGP study used a total of 707 cell lines and 139 drugs, only 653 cell lines had expression profiles and only 124 drugs had chemical information available. Out of the possible 653 × 124 cell line-drug combinations, only 76% have corresponding drug response data. With the cell similarity and drug similarity data, we could use our dual-layer integrated cell line-drug network model to predict the missing activity areas (Fig 6) and IC50 (S9 Fig), with a particular focus on three MEK inhibitors AZD6244, RDEA119, and PD-0325901, where a large number of response values were missing. When grouping the cell lines based on their BRAF mutation profiles, we found that the BRAF-mutated cell lines were significantly more sensitive to MEK inhibitors (Fig 6). These predictions were consistent with those in cell lines where response data were available, and were in agreement with previously published studies. The above findings suggest that our dual-layer integrated cell line-drug network model can be used to optimize the design of cell line screens with new drugs by combining in silico predicted response values from existing screen results and the structure of the new drugs.

Bottom Line: When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model.We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset.Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.

ABSTRACT
The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

No MeSH data available.


Related in: MedlinePlus