Anticancer drug synergy prediction in understudied tissues using transfer learning
Yejin Kim1, Shuyu Zheng2, Jing Tang2
1Center for Safe Artificial Intelligence for Healthcare, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Objective:
Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.
Materials And Methods:
We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues.
Results:
We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues.
Conclusions:
Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.
Insights
This study developed a drug synergy prediction model using multitask deep neural networks and transfer learning to improve cancer treatment options for understudied tissues. The model enhances accuracy in predicting drug combinations, aiding future experimental prioritization.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Drug combination screening offers potential for enhanced cancer treatment efficacy and safety.
- A significant challenge in drug synergy prediction is the variable availability of in vitro drug response data across different cancer types.
- Understudied cancer tissues present data scarcity issues, hindering the development of effective treatment strategies.
Purpose of the Study:
- To develop a drug synergy prediction model specifically for understudied cancer tissues.
- To overcome data scarcity limitations in cancer research.
- To improve the identification of effective cancer treatment options.
Main Methods:
- Collected comprehensive genetic, molecular, and phenotypic features from cancer cell lines.
- Developed a multitask deep neural network model for integrating multimodal data and multiple outputs.
- Employed transfer learning techniques to leverage data from data-rich tissues for application to data-poor tissues.
Main Results:
- Achieved improved accuracy in predicting drug synergy for both data-rich and understudied tissues.
- Demonstrated high prediction accuracy in data-rich tissues (0.9577 AUROC for classification, 174.3 MSE for regression).
- Confirmed that transfer learning significantly enhances prediction accuracy in understudied tissues.
Conclusions:
- The developed synergy prediction model effectively ranks synergistic drug combinations for understudied tissues.
- The model aids in prioritizing future in vitro experimental investigations.
- Open-source code is available to facilitate further research and application.
More Related Videos
15:04Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
Published on: January 19, 2019
13:34A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Related Concept Videos
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
02:43Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
02:53Adaptive Mechanisms in Cancer Cells
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
01:21Cancer Survival Analysis