Abstract 671: Evaluation of machine learning tools for cancer drug response prediction with genomic profile data and drug response data from PDX model studies

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

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Ensemble transfer learning for the prediction of anti-cancer drug response

Scientific Reports ◽

10.1038/s41598-020-74921-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yitan Zhu ◽

Thomas Brettin ◽

Yvonne A. Evrard ◽

Alexander Partin ◽

Fangfang Xia ◽

...

Keyword(s):

Tumor Cells ◽

Transfer Learning ◽

Drug Response ◽

Prediction Models ◽

Response Prediction ◽

Cancer Drug ◽

Precision Oncology ◽

Learning Framework ◽

Prediction Algorithms ◽

Anti Cancer

Abstract Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.

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Drug Response Prediction as a Link Prediction Problem

Scientific Reports ◽

10.1038/srep40321 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 27

Author(s):

Zachary Stanfield ◽

Mustafa Coşkun ◽

Mehmet Koyutürk

Keyword(s):

Machine Learning ◽

Cell Lines ◽

Link Prediction ◽

Drug Response ◽

Response Prediction ◽

Resistant Cell ◽

Resistant Cell Line ◽

Molecular Networks ◽

Molecular Profile ◽

Prediction Problem

Abstract Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.

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