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

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.

Learning curves for drug response prediction in cancer cell lines

“…Given recent advances in artificial neural networks (NNs), deep learning (DL) methods have become a favorite approach across a variety of scientific disciplines for discovering hidden patterns in large volumes of complex data. This trend is also observed in medical applications, including the prediction of drug response in cancer cell lines [10][11][12][13][14]. Regardless of the learning algorithm, supervised learning models are expected to improve generalization performance with increasing amounts of high-quality labeled data.…”

“…Contributed to the diversity and complexity of cancer pathologies, no single combination of a dataset, feature type, and prediction target serves as a universal benchmark for modeling drug response. Various learning methodologies and data pre-processing techniques have been explored to enhance the predictive capabilities of models, including multi-modal learning [11,26], feature encoding schemes [27,31], and transfer learning [13,42]. The performance of these models is assessed by comparing single-value performance measures such as prediction error or accuracy against baseline models obtained at the full sample size.…”

Artificial intelligence in cancer research: learning at different levels of data granularity

“…Transfer learning re‐uses the weights of pretrained models in a similar learning task [87]. For instance, it has been recently applied to model anticancer drug response in a small dataset transferring the information learnt from large datasets [88]. This study illustrates the potential of transfer learning to improve future drug response prediction performance on patients by transferring information from patient‐derived models, such as xenografts and organoids.…”

Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

“…Various methods of transfer learning have been proposed in the context of drug response prediction. These methods either address these discrepancies implicitly (Sharifi- Noghabi et al 2019;Snow et al 2020;Kuenzi et al 2020), or explicitly which means they assume that the model has access to the desired labeled or unlabeled target domain during training (Sharifi-Noghabi et al 2020;Mourragui et al 2019Mourragui et al , 2020Ma et al 2021;Zhu et al 2020;Warren et al 2020;Peres da Silva, Suphavilai, and Nagarajan 2021).…”