Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
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.
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
Repetitive hypoxic preconditioning creates long-lasting, endogenous protection in a mouse model of stroke, characterized by reductions in leukocyte-endothelial adherence, inflammation, and infarct volumes. The constitutively expressed chemokine CXCL12 can be upregulated by hypoxia and limits leukocyte entry into brain parenchyma during central nervous system inflammatory autoimmune disease. We therefore hypothesized that the sustained tolerance to stroke induced by repetitive hypoxic preconditioning is mediated, in part, by long-term CXCL12 upregulation at the bloodbrain barrier (BBB). In male Swiss Webster mice, repetitive hypoxic preconditioning elevated cortical CXCL12 protein levels, and the number of cortical CXCL12þ microvessels, for at least two weeks after the last hypoxic exposure. Repetitive hypoxic preconditioning-treated mice maintained more CXCL12-positive vessels than untreated controls following transient focal stroke, despite cortical decreases in CXCL12 mRNA and protein. Continuous administration of the CXCL12 receptor (CXCR4) antagonist AMD3100 for two weeks following repetitive hypoxic preconditioning countered the increase in CXCL12-positive microvessels, both prior to and following stroke. AMD3100 blocked the protective post-stroke reductions in leukocyte diapedesis, including macrophages and NK cells, and blocked the protective effect of repetitive hypoxic preconditioning on lesion volume, but had no effect on blood-brain barrier dysfunction. These data suggest that CXCL12 upregulation prior to stroke onset, and its actions following stroke, contribute to the endogenous, anti-inflammatory phenotype induced by repetitive hypoxic preconditioning.
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