Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine Approach

Abstract

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as cancer, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. While the "omics" era provides unique opportunities to dissect the molecular features of diseases, the ability to utilize it in targeted therapeutic efforts is hindered by both the massive size and diverse nature of the "omics" data.Recent advances with Deep Learning Neural Networks (DLNNs), suggests that DLNN could be trained on large data sets to efficiently predict therapeutic responses in cancer treatment. We present the application of Association Rule Mining combined with DLNNs for the analysis of high-throughput molecular profiles of 1001 cancer cell lines, in order to extract cancer-specific signatures in the form of easily interpretable rules and use these rules as input to predict pharmacological responses to a large number of anti-cancer drugs. The proposed algorithm outperformed Random Forests (RF) and Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction. Moreover, the in silico pipeline presented, introduces a novel strategy for identifying potential therapeutic targets, as well as possible drug combinations with high therapeutic potential. For the first time, we demonstrate that DLNNs trained on a large pharmacogenomics data-set can effectively predict the therapeutic response of specific drugs in different cancer types. These findings serve as a proof of concept for the application of DLNNs to predict therapeutic responsiveness, a milestone in precision medicine.

Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

“…Applications of deep learning recently demonstrate state-of-the-art performance for predicting cell phenotypes from transcriptomics data [122], drug response in cancer [123], seizure-inducing side effects of preclinical drugs [124], patient survival from multi-omic data [38], drug-induced liver injury prediction [62], and classifying genomic variants into adverse drug reactions [125].…”

Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis

“…Predicting patient response: Network analysis can be used to predict a patient's response to a specific treatment based on their individual network profile. This information can guide personalized treatment decisions and improve clinical outcomes [94].…”

A hybrid sampling combining Smote and RF algorithm for cancer chemotherapy protocols Classification

“…Some studies did run some protocols to investigate the best models (Machine learning, deep learning) for predicting drug response of the different chemotherapy regimens on breast cancer patients. This paper [16] presents a novel approach to predict the therapeutic responses of cancer patients to anti-cancer drugs using Deep Learning Neural Networks (DLNNs). Through the combination of Association Rule Mining and DLNNs, a large data set of molecular profiles from 1001 cancer cell lines was used to generate cancerspecific signatures, which were then used to predict pharmacological responses to a wide variety of anti-cancer drugs.…”