Big Data Analysis Illuminates New Strategies for Combinatorial Drug Therapy and Repurposing

A research team led by Prof. Hsueh-Fen Juan (阮雪芬) at NTU’s Department of Life Science and Graduate Institute of Biomedical Electronics and Bioinformatics (BEBI) and Prof. Hsuan-Cheng Huang (黃宣誠) at the Institute of Biomedical Informatics, National Yang-Ming University, has developed a series of computational and systems-biology approaches to identify new therapeutic opportunities for combinatorial drug discovery and repurposing in oncology. This was achieved by using over 1.3 million publicly accessible perturbational gene-expression profiles obtained from the Library of Integrated Network-based Cellular Signatures (LINCS), a project initiated by the US National Institute of Health. These studies were published in Clinical Cancer Research and iScience, an interdisciplinary open-access journal from Cell Press, and funded by the Ministry of Science and Technology and the National Health Research Institutes. The major contributors to these studies are the PhD students Chen-Tsung Huang (黃振綜; the first author) and Chiao-Hui Hsieh (謝巧慧; the second author). Other collaborators include Prof. Wen-Ming Hsu’s (許文明) team at NTU Hospital and Prof. Yen-Jen Oyang (歐陽彥正) at NTU’s BEBI.

Compared with the traditional drug-development paradigm whereby “a single drug should bind to a single target,” the phenomenon of drug promiscuity, or polypharmacology, whereby a single drug can bind to multiple targets, has been recently attracting much attention in the medical community. This polypharmacology is probably even indispensable for effective treatment in some medications. The research team investigated the complex polypharmacological interactions mirrored in the compound-perturbed gene expression profiles to explore new opportunities for drug repurposing and combination therapy, which will help to substantially reduce the cost and time spent on drug research and development.

The research team first developed a gene expression similarity metric that directly emphasizes the genes exhibiting the greatest changes in expression in response to a small-molecule perturbation. This metric was proved to outperform other state-of-the-art and commonly used metrics in a clustering task of given known drugs with diverse mechanisms of action. The research team then applied this metric to systematically compare thousands of small-molecule perturbations across 10 cell types and further investigated an anthelmintic and a loop diuretic as potential topoisomerase inhibitors for anticancer therapy.

In addition to the dependency on driver mutations that confer growth advantage, cancer cells can also develop an addiction to certain genes that are themselves not oncogenic but whose functions are required for maintenance of the tumorigenic state. These needs of both oncogenes and non-mutated genes for cancer cell survival are coined as oncogene and non-oncogene addictions, respectively. By systematically analyzing a large compendium of compound-perturbed data, the research team identified several perturbational gene-expression signatures that are highly correlated with cancer hallmark and drug sensitivity. The team then developed a computational approach that uses these small-molecule signatures to target non-oncogene tumor dependencies for combinatorial drug discovery, and experimentally confirmed two unexpected drug pairs with synergistic killing. This study provides an alternative drug discovery strategy from non-oncogene addiction and has potential clinical applicability to guide future combination therapy in precision medicine.

Neuroblastoma is a rare pediatric malignancy, whose heterogeneous mutational spectrum has restricted the development of targeted therapies. Despite intensive treatment, survival for high-risk neuroblastoma still remains below 40%. To address this unmet need, the research team performed an integrative transcriptomic analysis of nearly a thousand patients with primary neuroblastomas obtained from multiple Gene Expression Omnibus datasets to identify potential drugs that target non-oncogene dependencies in high-risk neuroblastoma. Among these predictions, the team demonstrated the in vivo efficacy of niclosamide, an anthelmintic drug approved by the US FDA to treat tapeworm infections, and further investigated its mechanism of action through proteomics.

Article links:

1. A Large-Scale Gene Expression Intensity-Based Similarity Metric for Drug Repositioning (https://www.cell.com/iscience/fulltext/S2589-0042(18)30129-9)
2. Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery (https://www.cell.com/iscience/fulltext/S2589-0042(19)30136-1)
3. Therapeutic Targeting of Non-Oncogene Dependencies in High-Risk Neuroblastoma (https://doi.org/10.1158/1078-0432.CCR-18-4117)