Reconciling Multiple Connectivity Scores for Drug Repurposing

Kewalin Samart https://github.com/KewalinSamart (Mathematics, Michigan State University)https://github.com/orgs/JRaviLab , Phoebe Tuyishime https://github.com/phoebetuyishime (Food Science and Nutrition, Michigan State University)https://github.com/orgs/JRaviLab , Arjun Krishnan https://www.thekrishnanlab.org/ (Computational Mathematics, Science, and Engineering; Biochemistry and Molecular Biology, Michigan State University)https://cmse.msu.edu/directory/faculty/ , Janani Ravi https://jravilab.github.io (Pathobiology and Diagnostic Investigations, Michigan State University)https://cvm.msu.edu/directory/ravi
2020-10-20

Abstract

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular ‘signature’ with minimal side-effects. This principle was defined and popularized by the influential ‘connectivity map’ study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 14 years, several studies have proposed variations in calculating connectivity scores towards improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using varied notations and terminologies even though they are based on a common set of conceptual and statistical ideas. Therefore, we present a systematic reconciliation of multiple disease-drug connectivity scores by defining them using consistent notation and terminology. In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this review will be available as a live document (https://jravilab.github.io/connectivity_score_review) coupled with a GitHub repository (https://github.com/jravilab/connectivity_score_review) that any researcher can build on and push changes to.

Keywords

drug repurposing | disease gene signature | drug profile | connectivity mapping | transcriptomics

Key points

Introduction

The manifestation of a disease or perturbation by a small molecule in a tissue leaves a characteristic imprint (a “signature”) in its gene expression profile [1]. These signatures, recorded for thousands of diseases and drugs, form the basis of a powerful and widely-adopted method for drug repurposing called “drug-disease connectivity analysis” [2]. In this analysis, novel drug indications for a specific disease of interest are identified based on the extent to which the ranked drug-gene signature is a “reversal” of the disease gene signature ([3] [4]; Fig. 1). Connectivity-based drug repurposing has been used to discover drugs in various cancers and non-cancer diseases [5].