A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
Abstract
Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL4)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.
Data availability
Sequencing data have been deposited in GEO under accession codes GSE180243 and GSE193897.All data generated or analysed during this study are included in the manuscript and supporting files.
-
A survey of optimal strategy for signature-based drug repositioning and an application to liver cancerNCBI Gene Expression Omnibus, GSE180243.
-
Gene expression profiles of 35 paired HCC and non-tumor tissues by RNA-seq dataNCBI Gene Expression Omnibus, GSE124535.
-
Next-generation characterization of the Cancer Cell Line Encyclopedia.Broad Institute Cancer Cell Line Encyclopedia (CCLE).
-
The Genotype-Tissue Expression (GTEx) projectGenotype-Tissue Expression (GTEx).
-
Gene expression data of human hepatocellular carcinoma (HCC)NCBI Gene Expression Omnibus,GSE14520.
-
Integrative omics analysis in HCC samples [mRNA expression]NCBI Gene Expression Omnibus, GSE84005.
-
Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinomaNCBI Gene Expression Omnibus,GSE6764.
-
Gene-expression profiles of hepatitis C-related, early-stage liver cirrhosisNCBI Gene Expression Omnibus, GSE15654.
-
Characterization of gene expression profile in HBV-related liver fibrosis patientsNCBI Gene Expression Omnibus, GSE84044.
-
Gene expression profile of liver tissue from carbon tetrachloride (CCl4)-treated mouse cultured ex vivoNCBI Gene Expression Omnibus, GSE71379.
-
Gene expression profiles of fractionated cells from cirrhotic rat liversNCBI Gene Expression Omnibus,GSE63726.
Article and author information
Author details
Funding
National Natural Science Foundation of China (81972208)
- Hui Wang
National Natural Science Foundation of China (82170646)
- Hualian Hang
Shanghai Natural Science Foundation (19ZR1452700)
- Hui Wang
The Interdisciplinary Program of Shanghai Jiao Tong University (YG2021ZD10)
- Hualian Hang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Arduino A Mangoni, Flinders Medical Centre, Australia
Ethics
Animal experimentation: All animals were manipulated according to protocols approved by the Shanghai Medical Experimental Animal Care Commission and Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine.
Version history
- Preprint posted: June 30, 2021 (view preprint)
- Received: July 2, 2021
- Accepted: February 16, 2022
- Accepted Manuscript published: February 22, 2022 (version 1)
- Version of Record published: March 3, 2022 (version 2)
Copyright
© 2022, Yang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,192
- views
-
- 406
- downloads
-
- 56
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
BRAFV600E mutation is a driver mutation in the serrated pathway to colorectal cancers. BRAFV600E drives tumorigenesis through constitutive downstream extracellular signal-regulated kinase (ERK) activation, but high-intensity ERK activation can also trigger tumor suppression. Whether and how oncogenic ERK signaling can be intrinsically adjusted to a ‘just-right’ level optimal for tumorigenesis remains undetermined. In this study, we found that FAK (Focal adhesion kinase) expression was reduced in BRAFV600E-mutant adenomas/polyps in mice and patients. In Vil1-Cre;BRAFLSL-V600E/+;Ptk2fl/fl mice, Fak deletion maximized BRAFV600E’s oncogenic activity and increased cecal tumor incidence to 100%. Mechanistically, our results showed that Fak loss, without jeopardizing BRAFV600E-induced ERK pathway transcriptional output, reduced EGFR (epidermal growth factor receptor)-dependent ERK phosphorylation. Reduction in ERK phosphorylation increased the level of Lgr4, promoting intestinal stemness and cecal tumor formation. Our findings show that a ‘just-right’ ERK signaling optimal for BRAFV600E-induced cecal tumor formation can be achieved via Fak loss-mediated downregulation of ERK phosphorylation.
-
- Cancer Biology
- Computational and Systems Biology
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.