A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
- Shanghai Jiao Tong University, China
- Huazhong University of Science and Technology, China
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.
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.
Reviewing Editor
- Arduino A Mangoni, Flinders Medical Centre, Australia
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
-
- 2,463
- Page views
-
- 327
- Downloads
-
- 26
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
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)
A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer
eLife 11:e71880.
https://doi.org/10.7554/eLife.71880
Further reading
-
- Cancer Biology
- Computational and Systems Biology
Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, i.e., possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer cell lines or characterize intrinsic drug resistance without a priori knowledge of its cause. We hypothesized that cell morphology could provide an unbiased readout of drug resistance. To test this hypothesis, we used HCT116 cells, a mismatch repair-deficient cancer cell line, to isolate clones that were resistant or sensitive to bortezomib, a well-characterized proteasome inhibitor and anticancer drug to which many cancer cells possess intrinsic resistance. We then expanded these clones and measured high-dimensional single-cell morphology profiles using Cell Painting, a high-content microscopy assay. Our imaging- and computation-based profiling pipeline identified morphological features that differed between resistant and sensitive cells. We used these features to generate a morphological signature of bortezomib resistance. We then employed this morphological signature to analyze a set of HCT116 clones (five resistant and five sensitive) that had not been included in the signature training dataset, and correctly predicted sensitivity to bortezomib in seven cases, in the absence of drug treatment. This signature predicted bortezomib resistance better than resistance to other drugs targeting the ubiquitin-proteasome system. Our results establish a proof-of-concept framework for the unbiased analysis of drug resistance using high-content microscopy of cancer cells, in the absence of drug treatment.
-
- Biochemistry and Chemical Biology
- Cancer Biology
Identification oncogenes is fundamental to revealing the molecular basis of cancer. Here, we found that FOXP2 is overexpressed in human prostate cancer cells and prostate tumors, but its expression is absent in normal prostate epithelial cells and low in benign prostatic hyperplasia. FOXP2 is a FOX transcription factor family member and tightly associated with vocal development. To date, little is known regarding the link of FOXP2 to prostate cancer. We observed that high FOXP2 expression and frequent amplification are significantly associated with high Gleason score. Ectopic expression of FOXP2 induces malignant transformation of mouse NIH3T3 fibroblasts and human prostate epithelial cell RWPE-1. Conversely, FOXP2 knockdown suppresses the proliferation of prostate cancer cells. Transgenic overexpression of FOXP2 in the mouse prostate causes prostatic intraepithelial neoplasia. Overexpression of FOXP2 aberrantly activates oncogenic MET signaling and inhibition of MET signaling effectively reverts the FOXP2-induced oncogenic phenotype. CUT&Tag assay identified FOXP2-binding sites located in MET and its associated gene HGF. Additionally, the novel recurrent FOXP2-CPED1 fusion identified in prostate tumors results in high expression of truncated FOXP2, which exhibit a similar capacity for malignant transformation. Together, our data indicate that FOXP2 is involved in tumorigenicity of prostate.
