# Discovery of new drug therapeutic indications from gene mutation information for hepatocellular carcinoma

**Authors:** Liang Yu, Fengdan Xu, Lin Gao  
**arXiv:** [1707.04586](https://arxiv.org/abs/1707.04586)  
**Date:** 2017-07-14  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1707.04586v1/

## Key points

- Hepatocellular carcinoma (HCC) is a primary liver malignancy responsible for many cancer-related deaths worldwide
- Currently used therapies do not offer a cure and there are limited approved targeted therapies for HCC
- Liang Yu, Fengdan Xu and Lin Gao used a systematic drug repositioning bioinformatics approach to identify novel candidate drugs to treat HCC
- Their approach considered aberrant genomic information and changes in transcriptional landscapes associated with the disease
- They screened the collection of HCC feature genes based on human mutation data and combined gene expression data from The Cancer Genome Atlas (TCGA) to classify the kernel genes of HCC
- They calculated the therapeutic score (TS) of each drug using the Kolmogorov-Smirnov statistical method
- Yu et al. identified five drugs associated with HCC: three drugs that could treat HCC and two drugs that might have side effects on HCC
- They conducted Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets
- Their approach is effective for accurately discovering novel therapeutic options for HCC and can be easily extended to other tumors.
- This research provides hope for new treatments that could improve outcomes for patients with this devastating disease while still many challenges remain in treating liver cancer effectively.

## Summary

Hepatocellular carcinoma (HCC) is a primary liver malignancy that is responsible for a significant number of cancer-related deaths worldwide. Unfortunately, currently used therapies do not offer a cure and there are limited approved targeted therapies for HCC despite numerous attempts and clinical trials. To address this challenge, Liang Yu, Fengdan Xu and Lin Gao sought to use a systematic drug repositioning bioinformatics approach to identify novel candidate drugs to treat HCC. Their approach considered not only aberrant genomic information but also changes in transcriptional landscapes associated with the disease. The researchers began by screening the collection of HCC feature genes that frequently mutated in most samples of HCC based on human mutation data. They then combined gene expression data from The Cancer Genome Atlas (TCGA) to classify the kernel genes of HCC and calculated the therapeutic score (TS) of each drug using the Kolmogorov-Smirnov statistical method. Using this strategy, Yu et al. identified five drugs associated with HCC: three drugs that could treat HCC and two drugs that might have side effects on HCC. In addition, they conducted Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. Their findings suggest that their approach is effective for accurately discovering novel therapeutic options for HCC and can be easily extended to other tumors. This research provides hope for new treatments that could improve outcomes for patients with this devastating disease while still many challenges remain in treating liver cancer effectively. With their systematic drug repositioning bioinformatics approach, Yu et al have opened up new possibilities for discovering novel therapeutic options for hepatocellular carcinoma which may lead to improved outcomes in the future.