An Integrative Pharmacogenomic Approach Identifies Two-drug Combination Therapies for Personalized Cancer Medicine
Yin Liu1,2,3, Teng Fei4,5, Xiaoqi Zheng6
1School of Life Science and Technology, Tongji University, Shanghai 200092, China.
Abstract:
An individual tumor harbors multiple molecular alterations that promote cell proliferation and prevent apoptosis and differentiation. Drugs that target specific molecular alterations have been introduced into personalized cancer medicine, but their effects can be modulated by the activities of other genes or molecules. Previous studies aiming to identify multiple molecular alterations for combination therapies are limited by available data. Given the recent large scale of available pharmacogenomic data, it is possible to systematically identify multiple biomarkers that contribute jointly to drug sensitivity, and to identify combination therapies for personalized cancer medicine. In this study, we used pharmacogenomic profiling data provided from two independent cohorts in a systematic in silico investigation of perturbed genes cooperatively associated with drug sensitivity. Our study predicted many pairs of molecular biomarkers that may benefit from the use of combination therapies. One of our predicted biomarker pairs, a mutation in the BRAF gene and upregulated expression of the PIM1 gene, was experimentally validated to benefit from a therapy combining BRAF inhibitor and PIM1 inhibitor in lung cancer. This study demonstrates how pharmacogenomic data can be used to systematically identify potentially cooperative genes and provide novel insights to combination therapies in personalized cancer medicine.
Insights
This study uses computational analysis of pharmacogenomic data to identify gene pairs that improve cancer drug effectiveness. Researchers found that combining therapies targeting specific gene mutations, like BRAF, and gene expression, like PIM1, can enhance personalized cancer medicine.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Cancer involves multiple molecular changes affecting cell growth and survival.
- Targeted cancer drugs can have complex interactions with other genes.
- Identifying multiple biomarkers for combination therapy is challenging due to data limitations.
Purpose of the Study:
- To systematically identify cooperative gene biomarkers for drug sensitivity using large-scale pharmacogenomic data.
- To discover novel combination therapies for personalized cancer medicine.
- To validate predicted biomarker pairs and their response to combined drug treatments.
Main Methods:
- Utilized pharmacogenomic profiling data from two independent cohorts.
- Performed systematic in silico investigation of perturbed genes associated with drug sensitivity.
- Focused on identifying cooperative gene interactions impacting treatment outcomes.
Main Results:
- Predicted numerous pairs of molecular biomarkers that could benefit from combination therapies.
- Validated a specific pair: BRAF gene mutation and PIM1 gene upregulation.
- Demonstrated that combined BRAF and PIM1 inhibition is effective in lung cancer models.
Conclusions:
- Pharmacogenomic data can be leveraged to systematically identify cooperative genes.
- This approach provides novel insights for developing combination therapies in personalized cancer medicine.
- The BRAF and PIM1 biomarker pair offers a promising strategy for targeted lung cancer treatment.
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