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Published on: November 22, 2021
Utilising multi-modal data-driven network analysis to identify monotherapy and combinational therapy targets in
Woochang Hwang1,2,3, Daniel Kottmann4, Wenrui Guo4
1Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
Abstract:
Drug discovery requires understanding disease mechanisms, making the integration of multi-modal data essential. These data types, including omics, disease-associated, and pathway information, must be combined to uncover therapeutic insights. We developed iPANDDA, a computational pipeline that integrates these data through a network-based approach to predict candidate drug targets for specific diseases. We applied iPANDDA to lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer representing ~25% of global cases. Despite advances in cancer therapeutics, targeted treatments for LUSC remain limited, partly due to a lack of robust models to study carcinogenesis and therapeutic response. The SOX2 gene, amplified in ~50% of patients, plays a critical role in sustaining the cancer phenotype. Using iPANDDA, we identified and validated SOX2-dependent therapeutic targets. In vitro inhibition studies confirmed AKT and mTOR complexes as key monotherapy and combination therapy targets and revealed pathways for SOX2-targeted combination therapies.
Insights
We created iPANDDA, a computational tool to integrate multi-modal data for identifying drug targets. This approach successfully pinpointed SOX2-dependent targets like AKT and mTOR for lung squamous cell carcinoma treatment.
Area of Science:
- Computational biology
- Genomics
- Oncology
Background:
- Drug discovery necessitates understanding complex disease mechanisms.
- Integrating multi-modal data (omics, disease, pathway) is crucial for therapeutic insights.
- Lung squamous cell carcinoma (LUSC) lacks targeted therapies due to limited models.
Purpose of the Study:
- To develop a computational pipeline (iPANDDA) for integrating multi-modal data.
- To predict and validate candidate drug targets for specific diseases, focusing on LUSC.
- To identify therapeutic strategies targeting the SOX2 gene in LUSC.
Main Methods:
- Developed iPANDDA, a network-based computational pipeline.
- Integrated omics, disease-associated, and pathway data.
- Applied iPANDDA to LUSC, focusing on SOX2-amplified tumors.
Main Results:
- iPANDDA successfully identified SOX2-dependent therapeutic targets in LUSC.
- In vitro studies validated AKT and mTOR complexes as key targets.
- Discovered potential monotherapy and combination therapy strategies for SOX2-driven LUSC.
Conclusions:
- iPANDDA is an effective tool for multi-modal data integration in drug discovery.
- Targeting SOX2, AKT, and mTOR offers promising therapeutic avenues for LUSC.
- This study provides a framework for developing targeted LUSC treatments.
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