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Published on: July 22, 2020
Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens
Ginte Kutkaite1,2, Göksu Avar1,2,3, Diyuan Lu1
1Computational Health Center, Helmholtz Munich, Neuherberg, Germany.
Abstract:
Precision oncology relies on molecular biomarkers to stratify patients into responders and non-responders to a given treatment. Although gene expression profiles have historically been explored for biomarker discovery, fewer studies investigated single-gene expression biomarkers. Additionally, many approaches are limited to cancer type-specific associations, which constrain statistical power. To address these limitations, we developed a regression-based framework that corrects for tissue-specific biases and enhances detection of pan-cancer single-gene expression biomarkers of drug sensitivity in cancer cell line high-throughput drug screens. Our method maintains predictive performance post-correction, and successfully recovers established biomarkers, such as SLFN11 expression for DNA damaging agents. Notably, we identified SPRY4 and NES expression as biomarkers of sensitivity for compounds targeting ERK/MAPK signaling (adjusted p-value = 4.016 × 10 ⁻ ⁵ and 7.221 × 10 ⁻ ⁶, respectively). This approach offers a scalable strategy for biomarker discovery and holds potential for translation to more complex biological models and patient-derived datasets. Ultimately, pan-cancer single-gene expression biomarkers may inform patient stratification and warrant clinical validation in precision oncology.
Insights
This study introduces a new method to find single-gene biomarkers for cancer drug sensitivity across many cancer types. The approach improves patient stratification in precision oncology.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Precision oncology utilizes molecular biomarkers for patient stratification.
- Gene expression profiles are explored for biomarker discovery, but single-gene approaches are less common.
- Existing methods often lack statistical power due to cancer type-specific limitations.
Purpose of the Study:
- To develop a framework for discovering pan-cancer single-gene expression biomarkers of drug sensitivity.
- To address limitations of current methods by correcting for tissue-specific biases.
- To enhance the detection of biomarkers in high-throughput drug screens.
Main Methods:
- Developed a regression-based framework to correct for tissue-specific biases.
- Applied the method to cancer cell line high-throughput drug screens.
- Evaluated the framework's ability to detect pan-cancer single-gene expression biomarkers.
Main Results:
- The method maintains predictive performance after bias correction.
- Successfully identified established biomarkers, such as SLFN11 for DNA damaging agents.
- Discovered SPRY4 and NES as novel biomarkers for ERK/MAPK signaling inhibitors (adjusted p-values 4.016×10⁻⁵ and 7.221×10⁻⁶).
Conclusions:
- The developed framework offers a scalable strategy for pan-cancer biomarker discovery.
- Identified SPRY4 and NES as potential biomarkers for specific targeted therapies.
- Findings support the potential translation to patient-derived datasets for clinical validation in precision oncology.
