Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens
Kutkaite, G.; Avar, G.; Lu, D.; ONeill, T.; Krappmann, D.; Menden, M. P.
Show 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=4.016x10- and 7.221x10-, 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 improve patient stratification and clinical outcomes in precision oncology.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The DiffInvex evolutionary model for conditional somatic selection identifies chemotherapy resistance genes in 10,000 cancer genomes 97%
- Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action 97%
- Drug and single-cell gene expression integration identifies sensitive and resistant glioblastoma cell populations 97%
Similar papers in this journal
- Non-oncology drugs are a source of previously unappreciated anti-cancer activity 96%
- Multiplexed mosaic tumor models reveal natural phenotypic variations in drug response within and between populations 96%
- Combined KRASG12C and SOS1 inhibition enhances and extends the anti-tumor response in KRASG12C-driven cancers by addressing intrinsic and acquired resistance 95%
Similar papers in this journal
- Aberrant transcript usage induces homologous recombination deficiency and predicts therapeutic responses 96%
- Quantitative in vivo analyses reveal a complex pharmacogenomic landscape in lung adenocarcinoma 95%
- CIP2A interacts with TopBP1 and is selectively essential for DNA damage-induced basal-like breast cancer tumorigenesis 95%
Similar papers in this journal
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 96%
- Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer 93%
- A single-cell based precision medicine approach using glioblastoma patient-specific models 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.