Characterizing the landscape of gene process dependencies in cancer
Abstract
Precision oncology aims to tailor cancer treatment to tumor genetics but it currently benefits only a small fraction of patients, in part because the primary focus is to match drug targets to single genes. The Cancer Dependency Map Project (DepMap) aimed to characterize the landscape of single-gene dependencies, which increased the universe of potential drug targets. However, the common challenges of drug off-target effects and polypharmacology may limit effectiveness of single genes as drug targets. To address this limitation, we apply BioBombe, an AI/ML framework, to DepMap gene dependency data. This approach characterizes gene process dependencies, which are groups of genes within a biological process that cells rely on for survival. BioBombe fits many hundreds of dimensionality reduction models, across a large range of latent dimensionalities. We find that this multiple-model approach discovers many more gene process dependencies than any single model alone. Using Reactome and CORUM-based gene set enrichment analyses, we characterize the landscape of gene process dependencies, identifying, for example, mitotic regulation and the citric acid cycle as targets, as well as many cancer type-specific dependencies. In gliomas, for example, TP53- and mitochondrial-related pathways emerged as key process vulnerabilities. Linking gene process dependencies with drug sensitivity scores on matched cell lines, we discovered both established and novel drug candidates. Furthermore, we developed a machine learning approach to predict gene process dependencies and associated drug sensitivities from RNA-seq data. Using this approach, we predicted cladribine as a potential new therapeutic for pediatric high-grade glioma and validated its effectiveness at killing these cells. Taken together, BioBombe provides a scalable and interpretable framework for uncovering complex gene process dependencies, which guides drug repurposing, and introduces a novel targeting paradigm for precision oncology.
Related
- The adaptive landscape of the miR482 gene family in tomato is rugged
- The changing landscape of Fabry disease: Impact of the inclusion of the GLA-gene in broader NGS or WES based panels on the phenotypic spectrum
- Characterizing the role of monocytes in T cell cancer immunotherapy using a 3d microfluidic model
- Multi-ancestry genome- and transcriptome-wide association studies expand the susceptibility landscape for breast cancer overall and across subtypes
- Systemic iron sequestration restricts the intratumoral CD8+ T cell landscape in pancreas cancer
BibTeX
@article{curd2026characterizing,
title = {{Characterizing the landscape of gene process dependencies in cancer}},
author = {Julia B. Curd and Navin Kumar Balagopal and Adam L. Green and Gregory P. Way},
journal = {BMC Genomics},
year = {2026},
month = oct,
doi = {10.1186/s12864-026-13457-5},
url = {https://link.springer.com/article/10.1186/s12864-026-13457-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Characterizing the landscape of gene process dependencies in cancer
AU - Julia B. Curd
AU - Navin Kumar Balagopal
AU - Adam L. Green
AU - Gregory P. Way
PY - 2026
DA - 2026/10/14
JO - BMC Genomics
DO - 10.1186/s12864-026-13457-5
UR - https://link.springer.com/article/10.1186/s12864-026-13457-5
AB - Precision oncology aims to tailor cancer treatment to tumor genetics but it currently benefits only a small fraction of patients, in part because the primary focus is to match drug targets to single genes. The Cancer Dependency Map Project (DepMap) aimed to characterize the landscape of single-gene dependencies, which increased the universe of potential drug targets. However, the common challenges of drug off-target effects and polypharmacology may limit effectiveness of single genes as drug targets. To address this limitation, we apply BioBombe, an AI/ML framework, to DepMap gene dependency data. This approach characterizes gene process dependencies, which are groups of genes within a biological process that cells rely on for survival. BioBombe fits many hundreds of dimensionality reduction models, across a large range of latent dimensionalities. We find that this multiple-model approach discovers many more gene process dependencies than any single model alone. Using Reactome and CORUM-based gene set enrichment analyses, we characterize the landscape of gene process dependencies, identifying, for example, mitotic regulation and the citric acid cycle as targets, as well as many cancer type-specific dependencies. In gliomas, for example, TP53- and mitochondrial-related pathways emerged as key process vulnerabilities. Linking gene process dependencies with drug sensitivity scores on matched cell lines, we discovered both established and novel drug candidates. Furthermore, we developed a machine learning approach to predict gene process dependencies and associated drug sensitivities from RNA-seq data. Using this approach, we predicted cladribine as a potential new therapeutic for pediatric high-grade glioma and validated its effectiveness at killing these cells. Taken together, BioBombe provides a scalable and interpretable framework for uncovering complex gene process dependencies, which guides drug repurposing, and introduces a novel targeting paradigm for precision oncology.
SN - 1471-2164
ER -