Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.
Cara AbecunasMohammad Fallahi-SichaniPublished in: bioRxiv : the preprint server for biology (2023)
Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tissue lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings using data from an independent set of cell lines, pharmacological screens, and via single-cell analysis of patient-derived tumors. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating these relationships could inform the development of more precise and context-specific, metabolism-targeted cancer therapies.
Keyphrases
- cancer therapy
- papillary thyroid
- single cell
- squamous cell
- machine learning
- drug delivery
- high throughput
- genome wide
- cell proliferation
- physical activity
- oxidative stress
- data analysis
- rna seq
- young adults
- squamous cell carcinoma
- electronic health record
- lymph node metastasis
- childhood cancer
- big data
- deep learning
- signaling pathway
- electron transfer