Small Cell Lung Cancer
Small-cell lung cancer (SCLC) is an extremely aggressive disease with a poor prognosis. A number of different subtypes exist. Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer.
Our Services include but are not limited to:
- Genomic Analysis
- K-means Clustering
- Gene Regulatory Network Construction
- Gene Ontology Enrichment Analysis
- BooleaBayes Network Simulations
- Drug Sensitivity Analysis
- High Throughput Screening
Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies. We utilize a computational biology approach, based on appropriate re-analyses of datasets followed by reliable data filtering, to analyze integrative and combinatorial deregulated interaction networks in SCLC.
| SCLC Targets and Drugs |
| Apoptotic agents |
PIC3CA |
Focal adhesion kinases (FAK) inhibitors |
| eIF4E inhibitors |
RET inhibitors |
Chemokine receptor 4 (CXCR4) inhibitors |
| Aurora kinase inhibitors |
EZH2 inhibitors |
Poly ADP ribose polymerase (PARP) inhibitors |
| Notch inhibitors |
Transcription-targeting drugs |
Antiangiogenic agents |
| FGFR inhibitors |
Novel cytotoxic chemotherapeutic agents |
Antibody drug and radiotherapeutic conjugates |
CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers. Contact us for more service details.
