|
| Status |
Public on Apr 04, 2025 |
| Title |
CAN-Scan: a multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
|
| Summary |
Application of machine learning (ML) on cancer-specific pharmacogenomic datasets shows immense promise for identifying predictive response-biomarkers to enable personalized treatment. We introduce CAN-Scan, a precision oncology platform, that applies ML on next-generation pharmacogenomic datasets generated from a freeze-viable biobank of patient-derived primary cell lines (PDCs). These PDCs are screened against 84 FDA-approved drugs at clinically relevant doses (Cmax), focusing on colorectal cancer (CRC) as a model system. CAN-Scan uncovers prognostic biomarkers and alternative treatment strategies, particularly for patients unresponsive to first-line chemotherapy. Specifically, it identifies gene expression signatures linked to resistance against 5-Fluorouracil (5FU)-based drugs and a focal copy number gain on chromosome 7q, harbouring critical resistance-associated genes. CAN-Scan-derived response signatures accurately predict clinical outcomes across four independent, ethnically-diverse CRC cohorts. Notably, drug-specific ML models reveal Regorafenib and Vemurafenib as alternative treatments for BRAF-expressing, 5FU-insensitive CRC. Altogether, this approach demonstrates significant potential in improving biomarker-discovery and guiding personalized treatments.
|
| |
|
| Overall design |
Patient derived cells (PDC) were generated from resected samples. Baseline gene expression profiles of these samples were generated.
|
| |
|
| Contributor(s) |
Chia S, Dasgupta R, Shirgaonkar N |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
| |
| Submission date |
Jan 24, 2025 |
| Last update date |
Apr 04, 2025 |
| Contact name |
Niranjan Shirgaonkar |
| E-mail(s) |
niranjan.shirgaonkar@gmail.com
|
| Organization name |
A*STAR
|
| Lab |
Laboratory of Precision Oncology and Cancer Evolution
|
| Street address |
60 Biopolis St
|
| City |
Singapore |
| State/province |
Singapore |
| ZIP/Postal code |
138672 |
| Country |
Singapore |
| |
|
| Platforms (1) |
| GPL24676 |
Illumina NovaSeq 6000 (Homo sapiens) |
|
