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A new algorithm Precision OncoPanels (PrOPs) identifies short individualized actionable panels that can guide cancer
Shrisruti Sriraman1, Debajyoti Das2, Nagasuma Chandra1,2,3
1IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India.
Abstract:
Precision oncology, enabled by next-generation sequencing (NGS), has shown tremendous potential for use in a clinical setting for cancer diagnosis and treatment. The biggest promise is to make treatment more precise and tailored for individual patients, departing from the one-size-fits-all approach. However, the translation of genomic panels into clinical practice and their wider implementation are met with challenges. Currently, only those patients who have frequently observed mutations in that cancer benefit from the NGS approach. There is an urgent need to expand the scope of this to all patients, for which new methods are required to be developed so as to identify key actionable gene panels in all patients. We address this need and present a new algorithm, PrOPs (Precision Onco Panels), that identifies short actionable driver panels by integrating genomics, transcriptomics, genome-wide protein-protein interactions, and precision network construction and analysis. We tested the algorithm on 2180 patients from six cancer types from TCGA (BRCA, COAD, GBM, LIHC, LUAD, and SKCM) and predicted patient-specific cancer driver genes. PrOPs outperforms the existing network-based methods that identify personalized drivers and also capture rare and patient-specific cancer drivers. Among the clinical cohorts, PrOPs identified clinically relevant actionable panels in 93% of patient cases. The extensive testing of our algorithm and demonstrated generalizability in six different cancers indicate the usefulness of our algorithm in precision oncology.
Insights
Precision oncology uses next-generation sequencing (NGS) for tailored cancer treatment. A new algorithm, PrOPs, identifies actionable gene panels for all patients, improving personalized cancer care.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Precision oncology leverages next-generation sequencing (NGS) for personalized cancer diagnosis and treatment.
- Current NGS approaches benefit limited patient groups with common mutations, necessitating broader applicability.
- A gap exists in identifying actionable gene panels for all cancer patients.
Purpose of the Study:
- To develop a novel algorithm, PrOPs (Precision Onco Panels), for identifying short, actionable driver gene panels.
- To integrate multi-omics data (genomics, transcriptomics, protein-protein interactions) for enhanced driver gene identification.
- To address the need for comprehensive genomic analysis in precision oncology.
Main Methods:
- Developed the PrOPs algorithm integrating genomics, transcriptomics, and protein-protein interaction networks.
- Constructed and analyzed precision networks to identify patient-specific cancer drivers.
- Validated the algorithm on 2180 patients across six cancer types from The Cancer Genome Atlas (TCGA).
Main Results:
- PrOPs successfully identified patient-specific cancer driver genes.
- The algorithm outperformed existing network-based methods in personalized driver identification.
- Clinically relevant actionable panels were identified in 93% of tested patient cases.
- PrOPs captured rare and patient-specific cancer drivers effectively.
Conclusions:
- The PrOPs algorithm enhances precision oncology by identifying actionable gene panels for a wider patient population.
- Demonstrated generalizability across six cancer types highlights its clinical utility.
- PrOPs represents a significant advancement in personalized cancer treatment strategies.
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