Personalizing Chemotherapy Using Network-based Stratification of Tumors
Personalizing Chemotherapy Using Network-based Stratification of Tumors
An algorithm to incorporate somatic mutation data into signaling networks resulting in more accurate and reproducible sub-typing of tumors. Using this technique called Network-based Stratification, the researchers were able to stratify patients into subgr
San Diego, CA, United States
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Background

The current classifications of cancer based on anatomic location and histology often end up grouping together tumors with diverse growth rates, metastatic potential and responses to chemotherapy. Current tumor stratification methods are mostly based on gene expression and often are not reproducible or clinically relevant. Prior attempts at incorporating somatic mutation data into the stratification were hampered by the fact that the population size of the mutations vary too much to have relevance.


Technology Description

UCSD researchers have developed an algorithm to incorporate somatic mutation data into signaling networks resulting in more accurate and reproducible sub-typing of tumors. Using this technique called Network-based Stratification, the researchers were able to stratify patients into subgroups with significant differences in survival, which indicates significant clinical relevance.


Advantages

This algorithm enables more accurate grouping of tumors and will increase the effectiveness of patient treatment as well as drug development. Somatic mutation data are integrated with a network of known protein-protein interactions. This network integration allows for the network-based method to succeed in clustering cancer patients based on somatic mutations, with more clinically relevant outcomes.


Applications

This technology platform can be applied to the diagnostic sub-typing of various types of cancers, used to help determine optimal therapy and prognostic monitoring of treatment. After a patient has a biopsy or surgery to remove a tumor, the Network-based Stratification test could be used to determine the best treatment option for the patient.


State Of Development

This technique has been tested by stratifying ovarian cancer tumors, and can be generalized to any cancer with existing exome sequencing data. In addition, network-based stratification also enabled the identification of several new drug targets in each cancer subtype. 


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