At Curesponse, we offer cResponse™, a cancer diagnostics that combines rapid genomic sequencing and an innovative functional test to assist your oncologist in prioritizing your personalized treatment.
cResponse™ functional cancer
drug selection assay
Curesponse has developed, cResponse™, a personalized drug sensitivity platform which enables the evaluation of different treatments on your tumor. The platform accurately preserves the cancer tissue architecture mirroring the cancer growth found in your body. The tissue is grown in its 3D microenvironment which has a major impact on drug response. Drugs are selected based on the oncologist’s recommendations along with our rapid genomic sequencing of actionable mutations. Upon completion, the patient is provided with a genomic report and prioritization of the different treatments based on a proprietary diagnostic algorithm.
A fresh biopsy arrives at a regional cResponse™ service center. Part of the sample undergoes rapid genomic sequencing to identify tumor-specific genomic alterations that may be targeted by specific anti-cancer drugs. Added to those selected by the physician, they create a panel of therapies to be tested. The biopsy is cut into thick sections which are placed in a proprietary 3D culture system which tests the cancer’s sensitivity to various drugs or novel drug combinations. cResponse’s proprietary evaluation system & response algorithm prioritizes the options according to demonstrated drug efficacy, providing results within two weeks.
The cResponse™ Report
Upon completion of cResponse™, we will provide a comprehensive report, summarizing the genomic information founded in our rapid panel, the different drugs tested and the effect of each drug on the patient’s tumor sample.
The cResponse™ system uses a proprietary algorithm, where each drug is assigned a score from 0-100, with 100 reflecting a very strong response to therapy in our assay and 0 reflecting a very weak response. The highest scoring drugs represent the most potent treatment option and may indicate higher chances of clinical effect.