TAS::75 0849::TAS SBIR TOPIC 277 - NOVEL CTC-BASED TEST FOR LUNG CANCER DRUG RESISTANCE

Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: N43CO110109
Agency Tracking Number: N43CO110109
Amount: $195,030.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2011
Solicitation Topic Code: NCI
Solicitation Number: N/A
Small Business Information
CYNVENIO BIOSYSTEMS, INC.
2260 TOWNSGATE RD UNIT 2, WESTLAKE VILLAGE, CA, 91361-2437
DUNS: 827484655
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 PAUL DEMPSEY
 (805) 777-0017
 PDEMPSEY@CYNVENIO.COM
Business Contact
 PAUL DEMPSEY
Phone: (805) 777-0017
Email: PDEMPSEY@CYNVENIO.COM
Research Institution
 Stub
Abstract
The evolving understanding of cancer indicates every tumor has distinct characteristics driven by the molecular pathology of the disease. Predicted by this heterogeneity, targeted therapeutic agents are much more successful than a one size fits all approach. Currently, there are no tools to analyze resistance in solid tissue tumor cells other than biopsy. This limitation excludes test for molecular markers that emerge during treatment. The goal of this project is to develop and characterize a CTC based molecular drug resistance test and demonstrate application in clinical samples. 15-20% of non-small cell lung cancers are driven by activating mutations in the epidermal growth factor receptor (EGFR), a mutation that can be very effectively treated with EGFRinhibitors such as gefitinib or erlotinib. Resistance to these drugs arises universally; caused half of the time by a T790M mutation. Currently, resistance is determined medically by demonstrating tumor progression. As an alternative approach, extractingthe small numbers of circulating tumor cells (CTC) released into the bloodstream for detection of the emergence of the T790M mutation offers a strategy for genotyping patient-specific tumor cells in real time. This will produce a fundamentally enabling technology for predictive cancer tests in personalized medicine.

* information listed above is at the time of submission.

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