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Manjusha Biswas
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P2.15 - Treatment in the Real World - Support, Survivorship, Systems Research (Not CME Accredited Session) (ID 964)
- Event: WCLC 2018
- Type: Poster Viewing in the Exhibit Hall
- Track:
- Presentations: 1
- Moderators:
- Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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P2.15-01 - CANscript™ as a Patient-Derived Predictive Platform for individualizing Treatment in Lung Cancer (ID 13851)
16:45 - 18:00 | Author(s): Manjusha Biswas
- Abstract
Background
Lung cancer causes nearly 1.69 million deaths globally and 5-year survival rate is less than 20%1. Several predictive and prognostic biomarkers have been identified, such as mutations in EGFR and KRAS, -rearrangement of ALK and ROS1, and PD-L1 expression status amongst others, which have improved treatment selection and overall prognosis in lung cancer to some extent2. However, clinical relevance of these biomarkers is limited to only a small percentage of patients who potentially benefit from these signatures2. Hence, there is a need for a personalized tool that can accurately predict an individual’s response to a therapy, especially in scenarios where there is a choice of equivalent treatment regimens.
References:
http://www.who.int/en/news-room/fact-sheets/detail/cancer
J. Natl. Compr. Canc. Netw. 2017;15(4):504
Nat. Commun. 2014, 6:6169 doi: 10.1038/ncomms7169
We have developed a platform technology (CANscript™), using multi-disciplinary systems biology-based approach that effectively recreates a patient’s tumor microenvironment ex vivo by preserving the native contexture and heterogeneity. The platform provides phenotypic assessment of response to the drug(s) tested for a given tumor and generates clinically relevant predictions for real-time treatment selection3.
4c3880bb027f159e801041b1021e88e8 Result
In the current study, 21 lung cancer patient tumors were evaluated using the CANscript™, were treated with one or more FDA approved regimens including targeted therapy and chemotherapy. We did not observe any advantage of targeted therapy (erlotinib and gefitinib) over chemotherapeutic agents used. Six tumors, which were non-responder to gefitinib, were predicted responders to chemotherapy (carboplatin/pemetrexed and/or carboplatin/docetaxel). Further, out of 4 gefitinib responders, 3 were predicted to respond to chemotherapies carboplatin/pemetrexed or carboplatin/docetaxel. Highest efficacy was observed in carboplatin/pemetrexed and carboplatin/docetaxel arms (47% and 46% respectively). One out of 4 tumors treated with anti-PD1 responded to the therapy showing a response rate of 25%, matching the reported clinical response rate of anti-PD-1 therapy.
8eea62084ca7e541d918e823422bd82e Conclusion
CANscript™ is used as an ex-vivo, personalized platform that can predict an individual’s response to various classes of anticancer drugs. The platform has been validated over a large number of clinical samples and the current study indicates that CANscript is a preferred platform for selecting individualized drug responses for treatment of lung cancer.
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