On May 28, 2019 Lunit reported an abstract presentation of its AI precision medicine research portfolio at the American Society of Clinical Oncology (ASCO) (Free ASCO Whitepaper) Annual Meeting 2019, held May 31 – June 4 in Chicago (Press release, Lunit, MAY 28, 2019, View Source [SID1234536610]).
Schedule your 30 min Free 1stOncology Demo!
Discover why more than 1,500 members use 1stOncology™ to excel in:
Early/Late Stage Pipeline Development - Target Scouting - Clinical Biomarkers - Indication Selection & Expansion - BD&L Contacts - Conference Reports - Combinatorial Drug Settings - Companion Diagnostics - Drug Repositioning - First-in-class Analysis - Competitive Analysis - Deals & Licensing
Schedule Your 30 min Free Demo!
The accepted abstract highlights the feasibility of AI-based biomarker in metastatic non-small cell lung cancer, based on the H&E analysis that predicts response to immune checkpoint inhibitors (ICI).
The abstract will be presented at ASCO (Free ASCO Whitepaper) poster sessions on Sunday, June 2. Lunit will also be hosting a booth exhibition during ASCO (Free ASCO Whitepaper), at booth #19129.
The study evaluated the predictive value of AI versus PD-L1, the main biomarker for ICI, in terms of both its comparative predictive value as well as additive predictive value. According to the research, within PD-L1(+) patient group, the treatment response and progression-free survival (PFS) significantly differed depending on the AI score. The same results were obtained within the PD-L1(-) group.
After reclassifying PD-L1(-) patient group based on the AI score, 52% of patients with high AI score had, in fact, shown response to ICI. These patients had three times longer PFS compared to the patients who had a low AI score. Similar outcomes were found among the PD-L1(+) patient group. Classified with AI profiling, 63% of low AI score patients were non-responsive to ICI. These patients had six times shorter PFS compared to high AI score patients.
Additionally, in an AI analysis independent of PD-L1, the team was able to identify more patients that showed response to ICI. Among PD-L1(+) patient group, 49% of the patients were responsive to ICI, whereas 65% of patients within high AI score patient group showed response.
"With our advanced deep-learning technology, we seek to push the boundary of precision medicine and navigate for opportunities that transcend current practices," said Brandon Suh, CEO of Lunit. "We look forward to accelerating our research and development in AI biomarkers for cancer treatment and outcome prediction through various research partnerships."
Full ASCO (Free ASCO Whitepaper) abstract: View Source
ASCO Poster Session Abstract by Lunit:
#9094 Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
Poster Session: Lung Cancer – Non-Small Cell Metastatic (Board #417)
Sunday, June 2, 2019, 8:00am to 11:00am, Hall A
Contacts
Media Contact
Lunit
Jussarang Lee, Communications Manager
+82-10-4884-0827
[email protected]