Entrectinib in locally advanced or metastatic ROS1 fusion-positive non-small cell lung cancer (NSCLC): Integrated analysis of ALKA-372-001, STARTRK-1 and STARTRK-2

Background Entrectinib is a potent inhibitor of ROS1 (in addition to TRKA/B/C), designed to effectively penetrate the central nervous system (CNS); brain metastases are common in patients with advanced ROS1 fusion-positive NSCLC. Entrectinib achieves therapeutic levels in the CNS with antitumor activity in multiple intracranial tumor models. We present an updated integrated safety and efficacy analysis from three Phase I/II studies of entrectinib (ALKA-372-001 [EudraCT 2012-000148-88], STARTRK-1 [NCT02097810], STARTRK-2 [NCT02568267]) in patients with locally advanced or metastatic ROS1 fusion-positive NSCLCs. Methods The analysis included patients with ROS1 inhibitor-naive NSCLC harboring a ROS1 fusion identified via nucleic acid-based diagnostic platforms. The ROS1 safety-evaluable population included patients with ROS1 fusion-positive NSCLC who received ≥1 dose of entrectinib; the integrated efficacy analysis included patients with at least 6 months of follow-up. Tumor assessments were done at week 4 and every 8 weeks thereafter. Blinded independent central review (BICR), RECIST v1.1 was performed. Primary endpoints by BICR: overall response rate (ORR) and duration of response (DOR). Key secondary endpoints: progression-free survival (PFS), safety. Additional endpoints: intracranial ORR (complete/partial responses), DOR in patients with an intracranial response, PFS in patients with baseline CNS disease. Results In the ROS1 safety-evaluable population (n=134), at least one treatment-related AE (TRAE) of any grade was seen in 93% of patients. Patients with at least one TRAE by highest grade were: grade 1/2, 59%; grade 3, 31%; grade 4, 4%. There were no grade 5 TRAEs. TRAEs led to dose reduction or discontinuation in 34% and 5% of patients, respectively. In the efficacy-evaluable population (n=53 patients with treatment-naive, ROS1 fusion-positive NSCLC; median age 53 years, 64% female, 59% never smokers), BICR-assessed ORR was 77% (95% CI 64-88), complete responses n=3 (6%). Median BICR-assessed DOR: 25 mo (95% CI 11-35). Median BICR-assessed PFS: 26 mo (95% CI 16-37) and 14 mo (95% CI 5-NR) for patients without (n=30) and with CNS disease (n=23) at baseline, respectively. In patients with baseline CNS disease (per BICR assessment, n=20), intracranial ORR was 55% (95% CI 32-77) and median intracranial DOR in patients with an intracranial response (n=11) was 13 mo (95% CI 6-not reached). Conclusion Entrectinib is highly active in patients with ROS1 fusion-positive NSCLC, including those with CNS disease. Entrectinib is well tolerated and has a manageable safety profile. Citation Format: Alexander Drilon, Fabrice Barlesi, Filippo De Braud, Byoung Chul Cho, Myung-Ju Ahn, Salvatore Siena, Matthew G. Krebs, Chia-Chi Lin, Tom John, Daniel SW Tan, Takashi Seto, Rafal Dziadziuszko, Hendrick-Tobias Arkenau, Christian Rolfo, Jurgen Wolf, Chenglin Ye, Todd Riehl, Susan Eng, Robert C. Doebele. Entrectinib in locally advanced or metastatic ROS1 fusion-positive non-small cell lung cancer (NSCLC): Integrated analysis of ALKA-372-001, STARTRK-1 and STARTRK-2 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT192

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings

Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology

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Global Assessment of Retinal Arteriolar, Venular and Capillary Microcirculations Using Fundus Photographs and Optical Coherence Tomography Angiography in Diabetic Retinopathy

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Lipidomic Profiling of Lung Pleural Effusion Identifies Unique Metabotype for EGFR Mutants in Non-Small Cell Lung Cancer

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25-years Trends and Risk factors related to Surgical Outcomes of Giant Retinal Tear-Rhegmatogenous Retinal Detachments

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A three gene immunohistochemical panel serves as an adjunct to clinical staging of patients with head and neck cancer

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Human Tumor-Infiltrating MAIT Cells Display Hallmarks of Bacterial Antigen Recognition in Colorectal Cancer

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