YBL-006 is a fully human anti-programmed death-1 (PD-1) antibody under evaluating its safety and efficacy in phase I clinical trial. We have reported interim analysis of dose escalation cohort which showed a tolerable safety profile. Here, we present the updated clinical activity of YBL-006 in dose escalation and expansion cohorts.
Dose escalation (0.5, 2, 5, 10 mg/kg) and dose expansion (flat doses of 200 mg every 2 weeks [Q2W] and 300 mg every 3 weeks [Q3W]) cohorts explored the safety, pharmacokinetics (PK) and objective response rate (ORR) in the patients with advanced solid tumors who failed or were ineligible to the standard of care. Adverse events (AEs) were graded using the CTCAE v5. ORR was assessed using the RECIST (v1.1). Lunit SCOPE IO, an H&E analysis tool, was applied as an exploratory biomarker, and samples with “Immune inflamed phenotype” were defined as those with high intratumoral TIL density in ≥ 20% of the tumor microenvironment. The cut-off date for analysis was Jan 4th, 2022.
A total of 67 patients (safety set) with advanced solid tumors were enrolled in the study. Median follow-up duration of the safety set was 1.6 months (range 0.2-16.8). There was no dose-limiting toxicity during dose escalation phase. Most frequent AEs were grade 1 or 2; fatigue (19.4%), pruritus (10.4%), and fever (7.5%), and two hypothyroidism (3.0%), one pneumonitis (1.5%), and one cytokine-releasing syndrome (1.5%) were observed. One subject experienced grade 3 diarrhea in the safety set. PK study showed that half-life was 8.0 days, and mean Cmax and AUC0-inf were 4.15 x 104 ng/ml and 1.12 x 107 ng/mlh for 200 mg dose, and 6.26 x 104 ng/ml and 1.53 x 107 ng/mlh for 300 mg dose, respectively. A total of 52 patients were evaluable for efficacy. ORR was 15.4%, including 1 complete response (penile squamous cell carcinoma [SqCC]), and 7 partial responses (two gastric adenocarcinomas, anal SqCC, paranasal sinus SqCC, nasopharyngeal carcinoma, neuroendocrine carcinoma, and thyroid Hurthle cell carcinoma). Durable responses were seen in 2 patients for over 12 months. Median duration of response was 4.9 weeks (range 1-65). Among efficacy set, 32 samples were available for Lunit SCOPE IO. ORR was significantly higher in inflamed immune phenotype compared to non-inflamed samples (62.5% vs 8.3% p = 0.005).
Interim analysis of phase I trial of YBL-006 shows a tolerable safety profile and clinical activity. Notable anti-tumor efficacy was observed in inflamed immune phenotype. Clinical trial information: NCT04450901.
Do-Youn Oh1, John J. Park2, Keun-Wook Lee3, Seung Tae Kim4, Virote Sriuranpong5, Sun Young Rha6, Changhoon Yoo7, Bhumsuk Keam1, Dhanusha Sabanathan2, Sehyun Kim3, Joon Oh Park4, Napa Parinyanitikul5, Min Hwan Kim6, Kyu-Pyo Kim7, Myungsuk Kim8, Jaebong Yoon8, Hanseung Lee8, Chan-Young Ock9.
1Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. 2Department of Clinical Medicine, Macquarie University, North Ryde, NSW, Australia. 3Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea. 4Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 5Division of Medical Oncology, Chulalongkorn University and the King Chulalongkorn Memorial Hospital, Bangkok, Thailand. 6Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea. 7Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 8Y-Biologics Inc., Daejeon, Republic of Korea. 9Lunit Inc., Seoul, Republic of Korea
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
Abstract: Comprehensive deep learning analysis of H&E tissue phenomics reveals distinct immune landscape and transcriptomic enrichment profile among immune inflamed, excluded and desert subtypes...
Abstract: Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor...
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
Reducing Domain Gap by Reducing Style Bias
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors
Abstract : Assistance with an artificial intelligence-powered PD-L1 analyzer reduces interobserver variation in pathologic reading of tumor proportion score in non-small cell lung cancer
Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma
Artificial Intelligence–Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non–Small-Cell Lung Cancer
Artificial Intelligence–Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non–Small-Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Analyzer Reduces Inter-observer Variation in PD-L1 Tumor Proportion Score of Non-Small Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates...
Abstract : Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations
Observer Performance Study to Examine the Feasibility of the AI-powered PD-L1 Analyzer to Assist Pathologists’ Assessment of PD-L1 Expression Using Tumor Proportion Score in Non-Small Cell Lung Cancer
Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation
Artificial intelligence-powered whole-slide image analyzer reveals a distinctive distribution of tumor-infiltrating lymphocytes in neuroendocrine tumors and carcinomas
Artificial Intelligence (AI) - powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC)
Artificial intelligence (AI)-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes
Trastuzumab plus FOLFOX for Gemcitabine/Cisplatin refractory HER2-positive biliary tract cancer: a multi-institutional phase II trial of the Korean Cancer Study Group (KCSG-HB19-14)
The Inflamed Immune Phenotype (IIP): a clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types
Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.