YBL-006 is a fully human anti-programmed death-1 (PD-1) antibody with a wider binding interface of the PD-1/YBL-006 complex and higher affinity compared to that of other PD-1 antibodies, which showed a favorable safety profile and a potent anti-tumor efficacy in animal models.
A modified “3+3” design, with the first patient dosed at 0.5 mg/kg (mpk), was followed by conventional dose escalation of 2, 5, and 10 mpk IV. Dose escalation cohort explored the safety, pharmacokinetics (PK), PD-1 receptor occupancy (RO), serum IFN-γ level and tumor response. Adverse events (AEs) were graded using the CTCAE v5. Tumor response was assessed using the RECIST v1.1 every 8 weeks. Exploratory biomarker analysis included whole exome sequencing to assess tumor mutational burden (TMB) and Lunit SCOPE IO to assess the density of intra-tumoral tumor-infiltrating lymphocyte (TIL). The cut-off date for analysis was Apr 27th, 2021.
Total of 11 patients with advanced solid tumors were enrolled in the escalation cohort. YBL-006 showed a linear PK prolife in terms of Cmax and area under the curve by dose escalation and approximately 8 days of T1/2. Both PD-1 RO and serum IFN-γ increased by > 2 times 8 h after the first dose. No dose limiting toxicity (DLTs) or deaths related to YBL-006 have been reported. The most common AEs of Grade 2 ≥ related to YBL-006 were rash (21.7%), fatigue (13%), fever (13%) and hypothyroidism (4.3%). Ten patients were available for tumor response evaluation and their best overall responses included 1 complete response (penile squamous cell carcinoma, 2 mpk), 1 partial response (anal squamous cell carcinoma, 2 mpk) with durable responses lasting more than 30+ and 14+ weeks respectively, and 4 stable disease. Tumor samples of both of 2 responders harbored high levels of TMB (8.3 and 9.3 per megabase) and intra-tumoral TIL density (66.1% and 95.8%).
YBL-006 is well tolerated, and AEs are manageable with the results of no DLTs occurred and the maximum tolerated dose was not reached until progressing to the 10 mpk. Dose expansion cohort using flat dosing is planned.
K. Lee1, J. Park2, D. Oh3, S.H. Kim1, D. Sabanathan2, T.M. Kim3, M. Kim4, J. Yoon4, H. Lee4, S. Park5, K. Paeng6, C. Ock7
1 Seoul National University College Of Medicine, Seoul National University Bundang Hospital, 13620 - Seongnam/KR
2 Department Of Clinical Medicine, Macquarie University, Macquarie Park/AU
3 Seoul National University College Of Medicine, Seoul National University Hospital, Cancer Research Institute, Seoul/KR
4 Development Division, Y-Biologics Inc., 34014 - Daejeon/KR
5 Research And Development, Genome Insight Inc., Daejeon/KR
6 Oncology Group, Medical Affairs, Lunit Inc., 06241 - Seoul/KR
7 Oncology Group, Medical Affairs, Lunit Inc., 6247 - Seoul/KR
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
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
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