Abstract A071: Inverse Biomarker Exploring Technology (IBMET) Mathematical Identification of Cancer-Specific Epitopes and Novel Targets (Biomarkers) from Big Data of Single-Domain Antibodies Recognizing Higher Structures
Authors: Akihiro Imura, Kazutaka Araki, Tsuyoshi Inoue, Ryota Maeda, Hiroyuki Yamazaki
Journal: Cancer Research (2024)

Abstract Currently, candidate molecules such as MUC1, Mesothelin, CEACAM6, and Claudin18.2 have been proposed as immunotherapy targets in pancreatic cancer. These molecules are significantly overexpressed in cancer cells, allowing differentiation from normal cells, and are being targeted for drug development using immunotherapy modalities like antibody drug conjugate (ADC), T cell bispecific (TCB), and CAR-T. Therefore, if more specific target molecules exist, it would be possible to develop even higher quality drug. However, do such target molecules exist? We conducted long-term immunization of alpacas, which are single-domain antibody producers, using more than 10 types of human pancreatic cancer cell lines. Over time, we constructed a large library of VHH antibodies recognizing the cell surface. Based on this, we developed an algorithm to cluster millions of antibody amino acid sequences in silico and identify characteristic clusters for each pancreatic cancer cell line. The Inverse Biomarker Exploring Technology (IBMET) is a bio-AI fusion technology that comprehensively converts biologically generated single-domain antibodies into a digital library to discover new biomarker molecules by using features correlated with epitopes as indicators. We validated antibodies recognizing new markers in patient tissues and ultimately identified several biomarkers. Among these, we here report the most distinctive biomarker, VHH89T antigen. The antigen annotation revealed by MSpec analysis identified it as CD166, known as ALCAM, a type I membrane protein. ALCAM is a membrane molecule widely expressed in the body, including immune cells. However, VHH89T specifically recognized only the complex formed by a small subset of ALCAM with its partner molecule. Furthermore, the partner molecule of the ALCAM complex was identified as membrane protein X and it was confirmed that the multimeric complex was rapidly internalized into the cell upon antibody binding. Consequently, we formulated VHH89T into an ADC bound to SN-38 and tested its cytotoxic function in cultured cells, confirming very favorable efficacy. Also efficacy has been confirmed in a mouse xenograft model. The novelty and advantages of IBMET are described as The ability to identify abnormalities (features) in post-translational modifications or complex formation, which are not detectable by genome or mRNA expression analysis, by converting antibodies that recognize target molecule structures into amino acid sequence information. Immediate formulation into ADCs and other drugs upon biomarker discovery, as antibodies are already available. Instant response to necessary companion diagnostics in parallel with drug formulation. This enables the provision of a super-rapid and highly probable next-generation antibody drug development platform. With IBMET, we have successfully identified similar marker molecules in not only pancreatic cancer but also cholangiocarcinoma and TNBC, and are currently aiming to pipeline multiple targets. Citation Format: Akihiro Imura, Kazutaka Araki, Tsuyoshi Inoue, Ryota Maeda, Hiroyuki Yamazaki. Inverse Biomarker Exploring Technology (IBMET) Mathematical Identification of Cancer-Specific Epitopes and Novel Targets (Biomarkers) from Big Data of Single-Domain Antibodies Recognizing Higher Structures [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A071.