Abstract 6925: Leveraging AI-enhanced multi-omics discovery of novel tumor-selective targets for ADC development
Abstract
Abstract TenSixty Biosciences employs an innovative AI-enhanced multi-omics discovery framework to enable the development of next-generation antibody-drug conjugates (ADCs). This integrated approach identified novel tumor-selective targets with high therapeutic potential by combining: 1. Multi-dimensional omics analysis integrated with advanced AI analytics to uncover therapeutically relevant cancer targets 2. Specialized proteomic profiling focused on unique post-translational modifications (PTMs) that reveal tumor-specific variants of targets, enabling enhanced tumor-selective targeting 3. Proprietary antibody discovery and engineering generates tumor-selective antibodies directed against these variants, yielding highly potent ADC candidates Applied to small cell lung cancer (SCLC), this framework identified a novel therapeutic target exhibiting tumor-specific hypoglycosylation. Antibodies directed against the epitope exposed by altered glycosylation showed exceptional tumor selectivity and markedly enhanced ADC efficacy. AI-driven integration across discovery and development stages further accelerated target validation and candidate optimization. Citation Format: Daniel Montoro, Christoph Muus, Karthik Jagadeesh, Cecile Rouleau, Yueyue Shi, Sho Takahashi, James Meador, Ritika Singh, . Leveraging AI-enhanced multi-omics discovery of novel tumor-selective targets for ADC development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6925.
Fields of Study & Subjects
HER2/EGFR in Cancer Research
Protein Degradation and Inhibitors
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- Cancer Research
- Publisher
- American Association for Cancer Research (AACR)
- Volume
- 86
- Issue
- 7_Supplement
- Pages
- 6925–6925
- Publication date
- 3 April 2026
- Language
- EN