Identifying optimal tumor-associated antigen combinations with single-cell genomics to enable multi-targeting therapies

Abstract

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Targeted antibody-based therapy for oncology represents a highly efficacious approach that has demonstrated robust responses against single tumor-associated antigen (TAA) targets. However, tumor heterogeneity presents a major obstacle for targeting most solid tumors due to a lack of single targets that possess the right on-tumor/off-tumor expression profile required for adequate therapeutic index. Multi-targeting antibodies that engage two TAAs simultaneously may address this challenge through Boolean logic-gating function by improving both therapeutic specificity and efficacy. In addition to the complex engineering of multi-targeting antibodies for ideal logic-gate function, selecting optimal TAA combinations ab initio is the critical step to initiate preclinical development but remains largely unexplored with modern data-generation platforms. Here, we propose that single-cell atlases of both primary tumor and normal tissues are uniquely positioned to unveil optimal target combinations for multi-targeting antibody therapeutics. We review the most recent progress in multi-targeting antibody clinical development, as well as the designs of current TAA combinations currently exploited. Ultimately, we describe how multi-targeting antibodies tuned to target pairs nominated through a data-driven process are poised to revolutionize therapeutic safety and efficacy, particularly for difficult-to-treat solid tumors.

Keywords

• antibody
• multi-specifics
• bispecifics
• ADC
• cell engager
• single-cell sequencing