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Identifying optimal tumor-associated antigen combinations with single-cell genomics to enable multi-targeting
Matthew A Nix1,2, Caleb A Lareau3, Jeffrey Verboon1
1Cartography Biosciences, South San Francisco, CA, United States.
Abstract:
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.
Insights
Multi-targeting antibodies offer improved cancer therapy by engaging two tumor antigens (TAAs) simultaneously. Single-cell atlases can identify optimal TAA pairs for enhanced specificity and efficacy in solid tumors.
Area of Science:
- Oncology
- Immunotherapy
- Bioinformatics
Background:
- Targeted antibody therapy shows promise in oncology but struggles with tumor heterogeneity.
- Single tumor-associated antigen (TAA) targeting is limited by on-tumor/off-tumor expression profiles, impacting therapeutic index.
- Multi-targeting antibodies engaging two TAAs offer a potential solution via Boolean logic-gating for improved specificity and efficacy.
Purpose of the Study:
- To propose single-cell atlases as a method for identifying optimal TAA combinations for multi-targeting antibody therapeutics.
- To review current progress in multi-targeting antibody development and TAA combination strategies.
- To highlight the potential of data-driven TAA pair selection for revolutionizing cancer therapy.
Main Methods:
- Utilizing single-cell atlases of primary tumors and normal tissues to identify ideal TAA combinations.
- Reviewing existing clinical development of multi-targeting antibodies.
- Analyzing current TAA combinations employed in antibody therapeutics.
Main Results:
- Single-cell atlases provide a unique approach to nominate optimal TAA pairs for multi-targeting antibodies.
- Current TAA combinations and clinical development strategies are being explored.
- A data-driven selection process is crucial for optimizing multi-targeting antibody function.
Conclusions:
- Multi-targeting antibodies, guided by data-driven TAA selection from single-cell atlases, can overcome limitations of single-target therapies.
- This approach promises to significantly enhance therapeutic safety and efficacy, especially for challenging solid tumors.
- Optimizing TAA combinations is critical for unlocking the full potential of multi-targeting antibody therapeutics in oncology.
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