Single-cell mapping of combinatorial target antigens for CAR switches using logic gates

Area of Science:

• Oncology
• Immunotherapy
• Bioinformatics

Background:

• Chimeric antigen receptor (CAR) cell therapy faces challenges in targeting tumors with intratumoral heterogeneity.
• Identifying specific antigens to distinguish cancer cells from normal cells is crucial for effective CAR T-cell therapy.

Purpose of the Study:

• To develop a method for identifying optimal target antigens for CAR cell therapy.
• To address the challenge of intratumoral heterogeneity in cancer treatment.

Main Methods:

• Constructed a single-cell expression atlas integrating ~1.4 million cells from 412 tumors and 12 normal organs.
• Employed a two-step screening using random forest and convolutional neural networks to select discriminatory gene pairs.
• Evaluated tumor coverage and specificity using AND, OR, and NOT logic gates based on combinatorial gene expression patterns.

Main Results:

• Identified gene pairs that effectively discriminate between individual malignant and normal cells.
• Validated the identified AND, OR, and NOT switch targets using single-cell transcriptome-coupled epitope profiling.
• Demonstrated the utility of the approach in ovarian and colorectal cancer models.

Conclusions:

• The developed single-cell atlas and screening method can identify precise CAR T-cell targets.
• This approach enhances specificity and coverage, crucial for overcoming tumor heterogeneity.
• The findings pave the way for more effective and safer CAR T-cell therapies.