Frontiers in immunology · 2026
A conditional multi-signal validation framework for cancer immunotherapy: the adaptive anti-error biological system.
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Kalondero Emery M
Аннотация
BACKGROUND: Cancer immunotherapy faces persistent limitations due to its reliance on single-signal therapeutic architectures, which are vulnerable to antigen loss, off-tumor toxicity, and tumor heterogeneity. A fundamental contributor to therapeutic failure is the generation of biological decision errors - false-positive activation in normal tissues and false-negative missed recognition in antigen-low tumors.
OBJECTIVE: We propose the Adaptive Anti-Error Biological System (AABS), a conceptual framework designed to improve therapeutic precision through structured multi-signal validation and conditional effector activation. A simplified implementation, AABS-01, is introduced as a trimodular conditional therapeutic model integrating tumor priming, dual-signal AND-gate logic, and conditional effector engagement.
FRAMEWORK: AABS-01 operates through three coordinated layers: (1) a Tumor Priming Layer enhancing antigen visibility through tumor-restricted IFN-γ conditioning, epigenetic modulation, and TME normalization; (2) a Validation Layer implementing Boolean AND-gate logic requiring simultaneous detection of two independent tumor-associated signals; and (3) an Effector Layer triggering localized immune activation exclusively upon validated dual-signal convergence.
BIOINFORMATIC SUPPORT: Analysis of TCGA Pan-Cancer Atlas and GTEx v8 transcriptomic data confirms that four candidate signal pairs (HER2/MUC4, EGFR/EpCAM, MSLN/HER2, PD-L1/GD2) achieve corrected Tumor Specificity Index (TSI) values of 18.3x to 46.8x across six cancer types, after correction for inter-signal correlation (r = 0.18-0.44). A revised probabilistic framework accounting for signal co-regulation demonstrates that AND-gate logic achieves 3-8x false-positive rate reduction versus single-signal approaches under empirically observed correlation conditions.
CONCLUSION: AABS represents a paradigm shift from reactive to decision-based cancer immunotherapy, grounded in established immunological principles including T-cell multi-signal activation and kinetic proofreading. A five-phase experimental roadmap with quantitative endpoints is provided to guide preclinical and translational validation.
OBJECTIVE: We propose the Adaptive Anti-Error Biological System (AABS), a conceptual framework designed to improve therapeutic precision through structured multi-signal validation and conditional effector activation. A simplified implementation, AABS-01, is introduced as a trimodular conditional therapeutic model integrating tumor priming, dual-signal AND-gate logic, and conditional effector engagement.
FRAMEWORK: AABS-01 operates through three coordinated layers: (1) a Tumor Priming Layer enhancing antigen visibility through tumor-restricted IFN-γ conditioning, epigenetic modulation, and TME normalization; (2) a Validation Layer implementing Boolean AND-gate logic requiring simultaneous detection of two independent tumor-associated signals; and (3) an Effector Layer triggering localized immune activation exclusively upon validated dual-signal convergence.
BIOINFORMATIC SUPPORT: Analysis of TCGA Pan-Cancer Atlas and GTEx v8 transcriptomic data confirms that four candidate signal pairs (HER2/MUC4, EGFR/EpCAM, MSLN/HER2, PD-L1/GD2) achieve corrected Tumor Specificity Index (TSI) values of 18.3x to 46.8x across six cancer types, after correction for inter-signal correlation (r = 0.18-0.44). A revised probabilistic framework accounting for signal co-regulation demonstrates that AND-gate logic achieves 3-8x false-positive rate reduction versus single-signal approaches under empirically observed correlation conditions.
CONCLUSION: AABS represents a paradigm shift from reactive to decision-based cancer immunotherapy, grounded in established immunological principles including T-cell multi-signal activation and kinetic proofreading. A five-phase experimental roadmap with quantitative endpoints is provided to guide preclinical and translational validation.