The X+n / X-n Thymic Selection Model: A Unified Framework for Cancer Immune Escape and Immunotherapy
Year: 2026
Type: preprint
Abstract: The persistent lineage-specific cancer vulnerabilities observed in immigrant populations and the clinical non-responsiveness to neoantigen vaccines challenge current classical paradigms of tumor immunology [1, 2]. Somatic mutations continuously generate neoantigens that should trigger host immunosurveillance; yet, primary variants routinely escape immune elimination [3, 4]. Building upon previous theoretical insights on thymic selection thresholds [5], this paper advances a comprehensive theoretical and falsifiable model — the X+n / X-n Thymic Selection Model—connecting central tolerance induction to peripheral immune blind spots [6, 7]. By introducing the "Arrow and Target" dynamic paradigm, we demonstrate that ectopic thymic expression of self- or embryonic antigen clusters (X+n) during early T-cell development obligatorily deletes high-affinity cross-reactive T-cell receptor (TCR) clones across quantitative binding affinity thresholds, leaving structural clonal vacuums (X-n) in the mature peripheral repertoire [8, 9, 10, 11]. We formalize this thymic antigen presentation profile as a "Microscopic Tissue Phenotype" tightly regulated by genetic and epigenetic architecture, providing a robust mechanistic basis for the multi-generational genetic inertia observed in epidemiological cancer migration studies [12, 13]. Furthermore, we resolve the dual clinical outcomes of personalized mRNA cancer vaccines, demonstrating that vaccination expands pre-existing peripheral clones when targets evade thymic tolerance, but fails when neoantigens fall within X-n blind spots [14, 15]. Finally, we establish empirical falsifiability by proposing rigorous validation protocols regarding HLA-dependent TCR blind spots and retrospective thymic mapping of vaccine-refractory non-responders [16, 17].
Author: LU, DAVID
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225419417