Integrative Multi-Omics and Artificial Intelligence Framework for Precision Oncology: Bridging Aging-Associated Tumor Biology with Ultra-Early Detection in Translational Cancer Research

Abstract

The convergence of big data analytics, artificial intelligence (AI), and multi-omics profiling has catalyzed a paradigm shift in translational oncology, yet the integration of aging-associated tumor biology with ultra-early detection strategies remains an unmet challenge. This study presents a comprehensive big data framework analyzing 11,285 tumor samples across 33 cancer types from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas, supplemented by 5,408 multi-omic profiles encompassing 60,112 molecular features. We integrated genomic, transcriptomic, epigenomic, and clinical data from over 500,000 comprehensively profiled solid tumors to construct an AI-driven precision oncology platform. Deep learning convolutional neural networks (CNNs) applied to whole-slide histopathology images achieved a diagnostic accuracy of 0.70, significantly surpassing the 0.61 accuracy of 29 pathologists (p = 0.002). A multidimensional cell-free DNA (cfDNA) fragmentomics model demonstrated 93.3% sensitivity at 94.6% specificity (AUC = 0.983) for early cancer detection, while multimodal methylation and fragmentomics analysis (SPOT-MAS) detected five cancer types with 72.4% sensitivity at 97.0% specificity. AI-enabled pancreatic cancer detection on routine CT imaging achieved an AUC of 0.82 with 73.0% sensitivity and a median lead time of 475 days before clinical diagnosis. Analysis of clonal hematopoiesis of indeterminate potential (CHIP) in 4,187 participants revealed significant associations between age-related somatic mutations and solid tumor risk. The senescence-associated secretory phenotype (SASP) was implicated as a dual mediator of tumor suppression and promotion in the aging tumor microenvironment. These findings demonstrate that integrative multi-omics and AI frameworks can bridge the gap between aging-associated cancer biology and ultra-early detection, establishing a scalable translational pipeline for precision oncology. The convergence of these technologies promises to reduce the projected 9.7 million annual global cancer deaths through earlier intervention and personalized therapeutic strategies.