Artificial Intelligence–Driven Multi-Omics Analysis for Early Cancer Detection and Personalized Treatment Strategies

Abstract

Early cancer detection and individualized therapy selection remain central to improving survival and quality of life. Advances in high-throughput sequencing, mass spectrometry, and liquid biopsy platforms now enable simultaneous profiling of genomes, epigenomes, transcriptomes, proteomes, metabolomes, and the tumor immune microenvironment from minimal input material—even as cell-free nucleic acids and proteins circulating in blood. Yet the information content of each omic is complementary and noisy, necessitating integrative analytics to recover weak but clinically meaningful signals at the earliest disease stages. Artificial Intelligence (AI)—particularly multimodal deep learning, graph representation learning, and probabilistic data fusion—has become the methodological scaffold for combining heterogeneous omics into robust risk scores, tissue-of-origin predictions, and therapy recommender systems. Landmark resources such as TCGA, CPTAC, and ICGC-ARGO have catalyzed model development and benchmarking, while prospective multi-cancer early detection (MCED) programs (e.g., CancerSEEK; targeted cfDNA methylation platforms) demonstrate high specificities (~99%) and improving sensitivities in real-world studies. At the same time, translation to routine screening hinges on demonstrable clinical utility, cost-effectiveness, equitable performance across populations, and transparent, auditable pipelines.

This paper synthesizes methodological and translational advances in AI-driven multi-omics for (i) pre-symptomatic detection; (ii) stratification of minimal residual disease; and (iii) personalization of systemic therapies. We outline principled data engineering (quality control, batch harmonization, drift monitoring), model architectures (early/late/hybrid fusion; attention mechanisms; contrastive multimodal representation learning), and mechanisms for uncertainty quantification and dynamic thresholding to keep false-positive rates acceptably low in screening contexts. We also present a deployable end-to-end workflow integrating liquid biopsy with radiomics and EHR metadata, and we benchmark detection metrics using literature-reported figures from contemporary MCED studies. Finally, we discuss ethical, regulatory, and reimbursement considerations, including privacy-preserving analytics and real-world evidence requirements for payers and regulators. Collectively, convergent multi-omics measured non-invasively and fused by AI paves a realistic path toward earlier stage shifts at diagnosis and treatment strategies tailored to each tumor’s evolving molecular circuitry. PMC+4Cancer.gov+4PMC+4.