Beyond the hallmarks of cancer: enabling technologies reshaping cancer diagnosis, prevention, and treatment.

Abstract

Cancer biology has been organized for 2 decades by the hallmarks framework, articulated in 2000 and most recently revised in 2026. The pace of technological change in oncology now requires reframing how cancer is detected, prevented, and treated. This review argues that the hallmarks framework, while historically essential, is no longer sufficient on its own to capture contemporary cancer care, and repositions the discussion around enabling technologies that operate across and beyond individual hallmarks. For diagnosis, multi-omic liquid biopsy, AI-assisted radiomics and digital pathology, spatial transcriptomics, single-cell sequencing, and machine-learning analysis of plasma proteomics now support earlier detection, prediction of cancer risk years before diagnosis, monitoring of clonal evolution, and prediction of treatment response. For prevention, the validated population-level success of Human papillomavirus and HBV vaccination is contrasted with investigational directions including mRNA platforms, CRISPR-enabled antigen optimization, personalized neoantigen prophylaxis, and biomarker-stratified molecular interception, none of which has yet been validated in prospective human cancer-prevention trials. For treatment, the discussion is organized by modality class: immune checkpoint inhibitors and resistance strategies, antibody-drug conjugates, bispecific antibodies and T-cell engagers, radioligand therapies, tumor-infiltrating lymphocyte therapy, CRISPR-edited CAR-T and TCR therapies, photonic and photoimmunologic therapies, RNA therapeutics, and adaptive combination regimens. Comparative tables summarize diagnostic platforms, immunotherapeutic modalities, photonic therapies, CRISPR-edited immunotherapy trials, neoantigen vaccine pipelines, and convergent strategies. The trajectory of oncology will be defined less by incremental refinement of hallmarks and more by convergent technologies that integrate diagnostics, prevention, and treatment into continuous, data-driven systems.