AI-Driven Multiomics Biomarkers for Precision Oncology: Navigating the Translational Gap and Regulatory Hurdles.

Artificial Intelligence-Driven Multiomics Integration in Lung Cancer: From Data Convergence to Precision Phenomics.

Precision phenomics is introduced as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management and has the potential to transform lung cancer research and improve patient outcomes.

Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions

This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.

Non-Oncology Biomarkers in Precision Medicine: A Narrative Review.

An integrated methodological roadmap for real-world biomarker studies: advancing oncology precision medicine through robust methodologies and machine learning integration.

The selection of biomarker-specific patient populations is essential in targeted cancer therapies to enhance precision and efficacy. To ensure a successful launch, it is vital to promote awareness and adoption of biomarker testing at diagnosis, tailor implementation strategies to accommodate local variations, and ensure testing is accessible and reimbursed. An integrated evidence generation plan should address critical questions, including the prevalence of biomarker expression and agreement between local and central labs, across different platforms, antibodies and pathologists. Furthermore, understanding prognostic effects and associations with other biomarkers is of considerable interest. Real-world studies (RWS) play a pivotal role in addressing these questions but are inherently challenged by confounding factors, biases (e.g. immortal time bias), missing data, agreement assessment complexities, and low biomarker expression prevalence. This manuscript provides an integrated methodological roadmap for designing and analyzing RWS. We address key challenges by integrating robust statistical methodologies with advanced machine learning (ML) methods. Core methods discussed include the use of time-dependent Cox models to mitigate immortal time bias, inverse probability of biomarker weighting to adjust for confounding, and ML-based tree ensemble approaches to model complex relationships between covariates and outcomes and handle missing data. By systematically applying this integrated roadmap, we demonstrate how to enhance the validity and robustness of RW biomarker research. This approach overcomes common analytical pitfalls, enabling more reliable evidence generation for clinical decision-making. Ultimately, this roadmap helps drive precision oncology forward by ensuring that biomarker-driven therapeutic strategies are based on sound, high-quality RW evidence.

Multi-Omics-Driven Insights into Cancer Biology and Therapeutic Targeting

The increasing biological complexity and heterogeneity of cancer have driven a shift in oncology drug discovery from single-target approaches toward system-level strategies capable of capturing multilayered disease regulation. Multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics, have emerged as powerful tools for elucidating cancer-driving mechanisms, identifying therapeutic targets, and enabling biomarker-guided drug development. This review examines how integrative multi-omics approaches support cancer drug discovery and therapeutic targeting, focusing on target identification, pathway elucidation, target validation, biomarker discovery, and therapeutic development. Genomics and transcriptomics facilitate the identification of driver alterations and dysregulated signaling pathways, whereas proteomics and metabolomics provide functional insights into protein activity, metabolic reprogramming, and treatment response. We further highlight the contributions of epigenomic and microbiomic profiling to biomarker discovery, therapeutic response prediction, and precision oncology. Given the complexity of multi-omics datasets, the review also explores the application of artificial intelligence (AI) and machine-learning methodologies for data integration, network modeling, biomarker discovery, and drug repurposing, including deep learning, Bayesian frameworks, graph-based models, and explainable AI approaches. Emerging computational frameworks and integration strategies that enable interpretation of heterogeneous molecular datasets and support therapeutic discovery are also discussed. Cancer-focused examples demonstrate how integrative multi-omics frameworks have enabled the identification of clinically relevant biomarkers, therapeutic targets, and rational combination therapies. Furthermore, the clinical translation of biomarker-driven precision oncology, exemplified by HER2-, EGFR-, and MSI-directed therapies, highlights the growing impact of omics-informed approaches on personalized cancer treatment. Overall, AI-enabled multi-omics approaches hold substantial promise for accelerating cancer drug discovery and precision oncology. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing. Multi-omics approach contributes at multiple stages of the cancer drug discovery and development pipeline. Integrated omics analyses enable systematic identification of therapeutic targets and predictive biomarkers in oncology. Artificial intelligence and machine learning facilitate efficient integration and interpretation of complex multi-omics datasets, supporting target discovery and drug repurposing.

A multi-layer framework for computational cancer epigenomics

Cancer epigenomics has become central to understanding tumor initiation, progression, heterogeneity, and therapeutic response. High-throughput profiling technologies including bisulfite sequencing, chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and RNA sequencing (RNA-seq) generate complex, multi-dimensional datasets that require robust computational frameworks for meaningful interpretation. This review outlines key bioinformatics workflows in cancer epigenomics, including data preprocessing, quality control, sequence alignment, signal detection, and differential analysis. While epigenomic data provide a mechanistic regulatory foundation, their full interpretive value emerges through integration with genomic, transcriptomic, and clinical data within computational oncology frameworks. Accordingly, we emphasize integrative modeling approaches that combine multi-omics data to uncover regulatory mechanisms, identify biomarkers, and define disease-associated molecular subtypes. Machine learning methods are increasingly applied for classification, prognosis prediction, and therapeutic response modeling; however, challenges remain in model interpretability, reproducibility, and external validation. We further highlight critical analytical limitations, including data heterogeneity, tumor complexity, lack of standardized workflows, and the persistent gap between association and biological mechanism. Emerging advances in single-cell epigenomics, spatial profiling, and explainable AI offer new opportunities to refine biological insight and clinical translation. Importantly, we propose a structured multi-layer interpretation framework that links computational outputs across data-level processing, epigenomics-informed integrative regulatory modeling, and multi-omics-informed clinical interpretation. This framework differs from existing pipelines by explicitly constraining how information is transformed across analytical layers, enabling traceable and mechanistically interpretable clinical inference.