ReviewAAPS PharmSciTech2026
Multi-Omics-Driven Insights into Cancer Biology and Therapeutic Targeting.
Review in AAPS PharmSciTech, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
42675339What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.