Volume 10 Number 1 2026
Integrating Genomics, Transcriptomics, and Organoid-Based Drug Testing for Precision Cancer Care
Yoghinni Manogaran 1*, Ahmed Ahmed Al-Akwaa 2
Integrative Biomedical Research 10 (1) 1-8 https://doi.org/10.25163/biomedical.10110907
Submitted: 14 January 2026 Revised: 01 March 2026 Accepted: 09 March 2026 Published: 11 March 2026
Genomic profiling has reshaped oncology, yet only a minority of sequenced patients ever receive a molecularly matched therapy, and identical driver mutations often yield inconsistent clinical outcomes. This gap suggests that static DNA-level data alone cannot capture the dynamic, evolving biology of tumors. We synthesized real-world implementation cohorts, diagnostic-platform comparisons, biomarker-pathobiology data, and workforce-competency evidence drawn from recently published precision oncology literature, and we outline a prospective multi-omic pipeline — combining targeted DNA panels, transcriptomics, methylation and copy-number signatures, and patient-derived organoid drug screening — layered onto machine-learning fusion models. Across programs such as KOSMOS and the pediatric integrative-genomics cohort, actionable alterations were identified in 55–100% of patients, yet molecularly matched treatment was ultimately delivered to only 38–51% of cases. Transcriptomic and functional assays improved concordance between predicted and observed drug sensitivity, while copy-number and methylation signatures added prognostic information beyond single-nucleotide variants. Diagnostic-platform trade-offs (depth versus breadth), clonal hematopoiesis confounding in liquid biopsy, and demographic underrepresentation in reference databases emerged as recurring, largely unresolved barriers. A closed-loop framework that pairs multi-omic profiling with functional validation and tiered workforce competencies offers a plausible route toward durable, equitable precision oncology, though prospective, adequately powered trials are still needed to confirm its clinical benefit.
Keywords: precision oncology; multi-omics; functional precision oncology; patient-derived organoids; liquid biopsy; molecular tumor board; machine learning
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