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Molecular Oncodiagnostics in Precision Oncology: Integrating Tumor Transcriptomics, Patient Pharmacogenetics, and Ex
1Independent Researcher, 57125 Livorno, Italy.
Abstract:
Background: Precision oncology has traditionally relied on genomic biomarkers to guide therapy selection; however, static molecular profiling often fails to predict real-world responses to cytotoxic chemotherapy. Increasing evidence suggests that treatment outcomes are determined by the interaction between tumor-intrinsic biology and host-specific pharmacology. Functional ex vivo platforms, including patient-derived organoids and tumor slice cultures, provide a complementary phenotypic readout of drug sensitivity that reflects tumor architecture and microenvironmental interactions. Methods: This narrative review integrates recent experimental, translational, and clinical evidence on molecular oncodiagnostics combining tumor transcriptomics, germline pharmacogenetics, and ex vivo drug sensitivity testing. Relevant literature was identified through targeted searches of major biomedical databases, focusing on studies describing multi-omic predictive models, functional precision oncology platforms, and patient-derived tumor models. Results: Converging data indicate that integrated oncodiagnostic strategies can improve prediction of chemotherapy response beyond genomics-only approaches. Transcriptomic profiling captures dynamic pathway activity and resistance programs, pharmacogenetic testing informs host-specific toxicity and dosing constraints, and ex vivo assays enable direct phenotypic validation of drug efficacy. Together, these complementary approaches provide a biologically grounded framework for individualized therapy selection. Conclusions: The convergence of molecular profiling and functional phenotyping represents an emerging paradigm in precision oncology. Integrating multi-omic and functional data may enhance treatment prediction and reduce ineffective therapy, although prospective validation and standardization remain necessary for routine clinical implementation.
Insights
Integrating multi-omic data and functional testing improves cancer treatment prediction beyond genomics alone. This approach combines tumor transcriptomics, pharmacogenetics, and ex vivo drug sensitivity for personalized therapy selection.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Precision oncology traditionally uses genomic biomarkers, but static profiling often fails to predict chemotherapy response.
- Treatment outcomes depend on tumor biology and host pharmacology interactions.
- Functional ex vivo platforms offer phenotypic drug sensitivity readouts, reflecting tumor microenvironment.
Purpose of the Study:
- To review evidence on molecular oncodiagnostics integrating transcriptomics, pharmacogenetics, and ex vivo drug sensitivity testing.
- To explore multi-omic predictive models and functional precision oncology platforms.
- To assess the role of patient-derived tumor models in predicting treatment response.
Main Methods:
- Narrative review of experimental, translational, and clinical studies.
- Searches of biomedical databases for multi-omic predictive models and functional platforms.
- Focus on studies utilizing patient-derived tumor models.
Main Results:
- Integrated oncodiagnostic strategies improve chemotherapy response prediction over genomics alone.
- Transcriptomics reveal dynamic pathway activity; pharmacogenetics inform host toxicity and dosing.
- Ex vivo assays validate drug efficacy phenotypically, enabling individualized therapy.
Conclusions:
- Convergence of molecular profiling and functional phenotyping is a new paradigm in precision oncology.
- Integrating multi-omic and functional data enhances treatment prediction and reduces ineffective therapies.
- Prospective validation and standardization are needed for clinical implementation.
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