Pharmacogenomics in Oncology

Why the Same Chemotherapy Can Help One Patient and Harm Another?

Introduction

Chemotherapy remains a cornerstone of cancer treatment across many malignancies, yet patient responses vary dramatically.

Two individuals receiving the same regimen can experience entirely different outcomes, ranging from excellent tumour control to severe, life-threatening toxicity. For decades, these differences were largely attributed to age, organ function, or chance. Pharmacogenomics has revealed a far more precise explanation. Genetic variations in drug-metabolising enzymes, transporters, and molecular targets determine how each patient activates, detoxifies, or eliminates chemotherapy.

Over the past decade, large cohort studies, pooled clinical analyses, and systematic reviews have confirmed that pharmacogenomic markers are strong, independent predictors of treatment response and safety. As a result, pharmacogenomics has evolved from an emerging concept into a central pillar of precision oncology.

Genomic Predictors of Toxicity and Treatment Response

One of the most well-established pharmacogenomic markers in oncology is DPYD, the gene encoding dihydropyrimidine dehydrogenase. This enzyme is responsible for breaking down fluoropyrimidines such as 5-fluorouracil and capecitabine. Meta-analyses involving more than twenty thousand patients consistently show that individuals carrying reduced-function DPYD variants face a markedly increased risk of severe toxicity, including neutropenia, gastrointestinal injury, cardiotoxicity, and treatment-related death. Prospective studies demonstrate that genotype-guided dose reduction significantly lowers toxicity without compromising efficacy, leading several regulatory bodies to recommend DPYD testing before treatment.

Strong evidence also supports pharmacogenomic testing for TPMT and NUDT15, genes involved in thiopurine metabolism in acute lymphoblastic leukemia. Meta-analyses show that patients with low or absent TPMT activity experience profound myelosuppression unless doses are drastically reduced. Variants in NUDT15, which are particularly prevalent in East Asian populations, confer a similar risk. These examples underscore the importance of population-specific genetic screening, as variant frequencies vary widely across ethnic groups.

Another clinically relevant gene is UGT1A1, which metabolises the active form of irinotecan. Pooled analyses of colorectal cancer trials demonstrate that patients carrying UGT1A1*28 or *6 variants have significantly higher rates of neutropenia and diarrhea when treated with standard doses. As a result, several clinical guidelines recommend dose adjustment is recommended based on UGT1A1 status, particularly when higher irinotecan doses are used.

Beyond these established gene–drug pairs, ongoing research suggests that variants affecting drug transporters such as ABCB1 and SLCO1B1 may influence exposure to taxanes, platinum agents, and targeted therapies. While these associations are not yet routinely actionable, they continue to be refined through large-scale analyses.

Pharmacogenomics and Precision Oncology

The integration of pharmacogenomics aligns closely with the broader shift toward precision medicine in oncology. Traditional chemotherapy dosing relies on population averages and body surface area calculations, despite well-documented inter-individual variability.

Pharmacogenomic testing provides a biological framework to explain this variability, allowing clinicians to identify patients at risk of toxicity before treatment begins.

Multiple prospective studies show that genotype-guided dosing reduces hospitalisations and treatment interruptions while maintaining tumour control.

Importantly, pharmacogenomics also supports therapeutic equity. Population-based studies reveal that individuals of African, South Asian, and East Asian ancestry are more likely to carry certain high-risk variants. Without genetic screening, these patients face disproportionate treatment-related harm. Incorporating genetic information into treatment planning allows clinicians to tailor therapy more fairly and effectively.

Expanding the Evidence Base Through Multi-omic and AI Approaches

Recent advances in whole-genome sequencing, proteomics, and machine learning are expanding the scope of pharmacogenomics. Instead of focusing on single gene–drug pairs, researchers are integrating genomic, transcriptomic, and metabolomic data to generate more accurate predictions of drug response.

Large multinational datasets linking genetic variation with treatment outcomes have enabled machine learning models to identify complex risk signatures that outperform single-gene testing. These models are currently being evaluated in clinical trials, pointing toward a future in which clinicians may use composite pharmacogenomic risk scores to guide treatment decisions.

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What does “multi-omic” mean in practice?

Genomics 

examines inherited genetic variants that influence drug metabolism, transport, and targets.

Transcriptomics measures gene expression, showing which genes are actively turned on or off in tumour or normal tissues.

Proteomics 

assesses protein levels and signaling activity, reflecting how drugs actually interact with cellular pathways.

Metabolomics captures drug breakdown products and metabolic activity, providing real-time insight into drug clearance.

How is this different from traditional genotyping?

Traditional pharmacogenomic testing typically evaluates one gene for one drug. Multi-omic approaches combine multiple biological layers to capture variability that single-gene testing may miss, improving prediction of both treatment response and toxicity.

How are these data combined?

Machine learning models integrate genomic mutations, gene expression profiles, and proteomic signaling patterns into a single predictive framework. This allows clinicians to move from isolated genetic results toward comprehensive pharmacogenomic risk profiles that better reflect real-world drug response.

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Challenges and Implementation Barriers

Despite robust evidence, clinical adoption of pharmacogenomic testing remains inconsistent. Barriers include limited reimbursement, variable access to testing, and differences in clinician familiarity with guidelines. Concerns about delaying treatment initiation also persist, although modern sequencing platforms can now deliver results within forty-eight hours.

Interpreting rare or novel genetic variants presents an additional challenge. Many variants remain classified as uncertain, highlighting the need for expanded reference databases and harmonised classification frameworks.

Conclusion

Pharmacogenomics has transformed the understanding of why chemotherapy affects patients differently. Evidence from large-scale studies consistently shows that genetic variation is a key determinant of treatment toxicity and effectiveness. By guiding safer dosing and more informed drug selection, pharmacogenomics brings oncology closer to the goal of delivering the right treatment at the right dose for each individual patient.

References

Dean, L. (2018). Irinotecan therapy and UGT1A1 genotype. In Pratt, V. M., Scott, S. A., Pirmohamed, M., et al. (Eds.), Medical Genetics Summaries [Internet]. National Center for Biotechnology Information (US). https://www.ncbi.nlm.nih.gov/books/NBK294473/

Filipski, K. K., Mechanic, L. E., Long, R., & Freedman, A. N. (2014). Pharmacogenomics in oncology care. Frontiers in Genetics, 5, 73. https://doi.org/10.3389/fgene.2014.00073 

Henricks, L. M., Lunenburg, C. A. T. C., de Man, F. M., Meulendijks, D., Frederix, G. W. J., Kienhuis, E., Creemers, G. J., Baars, A., Dezentjé, V. O., Imholz, A. L. T., Jeurissen, F. J. F., Portielje, J. E. A., Jansen, R. L. H., Hamberg, P., ten Tije, A. J., Droogendijk, H. J., Koopman, M., Nieboer, P., van de Poel, M. H. W., & Schellens, J. H. M. (2018). DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: A prospective safety analysis. The Lancet Oncology, 19(11), 1459–1467. https://doi.org/10.1016/S1470-2045(18)30686-7

Relling, M. V., & Evans, W. E. (2015). Pharmacogenomics in the clinic. Nature, 526(7573), 343–350. https://doi.org/10.1038/nature15817 

Xu, T., Ngan, D. K., Zheng, W., et al. (2025). Systematic identification of cancer pathways and potential drugs for intervention through multi-omics analysis. Pharmacogenomics Journal, 25, 2. https://doi.org/10.1038/s41397-025-00361-6 

Wang, C., Lye, X., Kaalia, R., & Rajapakse, J. C. (2022). Deep learning and multi-omics approach to predict drug responses in cancer. BMC Bioinformatics, 22(Suppl 10), Article 632. https://doi.org/10.1186/s12859-022-04964-9 

Wang, C., Zhang, M., Zhao, J., Li, B., Xiao, X., & Zhang, Y. (2023). The prediction of drug sensitivity by multi‑omics fusion reveals the heterogeneity of drug response in pan‑cancer. Computers in Biology and Medicine, 163, 107220. https://doi.org/10.1016/j.compbiomed.2023.107220 

Zack, M., Stupichev, D. N., Moore, A. J., Slobodchikov, I. D., Sokolov, D. G., Trifonov, I. F., & Gobbs, A. (2025). Artificial intelligence and multi-omics in pharmacogenomics: A new era of precision medicine. Mayo Clinic Proceedings: Digital Health, 3(3), 100246. https://doi.org/10.1016/j.mcpdig.2025.100246 

Assessed and Endorsed by the MedReport Medical Review Board