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. 2024 Feb 12;25(4):2197.
doi: 10.3390/ijms25042197.

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

Lydia Giannitrapani  1   2 , Francesca Di Gaudio  2 , Melchiorre Cervello  1 , Francesca Scionti  3 , Domenico Ciliberto  4 , Nicoletta Staropoli  3   4 , Giuseppe Agapito  5 , Mario Cannataro  6 , Pierfrancesco Tassone  3   4   7 , Pierosandro Tagliaferri  3   4 , Aurelio Seidita  2   8 , Maurizio Soresi  2 , Marco Affronti  2 , Gaetano Bertino  9 , Maurizio Russello  10 , Rosaria Ciriminna  11 , Claudia Lino  11 , Francesca Spinnato  8 , Francesco Verderame  8 , Giuseppa Augello  1 , Mariamena Arbitrio  12
Affiliations

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

Lydia Giannitrapani et al. Int J Mol Sci. .

Abstract

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

Keywords: anticancer drugs; genetic polymorphism; genotyping; pharmacogenomics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The Decision Tree is computed from the RandomTree classifier, available in Weka, using the sorafenib dataset. The RandomTree’s parameters were set up as follows: weka.classifiers.trees. RandomTree -K 0 m 1.0 -V 0.001 -S 1, the selected Test models 10-fold cross-validation, reaching an accuracy of 86.9565%.
Figure 2
Figure 2
The ROC curve displays the trade-off between the True Positive Rate (TPR) and False Positive Rate (FPR) across different thresholds, illustrating a binary classifier’s performance. The curve’s proximity to the top-left corner indicates better model accuracy, with the Area Under the Curve (AUC) metric summarizing overall effectiveness (0.8259). The y-axis represents the sensitivity while the x-axis represents the False Positive Rate.
Figure 3
Figure 3
Expression of genes identified by decision tree analysis in HCC patients treated with sorafenib. Data are expressed as mean ± SD, and the differences between the two groups were assessed with the Mann–Whitney U test.
Figure 4
Figure 4
The consolidated network was obtained for each seed gene by computing the neighborhoods with a radius of one.
Figure 5
Figure 5
Top 10 genes by their degree of relevance in the seed network, computed using CytoHubba: high significance values are represented by red, orange, and yellow, while all bluish colors represent less significant values.
See this image and copyright information in PMC

References

    1. Cervello M., Emma M.R., Augello G., Cusimano A., Giannitrapani L., Soresi M., Akula S.M., Abrams S.L., Steelman L.S. New landscapes and horizons in hepatocellular carcinoma therapy. Aging. 2020;12:3053–3094. doi: 10.18632/aging.102777. - DOI - PMC - PubMed
    1. Luo J., Gao B., Lin Z., Fan H., Ma W., Yu D., Yang Q., Tian J., Yang X., Li B. Efficacy and safety of lenvatinib versus sorafenib in first-line treatment of advanced hepatocellular carcinoma: A meta-analysis. Front. Oncol. 2022;12:1010726. doi: 10.3389/fonc.2022.1010726. - DOI - PMC - PubMed
    1. Finn R.S., Qin S., Ikeda M., Galle P.R., Ducreux M., Kim T.Y., Kudo M., Breder V., Merle P., Kaseb A.O., et al. IMbrave150 Investigators. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinom. N. Engl. J. Med. 2020;382:1894–1905. doi: 10.1056/NEJMoa1915745. - DOI - PubMed
    1. Wilhelm S., Carter C., Lynch M., Lowinger T., Dumas J., Smith R.A., Schwartz B., Simantov R., Kelley S. Discovery and development of sorafenib: A multikinase inhibitor for treating cancer. Nat. Rev. Drug Disc. 2006;5:835–844. doi: 10.1038/nrd2130. - DOI - PubMed
    1. Gong L., Giacomini M.M., Giacomini C., Maitland M.L., Altman R.B., Klein T.E. PharmGKB summary: Sorafenib pathways. Pharmacogenet. Genom. 2017;27:240–246. doi: 10.1097/FPC.0000000000000279. - DOI - PMC - PubMed

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