Machine learning for genetic prediction of chemotherapy toxicity in cervical cancer - 28/03/23
Abstract |
Background |
Locally advanced cervical cancer (LACC) is frequently treated with neoadjuvant chemotherapy (NACT), which includes paclitaxel and platinum. However, the development of severe chemotherapy toxicity is a barrier to successful NACT. Phosphatidylinositol 3-kinase (PI3K)/serine/threonine kinase (AKT) pathway is related to the occurrence of chemotherapeutic toxicity. In this research work, we employ a random forest (RF) machine learning model to forecast NACT toxicity (neurological, gastrointestinal, and hematological reactions).
Materials and Methods |
Twenty-four single nucleotide polymorphisms (SNPs) in the PI3K/AKT pathway from 259 LACC patients were used to construct a dataset. Following the data preprocessing, the RF model was trained. The Mean Decrease in Impurity approach was adopted to evaluate the relevance of 70 selected genotypes' importance by comparing chemotherapy toxicity grades 1–2 vs. 3.
Results |
In the Mean Decrease in Impurity analysis, neurological toxicity was much more likely to occur in LACC patients with homozygous AA in Akt2 rs7259541 than in those with AG or GG genotypes. The CT genotype of PTEN rs532678 and the CT genotype of Akt1 rs2494739 increased the risk of neurological toxicity. The top three loci were rs4558508, rs17431184, and rs1130233, which were attributed to an elevated risk of gastrointestinal toxicity. LACC patients who had heterozygous AG in Akt2 rs7259541 exhibited an obviously greater risk of hematological toxicity than those who had AA or GG genotypes. And the CT genotype for Akt1 rs2494739 and the CC genotype in PTEN rs926091 showed a tendency to increase the risk of suffering from hematological toxicity.
Conclusion |
Akt2 rs7259541 and rs4558508, Akt1 rs2494739 and rs1130233, PTEN rs532678, rs17431184, and rs926091 polymorphisms are associated with different toxic effects during the chemotherapy treatment of LACC.
Le texte complet de cet article est disponible en PDF.Graphical Abstract |
Highlights |
• | Chemotherapy toxicity is predicted by SNPs & genotypes from MDI-based feature importance analysis. |
• | The RF prediction model improves clinical treatment plans by reducing chemotherapeutic side effects. |
• | This methodology is applicable to a variety of features with additional genotype datasets. |
Keywords : Machine learning, Random forest, PI3K/AKT pathway, Single nucleotide polymorphisms, Locally advanced cervical cancer, Chemotherapy toxicity, Neoadjuvant chemotherapy
Plan
Vol 161
Article 114518- mai 2023 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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