Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer

The PI3K/Akt pathway involves in regulating resistance to platinum-based neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC) patients. Single nucleotide polymorphisms (SNPs) reflect the basic genetic variation between individuals. Random forest (RF) is one of the machine-learning models that can predict drug sensitivity with high accuracy. We applied the RF model for genomic prediction of NACT sensitivity in LACC patients. A total of 259 LACC patients were separated to two groups (i) effective and (ii) ineffective NACT group, depending on the NACT response. The 24 SNPs in four genes (PTEN, PIK3CA, Akt1, and Akt2) were genotyped by the Sequenom MassArray system in these patients. We implemented the SNPs as the feature to train the RF model, calculated the feature importance using mean decreases in impurity based on the model, and further analyzed the importance of each SNP. The importance analysis indicated that the top three SNPs (rs4558508, rs1130233, and rs7259541) and the last six loci (rs892120, rs62107593, rs34716810, rs10416620, rs41275748, and rs41275746) were all located in Akt. The patients carrying heterozygous GA in Akt2 rs4558508 had a considerably higher risk of chemoresistance than those carrying GG or AA genotype. The RF model could accurately predict the response to platinum-based NACT of LACC patients. The variables of Akt2 rs4558508 and rs7259541, and Akt1 rs1130233 were major polymorphic loci for NACT inefficiency. The LACC patients carrying heterozygous GA of Akt2 rs4558508 had a significantly increased risk of chemoresistance. Akt was an important gene in PI3K/Akt pathway that could predict the response of platinum-based NACT. The study applied the basis for an individualized approach to LACC patient therapy.