AUTHOR=Asensio-Puig Laura , Alemany Laia , Pavón Miquel Angel 

TITLE=A Straightforward HPV16 Lineage Classification Based on Machine Learning

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 5 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.851841

DOI=10.3389/frai.2022.851841

ISSN=2624-8212

ABSTRACT=Human Papillomavirus (HPV) is the causal agent of 5% of cancers worldwide and the main cause of cervical cancer and it is also associated with a significant percentage of oropharyngeal and anogenital cancers. More than 60% of cervical cancers are caused by HPV16 genotype, which has been classified into lineages (A, B, C and D). Lineages are related to the progression of cervical cancer and the current method to assess lineages is by building a Maximum Likelihood Tree (MLT); which is slow, it cannot assess poor sequenced samples and annotation is done manually. In this study, we have developed a new model to assess HPV16 lineage using machine learning tools.
A total of 647 HPV16 genomes were analysed using Genome-Wide Association Study (GWAS), which identified 53 lineage-specific Single Nucleotide Polymorphisms (SNPs). From the SNPs found, training-test models were constructed using different algorithms such as Random Forest (RF), Support Vector Machine (SVM) and K-nearest neighbour (KNN). A distinct set of HPV16 sequences (n=1014), whose lineage was previously determined by MLT, was used for validation. The RF-based model allowed a precise assignment of HPV16 lineage, showing an accuracy of 99.5% in the known lineage samples. Moreover, the RF model could assess lineage to 277 samples that ML could not determine. In terms of computer consuming time, the RF-based model was almost 400 times faster than ML. Having a fast and efficient method for assigning HPV16 lineages, could facilitate the implementation of lineage classification as triage or prognostic marker in the clinical setting.