Our team has developed a methylation classifier to detect CIN3 that could be used routinely as a molecular diagnostic tool. Although this classifier already has good sensitivity and specificity our aim was to improve it further. We used whole exome sequencing to find new biomarkers to add to the classifier.
The study was designed to compare the baseline and dyskaryotic samples of HPV positive women who developed CIN3 (cases) to women who did not (normal controls) and to HPV negative women (negative controls). The samples at baseline did not show any sign of dyskaryosis. From the archived material of the ARTISTIC trial, we selected 27 cases (2 samples per case, one at baseline, one at time of diagnosis) that were matched to 27 normal controls by age and HPV type to the cases. Twelve negative controls were also randomly selected. In total, we sequenced the whole exome of 93 samples on an IonProton using the IonAmpliSeq Exome RDY kit. We developed an in-house bioinformatics pipeline.
First, we validated our bioinformatics pipeline on the top 29 candidate single nucleotide polymorphisms (SNPs) using Sanger sequencing. All SNPs were successfully validated which showed that the sequencing and analytical methods were appropriate. Then, using a principal component analysis with both germline and somatic mutations, we showed a clear separation between both types of controls and the cases. We found that the cases harboured mutations at baseline and time of CIN3 diagnosis that were not present in any of the controls. We are now in the process of validating the top 200 variants using Fluidgm and Illumina technologies. More details of our ongoing investigations will be reported.
We have validated the use of whole exome sequencing on the IonProton platform to identify new biomarkers for detection of CIN3 and produced a list of 200 candidate SNPs to improve our classifier.