Molecular profiling of ovarian cancer to guide targeted treatment

© 2015 Olga KondrashovaOvarian cancer is a complex disease composed of multiple distinct molecular and clinical subtypes. The survival rate for ovarian cancer has remained largely unchanged over the past three decades, despite the rapid advancement of the knowledge of the molecular and genetic mechanisms underlying most of the subtypes of ovarian cancer. There is, therefore, an urgent need to rapidly translate this knowledge into improved clinical outcomes for patients with ovarian cancer. There have been significant clinical responses of certain types of cancer to targeted therapies that are designed to inhibit specific molecular defects that some tumours appear to be dependent upon. To assist in allocating patients with ovarian cancer to targeted therapies, two customised assays for mutation and copy number alteration detection were developed for molecular profiling. A panel of 29 genes, which are commonly mutated in ovarian cancer, and are potentially therapeutically targeted, was selected to be screened using an amplicon-based assay, designed for next generation sequencing. Seventy six ovarian cancer cases with matched formalin-fixed paraffin- embedded tumour tissue, snap-frozen tumour tissue and blood samples were used for the assay validation and estimation of the diagnostic yield. A panel of 11 commonly copy number altered genes in ovarian cancer was also selected for screening with a herein developed method for multiplex low-level copy number detection. Furthermore, a thorough assessment and optimisation of the available and developed analysis methods was performed to ensure accurate analysis and reporting of mutations and copy number alterations. Thirty five patients with advanced ovarian cancer were tested using the developed assays as part of the ALLOCATE study, with genetic changes detected in 90.9%, demonstrating a high diagnostic yield. Molecular profiling of these cases was not only useful in identification of possible targeted treatment strategies with the aim of improving clinical outcomes, but also assisted in determining the correct diagnosis. Moreover, a novel algorithm was proposed for the prediction of individual tumour response to PARP inhibitors, a promising targeted treatment in high-grade serous ovarian cancer