PURPOSE The ALLOCATE study was designed as a pilot to demonstrate the feasibility and clinical utility of real-time targeted molecular profiling of patients with recurrent or advanced ovarian cancer for identification of potential targeted therapies. PATIENTS AND METHODS A total of 113 patients with ovarian cancer of varying histologies were recruited from two tertiary hospitals, with 99 patient cases suitable for prospective analysis. Targeted molecular and methylation profiling of fresh biopsy and archived tumor samples were performed by screening for mutations or copy-number variations in 44 genes and for promoter methylation of BRCA1 and RAD51C. RESULTS Somatic genomic or methylation events were identified in 85% of all patient cases, with potentially actionable events with defined targeted therapies (including four resistance events) detected in 60% of all patient cases. On the basis of these findings, six patients received molecularly guided therapy, three patients had unsuspected germline cancer–associated BRCA1/ 2 mutations and were referred for genetic counseling, and two intermediate differentiated (grade 2) serous ovarian carcinomas were reclassified as low grade, leading to changes in clinical management. Additionally, secondary reversion mutations in BRCA1/ 2 were identified in fresh biopsy samples of two patients, consistent with clinical platinum/poly (ADP-ribose) polymerase inhibitor resistance. Timely reporting of results if molecular testing is done at disease recurrence, as well as early referral for patients with platinum-resistant cancers, were identified as factors that could improve the clinical utility of molecular profiling. CONCLUSION ALLOCATE molecular profiling identified known genomic and methylation alterations of the different ovarian cancer subtypes and was deemed feasible and useful in routine clinical practice. Better patient selection and access to a wider range of targeted therapies or clinical trials will further enhance the clinical utility of molecular profiling.
Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera