Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer.

PURPOSE: The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making. EXPERIMENTAL DESIGN: Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC). RESULTS: GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33-0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31-0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74-1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07-0.72). CONCLUSIONS: GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.

OutLyzer: software for extracting low-allele-frequency tumor mutations from sequencing background noise in clinical practice.

Highlighting tumoral mutations is a key step in oncology for personalizing care. Considering the genetic heterogeneity in a tumor, software used for detecting mutations should clearly distinguish real tumor events of interest that could be predictive markers for personalized medicine from false positives. OutLyzer is a new variant-caller designed for the specific and sensitive detection of mutations for research and diagnostic purposes. It is based on statistic and local evaluation of sequencing background noise to highlight potential true positive variants. 130 previously genotyped patients were sequenced after enrichment by capturing the exons of 22 genes. Sequencing data were analyzed by HaplotypeCaller, LofreqStar, Varscan2 and OutLyzer. OutLyzer had the best sensitivity and specificity with a fixed limit of detection for all tools of 1% for SNVs and 2% for Indels. OutLyzer is a useful tool for detecting mutations of interest in tumors including low allele-frequency mutations, and could be adopted in standard practice for delivering targeted therapies in cancer treatment.

Anti-cetuximab IgE ELISA for identification of patients at a high risk of cetuximab-induced anaphylaxis.

Cetuximab, a chimeric mouse-human IgG1 monoclonal antibody against the epidermal growth factor receptor, has proven effective in the treatment of metastatic colorectal cancer and squamous cell carcinoma of the head and neck. However, a high incidence of immediate hypersensitivity reactions (HSR) to cetuximab after the first infusion has been observed. We have developed a test for identification of patients likely to show treatment-related HSR to cetuximab. An enzyme-linked immunosorbent assay (ELISA) for detecting anti-cetuximab IgEs was developed and tested on serum samples collected from cancer patients before start of cetuximab treatment, and from healthy blood donors. Similar levels of anti-cetuximab IgE were detected in pre-treatment patient sera (24/92, 26.1%) and sera from healthy blood donors (33/117, 28.2%). HSR were observed in 14 out of the 92 patients (15.2%), and 8 of these (57.1%) were grade 3-4. Anti-cetuximab IgEs were detected in 7/8 of the patients (87.5%) with severe HSRs as compared with 14/78 patients (17.9%) with no HSR (p=0.0002). Predictive value of the anti-cetuximab IgE test for HSR events of grades 3-4 was calculated using Receiver Operating Characteristics analysis. With a cut-off value of 29 arbitrary units for the anti-cetuximab IgE, the ELISA test showed a sensitivity of 87.5%, specificity of 82.1%, positive predictive value of 33.3% and negative predictive value of 98.5%. Anti-cetuximab IgE ELISA detection could be a valuable tool to help the physician anticipate an anaphylaxis episode following cetuximab infusion and opt for a suitable alternative treatment.