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. 2023 Nov 1;29(21):4419-4429.
doi: 10.1158/1078-0432.CCR-23-0898.

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

Raphaël Leman  1   2 , Etienne Muller  1   2 , Angelina Legros  1 , Nicolas Goardon  1   2 , Imène Chentli  1 , Alexandre Atkinson  1   2 , Aurore Tranchant  1 , Laurent Castera  1   2 , Sophie Krieger  1   2 , Agathe Ricou  1   2 , Flavie Boulouard  1   2 , Florence Joly  3 , Romain Boucly  4 , Aurélie Dumont  4 , Noémie Basset  5   6 , Florence Coulet  5   6 , Louise-Marie Chevalier  7   8 , Etienne Rouleau  9 , Katharina Leitner  10   11 , Antonio González-Martin  12   13 , Piera Gargiulo  14   15 , Hans-Joachim Lück  16   17 , Catherine Genestie  18 ; PAOLA-1 investigators; Isabelle Ray-Coquard  19   20 , Eric Pujade-Lauraine  20 , Dominique Vaur  1   2
Collaborators, Affiliations

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

Raphaël Leman et al. Clin Cancer Res. .

Abstract

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.

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Figures

Figure 1. GIScar and the dataset used to develop the score. A, GIScar principle. B, Recruitment to the prospective collection. C, Clinical data used to validate GIScar. D, Proportion of BRCA-mutated and HRD-positive tumors according to MGMC on the prospective collection (n = 250 samples). E, Proportion of BRCA-mutated and HRD-positive tumors according to MGMC on the clinical collection (n = 469 samples).
Figure 1.
GIScar and the dataset used to develop the score. A, GIScar principle. B, Recruitment to the prospective collection. C, Clinical data used to validate GIScar. D, Proportion of BRCA-mutated and HRD-positive tumors according to MGMC on the prospective collection (n = 250 samples). E, Proportion of BRCA-mutated and HRD-positive tumors according to MGMC on the clinical collection (n = 469 samples).
Figure 2. Correlation of GIScar scores with MGMC status and BRCA mutation status (n = 250 samples). A, Distribution of tumor status according to GIScar. B, ROC curve of GIScar scores compared with MGMC status. C, Correlation between GIScar scores and MGMC scores. D, Distribution of GIScar scores among BRCA wild-type and BRCA-mutated tumors.
Figure 2.
Correlation of GIScar scores with MGMC status and BRCA mutation status (n = 250 samples). A, Distribution of tumor status according to GIScar. B, ROC curve of GIScar scores compared with MGMC status. C, Correlation between GIScar scores and MGMC scores. D, Distribution of GIScar scores among BRCA wild-type and BRCA-mutated tumors.
Figure 3. Kaplan–Meier estimates of PFS according to tumor GIScar status (n = 469). A, PFS in GIScar HRD-positive tumors with a BRCA mutation. B, PFS in GIScar HRD-positive tumors without a BRCA mutation. C, PFS in GIScar HRD-negative tumors.
Figure 3.
Kaplan–Meier estimates of PFS according to tumor GIScar status (n = 469). A, PFS in GIScar HRD-positive tumors with a BRCA mutation. B, PFS in GIScar HRD-positive tumors without a BRCA mutation. C, PFS in GIScar HRD-negative tumors.
Figure 4. Comparison of GIScar and MGMC results on the clinical data (n = 469 samples). A, Distribution of HRD-positive and HRD-negative tumors and tumors with inconclusive status according to GIScar (left) and MGMC (right). B, Correlation between GIScar score and MGMC score. C, PFS in BRCAwt tumors excluding the missing data of MGMC and GIScar (n = 275). D, PFS in tumors with discordant GIScar and MGMC results. E, PFS in tumors with GIScar HRD results but inconclusive MGMC HRD status.
Figure 4.
Comparison of GIScar and MGMC results on the clinical data (n = 469 samples). A, Distribution of HRD-positive and HRD-negative tumors and tumors with inconclusive status according to GIScar (left) and MGMC (right). B, Correlation between GIScar score and MGMC score. C, PFS in BRCAwt tumors excluding the missing data of MGMC and GIScar (n = 275). D, PFS in tumors with discordant GIScar and MGMC results. E, PFS in tumors with GIScar HRD results but inconclusive MGMC HRD status.
Figure 5. OS according to GIScar status and treatment arm (n = 469 samples). A, Tumors with a positive GIScar result. B, Tumors with a negative GIScar result.
Figure 5.
OS according to GIScar status and treatment arm (n = 469 samples). A, Tumors with a positive GIScar result. B, Tumors with a negative GIScar result.
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