Catherine Genestie

Spatial Profiling of Ovarian Carcinoma and Tumor Microenvironment Evolution under Neoadjuvant Chemotherapy

Abstract Purpose: This study investigates changes in CD8+ cells, CD8+/Foxp3 ratio, HLA I expression, and immune coregulator density at diagnosis and upon neoadjuvant chemotherapy (NACT), correlating changes with clinical outcomes. Experimental Design: Multiplexed immune profiling and cell clustering analysis were performed on paired matched ovarian cancer samples to characterize the immune tumor microenvironment (iTME) at diagnosis and under NACT in patients enrolled in the CHIVA trial (NCT01583322). Results: Several immune cell (IC) subsets and immune coregulators were quantified pre/post-NACT. At diagnosis, patients with higher CD8+ T cells and HLA I+-enriched tumors were associated with a better outcome. The CD8+/Foxp3+ ratio increased significantly post-NACT in favor of increased immune surveillance, and the influx of CD8+ T cells predicted better outcomes. Clustering analysis stratified pre-NACT tumors into four subsets: high Binf, enriched in B clusters; high Tinf and low Tinf, according to their CD8+ density; and desert clusters. At baseline, these clusters were not correlated with patient outcomes. Under NACT, tumors were segregated into three clusters: high BinfTinf, low Tinf, and desert. The high BinfTinf, more diverse in IC composition encompassing T, B, and NK cells, correlated with improved survival. PDL1 was rarely expressed, whereas TIM3, LAG3, and IDO1 were more prevalent. Conclusions: Several iTMEs exist during tumor evolution, and the NACT impact on iTME is heterogeneous. Clustering analysis of patients unravels several IC subsets within ovarian cancer and can guide future personalized approaches. Targeting different checkpoints such as TIM3, LAG3, and IDO1, more prevalent than PDL1, could more effectively harness antitumor immunity in this anti-PDL1–resistant malignancy.

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

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.

ESR1 Mutation in Endocrine Treatment-Naïve Endometrial Cancer: Prevalence, Characteristics, and Prognostic Implications, Results from the UTOLA Phase II GINECO Trial

Abstract Purpose: Aromatase inhibitors (AI) are used to treat estrogen receptor (ER)–positive low-grade endometrioid endometrial cancer. In breast cancer, ESR1 mutations are rare at diagnosis (<5%) but are frequently acquired in AI-resistant cases and are considered one of the major resistance mechanisms to endocrine therapy. This study aimed to assess the prevalence of ESR1 mutations in hormonotherapy-naïve endometrial cancer samples and correlate them with molecular profiles, ER expression, and clinical outcomes. Experimental Design: A total of 147 patients with advanced endometrial cancer who had responded to first-line chemotherapy were recruited into the UTOLA trial. Archival endometrial cancer tumor tissues underwent sequencing of 127 genes, including ESR1. Only hotspot mutations in the ligand-binding domain were evaluated. ESR1 mutation prevalence was validated in the Genomics England dataset. In UTOLA, tumors were classified as POLE, MMR deficient, TP53abn, or no specific molecular profiles (NSMP) based on the Proactive Molecular Risk Classifier for Endometrial Cancer (PROMISE) classification. Results: Of 147 patients, 137 had sufficient tumor material for sequencing. ESR1 mutations were identified in eight tumors (6%), including Y537S/C/N (n = 4), L536H/P (n = 2), and E380Q (n = 2). A similar prevalence (3.5%) was found among 1,311 tumors in the Genomics England dataset. All ESR1 mutation cases were low-grade endometrioid endometrial cancer, ER-positive, and PR-positive, and classified as NSMP. Among patients with metastatic NSMP low-grade endometrioid endometrial cancer, 22% (8/37) harbored ESR1 mutations. Survival outcomes after platinum chemotherapy were similar between patients with ESR1 mutation endometrial cancer and ESR1 wild type (median, not reached vs. 25.3 months; P = 0.114). Conclusions: ESR1 mutations, while rare overall in treatment-naïve endometrial cancer, are more prevalent in patients with NSMP low-grade endometrioid endometrial cancer, potentially affecting AI efficacy. ESR1 status should be considered in selecting hormonotherapy and as a stratification factor in AI trials.