Association of PD-L1 expression by immunohistochemistry and gene microarray with molecular subtypes of ovarian tumors

Identifying patients who respond to immune checkpoint blockade (ICB) is a significant challenge in oncology. While PD-L1 expression by immunohistochemistry (IHC) is the current diagnostic gold standard for patient selection, it nevertheless does not capture all patients who may respond to ICB. Recent gene expression studies in high-grade serous ovarian carcinoma have defined an immunoreactive molecular subtype that has a measurable difference in patient survival compared with non-immunoreactive subtypes, but no studies have yet demonstrated its impact on predicting response to ICB. As a step toward establishing the predictive value of gene expression classifiers in ICB, we assessed the relationship between PD-L1 IHC and molecular subtypes of ovarian epithelial cancer. This was done by analyzing a total of 93 tissue specimens from patients with stage III and IV disease, and comparing PD-L1 IHC with gene expression by Agilent microarrays using TCGA-defined subtypes. We showed that ovarian tumors with elevated IHC PD-L1 expression are most strongly associated with immunoreactive subtype as compared with other molecular subtypes, reaching statistical significance against differentiated (Dunn's test, 33.39, p = 0.0003) and mesenchymal (39.63, p < 0.0001) subtypes. Comparing PD-L1 scoring with CPS vs. TPS showed similar trends, but with stronger correlation strength when using CPS (Kruskal-Wallis, H = 27.52, p < 0.0001), as opposed to TPS (H = 25.04, p < 0.0001). Interestingly, while PD-L1 gene expression by microarray was significantly increased in the immunoreactive subtype (H = 20.25, p = 0.0002), it showed a positive but relatively poor correlation to IHC. Overall, the results demonstrate potential value in use of the molecular classifier to select patients for ICB, pending further studies that assess its ability to predict treatment outcomes. In the future, integration of cellular, protein, and genomic biomarkers in the tumor and tumor microenvironment may improve current methods of predicting treatment response.

Prognostic gene expression signature for high-grade serous ovarian cancer

BACKGROUND

Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC.

PATIENTS AND METHODS

Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies.

RESULTS

Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years.

CONCLUSION

The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches.

Overcoming platinum resistance in ovarian cancer by targeting pregnancy-associated plasma protein-A

OBJECTIVES

Inhibition of pregnancy-associated plasma protein-A (PAPP-A), an upstream activator of the insulin-like growth factor (IGF) pathway, is known to augment sensitivity to platinum-based chemotherapy. This study further tests the efficacy of PAPP-A inhibition with a monoclonal antibody inhibitor (mAb-PA) in ovarian cancer (OC) platinum-resistant patient-derived xenograft (PDX) models.

METHODS

PAPP-A expression was quantitated in platinum-resistant PDX models by ELISA. A subset with High (n = 5) and Low (n = 2) expression were revived in female SCID/beige mice for studies with either saline, carboplatin/paclitaxel (CP) + mAb-PA, or CP + IgG2a. The primary endpoint was tumor area by ultrasound on day 28 relative to baseline. Conversion to platinum-sensitive was defined by average tumor regression below baseline. Statistical analyses included linear mixed effects modeling and Kaplan Meier curves. Response to therapy was correlated with changes in the ratio of phosphorylated/total AKT and ERK 1/2 using Wes analysis.

RESULTS

The addition of mAb-PA to CP induced tumor regression below baseline in one High PAPP-A PDX model; another three models exhibited notable growth inhibition relative to CP + IgG2a. None of the Low PAPP-A PDX models regressed below baseline. The PDX model with the greatest magnitude of tumor regression from baseline after combination therapy was maintained on single agent mAb-PA or IgG2a, but no benefit was observed. Decreased phosphorylation of ERK1/2 correlated with conversion to platinum-sensitive.

CONCLUSIONS

The addition of mAb-PA to CP overcame platinum-resistance in one of five High PAPP-A PDX models; three other models demonstrated improved platinum-response. This supports further clinical development of this novel therapeutic.