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A Transcriptomic Signature Platform For Predicting Prognosis And Guiding Therapy In Ovarian Cancer Patients

Interests: Biomarker
Published:
Lead Inventor: Lei Huang
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SUMMARY

A 10-gene signature based on glucocorticoid receptor activity to predict survival risk in high-grade serous ovarian cancer patients, helping identify those who may benefit from specific therapies and improving personalized treatment strategies

 

The Unmet Need: Robust molecular profiling tools that can accurately reflect glucocorticoid receptor pathway activation to improve prognostic stratification

  • High-grade serous ovarian cancer (HGS OvCa) is the most lethal form of gynecological malignancy, accounting for the majority of ovarian cancer-related deaths. Despite advances in surgical techniques and chemotherapy, the prognosis for patients with advanced HGS OvCa remains poor, largely due to late-stage diagnosis and the heterogeneous nature of the disease. Accurate risk stratification is critical for guiding treatment decisions and improving patient outcomes, yet current prognostic tools are limited in their ability to capture the complex molecular landscape of HGS OvCa. There is a pressing need for more precise biomarkers that can identify high-risk patients and enable personalized therapeutic strategies, particularly as new targeted therapies and immunomodulatory approaches are being developed.

  • Existing prognostic approaches in HGS OvCa often rely on clinical parameters, such as tumor stage and residual disease after surgery, or on the expression of single biomarkers like the glucocorticoid receptor (GR) mRNA (NR3C1). However, these methods have significant limitations. Clinical factors do not account for underlying tumor biology, and single-gene markers like NR3C1 provide only a narrow view of the molecular drivers of disease progression. Moreover, the heterogeneity of gene expression within tumors means that single-marker approaches may fail to identify patients with high transcriptional activity of key pathways, such as GR signaling, that influence prognosis and therapeutic response. As a result, many patients are not optimally stratified for risk, and opportunities to tailor treatments—such as the use of GR antagonists—are missed, underscoring the need for more comprehensive and robust molecular signatures.

 

The Proposed Solution: A prognostic glucocorticoid receptor 10-gene transcriptomic signature for high-grade serous ovarian cancer

  • The faculty inventor developed a prognostic transcriptomic signature, known as GR-sig, to enhance risk stratification for patients with HGS OvCa. This solution identifies a set of glucocorticoid receptor-regulated differentially expressed genes (GR-DEGs) in ovarian cancer cell lines and validates their expression in primary human HGS OvCa samples using large-scale gene expression databases. Through comprehensive statistical modeling a robust 10-gene signature was identified enabling clinicians to classify advanced stage HGS OvCa patients into high- and low-risk categories for mortality, providing a more accurate prognostic tool than NR3C1 expression alone. This signature is also positioned to guide patient selection for clinical trials evaluating glucocorticoid receptor antagonist therapies.

ADVANTAGES

ADVANTAGES

  • Improves risk stratification for high-grade serous ovarian cancer (HGS OvCa) patients

  • Predicts overall survival more accurately

  • Enables identification of high-risk patients who may benefit from targeted GR antagonist therapies

  • Supports personalized treatment decisions and patient selection in clinical trials

  • Validated using large-scale, publicly available gene expression datasets

  • Facilitates development of novel therapeutic strategies

APPLICATIONS

  • Ovarian cancer patient risk stratification

  • Clinical trial patient enrollment optimization

  • Personalized ovarian cancer prognosis

  • Guiding GR antagonist therapy selection