Investigator

Juan Pardo

Universidad Cardenal Herrera CEU

JPJuan Pardo
Papers(1)
Prognostic Relevance …
Collaborators(6)
Juan Ramón Berenguer-…Raquel Bosch-RomeuAntonio FalcóIrene Tadeo-CerveraJavier Martín-VallejoJoan Climent
Institutions(3)
Universidad Cardenal …Generalitat ValencianaHospital Denia

Papers

Prognostic Relevance of Clinical and Tumor Mutational Profile in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of ovarian cancer, accounting for approximately 70% of cases. This study investigates genetic mutations and their associations with overall survival (OS), complete cytoreduction (R0), and platinum response in patients undergoing either primary debulking surgery followed by adjuvant chemotherapy (PDS) or neoadjuvant chemotherapy followed by interval debulking surgery (NACT). Genetic analysis was performed on 43 primary HGSOC tumor samples using targeted massive parallel sequencing via next-generation sequencing (NGS). Clinical and molecular data were evaluated collectively and through subgroup comparisons between PDS and NACT cohorts. All analyzed samples harbored genetic alterations. Univariate survival analysis revealed that the total number of mutations (p = 0.0035), as well as mutations in HRAS (p = 0.044), FLT3 (p = 0.023), TP53 (p = 0.03), and ERBB4 (p = 0.007), were significantly associated with poorer OS. Multivariate Cox regression integrating clinical and molecular data confirmed that ERBB4 mutations are independently associated with adverse outcomes. These findings reveal a distinctive mutational landscape between the PDS and NACT groups and suggest that ERBB4 alterations may define a particularly aggressive tumor phenotype. This study contributes to a deeper understanding of HGSOC biology and may support the development of novel therapeutic targets and personalized treatment strategies in the context of precision oncology.

96Works
1Papers
6Collaborators

Positions

Researcher

Universidad Cardenal Herrera CEU

2003–

Professor

CEU Cardenal Herrera University

Country

ES

Keywords
machine learningartificial intelligencestatistical learning
Links & IDs
0000-0003-4184-9855

Scopus: 56871346300

Researcher Id: E-3839-2017