From biobank to bedside Advancing ovarian cancer research, classification, and prognostication

The aim of this thesis is to improve ovarian cancer care by advancing research, diagnosis, and prognosis. 
In 2019, the Dutch nationwide, interdisciplinary research infrastructure and biobank for fundamental and translational ovarian cancer research, known as the Archipelago of Ovarian Cancer Research (AOCR), was established. Part I of this thesis describes its establishment and evaluation. Chapter 2 outlines the biobank protocol, Chapter 3 describes the IT infrastructure, and Chapter 4 evaluates the establishment process and describes the patient cohort included during the first three years.
Part II of this thesis focuses on improving the histological subtype classification of epithelial ovarian cancer. Chapter 5 evaluates immunohistochemistry-based decision-tree algorithms for subtype classification. To address discrepancies between morphology and immunohistochemistry, Chapter 6 assesses the performance of an externally developed AI-based subtype classification algorithm.
Part III of this thesis addresses prognostic stratification within the histological subtypes. Chapter 7 evaluates interobserver variability and prognostic differences between gene expression–based subtypes of high-grade serous carcinoma. Chapter 8 combines TP53 sequencing with shallow whole-genome sequencing of circulating tumor DNA to improve detection and assess prognostic value. Chapter 9 evaluates the added prognostic value of estrogen receptor immunohistochemistry in molecularly classified endometrioid carcinoma.