Unlocking personalized endometrial cancer treatment: the critical role of the BBIRE biobank in sample collection and distribution

Abstract

Introduction: Endometrial cancer (EC) is the most common gynecological malignancy and the sixth most common cancer in women. Although it primarily affects women around or after menopause, an increasing number of cases are now being found in women of reproductive age. This shift highlights the need for fertility-sparing treatments and research.

Methods: The tumor biobank of the Regina Elena National Cancer Institute (BBIRE) has played a central role in EC research by simplifying the collection and distribution of high-quality samples linked to clinical data. BBIRE follows strict protocols and uses secure databases to protect patient privacy, meet regulations, and keep clinical information accurate. These steps help maintain sample quality and reduce errors before analysis.

Results: This research highlights the importance of the BBIRE-tissue processing group in the coordinated management of 545 gynecological tumor samples, comprising 321 EC samples, underscoring its importance as a crucial instrument for translational research. The biobank supports a complete research process, from patient enrollment to molecular data analysis. Its flexible, standardized structure helps ensure reliable results in different research settings.

Discussion: As a gynecologic oncology resource, BBIRE facilitates large-scale studies and collaboration among team researchers. This support is essential for identifying new biomarkers, tailoring treatments, and advancing precision medicine. The development of personalized care and improved outcomes for women with EC can be accelerated when work is performed collaboratively by surgeons, biobanks, and researchers.

Statement of significance: The BBIRE Biobank is a game changer in cancer research that enables the integration of annotated samples, multiomics data, and organoid models to identify molecular drivers and accelerate personalized care.

Keywords: biobanking; digital pathology; endometrial cancer; multiomics; patient derived organoids; precision oncology; translational oncology.

FIGURE 1

Overview of the endometrial cancer samples workflow. Tumor samples are collected and pseudo-anonymized (1–2), processed into multiple formats (FFPE, frozen tissues, thin sections, and primary cells (3), and subjected to quality control (4). EC samples are archived and used for downstream analyses, including NGS-based expression and mutation profiling, cell type quantification, and organoid generation, in compliance with GDPR (5). Sample metadata are registered in the BBMRI-ERIC directory (6).

FIGURE 2

Circle plot of the biobanking workflow. The filled gray circles denote the operational units arranged in sequential order. Concentric levels 15 indicate the main workflow stages; each level spans the units involved in that stage.

FIGURE 3

IFOIRE Biobank summary. (A) Patients stratified by pathology with annotated totals of tissue and serum samples. (B) Pie chart of the tissue sample origin. (C) Pie chart of preservation categories. (D) Annotated heatmap of gynecological samples across pathologies by year.

FIGURE 4

Characterization and drug response of PDO ECs. (A) IHC comparison between the original biopsy sample (T0) and the matched PDO sample BBIRET1983. Representative images showing H&E staining and immunostaining for Vimentin, CKAE1/AE3, ER, PR, and TP53. Scale bars = 100 µm. (B) Representative brightfield images illustrating PDO growth dynamics over 12 days in culture. Scale bars = 100 µm. (C) Representative images of PDOs after treatment with carboplatin (100 µM) plus paclitaxel (100 nM) compared with untreated controls, showing morphological alterations and reduced viability under the treated conditions. Scale bars = 100 µm.

FIGURE 5

Digital pathology. (A) Representative digitized wholeslide image of an endometrial tissue section used for morphometric analysis (hematoxylin and eosin, H&E). (B) The section in (A) after image analysis, shown with a pseudocolor overlay highlighting regions classified as cancer, normal, or immune cells. (C) Percentage estimates for endometrial biobank samples.

FIGURE 6

Quality assessment of biobank samples. (A) Metrics evaluated at each processing stage. (B) Transcriptome metrics vs. RIN with color-coded threshold lines; accompanying bar chart of dropout rates for the RIN and transcriptome metrics. (C) Exome metrics vs. DIN with color-coded threshold lines; accompanying bar chart of dropout rates for DIN and exome metrics.

FIGURE 7

Biobank Information Management System (BIMS). Information flow and system interactions within the BIMS ecosystems.