Abstract 22P
Background
Establishing biobanking in cancer research is important for health research infrastructure to collect, store, process, and distribute high-quality human biological samples and associated data. The Biobank for Translational Medicine (B4MED) Unit at the European Institute of Oncology (IEO) is a landmark in this field. The aim of this analysis was to retrospectively examine the evolution of biobank activity for breast cancer research during the 10 years since the B4MED foundation.
Methods
All B4MED activities are controlled and tracked by Nautilus Laboratory Information Management System, a management system integrated with IEO medical records database. The files of B4MED were interrogated for the number of samples associated with patients treated for breast cancer at IEO, including their storage and use since the beginning of B4MED. All clinicopathologic characteristics, including histological subtype, grade, and stage, were retrieved into a pseudo-anonymized database. To extract the data, we performed a query with the following keywords: Breast, Carcinoma, and Primary.
Results
A total of 3,858 breast cancers and integrated clinicopathologic data were collected in our biobank (2012-2022). The samples, stored at -80°C, included tumor (n=3,858), matched non-neoplastic tissues (n=2,101; 55%), and biofluids (n=3,423; 89%), i.e. blood, serum, and plasma. For 1,774 (45%) patients both the normal tissue and blood samples were stored. Subtypes included 3,188 (82.6%) invasive carcinoma of no special type, 428 (11.1%) lobular, 134 (3.5%) mixed ductal-lobular, and 84 (2.2%) special types. Grade, stage, and molecular subtypes were available for all patients. Biomarkers were carefully annotated (e.g. we were able to retrieve 1,478 HER2-low breast cancers samples). All patients signed the Scientific Research Participation Agreement.
Conclusions
B4MED at IEO is an invaluable source of biomaterials that in the last decade led to remarkable scientific innovations related to new biomarkers and novel drugs, for increasingly personalized treatment strategies designed for breast cancer patients. The next challenge is to integrate digital and molecular pathology data to take advantage of machine learning protocols for next-generation biobanking.
Legal entity responsible for the study
The authors.
Funding
European Institute of Oncology and Italian Ministries of Health and Research.
Disclosure
All authors have declared no conflicts of interest.