The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks.

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

Detection of circulating tumour DNA after neoadjuvant chemoradiotherapy in patients with locally advanced oesophageal cancer.

Active surveillance instead of standard surgery after neoadjuvant chemoradiotherapy (nCRT) has been proposed for patients with oesophageal cancer. Circulating tumour DNA (ctDNA) may be used to facilitate selection of patients for surgery. We show that detection of ctDNA after nCRT seems highly suggestive of major residual disease. Tumour biopsies and blood samples were taken before, and 6 and 12 weeks after, nCRT. Biopsies were analysed with regular targeted next-generation sequencing (NGS). Circulating cell-free DNA (cfDNA) was analysed using targeted NGS with unique molecular identifiers and digital polymerase chain reaction. cfDNA mutations matching pre-treatment biopsy mutations confirmed the presence of ctDNA. In total, 31 patients were included, of whom 24 had a biopsy mutation that was potentially detectable in cfDNA (77%). Pre-treatment ctDNA was detected in nine of 24 patients (38%), four of whom had incurable disease progression before surgery. Pre-treatment ctDNA detection had a sensitivity of 47% (95% CI 24-71) (8/17), specificity of 85% (95% CI 42-99) (6/7), positive predictive value (PPV) of 89% (95% CI 51-99) (8/9), and negative predictive value (NPV) of 40% (95% CI 17-67) (6/15) for detecting major residual disease (>10% residue in the resection specimen or progression before surgery). After nCRT, ctDNA was detected in three patients, two of whom had disease progression. Post-nCRT ctDNA detection had a sensitivity of 21% (95% CI 6-51) (3/14), specificity of 100% (95% CI 56-100) (7/7), PPV of 100% (95% CI 31-100) (3/3), and NPV of 39% (95% CI 18-64) (7/18) for detecting major residual disease. The addition of ctDNA to the current set of diagnostics did not lead to more patients being clinically identified with residual disease. These results indicate that pre-treatment and post-nCRT ctDNA detection may be useful in identifying patients at high risk of disease progression. The addition of ctDNA analysis to the current set of diagnostic modalities may not improve detection of residual disease after nCRT. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Cell-free DNA mutations as biomarkers in breast cancer patients receiving tamoxifen.

The aim was to identify mutations in serum cell-free DNA (cfDNA) associated with disease progression on tamoxifen treatment in metastatic breast cancer (MBC). Sera available at start of therapy, during therapy and at disease progression were selected from 10 estrogen receptor (ER)-positive breast cancer patients. DNA from primary tumor and normal tissue and cfDNA from minute amounts of sera were analyzed by targeted next generation sequencing (NGS) of 45 genes (1,242 exons). At disease progression, stop-gain single nucleotide variants (SNVs) for CREBBP (1 patient) and SMAD4 (1 patient) and non-synonymous SNVs for AKAP9 (1 patient), PIK3CA (2 patients) and TP53 (2 patients) were found. Mutations in CREBBP and SMAD4 have only been occasionally reported in breast cancer. All mutations, except for AKAP9, were also present in the primary tumor but not detected in all blood specimens preceding progression. More sensitive detection by deeper re-sequencing and digital PCR confirmed the occurrence of circulating tumor DNA (ctDNA) and these biomarkers in blood specimens.

SIAH2 protein expression in breast cancer is inversely related with ER status and outcome to tamoxifen therapy.

Our previous study demonstrated that high mRNA levels for Seven in Absentia Homolog 2 (SIAH2) correlated with high Estrogen Receptor (ER) mRNA levels and with longer progression-free survival (PFS) after first-line tamoxifen. Others showed high SIAH2 protein levels in ER-negative breast cancer associated with an unfavorable relapse-free survival. In the current study, we investigated SIAH2 protein expression to clarify the discrepancy between protein and mRNA findings and to determine its diagnostic value in breast cancer patients. Tissue microarrays (TMAs) containing core specimens of primary breast tumors were immunohistochemically stained for SIAH2 protein. The TMAs analyzed a cohort of 746 patients with primary breast cancer (PBC) and a cohort of 245 patients with ER-positive metastatic breast cancer (MBC) treated with first-line tamoxifen. SIAH2 staining was scored for intensity and proportion of positive tumor cells and evaluated for its relationship with metastasis-free survival (MFS) and PFS. Multivariate survival analyses included traditional prognostic or predictive factors, respectively. The PBC-cohort had 263 patients with high SIAH2 protein expression and decreased expression of ER protein and mRNA levels (P = 0.005 and P = 0.003, respectively). High SIAH2 levels correlated with significant unfavorable MFS in lymph node negative, ER-positive breast cancer patients. The MBC-cohort had 86 patients with increased SIAH2 protein expression. High SIAH2 expression was associated with an unfavorable PFS after first-line tamoxifen in multivariate analyses (HR = 1.45; 95% CI, 1.07-1.96; P = 0.015). In conclusion, SIAH2 protein expression is especially observed in ER-negative tumors. Its prognostic value in breast cancer does not add to current prognostic markers. The proportion of SIAH2-positive cells can be used as biomarker to predict tamoxifen treatment failure in MBC patients.

Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.

BACKGROUND: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings. RESULTS: We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95%CI: 1.29-3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response. CONCLUSION: We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen.