Hyperactivation of WNT signaling is a hallmark of cancer, often driven by increased expression of WNT ligands. In pancreatic ductal adenocarcinoma (PDAC), elevated WNT7B and WNT10A correlate with aggressive, basal-like disease and poor patient survival, but the mechanisms underlying this association remain unclear. Using patient-derived organoids, we show that WNT7B promotes proliferation and maintains basal-like transcriptional states by preventing differentiation toward a more classical PDAC signature. Clonal WNT7B reporter organoids reveal that WNT-high cells are heterogeneously distributed and stably coexist with WNT-low/negative lineages. Hybrid co-cultures demonstrate that WNT7B-expressing cells support the survival and growth of neighboring WNT-negative cells via short-range, contact-dependent signaling. These findings highlight the functional importance of heterogeneous WNT7B/10A expression in driving PDAC aggressiveness and suggest that targeted WNT inhibition may shift tumors toward a more differentiated, less aggressive state, offering potential therapeutic benefit.

INTRODUCTION:In patients with pancreatic cancer, the risk of venous thromboembolism (VTE) is high compared to other cancer types, suggesting that tumor-intrinsic features drive hypercoagulability. Tumor gene expression analysis may help unravel the pathogenesis of VTE in these patients and help to identify high-risk patients. AIM:To evaluate the association between tumor gene expression patterns and VTE in patients with pancreatic cancer. METHODS:In this retrospective cohort study RNA-sequence data from surgically resected tumor material from patients with pancreatic ductal adenocarcinoma (PDAC) was used to identify genes associated with the presence of venous thromboembolism (i.e., pulmonary embolism or deep-vein thrombosis) within one year follow-up after surgery. Additionally, VTE risk and expression of coagulation related genes in two molecular subtypes of pancreatic cancer was assessed. RESULTS:Out of 151 patients, 10 (6.6 %) developed deep-vein thrombosis or pulmonary embolism within one year follow-up. Differential expression analysis yielded 89 genes significantly differentially expressed in patients with VTE compared to those without VTE, including ATP6V0A4, SYT14 and ZNF114. The incidence of VTE in classical subtype was higher (n = 9; 7.6 %) than in basal-like subtype (n = 1;4 %), but this difference was not statistically significant (SHR 1.79; 95 % CI 0.22-14.3). Forty-two coagulation-associated genes were identified that were differentially expressed between these molecular subtypes, including F5, PLAU, SERPINE1, and C4BPB. CONCLUSIONS:Patients with pancreatic cancer and VTE show a different tumor gene expression profile than those without VTE. Multiple coagulation-related genes were differentially expressed in classical versus basal-like molecular subtype, suggesting that there is a difference in pro-thrombotic phenotype.

Introduction/Background To improve epithelial ovarian cancer histotyping accuracy, Köbel et al. developed decision-tree algorithms based on immunohistochemistry. These algorithms included a six-split and a four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. This study evaluated the efficacy of these algorithms. Methodology A gynaecological pathologist, blinded for the original diagnosis, first determined the haematoxylin and eosin-stained (H&E)-based histotype of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E sections in combination with the immunohistochemical stainings. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, PR, TFF3, and WT1 were scored. The algorithmic predictions were compared with the final histotypes to assess the accuracy, for which the early- and advanced stage algorithms were assessed together as the six-split-stages algorithm. Results The six-split algorithm demonstrated 96.1% accuracy, while the six-split-stages and four-split algorithms both showed 93.5% accuracy. Of the 230 cases, 16 (7%) showed discordant original and final diagnoses; the algorithms concurred with the final diagnosis in 14/16 cases (87.5%). Moreover, in 12.4%-13.3% of the cases, the H&E-based histotype was changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%) compared to the other two algorithms, while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7–97.6%). Conclusion Considering the higher sensitivity of the six-split algorithm for endometrioid carcinoma compared to the four-split algorithm, and a higher sensitivity for low-grade serous carcinoma compared to the six-split-stages algorithm, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice. Disclosures The authors have no disclosures to declare that are relevant to the content of this study.

Aims Endoscopic or percutaneous bile duct brushing is often performed as first step to differentiate between benign and malignant biliary strictures. Although brush cytology has a high specificity (95-100%), the sensitivity for detection of malignancy has been reported to be poor (41-67%) (1-4). This results in repeated diagnostic procedures with potential treatment delay, adverse events, and additional costs. Aim of this study was to evaluate a change in protocol with optimization of obtaining, handling and rating of brush cytology in patients with suspected perihilar or intrahepatic cholangiocarcinoma (pCCA/iCCA).

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at metastatic stage and typically treated with fluorouracil, leucovorin, irinotecan and oxaliplatin (FOLFIRINOX). Few patients benefit from this treatment. Molecular subtypes are prognostic in particularly resectable PDAC and might predict treatment response. This study aims to correlate molecular subtypes in metastatic PDAC with FOLFIRINOX responses using real-world data, providing assistance in counselling patients. We collected 131 RNA-sequenced metastatic biopsies and applied a network-based meta-analysis using published PDAC classifiers. Subsequent survival analysis was performed using the most suitable classifier. For validation, we developed an immunohistochemistry (IHC) classifier using GATA6 and keratin-17 (KRT17), and applied it to 86 formalin-fixed paraffin-embedded samples of advanced PDAC. Lastly, GATA6 knockdown models were generated in PDAC organoids and cell lines. We showed that the PurIST classifier was the most suitable classifier. With this classifier, classical tumors had longer PFS and OS than basal-like tumors (PFS: 216 vs. 78 days, p = 0.0002; OS: 251 vs. 195 days, p = 0.049). The validation cohort showed a similar trend. Importantly, IHC GATA6low patients had significantly shorter survival with FOLFIRINOX (323 vs. 746 days, p = 0.006), but no difference in non-treated patients (61 vs. 54 days, p = 0.925). This suggests that GATA6 H-score predicts therapy response. GATA6 knockdown models did not lead to increased FOLFIRINOX responsiveness. These data suggest a predictive role for subtyping (transcriptomic and GATA6 IHC), though no direct causal relationship was found between GATA6 expression and chemoresistance. GATA6 immunohistochemistry should be seamlessly added to current diagnostics and integrated into upcoming clinical trials.

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.

To improve the precision of epithelial ovarian cancer histotyping, Köbel et al. (2016) developed immunohistochemical decision-tree algorithms. These included a six- and four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. In this study, we evaluated the efficacy of these algorithms. A gynecological pathologist determined the hematoxylin and eosin (H&E)-based histotypes of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E-stained sections and immunohistochemistry outcomes. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, progesterone receptor (PR), trefoil factor 3 (TFF3), and Wilms’ tumor 1 (WT1) were scored. The algorithmic predictions were compared with the final histotypes to assess their precision, for which the early- and advanced stage algorithms were assessed together as six-split-stages algorithm. The six-split algorithm demonstrated 96.1% precision, whereas the six-split-stages and four-split algorithms showed 93.5% precision. Of the 230 cases, 16 (7%) showed discordant original and final diagnoses; the algorithms concurred with the final diagnosis in 14/16 cases (87.5%). In 12.4%–13.3% of cases, the H&E-based histotype changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%), while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7–97.6%). Considering the higher sensitivity of the six-split algorithm for endometrioid and low-grade serous carcinoma compared with the four-split and six-split-stages algorithms, respectively, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice.

High-grade serous ovarian carcinoma (HGSOC) can be categorized into four gene expression-based subtypes, with supposedly distinct prognoses and treatment responses. Murakami et al. translated these gene expression-based subtypes into the histopathological mesenchymal, immunoreactive, solid and proliferative, and papilloglandular subtypes, showing differences in survival outcomes. Miyagawa et al. refined these criteria to improve the interobserver concordance. The current retrospective study evaluated the interobserver variability and the prognostic differences between the histopathologic subtypes using the criteria of both Murakami et al. and Miyagawa et al. in 208 HGSOC cases. The mesenchymal subtype was considered first, followed by the immunoreactive subtype. Non-conforming cases were categorized as solid and proliferative or papilloglandular. The mesenchymal subtype was identified in 122 patients (58.7

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10 −8 ), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10 −7 ), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10 −6 ) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10 −5 ). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read‐out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)‐based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho‐subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho‐subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype‐associated activity profiles can guide therapeutic combination approaches in tumor and stroma‐enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

Introduction/BackgroundImmunohistochemistry (IHC) is essential for correctly diagnosing and histotyping epithelial ovarian cancer. Key markers for origin determination include CDX2, cytokeratin 7, cytokeratin 20, GATA3, and PAX8, while oestrogen receptor (ER), Napsin A, p16, p53, progesterone receptor (PR), and Wilms' Tumour 1 (WT1) are essential for histotyping. We examined the use of these markers in diagnosing and histotyping epithelial ovarian cancer in the Netherlands.MethodologyIn this retrospective cohort study, clinical and pathological data from all patients diagnosed with ovarian cancer in 2018 in the Netherlands were retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank, respectively. Information about immunohistochemistry was extracted from the microscopy section of the pathology reports.ResultsIHC was conducted in 94.3% (n=1,020) of the 1,082 cases. In clear cell (CCC) and mucinous (MC) carcinoma, IHC was omitted most frequently (17.9% and 10.1%, respectively). Approximately 25% of CCC, MC, endometrioid (EC), and low-grade serous (LGSC) carcinomas lacked origin-determining IHC. The key markers p53 and WT1 were concurrently assessed in 81.6% of high-grade serous carcinoma (HGSC) and 72.1% of LGSC cases. The key markers WT1, and ER or PR, were used in 70% of EC and 34.3% of MC cases. In CCC, Napsin A was analysed in 39.2% of cases, but when absent, WT1 and ER or PR were assessed in 35.4%.ConclusionThe high number of tumours in which key markers were omitted, and the presence of cases in which no IHC was conducted, underscore the urgency for standardised histotype-specific IHC sets. We propose assessment of at least WT1 and p53 for HGSC and LGSC, WT1 and PR for EC and MC, and Napsin A for CCC, aiming for diagnostic precision and consistency in epithelial ovarian cancer histotyping.DisclosuresThe authors have no disclosures to declare that are relevant to the content of this study.

Abstract Background High ovarian cancer mortality rates motivate the development of effective and patient-friendly diagnostics. Here, we explored the potential of molecular testing in patient-friendly samples for ovarian cancer detection. Methods Home-collected urine, cervicovaginal self-samples, and clinician-taken cervical scrapes were prospectively collected from 54 patients diagnosed with a highly suspicious ovarian mass (benign n = 25, malignant n = 29). All samples were tested for nine methylation markers, using quantitative methylation-specific PCRs that were verified on ovarian tissue samples, and compared to non-paired patient-friendly samples of 110 age-matched healthy controls. Copy number analysis was performed on a subset of urine samples of ovarian cancer patients by shallow whole-genome sequencing. Results Three methylation markers are significantly elevated in full void urine of ovarian cancer patients as compared to healthy controls (C2CD4D, P = 0.008; CDO1, P = 0.022; MAL, P = 0.008), of which two are also discriminatory in cervical scrapes (C2CD4D, P = 0.001; CDO1, P = 0.004). When comparing benign and malignant ovarian masses, GHSR shows significantly elevated methylation levels in the urine sediment of ovarian cancer patients (P = 0.024). Other methylation markers demonstrate comparably high methylation levels in benign and malignant ovarian masses. Cervicovaginal self-samples show no elevated methylation levels in patients with ovarian masses as compared to healthy controls. Copy number changes are identified in 4 out of 23 urine samples of ovarian cancer patients. Conclusions Our study reveals increased methylation levels of ovarian cancer-associated genes and copy number aberrations in the urine of ovarian cancer patients. Our findings support continued research into urine biomarkers for ovarian cancer detection and highlight the importance of including benign ovarian masses in future studies to develop a clinically useful test.

Fundamental and translational research in ovarian cancer aims to enhance understanding of disease mechanisms and improve treatment and survival outcomes. To support this, we established the Dutch multicenter, interdisciplinary Archipelago of Ovarian Cancer Research (AOCR) infrastructure, which includes a nationwide biobank. In this study, we share our experiences in establishing the infrastructure, offer guidance for similar initiatives, and evaluate the AOCR patient cohort. Key challenges included obtaining Data Protection Impact Assessment (DPIA) clearance, drafting the consortium agreement, and securing ethical approval from all hospitals. Over three years, 1093 patients were enrolled across 17 hospitals, resulting in the collection of 1339 tissue samples and 2280 blood samples. Of the 523 patients with currently available clinical and pathological data, 74 % (n = 387) had primary ovarian cancer. Among these patients, 73.4 % was diagnosed with high-grade serous ovarian carcinoma, and 80.9 % presented with advanced-stage disease. Surgery was performed on 93 % of patients with primary ovarian cancer, and chemotherapy was administered to 90.4 % of these patients. In conclusion, the AOCR biobank has established a robust foundation for future fundamental and translational ovarian cancer research. This manuscript provides valuable insights and guidance for developing future research infrastructures and biobanks, and contains detailed information about the AOCR patient cohort to date.

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease, commonly characterized by multiple aberrant signaling. Phosphoproteomics provides a direct read-out of these complex signaling networks and the resultant clinically relevant phenotype, as well as a functional scaffold to identify new targets. In the absence of an oncogenic driver, low dose (LD) kinase inhibitor (KI) combinations against multiple (parallel) activated kinases might provide higher efficacy and reduce toxicity as compared to single drug treatment. Aims: 1) To identify targets and test combinations of multiple KIs at LDs for potential synergism in preclinical models. 2) To chart the phosphoproteome of 42 PDAC tumors to reveal signalling pathways that may be involved in PDAC progression. Methods: By using a two step phosphopeptide enrichment with phosphotyrosine immunoprecipitation and immobilized metal affinity chromatography, followed by label free MS analysis, we analyzed phosphoproteome data of 7 immortalized and 2 primary PDAC cell lines, and of 42 PDAC tumors. We used integrative inferred kinase activity (INKA) scoring of the maxquant output to identify hyperactive kinases. For the cell line panel, five KIs were selected based on targeting coverage of the INKA profiles. LD were set as IC20s for 2,3,4 drug combinations. Cell growth inhibition was assessed by SRB assay. Median-effect analysis was used to assess synergy. Functional testing was performed in immortalized 2D, xenoderived 2D and 3D cultures. Effective low dose 3 drug combinations were further validated using patient-derived xenografts. Results: High INKA scoring of multiple activities per cell line without clear outliers of single kinases underscores the need for combination therapies. Multiple LD combinations showed effective growth inhibition (70 to 92%) and synergism, mostly 3 drug combinations, which required targeting of several RTKs and downstream signaling. Different responses were further observed between epithelial and mesenchymal cell lines. These top performing combinations were further validated in PDAC organoids and in vivo. Clinical utility of these kinase targets was then confirmed in 42 tumor phosphoprofiles, which were characterized in different subtypes with distinct therapeutic options. Phosphoproteome signals and kinases activities with potential prognostic value and mutational associations were further described. Conclusion: Our INKA pipeline, which can rank kinase activities in individual tumors, is optimally suited to specifically prioritize actionable kinases with targeting purposes. Tailored LD combination strategies exhibited promising efficacy in preclinical models and may ultimately improve treatment outcomes. Next Steps: Multicellular patient-derived models will be used to further study and target the TME in PDAC tumors. Citation Format: Andrea Vallés Martí, Giulia Mantini, Cynthia Waasdorp, Richard R. de Goeij- de Haas, Alex A. Henneman, Sander R. Piersma, Thang V. Pham, Jaco C. Knol, Joanne Verheij, Frederike Dijk, Hans Halfwerk, Elisa Giovannetti, Connie R. Jiménez, Maarten F. Bijlsma. Profile guided low dose drug combination strategies and kinase activities with prognostic and therapeutic avenues in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6485.

DNA-based testing of pancreatic cyst fluid (PCF), obtained by endoscopic ultrasound fine needle aspiration (EUS-FNA) may improve diagnostic accuracy for differentiation between types of pancreatic cystic neoplasms (PCN),1 which is currently suboptimal. Mutations in DNA (a.o. KRAS, GNAS) can differentiate non-mucinous from (pre)-malignant, mucinous PCN. Various techniques are available to detect mutations in DNA; including targeted next generation sequencing (NGS), like Ion Torrent NGS, and since more recently droplet digital PCR (ddPCR).

Introduction Only a small number of risk factors for pancreatic ductal adenocarcinoma (PDAC) has been established. Several studies identified a role of epigenetics and of deregulation of DNA methylation. DNA methylation is variable across a lifetime and in different tissues; nevertheless, its levels can be regulated by genetic variants like methylation quantitative trait loci (mQTLs), which can be used as a surrogate. Materials and methods We scanned the whole genome for mQTLs and performed an association study in 14 705 PDAC cases and 246 921 controls. The methylation data were obtained from whole blood and pancreatic cancer tissue through online databases. We used the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case–Control Consortium genome-wide association study (GWAS) data as discovery phase and the Pancreatic Disease Research consortium, the FinnGen project and the Japan Pancreatic Cancer Research consortium GWAS as replication phase. Results The C allele of 15q26.1-rs12905855 showed an association with a decreased risk of PDAC (OR=0.90, 95% CI 0.87 to 0.94, p=4.93×10 −8 in the overall meta-analysis), reaching genome-level statistical significance. 15q26.1-rs12905855 decreases the methylation of a 'C-phosphate-G' (CpG) site located in the promoter region of the RCCD1 antisense ( RCCD1-AS1 ) gene which, when expressed, decreases the expression of the RCC1 domain-containing ( RCCD1 ) gene (part of a histone demethylase complex). Thus, it is possible that the rs12905855 C-allele has a protective role in PDAC development through an increase of RCCD1 gene expression, made possible by the inactivity of RCCD1-AS1 . Conclusion We identified a novel PDAC risk locus which modulates cancer risk by controlling gene expression through DNA methylation.

Pancreatic ductal adenocarcinoma (PDAC) is a dismal disease with a poor clinical prognosis and unsatisfactory treatment options. We previously found that the transcription factor CCAAT/Enhancer-Binding Protein Delta (C/EBPδ) is lowly expressed in PDAC compared to healthy pancreas duct cells, and that patient survival and lymph node involvement in PDAC is correlated with the expression of C/EBPδ in primary tumor cells. C/EBPδ shares a homologous DNA-binding sequence with other C/EBP-proteins, leading to the presumption that other C/EBP-family members might act redundantly and compensate for the loss of C/EBPδ. This implies that patient stratification could be improved when expression levels of multiple C/EBP-family members are considered simultaneously. In this study, we assessed whether the quantification of C/EBPβ or C/EBPγ in addition to that of C/EBPδ might improve the prediction of patient survival and lymph node involvement using a cohort of 68 resectable PDAC patients. Using Kaplan–Meier analyses of patient groups with different C/EBP-expression levels, we found that both C/EBPβ and C/EBPγ can partially compensate for low C/EBPδ and improve patient survival. Further, we uncovered C/EBPβ as a novel predictor of a decreased likelihood of lymph node involvement in PDAC, and found that C/EBPβ and C/EBPδ can compensate for the lack of each other in order to reduce the risk of lymph node involvement. C/EBPγ, on the other hand, appears to promote lymph node involvement in the absence of C/EBPδ. Altogether, our results show that the redundancy of C/EBP-family members might have a profound influence on clinical prognoses and that the expression of both C/EPBβ and C/EBPγ should be taken into account when dichotomizing patients according to C/EBPδ expression.

Objectives: Ovarian cancer has the worst overall survival rate of all gynecologic malignancies. For the majority of patients, the 5-year overall survival rate of less than 50% has hardly improved over the last decades. To improve the outcome of patients with all subtypes of ovarian cancer, large-scale fundamental and translational research is needed. To accommodate these types of ovarian cancer research, we have established a Dutch nationwide, interdisciplinary infrastructure and biobank: the Archipelago of Ovarian Cancer Research (AOCR). The AOCR will facilitate fundamental and translational ovarian cancer research and enhance interdisciplinary, national, and international collaboration. Design: The AOCR biobank is a prospective ovarian cancer biobank in which biomaterials are collected, processed, and stored in a uniform matter for future (genetic) scientific research. All 19 Dutch hospitals in which ovarian cancer surgery is performed participate and collaborate in the AOCR biobank. Participants/Materials, Setting, Methods: Patients of 16 years and older with suspected or diagnosed ovarian, fallopian tube, or primary peritoneal cancer are recruited for participation. Patients who agree to participate give written informed consent for collection, storage, and issue of their biomaterials for future studies. After inclusion, different blood samples are taken at various predefined time points both before and during treatment. In case of a diagnostic paracentesis or biopsy, the residual biomaterials of these procedures are stored in the biobank. During surgery, primary tumor tissue and, if applicable, tissue from metastatic sites are collected and stored. From each patient, a representative histological hematoxylin and eosin stained slide is digitalized for research purposes, including reassessment by a panel of gynecologic pathologists. Clinical and pathological data are obtained on a per-study basis from Dutch registries. Research proposals for the issue of biomaterials and data are evaluated by both the Archipelago Scientific Committee and the Steering Committee. Researchers using the biomaterials from the AOCR biobank are encouraged to enrich the biobank with data and materials resulting from their analyses and experiments. Limitations: The implementation and first 4 years of collection are financed by an infrastructural grant from the Dutch Cancer Society. Therefore, the main limitation is that the costs for sustaining the biobank after the funding period will have to be covered. This coverage will come from incorporation of budget for biobanking in future grant applications and from fees from external researchers and commercial parties using the biomaterials stored in the AOCR biobank. Moreover, we will apply for grants aimed at sustaining and improving research infrastructures and biobanks. Conclusions: With the establishment of the Dutch nationwide, interdisciplinary Archipelago of Ovarian Cancer Research infrastructure and biobank, fundamental and translational research on ovarian cancer can be greatly improved. The ultimate aim of this infrastructure is that it will lead to improved diagnostics, treatment, and survival of patients with ovarian cancer.

Pancreatic cancer has an incidence that almost matches its mortality. Only a small number of risk factors and 33 susceptibility loci have been identified. so Moreover, the relative rarity of pancreatic cancer poses significant hurdles for research aimed at increasing our knowledge of the genetic mechanisms contributing to the disease. Additionally, the inability to adequately power research questions prevents small monocentric studies from being successful. Several consortia have been established to pursue a better understanding of the genetic architecture of pancreatic cancers. The Pancreatic disease research (PANDoRA) consortium is the largest in Europe. PANDoRA is spread across 12 European countries, Brazil and Japan, bringing together 29 basic and clinical research groups. In the last ten years, PANDoRA has contributed to the discovery of 25 susceptibility loci, a feat that will be instrumental in stratifying the population by risk and optimizing preventive strategies.