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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., Competing Interests: Declaration of competing interest The authors have no competing interests to declare relevant to the content of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

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%) for both criteria. Using the criteria of Murakami et al., 10 cases (4.8%) were immunoreactive, 26 (12.5%) solid and proliferative, and 50 (24%) papilloglandular, with a concordance rate of 62.5% (κ = 0.34, p < .001). Using the Miyagawa et al. criteria, 23 cases (11%) were immunoreactive, 20 (9.6%) solid and proliferative, and 43 (20.7%) papilloglandular. No survival differences were observed between the subtypes. The fair reproducibility of the histopathological subtype classification of HGSOC and the lack of survival differences among these subtypes indicate the need for further refinement of the criteria and exploration of their correlation with overall survival outcomes before clinical application., (© 2024. The Author(s).)

Introduction: Prognostic gene expression signatures can be used in combination with classical clinicopathological factors to guide adjuvant chemotherapy decisions in ER-positive, HER2-negative breast cancer. However, long-term outcome data after introduction of genomic testing in the treatment decision-making process are limited., Methods: In the prospective RASTER study, the tumours of 427 patients with cTanyN0M0 breast cancer were tested to assess the 70-gene signature (MammaPrint). The results were provided to their treating physician to be incorporated in the decision-making on adjuvant systemic therapy. Here, we report the long-term outcome of the 310 patients with ER-positive, HER2-negative tumours by clinical and genomic risk categories at a median follow-up of 10.3 years., Results: Among the clinically high-risk patients, 45 (49%) were classified as genomically low risk. In this subgroup, at 10 years, distant recurrence free interval (DRFI) was similar between patients treated with (95.7% [95% CI 87.7-100]) and without (95.5% [95% CI 87.1-100]) chemotherapy. Within the group of clinically low-risk patients, 56 (26%) were classified as genomically high risk. Within the clinically low-risk group, beyond 5 years, a difference emerged between the genomically high- and low-risk subgroup resulting in a 10-year DRFI of 84.3% (95% CI 74.8-95.0) and 93.4% (95% CI 89.5-97.5), respectively. Interestingly, genomic ultralow-risk patients have a 10-year DRFI of 96.7% (95% CI 90.5-100), largely (79%) without systemic therapy., Conclusions: These data confirm that clinically high-risk, genomically low-risk tumours have an excellent outcome in the real-world setting of shared decision-making. Together with the updated results of the MINDACT trial, these data support the use of the MammaPrint, in ER-positive, HER2-negative, node-negative, clinically high-risk breast cancer patients., Registry: ISRCTN71917916., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SCL and VPR have received unrestricted research grants from Agendia outside of the context of this study. LvtV is co-founder, stockholder and part-time employee of Agendia NV. All remaining authors have declared no conflicts of interest., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The recently identified classical and basal-like molecular subtypes of pancreatic cancer impact on overall survival (OS). However, the added value of routine subtyping in both clinical practice and randomized trials is still unclear, as most studies do not consider clinicopathological parameters. This study examined the clinical prognostic value of molecular subtyping in patients with resected pancreatic cancer., Methods: Subtypes were determined on fresh-frozen resected pancreatic cancer samples from three Dutch centres using the Purity Independent Subtyping of Tumours classification. Patient, treatment, and histopathological variables were compared between subtypes. The prognostic value of subtyping in (simulated) pre- and postoperative settings was assessed using Kaplan-Meier and Cox regression analyses., Results: Of 199 patients with resected pancreatic cancer, 164 (82.4 per cent) were classified as the classical and 35 (17.6 per cent) as the basal-like subtype. Patients with a basal-like subtype had worse OS (11 versus 16 months (HR 1.49, 95 per cent c.i. 1.03 to 2.15; P = 0.035)) than patients with a classical subtype. In multivariable Cox regression analysis, including only clinical variables, the basal-like subtype was a statistically significant predictor for poor OS (HR 1.61, 95 per cent c.i. 1.11 to 2.34; P = 0.013). When histopathological variables were added to this model, the prognostic value of subtyping decreased (HR 1.49, 95 per cent c.i. 1.01 to 2.19; P = 0.045)., Conclusion: The basal-like subtype was associated with worse OS in patients with resected pancreatic cancer. Adding molecular classification to inform on tumor biology may be used in patient stratification., (© The Author(s) 2022. Published by Oxford University Press on behalf of BJS Society Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In estrogen-receptor-positive, HER2-negative (ER + HER2 - ) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER + HER2 - breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER + HER2 - breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER + HER2 - breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER + HER2 - breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER + HER2 - patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER + HER2 - breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions., (© 2022. The Author(s).)

Objectives: Programmed death-ligand 1 (PD-L1) is the only approved predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). However, predictive PD-L1 immunohistochemistry is subject to interobserver variability. We hypothesized that a pathologist's personality influences the interobserver variability and diagnostic accuracy of PD-L1 immunoscoring., Materials and Methods: Seventeen pathologists performed PD-L1 immunoscoring on 50 resected NSCLC tumors in three categories (<1%;1-49%;≥50%). Also, the pathologists completed a certified personality test (NEO-PI-r), assessing five personality traits: neuroticism, extraversion, openness, altruism and conscientiousness., Results: The overall agreement among pathologists for a series of 47 tumors was substantial (kappa = 0.63). Of these, 23/47 (49%) tumors were entirely negative or largely positive, resulting in a kappa value of 0.93. The remaining 24/47 (51%) tumors had a PD-L1 score around the cutoff value, generating a kappa value of 0.32. Pathologists with high scores for conscientiousness (careful, diligent) had the least interobserver variability (r = 0.6, p = 0.009). Also, they showed a trend towards higher sensitivity (74% vs. 68%, p = 0.4), specificity (86% vs. 82%, p = 0.3) and percent agreement (83% vs. 79%, p = 0.3), although not significant. In contrast, pathologists with high scores for neuroticism (sensitive, anxious) had significantly lower specificity (80% vs. 87%, p = 0.03) and percent agreement (78% vs. 85%, p = 0.03). Also, a trend towards high interobserver variability (r = -0.3, p = 0.2) and lower sensitivity (68% vs. 74%, p = 0.3) was observed, although not significant. Pathologists with relatively high scores for conscientiousness scored fewer tumors PD-L1 positive at the ≥ 1% cut-off (r = -0.5, p = 0.03). In contrast, pathologists with relatively high scores for neuroticism score more tumors PD-L1 positive at ≥ 1% (r = 0.6, p = 0.017) and ≥ 50% cut-offs (r = 0.6, p = 0.009)., Conclusions: This study is the first to demonstrate the impact of a pathologist's personality on the interobserver variability and diagnostic accuracy of immunostaining, in the context of PD-L1 in NSCLC. Larger studies are needed for validation of these findings., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2022 The Author(s). Published by S. Karger AG, Basel.)

Purpose: This study aimed to find indicators for early response to radiation therapy in breast cancer. These would be of help in tailoring treatment for individual patients., Methods and Materials: We analyzed 66 patients with low-risk breast cancer (≥60 years; cT1-2pN0) treated within the Preoperative Accelerated Partial Breast Irradiation (PAPBI) trial. Patients received radiation therapy (RT; 10 x 4 Gray or 5 x 6 Gray), followed by a wide local excision after 6 weeks. Patients underwent magnetic resonance imaging (MRI) and 18F-fluorodexoyglucose (FDG) positron emission tomography/computed tomography (PET/CT) before RT and 5 weeks after RT, before surgery. We assessed the response to PAPBI using a histopathologic assessment and correlated this with responses on MRI and FDG PET/CT. We calculated the positive predictive values (PPVs) of MRI and PET/CT as the number of true positives (complete response on MRI/normalized at visual evaluation on PET/CT and pathologic complete response) divided by the number of patients with a complete response on MRI/normalized at visual evaluation on PET/CT. Similarly, the negative predictive values (NPVs) of MRI and PET/CT were calculated., Results: The pathologic response was (nearly) complete in 15 (23%) of the 66 patients and partially complete in 28 (42%). The remaining 23 patients (35%) were nonresponders. The PPV of MRI (Response evaluation criteria in solid tumors [RECIST]) was 87.5% and the NPV was 85%. The PPV and NPV of PET/CT were 25% and 92%, respectively., Conclusions: The most accurate method to predict a response and residual disease after preoperative RT in low-risk breast cancer was MRI, using RECIST., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: To investigate risk factors for local recurrence (LR) after breast conserving therapy in young breast cancer patients randomized in the "Young Boost Trial"., Material & Methods: In the "Young Boost Trial" 2421 patients ≤50 years old were randomized between a 16 Gy and 26 Gy boost after breast conserving surgery and whole breast radiation (50 Gy). We performed a case-control study comparing patients who developed a LR (cases) and for each of them three control patients free of recurrence (controls). Clinicopathological factors, copy number- and gene expression profiles of primary tumors were compared between cases and controls, and between primary tumors and local recurrences., Results: The cumulative 5-year LR rate was 1.07% (95% CI 0.72-1.59%) and 10-year LR rate 2.56% (1.81-3.62%). Analysis of a subset of primary tumors and local recurrences showed similar histopathological characteristics (n = 15), copy number (n = 13) and gene expression profiles (n = 14). Basal subtype was strongly associated with LR in univariable and multivariable analysis. Gains of CCND1 were identified more frequently among controls, while more frequent gains of FGFR1 and IGF1R were observed among cases. Upregulation of genes involved in the p53-pathway was observed in recurring tumors compared to non-recurring tumors. We could not identify a genomic classifier for LR., Conclusions: All investigated local recurrences were true genomic recurrences. Although differences in copy number variation and gene expression pathways were observed in recurring tumors compared to non-recurring tumors, no genomic classifier for LR could be identified., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Patient stratification based on biological variation in pancreatic ductal adenocarcinoma (PDAC) subtypes could help to improve clinical outcome. However, noninvasive assessment of the entire tumor microenvironment remains challenging. In this study, we investigate the biological basis of dynamic contrast-enhanced (DCE), intravoxel incoherent motion (IVIM), and R2*-derived magnetic resonance imaging (MRI) parameters for the noninvasive characterization of the PDAC tumor microenvironment and evaluate their prognostic potential in PDAC patients. Patients diagnosed with treatment-naïve resectable PDAC underwent MRI. After resection, a whole-mount tumor slice was analyzed for collagen fraction, vessel density, and hypoxia and matched to the MRI parameter maps. MRI parameters were correlated to immunohistochemistry-derived tissue characteristics and evaluated for prognostic potential. Thirty patients were included of whom 21 underwent resection with whole-mount histology available in 15 patients. DCE K trans and v e , ADC, and IVIM D correlated with collagen fraction. DCE k ep and IVIM f correlated with vessel density and R2* with tissue hypoxia. Based on MRI, two main PDAC phenotypes could be distinguished; a stroma-high phenotype demonstrating high vessel density and high collagen fraction and a stroma-low phenotype demonstrating low vessel density and low collagen fraction. Patients with the stroma-high phenotype (high k ep and high IVIM D, n = 8) showed longer overall survival (not reached vs. 14 months, P = 0.001, HR = 9.1, P = 0.004) and disease-free survival (not reached vs. 2 months, P < 0.001, HR 9.3, P = 0.003) compared to the other patients (n = 22). Median follow-up was 41 (95% CI: 36-46) months. MRI was able to accurately characterize tumor collagen fraction, vessel density, and hypoxia in PDAC. Based on imaging parameters, a subgroup of patients with significantly better prognosis could be identified. These first results indicate that stratification-based MRI-derived biomarkers could help to tailor treatment and improve clinical outcome and warrant further research., (© 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)