Journal of Pathology Clinical Research最新文献

Inter-observer concordance data for the HER2 category as assessed by a group of 16 specialist breast pathologists on 50 diagnostic core biopsies was compared with that produced by digital image analysis (DIA) using the HER2 APP, CE2797 (VP APP; Visiopharm, Hoersholm, Denmark). Comparing pathologists' consensus scores and DIA scores, 36 cases (73.5%) agreed. Fleiss' kappa statistic was 0.433 (indicative of moderate agreement). Cohen's weighted kappa was used to compare the scores of individual raters to consensus scores; for all 50 cases the kappa scores had a range between 0.412 and 0.854; the VP APP was ranked 12th of 17 raters (kappa score 0.638 indicating substantial agreement). Results for HER2-low cases (N = 44) showed a kappa score range of 0.295 to 0.823; the VP APP ranked 12th of 17 (score 0.535 indicating moderate agreement). For high agreement cases the kappa score range was 0.664 to 1.000 for all HER2 scores (N = 24) and the VP APP scored 0.916 (indicating almost perfect agreement). For the HER2-low scores (N = 20), the kappa score range was 0.506–1.000 and the VP APP scored 0.860 (almost perfect agreement). DIA of the proportions of tumour cells showing expression within each of the HER2 categories demonstrated that the majority of cases showing a low level of agreement between pathologists showed heterogeneity and/or a level of expression close to a cut-point for decision making. This study demonstrates that the VP APP produces results that are extremely well-aligned to those of expert pathologists in cases with good overall agreement, and in difficult cases its reproducibility will outperform that of the visual scorer. The results also suggest that use of the VP APP has the potential to reduce the proportion of cases referred for gene amplification testing by reducing the number of cases incorrectly classified as HER2 2+.

Cribriform pattern and intraductal carcinoma of the prostate are recognized adverse histological features, yet their prognostic value in treatment-naïve metastatic disease remains uncertain. We conducted a single-center retrospective study of 183 biopsy-proven prostate carcinomas (105 with metastatic castration-sensitive prostate carcinoma and 78 non-metastatic high-grade cases) diagnosed between 2017 and 2024. Cribriform pattern, intraductal carcinoma of the prostate, and coagulative tumor necrosis were recorded per core and summarized as patient-level binary status and as semiquantitative proportions per cancer-positive core. Two multivariable logistic regression models (binary and semiquantitative) were fitted, and receiver operating characteristic (ROC) analysis evaluated the discriminatory performance of the cribriform proportion. Cribriform pattern and intraductal carcinoma of the prostate were more frequent in metastatic castration-sensitive prostate carcinoma. In the semiquantitative model, the cribriform proportion remained independently associated with metastatic status [odds ratio (OR) 1.29, 95% CI 1.07–1.55, p = 0.008; per 1.0 increase in the proportion, equivalent to OR 1.03 per 10%-point increase], whereas necrosis remained significant only in the binary model. The cancer-positive core rate and a lower total number of biopsy cores were predictive in both models, whereas prostate-specific antigen, intraductal carcinoma of the prostate, and Grade Group composition were not independent predictors. ROC analysis for the cribriform proportion yielded an area under the curve of 0.704, with a Youden Index cut-off of 0.445 (approximately half of cancer-positive cores), corresponding to a sensitivity of 57.1% and a specificity of 75.6%. These findings indicate that semiquantitative reporting of cribriform pattern – expressed as the proportion of cancer-positive cores – adds discriminatory information for metastatic status at presentation and could complement binary reporting in high-grade disease. From a clinical perspective, such evaluation may refine risk stratification at diagnosis and support treatment intensification strategies in very-high-risk patients.

The tumour microenvironment (TME) is a key factor in carcinogenesis by affecting tumour growth, invasion, metastasis, and drug resistance. Histopathology and immunohistochemistry provide accessible and reproducible methodologies for TME evaluation, with growing evidence supporting their prognostic and predictive significance in gastrointestinal cancers. This commentary examines four papers published in the Journal of Pathology: Clinical Research that illustrate this methodology across various gastrointestinal cancers. The Tumour Microenvironment Score, Combined Immune Checkpoint Stromal Score, Immune Score for Gastric Cancer, and the multicomponent microenvironment and tumour inflammatory features panel have been developed to assess TME characteristics in colorectal cancer, gastric cancer, and gastroenteropancreatic neuroendocrine neoplasm. The reviewed studies demonstrate that basic morphological characteristics together with the expression of immune checkpoint markers and immune cell markers enable the development of effective clinical tools to enhance risk assessment and treatment planning. These practical, cost-effective, and promising methods need to undergo extensive multicentre testing with established protocols, digital pathology, and molecular information integration.

In breast cancer (BC), the transcription factor GATA3 is linked to estrogen receptor (ER) alpha biology, and its loss is associated with aggressive tumor features. Little is reported about potential roles and implications of GATA3 independent of ER, and possible relationships to the BC tumor microenvironment (TME) have not been much explored. Thus, the discovery of novel biomarkers potentially linked to ER and GATA3 functions and predicting aspects of the TME could significantly improve precision in the management of patient subgroups. We examined GATA3 protein and mRNA expression in a large in-house population-based BC series (n = 837), and in the METABRIC datasets (METABRIC Discovery, n = 997 and METABRIC Validation, n = 995). Associations with primary BC phenotypes, transcriptional programs, TME features, clinical outcomes, and potentially independent roles of GATA3 are reported. We find that low GATA3 expression associates with aggressive features like increased tumor diameter, higher histological grade, triple negative BC, and a basal-like (CK5/6 positive) phenotype. Low GATA3 mRNA expression associated with downregulation of ER-related genes, upregulation of transcriptional signatures reflecting hypoxia, and enrichment of gene sets reflecting tumor cell proliferation, epithelial-mesenchymal transition, and stemness. Low GATA3 protein and mRNA expression both associated with overall reduced BC-specific survival. Notably, low GATA3 expression strongly associated with upregulation of immune checkpoint markers, T-cell activation, and metabolic alterations not previously described in BC. Gene expression patterns underlying GATA3-low tumors, independent of ER status, reflected activation of immunological and metabolic processes. This study suggests that GATA3 might influence the TME independent of ER status. Our results point to metabolic and immunophenotypic alterations in GATA3-low BCs, in particular with T-cell activation and increased expression of immune checkpoints. These findings could be relevant for patient selection in the context of immunotherapies and potential targeting of metabolic pathways.

Anatomical pathology has traditionally relied on the interpretation of histomorphological features under a light microscope by trained pathologists for diagnosis. Technological advancements have enabled the digitisation of tissue slides to produce high-resolution whole slide images, heralding the era of digital pathology (DP). Many laboratories around the world have incorporated DP into their routine workflows owing to the myriad applications it offers in facilitating tumour board discussions, remote reporting, teaching, and research. Most significantly, DP has engendered the field of computational pathology, a novel branch of histopathology incorporating artificial intelligence (AI) models. Computational pathology has been utilised in histomorphological quantification and diagnostic, predictive, and prognostic applications due to its potential to improve diagnostic accuracy, personalise treatment, and streamline workflows. Here, we highlight the work of Meier et al, Shen et al, and Lee et al, published in this journal in recent years, as they apply AI models to predict survival and treatment responses in gastric cancer, breast cancer, and diffuse large B-cell lymphoma, respectively. Collectively, these studies illustrate various approaches to incorporating AI into the DP pipeline and their potential clinical applications. Issues related to diagnostic accuracy, cost, patient confidentiality, and regulatory ethics still need to be addressed within the field. Despite this, the overall sentiment among pathologists is one of cautious optimism.

The tumor immune microenvironment plays a critical role in colorectal cancer (CRC) prognosis. However, most studies assess a limited set of immune markers manually in the tumor region, without considering immune heterogeneity across multiple tissue regions. This study aims to enhance CRC prognostic assessment by developing an automated multi-regional immunohistochemistry (IHC) scoring system for 15 immune markers. Two representative tissue cores were extracted from CRC surgical specimens (n = 154) across four regions: tumor center, invasive margin, paracancerous tissues, and normal tissues. IHC staining was performed for 15 immune markers, and digitized slides were analyzed using computational algorithms to classify tissue types (e.g., glands, tumor, and stroma) and identify stained pixels. Immune infiltration was quantified in different tissue types across regions, and a tumor-to-healthy immune ratio (THIR) score was introduced to compare immune marker expression in tumor versus healthy stroma. Associations between IHC scores and overall survival (OS) and relapse-free survival (RFS) were evaluated. Computational models achieved 95.19% accuracy in tissue classification and 97.90% in staining identification. Analysis of 120 IHC scores (15 markers × 8 tissue types) revealed significant immune heterogeneity, with 56 scores correlating with OS and 54 with RFS. Notably, markers such as Granzyme B and CD4 had higher prognostic relevance at the invasive margin than the tumor center, while markers like S100 and CD20 exhibited opposing prognostic effects across regions. Integrating multiple markers significantly improved prognostic accuracy, with the combined marker score in normal stroma providing the most significant risk stratification (log-rank test, p = 1.56e-7, OS). The THIR score also strongly correlated with patient outcomes. This study advances CRC prognostication through automated multi-regional IHC scoring, highlighting the importance of immune heterogeneity across tissue regions. These findings support integrating region-specific immune profiling into clinical workflows for more personalized and precise patient care.

Breast cancer can be categorised into a number of histological types, based on microscopic appearances. There is some evidence that the different breast cancer histological types are associated with different patient and tumour characteristics, but few previous studies have been large enough to investigate this systematically, especially for rare histological types. National cancer registration data were used to describe trends in the incidence of specific histological types of invasive breast cancer in women diagnosed when aged 18–89 years in England from January 1988 to December 2016, and to investigate associations between breast cancer histological types and patient and tumour characteristics. There were 838,776 women diagnosed with a first primary invasive breast cancer in this 29-year period, including 614,698 (73%) cases of ductal carcinoma NST [no special type (NST)], 90,028 (11%) cases of lobular carcinoma, and more than 16,000 (2%) cases each of tubular and mucinous carcinomas. Rarer histological types included medullary, papillary, metaplastic, and cribriform carcinomas, with >1000 cases of each type. Data quality and completeness improved substantially during the study period. The different histological types of breast cancer showed different patterns in incidence by calendar period of diagnosis, age at diagnosis, and screen-detection status, as well as different associations with tumour characteristics such as grade, stage at diagnosis, and molecular subtype. This large nationwide study provides an overview of the changing incidence of the different histological types of invasive breast cancer in England over almost 30 years. It also gives an opportunity to investigate the characteristics of rare histological types, which smaller studies have been unable to explore. In addition, the results demonstrate the continuing value of histological types defined by microscopic morphology, alongside newer molecular classifications.

Colorectal carcinoma (CRC) is a frequent cause of morbidity and mortality in sub-Saharan Africa. The incidence of early-onset, microsatellite stable (MSS) CRC is on the rise, and the tumour biology of these lesions is poorly categorised. Preliminary data from one centre in Nigeria found differences in the frequencies of mutations in driver genes and altered signalling pathways. We sought to investigate potential alternative driver genes and signalling pathways by whole exome sequencing. Eighty-three cases passed quality control filters and were included in the analysis (77 MSS, 4 microsatellite instability-high, and 2 POLE mutant). APC, TP53, and KRAS were among the most frequently mutated driver genes, although at a lower frequency than expected. BRAF V600E mutations were absent in our cohort. Although there were differences in the frequencies of mutations in the major driver genes, the frequencies of oncogenic pathway alterations were found to be similar. FAT4 (26%) and TET2 (15%) emerged as important mutated driver genes and potential therapeutic targets for further investigation. We have highlighted distinct differences in driver gene mutations in our cohort of young CRC from sub-Saharan Africa and have identified FAT4 and TET2 as potential drivers that are more common and are potential therapeutic targets.

Myeloid sarcoma (MS) is a mass-forming extramedullary manifestation of myeloid blasts, either in relation to an underlying acute myeloid leukemia (AML), another myeloid neoplasm (MN) or as a de novo occurrence. Data on the genetic profile of MS are sparse. In this study, 41 MS of 34 patients, including 7 de novo cases and 24 patients with antecedent or synchronous MN, were analyzed with targeted next-generation sequencing (NGS), RNA-based fusion detection, and gene expression profiling (GEP). In 10 patients, a MS developed after stem cell transplantation for MN. Additionally, 21 available pre-transplant bone marrow biopsies (BMB) from 20 patients and 6 post-transplant BMB from 6 patients were investigated. The most frequently mutated gene was TET2 (41%), followed by NPM1 (38%) and NRAS (35%). Overall, 74% of the cases exhibited mutations affecting the MAPK/ERK pathway. AML-type fusions were detected in seven MS patients, who were younger than those without fusions (median 49 versus 67 years). Nine of 13 patients with a MN and available pre-transplant BMB showed additional mutations restricted to the MS, including an additional NRAS mutation in 3/5 cases with AML. Five of seven of patients with pre-transplant BMB without evidence of a MN revealed clonal hematopoiesis (CH), mostly shared TET2 mutations. Comparative GEP between BM and MS revealed upregulation of the MAPK/ERK pathway in MS and of gene sets relevant for interaction with the microenvironment. In conclusion, MS is characterized by a high incidence of MAPK/ERK pathway mutations and activation, frequent clonal evolution, and association with CH in elderly patients without recurrent AML-type fusions.

In its first decade, The Journal of Pathology: Clinical Research has become a leading source of translational studies advancing molecular diagnostics in cancer, particularly in urologic pathology. This commentary highlights recent contributions that collectively place precision oncology at the forefront of pathology research. One review examines cancer stem cells in renal cell carcinoma, emphasizing the complexity of cellular plasticity and the tumor microenvironment in driving resistance and recurrence. In prostate cancer, epithelial-to-mesenchymal transition (EMT) regulators, including Twist, Slug, and Snail, are identified as synergistic markers of poor prognosis, linked to hypoxia and invasiveness. Another review details the integration of homologous recombination repair gene testing into clinical workflows, supporting targeted treatment strategies with poly (ADP-ribose) polymerase inhibitors. In pediatric oncology, TP53 alterations in Wilms tumor are shown to occur beyond anaplastic cases, expanding their prognostic significance. Advances in molecular subtyping are also demonstrated in bladder cancer, where transcriptomic profiling could enable tailored neoadjuvant therapy. In clear cell renal cell carcinoma, re-evaluation of a prognostic model revealed that while necrosis or sarcomatoid differentiation correlated with poor outcomes, only DNA methylation markers improved prognostic accuracy, underscoring their utility for biopsy-based risk stratification. Finally, digital spatial profiling of sarcomatoid urothelial carcinoma reveals an immunosuppressive microenvironment with CD163-positive cells, implicating them in EMT and aggressive phenotype. Together, these studies highlight the transformative role of integrated molecular diagnostics in guiding individualized therapies and improving outcomes in urologic cancers.