Journal of Pathology Clinical Research最新文献

{"title":"Large language models as a diagnostic support tool in neuropathology.","authors":"Katherine J Hewitt, Isabella C Wiest, Zunamys I Carrero, Laura Bejan, Thomas O Millner, Sebastian Brandner, Jakob Nikolas Kather","doi":"10.1002/2056-4538.70009","DOIUrl":"10.1002/2056-4538.70009","url":null,"abstract":"<p><p>The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro-oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro-oncology cases from free-text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT-4o, Claude-3.5-sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval-Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homologous recombination deficiency score is an independent prognostic factor in esophageal squamous cell carcinoma","authors":"Yulu Wang,&nbsp;Bowen Ding,&nbsp;Yunlan Tao,&nbsp;Lingli Huang,&nbsp;Qian Zhu,&nbsp;Chengying Gao,&nbsp;Mingli Feng,&nbsp;Yuchen Han","doi":"10.1002/2056-4538.70007","DOIUrl":"10.1002/2056-4538.70007","url":null,"abstract":"<p>Homologous recombination deficiency (HRD) represents an impairment in the homologous recombination repair (HRR) pathway, crucial for repairing DNA double-strand breaks and contributing to genomic instability in cancer. The HRD score may be a more reliable biomarker than HRR-related gene mutations for identifying patients sensitive to poly(ADP-ribose) polymerase inhibitors. Despite its relevance in various cancers, the HRD score remains underexplored in esophageal squamous cell carcinoma (ESCC). We retrospectively analyzed HRD scores in 96 ESCC patients, examining correlations with clinical characteristics and survival outcomes, and validated our findings using the TCGA dataset. Genomic sequencing utilized a custom superHRD next-generation sequencing panel, and HRD scores were calculated from 54,000 single-nucleotide polymorphisms using Kruskal–Wallis rank-sum tests and two cut-off points for analysis. Higher HRD scores correlated with advanced tumor stages, recurrence, and mutations in <i>TP53</i> and <i>ABCB1</i>, while <i>APC</i> mutations were linked to lower HRD scores. Patients with high HRD scores had significantly shorter disease-free survival (<i>p</i> = 0.013) and a trend toward shorter overall survival (OS) (<i>p</i> = 0.005), particularly those not receiving adjuvant therapy. Conversely, HRD-high patients undergoing adjuvant therapy showed a trend toward longer OS (<i>p</i> = 0.015). Multivariate analysis identified HRD as an independent prognostic factor (hazard ratio = 2.814 for recurrence, <i>p</i> = 0.015). Validation with the TCGA dataset supported these findings. This study highlights the associations between HRD scores, clinical characteristics, and genomic mutations in ESCC, suggesting HRD as a potential prognostic biomarker. HRD assessment may aid in patient stratification and personalized treatment strategies, warranting further investigation to validate the therapeutic implications of HRD scores in ESCC.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast cancer survival prediction using an automated mitosis detection pipeline","authors":"Nikolas Stathonikos,&nbsp;Marc Aubreville,&nbsp;Sjoerd de Vries,&nbsp;Frauke Wilm,&nbsp;Christof A Bertram,&nbsp;Mitko Veta,&nbsp;Paul J van Diest","doi":"10.1002/2056-4538.70008","DOIUrl":"10.1002/2056-4538.70008","url":null,"abstract":"<p>Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm². We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (<i>N</i> = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes","authors":"Joep MA Bogaerts,&nbsp;Miranda P Steenbeek,&nbsp;John-Melle Bokhorst,&nbsp;Majke HD van Bommel,&nbsp;Luca Abete,&nbsp;Francesca Addante,&nbsp;Mariel Brinkhuis,&nbsp;Alicja Chrzan,&nbsp;Fleur Cordier,&nbsp;Mojgan Devouassoux-Shisheboran,&nbsp;Juan Fernández-Pérez,&nbsp;Anna Fischer,&nbsp;C Blake Gilks,&nbsp;Angela Guerriero,&nbsp;Marta Jaconi,&nbsp;Tony G Kleijn,&nbsp;Loes Kooreman,&nbsp;Spencer Martin,&nbsp;Jakob Milla,&nbsp;Nadine Narducci,&nbsp;Chara Ntala,&nbsp;Vinita Parkash,&nbsp;Christophe de Pauw,&nbsp;Joseph T Rabban,&nbsp;Lucia Rijstenberg,&nbsp;Robert Rottscholl,&nbsp;Annette Staebler,&nbsp;Koen Van de Vijver,&nbsp;Gian Franco Zannoni,&nbsp;Monica van Zanten,&nbsp;AI-STIC Study Group,&nbsp;Joanne A de Hullu,&nbsp;Michiel Simons,&nbsp;Jeroen AWM van der Laak","doi":"10.1002/2056-4538.70006","DOIUrl":"10.1002/2056-4538.70006","url":null,"abstract":"<p>In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&amp;E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (<i>p</i> &lt; 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (<i>p</i> &lt; 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (<i>p</i> &lt; 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11496567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A clinically feasible algorithm for the parallel detection of glioma-associated copy number variation markers based on shallow whole genome sequencing","authors":"Shuai Wu,&nbsp;Chenyu Ma,&nbsp;Jiawei Cai,&nbsp;Chenkang Yang,&nbsp;Xiaojia Liu,&nbsp;Chen Luo,&nbsp;Jingyi Yang,&nbsp;Zhang Xiong,&nbsp;Dandan Cao,&nbsp;Hong Chen","doi":"10.1002/2056-4538.70005","DOIUrl":"10.1002/2056-4538.70005","url":null,"abstract":"<p>Molecular features are incorporated into the integrated diagnostic system for adult diffuse gliomas. Of these, copy number variation (CNV) markers, including both arm-level (1p/19q codeletion, +7/−10 signature) and gene-level (<i>EGFR</i> gene amplification, <i>CDKN2A/B</i> homozygous deletion) changes, have revolutionized the diagnostic paradigm by updating the subtyping and grading schemes. Shallow whole genome sequencing (sWGS) has been widely used for CNV detection due to its cost-effectiveness and versatility. However, the parallel detection of glioma-associated CNV markers using sWGS has not been optimized in a clinical setting. Herein, we established a model-based approach to classify the CNV status of glioma-associated diagnostic markers with a single test. To enhance its clinical utility, we carried out hypothesis testing model-based analysis through the estimation of copy ratio fluctuation level, which was implemented individually and independently and, thus, avoided the necessity for normal controls. Besides, the customization of required minimal tumor fraction (TF) was evaluated and recommended for each glioma-associated marker to ensure robust classification. As a result, with 1× sequencing depth and 0.05 TF, arm-level CNVs could be reliably detected with at least 99.5% sensitivity and specificity. For <i>EGFR</i> gene amplification and <i>CDKN2A/B</i> homozygous deletion, the corresponding TF limits were 0.15 and 0.45 to ensure the evaluation metrics were both higher than 97%. Furthermore, we applied the algorithm to an independent glioma cohort and observed the expected sample distribution and prognostic stratification patterns. In conclusion, we provide a clinically applicable algorithm to classify the CNV status of glioma-associated markers in parallel.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images","authors":"Jun Hyeong Park,&nbsp;June Hyuck Lim,&nbsp;Seonhwa Kim,&nbsp;Chul-Ho Kim,&nbsp;Jeong-Seok Choi,&nbsp;Jun Hyeok Lim,&nbsp;Lucia Kim,&nbsp;Jae Won Chang,&nbsp;Dongil Park,&nbsp;Myung-won Lee,&nbsp;Sup Kim,&nbsp;Il-Seok Park,&nbsp;Seung Hoon Han,&nbsp;Eun Shin,&nbsp;Jin Roh,&nbsp;Jaesung Heo","doi":"10.1002/2056-4538.70004","DOIUrl":"10.1002/2056-4538.70004","url":null,"abstract":"<p><i>EGFR</i> mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of <i>EGFR</i> mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the <i>EGFR</i> mutation prevalence (EMP) score, which quantifies <i>EGFR</i> prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of <i>EGFR</i> mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of <i>EGFR</i> mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607–0.7720) and an area under the precision-recall curve of 0.8391 (0.8326–0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (<i>p</i> = 0.0087) for p.L858R and 0.5918 (<i>p</i> = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (<i>p</i> = 0.0038 and <i>p</i> = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (<i>p</i> = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning","authors":"Binshen Wei,&nbsp;Linqing Li,&nbsp;Yenan Feng,&nbsp;Sihan Liu,&nbsp;Peng Fu,&nbsp;Lin Tian","doi":"10.1002/2056-4538.70003","DOIUrl":"10.1002/2056-4538.70003","url":null,"abstract":"<p>Hematoxylin and eosin (H&amp;E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (<i>p</i> &lt; 0.001) and the TCGA cohort (<i>p</i> &lt; 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in <i>APC</i>, <i>SMAD2</i>, <i>EEF1AKMT4</i>, <i>EPG5</i>, and <i>TANC1</i>. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a whole slide image management system for metabolic-associated steatohepatitis for clinical trials","authors":"Hanna Pulaski,&nbsp;Shraddha S Mehta,&nbsp;Laryssa C Manigat,&nbsp;Stephanie Kaufman,&nbsp;Hypatia Hou,&nbsp;ILKe Nalbantoglu,&nbsp;Xuchen Zhang,&nbsp;Emily Curl,&nbsp;Ross Taliano,&nbsp;Tae Hun Kim,&nbsp;Michael Torbenson,&nbsp;Jonathan N Glickman,&nbsp;Murray B Resnick,&nbsp;Neel Patel,&nbsp;Cristin E Taylor,&nbsp;Pierre Bedossa,&nbsp;Michael C Montalto,&nbsp;Andrew H Beck,&nbsp;Katy E Wack","doi":"10.1002/2056-4538.12395","DOIUrl":"10.1002/2056-4538.12395","url":null,"abstract":"<p>The gold standard for enrollment and endpoint assessment in metabolic dysfunction-associated steatosis clinical trials is histologic assessment of a liver biopsy performed on glass slides. However, obtaining the evaluations from several expert pathologists on glass is challenging, as shipping the slides around the country or around the world is time-consuming and comes with the hazards of slide breakage. This study demonstrated that pathologic assessment of disease activity in steatohepatitis, performed using digital images on the AISight whole slide image management system, yields results that are comparable to those obtained using glass slides. The accuracy of scoring for steatohepatitis (nonalcoholic fatty liver disease activity score ≥4 with ≥1 for each feature and absence of atypical features suggestive of other liver disease) performed on the system was evaluated against scoring conducted on glass slides. Both methods were assessed for overall percent agreement with a consensus “ground truth” score (defined as the median score of a panel of three pathologists’ glass slides). Each case was also read by three different pathologists, once on glass and once digitally with a minimum 2-week washout period between the modalities. It was demonstrated that the average agreement across three pathologists of digital scoring with ground truth was noninferior to the average agreement of glass scoring with ground truth [noninferiority margin: −0.05; difference: −0.001; 95% CI: (−0.027, 0.026); and <i>p</i> &lt; 0.0001]. For each pathologist, there was a similar average agreement of digital and glass reads with glass ground truth (pathologist A, 0.843 and 0.849; pathologist B, 0.633 and 0.605; and pathologist C, 0.755 and 0.780). Here, we demonstrate that the accuracy of digital reads for steatohepatitis using digital images is equivalent to glass reads in the context of a clinical trial for scoring using the Clinical Research Network scoring system.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.12395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges for pathologists in implementing clinical microbiome diagnostic testing","authors":"Yulia Gerasimova,&nbsp;Haroon Ali,&nbsp;Urooba Nadeem","doi":"10.1002/2056-4538.70002","DOIUrl":"https://doi.org/10.1002/2056-4538.70002","url":null,"abstract":"<p>Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The expression of YAP1 and other transcription factors contributes to lineage plasticity in combined small cell lung carcinoma","authors":"Naoe Jimbo,&nbsp;Chiho Ohbayashi,&nbsp;Tomomi Fujii,&nbsp;Maiko Takeda,&nbsp;Suguru Mitsui,&nbsp;Yugo Tanaka,&nbsp;Tomoo Itoh,&nbsp;Yoshimasa Maniwa","doi":"10.1002/2056-4538.70001","DOIUrl":"https://doi.org/10.1002/2056-4538.70001","url":null,"abstract":"<p>Lineage plasticity in small cell lung carcinoma (SCLC) causes therapeutic difficulties. This study aimed to investigate the pathological findings of plasticity in SCLC, focusing on combined SCLC, and elucidate the involvement of YAP1 and other transcription factors. We analysed 100 surgically resected SCLCs through detailed morphological observations and immunohistochemistry for YAP1 and other transcription factors. Component-by-component next-generation sequencing (<i>n</i> = 15 pairs) and immunohistochemistry (<i>n</i> = 35 pairs) were performed on the combined SCLCs. Compared with pure SCLCs (<i>n</i> = 65), combined SCLCs (<i>n</i> = 35) showed a significantly larger size, higher expression of NEUROD1, and higher frequency of double-positive transcription factors (<i>p</i> = 0.0009, 0.04, and 0.019, respectively). Notably, 34% of the combined SCLCs showed morphological mosaic patterns with unclear boundaries between the SCLC and its partner. Combined SCLCs not only had unique histotypes as partners but also represented different lineage plasticity within the partner. NEUROD1-dominant combined SCLCs had a significantly higher proportion of adenocarcinomas as partners, whereas POU2F3-dominant combined SCLCs had a significantly higher proportion of squamous cell carcinomas as partners (<i>p</i> = 0.006 and <i>p</i> = 0.0006, respectively). YAP1 expression in SCLC components was found in 80% of combined SCLCs and 62% of pure SCLCs, often showing mosaic-like expression. Among the combined SCLCs with component-specific analysis, the identical <i>TP53</i> mutation was found in 10 pairs, and the identical <i>Rb1</i> abnormality was found in 2 pairs. On immunohistochemistry, the same abnormal p53 pattern was found in 34 pairs, and Rb1 loss was found in 24 pairs. In conclusion, combined SCLC shows a variety of pathological plasticity. Although combined SCLC is more plastic than pure SCLC, pure SCLC is also a phenotypically plastic tumour. The morphological mosaic pattern and YAP1 mosaic-like expression may represent ongoing lineage plasticity. This study also identified the relationship between transcription factors and partners in combined SCLC. Transcription factors may be involved in differentiating specific cell lineages beyond just ‘neuroendocrine’.</p>","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/2056-4538.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}