Laboratory Investigation最新文献_第8页

Unbiased DNA Pathogen Detection in Tissues: Real-World Experience With Metagenomic Sequencing in Pathology 组织中无偏DNA病原体检测：病理学中宏基因组测序的真实世界经验。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-29 DOI: 10.1016/j.labinv.2025.104254

Baptiste Hamelin , Salome Hosch , Claudio Neidhöfer , Marie-Thérèse Ruf , Jasmin D. Haslbauer , Christopher M. Field , Pascal Schläpfer , Massimiliano Manzo , Andreas Neumayr , Esther Kuenzli , Maria Mancuso , Melanie Sachs , Nadine Mensah , Regula Bernhard , Barbara Klaus-Wirthner , Maura Concu , Ronny Nienhold , Richard Kuehl , Veronika Baettig , Maja Weisser-Rohacek , Kirsten D. Mertz

Pathogen detection in formalin-fixed paraffin-embedded (FFPE) tissue remains challenging. We implemented metagenomic next-generation sequencing (mNGS) in our clinical diagnostic workflow to evaluate its feasibility, diagnostic yield, and pathogen spectrum in routine infectious pathology cases. Between November 2021 and April 2025, we analyzed 623 FFPE tissue samples using a low-depth mNGS workflow on the Thermo Fisher Ion Torrent platform with a CLC Genomics Workbench (Qiagen) bioinformatics pipeline. Our assay was designed to detect DNA pathogens. When possible, results were validated by orthogonal methods, including species-specific PCRs, 16S/internal transcribed spacer PCR, and immunohistochemistry on tissue sections. Among 623 samples analyzed, at least 1 potentially pathogenic and plausible microorganism was identified in 229 samples (36.8%), whereas 334 (53.6%) were negative and 60 (9.6%) were uninterpretable due to quality control failures or suspected contamination. Of the 229 positive samples, 145 (63.3%) involved bacteria, 37 (16.2%) viruses, 28 (12.2%) fungi, and 9 (3.9%) parasites; mixed infections with >1 pathogen were detected in 10 (4.4%) samples. The most frequently identified bacterial family was Mycobacteriaceae (n = 27), including Mycobacterium xenopi (n = 8), which is not routinely covered by syndromic multiplex PCR panels. Notable viral and fungal detections included a novel human circovirus and Coccidioides posadasii. Despite variable sample quality and DNA input, mNGS yielded reliable results in a wide range of tissue types. mNGS is a feasible, valuable addition to routine infectious pathology diagnostics, particularly in complex or inconclusive cases. The assay improved the diagnostic yield compared with conventional PCR, expanded the range of detectable pathogens, and proved robust even in low-quality FFPE samples. These results support broader adoption of mNGS in tissue-based pathogen diagnostics.

福尔马林固定石蜡包埋（FFPE）组织中的病原体检测仍然具有挑战性。我们在临床诊断工作流程中实施了新一代宏基因组测序（mNGS），以评估其在常规感染病理病例中的可行性、诊断率和病原体谱。在2021年11月至2025年4月期间，我们使用Thermo Fisher Ion Torrent平台上的低深度mNGS工作流程和CLC Genomics Workbench生物信息学管道分析了623个FFPE组织样本。我们的试验设计用于检测DNA病原体。在可能的情况下，通过正交方法验证结果，包括物种特异性PCR， 16S/ITS PCR和组织切片的免疫组织化学。在分析的623份样品中，229份（36.8%）样品中至少鉴定出一种潜在致病性和似是而非的微生物，334份（53.6%）样品为阴性，60份（9.6%）样品由于质量控制失败或疑似污染而无法解释。229份阳性标本中，细菌145份（63.3%），病毒37份（16.2%），真菌28份（12.2%），寄生虫9份（3.9%）；在10份（4.4%）样本中检出一种以上病原菌的混合感染。最常发现的细菌家族是分枝杆菌科（n=27），包括xenopi分枝杆菌（n=8），该分支杆菌通常未被综合征多重PCR检测覆盖。值得注意的病毒和真菌检测包括一种新的人类圆环病毒和波萨达球螨。尽管样品质量和DNA输入不同，但mNGS在广泛的组织类型中产生了可靠的结果。宏基因组NGS是常规感染病理学诊断的一种可行的、有价值的补充，特别是在复杂或不确定的病例中。与传统PCR相比，该方法提高了诊断率，扩大了可检测病原体的范围，并且即使在低质量的FFPE样品中也证明了其可靠性。这些结果支持在基于组织的病原体诊断中更广泛地采用mNGS。

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引用次数: 0

Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data 机器学习在组学和临床数据建模方面的最佳推广性能。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-15 DOI: 10.1016/j.labinv.2025.104253

Fei Deng , Yongfeng Zhang , Lanjing Zhang

There are often performance differences between intra-data set and cross-data set tests in machine learning (ML) modeling. However, reducing these differences may reduce ML performance. It is thus a challenging dilemma for developing models that excel in intra-data set testing and are generalizable to cross-data set testing. Therefore, we aimed to understand and improve the performance and generalizability of ML in intra-data set and cross-data set testing. We evaluated 4200 ML models of classifying lung adenocarcinoma deaths using The Cancer Genome Atlas (n = 286) and Oncogenomic-Singapore (n = 167) data sets and 1680 models of classifying glioblastoma deaths using The Cancer Genome Atlas (n = 151) and Clinical Proteomic Tumor Analysis Consortium (n = 97) data sets. After examining performance distributions of these ML models, we applied a dual analytical framework, including statistical analyses and SHapley Additive exPlanations–based meta-analysis, to quantify factors’ importance and trace model success back to design principles. We also developed a framework to identify the best generalizable model. Strikingly, the Jarque-Bera test revealed significant deviations of model performances from normality in both cancer types and testing contexts. Simple linear models with sparse feature sets consistently dominated in lung adenocarcinoma experiments, whereas nonlinear models dominated in glioblastoma ones, suggesting that the best modeling strategy appears to be cancer type/disease dependent. Importantly, both robust analysis of variance and Kruskal-Wallis tests consistently identified differentially expressed genes as one of the most influential factors in both cancer types. The proposed multicriteria framework successfully identified the model that achieved both the best cross-data set performance and similar intra-data set performance. In summary, ML performance distributions significantly deviated from normality, which motivates using both robust parametric and nonparametric statistical tests. We quantified and provided possible exploitability on the factors associated with cross-data set performances and generalizability of ML models in 2 cancer types. A multicriteria framework was developed and validated to identify the models that are accurate and consistently robust across data sets.

在机器学习（ML）建模中，数据集内测试和跨数据集测试之间通常存在性能差异。然而，减少这些差异可能会降低机器学习的性能。因此，开发在数据集内部测试中表现出色并可推广到跨数据集测试的模型是一个具有挑战性的困境。因此，我们的目标是理解和提高机器学习在数据集内和跨数据集测试中的性能和可泛化性。我们使用the Cancer Genome Atlas （TCGA, n=286）和oncogenome - singapore （OncoSG, n=167）数据集评估了4200 ML肺腺癌（LUAD）死亡分类模型，以及使用TCGA （n=151）和临床蛋白质组学肿瘤分析联盟（CPTAC, n=97）数据集评估了1680个胶质母细胞瘤死亡分类模型。在检查了这些机器学习模型的性能分布后，我们应用了双重分析框架，包括统计分析和基于SHapley Additive explations的元分析，来量化因素的重要性，并将模型的成功追溯到设计原则。我们还开发了一个框架来确定最佳的可推广模型。引人注目的是，Jarque-Bera测试揭示了在癌症类型和测试环境下，模型性能与正常情况的显著偏差。具有稀疏特征集的简单线性模型在LUAD实验中始终占主导地位，而非线性模型在胶质母细胞瘤实验中占主导地位，这表明最佳建模策略似乎依赖于癌症类型/疾病。重要的是，稳健的方差分析（ANOVA）和Kruskal-Wallis测试一致地确定差异表达基因是两种癌症类型中最具影响力的因素之一。所提出的多准则框架成功地识别出具有最佳跨数据集性能和相似数据集性能的模型。总之，机器学习性能分布明显偏离正态性，这促使我们使用鲁棒参数和非参数统计检验。我们量化并提供了与两种癌症类型的ML模型的跨数据集性能和泛化性相关的因素的可能利用。开发并验证了一个多标准框架，以确定准确且始终可靠的跨数据集模型。

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引用次数: 0

Stain-Free Characterization of Membranous Glomerulonephritis via Fourier Ptychographic Microscopy 膜性肾小球肾炎的傅立叶显微染色特征。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-13 DOI: 10.1016/j.labinv.2025.104251

Marika Valentino , Vittorio Bianco , Gioacchino D’Ambrosio , Marco Paulli , Giovanni Smaldone , Valentina Brancato , Lisa Miccio , Marco Salvatore , Marcello Gambacorta , Pietro Ferraro

Histology remains a cornerstone in diagnosis and prognosis of renal diseases. Histopathological analysis of kidney tissue is crucial for understanding renal pathophysiology. The availability of multiple stained sections is essential for conducting comprehensive histopathological analysis and achieving accurate diagnosis. Fourier ptychographic microscopy (FPM) has emerged as a promising microscopy technique. The ability to provide high-resolution, quantitative phase-contrast images over a wide area, particularly in a stain-free mode, makes FPM highly appealing to experts in histopathology. As renal pathologies are characterized by subtle morphologic changes encoded in tissue slides, phase maps obtained using FPM are well suited for providing detailed, high-contrast images of tissue structures. FPM provides a quantitative imaging tool that can be descriptive of the sample and/or expressive of the disease. Here, we explored FPM capability to image pathological kidney tissue, enabling pathologists to select regions of interest within the intricate architecture of renal tissue and zoom in to observe minute submicron structures, ranging from overall tissue organization and glomeruli distribution to individual basal membranes. Membranous glomerulonephritis (MG) was focused on because of its high dependence on histologic examination. We extracted descriptive features able to discriminate between healthy kidney tissues and those affected by MG. Moreover, FPM phase-contrast images allowed observing in detail the glomerular basal membranes and measuring the differences in lateral thickness with respect to healthy specimens. This is because of better glomerular membranes contrast in FPM images with respect to the hematoxylin-and-eosin–stained images. Our study shows the broad potential of FPM in characterizing hallmarks of MG disease even in stain-free tissue slides.

组织学仍然是肾脏疾病诊断和预后的基础。肾脏组织病理分析是了解肾脏病理生理的关键。多重染色切片的可用性对于进行全面的组织病理学分析和实现准确诊断至关重要。傅里叶显微术（FPM）是一种很有前途的显微技术。FPM能够在大范围内提供高分辨率、定量的相衬图像，特别是在无染色模式下，这使得FPM对组织病理学专家非常有吸引力。由于肾脏病理的特点是组织载玻片中编码的细微形态变化，因此使用FPM获得的相图非常适合提供详细的、高对比度的组织结构图像。FPM提供了一种定量成像工具，可以描述样本和/或表达疾病。在这里，我们探索FPM成像病理肾组织的能力，使病理学家能够在复杂的肾组织结构中选择感兴趣的区域，并放大观察微小的亚微米结构，从整体组织和肾小球分布到单个基底膜。膜性肾小球肾炎（MG）是一种高度依赖于组织学检查的肾病。我们提取能够区分健康肾脏组织和受MG影响的肾脏组织的描述性特征。此外，FPM期图像可以详细观察肾小球基底膜，并测量相对于健康标本的侧壁厚度差异。这是由于FPM图像与h&e染色图像相比有更好的肾小球膜对比。我们的研究表明，FPM在无染色组织载玻片中表征MG疾病特征方面具有广阔的潜力。

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引用次数: 0

Artificial Intelligence–Powered Spatial Analysis of the Tumor Microenvironment in Pulmonary Lymphoepithelial Carcinoma 基于人工智能的肺淋巴上皮癌肿瘤微环境空间分析。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-13 DOI: 10.1016/j.labinv.2025.104252

Haohua Teng , Yueyuxiao Yang , Chan Xiang , Jikai Zhao , Zhanxian Shang , Qian Zhu , Lianying Guo , Qiushun He , Meng Yang , Yuchen Han

Lymphoepithelial carcinoma (LEC) can occur in various organs, such as the lung, nasopharynx, and thymus. We investigated the spatial characteristics of the tumor immune microenvironment (TIME) among primary pulmonary LECs (pLECs), pulmonary metastatic nasopharyngeal carcinomas (pmNPCs), and thymic LECs (tLECs). In this retrospective study, a total of 160 surgically resected LEC cases, comprising 116 pLECs, 26 tLECs, and 18 pmNPCs, were included. The TIME features, based on hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) and multiplexed immunofluorescence staining images, were obtained and their association with patient prognosis was analyzed. We developed a semisupervized model for automated tumor segmentation based on H&E WSIs. The performance of the model was robust, with a mean accuracy rate of 0.847 on the testing set. Subsequent TIME analysis revealed different spatial distribution patterns of lymphocytes on H&E WSIs among pLECs, tLECs, and pmNPCs. Lymphocyte count and distribution were prognostically relevant in pLECs, with an increasing trend of lymphocytes from the peripheral normal lung area to the tumor core in patients with a good prognosis. Further TIME analysis based on multiplexed immunofluorescence images uncovered that spatial arrangement and spatial interaction pattern characteristics were dependent on specific tumor types and cell subtypes. Our semisupervized learning model offers an automated and reproducible method for tumor segmentation for the TIME of rare LECs. Our analysis revealed different TIME patterns that distinguish among pLEC, tLEC, and pmNPC and demonstrates that the spatial arrangement and positional interaction patterns of PDL1⁺ tumor cells and FOXP3⁺ regulatory T cells could stratify prognosis in patients with pLEC.

淋巴上皮癌（LEC）可发生在各种器官，如肺、鼻咽部和胸腺。我们研究了原发性肺LECs （pLECs）、肺转移性鼻咽癌（pmnpc）和胸腺LECs （tLECs）肿瘤免疫微环境（TIME）的空间特征。在这项回顾性研究中，共包括160例手术切除的LEC病例，包括116例plec， 26例tLECs和18例pmnpc。基于苏木精和伊红（H&E）染色的全片图像（WSIs）和多路免疫荧光（mIF）染色图像获得TIME特征，并分析其与患者预后的关系。我们开发了一种基于H&E wsi的半监督自动肿瘤分割模型。该模型具有良好的鲁棒性，在测试集上的平均准确率为0.847。随后的TIME分析显示，pLECs、tLECs和pmnpc在H&E wsi上的淋巴细胞空间分布模式不同。淋巴细胞计数和分布与pLECs预后相关，在预后良好的患者中，淋巴细胞从正常肺外周区向肿瘤核心区增加。基于mIF图像的进一步TIME分析发现，空间排列和空间相互作用模式特征依赖于特定的肿瘤类型和细胞亚型。我们的半监督学习模型为罕见LECs的肿瘤分割提供了一种自动化和可重复的方法。我们的分析揭示了pLEC、tLEC和pmNPC之间不同的时间模式，并表明PDL1+肿瘤细胞和FOXP3+ Tregs的空间排列和位置相互作用模式可以对pLEC患者的预后进行分层。

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引用次数: 0

Glycolysis Inhibition Restores Immune Sensitivity in GSNOR–Deficient Colorectal Cancer 糖酵解抑制可恢复gsnorn缺陷结直肠癌的免疫敏感性。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-11 DOI: 10.1016/j.labinv.2025.104250

Ana Mantrana , María Teresa Sánchez-Montero , Carmen Navarrete-Sirvent , Nerea Herrera-Casanova , Rafael Mena-Osuna , Aurora Rivas-Crespo , Alejandra Díaz-Chacón , Virginia Ávila-Oca , Marta Toledano-Fonseca , María Victoria García-Ortíz , Rafael González-Fernández , María Auxiliadora Gómez-España , Carlos Villar , Francisco Javier Medina-Fernández , Enrique Aranda , Silvia Guil-Luna , Antonio Rodríguez-Ariza

S-nitrosoglutathione reductase (GSNOR) is increasingly recognized as a tumor suppressor, and we have recently reported that its deficiency drives an aggressive and immune-evasive phenotype in colorectal cancer (CRC). However, the mechanisms linking GSNOR loss to immune escape remain incompletely understood. In this study, we uncover a previously unrecognized connection between metabolic reprogramming and immune escape in GSNOR-deficient CRC and identify a therapeutic vulnerability that can be exploited to restore immune responsiveness. A comprehensive analysis of 137 clinical CRC samples revealed that GSNOR-deficient tumors exhibit high-grade tumor budding, an established marker of poor prognosis, and reduced CD4⁺ and CD8⁺ T-cell infiltration, consistent with an immunosuppressive tumor microenvironment. Integrating transcriptomic, immunohistochemical, and single–cell RNA-sequencing data, we demonstrate that GSNOR-deficient tumors undergo a striking metabolic reprogramming toward glycolytic dependence, with elevated lactate production contributing to T-cell exclusion. Based on these findings, we show that pharmacologic glycolysis inhibition with 2-deoxyglucose reverses immune resistance in GSNOR-knockout models, enhancing CD8⁺ T-cell infiltration and sensitizing tumors to anti–PD-1 therapy both in vitro and in vivo. Notably, this is the first demonstration that metabolic intervention can restore immune sensitivity in GSNOR-deficient CRC. Our results identify GSNOR expression as a predictive biomarker for metabolic-immune combinatorial strategies and support the clinical translation of 2-deoxyglucose plus anti–PD-1 as a precision immunotherapy approach for this high-risk CRC phenotype.

s -亚硝基谷胱甘肽还原酶（GSNOR）越来越被认为是一种肿瘤抑制因子，我们最近报道了它的缺乏导致结直肠癌（CRC）的侵袭性和免疫逃避表型。然而，将GSNOR丢失与免疫逃逸联系起来的机制仍然不完全清楚。在这项研究中，我们揭示了gsnorr缺陷CRC中代谢重编程和免疫逃逸之间先前未被认识到的联系，并确定了一种可用于恢复免疫反应性的治疗脆弱性。对137例临床CRC样本的综合分析显示，gsnorr缺陷肿瘤表现为高级别肿瘤出芽，这是一种预后不良的标志，CD4+和CD8+ t细胞浸润减少，与免疫抑制肿瘤微环境一致。综合转录组学、免疫组织化学和单细胞RNA-seq数据，我们证明了gsnorr缺陷肿瘤经历了惊人的代谢重编程，导致糖酵解依赖，乳酸产量升高导致t细胞排斥。基于这些发现，我们发现2-脱氧葡萄糖（2-DG）的药理学糖酵解抑制逆转了gsnoro - ko模型的免疫抵抗，增强了CD8+ t细胞的浸润，并使肿瘤对抗pd -1治疗增敏。值得注意的是，这是首次证明代谢干预可以恢复gsnorr缺陷CRC的免疫敏感性。我们的研究结果确定GSNOR表达作为代谢-免疫组合策略的预测性生物标志物，并支持2-DG加抗pd -1的临床翻译作为这种高风险CRC表型的精确免疫治疗方法。

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引用次数: 0

Assessing the Status of Cyclin E1 (CCNE1) From Gene to Protein Level in Ovarian and Endometrial Carcinomas: A Systematic Review 评估周期蛋白E1 （CCNE1）在卵巢癌和子宫内膜癌中从基因到蛋白水平的状态：一项系统综述。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-10 DOI: 10.1016/j.labinv.2025.104249

Alexis Trecourt , Catherine Genestie , Alexander Valent , Mojgan Devouassoux-Shisheboran , Etienne Rouleau , Elisa Yaniz-Galende , Audrey Leformal , Valeria Naim , Alexandra Leary

Twenty percent and 45.4% of high-grade ovarian carcinomas (OC) and endometrial carcinomas (EC) exhibit CCNE1 amplification (CCNE1-amp), respectively, which is related to poor prognosis, but could serve as predictive biomarker for response to innovative targeted therapies. However, there is no consensus regarding how to evaluate the CCNE1 status (at the DNA, RNA, and/or protein level). Therefore, we conducted a systematic review of CCNE1 status testing in tubo-ovarian neoplasms and EC, comparing their performance for clinical purposes and highlighting the test’s interpretation criteria (CRD420250651291). Among the 734 records initially found on PubMed and Google Scholar, 48 reports were finally included. Molecular analyses and immunohistochemistry (IHC) were reported on 9774 tubo-ovarian neoplasms and 750 EC, and 6966 tubo-ovarian neoplasms and 856 EC, respectively. The most frequently morphological used method to detect CCNE1-amp was fluorescent in situ hybridization (13/16 studies, 81.3%), with quite consensual criteria to defined amplification (ie, CCNE1/chromosome 19 ratio ≥2, and/or >8/≥8 copies of CCNE1 per nucleus, and/or ≥4 CCNE1 copies in ≥40% of cells). The proportion of tubo-ovarian neoplasms with CCNE1 immunohistochemical overexpression varied from 13.5% to 96%, and 14.6% to 86.1% in EC. The sensitivity and specificity of CCNE1 IHC to detect/exclude CCNE1-amp varied from 54.5% to 100% and 59.3% to 90.1%, respectively. Given the reported data, CCNE1 overexpression should be considered either when an H-score is ≥100 or when the staining is >60% with >5% of cells strongly stained. Both CCNE1-amp and CCNE1 overexpressions were associated with poor prognosis and with response to Wee1 and CDK2 inhibitors in high-grade serous OC (overall response rate up to 53%, objective response rate of 32%-40%). In contrast, CCNE1 messenger RNA overexpression had no prognostic value. Thus, both CCNE1-amp detection by fluorescent in situ hybridization and CCNE1 protein levels quantification using IHC represent today the most validated tools to determine the CCNE1 status in OC/EC.

高级别卵巢癌（OC）和子宫内膜癌（EC）分别有20%和45.4%表现出CCNE1扩增（CCNE1-amp），这与预后不良有关，但可以作为对创新靶向治疗反应的预测性生物标志物。然而，关于如何评估CCNE1状态（在DNA、RNA和/或蛋白质水平上）尚无共识。因此，我们对输卵管卵巢肿瘤和EC的CCNE1状态检测进行了系统综述，比较了它们在临床中的表现，并强调了检测的解释标准（CRD420250651291）。在PubMed和b谷歌Scholar上最初发现的734条记录中，最终收录了48篇报告。分别对9774例输卵管卵巢肿瘤和750例EC、6966例输卵管卵巢肿瘤和856例EC进行了分子分析和免疫组化（IHC）。检测CCNE1-amp最常用的形态学方法是荧光原位杂交（FISH； 13/16项研究，81.3%），对扩增的定义标准是相当一致的（即CCNE1/ 19号染色体的比率≥2，和/或每个细胞核有8/≥8个CCNE1拷贝，和/或≥4个CCNE1拷贝在≥40%的细胞中）。CCNE1免疫组化过表达的输卵管卵巢肿瘤比例为13.5% ~ 96%，EC为14.6% ~ 86.1%。CCNE1 IHC检测/排除CCNE1-amp的敏感性和特异性分别为54.5% ~ 100%和59.3% ~ 90.1%。根据已报道的数据，当h评分至少为>00时，或者当>染色为60%，>染色为5%的细胞强烈染色时，应考虑CCNE1过表达。CCNE1-amp和CCNE1过表达均与HGSOC患者预后不良以及对Wee1和CDK2抑制剂的反应相关（总缓解率高达53%，客观缓解率为32-40%）。相比之下，CCNE1 mRNA过表达没有预后价值。因此，FISH检测CCNE1-amp和IHC定量CCNE1蛋白水平是目前确定OC/EC中CCNE1状态最有效的工具。

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引用次数: 0

Prevalence, Immune Checkpoint Expression, and Spatial Interplay of Immune Cells Are Linked to Favorable Tumor Phenotype in 4915 Human Carcinomas 在4915例人类癌症中，患病率、免疫检查点表达和免疫细胞的空间相互作用与有利的肿瘤表型有关。

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-09 DOI: 10.1016/j.labinv.2025.104248

Zhihao Huang , Tim Mandelkow , Jonas B. Raedler , Elena Bady , Jan H. Müller , Ronald Simon , Eik Vettorazzi , Guido Sauter , Julia Ebner , Niclas C. Blessin

Although there is rising evidence that immune cell subpopulations that are in direct contact with the tumor cells (intraepithelial) can predict response to immune checkpoint therapy and patients’ outcome, a comprehensive assessment of intraepithelial immune cells and their spatial interplay is lacking. To assess intraepithelial leukocyte densities, immune checkpoint expression, and spatial interactions in 43 carcinoma entities, 4915 tumor samples in a tissue microarray format were analyzed using a deep learning framework and BLEACH&STAIN multiplex fluorescence immunohistochemistry. This approach enabled single-cell resolution quantification of 21 biomarkers through 7 sequential staining and imaging rounds. Immune and tumor cells were classified into 54 subpopulations. The mean intraepithelial immune cell density of CD8⁺ cytotoxic T cells, CD4⁺ T-helper cells, FOXP3⁺ regulatory T cells, CD20⁺ B cells, M1/M2 macrophages, and CD11c⁺ dendritic cells varied markedly between tumor entities and individual tumors. For instance, 88(±90) cells/mm² were found in tubular breast cancer, 661(±729) cells/mm² in colorectal cancer, and up to 2325(±2131) cells/mm² in squamous cell cancers from various origins. Unsupervised cluster analysis revealed a “cluster a” of 634 patients from almost all different tumor entities with an exceptionally high density of intraepithelial immune cells that was characterized by a unique interaction profile along with the highest immune checkpoint expression. Across all analyzed tumor entities, the intraepithelial highly inflamed cluster a was significantly linked to low pathologic tumor stage (P < .001). The data from this study provide a comprehensive characterization of intraepithelial immune cells across 43 different human carcinomas and identified an inflamed pan-cancer phenotype characterized by strong interactions of intraepithelial CD8⁺ cytotoxic T cells, CD4⁺ T cells, dendritic cells, and M2 macrophages, along with highest levels of TIM3, PD-1, and CTLA-4 expression that is linked to a favorable tumor phenotype.

尽管越来越多的证据表明，与肿瘤细胞（上皮内）直接接触的免疫细胞亚群可以预测对免疫检查点治疗的反应和患者的预后，但缺乏对上皮内免疫细胞及其空间相互作用的全面评估。为了评估43种癌症实体的上皮内白细胞密度、免疫检查点表达和空间相互作用，使用深度学习框架和BLEACH&STAIN多重荧光免疫组织化学（mfIHC）分析了组织微阵列格式的4915例肿瘤样本。该方法通过7轮连续染色和成像，实现了21种生物标志物的单细胞分辨率定量。免疫细胞和肿瘤细胞可分为54个亚群。上皮内CD8+细胞毒性t细胞、CD4+ t辅助细胞、FOXP3+Tregs细胞、CD20+ b细胞、M1/ m2巨噬细胞和CD11c+树突状细胞的平均免疫细胞密度在肿瘤实体和个体之间存在显著差异。例如，在管状乳腺癌中发现88（±90）个细胞/mm2，在结直肠癌中发现661（±729）个细胞/mm2，在各种来源的鳞状细胞癌中发现高达2325（±2131）个细胞/mm2。无监督聚类分析显示，来自几乎所有不同肿瘤实体的634例患者的“聚类a”具有异常高密度的上皮内免疫细胞，其特征是独特的相互作用谱以及最高的免疫检查点表达。在所有分析的肿瘤实体中，上皮内高度炎症的簇a与低pT (p) +细胞毒性t细胞、CD4+ t细胞、树突状细胞和M2巨噬细胞以及最高水平的TIM3、PD-1和CTLA-4表达显著相关，这与有利的肿瘤表型有关。

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引用次数: 0

Corrigendum to “Tumor Necrosis Factor-α–Dependent Inflammation Upregulates High Mobility Group Box 1 To Induce Tumor Promotion and Anti–Programmed Cell Death Protein-1 Immunotherapy Resistance in Lung Adenocarcinoma” [Laboratory Investigation 105 (2025) 102164] “肿瘤坏死因子-α -依赖性炎症上调高迁移率组1诱导肺腺癌肿瘤促进和抗程序性细胞死亡蛋白-1免疫治疗耐药性”的勘误表[实验室研究105 (2025)102164]

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-10-02 DOI: 10.1016/j.labinv.2025.104228

Lifei Kang , Jingjing Cao , Wenli Guo , Xiaohui Cui , Yangxuan Wei , Jiayu Zhang , Feiran Liu , Chenyang Duan , Qiang Lin , Ping Lvx , Zhiyu Ni , Jing Zuo , Haitao Shen

引用次数: 0

Ultrarapid EGFR Testing in Non–Small Cell Lung Carcinoma Patients: Findings From a Canadian Clinical Testing Workflow 非小细胞肺癌患者的超快速EGFR检测：来自加拿大临床检测工作流程的发现

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-09-24 DOI: 10.1016/j.labinv.2025.104245

Kate Fitzsimmons , Curtis Hughesman , Reka Pataky , Deirdre Weymann , Marie-Frédérique D’Amours , Deepu Alex , Diana N. Ionescu , Barb Melosky , Hannah Carolan , Kelly McNeil , Cheryl Ho , Anna McGuire , Stephen Yip , Julia R. Naso

Single vs multigene molecular testing modalities for lung cancer offer distinct advantages and risks. We examined lung cancer cases with clinically requested ultrarapid EGFR testing to (1) identify clinical features of rapid tested cases and their association with EGFR mutations, (2) evaluate performance of single-gene and multigene panel testing for ultrarapid tested patients, and (3) estimate laboratory costs and clinical outcomes. We include all retrospectively identified lung cancer patients who had ultrarapid Idylla EGFR testing during the study period. Demographic data were retrieved from clinical charts, and cost estimates were obtained from the BC Cancer Genetics and Genomics Laboratory. Of the 109 ultrarapid tests, 94 (86%) were technically successful, yielding a positive or negative result. Of these, 62 tests (66%) identified an EGFR mutation. Patients with negative or failed testing were offered panel sequencing (n = 47, 43%). Ultrarapid testing had a median 1-day turnaround time and 95% sensitivity for EGFR mutation detection relative to panel sequencing. East/Southeast Asian ethnicity and female sex were significantly associated with EGFR mutation positivity in a multivariate logistic regression model (P = .0001 and .029, respectively). The mean molecular testing cost per ultrarapid tested patient, including panel sequencing for cases with negative/failed rapid tests, was $550.53 (SD: $284), slightly less than the $571 cost for panel sequencing. Single-gene testing of patients with urgent clinical need or high probability of mutation may allow a rapid time to treatment at similar testing costs.

肺癌单基因与多基因分子检测方式具有明显的优势和风险。我们对肺癌病例进行了临床要求的超快速EGFR检测，以(i)确定快速检测病例的临床特征及其与EGFR突变的关系，（ii）评估超快速检测患者的单基因和多基因面板检测的性能；（iii）估计实验室费用和临床结果。我们纳入了所有在研究期间进行超快速Idylla EGFR检测的回顾性肺癌患者。人口统计数据来自临床图表，成本估算来自BC省癌症遗传学和基因组学实验室。在109项超快速检测中，94项（86%）在技术上是成功的，产生阳性或阴性结果。其中，62项（66%）检测发现EGFR突变。阴性或检测失败的患者接受小组测序（n=47, 43%）。与面板测序相比，超快速检测的平均周转时间为1天，EGFR突变检测的灵敏度为95%。多因素logistic回归模型显示，东亚/东南亚族裔和女性与EGFR突变阳性显著相关（P分别为0.0001和0.029）。每位超快速检测患者的平均分子检测成本，包括快速检测阴性/失败病例的小组测序，为550.53美元（标准偏差284美元），略低于小组测序的571美元。对有迫切临床需要或突变可能性高的患者进行单基因检测，可以在相同的检测费用下快速获得治疗。

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引用次数: 0

Comparison of QuPath and HALO Platforms for Analysis of the Tumor Microenvironment in Prostate Cancer QuPath与HALO平台在前列腺癌肿瘤微环境分析中的比较

IF 4.2 2区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Laboratory Investigation

Pub Date : 2025-09-24 DOI: 10.1016/j.labinv.2025.104246

Wei Zhang , Qin Zhou , Jonathan V. Nguyen , Erika Egal , Qian Yang , Michael R. Freeman , Siwen Hu-Lieskovan , Gita Suneja , Anna Coghill , Beatrice S. Knudsen

QuPath, an open-source digital pathology platform, has gained widespread use for image analysis in biomedical research since its release in 2016. However, its reproducibility and reliability compared with commercial software, such as HALO, require further validation, particularly for multiplex immunofluorescence analysis. In this study, we performed a direct comparison of QuPath and HALO using a multiplex immunofluorescence–stained prostate cancer tissue microarray inclusive of 192 unique cores. We evaluated performance across 3 key analytical modules: immune cell phenotyping, tumor infiltration with immune cells, and nearest neighbor analysis. Furthermore, we integrated QuPath with CytoMap, an open-source spatial analysis tool, to perform unsupervised clustering of immune cell infiltration—a feature not available in HALO. Our results demonstrated high concordance between 2 platforms, with correlation coefficients >0.89 for immune cell density, distance, and pattern of cell organization in tumor microenvironment. A neighborhood analysis using CytoMap was further performed and provided a more detailed spatial analysis of immune cell distribution across different prostate cancer grades. A significant increase of CD103+ T-cell infiltration into tumor microenvironment was observed in prostate cancer. In conclusion, our findings validate QuPath as a robust and reproducible alternative to commercial platforms for fluorescence-based digital pathology. By demonstrating QuPath’s capability to perform high-quality quantitative analysis with additional flexibility for integration with external tools, our study underscores its potential for advancing tumor microenvironment research in translational oncology.

QuPath是一个开源的数字病理平台，自2016年发布以来，在生物医学研究中获得了广泛的图像分析应用。然而，与商业软件（如HALO）相比，其再现性和可靠性需要进一步验证，特别是对于多重免疫荧光（mIF）分析。在这项研究中，我们使用包含192个独特核心的mif染色前列腺癌组织微阵列（TMA）对QuPath和HALO进行了直接比较。我们评估了三个关键分析模块的性能：免疫细胞表型、免疫细胞浸润肿瘤和最近邻分析。此外，我们将QuPath与开放源代码的空间分析工具CytoMap结合起来，对免疫细胞浸润进行无监督聚类，这是HALO中不具备的功能。我们的结果表明，两个平台之间的一致性很高，免疫细胞密度、距离和肿瘤微环境（TME）中细胞组织模式的相关系数超过0.89。进一步使用细胞图谱进行邻域分析，并对不同级别前列腺癌的免疫细胞分布提供了更详细的空间分析。前列腺癌中CD103+ T细胞对TME的浸润明显增加。总之，我们的研究结果验证了QuPath作为基于荧光的数字病理商业平台的强大且可重复的替代方案。通过展示QuPath进行高质量定量分析的能力，以及与外部工具集成的额外灵活性，我们的研究强调了其在转化肿瘤学中推进肿瘤微环境研究的潜力。

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引用次数: 0