Modern Pathology最新文献

Epithelioid sarcoma (ES) is a rare tumor hallmarked by the loss of INI1/SMARCB1 expression. Apart from this alteration, little is known about the biology of ES. Despite recent advances in treatment, the prognosis of ES remains unsatisfactory. To elucidate the molecular underpinnings of ES, and to identify diagnostic biomarkers and potential therapeutic vulnerabilities, we performed an integrated omics profiling (RNA sequencing and methylation array) of 24 primary, untreated ESs. Transcriptome and methylome analysis identified two distinct molecular clusters that essentially corresponded to the morphologic variants of ES, classic ES (C-ES) and the more aggressive proximal ES (P-ES). The P-ES group was characterized by hyperactivation of GATA3 and MYC pathways, with extensive epigenetic rewiring associated with EZH2 overexpression. Both DNA methylation and gene expression analysis indicated a striking similarity with the "MYC subgroup" of ATRT, another SMARCB1-deficient tumor, implying a shared molecular background and potential therapeutic vulnerabilities. Conversely, the C-ES group exhibited an endothelial-like molecular profile, with expression of vascular genes and elevated pro-angiogenic SOX17 signaling. Immunohistochemistry validated the overexpression of the chromatin regulators GATA3 (9/12 vs. 0/16) and EZH2 (7/7 vs. 2/6) in P-ESs, and of the vascular factors SOX17 (8/8 vs. 1/10) and N-cadherin (5/9 vs 0/10) in C-ESs. Therefore, these molecules emerge as potential diagnostic tools to fill the gap represented by the lack of ES subtype-specific biomarkers. In summary, our study shows that P-ES and C-ES represent distinct molecular entities defined by MYC/GATA3 and SOX17/endothelial molecular traits, respectively. Besides providing insights into the biology of ES, our study pinpoints subtype-specific biomarkers and potential therapeutic vulnerabilities.

Peripheral T-cell lymphoma (PTCL) is a heterogeneous category, and many cases are unclassifiable and designated as PTCL-not otherwise specified (PTCL-NOS). Gene expression profiling (GEP) has delineated two prognostic subtypes within PTCL-NOS, PTCL-TBX21, and PTCL-GATA3, characterized by distinctive transcriptomes and a different prognosis. To further evaluate the pathologic features of these subgroups, 101 PTCL cases that did not meet specific criteria for well-defined T-cell lymphoma entities underwent detailed pathologic, immunophenotypic (including T_FH biomarkers) and GEP analyses, separating them into PTCL-NOS (n=63) and PTCL-TFH (a.k.a. nodal PTCL-TFH, NOS, and TFH lymphoma, NOS) (n=38). PTCL-NOS cases were further categorized into PTCL-GATA3 (n=22; 34%) and PTCL-TBX21 (n=41; 66%), and a significant association (p < 0.02) with overall survival (OS) was reaffirmed. Histopathological assessment showed PTCL-GATA3 cases were characterized by monotonous medium-sized or large transformed cells with a minimal tumor microenvironment (TME) compared to PTCL-TBX21 cases, which consisted of pleomorphic cells in a polymorphous TME (p < 0.05). GEP analysis validated these TME distinctions. Immunophenotypic analysis showed that PTCL-GATA3 cases were predominantly CD4+CD8- and associated with significantly higher LEF1, MYC, and CD30 expression (p < 0.05). PTCL-TBX21 displayed a more diverse biomarker profile with two subgroups: one expressing cytotoxic antigens and enriched in CD8+CD4- or CD8-CD4- phenotype, and another lacking cytotoxic markers but showing a CD4+CD8- phenotype with increased ICOS expression, but devoid of other T_FH markers. The PTCL-TFH cases correlated with an angioimmunoblastic T-cell lymphoma (AITL) gene signature, had more EBER-positive cells than the PTCL-GATA3 and PTCL-TBX21 cases, and a subset had some morphologic features of AITL (p < 0.01). This study highlights the unique morphologic and phenotypic variations within the newly-identified PTCL subtypes and should enable more precise diagnosis and tailored therapeutic strategies in the future.

Human epidermal growth factor receptor 2 (HER2)-positive breast carcinoma (BC) encompasses a spectrum of molecular subtypes, characterized by varying HER2/CEP17 ratios and HER2 copy numbers, influencing responses to anti-HER2 therapy. This study stratified HER2 fluorescence in situ hybridization (FISH) positive patients into three distinct groups: Group 1 with high copy number (G1-HC: ratio ≥2, copy number ≥6), Group 1 with low copy number (G1-LC: ratio ≥2, copy number ≥4 and <6), and Group 3 (G3: ratio <2.0, copy number ≥6.0), and evaluated their clinicopathologic features, response to anti-HER2 therapy and outcomes. In a cohort of 2,702 continuous primary BCs, G1-HC BCs accounted for 304 cases (11.3%), G1-LC for 37 cases (1.4%) and G3 for 75 cases (2.8%). G1-HC BCs were associated with younger age, higher tumor grade, and estrogen receptor negativity compared to G1-LC BCs. Furthermore, G1-HC BCs exhibited increased progesterone receptor negativity and HER2 immunohistochemistry (IHC) 3+ compared to G1-LC and G3 BCs. Analysis of the subgroup of HER2 IHC 2+ only cases (n=166) showed similar results. Notably, G1-HC patients exhibited significantly enhanced responses to anti-HER2 neoadjuvant chemotherapy compared to G1-LC and G3 patients. Conversely, G1-LC patients displayed a lower likelihood of disease-free status compared to G1-HC and G3 patients, albeit with no significant differences in overall survival, distant metastasis, or local recurrence among the groups. These findings offer valuable clinicopathologic insights into different HER2 FISH positive subgroups, potentially informing future criteria for interpreting HER2 FISH results.

An integral stage in typical digital pathology workflows involves deriving specific features from tiles extracted from a tessellated whole slide image. Notably, various computer vision neural network architectures, particularly the ImageNet pre-trained, have been extensively used in this domain. This study critically analyzes multiple strategies for encoding tiles to understand the extent of transfer learning and identify the most effective approach. The study categorizes neural network performance into three weight initialization methods: random, ImageNet-based, and self-supervised learning. Additionally, we propose a framework based on task-specific self-supervised learning (TS-SSL) which introduces a shallow feature extraction method, employing a spatial-channel attention block to glean distinctive features optimized for histopathology intricacies. Across two different downstream classification tasks (patch classification, and weakly supervised whole slide image classification) with diverse classification datasets, including Colorectal cancer histology, Patch Camelyon, PANDA, TCGA and CIFAR-10, our task specific self-supervised encoding approach consistently outperforms other CNN-based encoders. The better performances highlight the potential of task-specific-attention based self-supervised training in tailoring feature extraction for histopathology, indicating a shift from utilizing pretrained models originating outside the histopathology domain. Our study supports the idea that task-specific self-supervised learning allows domain-specific feature extraction, encouraging a more focused analysis.

GLI1-altered mesenchymal tumors have recently emerged as a distinctive group of neoplasms characterized by GLI1 fusions or amplifications. While there is clearly metastatic potential, the clinicopathological features predicting for metastasis are currently unknown. Herein, we present 6 cases of GLI1-altered mesenchymal tumors with multiomics analysis. The median patient age was 50 years (range: 3 to 68). They arose from extremities and trunk (2/6), head and neck region (2/6), and gastrointestinal tract (2/6). Histologically, they featured uniform round-to-ovoid cells with nested architecture and a rich vascular network. One case displayed abundant multinucleated giant cells. All stained positive for GLI1 (5/5) and CD56 (6/6). Molecularly, they featured GLI1 fusion (5/6) and amplification (1/6). Fusion partner included ACTB (3/5), TXNIP (1/5) and novel TUBA1B (1/5). Multiomics analysis revealed they possessed distinct expression and epigenomic profiles. All the 6 cases had follow-up information, with 5 of them having no evidence of disease at median follow-up of 30 months (range 17.3 to 102 months), and one case being died of disease with regional neck lymph node and bilateral lung metastasis at 81.5 months of follow-up. By incorporating cases reported in the literature, we analyzed clinicopathological features of a total of 167 cases predictive of malignant behavior. We found that size ≥6 cm and mitotic count ≥ 5 per 10 high power fields are predicting of metastasis. Cases with both high-risk features had significantly poorer survival. This study expands the literature database of GLI1-altered mesenchymal tumors and identifies features which can be used for risk stratification.

Clear cell stromal tumor (CCST) is a recently described mesenchymal neoplasm of the lung, characterized by spindle cells with variably clear-to-pale eosinophilic cytoplasm and prominent vascularity, as well as a recurrent YAP1::TFE3 gene fusion in most cases. Diagnosis can be challenging given its rarity and the lack of supportive immunohistochemical markers aside from TFE3. To date, less than 20 cases have been reported and data on clinical behavior are also limited. While most appear to be benign, aggressive behavior has been reported rarely. Here, we present the largest multi-institutional series of CCST to date, comprising a total of 8 cases and including 6 previously unpublished cases. We investigate its clinicopathologic and genomic features, while also assessing the diagnostic utility of immunohistochemistry (IHC) for YAP1 C-terminus (YAP-CT). Five patients were male and three were female. The median age was 59 years (range: 35 - 84). In all cases, a TFE3 rearrangement was demonstrated by either FISH or DNA / RNA sequencing. In 7 tumors, the YAP1::TFE3 fusion was identified by sequencing. We demonstrate that the combination of YAP1-CT loss and TFE3 overexpression by IHC reliably predicts an underlying YAP1::TFE3 fusion in these neoplasms and may be more sensitive than TFE3 FISH. Although the median follow-up time for our study was short (18 months, available in 7 cases), all cases pursued a benign clinical course, with no recurrences or metastases. Our study provides further characterization of this novel entity, supporting its wider recognition.

Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.

Bladder cancer (BC) remains a major disease burden in terms of incidence, morbidity, mortality, and economic cost. Deciphering the intrinsic molecular subtypes and identification of key drivers of BC has yielded successful novel therapeutic strategies. Advances in computational and digital pathology are reshaping the field of anatomic pathology. This review offers an update on the most relevant computational algorithms in digital pathology that have been proposed to enhance bladder cancer management. These tools promise to enhance diagnostics, staging and grading accuracy, and streamline efficiency while advancing practice consistency. Computational applications that enable intrinsic molecular classification, predict response to neoadjuvant therapy, and identify targets of therapy are also reviewed.

Ovarian clear cell carcinoma (OCCC) has a high incidence in Asia, with frequent occurrence at an early stage, but without sufficient data on molecular stratification for high-risk patients. Recently, immune-hot features have been proposed as indicators of poor prognosis in early stage OCCC. Specific patterns of intratumoral heterogeneity associated with immune-hot features must be defined. NanoString Digital Spatial Profiling technology was used to decipher the spatial distribution of the 18-plex protein panel. ROIs were collected based on the reference hematoxylin and eosin (H&E)-stained morphology. Areas of illumination (AOIs) were defined according to the ROI segmentation using the fluorescence signals of the visualization markers pan-cytokeratin (PanCK), CD45, or DNA. Unsupervised hierarchical clustering of 595 AOIs from 407 ROIs showed that the PanCK segments expressed different combinations of immune markers, suggestive of immune mimicry. Three immune-hot clusters were identified: granzyme B high (GZMB), immune signal high (IH), and immune-like cells (IL); two immune-cold clusters were identified: fibronectin high (FN) and immune checkpoint high cells (IC). In tumor samples at FIGO stage IC1/2 experiencing recurrence, there was an increased occurrence of PanCK+ AOIs with IH and IL groups in the papillary morphology surrounded by macrophage lineage tumor-infiltrating immune cells (TIIs). In contrast, for tumor samples at FIGO stage IC3/II with recurrence, PanCK + AOIs were prevalent in the FN group, particularly those with tubulocystic morphology surrounded by lymphoid lineage non-TIIs. Our work on the spatial profiling of early stage OCCC tumors revealed that the immune mimicry of tumor cells, presence of TIIs, and morphological patterns were associated with recurrence, which switched during tumor progression.