Laboratory Investigation最新文献

About 20% of human cancers harbor mutations of genes encoding switch/sucrose nonfermentable (SWI/SNF) complex subunits. Deficiency of subunits of the complex is present in 10% of non-small-cell lung cancers (NSCLC; SMARCA4/SMARCA2 deficient), 100% thoracic SMARCA4/A2-deficient undifferentiated tumors (TSADUDT; SMARCA4/A2 deficient), malignant rhabdoid tumor, and atypical/teratoid tumor (SMARCB1-deficient), >90% of small cell carcinoma of the ovary, hypercalcemic type (SMARCA4/SMARCA2 deficient), frequently in undifferentiated/dedifferentiated endometrial carcinoma (SMARCA4, SMARCA2, SMARCB1, and ARID1A/B deficient), 100% SMARCA4 deficient undifferentiated uterine sarcoma (SMARCA4 deficient); and in various other tumors from multifarious anatomical sites. Silencing of SWI/SNF gene expression may be genomically or epigenetically driven, causing loss of tumor suppression function or facilitating other oncogenic events. The SWI/SNF-deficient tumors share the phenotype of poor or no differentiation, often with a variable component of rhabdoid tumor cells. They present at advanced stages with poor prognosis. Rhabdoid tumor cell phenotype is a useful feature to prompt investigation for this group of tumors. In the thoracic space, the overlap in morphology, immunophenotype, genetics, and epigenetics of SMARCA4/A2-deficient NSCLC and TSADUDT appears more significant. This raises a possible nosologic relationship between TSADUDT and SMARCA4/A2-deficient NSCLC. Increased understanding of the genetics, epigenetics, and mechanisms of oncogenesis in these poor prognostic tumors, which are often resistant to conventional treatment, opens a new horizon of therapy for the tumors.

Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma (ESCC) is unknown. Artificial intelligence (AI) can quantitatively evaluate histopathological findings. The aim was to measure NS in ESCC using AI and elucidate its clinical significance. We investigated the relationship between NS assessed by AI and prognosis in 138 patients with ESCC who underwent curative esophagectomy. Hematoxylin and eosin-stained slides from the deepest tumor sections were digitized. Using HALO-AI DenseNet v2, we created a deep-learning classifier that identified tumor cells with an NS area >20 μm². Median NS was 40.14 μm², which was used to divide patients into NS-high and NS-low groups (n = 69 per group). Five-year overall survival (OS) and relapse-free survival rates were significantly lower in the NS-high group (43.2% and 39.6%) than in the NS-low group (67.7% and 49.6%). Multivariate analysis showed that greater tumor depth and NS-high status (hazard ratio: 1.79; P = .032) were independent risk factors for OS. In 77 cases with neoadjuvant chemotherapy, increased tumor depth and NS-high status (hazard ratio: 1.99; P = .048) were independent prognostic factors for unfavorable OS. Compared with the NS-low group, the NS-high group had significantly higher anisokaryosis, higher Ki-67 expression as calculated by AI analysis of immunostaining, and higher NS heterogeneity as examined by equidividing the tumors into square tiles. In conclusion, NS assessed by AI is a simple and useful prognostic factor for ESCC.

Acute kidney frailty (AKF) is a condition of increased susceptibility to acute kidney injury (AKI), an abrupt impairment of renal excretory function potentially leading to severe complications. Prevention of AKI relies on the recognition of risk factors contributing to AKF. At the population level, dehydration constitutes a predisposing factor for AKI. However, renal frailty may be context-specific, with variations among patients in the types of damage and the distinct pathological mechanisms. In this regard, we studied the combined effect of dehydration with other factors on renal homeostasis, such as increasing age and hypertension. AKF status was studied in rats bearing risk factors individually and in combination and was evaluated as the level of AKI induced by a triggering dose of cisplatin, which is known to be mildly nephrotoxic for young, healthy rats. AKI was assessed through parameters of renal function (including creatinine, urea, creatinine clearance, proteinuria, and fractional excretion of sodium) and histopathology of renal tissue specimens. The hydration status was measured by bioelectric impedance and other techniques. Water deprivation induces a dehydration state characterized by reductions in body weight and urinary flow and increases in hematocrit and plasma and urine osmolality. Bioelectric impedance showed a net loss of body water after water deprivation with no relevant changes in body mass distribution. Dehydration is not sufficient to predispose young control rats to intrinsic AKI. However, the combination of dehydration with advanced age or hypertension induces AKF evidenced by a magnified response of renal dysfunction (reduced filtration and tubular function) and tubular necrosis caused by low-dose cisplatin treatment. This study highlights the relevance of addressing AKF as a premorbid condition providing prophylactic opportunities and shows that dehydration differentially predisposes to prerenal and intrinsic AKI.

The success of immune checkpoint inhibitors (ICI) in melanoma therapy has catalyzed the introduction of ICI in increasingly early stages of the disease. This exposes many patients with a lower risk of relapse to the risk of protracted adverse events, highlighting the need for biomarkers guiding the use of ICI. Already many years ago, brisk infiltration of primary melanomas by lymphocytes has been linked to improved patient outcome, but controversial findings due to a high variability in classification systems have been described CD8+ T cells have been identified as a primary mediator of antitumor immunity in patients treated with ICI. As CD8+ T cells require the presentation of antigens via MHC-I on target cells, downregulation and loss of MHC-I have been observed as resistance mechanisms to ICI. In this study, we revisit the role of MHC-I expression and CD8+ T-cell infiltration in melanoma evolution using a cohort of advanced primary and matched metastatic melanomas by using an automated immunohistochemistry and digital pathology workflow. Our results show that downregulation of MHC-I expression is a frequent event in advanced primary melanomas that is associated with decreased CD8+ T-cell infiltration and an early metastatic spread to sentinel lymph nodes. Furthermore, MHC-I downregulation and decreased infiltration with CD8+ T cells are also associated with resistance to ICI. Our results suggest that analyses of MHC-I expression and CD8+ T-cell infiltration patterns could serve as future biomarkers to guide the decision to treat patients in early stages of melanoma with ICI.

Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional hematoxylin and eosin (H&E) staining primarily reveals cellular inflammation with limited detail on fibrosis. To address these issues, we introduce a pioneering label-free quantitative multiphoton fiber histology (MPFH) technique that delineates the intricate characteristics of collagen and elastin fibers for ILD diagnosis. We acquired colocated multiphoton and H&E-stained images from a single tissue slice. Multiphoton imaging was performed on the deparaffinized section to obtain fibrotic tissue information, followed by H&E staining to capture cellular information. This approach was tested in a blinded diagnostic trial among 7 pathologists involving 14 patients with relatively normal lung and 31 patients with ILD (11 idiopathic pulmonary fibrosis/usual interstitial pneumonia, 14 nonspecific interstitial pneumonia, and 6 pleuroparenchymal fibroelastosis). A customized algorithm extracted quantitative fiber indicators from multiphoton images. These indicators, combined with clinical and radiologic features, were used to develop an automatic multiclass ILD classifier. Using MPFH, we can acquire high-quality, colocalized images of collagen fibers, elastin fibers, and cells. We found that the type, distribution, and degree of fibrotic proliferation can effectively distinguish between different subtypes. The blind study showed that MPFH enhanced diagnostic consistency (κ values from 0.56 to 0.72) and accuracy (from 73.0% to 82.5%, P = .0090). The combination of quantitative fiber indicators effectively distinguished between different tissues, with areas under the receiver operating characteristic curves exceeding 0.92. The automatic classifier achieved 93.8% accuracy, closely paralleling the 92.2% accuracy of expert pathologists. The outcomes of our research underscore the transformative potential of MPFH in the field of fibrotic-ILD diagnostics. By integrating quantitative analysis of fiber characteristics with advanced machine learning algorithms, MPFH facilitates the automatic and accurate identification of various fibrotic disease subtypes, showcasing a significant leap forward in precision diagnostics.

Over the past decade, a scientific field has been developed demonstrating microRNAs (miRNAs) to be actively sorted into extracellular vesicles via specific nucleotide motifs that interact with discrete RNA-binding proteins. These miRNAs are proposed to be transported into recipient cells in which they can regulate specific cellular pathways. This mechanism could have enormous potential in explaining how cells signal and regulate other cells nearby or at a distance. Tens of studies have built this theme of a regulated transport of miRNAs. However, some concerns exist about this field. Taken together, there are concerns of a lack of a consistent motif, RNA-binding protein, or preferential miRNA involved in this process. In this study, we provide an expert and extensive analysis of the field that makes the cases for and against an active sorting mechanism. We provide potential explanations on why there is a lack of agreement. Most importantly, we provide ideas on how to move this field forward with more rigor and reproducibility. It is hoped that by engaging in a scientific debate of the pros and cons of this field, more rigorous experiments can be performed to conclusively demonstrate this biological activity.

Adult granulosa cell tumors (AGCTs) of the ovary are characterized by their propensity for late recurrences and are primarily managed surgically due to the limited efficacy of systemic treatment. The FOXL2 p.C134W somatic mutation has been identified in ∼95% of AGCT cases, and TERT promoter alterations have been linked to worse overall survival. This study highlights the potential prognostic significance of FOXO1 mutations, suggesting that they may be associated with poorer overall survival and shorter time to recurrence. A total of 183 primary AGCTs and 44 recurrences without corresponding primary tumors were analyzed. The primary AGCTs were categorized into 3 groups: 77 nonrecurrent tumors, 18 tumors that later recurred (including 9 cases with matched primary-recurrence pairs), and 88 tumors with unknown recurrence status. Targeted next-generation sequencing was conducted on 786 cancer-related genes to investigate their genetic profile. The study aimed to identify the molecular alterations associated with AGCT pathogenesis and recurrence rate, comparing primary versus recurrent tumors, and primary recurrent versus primary nonrecurrent cases. Our findings confirmed the high prevalence (99%) of the FOXL2 p.C134W mutation in AGCTs. Secondary truncating FOXL2 mutations were observed in 5% of cases. Two cases with typical AGCT morphology were FOXL2 wild-type, harboring mutations in KRAS or KMT2D instead, suggesting alternative genetic pathways. TERT promoter mutations were found in 43% of cases, more frequently in recurrences. Other recurrent mutations detected in the cohort included KMT2D (10%), FOXO1 (7%), CHEK2 (5%), TP53 (3.5%), PIK3CA (3.5%), and AKT1 (3%). Two recurrent, FOXL2-mutated cases also carried DICER1 mutations. One tumor exhibited MSI-high status and a tumor mutation burden of 19 mut/Mb.Our results indicate the need for further investigation into the role of FOXO1 as a potential prognostic marker in AGCTs.

Hyalinized clear cell carcinoma (HCCC) is a rare tumor of the minor salivary gland, characterized by pale cytoplasm and EWSR1::ATF1 fusion. Recently, new fusions, such as EWSR1::LARP4 and SMARCA2::CREM, have also been identified. Histologically, HCCC closely resembles other salivary gland tumors like mucoepidermoid carcinoma and myoepithelial carcinoma, and there are no specific immunohistological markers for its identification. In this study, we investigated potential markers for HCCC based on the characteristics of minor salivary gland acini, from which these tumors may originate. SOX10 is a known marker for serous gland clusters and NKX3.1 for mucus gland clusters. Fluorescence intensity analysis of double staining, objectively evaluated by artificial intelligence, revealed variations in the positive intensity of cells single positive for NKX3.1 and SOX10, as well as cells positive for both markers, which are commonly observed in normal minor salivary glands. We evaluated NKX3.1 expression by immunohistochemistry in 12 HCCC cases (including 9 EWSR1::ATF1, 1 EWSR1::LARP4, and 1 SMARCA2::CREM), 12 myoepithelial carcinoma cases, and tissue microarray containing 88 cases of multiple salivary gland tumors using immunohistochemistry. NKX3.1 was expressed in all 12 HCCC cases (100%), with NKX3.1-positive cells ≧90% in 3 cases, ≧60% 1 case, ≧30% 4 cases, and <30% 4 cases, respectively. SOX10 was negative in 10 cases and weakly positive in 2 cases. This finding mimics the pattern of expression in minor salivary glands and may explain the occurrence of weak NKX3.1 staining and SOX10-positive cases in HCCC. Additionally, in the tissue microarray analysis, NKX3.1 staining was observed in only 1 HCCC case. These findings indicate that NKX3.1 is a useful marker for distinguishing HCCC from other clear cell salivary gland neoplasms. This study suggests that NKX3.1, along with SOX10 and CK7, can be utilized to improve the accuracy of HCCC diagnosis.

Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists, and other researchers to ensure data quality and consistency. The lack of standardization and scalability is a significant challenge when generating annotations and annotated data sets. Recognizing these limitations, the Scientific Committee of the European Society of Digital and Integrative Pathology (ESDIP) performed a comprehensive international survey to understand the current state of annotation practices and identify actionable areas to address critical needs in the annotation process. The analysis and summary of the survey results provide several insights for all stakeholders involved in data preparation and ground truthing, ultimately contributing to the advancement of AI in computational pathology.

Desmoid-type fibromatosis (DTF) is a benign but locally aggressive neoplasm characterized by persistent fibroblast activation, unlike reactive fibrosis (RF), where fibroblast activation is transient. Although the Wnt/β-catenin signaling pathway is known to play a role in DTF pathogenesis, the specific genetic drivers contributing to this abnormal fibroblast activation are not fully understood. To identify additional driver genes that underlie the persistent activation of fibroblasts in DTF, we conducted a comparative transcriptome analysis between 29 DTF and 14 RF tissue samples, identifying 4267 differentially expressed genes (DEGs) specific to DTF. These DTF-specific DEGs were significantly associated with pathways involved in embryonic limb morphogenesis and muscle contraction, whereas RF-specific DEGs were linked to immune response and apoptosis. Using weighted gene coexpression network analysis to further elucidate the key regulatory circuits associated with persistent activation of DTF fibroblasts, we identified a highly DTF-specific gene module comprising 120 genes. This module was also significantly enriched in other fibroproliferative conditions showing persistent fibroblast activation, such as keloid disease and idiopathic pulmonary fibrosis. Subsequent analyses identified 7 driver transcription factors (ZNF536, IRX5, TWIST2, NKD2, PAX9, SHOX2, and SALL4) within this DTF-specific module that may contribute to the sustained activation of DTF fibroblasts. We further assessed the utility of 5 key genes from this module (TWIST2, LRRC15, CTHRC1, SHOX2, and SALL4) as potential biomarkers to distinguish DTF from RF using immunohistochemistry. All markers demonstrated excellent diagnostic performance, with TWIST2 showing exceptionally high sensitivity and specificity, surpassing β-catenin, the current standard biomarker for DTF. In conclusion, our study identifies gene modules and driver transcription factors that are highly specific to DTF, offering new insights into the genetic underpinnings of abnormal fibroblast activation in DTF. We also propose novel biomarkers that could improve the diagnostic accuracy and clinical management of DTF.