Laboratory Investigation最新文献

Junctional epidermolysis bullosa is an intractable cutaneous disorder in humans causing skin fragility and blistering due to mutations in genes encoding essential molecules adhering epidermis and dermis including collagen XVII. However, the pathogenesis still remains to be not fully understood perhaps because of a lack of appropriate animal models. In this study, we report novel mutant rats experiencing junctional epidermolysis bullosa, which was confirmed to be caused by a frameshift mutation of Col17a1 gene, as a rat model for investigating the underlying mechanism of pathogenesis. The mutant rats completely lacked the expression of collagen XVII and had blisters leading to infantile deaths as a homozygous condition, although their skin was apparently normal at birth by light microscopic evaluation except that immunohistochemical examination could not detect collagen XVII in any organs. These observations suggest that collagen XVII is not essential for the development of skin during the prenatal period but is indispensable for keeping epidermal-dermal connections stable after birth. Subsequent electron microscopic examinations further revealed an absence of hemidesmosomal inner plaques being composed of BP230, a binding partner of collagen XVII, and plectin in Col17a1-null newborns, albeit mRNA expressions of these molecules seemed to be unaffected at least during the fetal period. These results suggest that the lack of collagen XVII induces attenuation of hemidesmosomal inner plaques, which in turn destabilizes the epidermis-dermis connection and results in deterioration of epidermal physiology with formation of blisters after birth.

Targeting novel inhibitory ligands beyond anti-PD-1 and PD-L1 and CTLA-4 therapies is essential for the next decade of the immunotherapy era. Agents for the B7 family molecules B7-H3, B7-H4, and B7-H5 are emerging in clinical trial phases; therefore, further accumulation of evidence from both clinical and basic aspects is vital. Here, we applied a 7-color multiplexed imaging technique to analyze the profile of B7 family B7-H3/B7-H4/B7-H5 expression, in addition to PD-L1, and the spatial characteristics of immune cell infiltrates in urothelial carcinoma (UC). The results revealed that B7-H3 and B7-H4 were mainly expressed on tumor cells and B7-H5 on immune cells in UC, and most of the B7-H3/B7-H4/B7-H5-positive cells were mutually exclusive with PD-L1-positive cells. Also, the expression of B7-H4 was elevated in patients with advanced pathologic stages, and high B7-H4 expression was a significant factor affecting overall mortality following surgery in UC. Furthermore, spatial analysis revealed that the distance from the B7-H4⁺ cells to the nearest CD8⁺ cells was markedly far compared with other B7 family-positive tumor cells. Interestingly, the distance from B7-H4⁺ cells to the nearest CD8⁺ cells was significantly farther in patients dying from cancer after surgery or immune checkpoint inhibitors compared with cancer survivors; thus, high B7-H4 expression in tumor cells may inhibit CD8 infiltration into the tumor space and that B7-H4-positive cells form a specific spatial niche. In summary, we performed a comprehensive evaluation of B7 family member expression and found that the spatial distribution of B7-H4 suggests the potentially useful role of combination blockade with both B7-H4 and the current anti-PD-1/PD-L1 axis in the treatment of UC.

In digital pathology, accurate mitosis detection in histopathological images is critical for cancer diagnosis and prognosis. However, this remains challenging due to the inherent variability in cell morphology and the domain shift problem. This study introduces ConvNext Mitosis Identification-You Only Look Once (CNMI-YOLO), a new 2-stage deep learning method that uses the YOLOv7 architecture for cell detection and the ConvNeXt architecture for cell classification. The goal is to improve the identification of mitosis in different types of cancers. We utilized the Mitosis Domain Generalization Challenge 2022 data set in the experiments to ensure the model’s robustness and success across various scanners, species, and cancer types. The CNMI-YOLO model demonstrates superior performance in accurately detecting mitotic cells, significantly outperforming existing models in terms of precision, recall, and F1 score. The CNMI-YOLO model achieved an F1 score of 0.795 on the Mitosis Domain Generalization Challenge 2022 and demonstrated robust generalization with F1 scores of 0.783 and 0.759 on the external melanoma and sarcoma test sets, respectively. Additionally, the study included ablation studies to evaluate various object detection and classification models, such as Faster-RCNN and Swin Transformer. Furthermore, we assessed the model’s robustness performance on unseen data, confirming its ability to generalize and its potential for real-world use in digital pathology, using soft tissue sarcoma and melanoma samples not included in the training data set.

Lymph node status is a key factor in determining stage, treatment, and prognosis in cancers. Small lymph nodes in fat-rich gastrointestinal and breast cancer specimens are easily missed in conventional sampling methods. This study examined the effectiveness of the degreasing pretreatment with dimethyl sulfoxide (DMSO) in lymph node detection and its impact on the analysis of clinical treatment–related proteins and molecules. Thirty-three cases of gastrointestinal cancer specimens from radical gastrectomy and 63 cases of breast cancer specimens from modified radical mastectomy were included. After routine sampling of lymph nodes, the specimens were immersed in DMSO for 30 minutes for defatting. We assessed changes in the number of detected lymph nodes and pN staging in 33 gastrointestinal cancer specimens and 37 breast cancer specimens. In addition, we analyzed histologic characteristics, Masson trichrome special staining, and immunohistochemistry (gastrointestinal cancer: MMR, HER2, and PD-L1; breast cancer: ER, PR, AR, HER2, Ki-67, and PD-L1). Molecular status was evaluated for colorectal cancer (KRAS, NRAS, BRAF, and microsatellite instability) and breast cancer (HER2) in gastrointestinal cancer specimens and the remaining 26 breast cancer specimens. Compared with conventional sampling, DMSO pretreatment increased the detection rate of small lymph nodes (gastrointestinal cancer: P < .001; breast cancer: P < .001) and improved pN staging in 1 case each of gastric cancer, colon cancer, and rectal cancer (3/33; 9.1%). No significant difference in the morphology, special staining, protein, and molecular status of cancer tissue after DMSO treatment was found. Based on these results and our institutional experience, we recommend incorporating DMSO degreasing pretreatment into clinical pathologic sampling practices.

Tumor–stroma ratio (TSR) has been recognized as a valuable prognostic indicator in various solid tumors. This study aimed to examine the clinicopathologic relevance of TSR in Merkel cell carcinoma (MCC) using artificial intelligence (AI)-based parameterization of the stromal landscape and validate TSR scores generated by our AI model against those assessed by humans. One hundred twelve MCC cases with whole-slide images were collected from 4 different institutions. Whole-slide images were first partitioned into 128 × 128-pixel “mini-patches,” then classified using a novel framework, termed pre-tumor and stroma (Pre-TOAST) and TOAST, whose output equaled the probability of the minipatch representing tumor cells rather than stroma. Hierarchical random samplings of 50 minipatches per region were performed throughout 50 regions per slide. TSR and tumor–stroma landscape (TSL) parameters were estimated using the maximum-likelihood algorithm. Receiver operating characteristic curves showed that the area under the curve value of Pre-TOAST in discriminating classes of interest including tumor cells, collagenous stroma, and lymphocytes from nonclasses of interest including hemorrhage, space, and necrosis was 1.00. The area under the curve value of TOAST in differentiating tumor cells from related stroma was 0.93. MCC stroma was categorized into TSR high (TSR ≥ 50%) and TSR low (TSR < 50%) using both AI- and human pathology–based methods. The AI-based TSR-high subgroup exhibited notably shorter metastasis-free survival (MFS) with a statistical significance of P = .029. Interestingly, pathologist-determined TSR subgroups lacked statistical significance in recurrence-free survival, MFS, and overall survival (P > .05). Density-based spatial clustering of applications with noise analysis identified the following 2 distinct TSL clusters: TSL1 and TSL2. TSL2 showed significantly shorter recurrence-free survival (P = .045) and markedly reduced MFS (P < .001) compared with TSL1. TSL classification appears to offer better prognostic discrimination than traditional TSR evaluation in MCC. TSL can be reliably calculated using an AI-based classification framework and predict various prognostic features of MCC.