Anti-PD-1 immunotherapy is the standard of care for treating many patients with non-small cell lung cancer (NSCLC), yet mechanisms of treatment failure are emerging. We present a case of NSCLC, who rapidly progressed during a trial (NCT02318771) combining palliative radiotherapy and pembrolizumab. Planned tumor biopsy demonstrated PD-1 expression by NSCLC cells. We validated this observation by detecting PD-1 transcript in lung cancer cells and by co-localizing PD-1 and lung cancer-specific markers in resected lung cancer tissues. We further investigated the biological role of cancer-intrinsic PD-1 in a mouse lung cancer cell line, M109. Knockout or antibody blockade of PD-1 enhanced M109 viability in-vitro, while PD-1 overexpression and exposure to recombinant PD-L1 diminished viability. PD-1 blockade accelerated growth of M109-xenograft tumors with increased proliferation and decreased apoptosis in immune-deficient mice. This represents a first-time report of NSCLC-intrinsic PD-1 expression and a potential mechanism by which PD-1 blockade may promote cancer growth.

Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images.Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests.Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.01±17.10 vs 47.00±24.08, p=0.000, 134.99±21.42 vs 62.40±29.15, p=0.000, 1770.89±627.22 vs 1157.27±722.23, p=0.013, 165.84±26.33 vs 132.94±28.73, p=0.000), respectively.Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.

SYNOPSISThis article reports the influence of oscillating pressure on the mechanical performance of general-grade high-density polyethylene (HDPE). The tensile strength of 93 MPa and the Young's modulus of 5 GPa were obtained by an oscillating packing technique. The great improvement of the mechanical properties of HDPE specimens is due to the existence of the shish-kebab crystalline structure, the orientation of the molecular chains along the flow direction, and the more perfect crystallites.

The mammalian peptide hormone stanniocalcin 2 (STC2) plays an oncogenic role in many human cancers. However, the exact function of STC2 in human head and neck squamous cell carcinoma (HNSCC) is unclear. We aimed to examine the function and clinical significance of STC2 in HNSCC. Using in vitro and in vivo assays, we show that overexpression of STC2 suppressed cell apoptosis, promoted cell proliferation, migration, invasion, and cell cycle arrest at the G1/S transition. By contrast, silencing of STC2 inhibited these activities. We further show that STC2 upregulated the phosphorylation of AKT and enhanced HNSCC metastasis via Snail-mediated increase of vimentin and decrease of E-cadherin. These responses were blocked by silencing of STC2/Snail expression or inhibition of pAKT activity. Furthermore, clinical data indicate that high STC2 expression was associated with high levels of pAKT and Snail in tumor samples from HNSCC patients with regional lymph node metastasis (P < 0.01). Thus, we conclude that STC2 controls HNSCC metastasis via the PI3K/AKT/Snail signaling axis and that targeted therapy against STC2 may be a novel strategy to effectively treat patients with metastatic HNSCC.

Based on the quantified risk stratification offered by our nomogram, clinicians might have a thorough discussion with PTMC patients during the both pre- and postoperative period. Prophylactic CLND and strict postoperative evaluation may be indicated when the patients have a high nomogram score.

Background: To explore whether deep convolutional neural networks (DCNNs) have the potential to improve diagnostic efficiency and increase the level of interobserver agreement in the classification of thyroid nodules in histopathological slides.Methods: A total of 11,715 fragmented images from 806 patients' original histological images were divided into a training dataset and a test dataset. Inception-ResNet-v2 and VGG-19 were trained using the training dataset and tested using the test dataset to determine the diagnostic efficiencies of different histologic types of thyroid nodules, including normal tissue, adenoma, nodular goiter, papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). Misdiagnoses were further analyzed.Results: The total 11,715 fragmented images were divided into a training dataset and a test dataset for each pathology type at a ratio of 5:1. Using the test set, VGG-19 yielded a better average diagnostic accuracy than did Inception-ResNet-v2 (97.34% vs. 94.42%, respectively). The VGG-19 model applied to 7 pathology types showed a fragmentation accuracy of 88.33% for normal tissue, 98.57% for ATC, 98.89% for FTC, 100% for MTC, 97.77% for PTC, 100% for nodular goiter and 92.44% for adenoma. It achieved excellent diagnostic efficiencies for all the malignant types. Normal tissue and adenoma were the most challenging histological types to classify. Conclusions:The DCNN models, especially VGG-19, achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology. Analysis of the misdiagnosed cases revealed that normal tissue and adenoma were the most challenging histological types for the DCNN to differentiate, while all the malignant classifications achieved excellent diagnostic efficiencies. The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis.

No abstract

KMT5A (known as PR-Set7/9, SETD8 and SET8), a member of the SET domain containing methyltransferase family specifically targeting H4K20 for methylation, has been implicated in multiple biological processes. In the present study, we identified that KMT5A was elevated in 50 pairs of papillary thyroid cancer tissue samples and in cell lines K1 and TPC-1 by qRT-PCR and western blotting, as well as by immunohistochemical staining. CCK-8 assay and flow cytometric analysis revealed that inhibition of KMT5A attenuated proliferation and induced apoptosis. Transwell assays revealed that cell migration and invasion were suppressed in KMT5A-knockdown cells. Moreover, the inhibition of KMT5A arrested the cell cycle in the G1/S phase of papillary thyroid cancer cells. The TCGA data revealed that elevated KMT5A expression was significantly correlated with extrathyroidal extension, lymph node metastasis and advanced pathological stage of papillary thyroid cancer. Furthermore, we observed that inhibition of KMT5A suppressed the expression of SREBP1, SCD, FASN and ACC, key molecules involved in lipid metabolism and decreased the level of malondialdehyde in papillary thyroid cancer cells. In conclusion, KMT5A may be a novel oncogenic factor, specifically a regulator for lipid metabolism in papillary thyroid carcinoma.