Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies.

Pancreatic cancer is one of the most lethal cancers worldwide, with a 5-year survival rate of less than 5%, the lowest of all cancer types. Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive pancreatic cancer and has been classified as a health emergency in the past few decades. The histopathological diagnosis and prognosis evaluation of PDAC is time-consuming, laborious, and challenging in current clinical practice conditions. Pathological artificial intelligence (AI) research has been actively conducted lately. However, accessing medical data is challenging; the amount of open pathology data is small, and the absence of open-annotation data drawn by medical staff makes it difficult to conduct pathology AI research. Here, we provide easily accessible high-quality annotation data to address the abovementioned obstacles. Data evaluation is performed by supervised learning using a deep convolutional neural network structure to segment 11 annotated PDAC histopathological whole slide images (WSIs) drawn by medical staff directly from an open WSI dataset. We visualized the segmentation results of the histopathological images with a Dice score of 73% on the WSIs, including PDAC areas, thus identifying areas important for PDAC diagnosis and demonstrating high data quality. Additionally, pathologists assisted by AI can significantly increase their work efficiency. The pathological AI guidelines we propose are effective in developing histopathological AI for PDAC and are significant in the clinical field.

Rapid Androgen-Responsive Proteome Is Involved in Prostate Cancer Progression.

Androgen exerts its functions by binding with an androgen receptor (AR). It can activate many signaling pathways that are important to the progression of castration-resistant prostate cancer (CRPC). Here, we characterized the rapid proteomic changes seen at 5, 15, 30, and 60 min after the androgen treatment of VCaP cells via the tandem mass tag (TMT) labeling strategy. A total of 5529 proteins were successfully identified and quantified. Dynamic time profiling of protein expression patterns allowed us to identify five protein clusters involved in various stages of androgen-initiated signal transmission and processing. More details of protein functions and localization patterns, and our elucidation of an AR-interacting protein network, were obtained. Finally, we validated the expression level of AR-regulated proteins known to be significantly regulated in CRPC patients using the mouse xenograft model and patient samples. Our work offers a systematic analysis of the rapid proteomic changes induced by androgen and provides a global view of the molecular mechanisms underlying CRPC progression.

Local adenoviral delivery of soluble CD200R-Ig enhances antitumor immunity by inhibiting CD200-ß-catenin-driven M2 macrophage.

CD200 is known as an immune checkpoint molecule that inhibits innate immune cell activation. Using a head and neck squamous cell carcinoma (HNSCC) model, we sought to determine whether localized delivery of adenovirus-expressing sCD200R1-Ig, the soluble extracellular domain of CD200R1, enhances antitumor immunity. Mouse-derived bone marrow cells and M1/M2-like macrophages were cocultured with tumor cells and analyzed for macrophage polarization. As an in vivo model, C57BL/6 mice were subcutaneously injected with MEER/CD200High cells, CD200-overexpressing mouse HNSCC cells. Adenovirus-expressing sCD200R1-Ig (Ad5sCD200R1) was designed, and its effect was tested. Components in the tumor-immune microenvironment (TIME) were quantified using flow cytometry. CD200 promoted tumor growth and induced the expression of immune-related genes, especially macrophage colony-stimulating factor (M-CSF). Interestingly, CD200 induced M2-like polarization both in vitro and in vivo. Consequently, CD200 recruited more regulatory T (Treg) cells and fewer CD8+ effector T cells. These effects were effectively abolished by local injection of Ad5sCD200R1. These protumor effects of CD200 were driven through the ß-catenin/NF-κB/M-CSF axis. CD200 upregulated PD-L1, and the combined targeting of CD200 and PD-1 thus showed synergy. The immune checkpoint CD200 upregulated immune-related genes through ß-catenin signaling, reprogrammed the TIME, and exerted protumor effects. Ad5sCD200R1 injection could be an effective targeted strategy to enhance antitumor immunoediting.

Characterization of Proteins Regulated by Androgen and Protein Kinase a Signaling in VCaP Prostate Cancer Cells.

Androgen signaling via the androgen receptor (AR) is involved in normal prostate development and prostate cancer progression. In addition to androgen binding, a variety of protein kinases, including cyclic AMP-dependent protein kinase A (PKA), can activate the AR. Although hormone deprivation, especially that of androgen, continues to be an important strategy for treating prostate cancer patients, the disease ultimately progresses to castration-resistant prostate cancer (CRPC), despite a continuous hormone-deprived environment. To date, it remains unclear which pathways in this progression are active and targetable. Here, we performed a proteomic analysis of VCaP cells stimulated with androgen or forskolin to identify proteins specific for androgen-induced and androgen-bypassing signaling, respectively. Patterns of differentially expressed proteins were quantified, and eight proteins showing significant changes in expression were identified. Functional information, including a Gene Ontology analysis, revealed that most of these proteins are involved in metabolic processes and are associated with cancer. The mRNA and protein expression of selected proteins was validated, and functional correlations of identified proteins with signaling in VCaP cells were assessed by measuring metabolites related to each enzyme. These analyses offered new clues regarding effector molecules involved in prostate cancer development, insights that are supported by the demonstration of increased expression levels of the eight identified proteins in prostate cancer patients and assessments of the progression-free interval. Taken together, our findings show that aberrant levels of eight proteins reflect molecular changes that are significantly regulated by androgen and/or PKA signaling pathways, suggesting possible molecular mechanisms of CRPC.

CD200 Induces Epithelial-to-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma via ß-Catenin-Mediated Nuclear Translocation.

The membrane glycoprotein CD200 binds to its receptor CD200R1 and induces tolerance, mainly in cells of the myeloid lineage; however, information regarding its role in solid tumors is limited. Here, we investigated whether CD200 expression, which is enriched mainly in high-grade head and neck squamous cell carcinoma (HNSCC), correlates with cancer progression, particularly the epithelial-to-mesenchymal transition (EMT). The forced overexpression of CD200 in the HNSCC cell line, UMSCC84, not only increased the expression of EMT-related genes, but also enhanced invasiveness. The cleaved cytoplasmic domain of CD200 interacted with ß-catenin in the cytosol, was translocated to the nucleus, and eventually enhanced EMT-related gene expression. CD200 increased the invasiveness of mouse tonsillar epithelium immortalized with E6, E7, and Ras (MEER), a model of tonsillar squamous cell carcinoma. siRNA inhibition of CD200 or extracellular domain of CD200R1 down-regulated the expression of EMT-related genes and decreased invasiveness. Consistently, compared to CD200-null MEER tumors, subcutaneous CD200-expressing MEER tumors showed significantly increased metastatic migration into draining lymph nodes. Our study demonstrates a novel and unique role of CD200 in inducing EMT, suggesting the potential therapeutic target for blocking solid cancer progression.

False-negative errors in next-generation sequencing contribute substantially to inconsistency of mutation databases.

BACKGROUND: More than 11,000 laboratories and companies developed their own next-generation sequencing (NGS) for screening and diagnosis of various diseases including cancer. Although inconsistencies of mutation calls as high as 43% in databases such as GDSC (Genomics of Drug Sensitivity in Cancer) and CCLE (Cancer Cell Line Encyclopedia) have been reported, not many studies on the reasons for the inconsistencies have been published. Methods: Targeted-NGS analysis of 151 genes in 35 cell lines common to GDSC and CCLE was performed, and the results were compared with those from GDSC and CCLE wherein whole-exome- or highly-multiplex NGS were employed. RESULTS: In the comparison, GDSC and CCLE had a high rate (40-45%) of false-negative (FN) errors which would lead to high rate of inconsistent mutation calls, suggesting that highly-multiplex NGS may have high rate of FN errors. We also posited the possibility that targeted NGS, especially for the detection of low-level cancer cells in cancer tissues might suffer significant FN errors. CONCLUSION: FN errors may be the most important errors in NGS testing for cancer; their evaluation in laboratory-developed NGS tests is needed.

Mitotic cell death caused by follistatin-like 1 inhibition is associated with up-regulated Bim by inactivated Erk1/2 in human lung cancer cells.

Follistatin-like 1 (FSTL1) was identified as a novel pro-inflammatory protein showing high-level expression in rheumatoid arthritis. The protective effect of FSTL1 via the inhibition of apoptosis was reported in myocardial injury. However, the functional mechanism of FSTL1 in cancer is poorly characterized, and its proliferative effects are ambiguous. Here, we examined the effects of FSTL1 on cellular proliferation and cell cycle checkpoints in lung cancer cells. FSTL1 inhibition induced the cellular portion of G2/M phase in human lung cancer cells via the accumulation of regulators of the transition through the G2/M phase, including the cyclin-dependent kinase 1 (Cdk1)-cyclin B1 complex. An increase in histone H3 phosphorylation (at Ser10), another hallmark of mitosis, indicated that the knockdown of FSTL1 in lung cancer cells stimulated a mitotic arrest. After that, apoptosis was promoted by the activation of caspase-3 and -9. Protein level of Bim, a BH3 domain-only, pro-apoptotic member and its isoforms, BimL, BimS, and BimEL were up-regulated by FSTL1 inhibition. Degradation of Bim was blocked in FSTL1-knockdown cells by decreased phosphorylation of Bim. Increased BimEL as well as decreased phosphorylated Erk1/2 is essential for cell death by FSTL1 inhibition in NCI-H460 cells. Taken together, our results suggest that the knockdown of FSTL1 induces apoptosis through a mitotic arrest and caspase-dependent cell death. FSTL1 plays the important roles in cellular proliferation and apoptosis in lung cancer cells, and thus can be a new target for lung cancer treatment.

Cigarette smoking decreases global microRNA expression in human alveolar macrophages.

Human alveolar macrophages are critical components of the innate immune system. Cigarette smoking-induced changes in alveolar macrophage gene expression are linked to reduced resistance to pulmonary infections and to the development of emphysema/COPD. We hypothesized that microRNAs (miRNAs) could control, in part, the unique messenger RNA (mRNA) expression profiles found in alveolar macrophages of cigarette smokers. Activation of macrophages with different stimuli in vitro leads to a diverse range of M1 (inflammatory) and M2 (anti-inflammatory) polarized phenotypes that are thought to mimic activated macrophages in distinct tissue environments. Microarray mRNA data indicated that smoking promoted an "inverse" M1 mRNA expression program, defined by decreased expression of M1-induced transcripts and increased expression of M1-repressed transcripts with few changes in M2-regulated transcripts. RT-PCR arrays identified altered expression of many miRNAs in alveolar macrophages of smokers and a decrease in global miRNA abundance. Stratification of human subjects suggested that the magnitude of the global decrease in miRNA abundance was associated with smoking history. We found that many of the miRNAs with reduced expression in alveolar macrophages of smokers were predicted to target mRNAs upregulated in alveolar macrophages of smokers. For example, miR-452 is predicted to target the transcript encoding MMP12, an important effector of smoking-related diseases. Experimental antagonism of miR-452 in differentiated monocytic cells resulted in increased expression of MMP12. The comprehensive mRNA and miRNA expression profiles described here provide insight into gene expression regulation that may underlie the adverse effects cigarette smoking has on alveolar macrophages.

Multi-analyte network markers for tumor prognosis.

Deregulation of gene expression, a hallmark of cancer, is caused by both genetic and epigenetic mechanisms. The rapid accumulation of epigenome maps of various cancers suggests a new avenue of research, namely integrating epigenomic data with other types of omic data for cancer diagnosis, prognosis, and biomarker discovery. We introduce the MAPIT algorithm (Multi Analyte Pathway Inference Tool), to enable principled integration of epigenomic, transcriptomic, and protein interactome data. As a proof-of-principle, we apply MAPIT to glioblastoma multiforme (GBM), the most common and aggressive form of brain tumor. Few predictive markers were reported for the prognosis of GBM patients. By integrating mRNA transcriptome, promoter DNA methylome and protein-protein physical interactome, we find ten expression- and three methylation-based network markers, involving 118 genes. When tested on additional GBM patient samples, the prognostic accuracy of the multi-analyte network markers (73.5%) is 9.7% and 8.6% higher than previous prognostic signatures built on gene expression or DNA methylation alone. Our results highlight the critical role of two novel pathways in the prognosis of GBM patients, small GTPase-mediated protein trafficking and ubiquitination-dependent protein degradation. A better understanding of these two pathways could lead to personalized therapies for subgroups of GBM patients. Our study demonstrates that integrating epigenomic, transcriptomic, and interactomic data can improve the accuracy network-based prognosis markers and lead to novel mechanistic understanding of cancer.

Discover protein complexes in protein-protein interaction networks using parametric local modularity.

BACKGROUND: Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data. RESULTS: We present an algorithm, miPALM (Module Inference by Parametric Local Modularity), to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. CONCLUSIONS: miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.