A Fast-Tracking Sample Preparation Protocol for Proteomics of Formalin-Fixed Paraffin-Embedded Tumor Tissues.

Archived tumor specimens are routinely preserved by formalin fixation and paraffin embedding. Despite the conventional wisdom that proteomics might be ineffective due to the cross-linking and pre-analytical variables, these samples have utility for both discovery and targeted proteomics. Building on this capability, proteomics approaches can be used to maximize our understanding of cancer biology and clinical relevance by studying preserved tumor tissues annotated with the patients' medical histories. Proteomics of formalin-fixed paraffin-embedded (FFPE) tissues also integrates with histological evaluation and molecular pathology strategies, so that additional collection of research biopsies or resected tumor aliquots is not needed. The acquisition of data from the same tumor sample also overcomes concerns about biological variation between samples due to intratumoral heterogeneity. However, the protein extraction and proteomics sample preparation from FFPE samples can be onerous, particularly for small (i.e., limited or precious) samples. Therefore, we provide a protocol for a recently introduced kit-based EasyPep method with benchmarking against a modified version of the well-established filter-aided sample preparation strategy using laser-capture microdissected lung adenocarcinoma tissues from a genetically engineered mouse model. This model system allows control over the tumor preparation and pre-analytical variables while also supporting the development of methods for spatial proteomics to examine intratumoral heterogeneity. Data are posted in ProteomeXchange (PXD045879).

Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).

Preclinical genetically engineered mouse models (GEMMs) of lung adenocarcinoma are invaluable for investigating molecular drivers of tumor formation, progression, and therapeutic resistance. However, histological analysis of these GEMMs requires significant time and training to ensure accuracy and consistency. To achieve a more objective and standardized analysis, we used machine learning to create GLASS-AI, a histological image analysis tool that the broader cancer research community can utilize to grade, segment, and analyze tumors in preclinical models of lung adenocarcinoma. GLASS-AI demonstrates strong agreement with expert human raters while uncovering a significant degree of unreported intratumor heterogeneity. Integrating immunohistochemical staining with high-resolution grade analysis by GLASS-AI identified dysregulation of Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced tumor regions. Our work demonstrates the benefit of employing GLASS-AI in preclinical lung adenocarcinoma models and the power of integrating machine learning and molecular biology techniques for studying the molecular pathways that underlie cancer progression.

SINE RNA of the imprinted miRNA clusters mediates constitutive type III interferon expression and antiviral protection in hemochorial placentas.

Hemochorial placentas have evolved defense mechanisms to prevent the vertical transmission of viruses to the immunologically underdeveloped fetus. Unlike somatic cells that require pathogen-associated molecular patterns to stimulate interferon production, placental trophoblasts constitutively produce type III interferons (IFNL) through an unknown mechanism. We demonstrate that transcripts of short interspersed nuclear elements (SINEs) embedded in miRNA clusters within the placenta trigger a viral mimicry response that induces IFNL and confers antiviral protection. Alu SINEs within primate-specific chromosome 19 (C19MC) and B1 SINEs within rodent-specific microRNA cluster on chromosome 2 (C2MC) produce dsRNAs that activate RIG-I-like receptors (RLRs) and downstream IFNL production. Homozygous C2MC knockout mouse trophoblast stem (mTS) cells and placentas lose intrinsic IFN expression and antiviral protection, whereas B1 RNA overexpression restores C2MCΔ/Δ mTS cell viral resistance. Our results uncover a convergently evolved mechanism whereby SINE RNAs drive antiviral resistance in hemochorial placentas, placing SINEs as integral players in innate immunity.

Deep Learning of Histopathology Images at the Single Cell Level.

The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to "weakly-label" the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

TIMEx: tumor-immune microenvironment deconvolution web-portal for bulk transcriptomics using pan-cancer scRNA-seq signatures.

SUMMARY: The heterogeneous cell types of the tumor-immune microenvironment (TIME) play key roles in determining cancer progression, metastasis and response to treatment. We report the development of TIMEx, a novel TIME deconvolution method emphasizing on estimating infiltrating immune cells for bulk transcriptomics using pan-cancer single-cell RNA-seq signatures. We also implemented a comprehensive, user-friendly web-portal for users to evaluate TIMEx and other deconvolution methods with bulk transcriptomic profiles. AVAILABILITY AND IMPLEMENTATION: TIMEx web-portal is freely accessible at http://timex.moffitt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Self-assembled miRNA-switch nanoparticles target denuded regions and prevent restenosis.

Cardiovascular disease is the leading cause of death and disability worldwide. Effective delivery of cell-selective therapies that target atherosclerotic plaques and neointimal growth while sparing the endothelium remains the Achilles heel of percutaneous interventions. The current study utilizes synthetic microRNA switch therapy that self-assembles to form a compacted, nuclease-resistant nanoparticle <200 nM in size when mixed with cationic amphipathic cell-penetrating peptide (p5RHH). These nanoparticles possess intrinsic endosomolytic activity that requires endosomal acidification. When administered in a femoral artery wire injury mouse model in vivo, the mRNA-p5RHH nanoparticles deliver their payload specifically to the regions of endothelial denudation and not to the lungs, liver, kidney, or spleen. Moreover, repeated administration of nanoparticles containing a microRNA switch, consisting of synthetically modified mRNA encoding for the cyclin-dependent kinase inhibitor p27Kip1 that contains one complementary target sequence of the endothelial cell-specific miR-126 at its 5' UTR, drastically reduced neointima formation after wire injury and allowed for vessel reendothelialization. This cell-selective nanotherapy is a valuable tool that has the potential to advance the fight against neointimal hyperplasia and atherosclerosis.

Anticipating designer drug-resistant cancer cells.

Successful use of anticancer designer drugs is likely to depend on simultaneous combinations of these drugs to minimize the development of resistant cancer cells. Considering the knowledge base of cancer signaling pathways, mechanisms of designer drug resistance should be anticipated, and early clinical trials could be designed to include arms that combine new drugs specifically with currently US Food and Drug Administration (FDA)-approved drugs expected to blunt alternative signaling pathways. In this review, we indicate examples of alternative signal pathways for recent anticancer drugs, and the use of original, Python-based software to systematically identify signaling pathways that could facilitate resistance to drugs targeting a particular protein. Pathway alternatives can be assessed at http://www.alternativesignalingpathways.com, developed with this review article.