Slit2/Robo1 signaling inhibits small-cell lung cancer by targeting ß-catenin signaling in tumor cells and macrophages.

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with poor patient prognosis. However, the mechanisms that regulate SCLC progression and metastasis remain undefined. Here, we show that the expression of the slit guidance ligand 2 (SLIT2) tumor suppressor gene is reduced in SCLC tumors relative to adjacent normal tissue. In addition, the expression of the SLIT2 receptor, roundabout guidance receptor 1 (ROBO1), is upregulated. We find a positive association between SLIT2 expression and the Yes1 associated transcriptional regulator (YAP1)-expressing SCLC subtype (SCLC-Y), which shows a better prognosis. Using genetically engineered SCLC cells, adenovirus gene therapy, and preclinical xenograft models, we show that SLIT2 overexpression or the deletion of ROBO1 restricts tumor growth in vitro and in vivo. Mechanistic studies revealed significant inhibition of myeloid-derived suppressor cells (MDSCs) and M2-like tumor-associated macrophages (TAMs) in the SCLC tumors. In addition, SLIT2 enhances M1-like and phagocytic macrophages. Molecular analysis showed that ROBO1 knockout or SLIT2 overexpression suppresses the transforming growth factor beta 1 (TGF-ß1)/ß-catenin signaling pathway in both tumor cells and macrophages. Overall, we find that SLIT2 and ROBO1 have contrasting effects on SCLC tumors. SLIT2 suppresses, whereas ROBO1 promotes, SCLC growth by regulating the Tgf-ß1/glycogen synthase kinase-3 beta (GSK3)/ß-catenin signaling pathway in tumor cells and TAMs. These studies indicate that SLIT2 could be used as a novel therapeutic agent against aggressive SCLC.

XDeathDB: a visualization platform for cell death molecular interactions.

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

BACKGROUND: RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment. RESULTS: Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/. CONCLUSION: The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

Lung cancer stem cells, p53 mutations and MDM2.

Over the past few decades, advances in cancer research have enabled us to understand the different mechanisms that contribute to the aberrant proliferation of normal cells into abnormal cells that result in tumors. In the pursuit to find cures, researchers have primarily focused on various molecular level changes that are unique to cancerous cells. In humans, about 50 % or more cancers have a mutated tumor suppressor p53 gene thereby resulting in accumulation of p53 protein and losing its function to activate the target genes that regulate cell cycle and apoptosis. Extensive research conducted in murine cancer models with activated p53, loss of p53, or p53 missense mutations have facilitated researchers to understand the role of this key protein. Despite the identification of numerous triggers that causes lung cancer specific cure still remain elusive. One of the primary reasons attributed to this is due to the fact that the tumor tissue is heterogeneous and contains numerous sub-populations of cells. Studies have shown that a specific sub-population of cells termed as cancer stem cells (CSCs) drive the recurrence of cancer in response to standard chemotherapy. These CSCs are mutated cells with core properties similar to those of adult stem cells. They reside in a microenvironment within the tumor tissue that supports their growth and make them less susceptible to drug treatment. These cells possess properties of symmetric self-renewal and migration thus driving tumor formation and metastasis. Therefore, research specifically targeting these cells has gained prominence towards developing new therapeutic agents against cancer. This chapter focuses on lung cancer stem cells, p53 mutations noted in these cells, and importance of MDM2 interactions. Further, research approaches for better understanding of molecular mechanisms that drive CSC function and developing appropriate therapies are discussed.