Schwann cell reprogramming and lung cancer progression: a meta-analysis of transcriptome data

Schwann cells in the normal and pathological lung microenvironment

The lungs are a key organ in the respiratory system. They are regulated by a complex network of nerves that control their development, structure, function, and response to various pathological stimuli. Accumulating evidence suggests the involvement of a neural mechanism in different pathophysiological conditions in the lungs and the development and progression of common respiratory diseases. Lung diseases are the chief source of death globally. For instance, lung cancer is the second most commonly diagnosed malignancy, after prostate cancer in men and breast cancer in women, and is the most lethal cancer worldwide. However, although airway nerves are accepted as a mechanistically and therapeutically important feature that demands appropriate emphasizing in the context of many respiratory diseases, significantly less is known about the role of the neuroglial cells in lung physiology and pathophysiology, including lung cancer. New data have uncovered some cellular and molecular mechanisms of how Schwann cells, as fundamental components of the peripheral nervous system, may regulate lung cancer cells' survival, spreading, and invasiveness in vitro and in vivo. Schwann cells control the formation and maintenance of the lung cancer microenvironment and support metastasis formation. It was also reported that the number of lung cancer-associated Schwann cells correlates with patients' survival. Different factors secreted by Schwann cells, including microRNA, are known to sharpen the lung cancer environment by regulating the tumor-neuro-immune axis. Further clinical and experimental studies are required to elucidate the detailed role of Schwann cells in creating and maintaining pulmonary tumor-neuro-immune axis, which will advance our understanding of the pathogenesis of lung cancer and may inform therapeutic hypotheses aiming neoplasms and metastases in the lung.

Establishment of a Machine Learning Model for the Risk Assessment of Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.

Melanoma-associated repair-like Schwann cells suppress anti-tumor T-cells via 12/15-LOX/COX2-associated eicosanoid production

Peripheral glia, specifically the Schwann cells (SCs), have been implicated in the formation of the tumor microenvironment (TME) and in cancer progression. However, in vivo and ex vivo analyses of how cancers reprogram SC functions in different organs of tumor-bearing mice are lacking. We generated Plp1-CreERT/tdTomato mice which harbor fluorescently labeled myelinated and non-myelin forming SCs. We show that this model enables the isolation of the SCs with high purity from the skin and multiple other organs. We used this model to study phenotypic and functional reprogramming of the SCs in the skin adjacent to melanoma tumors. Transcriptomic analyses of the peritumoral skin SCs versus skin SCs from tumor-free mice revealed that the former existed in a repair-like state typically activated during nerve and tissue injury. Peritumoral skin SCs also downregulated pro-inflammatory genes and pathways related to protective anti-tumor responses. In vivo and ex vivo functional assays confirmed immunosuppressive activities of the peritumoral skin SCs. Specifically, melanoma-reprogrammed SCs upregulated 12/15-lipoxygenase (12/15-LOX) and cyclooxygenase (COX)-2, and increased production of anti-inflammatory polyunsaturated fatty acid (PUFA) metabolites prostaglandin E2 (PGE2) and lipoxins A4/B4. Inhibition of 12/15-LOX or COX2 in SCs, or EP4 receptor on lymphocytes reversed SC-dependent suppression of anti-tumor T-cell activation. Therefore, SCs within the skin adjacent to melanoma tumors demonstrate functional switching to repair-like immunosuppressive cells with dysregulated lipid oxidation. Our study suggests the involvement of the melanoma-associated repair-like peritumoral SCs in the modulation of locoregional and systemic anti-tumor immune responses.

A Novel Therapeutic Target for Small-Cell Lung Cancer: Tumor-Associated Repair-like Schwann Cells

Small-cell lung cancer (SCLC), representing 15-20% of all lung cancers, is an aggressive malignancy with a distinct natural history, poor prognosis, and limited treatment options. We have previously identified Schwann cells (SCs), the main glial cells of the peripheral nervous system, in tumor tissues and demonstrated that they may support tumor spreading and metastasis formation in the in vitro and in vivo models. However, the role of SCs in the progression of SCLC has not been investigated. To clarify this issue, the cell proliferation assay, the annexin V apoptosis assay, and the transwell migration and invasion assay were conducted to elucidate the roles in SCLC of tumor-associated SCs (TA-SCs) in the proliferation, apoptosis, migration, and invasion of SCLC cells in vitro, compared to control group. In addition, the animal models to assess SC action's effects on SCLC in vivo were also developed. The result confirmed that TA-SCs have a well-established and significant role in facilitating SCLC cell cancer migration and invasion of SCLC in vitro, and we also observed that SC promotes tumor growth of SCLC in vivo and that TA-SCs exhibited an advantage and show a repair-like phenotype, which allowed defining them as tumor-associated repair SCs (TAR-SCs). Potential molecular mechanisms of pro-tumorigenic activity of TAR-SCs were investigated by the screening of differentially expressed genes and constructing networks of messenger-, micro-, and long- non-coding RNA (mRNA-miRNA-lncRNA) using DMS114 cells, a human SCLC, stimulated with media from DMS114-activated SCs, non-stimulated SCs, and appropriate controls. This study improves our understanding of how SCs, especially tumor-activated SCs, may promote SCLC progression. Our results highlight a new functional phenotype of SCs in cancer and bring new insights into the characterization of the nervous system-tumor crosstalk.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Melatonin signalling in Schwann cells during neuroregeneration

It has widely been thought that in the process of nerve regeneration Schwann cells populate the injury site with myelinating, non–myelinating, phagocytic, repair, and mesenchyme–like phenotypes. It is now clear that the Schwann cells modify their shape and basal lamina as to accommodate re–growing axons, at the same time clear myelin debris generated upon injury, and regulate expression of extracellular matrix proteins at and around the lesion site. Such a remarkable plasticity may follow an intrinsic functional rhythm or a systemic circadian clock matching the demands of accurate timing and precision of signalling cascades in the regenerating nervous system. Schwann cells react to changes in the external circadian clock clues and to the Zeitgeber hormone melatonin by altering their plasticity. This raises the question of whether melatonin regulates Schwann cell activity during neurorepair and if circadian control and rhythmicity of Schwann cell functions are vital aspects of neuroregeneration. Here, we have focused on different schools of thought and emerging concepts of melatonin–mediated signalling in Schwann cells underlying peripheral nerve regeneration and discuss circadian rhythmicity as a possible component of neurorepair.

Reprogrammed Schwann Cells Organize into Dynamic Tracks that Promote Pancreatic Cancer Invasion

Nerves are a component of the tumor microenvironment contributing to cancer progression, but the role of cells from nerves in facilitating cancer invasion remains poorly understood. Here we show that Schwann cells (SC) activated by cancer cells collectively function as tumor-activated Schwann cell tracks (TAST) that promote cancer cell migration and invasion. Nonmyelinating SCs form TASTs and have cell gene expression signatures that correlate with diminished survival in patients with pancreatic ductal adenocarcinoma. In TASTs, dynamic SCs form tracks that serve as cancer pathways and apply forces on cancer cells to enhance cancer motility. These SCs are activated by c-Jun, analogous to their reprogramming during nerve repair. This study reveals a mechanism of cancer cell invasion that co-opts a wound repair process and exploits the ability of SCs to collectively organize into tracks. These findings establish a novel paradigm of how cancer cells spread and reveal therapeutic opportunities.

SIGNIFICANCE

How the tumor microenvironment participates in pancreatic cancer progression is not fully understood. Here, we show that SCs are activated by cancer cells and collectively organize into tracks that dynamically enable cancer invasion in a c-Jun-dependent manner. See related commentary by Amit and Maitra, p. 2240. This article is highlighted in the In This Issue feature, p. 2221.

The Role of Schwann Cells in Cancer

Schwann cells (SCs) are the most abundant cell type in the nerves in the peripheral nervous system and compose a family of subtypes that are endowed with a variety of different functions. SCs facilitate the transmission of neural impulses, provide nutrients and protection for neurons, guide axons in nerve repair, and regulate immune functions. In the context of cancer, recent studies have revealed an active role of SCs in promoting cancer cell invasion, modulating immune responses, and transmitting pain sensation.

Schwann Cells in the Tumor Microenvironment: Need More Attention

The tumor microenvironment (TME), which is composed of various cell components and signaling molecules, plays an important role in the occurrence and progression of tumors and has become the central issue of current cancer research. In recent years, as a part of the TME, the peripheral nervous system (PNS) has attracted increasing attention. Moreover, emerging evidence shows that Schwann cells (SCs), which are the most important glial cells in the PNS, are not simply spectators in the TME. In this review article, we focused on the up-to-date research progress on SCs in the TME and introduced our point of view. In detail, we described that under two main tumor-nerve interaction patterns, perineural invasion (PNI) and tumor innervation, SCs were reprogrammed and acted as important participants. We also investigated the newest mechanisms between the interactions of SCs and tumor cells. In addition, SCs can have profound impacts on other cellular components in the TME, such as immune cells and cancer-associated fibroblasts (CAFs), involving immune regulation, tumor-related pain, and nerve remodeling. Overall, these innovative statements can expand the scope of the TME, help fully understand the significant role of SCs in the tumor-nerve-immune axis, and propose enlightenments to innovate antitumor therapeutic methods and future research.

Integrating Multi–Omics Data for Gene-Environment Interactions

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression

Carcinoma diagnosis and prognosis are still hindered by the lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method's robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic least absolute shrinkage and selection operator (LASSO) regression method [the averaged 1- to 4-years predictive area under the receiver operating characteristic curve (AUC) measure, 0.6998], the proposed one outperforms more accurately by 0.7208 with fewer predictors, particularly its averaged 1- to 3-years AUC reaches 0.723, vs. classic 0.6917 on The Cancer Genome Atlas (TCGA). In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results, are available at https://github.com/gladex/PM-FSOR.