A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk

Cell communication pathway prognostic model identified detrimental neurodevelopmental pathways in neuroblastoma

Neurodevelopmental cell communication plays a crucial role in neuroblastoma prognosis. However, determining the impact of these communication pathways on prognosis is challenging due to limited sample sizes and patchy clinical survival information of single cell RNA-seq data. To address this, we have developed the cell communication pathway prognostic model (CCPPM) in this study. CCPPM involves the identification of communication pathways through single-cell RNA-seq data, screening of prognosis-significant pathways using bulk RNA-seq data, conducting functional and attribute analysis of these pathways, and analyzing the post-effects of communication within these pathways. By employing the CCPPM, we have identified ten communication pathways significantly influencing neuroblastoma, all related to axongenesis and neural projection development, especially the BMP7-(BMPR1B-ACVR2B) communication pathway was found to promote tumor cell migration by activating the transcription factor SMAD1 and regulating UNK and MYCBP2. Notably, BMP7 expression was higher in neuroblastoma samples with distant metastases. In summary, CCPPM offers a novel approach to studying the influence of cell communication pathways on disease prognosis and identified detrimental communication pathways related to neurodevelopment.

Single-Cell Analysis Identifies NOTCH3-Mediated Interactions between Stromal Cells That Promote Microenvironment Remodeling and Invasion in Lung Adenocarcinoma

Cancer immunotherapy has revolutionized the treatment of lung adenocarcinoma (LUAD); however, a significant proportion of patients do not respond. Recent transcriptomic studies to understand determinants of immunotherapy response have pinpointed stromal-mediated resistance mechanisms. To gain a better understanding of stromal biology at the cellular and molecular level in LUAD, we performed single-cell RNA sequencing of 256,379 cells, including 13,857 mesenchymal cells, from 9 treatment-naïve patients. Among the mesenchymal cell subsets, FAP+PDPN+ cancer-associated fibroblasts (CAF) and ACTA2+MCAM+ pericytes were enriched in tumors and differentiated from lung-resident fibroblasts. Imaging mass cytometry revealed that both subsets were topographically adjacent to the perivascular niche and had close spatial interactions with endothelial cells (EC). Modeling of ligand and receptor interactomes between mesenchymal and ECs identified that NOTCH signaling drives these cell-to-cell interactions in tumors, with pericytes and CAFs as the signal receivers and arterial and PLVAPhigh immature neovascular ECs as the signal senders. Either pharmacologically blocking NOTCH signaling or genetically depleting NOTCH3 levels in mesenchymal cells significantly reduced collagen production and suppressed cell invasion. Bulk RNA sequencing data demonstrated that NOTCH3 expression correlated with poor survival in stroma-rich patients and that a T cell-inflamed gene signature only predicted survival in patients with low NOTCH3. Collectively, this study provides valuable insights into the role of NOTCH3 in regulating tumor stroma biology, warranting further studies to elucidate the clinical implications of targeting NOTCH3 signaling.

SIGNIFICANCE

NOTCH3 signaling activates tumor-associated mesenchymal cells, increases collagen production, and augments cell invasion in lung adenocarcinoma, suggesting its critical role in remodeling tumor stroma.

High-resolution transcriptomics analysis of CXCL13+ EPSTI1+ CDK1+ cells with a specific focus on lung adenocarcinoma

Background

Programmed cell death ligand 1 (PD-L1) blocking therapy has transformed the treatment of lung adenocarcinoma (LUAD), which has significantly changed the landscape of immunotherapy. We aimed to explore specific cell subpopulations to understand tumor progression and identify markers of response to PD-L1 blocking therapy.

Methods

Bulk, fluorescence-activated cell sorting (FACS), and single-cell RNA (scRNA) sequencing were used to profile CXCL13, EPSTI1, and CDK1. The gene set variation analysis (GSVA) R package was utilized for score calculation, and prognostic analyses included receiver operating characteristic (ROC) curves, Cox proportional hazard models, and meta-analysis. Additionally, we analyzed tumor microenvironment (TME), genomics, compound perturbations, and clinical indicators. The high-dimensional analysis captured the intrinsic characteristics of the subpopulation. Furthermore, subpopulation differential genes were used for enrichment analysis of transcription factors and compounds.

Results

Literature and website analyses supported the essential role of CXCL13, CDK1, and EPSTI1 in immunotherapy. This led us to focus specifically on LUAD by representing a pan-cancer profile of immune-sensitive genes. Logically, the high-characteristic population may consist of samples positive for CXCL13, EPSTI1, and CDK1. The three-gene signature was a favorable indicator of immunotherapy response in the Stand Up to Cancer-Mark Foundation (SU2C-MARK) LUAD cohort but showed a poor prognosis before treatment in the Lung Cancer Explorer (LCE) database. Further mechanistic exploration revealed specific mutations associated with the three-gene signature in SU2C-MARK LUAD, such as STK11. In The Cancer Genome Atlas (TCGA)-LUAD cohort, the high-scoring group exhibited a higher tumor mutational burden (TMB) and global methylation but a lower fraction genome altered (FGA) and estimated tumor purity. Moreover, dasatinib demonstrated sensitivity in the high-scoring group. The co-localization of the CXCL13, EPSTI1, and CDK1 subpopulation was validated through spatial transcriptome and immunohistochemical databases. Assessment of the subpopulation depicted high-resolution intercellular communication. Maintenance of specific pathways, such as TNF, CD74, and CD44, contributed to immunotherapy sensitivity. Finally, the subpopulation-enriched targets and drugs were confirmed through ConnectivityMap (CMAP) analysis and multi-omics, respectively.

Conclusions

In this study, positive samples for CXCL13, EPSTI1, and CDK1 exhibited poor prognostic significance in treatment-naïve LUAD cases but demonstrated benefits from PD-L1 blockade and dasatinib therapies.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

Microenvironmental regulation in tumor progression: Interactions between cancer-associated fibroblasts and immune cells

The tumor microenvironment (TME), the "soil" on which tumor cells grow, has an important role in regulating the proliferation and metastasis of tumor cells as well as their response to treatment. Cancer-associated fibroblasts (CAFs), as the most abundant stromal cells of the TME, can not only directly alter the immunosuppressive effect of the TME through their own metabolism, but also influence the aggregation and function of immune cells by secreting a large number of cytokines and chemokines, reducing the body's immune surveillance of tumor cells and making them more prone to immune escape. Our study provides a comprehensive review of fibroblast chemotaxis, malignant transformation, metabolic characteristics, and interactions with immune cells. In addition, the current small molecule drugs targeting CAFs have been summarized, including both natural small molecules and targeted drugs for current clinical therapeutic applications. A complete review of the role of fibroblasts in TME from an immune perspective is presented, which has important implications in improving the efficiency of immunotherapy by targeting fibroblasts.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

MicroRNAs in Tumor Endothelial Cells: Regulation, Function and Therapeutic Applications

Tumor endothelial cells (TECs) are key stromal components of the tumor microenvironment, and are essential for tumor angiogenesis, growth and metastasis. Accumulating evidence has shown that small single-stranded non-coding microRNAs (miRNAs) act as powerful endogenous regulators of TEC function and blood vessel formation. This systematic review provides an up-to-date overview of these endothelial miRNAs. Their expression is mainly regulated by hypoxia, pro-angiogenic factors, gap junctions and extracellular vesicles, as well as long non-coding RNAs and circular RNAs. In preclinical studies, they have been shown to modulate diverse fundamental angiogenesis-related signaling pathways and proteins, including the vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) pathway; the rat sarcoma virus (Ras)/rapidly accelerated fibrosarcoma (Raf)/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) pathway; the phosphoinositide 3-kinase (PI3K)/AKT pathway; and the transforming growth factor (TGF)-β/TGF-β receptor (TGFBR) pathway, as well as krüppel-like factors (KLFs), suppressor of cytokine signaling (SOCS) and metalloproteinases (MMPs). Accordingly, endothelial miRNAs represent promising targets for future anti-angiogenic cancer therapy. To achieve this, it will be necessary to further unravel the regulatory and functional networks of endothelial miRNAs and to develop safe and efficient TEC-specific miRNA delivery technologies.

Thymidine phosphorylase facilitates retinoic acid inducible gene-I induced endothelial dysfunction

Activation of nucleic acid sensors in endothelial cells (ECs) has been shown to drive inflammation across pathologies including cancer, atherosclerosis and obesity. We previously showed that enhancing cytosolic DNA sensing by inhibiting three prime exonuclease 1 (TREX1) in ECs led to EC dysfunction and impaired angiogenesis. Here we show that activation of a cytosolic RNA sensor, Retinoic acid Induced Gene 1 (RIG-I) diminishes EC survival, angiogenesis and triggers tissue specific gene expression programs. We discovered a RIG-I dependent 7 gene signature that affects angiogenesis, inflammation and coagulation. Among these, we identified the thymidine phosphorylase TYMP as a key mediator of RIG-I induced EC dysfunction via its regulation of a subset of interferon stimulated genes. Our RIG-I induced gene signature was also conserved in the context of human diseases - in lung cancer vasculature and herpesvirus infection of lung endothelial cells. Pharmacological or genetic inhibition of TYMP rescues RIG-I induced EC death, migration arrest and restores sprouting angiogenesis. Interestingly, using RNAseq we identified a gene expression program that was RIG-I induced but TYMP dependent. Analysis of this dataset indicated that IRF1 and IRF8 dependent transcription is diminished in RIG-I activated cells when TYMP is inhibited. Functional RNAi screen of our TYMP dependent EC genes, we found that a group of 5 genes - Flot1, Ccl5, Vars2, Samd9l and Ube2l6 are critical for endothelial cell death mediated by RIG-I activation. Our observations identify mechanisms by which RIG-I drives EC dysfunction and define pathways that can be pharmacologically targeted to ameliorate RIG-I induced vascular inflammation.

Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer

Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Gastric cancer (GC) ranks third for cancer-related fatalities worldwide. It is still unclear what causes GC to progress. Using integrated bioinformatics analysis, COL5A2 has been proved to be related to GC development, which may identify the likely pathogenic mechanism. Data from GC patients were gathered using The Cancer Gene Atlas (TCGA) and the gene expression omnibus (GEO). The level of COL5A2 expression was compared between paired GC and normal tissues. The differentially expressed genes (DEGs) in GC patients with high and low COL5A2 expression were identified using functional enrichment analysis to identify the signature pathways linked to the DEGs. The clinical pathologic traits connected to overall survival (OS) of GC patients were examined utilizing Cox regression and the Kaplan-Meier method. To assess the prognostic significance of COL5A2, receiver operating characteristic (ROC) curves was drawn. How the immune system infiltrate both normal gastric and GC tumor tissues was investigated. Using the human protein atlas (HPA) database, regression, and the Kaplan-Meier method, immunohistochemical analysis of DEG COL5A2 expression in GC tissues was carried out. The correlation between COL5A2 expression and the GC grouping was found to be highly significant. Functional annotations revealed that COL5A2 participates in extracellular matrix structure, collagen metabolism, and other biological processes (BPs). High COL5A2 expression was associated with poor prognostic and clinical features, such as clinical T, N, and M stages. ROC curves exhibited that COL5A2 might predict the occurrence of gastric cancer. The infiltration degree of 21 immune cell subsets, including activated dendritic cells (aDCs), CD8+ T cells, and cytotoxic cells, was found to be dramatically relevant to COL5A2. Immunohistochemical analysis indicated that the expression of COL5A2 in tumor tissues is higher than that in normal tissues. The COL5A2 gene may offer fresh perspectives on the pathogenic mechanism underlying GC, as well as potential biomarkers for estimating GC patient prognosis. As a result, COL5A2 may be a useful biomarker for predicting patient survival.

Marsdenia tenacissima extract disturbs the interaction between tumor-associated macrophages and non-small cell lung cancer cells by targeting HDGF

ETHNOPHARMACOLOGICAL RELEVANCE

Marsdenia tenacissima (Roxb.) Wight et Arn. is a traditional Chinese herbal medicine, and its water-soluble ingredient Marsdenia tenacissima extract (MTE), was widely used for cancer treatment. The multi-pharmacological efficacies and mechanisms of MTE in directly inhibiting tumor cells have been extensively studied. However, the anti-tumor effects of MTE in the tumor-associated macrophages (TAMs) microenvironment remain unclear.

AIM OF THE STUDY

To uncover the role of hepatoma-derived growth factor (HDGF) in the interaction between TAMs and non-small cell lung cancer (NSCLC) cells. To evaluate the anti-tumor effects of MTE on the vicious crosstalk between TAMs and NSCLC by targeting HDGF.

MATERIALS AND METHODS

HDGF-overexpression PC-9 and H292 NSCLC cell lines were constructed and verified. RNA-sequencing (RNA-seq) was performed in HDGF-overexpression PC-9 cells to probe the differential expression of genes. THP-1-derived macrophages were characterized using specific markers after stimulation with phorbol-12-myristate 13-acetate (PMA) and rhIL-4 or rhHDGF. The role of HDGF both in NSCLC cells and TAMs was determined using approaches like Western blot, qRT-PCR, ELISA, and flow cytometry. The interaction between tumor cells and TAMs were assessed by indirect co-culture H1975, PC-9 cells with M2 type macrophages. The effects of MTE on anti-tumor and macrophage polarization were evaluated in vitro and in vivo.

RESULTS

RNA-seq results identified IL-4 as a critical response to HDGF in NSCLC. HDGF induced macrophages polarizing toward M2 type, and promoted NSCLC cells proliferation, migration and invasion in vitro. On the one hand, HDGF dose-dependently promoted IL-4 expression in NSCLC cells. On the other hand, HDGF induced M2 macrophage polarization through the IL-4/JAK1/STAT3 signaling pathway. MTE treatment significantly decreased the expression and secretion of HDGF in NSCLC cells. Meanwhile, MTE treatment led to M2 macrophage repolarization, as evidenced by decreased expression of M2 markers and increased levels of M1 markers. Importantly, MTE treatment significantly suppressed tumor development in C57BL/6 mice bearing Lewis lung cancer (LLC) cells in vivo, accompanied by decreased plasma HDGF levels, reduced M2 macrophages infiltration and increased M1 macrophages proportion in mice tumor tissues.

CONCLUSIONS

HDGF upregulated IL-4 expression in NSCLC cells, and promoted M2 polarization by the IL-4/JAK1/STAT3 signaling pathway in macrophages. MTE disturbed the interaction between NSCLC and TAMs in vitro, and inhibited tumor growth in vivo, at least in part, by suppressing HDGF. Therefore, our present study revealed a novel anti-tumor mechanism of MTE through inhibiting HDGF expression and enhancing macrophage polarization from M2 to M1 phenotype.

Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

LMNA Reduced Acquired Resistance to Erlotinib in NSCLC by Reversing the Epithelial-Mesenchymal Transition via the FGFR/MAPK/c-fos Signaling Pathway

For patients exhibiting non-small-cell lung cancer (NSCLC) with activating epidermal growth factor receptor (EGFR) mutations, epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are a first-line treatment. However, most patients who initially responded to EGFR-TKIs eventually developed acquired resistance, limiting the effectiveness of therapy. It has long been known that epithelial-mesenchymal transition (EMT) leads to acquired resistance to EGFR-TKIs in NSCLC. However, the mechanisms underlying the resistance dependent on EMT are unknown. This research aimed to reveal the effects of LMNA in the regulation of acquired resistance to erlotinib by EMT in NSCLC. The acquired erlotinib-resistant cells (HCC827/ER) were induced by gradual increase of concentrations of erlotinib in erlotinib-sensitive HCC827 cells. RNA sequencing and bioinformatics analysis were performed to uncover the involvement of LMNA in the EMT process that induced acquired resistance to erlotinib. The effect of LMNA on cell proliferation and migration was measured by clone-formation, wound-healing, and transwell assays, respectively. The EMT-related protein, nuclear shape and volume, and cytoskeleton changes were examined by immunofluorescence. Western blot was used to identify the underlying molecular mechanism of LMNA regulation of EMT. HCC827/ER cells with acquired resistance to erlotinib underwent EMT and exhibited lower LMNA expression compared to parental sensitive cells. LMNA negatively regulated the expression of EMT markers; HCC827/ER cells showed a significant up-regulation of mesenchymal markers, such as CDH2, SNAI2, VIM, ZEB1, and TWIST1. The overexpression of LMNA in HCC827/ER cells significantly inhibited EMT and cell proliferation, and this inhibitory effect of LMNA was enhanced in the presence of 2.5 μM erlotinib. Furthermore, a decrease in LMNA expression resulted in a higher nuclear deformability and cytoskeletal changes. In HCC827/ER cells, AKT, FGFR, ERK1/2, and c-fos phosphorylation levels were higher than those in HCC827 cells; Furthermore, overexpression of LMNA in HCC827/ER cells reduced the phosphorylation of AKT, ERK1/2, c-fos, and FGFR. In conclusion, our findings first demonstrated that downregulation of LMNA promotes acquired EGFR-TKI resistance in NSCLC with EGFR mutations by EMT. LMNA inhibits cell proliferation and migration of erlotinib-resistant cells via inhibition of the FGFR/MAPK/c-fos signaling pathway. These findings indicated LMNA as a driver of acquired resistance to erlotinib and provided important information about the development of resistance to erlotinib treatment in NSCLC patients with EGFR mutations.

Adenocarcinomas of the Lung and Neurotrophin System: A Review

Neurotrophins (NTs) represent a group of growth factors with pleiotropic activities at the central nervous system level. The prototype of these molecules is represented by the nerve growth factor (NGF), but other factors with similar functions have been identified, including the brain derived-growth factor (BDNF), the neurotrophin 3 (NT-3), and NT-4/5. These growth factors act by binding specific low (p75) and high-affinity tyrosine kinase (TrkA, TrkB, and TrkC) receptors. More recently, these growth factors have shown effects outside the nervous system in different organs, particularly in the lungs. These molecules are involved in the natural development of the lungs, and their homeostasis. However, they are also important in different pathological conditions, including lung cancer. The involvement of neurotrophins in lung cancer has been detailed most for non-small cell lung cancer (NSCLC), in particular adenocarcinoma. This review aimed to extensively analyze the current knowledge of NTs and lung cancer and clarify novel molecular mechanisms for diagnostic and therapeutic purposes. Several clinical trials on humans are ongoing using NT receptor antagonists in different cancer cell types for further therapeutic applications. The pharmacological intervention against NT signaling may be essential to directly counteract cancer cell biology, and also indirectly modulate it in an inhibitory way by affecting neurogenesis and/or angiogenesis with potential impacts on tumor growth and progression.

GREM1 is a novel serum diagnostic marker and potential therapeutic target for pancreatic ductal adenocarcinoma

Objective

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm with rising incidence worldwide. Gremlin 1 (GREM1), a regulator of bone morphogenetic protein (BMP) signaling, fine-tunes extensive biological processes, including organ morphology, cellular metabolism, and multiple pathological developments. The roles of GREM1 in PDAC remain unknown.

Methods

Varieties of public databases and online software were employed to analyze the expressions at transcription and protein levels of GREM1 in multiple malignant neoplasms including PDAC, and in addition, its potential pro-tumoral functions in PDAC were further evaluated. A total of 340 serum samples of pancreatic disease, including PDAC, low-grade malignant pancreatic neoplasm, benign pancreatic neoplasm, pancreatitis, and 132 healthy controls, were collected to detect GREM1. The roles of serum GREM1 in the diagnosis and prediction of survival of PDAC after radical resection were also analyzed.

Results

Bioinformatics analyses revealed that GREM1 was overexpressed in PDAC and predicted a poorer survival in PDAC. A higher protein level of GREM1 in PDAC correlated with stroma formation and immunosuppression by recruiting varieties of immunosuppressive cells, including T regulatory cells (Tregs), M2 macrophages, myeloid-derived suppressor cells (MDSCs), and exhaustion T cells into the tumor microenvironment. A higher level of serum GREM1 was observed in PDAC patients, compared to healthy control (p < 0.001). Serum GREM1 had a good diagnostic value (area under the curve (AUC) = 0.718, p < 0.001), and its combination with carbohydrate antigen 199 (CA199) achieved a better diagnostic efficacy (AUC = 0.914, p < 0.001), compared to CA199 alone. The cutoff value was calculated by receiver operating characteristic (ROC) analysis, and PDAC patients were divided into two groups of low and high GREM1. Logistic analyses showed serum GREM1 positively correlated with tumor size (hazard ratio (HR) = 7.097, p = 0.032) and histopathological grades (HR = 2.898, p = 0.014). High-level serum GREM1 (1,117.8 pg/ml) showed a shorter postoperative survival (p = 0.0394).

Conclusion

Higher intra-tumoral expression of GREM1 in PDAC contributes to tumor stroma and immunosuppressive tumor microenvironment, presenting its therapeutic potential. High-level serum GREM1 predicts poorer survival after resection. A combination of serum CA199 and GREM1 shows a stronger diagnostic efficacy in PDAC.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti-PD-1 Therapy

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type-specific gene expression in each sample from bulk expression, and LIRICS (Ligand-Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand-receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type-specific ligand-receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti-programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type-specific ligand-receptor interactions in the melanoma TME that stratify survival of patients receiving anti-PD-1 therapy better than some recently published bulk transcriptomics-based methods.

SIGNIFICANCE

This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type-specific gene expression profiles and identify cell type-specific ligand-receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873.

Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI

Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset, then leverage single-cell transcriptomics data to increase the cell type resolution and identify LR prognostic signatures among tumor-stroma-immune subpopulations. We experimentally confirmed colocalization of CTGF:LRP6 among malignant cell subtypes as an interaction predicted to be associated with LUAD progression. Our work presents a computational approach to reconstruct interactomes and identify clinically relevant CCIs.

Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

Objective

The treatment plans designed with the guidance of the mathematical model and adaptive strategy can trap tumor subpopulations in a periodic and controllable loop. But this process requires detailed information about the tumor system, which is difficult to obtain. Therefore, we wondered whether the fixed periodic treatment plans designed with the typical values of population parameters could be applied to a similar tumor system without complete information.

Methods

A binary tumor system constructed by an EGFR-mutant and a KRAS-mutant cell line was used to explore the applicability of the fixed periodic treatment plans. The dynamics of this system were described by combining the Lotka-Volterra model with the framework of the nonlinear mixed-effects model. The typical values of population parameters were used to design the plans, and the robust plans were screened through parameter variation. These screened plans were examined their applicability in animal experiments and simulations.

Results

In animal experiments where system parameters vary from -30% to 30%, the "osimertinib administration, withdrawal, FK866 administration and withdrawal" plan can trap subpopulations of the system in periodic cycles. In simulation, when there was an unknown resistant subpopulation, the screened fixed periodic treatment plans can still delay the evolution of resistance. The median outcomes of screened plans were better than conventional sequential treatment in most cases. There was no significant difference between the outcomes of the screened plan with median stability and the optimal therapy. The evolutionary trajectories of these two plans were similar.

Conclusion

According to the results, these fixed periodic plans should be tried in treatment even the information of the tumor system was incomplete.

Atlas of clinically distinct cell states and ecosystems across human solid tumors

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives

Cancer-associated fibroblasts (CAFs), a stromal cell population with cell-of-origin, phenotypic and functional heterogeneity, are the most essential components of the tumor microenvironment (TME). Through multiple pathways, activated CAFs can promote tumor growth, angiogenesis, invasion and metastasis, along with extracellular matrix (ECM) remodeling and even chemoresistance. Numerous previous studies have confirmed the critical role of the interaction between CAFs and tumor cells in tumorigenesis and development. However, recently, the mutual effects of CAFs and the tumor immune microenvironment (TIME) have been identified as another key factor in promoting tumor progression. The TIME mainly consists of distinct immune cell populations in tumor islets and is highly associated with the antitumor immunological state in the TME. CAFs interact with tumor-infiltrating immune cells as well as other immune components within the TIME via the secretion of various cytokines, growth factors, chemokines, exosomes and other effector molecules, consequently shaping an immunosuppressive TME that enables cancer cells to evade surveillance of the immune system. In-depth studies of CAFs and immune microenvironment interactions, particularly the complicated mechanisms connecting CAFs with immune cells, might provide novel strategies for subsequent targeted immunotherapies. Herein, we shed light on recent advances regarding the direct and indirect crosstalk between CAFs and infiltrating immune cells and further summarize the possible immunoinhibitory mechanisms induced by CAFs in the TME. In addition, we present current related CAF-targeting immunotherapies and briefly describe some future perspectives on CAF research in the end.

A Signaling View into the Inflammatory Tumor Microenvironment

The development of tumors requires an initiator event, usually exposure to DNA damaging agents that cause genetic alterations such as gene mutations or chromosomal abnormalities, leading to deregulated cell proliferation. Although the mere stochastic accumulation of further mutations may cause tumor progression, it is now clear that an inflammatory microenvironment has a major tumor-promoting influence on initiated cells, in particular when a chronic inflammatory reaction already existed before the initiated tumor cell was formed. Moreover, inflammatory cells become mobilized in response to signals emanating from tumor cells. In both cases, the microenvironment provides signals that initiated tumor cells perceive by membrane receptors and transduce via downstream kinase cascades to modulate multiple cellular processes and respond with changes in cell gene expression, metabolism, and morphology. Cytokines, chemokines, and growth factors are examples of major signals secreted by immune cells, fibroblast, and endothelial cells and mediate an intricate cell-cell crosstalk in an inflammatory microenvironment, which contributes to increased cancer cell survival, phenotypic plasticity and adaptation to surrounding tissue conditions. Eventually, consequent changes in extracellular matrix stiffness and architecture, coupled with additional genetic alterations, further fortify the malignant progression of tumor cells, priming them for invasion and metastasis. Here, we provide an overview of the current knowledge on the composition of the inflammatory tumor microenvironment, with an emphasis on the major signals and signal-transducing events mediating different aspects of stromal cell-tumor cell communication that ultimately lead to malignant progression.

Cancer-associated fibroblasts in non-small cell lung cancer: Recent advances and future perspectives

Non-small cell lung cancer (NSCLC) constitutes the majority of lung cancer, which is the leading cause of cancer-related deaths in the world. Nearly 70% of NSCLC patients were diagnosed at advanced stage with only 15% of five-year survival rate. Cancer-associated fibroblasts (CAFs) are the major component of tumor microenvironment and account for almost 70% of the cells in tumor tissues. By the crosstalk with cancer cells, CAFs reprogrammed cancer cell metabolism, remodeled extracellular matrix (ECM) and created a supportive niche for cancer stem cells. CAFs lead collective invasion of tumor cells and shape tumor immune microenvironment, promoting tumor metastasis and immune escape. In this review, we have summarized the progress of studies regarding CAFs influences on NSCLC in recent five years from the aspects of cell growth, metabolism, therapy resistance, invasion and metastasis and immune suppression. We have discussed the involved mechanisms and implications for the development of anti-NSCLC therapies. The current strategies of CAFs targeting and elimination have also been generalized. Only better understanding of the molecular biology of CAFs may contribute to the development of novel anti-NSCLC strategies.