Whole Exome and Transcriptome Analyses Integrated with Microenvironmental Immune Signatures of Lung Squamous Cell Carcinoma

Pan-cancer prognosis, immune infiltration, and drug resistance characterization of lung squamous cell carcinoma tumor microenvironment-related genes

Background

The tumor microenvironment (TME) plays an important role in cancer development; however, its implications in lung squamous cell carcinoma (LUSC) and pan-cancer have been poorly understood.

Methods

In this study, The Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissue using Expression Data (ESTIMATE) datasets were applied to identify differentially expressed genes. Additionally, online public databases were utilized for in-depth bioinformatics analysis of pan-cancer datasets to investigate the prognostic implications of TME-related genes further.

Results

Our study demonstrated a significant association between stromal scores, immune scores, and specific clinical characteristics in LUSC patients. C3AR1, CSF1R, CCL2, CCR1, and CD14 were identified as prognostic genes related to the TME. All TME-related prognostic genes demonstrated varying degrees of correlation with immune infiltration subtypes and tumor cell stemness. Moreover, our study revealed that TME-related prognostic genes, particularly C3AR1 and CCR1, might contribute to drug resistance in cancer cells.

Conclusions

The identified TME-related prognostic genes, particularly C3AR1 and CCR1, have potential implications for understanding and targeting drug resistance mechanisms in cancer cells.

Characterization of virus-mediated autoimmunity and the consequences for pathological process in patients with systemic lupus erythematosus

INTRODUCTION/OBJECTIVES

This study aimed to identify differentially expressed genes (DEGs) of systemic lupus erythematosus (SLE) using gene expression-based computational methodologies to analyze disease-immune interactions, which affect the development and progression of SLE.

METHOD

Twenty-six patients with SLE and 46 healthy controls were selected from the Gene Expression Omnibus (GEO) database. The significantly enriched immune and virus-related gene lists were computed and visualized by using the DEGs from the gene set enrichment analysis (GSEA). Quantification of 38 immune cells was performed in determining the impact of immune cells on the virus mediated immunity in SLE by using ImmQuant algorithm.

RESULTS

Thirty-nine upregulated and 57 downregulated were identified in SLE patient compared to the healthy controls. Upregulated genes were significantly implicated in Gene Ontology gene sets as cytokine mediated signaling, secretion, and exocytosis in immune response pathways in 26 female SLE patients. In addition, these genes were enriched in hepatitis C, influenza A, measles, Epstein-Barr virus, and herpes simplex virus 1 infection in Kyoto Encyclopedia of Genes and Genomes pathways. Especially, FCGR1A, IRF7, OAS2, CAMP, MX1, OAS3, OAS1, DEFA3, ISG15, and RSAD2 were involved in virus mediated SLE mechanism, and the expression for OAS1, OAS2, and IRF7 was closely associated with the quantities of colony forming unit-monocyte and colony forming unit-granulocyte.

CONCLUSIONS

Identifying virus-mediated SLE genes and quantifies of immune cells were used to understand the pathological process and perform early diagnosis of female SLE, and will lead to clinical tools for treating SLE in patients. Key Points • Using gene expression-based computational methodologies, the 57 immune and viral genes were significantly upregulated in 26 SLE patients. • The identified three key  viral genes such as OAS1, OAS2, and IF7 were closely associated with colony-forming unit-monocytes and colony-forming unit-granulocytes, which affect the virus mediated immunity in SLE. • The viral genes and quantifies of immune cells are useful in understanding pathogenesis of SLE, and this will provide clinical strategies of potential treatment choices in SLE patients.

Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.

Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.

Identification of the Immune-Related Genes in Tumor Microenvironment That Associated With the Recurrence of Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCC) are still one of the most common malignant tumors in China, with a high metastasis rate and poor prognosis. The tumor immune microenvironment can affect the occurrence, development and prognosis of tumors, but the underlying mechanism is still unclear. In this study, we tried to describe the correlation between the recurrence of HNSCC and the tumor microenvironment (TME). The expression data [estimate the level of tumor stromal and immune infiltration, expression data (ESTIMATE)] algorithm was used to identify and estimate highly correlated stromal cells, immune cells, and prognostic scores in 116 samples of head and neck cancer patients from The Cancer Genome Atlas (TCGA) dataset. The functional enrichment analysis and protein-protein interaction (PPI) networks of differential expressed genes (DEGs) were constructed. Subsequently, the abundance of various infiltrating immune cells was estimated with the tumor immune estimation resource (TIMER) and the infiltration pattern of immune cells were explored in HNSCC. A total of 407 immune-related genes were identified to involve in the TME. We found that CCR5, CD3E, CD4, and HLA -DRB1 were the most obvious DEGs and the dendritic cells (DCs) showed the highest abundance in the TME of HNSCC. In addition, the unsupervised cluster analysis determined 10 clusters of immune infiltration patterns, and indicated that immune infiltrated CD4 + T and B cells may be related to the prognosis of HNSCC. In conclusion, our research determined the list of immune genes and immune infiltrating cells related to the prognosis of HNSCC, and provided a perspective for HNSCC evolution, anti-tumor drugs selection, and drug resistance research.

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Background

Ovarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).

Results

We identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.

Conclusions

In this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.

Development and validation of an individualized immune prognostic model in stage I-III lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) possesses a poor prognosis even for stages I-III resected patients. Reliable prognostic biomarkers that can stratify and predict clinical outcomes for stage I-III resected LUSC patients are urgently needed. Based on gene expression of LUSC tissue samples from five public datasets, consisting of 687 cases, we developed an immune-related prognostic model (IPM) according to immune genes from ImmPort database. Then, we comprehensively analyzed the immune microenvironment and mutation burden that are significantly associated with this model. According to the IPM, patients were stratified into high- and low-risk groups with markedly distinct survival benefits. We found that patients with high immune risk possessed a higher proportion of immunosuppressive cells such as macrophages M0, and presented higher expression of CD47, CD73, SIRPA, and TIM-3. Moreover, When further stratified based on the tumor mutation burden (TMB) and risk score, patients with high TMB and low immune risk had a remarkable prolonged overall survival compared to patients with low TMB and high immune risk. Finally, a nomogram combing the IPM with clinical factors was established to provide a more precise evaluation of prognosis. The proposed immune relevant model is a promising biomarker for predicting overall survival in stage I-III LUSC. Thus, it may shed light on identifying patient subset at high risk of adverse prognosis from an immunological perspective.

The prognostic landscape of tumor-infiltrating immune cells in lung squamous cell carcinoma

Background

Exploring novel biomarkers and developing effective therapeutic strategies can improve the prognosis of lung squamous cell carcinoma (LUSC) in the future. The prognostic value of tumor-infiltrating immune cells (TICs) in solid tumors has been extensively studied. However, the landscape of TICs involved in the prognosis of non-small cell lung cancer (NSCLC), especially in LUSC, remains unclear and should be systematically investigated.

Methods

This retrospective study analyzed the immune-related transcriptional profiles of 490 LUSC patients from The Cancer Genome Atlas (TCGA) cohort. Using the CIBERSORT method, TICs were evaluated and examined for their association with overall survival (OS) in LUSC.

Results

Out of the 27 TICs, 14 were correlated with prognosis in LUSC. A novel prognostic model characterized by fewer memory B cells and more central memory CD8 T cells, regulatory T cells (Tregs), and plasmacytoid dendritic cell (pDC) infiltration predicted poor OS in LUSC with high accuracy. The 1-, 3-, and 5-year areas under the curve (AUC) were 0.95, 0.98, and 0.96, respectively, in the training cohort. This finding was further validated in the validation cohort, where the 1-, 3-, and 5-year AUCs were 0.95, 0.98, and 0.95, respectively.

Conclusions

These findings may provide more effective prognostic biomarkers and potential therapeutic targets for the immunotherapy of LUSC.

Characterization of the Tumor Immune Microenvironment in Lung Squamous Cell Carcinoma Using Imaging Mass Cytometry

Background

Lung squamous cell carcinoma (LUSC) is a major subtype of non-small cell lung cancer. The tumor immune microenvironment (TIME) affects the anti-tumor immune response and the patient’s prognosis, although the TIME in LUSC patients is incompletely understood.

Methods

We retrospectively collected surgical specimens from patients with previously untreated primary LUSC. Histopathological examination was used to identify tumor regions and adjacent regions, and imaging mass cytometry was used to characterize the immune cells in those regions. The results were compared between regions and between patients.

Results

We identified heterogeneity in the TIME on comparing different patients with LUSC, although the tumor region and adjacent region both exhibited an immune response to the tumor. The TIME typically included a large number of infiltrating and activated T-cells, especially CD8⁺ T-cells, which closely interacted with the tumor cells in the tumor region. There was limited infiltration of B-cells, NK cells, and NKT cells, while the major immune suppressor cells were CD33⁺ myeloid-derived cells. We also identified a novel population of CD3⁻CD4⁺ cells with high expression of Foxp3 and TNFα, which might modulate the tumor microenvironment and play a proinflammatory role in the TIME.

Conclusions

The TIME of LUSC appears to be immunogenic and heterogenous, with predominant infiltration of activated CD8⁺ T-cells. The interactions between the tumor cells and T-cells facilitate the anti-tumor activity. A novel subpopulation of CD3⁻CD4⁺ cells with high TNFα and Foxp3 expression may modulate the tumor microenvironment and play a proinflammatory role.

Integrative Genomic and Transcriptomic Analyses of Tumor Suppressor Genes and Their Role on Tumor Microenvironment and Immunity in Lung Squamous Cell Carcinoma

Non-small-cell lung cancers (NSCLCs) are largely classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), which have different therapeutic options according to its molecular profiles and immune checkpoint expression, especially PD-L1, which is a suppressive factor in the tumor microenvironment. The tumor microenvironment can be altered by the genomic mutations on specific innate immune genes as well as tumor suppressor genes, so it is essential to comprehend the association between tumor microenvironment and tumor suppressor genes to discover the promising immunotherapeutic strategy to overcome the resistance of immune check point blockade. In this study, we aimed to analyze how the somatic mutations in tumor suppressor genes affect the tumor immune microenvironment through a comprehensive analysis of mutational profiling on the representative tumor suppressor genes (TP53, CDKN2A, PTEN, RB1, BRCA1, BRCA2) and immune gene expression in The Cancer Genome Atlas (TCGA) 155 lung squamous cell carcinoma (LUSC) and 196 lung adenocarcinoma (LUAD) samples. Several microenvironmental factors, such as the infiltrating immune and stromal cells, were suppressed by the mutated tumor suppressor genes in LUSC, unlike in the LUAD samples. In particular, infiltrating immune cells such as macrophage, neutrophil, and dendritic cells were significantly reduced in tumors with mutated tumor suppressor genes' group. In addition, the gene expressions for interleukin production and lymphocyte differentiation and PGC, C7, HGF, PLA2G2A, IL1RL1, CCR2, ALOX15B, CXCL11, FCN3 were significantly down-regulated, which were key immune genes for the cross-talk between LUSC microenvironment and tumor suppressors. Therefore, we generated evidence that TSG mutations in LUSC have an impact on tumor immune microenvironment, which suggests that TSG non-mutated patients will have the more inflamed tumors and are more likely to respond to immune checkpoint blockade therapy.

Reciprocal change in Glucose metabolism of Cancer and Immune Cells mediated by different Glucose Transporters predicts Immunotherapy response

The metabolic properties of tumor microenvironment (TME) are dynamically dysregulated to achieve immune escape and promote cancer cell survival. However, in vivo properties of glucose metabolism in cancer and immune cells are poorly understood and their clinical application to development of a biomarker reflecting immune functionality is still lacking.

Methods: We analyzed RNA-seq and fluorodeoxyglucose (FDG) positron emission tomography profiles of 63 lung squamous cell carcinoma (LUSC) specimens to correlate FDG uptake, expression of glucose transporters (GLUT) by RNA-seq and immune cell enrichment score (ImmuneScore). Single cell RNA-seq analysis in five lung cancer specimens was performed. We tested the GLUT3/GLUT1 ratio, the GLUT-ratio, as a surrogate representing immune metabolic functionality by investigating the association with immunotherapy response in two melanoma cohorts.

Results: ImmuneScore showed a negative correlation with GLUT1 (r = -0.70, p < 0.01) and a positive correlation with GLUT3 (r = 0.39, p < 0.01) in LUSC. Single-cell RNA-seq showed GLUT1 and GLUT3 were mostly expressed in cancer and immune cells, respectively. In immune-poor LUSC, FDG uptake was positively correlated with GLUT1 (r = 0.27, p = 0.04) and negatively correlated with ImmuneScore (r = -0.28, p = 0.04). In immune-rich LUSC, FDG uptake was positively correlated with both GLUT3 (r = 0.78, p = 0.01) and ImmuneScore (r = 0.58, p = 0.10). The GLUT-ratio was higher in anti-PD1 responders than nonresponders (p = 0.08 for baseline; p = 0.02 for on-treatment) and associated with a progression-free survival in melanoma patients who treated with anti-CTLA4 (p = 0.04).

Conclusions: Competitive uptake of glucose by cancer and immune cells in TME could be mediated by differential GLUT expression in these cells.

Immune-infiltration based signature as a novel prognostic biomarker in gastrointestinal stromal tumour

BACKGROUND

Accumulating evidence indicates that tumour-infiltrating lymphocytes (TILs) are the primary determinant of survival outcomes in various tumours. Thus, we sought to investigate the TIL distribution and density in gastrointestinal stromal tumours (GISTs) and to develop an immune infiltration (II)-based signature to predict prognosis.

METHODS

The expression of 8 immune features in the tumour centre (TC) and tumour margin (TM) and PD-L1 in 435 GIST patients was investigated by immunohistochemistry. Then, a 4-feature-based II-GIST signature integrating the CD3⁺ TC, CD3⁺ TM, CD8⁺ TM and CD45RO⁺ TM parameters was developed using a LASSO Cox regression model in the training cohort and was validated in two separate validation cohorts.

FINDINGS

High CD3⁺ TC, CD3⁺ TM, CD8⁺ TC, CD8⁺ TM, CD45RO⁺ TM, NKp46⁺ TM and CD20⁺ TM correlated with improved survival. Patients with high II-GIST scores have better RFS and OS outcomes than those with low II-GIST scores. Multivariable analyses demonstrated that the II-GIST signature is an independent prognostic factor. The receiver operating characteristic (ROC) curve demonstrated that the prognostic accuracy of the II-GIST signature is superior to that of the NIH risk criteria. Further analysis showed that moderate- and high-risk GIST patients with high II-GIST scores could gain survival benefits from adjuvant imatinib therapy.

INTERPRETATION

The novel II-GIST signature accurately predicted the survival outcomes of GIST patients. In addition, the II-GIST signature was a useful predictor of survival benefit from imatinib therapy amongst moderate- and high-risk patients with GIST.

FUNDING

This project was supported by National Natural Science Foundation of China (81702325), Natural Science Foundation of Guangdong Province (2017A030310565), and 3&3 Project of the First Affiliated Hospital of Sun Yat-sen University.

The tumor immune microenvironmental analysis of 2,033 transcriptomes across 7 cancer types

Understanding the tumor microenvironment is important to efficiently identify appropriate patients for immunotherapies in a variety of cancers. Here, we presented the tumor microenvironmental analysis of 2,033 cancer samples across 7 cancer types: colon adenocarcinoma, skin cutaneous melanoma, kidney renal papillary cell carcinoma, sarcoma, pancreatic adenocarcinoma, glioblastoma multiforme, and pheochromocytoma / paraganglioma from The Cancer Genome Atlas cohort. Unsupervised hierarchical clustering based on the gene expression profiles separated the cancer samples into two distinct clusters, and characterized those into immune-competent and immune-deficient subtypes using the estimated abundances of infiltrated immune and stromal cells. We demonstrated differential tumor microenvironmental characteristics of immune-competent subtypes across 7 cancer types, particularly immunosuppressive tumor microenvironment features in kidney renal papillary cell carcinoma with significant poorer survival rates and immune-supportive features in sarcoma and skin cutaneous melanoma. Additionally, differential genomic instability patterns between the subtypes were found across the cancer types, and discovered that immune-competent subtypes in most of cancer types had significantly higher immune checkpoint gene expressions. Overall, this study suggests that our subtyping approach based on transcriptomic data could contribute to precise prediction of immune checkpoint inhibitor responses in a wide range of cancer types.

Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein-protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Background: Tumor microenvironment is essential for breast cancer progression and metastasis. Our study sets out to examine the genes affecting stromal and immune infiltration in breast cancer progression and prognosis.

Materials and Methods: This work provides an approach for quantifying stromal and immune scores by using ESTIMATE algorithm based on gene expression matrix of breast cancer patients in TCGA database. We found differentially expressed genes (DEGs) through limma R package. Functional enrichments were accessed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Besides, we constructed a protein-protein network, identified several hub genes in Cytoscape, and discovered functionally similar genes in GeneMANIA. Hub genes were validated with prognostic data by Kaplan-Meier analysis both in The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and a meta-analysis of hub genes prognosis data was utilized in multiple databases. Furthermore, their relationship with infiltrating immune cells was evaluated by Tumor IMmune Estimation Resource (TIMER) web tool. Cox regression was utilized for overall survival (OS) and recurrence-free survival (RFS) in TCGA database and OS in METABRIC database in order to evaluate the impact of stromal and immune scores on patients prognosis.

Results: One thousand and eighty-five breast cancer patients were investigated and 480 differentiated expressed genes (DEGs) were found based on the analysis of mRNA expression profiles. Functional analysis of DEGs revealed their potential functions in immune response and extracellular interaction. Protein-protein interaction network gave evidence of 10 hub genes. Some of the hub genes could be used as predictive markers for patients prognosis. In this study, we found that tumor purity and specific immune cells infiltration varied in response to hub genes expression. The multivariate cox regression highlighted the fact that immune score played a detrimental role in overall survival (HR = 0.45, 95% CI: 0.27–0.74, p = 0.002) and recurrence-free survival (HR = 0.41, 95% CI: 0.22–0.77, p = 0.006) in TCGA database. These result was confirmed in METABRIC database that immune score was a protector of OS (HR = 0.88, 95% CI: 0.77–0.99, p = 0.039).

Conclusions: Our findings promote a better understanding of the potential genes behind the regulation of tumor microenvironment and cells infiltration. Immune score should be considered as a prognostic factor for patients' survival.

Transcriptome-based molecular subtyping of non-small cell lung cancer may predict response to immune checkpoint inhibitors

OBJECTIVES

We set out to investigate whether transcriptome-based molecular subtypes in lung adenocarcinoma and lung squamous cell carcinoma are predictive of the response to programmed cell death 1 blockade.

METHODS

Molecular classification of non-small cell lung cancer was performed by unsupervised clustering of mRNA sequencing data from 87 lung adenocarcinoma and 101 lung squamous cell carcinoma specimens, and molecular subtypes were characterized according to their immunogenomic determinants. A prediction algorithm of molecular subtypes was applied to 35 patients with non-small cell lung cancer treated with programmed cell death 1 blockade to test its association with treatment response (GSE93157; the Barcelona cohort).

RESULTS

Unsupervised hierarchical clustering of transcriptome sequencing data in lung adenocarcinoma and lung squamous cell carcinoma revealed 3 and 2 distinct clusters, respectively. Cluster 1 in each histology had a higher expression of immune regulatory molecules, increased cytolytic activity, higher interferon-γ signature, and more abundant infiltration of immune cells. Cluster 1 and other cluster(s) in lung adenocarcinoma and lung squamous cell carcinoma had immunologically-hot and immunologically-cold tumor-immune microenvironments, respectively. Immunologically-hot cluster 1 subtype is hereafter referred to as "good-tumor-immune microenvironments" and the other subtypes as "bad-tumor-immune microenvironments." The "good-tumor-immune microenvironments" subtype in lung adenocarcinoma included a high fraction of CD8 T cells and memory B cells, but a low fraction of regulatory CD4 T cells and tumor-associated myeloid cells. Forward and backward application of our molecular subtyping to the Barcelona cohort revealed that transcriptome-based molecular subtyping is significantly associated with response to programmed cell death 1 blockade.

CONCLUSIONS

Molecular stratification by transcriptome sequencing data in non-small cell lung cancer identifies distinct immunomolecular subtypes that predict the response to programmed cell death 1 blockade.

Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

The development of single cell transcriptome sequencing has allowed researchers the possibility to dig inside the role of the individual cell types in a plethora of disease scenarios. It also expands to the whole transcriptome what before was only possible for a few tenths of antibodies in cell population analysis. More importantly, it allows resolving the permanent question of whether the changes observed in a particular bulk experiment are a consequence of changes in cell type proportions or an aberrant behavior of a particular cell type. However, single cell experiments are still complex to perform and expensive to sequence making bulk RNA-Seq experiments yet more common. scRNA-Seq data is proving highly relevant information for the characterization of the immune cell repertoire in different diseases ranging from cancer to atherosclerosis. In particular, as scRNA-Seq becomes more widely used, new types of immune cell populations emerge and their role in the genesis and evolution of the disease opens new avenues for personalized immune therapies. Immunotherapy have already proven successful in a variety of tumors such as breast, colon and melanoma and its value in other types of disease is being currently explored. From a statistical perspective, single-cell data are particularly interesting due to its high dimensionality, overcoming the limitations of the "skinny matrix" that traditional bulk RNA-Seq experiments yield. With the technological advances that enable sequencing hundreds of thousands of cells, scRNA-Seq data have become especially suitable for the application of Machine Learning algorithms such as Deep Learning (DL). We present here a DL based method to enumerate and quantify the immune infiltration in colorectal and breast cancer bulk RNA-Seq samples starting from scRNA-Seq. Our method makes use of a Deep Neural Network (DNN) model that allows quantification not only of lymphocytes as a general population but also of specific CD8+, CD4Tmem, CD4Th and CD4Tregs subpopulations, as well as B-cells and Stromal content. Moreover, the signatures are built from scRNA-Seq data from the tumor, preserving the specific characteristics of the tumor microenvironment as opposite to other approaches in which cells were isolated from blood. Our method was applied to synthetic bulk RNA-Seq and to samples from the TCGA project yielding very accurate results in terms of quantification and survival prediction.

Proteogenomic landscape of squamous cell lung cancer

How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.

Squamous cell lung cancer has dismal prognosis due to the dearth of effective treatments. Here, the authors perform an integrated proteogenomic analysis of the disease, revealing three proteomics-based subtypes and suggesting potential therapeutic opportunities.

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein-protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8⁺ T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.

Comprehensive analysis of the tumor immune micro-environment in non-small cell lung cancer for efficacy of checkpoint inhibitor

Characterizing the molecular immune subtype and micro-environment of lung cancer is necessary to understand immunogenic interactions between infiltrating immune and stromal cells, and how tumor cells overcome immune checkpoint blockades. This study seeks to identify computational methodologies for subtyping gene expression-based tumor-immune micro-environment interactions, which differentiate non-small cell lung cancer (NSCLC) into immune-defective and immune-competent subtypes. Here, 101 lung squamous cell carcinomas (LUSCs) and 87 lung adenocarcinomas (LUADs) tumor samples have been analyzed. Several micro-environmental factors differentially induce LUAD or LUSC immune subtypes, as well as immune checkpoint expression. In particular, tumor-associated macrophages (TAMs) are key immune cells play a vital role in inflammation and cancer micro-environments of LUSCs; whereas, regulatory B cells are immunosuppressive and tumorigenic in LUADs. Additionally, cytolytic activity upon CD8⁺ T cell activation is decreased by the abundance of B cells and macrophages in immune-competent subtypes. Therefore, identifying immune subtypes in lung cancer and their impact on tumor micro-environment will lead to clinical tools for assessing LUADs and LUSCs in patients, as well as maximize the efficacy of immune checkpoint inhibitors.