A 50-gene signature is a novel scoring system for tumor-infiltrating immune cells with strong correlation with clinical outcome of stage I/II non-small cell lung cancer

Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.

A prognostic and immunotherapeutic predictive model based on the cell-originated characterization of tumor microenvironment in lung adenocarcinoma

Tumor microenvironment (TME) plays a crucial role in predicting prognosis and response to therapy in lung cancer. Our study established a prognostic and immunotherapeutic predictive model, the tumor immune cell score (TICS), by differentiating cell origins in lung adenocarcinoma (LUAD) based on the transcriptomic data of 2,510 patients in 14 independent cohorts, including 12 public datasets and two in-house cohorts. The high TICS was associated with prolonged overall survival (OS), especially in the early-stage LUAD. For the advanced-stage LUAD, high TICS predicted a superior OS in patients who were treated with immunotherapy instead of chemotherapy or TKI. The result suggested that TICS could serve as an indicator for the prognostic stratification management of patients in the early-stage LUAD, and as a potential guide for therapeutic decision-marking in the advanced-stage LUAD. Our findings provided an insight into prognosis stratification and potential guidance for treatment strategy selection.

B cell-dependent subtypes and treatment-based immune correlates to survival in stage 3 and 4 lung adenocarcinomas

Lung cancer is the leading cause of cancer-related deaths worldwide. Surgery and chemoradiation are the standard of care in early stages of non-small cell lung cancer (NSCLC), while immunotherapy is the standard of care in late-stage NSCLC. The immune composition of the tumor microenvironment (TME) is recognized as an indicator for responsiveness to immunotherapy, although much remains unknown about its role in responsiveness to surgery or chemoradiation. In this pilot study, we characterized the NSCLC TME using mass cytometry (CyTOF) and bulk RNA sequencing (RNA-Seq) with deconvolution of RNA-Seq being performed by Kassandra, a recently published deconvolution tool. Stratification of patients based on the intratumoral abundance of B cells identified that the B-cell rich patient group had increased expression of CXCL13 and greater abundance of PD1+ CD8 T cells. The presence of B cells and PD1+ CD8 T cells correlated positively with the presence of intratumoral tertiary lymphoid structures (TLS). We then assessed the predictive and prognostic utility of these cell types and TLS within publicly available stage 3 and 4 lung adenocarcinoma (LUAD) RNA-Seq datasets. As previously described by others, pre-treatment expression of intratumoral 12-chemokine TLS gene signature is associated with progression free survival (PFS) in patients who receive treatment with immune checkpoint inhibitors (ICI). Notably and unexpectedly pre-treatment percentages of intratumoral B cells are associated with PFS in patients who receive surgery, chemotherapy, or radiation. Further studies to confirm these findings would allow for more effective patient selection for both ICI and non-ICI treatments.

The prognostic impact of tumor-infiltrating B lymphocytes in patients with solid malignancies: A systematic review and meta-analysis

This study reviewed the prognostic effect of tumor-infiltrating B lymphocytes (TIBLs) on solid malignancies, to determine the potential role of TIBLs in predicting cancer patient's prognosis and their response to immunotherapy. A total of 45 original papers involving 11,099 individual patients were included in this meta-analysis covering 7 kinds of cancer. The pooled results suggested that high levels of TIBLs were correlated with favorable OS in lung, esophageal, gastric, colorectal, liver, and breast cancer; improved RFS in lung cancer; and improved DFS in gastrointestinal neoplasms. Additionally, TIBLs were significantly correlated with negative lymphatic invasion in gastric cancer, small tumor size in hepatocellular carcinoma, and negative distant metastasis in colorectal cancer. Additionally, TIBLs were reported as a discriminative feature of patients treated with immunotherapy with improved survival. We concluded that TIBLs play a favorable prognostic role among the common solid malignancie, providing theoretical evidence for further prognosis prediction for solid tumors.

Tumor Immune Microenvironment and Immunotherapy in Non-Small Cell Lung Cancer: Update and New Challenges

Non-small cell lung cancer (NSCLC) is a serious threat to the health of older adults. Despite the significant progress in immunotherapy, effective treatments for NSCLC remain limited. The development of tumors indicates failure in immune surveillance and the successful immune escape of tumor cells. Research on the tumor immune microenvironment (TIME) revealed these opposing immune processes and contributed to the discovery of new methods to suppress the immune escape and restore the immune surveillance functions. This paper aimed to provide updates on the current findings regarding the relevance of TIME in NSCLC treatment. It also aimed to introduce the TIME, immune editing, cancer immunotherapy, and new challenges. Based on the clinical data, the combination of neoadjuvant chemotherapy and immune checkpoint inhibitor (ICI) therapy is suitable for patients with NSCLC who are not eligible to undergo surgery. Combined ICI therapy after epidermal growth factor receptor (EGFR)/tyrosine kinase inhibitor (TKI) therapy should be considered in patients with EGFR mutations. Chemoradiotherapy may increase the density of CD8⁺ lymphocytes, which is significantly associated with better prognosis. For older patients and those with advanced-stage disease, regional tumor treatments, such as stereotactic radiation therapy and percutaneous cryoablation, may be more suitable, but further studies are needed to confirm this. In conclusion, restoring immune surveillance is as important as removing cancerous tissues; further studies that include the use of combined treatment methods, individualized treatment plans, and immunonutrition are warranted.

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.

METHODS

The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.

RESULTS

22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).

CONCLUSION

Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

B Cell Function in the Tumor Microenvironment

The tumor microenvironment (TME) is a heterogeneous, complex organization composed of tumor, stroma, and endothelial cells that is characterized by cross talk between tumor and innate and adaptive immune cells. Over the last decade, it has become increasingly clear that the immune cells in the TME play a critical role in controlling or promoting tumor growth. The function of T lymphocytes in this process has been well characterized. On the other hand, the function of B lymphocytes is less clear, although recent data from our group and others have strongly indicated a critical role for B cells in antitumor immunity. There are, however, a multitude of populations of B cells found within the TME, ranging from naive B cells all the way to terminally differentiated plasma cells and memory B cells. Here, we characterize the role of B cells in the TME in both animal models and patients, with an emphasis on dissecting how B cell heterogeneity contributes to the immune response to cancer.

The role of B cells in the development, progression, and treatment of lymphomas and solid tumors

B cells are integral components of the mammalian immune response as they have the ability to generate antibodies against an almost infinite array of antigens. Over the past several decades, significant scientific progress has been made in understanding that this enormous B cell diversity contributes to pathogen clearance. However, our understanding of the humoral response to solid tumors and to tumor-specific antigens is unclear. In this review, we first discuss how B cells interact with other cells in the tumor microenvironment and influence the development and progression of various solid tumors. The ability of B lymphocytes to generate antibodies against a diverse repertoire of antigens and subsequently tailor the humoral immune response to specific pathogens relies on their ability to undergo genomic alterations during their development and differentiation. We will discuss key transforming events that lead to the development of B cell lymphomas. Overall, this review provides a foundation for innovative therapeutic interventions for both lymphoma and solid tumor malignancies.

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions

Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at "https://github.com/yushanqiu/yushan.qiu-szu.edu.cn".

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Prognostic Significance of Tumor-Infiltrating Natural Killer Cells in Solid Tumors: A Systematic Review and Meta-Analysis

Background: Tumor-infiltrating natural killer (NK) cells (TINKs) are crucial immune cells in tumor defense, and might be related to tumor prognosis. However, the results were discrepant among different studies. The present meta-analysis was performed to comprehensively assess the prognostic value of NK cell markers in solid tumor tissues.

Methods: PubMed, Web of Science, and EMBASE were searched to identify original researches reporting the prognostic significance of TINKs in solid tumors. NK cell markers CD56, CD57, NKp30, and NKp46 were included in the analysis. The hazard ratios (HRs) and 95% confidence intervals (CIs) of pooled overall survival (OS), disease-free survival (DFS), metastasis-free survival (MFS), progression-free survival (PFS), and recurrence-free survival (RFS) were calculated by STATA software 14.0 to assess the prognostic significance.

Results : Of the 56 included studies, there were 18 studies on CD56, 31 studies on CD57, 1 study on NKp30, and 7 studies on NKp46. High levels of CD56, CD57, NKp30, and NKp46 were significantly correlated with better OS of patients with solid malignancies (HR = 0.473, 95%CI: 0.315–0.710, p < 0.001; HR = 0.484, 95%CI: 0.380–0.616, p < 0.001; HR = 0.34, 95%CI: 0.14–0.80, p = 0.014; HR = 0.622, 95%CI: 0.470–0.821, p < 0.001, respectively). Our results also revealed that CD56, CD57, and NKp46 could act as independent prognostic predictors for favorable OS (HR = 0.372, 95%CI: 0.261–0.531, p < 0.001; HR = 0.525, 95%CI: 0.346–0.797, p = 0.003; HR = 0.559, 95%CI: 0.385–0.812, p = 0.002, respectively).

Conclusions : Our results indicated that high levels of NK cell markers in solid tumor tissues could predict favorable prognosis for solid tumor patients.

The role of B lymphocytes in the immuno-biology of non-small-cell lung cancer

Tumour-infiltrating immune cells have been widely implicated to play a significant role in carcinogenesis, through both pro- or anti-tumour effects. The multi-faceted effects of lung cancer associated T lymphocytes have been extensively studied, and yet, the role of B lymphocytes remains an area less studied. In this review, we will describe the current understanding of the role of tumour-infiltrating B lymphocytes in NSCLC, discuss their prognostic significance, their functionality within the tumour microenvironment and ultimately how we might harness B-cell biology to develop B-cell therapeutic strategies in cancer.

Prognostic value of immune cells in the tumor microenvironment of early-stage lung cancer: a meta-analysis

BACKGROUND

Early-stage non-small cell lung cancer (NSCLC) patients carry significant risk of recurrence post-surgery. In-depth characterization of the immune tumor microenvironment (TME) can have prognostic value. This study aimed to evaluate the association of individual immune cell types in the TME with clinical outcomes in surgically resected, early-stage NSCLC.

METHODS

We performed a systematic literature search of the National Library of Medicine database through November 2019, investigating predefined biomarkers (CD3+ T cells, CD4+ T helper cells, CD8+ cytotoxic T cells, CD20+ B cells, CD56+ & CD57+ Natural Killer (NK) cells, CD68+ Tissue Associated Macrophages (TAMS), FoxP3+ T regulatory cells, and Mast Cells (MC)), and their association with survival following PRISMA guidelines.

RESULTS

Studies that adjusted for important clinical covariates (such as stage and age) showed that higher levels of CD8+ cytotoxic T cells were associated with improved OS (HR = 0.68; 95% CI, 0.50-0.93) and DFS (HR = 0.60; 95% CI, 0.41-0.87), while increased CD20+ B cells (HR = 0.16; 95% CI, 0.04-0.64) and CD 56/57+ NK cells (HR = 0.50; 95% CI, 0.26-0.95) were associated with improved OS; lung cancers with increased FoxP3+ T regulatory cells (HR = 2.22; 95% CI, 1.47-3.34) had worse OS.

CONCLUSIONS

Immune cell components of the TME have prognostic value in early-stage, surgically resected NSCLC, and may reveal which patients are more likely to need additional systemic treatment, including immunotherapy. Clinical covariates need to be considered when evaluating the prognostic value of immune cells in the TME.

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (-0.2491 × EXP_RRAGB) + (-0.0679 × EXP_RSPH9) + (-0.2317 × EXP_RPS6KL1) + (-0.1035 × EXP_RXFP1) + 0.1571 × EXP_RRM2 + 0.1104 × EXP_RTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

Bioinformatic profiling of prognosis-related genes in the breast cancer immune microenvironment

In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.

Intratumoral immunoglobulin isotypes predict survival in lung adenocarcinoma subtypes

BACKGROUND

The role of tumor-infiltrating B-cells (TIBs) and intratumorally-produced antibodies in cancer-immunity interactions essentially remains terra incognita. In particular, it remains unexplored how driver mutations could be associated with distinct TIBs signatures and their role in tumor microenvironment.

METHODS

Here we analyzed associations of immunoglobulin isotypes and clonality with survival in TCGA RNA-Seq data for lung adenocarcinoma (LUAD), stratifying patients into 12 driver mutation and phenotypic tumor subgroups.

RESULTS

We revealed several unexpected associations between TIBs behavior and prognosis. Abundance and high proportion of IgG1 isotype, and low proportion of IgA among all intratumorally produced immunoglobulins were specifically associated with improved overall survival for KRAS^mut but not KRAS^wt LUAD, revealing the first link between a driver mutation and B-cell response. We found specific IgG1 signature associated with long survival, which suggests that particular specificities of IgG1+ TIBs could be beneficial in KRAS^mut LUAD. In contrast to our previous observations for melanoma, highly clonal IgG1 production by plasma cells had no meaningful effect on prognosis, suggesting that IgG1+ TIBs may exert a beneficial effect in KRAS^mut cases in an alternative way, such as efficient presentation of cognate antigens or direct B cell attack on tumor cells. Notably, a high proportion of the IgG1 isotype is positively correlated with the non-silent mutation burden both in the general LUAD cohort and in most patient subgroups, supporting a role for IgG1⁺ TIBs in antigen presentation. Complementing the recent finding that the presence of stromal IgG4-producing cells is associated with a favorable prognosis for patients with stage I squamous cell carcinoma, we show that the abundance of IgG4-producing TIBs likewise has a strong positive effect on overall survival in STK11^mut and proximal proliferative subgroups of LUAD patients. We hypothesize that the positive role of IgG4 antibodies in some of the lung cancer subtypes could be associated with reported inability of IgG4 isotype to form immune complexes, thus preventing immunosuppression via activation of the myeloid-derived suppressor cell (MDSC) phenotype.

CONCLUSIONS

We discover prominent and distinct associations between TIBs antibody isotypes and survival in lung adenocarcinoma carrying specific driver mutations. These findings indicate that particular types of tumor-immunity relations could be beneficial in particular driver mutation context, which should be taken into account in developing strategies of cancer immunotherapy and combination therapies. Specificity of protective B cell populations in specific cancer subgroups could become a clue to efficient targeted immunotherapies for appropriate cohorts of patients.

Tumor characteristics associated with engraftment of patient-derived non-small cell lung cancer xenografts in immunocompromised mice

BACKGROUND

Patient-derived xenograft (PDX) models increasingly are used in translational research. However, the engraftment rates of patient tumor samples in immunodeficient mice to PDX models vary greatly.

METHODS

Tumor tissue samples from 308 patients with non-small cell lung cancer were implanted in immunodeficient mice. The patients were followed for 1.5 to approximately 6 years. The authors performed histological analysis of PDXs and some residual tumor tissues in mice with failed PDX growth at 1 year after implantation. Quantitative polymerase chain reaction and enzyme-linked immunoadsorbent assay were performed to measure the levels of Epstein-Barr virus genes and human immunoglobulin G in PDX samples. Patient characteristics were compared for PDX growth and overall survival as outcomes using Cox regression analyses. Disease staging was based on the 7th TNM staging system.

RESULTS

The overall engraftment rate for PDXs from patients with non-small cell lung cancer was 34%. Squamous cell carcinomas had a higher engraftment rate (53%) compared with adenocarcinomas. Tumor samples from patients with stage II and stage III disease and from larger tumors were found to have relatively high engraftment rates. Patients whose tumors successfully engrafted had worse overall survival, particularly those individuals with adenocarcinoma, stage III or stage IV disease, and moderately differentiated tumors. Lymphoma formation was one of the factors associated with engraftment failure. Human CD8-positive and CD20-positive cells were detected in residual samples of tumor tissue that failed to generate a PDX at 1 year after implantation. Human immunoglobulin G was detected in the plasma of mice that did not have PDX growth at 14 months after implantation.

CONCLUSIONS

The results of the current study indicate that the characteristics of cancer cells and the tumor immune microenvironment in primary tumors both can affect engraftment of a primary tumor sample.

The clinical role of the TME in solid cancer

The highly complex and heterogenous ecosystem of a tumour not only contains malignant cells, but also interacting cells from the host such as endothelial cells, stromal fibroblasts, and a variety of immune cells that control tumour growth and invasion. It is well established that anti-tumour immunity is a critical hurdle that must be overcome for tumours to initiate, grow and spread and that anti-tumour immunity can be modulated using current immunotherapies to achieve meaningful anti-tumour clinical responses. Pioneering studies in melanoma, ovarian and colorectal cancer have demonstrated that certain features of the tumour immune microenvironment (TME)-in particular, the degree of tumour infiltration by cytotoxic T cells-can predict a patient's clinical outcome. More recently, studies in renal cell cancer have highlighted the importance of assessing the phenotype of the infiltrating T cells to predict early relapse. Furthermore, intricate interactions with non-immune cellular players such as endothelial cells and fibroblasts modulate the clinical impact of immune cells in the TME. Here, we review the critical components of the TME in solid tumours and how they shape the immune cell contexture, and we summarise numerous studies evaluating its clinical significance from a prognostic and theranostic perspective.

Prognostic Significance of Tumor-Infiltrating B Cells and Plasma Cells in Human Cancer

There is abundant evidence that tumor-infiltrating CD8⁺ T cells contribute positively to antitumor immunity; however, the role of tumor-infiltrating B cells (TIL-B) and plasma cells (PC) remains controversial, leading to differing opinions about whether immunotherapies should be designed to enhance or inhibit these cells. Through a comprehensive PubMed search, we reviewed publications with cohorts of 50 or more cases in which the prognostic value of TIL-B/PC was assessed by immunohistochemistry and/or gene-expression analysis. Sixty-nine studies representing 19 cancers met our review criteria. The large majority of studies assessed TIL-B by immunohistochemical detection of CD20. Of these, 50.0% reported a positive prognostic effect for CD20⁺ TIL-B, whereas the remainder found a neutral (40.7%) or negative (9.3%) effect. These differences in prognostic effect were not attributable to cancer type, other clinicopathologic factors, or differing technical approaches. The prognostic significance of TIL-B/PC was generally concordant with that of CD3⁺ and/or CD8⁺ T cells, and the prognostic effect of T cells was generally stronger when TIL-B and/or PC were also present. Additionally, 21 studies inferred the presence of TIL-B/PC from gene-expression data, and a large majority reported a positive prognostic effect. Although more studies are required involving additional cancer types and independent patient cohorts, the weight of evidence supports a positive role for TIL-B and PC in antitumor immunity, suggesting that enhancement of these responses should be considered in the design of cancer immunotherapies.

Prognostic significance of immune cells in non-small cell lung cancer: meta-analysis

Background

Tumor-associated immune cells are prognostic in non-small cell lung cancer (NSCLC) but findings have been conflicting.

Objectives

To determine the prognostic role of immune cells according to localization in NSCLC patients.

Methods

A systematic literature review and meta-analysis was performed on dendritic cell (DC), tumor associated macrophages (TAM), mast cells (MC), natural killer (NK) cells, T and B cells and tumor CTLA-4 and PD-L1 studies.

Results

We analysed 96 articles (n= 21,752 patients). Improved outcomes were seen with increased tumor DCs (overall survival (OS) hazard ratio (HR) 0.55; 95% confidence interval (CI) 0.44-0.68), NK cells (OS HR 0.45; 0.31-0.65), TAMs (OS HR 0.33; 0.17-0.62), M1 TAMs (OS HR 0.10; 0.05-0.21), CD3+ T cells (disease specific survival (DSS) HR 0.64; 0.48-0.86), CD8+ T cells (OS HR 0.78; 0.66-0.93), B cells (OS HR 0.65; 0.42-0.99) and with increased stroma DC (DSS HR 0.62; 0.47-0.83), NK cells (DSS HR 0.51; 0.32-0.82), M1 TAMs (OS HR 0.63; 0.42-0.94), CD4+ T cells (OS HR 0.45; 0.21-0.94), CD8+ T cells (OS HR 0.77; 0.69-0.86) and B cells (OS HR 0.74;0.56-0.99). Poor outcomes were seen with stromal M2 TAMs (OS HR 1.44; 1.06-1.96) and Tregs (relapse free survival (RFS) HR 1.80; 1.34-2.43). Tumor PD-L1 was associated with worse OS (1.40; 1.20-1.69), RFS (1.67) and DFS (1.24).

Conclusion

Tumor and stroma DC, NK cells, M1 TAMs, CD8+ T cells and B cells were associated with improved prognosis and tumor PD-L1, stromal M2 TAMs and Treg cells had poorer prognosis. Higher quality studies are required for confirmation.

Tumor-infiltrating B cells: their role and application in anti-tumor immunity in lung cancer

Evidence indicates that lung cancer development is a complex process that involves interactions between tumor cells, stromal fibroblasts, and immune cells. Tumor-infiltrating immune cells play a significant role in the promotion or inhibition of tumor growth. As an integral component of the tumor microenvironment, tumor-infiltrating B lymphocytes (TIBs) exist in all stages of cancer and play important roles in shaping tumor development. Here, we review recent clinical and preclinical studies that outline the role of TIBs in lung cancer development, assess their prognostic significance, and explore the potential benefit of B cell-based immunotherapy for lung cancer treatment.

Analysis of the immune microenvironment in resected non-small cell lung cancer: the prognostic value of different T lymphocyte markers

The prognosis of non-small cell lung cancer (NSCLC) remains poor and heterogeneous and new biomarkers are needed. As the immune system plays a pivotal role in cancer, the study of immune-related markers may provide valuable prognostic information of NSCLC. In 122 formalin-fixed, paraffin-embedded tumor tissue samples from early-stage NSCLC, tumor and tumor-near stromal areas were microdissected and gene expression levels of conventional and regulatory T cell markers were assessed by quantitative polymerase chain reaction. Also, the presence of infiltrating CD4+, CD8+, and FOXP3+ cells in tumor samples was assessed by immunohistochemistry. The relative proportion of conventional and regulatory T cells present in the tumor environment was assessed and found to be key to understand the importance that the immune system analysis has in the prognostics of NSCLC patients. The presence of CD8+ cells in the tumor compartment was associated with better outcome, whereas the presence of FOXP3+ cells was associated with worse overall survival. The negative prognostic value of combined biomarkers, indicating high levels of FOXP3 in the stroma and low levels of CD4 or CD8 in tumors, was observed at mRNA level and was validated by immunohistochemistry.In conclusion, the proportion of T helper and cytotoxic cells vs. regulatory T cells in different locations of the tumor microenvironment have opposite prognostic impacts in resected NSCLC.

Prognostic role of tumor-infiltrating CD57-positive lymphocytes in solid tumors: a meta-analysis

The prognostic role of tumor-infiltrating CD57-positive lymphocytes (CD57⁺ lymphocytes) in human solid tumors remains controversial. Herein, we conducted a meta-analysis including 26 published studies with 7656 patients identified from PubMed and EBSCO to assess the prognostic impact of tumor-infiltrating CD57⁺ lymphocytes in human solid tumors. We found that CD57⁺ lymphocyte infiltration significantly improved overall survival (OS) including 1 – year, 3 – year and 5 – year survival, and disease – free survival (DFS) in all types of solid tumors. In stratified analyses, CD57⁺ lymphocyte infiltration was significantly associated with better OS in hepatocellular, esophageal, head and neck carcinoma, non-small cell lung cancer, 5 – year survival in colorectal cancer, and 3 – year and 5 – year survival in gastric cancer, but not with 1 – year survival in gastric cancer, or 1 – year or 3 – year survival in colorectal cancer. In addition, high density of intratumoral CD57⁺ lymphocytes was significantly inversely correlated with lymph node metastasis and TNM stage of solid tumor. In conclusion, CD57⁺ lymphocyte infiltration leads to a favorable clinical outcome in solid tumors, implicating that it is a useful biomarker for prognosis and adoptive immunotherapy based on these cells may be a promising choice for treatment.

Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.