Prognostic Immune Genes Research Articles

A systematic analysis of immune genes and overall survival in cancer patients

BackgroundOverall survival (OS) is a key endpoint measure in the management of patients with cancer. Immunotherapy has become a dominant strategy in cancer therapy. To investigate the relationship between OS and the immune system, we assessed the role of immune genes in OS in 8648 patients across 22 cancer types.MethodsGene expression data and clinical information were collected from The Cancer Genome Atlas (TCGA) and cBioPortal. Survival analysis was performed with a Cox proportional hazards regression model.Results(1) The number of prognostic genes, prognostic immune genes (PIGs) and the hazard ratio (HR) of PIGs in different cancer types all varied greatly; (2) KEGG pathway enrichment analyses indicated that the prognostic genes of 6 cancer types were significantly enriched in multiple (≥5) immune system-related pathways. Of the PIGs in these 6 cancer types, we screened 48 common PIGs in at least 5 cancer types. Eleven out of the 48 PIGs were found to participate in the T cell receptor (TCR) signaling pathway according to the STRING database. Among these genes, ZAP70, CD3E, CD3G, CD3D, and CD247 were part of the TCR ‘signal-triggering module’; (3) High expression of the PIGs involved in the TCR signaling pathway was associated with improved OS in 5 cancer types (breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), and sarcoma (SARC)), but was associated with decreased OS in brain lower-grade glioma (LGG).ConclusionsThe TCR signaling pathway played a distinct role in the OS of these 6 cancer types.

Exploration of the immune-related signature and immune infiltration analysis for breast ductal and lobular carcinoma

In this study, we aimed to explore the tumour associated immune signature of breast cancer (BC) and conduct integrative analyses with immune infiltrates in BC. We downloaded the transcriptome profiling and clinical data of BC from The Cancer Genome Atlas (TCGA) database. The list of immune-related signatures was from the Innate database. The limma package was utilized to conduct the normalization, and we screened the differential immune signatures in BC. A univariate Cox regression model and the LASSO method were used to find the hub prognostic immune genes. The TAIG risk model was calculated based on the multivariate Cox regression results, and a receiver operating characteristic (ROC) curve was generated to assess the predictive power of TAIG. Moreover, we also conducted a correlation analysis between TAIG and the clinical characteristics. Additionally, we utilized the METABRIC cohort as the validation data set. The TIMER database is a comprehensive resource for performing systematic analyses of immune infiltrates across various malignancies. We evaluated the associations of immune signatures with several immune cells based on TIMER. Furthermore, we used the CIBERSORT algorithm to determine the fractions of immune cells in each sample and compared the differential distributions of immune infiltrates between two TAIG groups using the Wilcoxon rank-sum test. A total of 1,178 samples were obtained from the TCGA-BRCA database, but only 1,045 breast tumour samples were matched with complete transcriptome expression data. Meanwhile, we collected a total of 1,094 BC patients from the METABRIC cohort. We found a list of 1,399 differential immune signatures associated with survival, and functional analysis revealed that these genes participated in cytokine-cytokine receptor interactions, Th1 and Th2 cell differentiation and the JAK-STAT signalling pathway. The TAIG risk model was established from the multivariate Cox analysis, and we observed that high TAIG levels correlated with poor survival outcomes based on Kaplan-Meier analysis. The Kruskal-Wallis test suggested that high TAIG levels correlated with high AJCC-TNM stages and advanced pathological stages (P<0.01). We validated the well robustness of TAIG in METABRIC cohort and 5-year AUC reached up to 0.829. Moreover, we further uncovered the associations of hub immune signatures with immune cells and calculated the immune cell fractions in specific tumour samples based on gene signature expression. Last, we used the Wilcoxon rank-sum test to compare the differential immune density in the two groups and found that several immune cells had a significantly lower infiltrating density in the high TAIG groups, including CD8+ T cells (P=0.031), memory resting CD4+ T cells (P=0.026), M0 macrophages (P=0.023), and M2 macrophages (P=0.048). In summary, we explored the immune signature of BC and constructed a TAIG risk model to predict prognosis. Moreover, we integrated the identified immune signature with tumour-infiltrating immune cells and found adverse associations between the TAIG levels and immune cell infiltrating density.

Potential Prognostic Immune Biomarkers for Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Study: Two Precision Medical Predictive Tools

Background: More and more evidences proved that tumor immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. The current research aimed at exploring the potential associations of immune genes and tumor immune infiltrating cells with overall survival of ovarian cancer (OC). Methods: Differentially expression analyses were carried out between normal tissues and tumor tissues. Potential prognostic biomarkers were recognized by univariate Cox regression. Immune regulatory network was constructed on prognostic immune genes and their highly related transcription factors. Findings: Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL,ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5 and LTN1) were recognized as independent risk factors through multivariate Cox analyses. A prognostic nomogram was developed by using these prognostic genes. Concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3- and 5- year overall survival. This prognostic model could identify high risk patients with poor overall survival. Interpretation: The current study recognized fourteen prognostic immune biomarkers for ovarian cancer and revealed potential regulatory associations among immune genes and transcription factors. The current research further developed and validated two precision medical predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.ioSmart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.ioGene_Survival_Subgroup_Analysis_17_OC_ F1001/. These two precision medical predictive tools were helpful to improve individualized treatment decision-making. Funding Statement: The current research was funded by Medical Science and Technology Foundation of Guangdong Province (A2016450 and B2018237). Declaration of Interests: The authors have no conflict of interest to declare. Ethics Approval Statement: The studies in TCGA database and GEO database have received ethical approval from ethics committees of their respective research institutes. These studies obtained informed consent from patients before admission. The current study is a second study based on public datasets from TCGA database and GEO database. Details of all patients in public datasets have been anonymously processed and therefore the current research does not involve patients' privacy information. This study was performed according to public database policy and declaration of Helsinki. Ethical approval and informed consent were not applicable.

Immune gene signatures and integrative spatially-resolved digital profiling of prognostic biomarkers for mesothelioma.

e20071 Background: Malignant mesothelioma has been an incurable disease with few effective therapies. While PD-1 targeted therapies have elicited some patient responses, the overall response rate for mesothelioma is low. Since mesothelioma is derived from the mesothelium of the lung, we hypothesize that immune cells in the tumor microenvironment (TME) may behave differently than other solid tumors that are responsive to immunotherapy. Here we characterize prognostic immune gene signatures and spatial protein expression in the mesothelioma TME. Methods: 50 FFPE mesothelioma samples were analyzed using the NanoString PanCancer IO360 assay which measures expression of 770 genes, including the abundance of 14 immune cell types and of 32 IO signatures. All genes and signatures were correlated with overall survival (OS). GeoMx digital spatial profiling (DSP) was performed on 40 samples assessing the protein expression along 12 geometric circular regions-of-interest (ROI). Tissue slides were stained with a combination of fluorescent-labeled antibodies (pan-cytokeratin, CD3, CD68) and a panel of 38-antibodies each conjugated to a unique UV-photocleavable DNA barcode. UV light was applied to the defined ROI, which releases the DNA barcodes for quantitation on the nCounter platform. Results: Unsupervised clustering of samples based on gene signatures showed two distinct groups; one, with low expression of lymphocyte activation/exhaustion signatures and the second, with moderate expression of immune signatures. Two samples had high expression of all immune gene signatures, also confirmed by DSP had increased expression of T cell markers. Tumor proliferation (p &lt; 0.001), hypoxia (p = 0.04), glycolysis (p &lt; 0.001), B7-H3 (p = 0.007) and TGF-β (p = 0.001) signatures were significantly associated with shorter OS. Additional DSP profiling of these mesothelioma samples showed both T cell excluded and desert TMEs. Conclusions: We show that the mesothelioma TME has distinct immune biology associated with OS. Tumors from patients with poor survival had expression profiles previously described to be associated with immune excluded and desert phenotypes. We show that gene expression and DSP identifies unique targets for immunotherapy and we hypothesize that these findings may guide the development of combination trials that will be effective against mesothelioma.

Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II-III Melanoma.

Biomarkers are needed to stratify patients with stage II-III melanoma for clinical trials of adjuvant therapy because, while immunotherapy is protective, it also confers the risk of severe toxicity. We previously defined and validated a 53-immune gene melanoma immune profile (MIP) predictive both of distant metastatic recurrence and of disease-specific survival (DSS). Here, we test MIP on a third independent population. A retrospective cohort of 78 patients with stage II-III primary melanoma was analyzed using the NanoString assay to measure expression of 53 target genes, and MIP score was calculated. Statistical analysis correlating MIP with DSS, overall survival, distant metastatic recurrence, and distant metastasis-free interval was performed using ROC curves, Kaplan-Meier curves, and standard univariable and multivariable Cox proportional hazards models. MIP significantly distinguished patients with distant metastatic recurrence from those without distant metastatic recurrence using ROC curve analysis (AUC = 0.695; P = 0.008). We defined high- and low-risk groups based on the cutoff defined by this ROC curve and find that MIP correlates with both DSS and overall survival by ROC curve analysis (AUC = 0.719; P = 0.004 and AUC = 0.698; P = 0.004, respectively). Univariable Cox regression reveals that a high-risk MIP score correlates with DSS (P = 0.015; HR = 3.2). MIP identifies patients with low risk of death from melanoma and may constitute a clinical tool to stratify patients with stage II-III melanoma for enrollment in clinical trials.

Abstract P4-04-17: Evaluation of a prognostic immune gene expression signature in different breast cancer molecular subtypes

Abstract Background: Cancer immunotherapy is increasingly important in many malignancies. Breast cancers with greater numbers of tumor-infiltrating lymphocytes (TILs) exhibit better responses to neoadjuvant therapy. Additionally, increased CD8+ TILs in the breast tumor microenvironment have better outcomes. The Total Cancer Care (TCC) biorepository at Moffitt Cancer Center allows for the collection of all clinical patient data and tissue for analysis of novel biomarkers with prognostic and predictive abilities. Using TCC consented patients, we sought to determine if an immune based signature score derived from gene expression profile data would have a significant prognostic ability, and determine if this signature is differentially expressed within different PAM50 molecular subtypes. Methods: Tumors were arrayed on Affymetrix HuRSTA-2a520709 GeneChips (Affymetrix, Santa Clara, CA). The chips contain ∼60,000 probe sets representing ∼25,037 unique genes (Affymetrix HuRSTA-2a520709). Gene expression data was normalized using iterative rank-order normalization and de-batched using Principal Component Analysis (PCA). The immune score for each tumor was derived using the first component from a PCA model based on all immune related genes in breast primary tumor samples. Mean immune score between molecular subtypes was compared using one way ANOVA. Results: A total sample of 310 non-metastatic patients with full clinical and gene expression data were available for the analysis. Mean age was 55. Staging breakdown was Stage I: 26%, stage II: 46%, and stage III: 17%. For tumor grades, 10% were well differentiated, 45% poorly or undifferentiated, 41% moderately differentiated, and 3% missing. Mean follow up for overall survival (OS) was 6.84 years and 6.10 years for recurrence free survival (RFS). For IHC subtypes 62% were hormone receptor positive, 18% triple negative, and 20% HER2 positive. The mean immune score across the whole population was 0.05 (range -2.22 to 2.27). Mean scores were higher in PAM50 basal (.379, 95% CI .143 to .615) and HER2 enriched (.316, 95% CI .143 to .6155) vs. luminal A (-.215, 95% CI -.34 to -.09)/B (-.168, 95% CI -.332 to -.004) subtypes. Immune score was not prognostic for OS hazard ratio (HR) 1.11 (95% CI: 0.91-1.36) p=.30, but was for RFS HR=0.71 (95% CI: 0.53-0.96) p=.02 in a multivariate analysis controlling for age, race, stage, grade, adjuvant treatment, and molecular subtype. Conclusions: The mean immune score is higher in basal and HER2 enriched tumors, suggesting we can potentially identify immune infiltrate enriched patients for immunotherapy trials using existing gene expression profiling data routinely obtained on breast cancer primary tumors. Correlation between score and lymphocyte infiltrate on histology is ongoing and will be presented. A higher score also appears to be an independent predictor of RFS which will need to be confirmed in additional independent well annotated datasets. Citation Format: Sheen MA, Connor CG, Berglund AE, Prabhakaran S, Rizk VT, Soliman HH. Evaluation of a prognostic immune gene expression signature in different breast cancer molecular subtypes. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-04-17.

Immune gene signatures and tumor intrinsic markers delineate novel immunogenic subtypes of breast cancer

The abundance and functional orientation of tumor-infiltrating effector cells has long been observed to predict for reduced incidence of clinical metastasis and cancer-specific death. Using bioinformatics to mine large breast tumor microarray datasets, we and others have identified prognostic immune gene signatures, or metagenes. Robust evidence indicates that these metagenes are: 1) positively correlated with distant metastasis-free survival (DMFS) of patients, 2) comprised of genes that regulate immune cell-specific biology, and 3) reflective of the relative abundance of discernible populations of tumor infiltrating leukocytes. In recent work we have leveraged the statistical associations between the immune metagenes and the DMFS of breast cancer patients to explore the underlying phenotypes that differ in their ability to potentiate long-term, immune-mediated tumor rejection. Using a tumor classification model that combines the prognostic attributes of three distinct immune metagenes, termed the B/P, T/NK and M/D metagenes, we have identified molecular subtypes of breast cancer that either permit or prohibit prognostication by the immune metagenes. On this basis, we have delineated the phenotypic attributes of breast cancer that distinguish two novel immunogenic tumor subtypes, which we have defined as: immune benefit-enabled (IBE) and immune benefit-disabled (IBD). Phenotypically, IBE tumors comprise of Basal-like tumors and highly-proliferative HER2-Enriched and Luminal-B subtypes, while IBD tumors comprise of Claudin-Low, Luminal-A, and low-proliferative HER2-Enriched and Luminal-B tumors. Prognostically, IBE tumors (n = 666) can be stratified by the immune metagene model into prognostic subgroups with high statistical significance (P<0.0001,log-rank test), while IBD tumors cannot (n = 1005, P = 0.3) consistent with the capacity for an innate anti-tumor immunity against IBE tumors, but not IBD tumors, that guards against distant metastasis. Furthermore, these observations were independent of adjuvant treatment, and may owe to differential activation of immunomodulatory pathways. Network analysis revealed that IBE/IBD differentially-expressed genes (q<0.01) underlie highly-significant pathway activation scores for TGF-beta signaling in IBD (p < 0.0001), and Interferon-gamma signaling in IBE (p < 0.0001). Furthermore, 15 of 19 genes comprising the previously described Immunologic Constant of Rejection (Marincola and colleagues) were significantly overexpressed in IBE tumors (P-value range: 0.05-3.5E-14). Thus, we conclude that breast tumors can be dichotomized into two subtypes fundamentally distinct with respect to their potential for metastasis-protective immune responsiveness. These findings indicate new contexts for studying anti-tumor immunity and oncogenic mechanisms of immunosuppression in breast cancer. Whether IBE and IBD subtypes represent clinically-relevant contexts for assessing patient prognosis or evaluating the efficacy of immunotherapeutic treatments warrants further investigation (See Figure ​Figure11). Figure 1 (A-D) The immune metagenes are prognostic of IBE but no IBD breast cancer. (A) Heatmaps of metagene expression levels (rows) across 1,954 tumors (columns). Key shows color scale of mean centered, log2-transformed gene signal intensities. For each metagene, ...

Increased Immune Gene Expression and Immune Cell Infiltration in High-Grade Astrocytoma Distinguish Long-Term from Short-Term Survivors

Survival in the majority of high-grade astrocytoma (HGA) patients is very poor, with only a rare population of long-term survivors. A better understanding of the biological factors associated with long-term survival in HGA would aid development of more effective therapy and survival prediction. Factors associated with long-term survival have not been extensively studied using unbiased genome-wide expression analyses. In the current study, gene expression microarray profiles of HGA from long-term survivors were interrogated for discovery of survival-associated biological factors. Ontology analyses revealed that increased expression of immune function-related genes was the predominant biological factor that positively correlated with longer survival. A notable T cell signature was present within this prognostic immune gene set. Using immune cell-specific gene classifiers, both T cell-associated and myeloid linage-associated genes were shown to be enriched in HGA from long-term versus short-term survivors. Association of immune function and cell-specific genes with survival was confirmed independently in a larger publicly available glioblastoma gene expression microarray data set. Histology was used to validate the results of microarray analyses in a larger cohort of long-term survivors of HGA. Multivariate analyses demonstrated that increased immune cell infiltration was a significant independent variable contributing to longer survival, as was Karnofsky/Lansky performance score. These data provide evidence of a prognostic anti-tumor adaptive immune response and rationale for future development of immunotherapy in HGA.

Prognostic Immune Markers in Non–Small Cell Lung Cancer

Tumor-associated immune responses have polarized effects in regulating tumor growth. Although a clear association has been shown between the tumor immune response and clinical outcome in colorectal and ovarian cancers, the role of immune markers for stratifying prognosis in non-small cell lung cancer (NSCLC) is less defined. Herein, we review the prognostic significance of published immune markers in the tumor microenvironment and peripheral blood of NSCLC patients. To identify prognostic immune genes, we reviewed all published gene-profiling studies in NSCLC and delineated the significance of immune genes by doing subanalysis on the microarray database of the NIH Director's Challenge study. This first comprehensive review of prognostic immune markers provides a foundation for further investigating immune responses in NSCLC.