Peripheral immune cell gene expression predicts survival of patients with non-small cell lung cancer

Development of a Molecular Blood-Based Immune Signature Classifier as Biomarker for Risks Assessment in Lung Cancer Screening

BACKGROUND

Low-dose CT (LDCT) screening trials have shown that lung cancer early detection saves lives. However, a better stratification of the screening population is still needed. In this respect, we generated and prospectively validated a plasma miRNA signature classifier (MSC) able to categorize screening participants according to lung cancer risk. Here, we aimed to deeply characterize the peripheral immune profile and develop a diagnostic immune signature classifier to further implement blood testing in lung cancer screening.

METHODS

Peripheral blood mononuclear cell (PBMC) samples collected from 20 patients with LDCT-detected lung cancer and 20 matched cancer-free screening volunteers were analyzed by flow cytometry using multiplex panels characterizing both lymphoid and myeloid immune subsets. Data were validated in PBMC from 40 patients with lung cancer and 40 matched controls and in a lung cancer specificity set including 27 subjects with suspicious lung nodules. A qPCR-based gene expression signature was generated resembling selected immune subsets.

RESULTS

Monocytic myeloid-derived suppressor cell (MDSC), polymorphonuclear MDSC, intermediate monocytes and CD8+PD-1+ T cells distinguished patients with lung cancer from controls with AUCs values of 0.94/0.72/0.88 in the training, validation, and lung cancer specificity set, respectively. AUCs raised up to 1.00/0.84/0.92 in subgroup analysis considering only MSC-negative subjects. A 14-immune genes expression signature distinguished patients from controls with AUC values of 0.76 in the validation set and 0.83 in MSC-negative subjects.

CONCLUSIONS

An immune-based classifier can enhance the accuracy of blood testing, thus supporting the contribution of systemic immunity to lung carcinogenesis.

IMPACT

Implementing LDCT screening trials with minimally invasive blood tests could help reduce unnecessary procedures and optimize cost-effectiveness.

The lung microbiome, peripheral gene expression, and recurrence-free survival after resection of stage II non-small cell lung cancer

BACKGROUND

Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence.

METHODS

In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves.

RESULTS

Over a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction.

CONCLUSIONS

We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

Evaluation of an RNAseq-Based Immunogenomic Liquid Biopsy Approach in Early-Stage Prostate Cancer

The primary objective of this study is to detect biomarkers and develop models that enable the identification of clinically significant prostate cancer and to understand the biologic implications of the genes involved. Peripheral blood samples (1018 patients) were split chronologically into independent training (n = 713) and validation (n = 305) sets. Whole transcriptome RNA sequencing was performed on isolated phagocytic CD14+ and non-phagocytic CD2+ cells and their gene expression levels were used to develop predictive models that correlate to adverse pathologic features. The immune-transcriptomic model with the highest performance for predicting adverse pathology, based on a subtraction of the log-transformed expression signals of the two cell types, displayed an area under the curve (AUC) of the receiver operating characteristic of 0.70. The addition of biomarkers in combination with traditional clinical risk factors (age, serum prostate-specific antigen (PSA), PSA density, race, digital rectal examination (DRE), and family history) enhanced the AUC to 0.91 and 0.83 for the training and validation sets, respectively. The markers identified by this approach uncovered specific pathway associations relevant to (prostate) cancer biology. Increased phagocytic activity in conjunction with cancer-associated (mis-)regulation is also represented by these markers. Differential gene expression of circulating immune cells gives insight into the cellular immune response to early tumor development and immune surveillance.

The peripheral immune response and lung cancer prognosis

Attempts to refine and improve outcome predictions using tumor gene expression have been recently reported. We show that peripheral blood mononuclear cell (PBMC)-associated gene signatures can predict outcome in non-small cell lung carcinoma patients independent of demographic data or TNM staging, and that this information may persist after tumor resection.

Expression of potassium channel genes predicts clinical outcome in lung cancer

Lung cancer is the most common cause of cancer deaths worldwide and several molecular signatures have been developed to predict survival in lung cancer. Increasing evidence suggests that proliferation and migration to promote tumor growth are associated with dysregulated ion channel expression. In this study, by analyzing high-throughput gene expression data, we identify the differentially expressed K⁺ channel genes in lung cancer. In total, we prioritize ten dysregulated K⁺ channel genes (5 up-regulated and 5 down-regulated genes, which were designated as K-10) in lung tumor tissue compared with normal tissue. A risk scoring system combined with the K-10 signature accurately predicts clinical outcome in lung cancer, which is independent of standard clinical and pathological prognostic factors including patient age, lymph node involvement, tumor size, and tumor grade. We further indicate that the K-10 potentially predicts clinical outcome in breast and colon cancers. Molecular signature discovered through K⁺ gene expression profiling may serve as a novel biomarker to assess the risk in lung cancer.

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

Low-dose CT (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign or malignant involves either repeated scans or invasive procedures that sample the lung tissue. Noninvasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high-risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. Malignant samples were predominantly stage 1 by pathologic diagnosis and 97% of the benign samples were confirmed by 4 years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data were then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC), which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed three clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20 mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, noninvasive method for the diagnosis of pulmonary nodules in patients with non-small cell lung cancer. SIGNIFICANCE: These findings describe a minimally invasive and clinically practical pulmonary nodule classifier that has good diagnostic ability at distinguishing benign from malignant pulmonary nodules.

Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms

BACKGROUND

Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA.

METHODS

Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls).

RESULTS

The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort.

CONCLUSIONS

Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Screening for early stage lung cancer and its correlation with lung nodule detection

Currently, the most effective way of reducing lung cancer mortality is early diagnosis of lung cancer. The National Lung Screening Trial has proved the efficacy of lung cancer screening using low-dose computed tomography to reduce lung cancer mortality. However, many questions remain surrounding lung cancer screening implementation, among which include how to select the optimal risk population, the personalized screening interval based different levels of risk, methods to improve diagnostic discrimination between malignant and benign disease in detected lung nodules, and the roles of biomolecular markers in stratifying risk and in guiding the management of indeterminate nodules. This review concentrates on the latest developments of lung cancer screening and provides an overview of the main unanswered questions on lung nodule detection.

Repeated observation of immune gene sets enrichment in women with non-small cell lung cancer

There are different biological and clinical patterns of lung cancer between genders indicating intrinsic differences leading to increased sensitivity to cigarette smoke-induced DNA damage, mutational patterns of KRAS and better clinical outcomes in women while differences between genders at gene-expression levels was not previously reported. Here we show an enrichment of immune genes in NSCLC in women compared to men. We found in a GSEA analysis (by biological processes annotated from Gene Ontology) of six public datasets a repeated observation of immune gene sets enrichment in women. "Immune system process", "immune response", "defense response", "cellular defense response" and "regulation of immune system process" were the gene sets most over-represented while APOBEC3G, APOBEC3F, LAT, CD1D and CCL5 represented the top-five core genes. Characterization of immune cell composition with the platform CIBERSORT showed no differences between genders; however, there were differences when tumor tissues were compared to normal tissues. Our results suggest different immune responses in NSCLC between genders that could be related with the different clinical outcome.

Panels of tumor-derived RNA markers in peripheral blood of patients with non-small cell lung cancer: their dependence on age, gender and clinical stages

Peripheral blood mononuclear cell (PBMC)-derived gene signatures were investigated for their potential use in the early detection of non-small cell lung cancer (NSCLC). In our study, 187 patients with NSCLC and 310 age- and gender-matched controls, and an independent set containing 29 patients for validation were included. Eight significant NSCLC-associated genes were identified, including DUSP6, EIF2S3, GRB2, MDM2, NF1, POLDIP2, RNF4, and WEE1. The logistic model containing these significant markers was able to distinguish subjects with NSCLC from controls with an excellent performance, 80.7% sensitivity, 90.6% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.924. Repeated random sub-sampling for 100 times was used to validate the performance of classification training models with an average AUC of 0.92. Additional cross-validation using the independent set resulted in the sensitivity 75.86%. Furthermore, six age/gender-dependent genes: CPEB4, EIF2S3, GRB2, MCM4, RNF4, and STAT2 were identified using age and gender stratification approach. STAT2 and WEE1 were explored as stage-dependent using stage-stratified subpopulation. We conclude that these logistic models using different signatures for total and stratified samples are potential complementary tools for assessing the risk of NSCLC.

Distinct transcriptional changes in non-small cell lung cancer patients associated with multi-antigenic RNActive® CV9201 immunotherapy

We recently completed a phase I/IIa trial of RNActive® CV9201, a novel mRNA-based therapeutic vaccine targeting five tumor-associated antigens in non-small cell lung cancer (NSCLC) patients. The aim of the study presented here was to comprehensively analyze changes in peripheral blood during the vaccination period and to generate hypotheses facilitating the identification of potential biomarkers correlating with differential clinical outcomes post RNActive® immunotherapy. We performed whole-genome expression profiling in a subgroup of 22 stage IV NSCLC patients before and after initiation of treatment with CV9201. Utilizing an analytic approach based on blood transcriptional modules (BTMs), a previously described, sensitive tool for blood transcriptome data analysis, patients segregated into two major clusters based on transcriptional changes post RNActive® treatment. The first group of patients was characterized by the upregulation of an expression signature associated with myeloid cells and inflammation, whereas the other group exhibited an expression signature associated with T and NK cells. Patients with an enrichment of T and NK cell modules after treatment compared to baseline exhibited significantly longer progression-free and overall survival compared to patients with an upregulation of myeloid cell and inflammatory modules. Notably, these gene expression signatures were mutually exclusive and inversely correlated. Furthermore, our findings correlated with phenotypic data derived by flow cytometry as well as the neutrophil-to-lymphocyte ratio. Our study thus demonstrates non-overlapping, distinct transcriptional profiles correlating with survival warranting further validation for the development of biomarker candidates for mRNA-based immunotherapy.

Biosynthesis and Functional Significance of Peripheral Node Addressin in Cancer-Associated TLO

Peripheral node addressin (PNAd) marks high endothelial venules (HEV), which are crucial for the recruitment of lymphocytes into lymphoid organs in non-mucosal tissue sites. PNAd is a sulfated and fucosylated glycoprotein recognized by the prototypic monoclonal antibody, MECA-79. PNAd is the ligand for L-selectin, which is expressed on the surface of naive and central memory T cells, where it mediates leukocyte rolling on vascular endothelial surfaces. Although PNAd was first identified in the HEV of peripheral lymph nodes, recent work suggests a critical role for PNAd in the context of chronic inflammatory diseases, where it can be used as a marker for the formation of tertiary lymphoid organs (TLOs). TLO form in tissues impacted by sustained inflammation, such as the tumor microenvironment where they function as local sites of adaptive immune cell priming. This allows for specific B- and T-cell responses to be initiated or reactivated in inflamed tissues without dependency on secondary lymphoid organs. Recent studies of cancer in mice and humans have identified PNAd as a biomarker of improved disease prognosis. Blockade of PNAd or its ligand, L-selectin, can abrogate protective antitumor immunity in murine models. This review examines pathways regulating PNAd biosynthesis by the endothelial cells integral to HEV and the formation and maintenance of lymphoid structures throughout the body, particularly in the setting of cancer.

AKAP4 is a circulating biomarker for non-small cell lung cancer

Cancer testis antigens (CTAs) are widely expressed in tumor tissues, circulating tumor cells (CTCs) and in cancer derived exosomes that are frequently engulfed by lymphoid cells. To determine whether tumor derived CTA mRNAs could be detected in RNA from purified peripheral blood mononuclear cells (PBMC) of non-small cell lung cancer (NSCLC) patients, we assayed for the expression of 116 CTAs in PBMC RNA in a discovery set and identified AKAP4 as a potential NSCLC biomarker. We validated AKAP4 as a highly accurate biomarker in a cohort of 264 NSCLCs and 135 controls from 2 different sites including a subset of controls with high risk lung nodules. When all (264) lung cancers were compared with all (135) controls the area under the ROC curve (AUC) was 0.9714. When 136 stage I NSCLC lung cancers are compared with all controls the AUC is 0.9795 and when all lung cancer patients were compared to 27 controls with histologically confirmed benign lung nodules, a comparison of significant clinical importance, the AUC was 0.9825. AKAP4 expression increases significantly with tumor stage, but independent of age, gender, smoking history or cancer subtype. Follow-up studies in a small number of resected NSCLC patients revealed a decrease of AKAP4 expression post-surgical resection that remained low in patients in remission and increased with tumor recurrence. AKAP4 is a highly accurate biomarker for the detection of early stage lung cancer.

Therapeutic Lymphoid Organogenesis in the Tumor Microenvironment

The inflammatory status of the tumor microenvironment (TME) has been heavily investigated in recent years. Chemokine- and cytokine-signaling pathways such as CCR7, CXCR5, lymphotoxin, and IL-36, which are involved in the generation of secondary lymphoid organs and effector immune responses, are now recognized as having value both as prognostic factors and as immunomodulatory therapeutics in the context of cancer. Furthermore, when produced in the TME, these mediators have been shown to promote the recruitment of immune cells, including T cells, B cells, dendritic cells (DCs), and other specialized immune cell subsets such as follicular DCs and T follicular helper cells, in association with the formation of "tertiary" lymphoid structures (TLSs) within or adjacent to sites of disease. Although TLSs are composed of a heterogeneous collection of immune cell types, whose composition differs based on cancer subtype, the qualitative presence of TLSs has been shown to represent a biomarker of good prognosis for cancer patients. A comprehensive understanding of the role each of these pathways plays within the TME may support the rational design of future immunotherapies to selectively promote/bolster TLS formation and function, leading to improved clinical outcomes across the vast range of solid cancer types.

Validation of a multiprotein plasma classifier to identify benign lung nodules

INTRODUCTION

Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs.

METHODS

A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples.

RESULTS

The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model.

CONCLUSIONS

This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.

Deconvolution of the gene expression profiles of valuable banked blood specimens for studying the prognostic values of altered peripheral immune cell proportions in cancer patients

Background

The altered composition of immune cells in peripheral blood has been reported to be associated with cancer patient survival. However, analysis of the composition of peripheral immune cells are often limited in retrospective survival studies employing banked blood specimens with long-term follow-up because the application of flow cytometry to such specimens is problematic. The aim of this study was to demonstrate the feasibility of deconvolving blood-based gene expression profiles (GEPs) to estimate the proportions of immune cells and determine their prognostic values for cancer patients.

Methods and Results

Here, using GEPs from peripheral blood mononuclear cells (PBMC) of 108 non-small cell lung cancer (NSCLC) patients, we deconvolved the immune cell proportions and analyzed their association with patient survival. Univariate Kaplan-Meier analysis showed that a low proportion of T cells was significantly associated with poor patient survival, as was the proportion of T helper cells; however, only the proportion of T cells was independently prognostic for patients by a multivariate Cox regression analysis (hazard ratio = 2.23; 95% CI, 1.01–4.92; p = .048). Considering that altered peripheral blood compositions can reflect altered immune responses within the tumor microenvironment, based on a tissue-based GEPs of NSCLC patients, we demonstrated a significant association between poor patient survival and the low level of antigen presentation, which play a critical role in T cell proliferation.

Conclusions

These results demonstrate that it is feasible to deconvolve GEPs from banked blood specimens for retrospective survival analysis of alterations of immune cell composition, and suggest the proportion of T cells in PBMC which might reflect the antigen presentation level within the tumor microenvironment can be a prognostic marker for NSCLC patients.

Role of Tertiary Lymphoid Structures (TLS) in Anti-Tumor Immunity: Potential Tumor-Induced Cytokines/Chemokines that Regulate TLS Formation in Epithelial-Derived Cancers

Following the successes of monoclonal antibody immunotherapies (trastuzumab (Herceptin^®) and rituximab (Rituxan^®)) and the first approved cancer vaccine, Provenge^® (sipuleucel-T), investigations into the immune system and how it can be modified by a tumor has become an exciting and promising new field of cancer research. Dozens of clinical trials for new antibodies, cancer and adjuvant vaccines, and autologous T and dendritic cell transfers are ongoing in hopes of identifying ways to re-awaken the immune system and force an anti-tumor response. To date, however, few consistent, reproducible, or clinically-relevant effects have been shown using vaccine or autologous cell transfers due in part to the fact that the immunosuppressive mechanisms of the tumor have not been overcome. Much of the research focus has been on re-activating or priming cytotoxic T cells to recognize tumor, in some cases completely disregarding the potential roles that B cells play in immune surveillance or how a solid tumor should be treated to maximize immunogenicity. Here, we will summarize what is currently known about the induction or evasion of humoral immunity via tumor-induced cytokine/chemokine expression and how formation of tertiary lymphoid structures (TLS) within the tumor microenvironment may be used to enhance immunotherapy response.

Analysis of the transcriptome in molecular epidemiology studies

The human transcriptome is complex, comprising multiple transcript types, mostly in the form of non-coding RNA (ncRNA). The majority of ncRNA is of the long form (lncRNA, ≥ 200 bp), which plays an important role in gene regulation through multiple mechanisms including epigenetics, chromatin modification, control of transcription factor binding, and regulation of alternative splicing. Both mRNA and ncRNA exhibit additional variability in the form of alternative splicing and RNA editing. All aspects of the human transcriptome can potentially be dysregulated by environmental exposures. Next-generation RNA sequencing (RNA-Seq) is the best available methodology to measure this although it has limitations, including experimental bias. The third phase of the MicroArray Quality Control Consortium project (MAQC-III), also called Sequencing Quality Control (SeQC), aims to address these limitations through standardization of experimental and bioinformatic methodologies. A limited number of toxicogenomic studies have been conducted to date using RNA-Seq. This review describes the complexity of the human transcriptome, the application of transcriptomics by RNA-Seq or microarray in molecular epidemiology studies, and limitations of these approaches including the type of cell or tissue analyzed, experimental variation, and confounding. By using good study designs with precise, individual exposure measurements, sufficient power and incorporation of phenotypic anchors, studies in human populations can identify biomarkers of exposure and/or early effect and elucidate mechanisms of action underlying associated diseases, even at low doses. Analysis of datasets at the pathway level can compensate for some of the limitations of RNA-Seq and, as more datasets become available, will increasingly elucidate the exposure-disease continuum.

Peripheral immune cell gene expression changes in advanced non-small cell lung cancer patients treated with first line combination chemotherapy

Introduction

Increasing evidence has shown that immune surveillance is compromised in a tumor-promoting microenvironment for patients with non-small cell lung cancer (NSCLC), and can be restored by appropriate chemotherapy.

Methods

To test this hypothesis, we analyzed microarray gene expression profiles of peripheral blood mononuclear cells from 30 patients with newly-diagnosed advanced stage NSCLC, and 20 age-, sex-, and co-morbidity-matched healthy controls. All the patients received a median of four courses of chemotherapy with cisplatin and gemcitabine for a 28-day cycle as first line treatment.

Results

Sixty-nine differentially expressed genes between the patients and controls, and 59 differentially expressed genes before and after chemotherapy were identified. The IL4 pathway was significantly enriched in both tumor progression and chemotherapy signatures. CXCR4 and IL2RG were down-regulated, while DOK2 and S100A15 were up-regulated in the patients, and expressions of all four genes were partially or totally reversed after chemotherapy. Real-time quantitative RT-PCR for the four up-regulated (S100A15, DOK2) and down-regulated (TLR7, TOP1MT) genes in the patients, and the six up-regulated (TLR7, CRISP3, TOP1MT) and down-regulated (S100A15, DOK2, IL2RG) genes after chemotherapy confirmed the validity of the microarray results. Further immunohistochemical analysis of the paraffin-embedded lung cancer tissues identified strong S100A15 nuclear staining not only in stage IV NSCLC as compared to stage IIIB NSCLC (p = 0.005), but also in patients with stable or progressive disease as compared to those with a partial response (p = 0.032). A high percentage of S100A15 nuclear stained cells (HR 1.028, p = 0.01) was the only independent factor associated with three-year overall mortality.

Conclusions

Our results suggest a potential role of the IL4 pathway in immune surveillance of advanced stage NSCLC, and immune potentiation of combination chemotherapy. S100A15 may serve as a potential biomarker for tumor staging, and a predictor of poor prognosis in NSCLC.