Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small cell lung cancer from patients with nonmalignant lung disease

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base

BACKGROUND

Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.

RESULTS

We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.

CONCLUSIONS

We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

An engineered niche delineates metastatic potential of breast cancer

Metastatic breast cancer is often not diagnosed until secondary tumors have become macroscopically visible and millions of tumor cells have invaded distant tissues. Yet, metastasis is initiated by a cascade of events leading to formation of the pre-metastatic niche, which can precede tumor formation by a matter of years. We aimed to distinguish the potential for metastatic disease from nonmetastatic disease at early times in triple-negative breast cancer using sister cell lines 4T1 (metastatic), 4T07 (invasive, nonmetastatic), and 67NR (nonmetastatic). We used a porous, polycaprolactone scaffold, that serves as an engineered metastatic niche, to identify metastatic disease through the characteristics of the microenvironment. Analysis of the immune cell composition at the scaffold was able to distinguish noninvasive 67NR tumor-bearing mice from 4T07 and 4T1 tumor-bearing mice but could not delineate metastatic potential between the two invasive cell lines. Gene expression in the scaffolds correlated with the up-regulation of cancer hallmarks (e.g., angiogenesis, hypoxia) in the 4T1 mice relative to 4T07 mice. We developed a 9-gene signature (Dhx9, Dusp12, Fth1, Ifitm1, Ndufs1, Pja2, Slc1a3, Soga1, Spon2) that successfully distinguished 4T1 disease from 67NR or 4T07 disease throughout metastatic progression. Furthermore, this signature proved highly effective at distinguishing diseased lungs in publicly available datasets of mouse models of metastatic breast cancer and in human models of lung cancer. The early and accurate detection of metastatic disease that could lead to early treatment has the potential to improve patient outcomes and quality of life.

Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

Carbohydrate sulfotransferases: a review of emerging diagnostic and prognostic applications

Carbohydrate sulfotransferases (CHST) catalyse the biosynthesis of proteoglycans that enable physical interactions and signalling between different neighbouring cells in physiological and pathological states. The study aim was to provide an overview of emerging diagnostic and prognostic applications of CHST. PubMed database search was conducted using the keywords "carbohydrate sulfotransferase" together with appropriate inclusion and exclusion criteria, whereby 41 publications were selected. Additionally, 40 records on CHST genetic and biochemical properties were hand-picked from UniProt, GeneCards, InterPro, and neXtProt databases. Carbohydrate sulfotransferases have been applied mainly in diagnostics of connective tissue disorders, cancer and inflammations. The lack of CHST activity was found in congenital connective tissue disorders while CHST overexpression was detected in different malignancies. Mutations of CHST3 gene cause skeletal dysplasia, chondrodysplasia, and autosomal recessive multiple joint dislocations while increased tissue expression of CHST11, CHST12 and CHST15 is an unfavourable prognostic factor in ovarian cancer, glioblastoma and pancreatic cancer, respectively. Recently, CHST11 and CHST15 overexpression in the vascular smooth muscle cells was linked to the severe lung pathology in COVID-19 patients. Promising CHST diagnostic and prognostic applications have been described but larger clinical studies and robust analytical procedures are required for the more reliable diagnostic performance estimations.

Improved predictability of pancreatic ductal adenocarcinoma diagnosis using a blood immune cell biomarker panel developed from bulk mRNA sequencing and single-cell RNA-sequencing

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) remains a devastating cancer due to its poor survival rate, early detection, and resectability. This study aimed to determine the peripheral blood mononuclear cell (PBMC) immune biomarkers in patients with PDAC and investigate the PDAC-specific peripheral blood biomarker panel and validate its clinical performance.

METHODS

In this prospective, blinded, case-control study, a biomarker panel formula was generated using a development cohort-including healthy controls, patients at high risk of PDAC, and patients with benign pancreatic disease, PDAC, or other gastrointestinal malignancies-and its diagnostic performance was verified using a validation cohort, including patients with ≥ 1 lesion suspected as PDAC on computed tomography (CT).

RESULTS

RNA-sequencing of PBMCs from patients with PDAC identified three novel immune cell markers, IL-7R, PLD4, and ID3, as specific markers for PDAC. Regarding the diagnostic performance of the regression formula for the three biomarker panels, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 84.0%, 78.8%, 47.2%, 95.6%, and 79.8%, respectively. Based on the formula scores for the biomarker panel, the false-negative rate (FNR) of the biomarkers was 8% (95% confidence interval [CI] 3.0-13.0), which was significantly lower than that based on CT in the validation cohort (29.2%, 95% CI 20.8-37.6).

CONCLUSIONS

The regression formula constructed using three PBMC biomarkers is an inexpensive, rapid, and convenient method that shows clinically useful performance for the diagnosis of PDAC. It aids diagnoses and differential diagnoses of PDAC from pancreatic disease by lowering the FNR compared to CT. Clinical trial registration Clinical Research Information Service, KCT0004614 (08 January 2020).

Improper preanalytical processes on peripheral blood compromise RNA quality and skew the transcriptional readouts of mRNA and LncRNA

Genetic and epigenetic reprogramming caused by disease states in other tissues is always systemically reflected in peripheral blood leukocytes (PBLs). Accurate transcriptional readouts of Messenger RNA (mRNA) and Long non-coding RNA (lncRNA) in peripheral blood leukocytes are fundamental for disease-related study, diagnosis and treatment. However, little is known about the impact of preanalytical variables on RNA quality and downstream messenger RNA and Long non-coding RNA readouts. In this study, we explored the impact of RNA extraction kits and timing of blood placement on peripheral blood leukocyte-derived RNA quality. A novel enhanced evaluation system including RNA yields, purity, RNA integrity number (RIN) values and β-actin copies was employed to more sensitively identify RNA quality differences. The expression levels of informative mRNAs and Long non-coding RNAs in patients with chronic obstructive pulmonary disease (COPD) or triple-negative breast cancer (TNBC) were measured by Quantitative reverse transcription polymerase chain reaction (qRT-PCR) to investigate the impact of RNA quality on transcriptional readouts. Our results showed that the quality of RNA extracted by different kits varies greatly, and commercial kits should be evaluated and managed before batch RNA extraction. In addition, the quality of extracted RNA was highly correlated with the timing of blood placement, and the copy number of β-actin was significantly decreased after leaving blood at RT over 12 h. More importantly, compromised RNA leads to skewed transcriptional readouts of informative mRNAs and Long non-coding RNAs in patients with chronic obstructive pulmonary disease or triple-negative breast cancer. These findings have significant implications for peripheral blood leukocyte-derived RNA quality management and suggest that quality control is necessary prior to the analysis of patient messenger RNA and Long non-coding RNA expression.

Differential expression of immune-regulatory proteins C5AR1, CLEC4A and NLRP3 on peripheral blood mononuclear cells in early-stage non-small cell lung cancer patients

Changes in gene expression profiling of peripheral blood mononuclear cells (PBMC) appear to represent the host's response to the cancer cells via paracrine signaling. We speculated that protein expression on circulating T-lymphocytes represent T-lymphocyte trafficking before infiltration into the tumor microenvironment. The possibility of using protein expression on circulating T-lymphocytes as a biomarker to discriminate early-stage non-small cell lung cancer (NSCLC) was explored. Four independent PBMC gene expression microarray datasets (GSE12771, GSE13255, GSE20189 and GSE3934) were analyzed. We selected C5AR1, CLEC4A and NLRP3 based on their significant protein expression in tumor-infiltrating lymphocytes, but not in normal lymphoid tissue. A validation study using automated flow cytometry was conducted in 141 study participants including 76 treatment-naive early-stage non-small cell lung cancer patients (NSCLC), 12 individuals with non-malignant pulmonary diseases, and 53 healthy individuals. Median ratios of C5AR1, CLEC4A and NLRP3 specific antibody staining to CD3 positive cells in early-stage NSCLC patients compared to healthy controls were 0.014 [0-0.37] vs. 0.01 [0-0.07, p = 0.13], 0.03 [0-0.87] vs. 0.02 [0-0.13, p = 0.10] and 0.19 [0-0.60] vs. 0.09 [0.02-0.31, p < 0.0001], respectively. Median fluorescence intensity (MFI) of CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ expression in early-stage NSCLC patients compared to healthy volunteers was 185 [64.2-4801] vs. 107.5 [27-229, p < 0.0001], 91.2 [42.4-2355] vs. 71.25 [46.2-103, p = 0.0005], and 1585 [478-5224] vs. 758.5 [318-1976, p < 0.0001], respectively. NLRP3:CD3 ratio, CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ MFI were significantly higher in early-stage NSCLC than healthy volunteers with an area under the ROC curve of 0.69-0.76. The CD3⁺NLRP3⁺ MFI provided the most distinguishable expression at 71.5% sensitivity and 70% specificity. Furthermore, CD3⁺NLRP3⁺ MFI potentially discriminated between early-stage NSCLC from malignant-mimic inflammation and infection pulmonary disease. Further validation in various pulmonary inflammatory disease might be warranted. Our proof-of-principle findings strengthen the hypothesis that malignancies generate distinctive protein expression fingerprints on circulating T-lymphocytes.

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.

PBMCs: a new source of diagnostic and prognostic biomarkers

There are various types of molecular biomarkers that are derived from distinct starting materials. Although many indirect biomarkers are found in blood, their detection remains a challenging issue because of the high degree of fragmentation, minute quantity and a vast amount of non-specific background. The present review points out the sensitivity and specificity of peripheral blood mononuclear cells (PBMCs) as an intact source of biomarkers in a variety of diseases. Multiple recent studies that have used PBMCs as a source of biomarkers reveal the alteration of mRNAs/microRNAs (miRNAs) signature and methylation profile in many kinds of disorders; for instance, dysregulation of mRNAs/miRNAs in schizophrenia, diabetes and different types of cancers and change in the methylation status of LINE-1 in neoplasms. In conclusion with a strong probability, PBMCs mimic conditions of some tissues which are in contact with them like the tumour cells, hence providing a non-invasive and suitable source of biomarkers.

Transmissible cancer influences immune gene expression in an endangered marsupial, the Tasmanian devil (Sarcophilus harrisii)

Understanding the effects of wildlife diseases on populations requires insight into local environmental conditions, host defence mechanisms, host life‐history trade‐offs, pathogen population dynamics, and their interactions. The survival of Tasmanian devils (Sarcophilus harrisii) is challenged by a novel, fitness limiting pathogen, Tasmanian devil facial tumour disease (DFTD), a clonally transmissible, contagious cancer. In order to understand the devils’ capacity to respond to DFTD, it is crucial to gain information on factors influencing the devils’ immune system. By using RT‐qPCR, we investigated how DFTD infection in association with intrinsic (sex and age) and environmental (season) factors influences the expression of 10 immune genes in Tasmanian devil blood. Our study showed that the expression of immune genes (both innate and adaptive) differed across seasons, a pattern that was altered when infected with DFTD. The expression of immunogbulins IgE and IgM:IgG showed downregulation in colder months in DFTD infected animals. We also observed strong positive association between the expression of an innate immune gene, CD16, and DFTD infection. Our results demonstrate that sampling across seasons, age groups and environmental conditions are beneficial when deciphering the complex ecoevolutionary interactions of not only conventional host‐parasite systems, but also of host and diseases with high mortality rates, such as transmissible cancers.

The potential of using blood circular RNA as liquid biopsy biomarker for human diseases

Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. The majority of circRNAs are formed by a back-splicing process in pre-mRNA splicing. Their expression is dynamically regulated and shows spatiotemporal patterns among cell types, tissues and developmental stages. CircRNAs have important biological functions in many physiological processes, and their aberrant expression is implicated in many human diseases. Due to their high stability, circRNAs are becoming promising biomarkers in many human diseases, such as cardiovascular diseases, autoimmune diseases and human cancers. In this review, we focus on the translational potential of using human blood circRNAs as liquid biopsy biomarkers for human diseases. We highlight their abundant expression, essential biological functions and significant correlations to human diseases in various components of peripheral blood, including whole blood, blood cells and extracellular vesicles. In addition, we summarize the current knowledge of blood circRNA biomarkers for disease diagnosis or prognosis.

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.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

A proteogenomic portrait of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Genome-wide gene-smoking interaction study identified novel susceptibility loci for non-small cell lung cancer in Chinese populations

Gene-smoking interactions play important roles in the development of non-small cell lung cancer (NSCLC). To identify single nucleotide polymorphisms (SNPs) that modify the association of smoking behavior with NSCLC risk, we conducted a genome-wide gene-smoking interaction study in Chinese populations. The genome-wide interaction analysis between SNPs and smoking status (ever- versus never-smokers) was carried out using genome-wide association studies (GWAS) of NSCLC, which included 13,327 cases and 13,328 controls. Stratified analysis by histological subtypes was also conducted. We used a genome-wide significance threshold of 5×10 -8 for identifying significant gene-smoking interactions and 1×10 -6 for identifying suggestive results. Functional annotation was performed to identify potential functional SNPs and target genes. We identified three novel loci with significant or suggestive gene-smoking interaction. For NSCLC, the interaction between rs2746087 (20q11.23) and smoking status reached genome-wide significance threshold (OR = 0.63, 95%CI: 0.54-0.74, P = 3.31×10 -8), and the interaction between rs11912498 (22q12.1) and smoking status reached suggestive significance threshold (OR = 0.72, 95%CI: 0.63-0.82, P = 8.10×10 -7). Stratified analysis by histological subtypes identified suggestive interactions between rs459724 (5q11.2) and smoking status (OR = 0.61, 95%CI: 0.51-0.73, P = 7.55×10 -8) in the risk of lung squamous cell carcinoma. Functional annotation indicated that both classic and novel biological processes, including nicotine addiction and airway clearance, may modulate the susceptibility to NSCLC. These novel loci provide new insights into the biological mechanisms underlying NSCLC risk. Independent replication in large-scale studies is needed and experimental studies are warranted to functionally validate these associations.

Identification of BHLHE40 expression in peripheral blood mononuclear cells as a novel biomarker for diagnosis and prognosis of hepatocellular carcinoma

Novel and sensitive biomarkers is highly required for early detection and predicting prognosis of hepatocellular carcinoma (HCC). Here, we investigated transcription profiles from peripheral blood mononuclear cells (PBMCs) of 8 patients with HCC and PBMCs from co-culture model with HCC using RNA-Sequencing. These transcription profiles were cross compared with published microarray datasets of PBMCs in HCC to identify differentially expressed genes (DEGs). A total of commonly identified of 24 DEGs among these data were proposed as cancer-induced genes in PBMCs, including 18 upregulated and 6 downregulated DEGs. The KEGG pathway showed that these enriched genes were mainly associated with immune responses. Five up-regulated candidate genes including BHLHE40, AREG, SOCS1, CCL5, and DDIT4 were selected and further validated in PBMCs of 100 patients with HBV-related HCC, 100 patients with chronic HBV infection and 100 healthy controls. Based on ROC analysis, BHLHE40 and DDIT4 displayed better diagnostic performance than alpha-fetoprotein (AFP) in discriminating HCC from controls. Additionally, BHLHE40 and DDIT4 had high sensitivity for detecting AFP-negative and early-stage HCC. BHLHE40 was also emerged as an independent prognostic factor of overall survival of HCC. Together, our study indicated that BHLHE40 in PBMCs could be a promising diagnostic and prognostic biomarker for HBV-related HCC.

Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis

Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case–control design. We employed several approaches to statistically analyze the data and the methods indicated that the case–control differences were subtle but most distinguishable in metastatic case–control pairs in the period 0–3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case–control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes.

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.

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.

Lung Cancer and Immunity Markers

An in-depth understanding of lung cancer biology and mechanisms of tumor progression has facilitated significant advances in the treatment of lung cancer. There remains a pressing need for the development of innovative approaches to detect and intercept lung cancer at its earliest stage of development. Recent advances in genomics, computational biology, and innovative technologies offer unique opportunities to identify the immune landscape in the tumor microenvironment associated with early-stage lung carcinogenesis, and provide further insight in the mechanism of lung cancer evolution. This review will highlight the concept of immunoediting and focus on recent studies assessing immune changes and biomarkers in pulmonary premalignancy and early-stage non-small cell lung cancer. A protumor immune response hallmarked by an increase in checkpoint inhibition and inhibitory immune cells and a simultaneous reduction in antitumor immune response have been correlated with tumor progression. The potential systemic biomarkers associated with early lung cancer will be highlighted along with current clinical efforts for lung cancer interception. Research focusing on the development of novel strategies for cancer interception prior to the progression to advanced stages will potentially lead to a paradigm shift in the treatment of lung cancer and have a major impact on clinical outcomes.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Engineered Niches to Analyze Mechanisms of Metastasis and Guide Precision Medicine

Cancer metastasis poses a challenging problem both clinically and scientifically, as the stochastic nature of metastatic lesion formation introduces complexity for both early detection and the study of metastasis in preclinical models. Engineered metastatic niches represent an emerging approach to address this stochasticity by creating bioengineered sites where cancer can preferentially metastasize. As the engineered niche captures the earliest metastatic cells at a nonvital location, both noninvasive and biopsy-based monitoring of these sites can be performed routinely to detect metastasis early and monitor alterations in the forming metastatic niche. The engineered metastatic niche also provides a new platform technology that serves as a tunable site to molecularly dissect metastatic disease mechanisms. Ultimately, linking the engineered niches with advances in sensor development and synthetic biology can provide enabling tools for preclinical cancer models and fosters the potential to impact the future of clinical cancer care.

Lung Cancer and Immunity Markers

An in-depth understanding of lung cancer biology and mechanisms of tumor progression has facilitated significant advances in the treatment of lung cancer. There remains a pressing need for the development of innovative approaches to detect and intercept lung cancer at its earliest stage of development. Recent advances in genomics, computational biology, and innovative technologies offer unique opportunities to identify the immune landscape in the tumor microenvironment associated with early-stage lung carcinogenesis, and provide further insight in the mechanism of lung cancer evolution. This review will highlight the concept of immunoediting and focus on recent studies assessing immune changes and biomarkers in pulmonary premalignancy and early-stage non-small cell lung cancer. A protumor immune response hallmarked by an increase in checkpoint inhibition and inhibitory immune cells and a simultaneous reduction in antitumor immune response have been correlated with tumor progression. The potential systemic biomarkers associated with early lung cancer will be highlighted along with current clinical efforts for lung cancer interception. Research focusing on the development of novel strategies for cancer interception prior to the progression to advanced stages will potentially lead to a paradigm shift in the treatment of lung cancer and have a major impact on clinical outcomes.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Noninvasive biomarkers for lung cancer diagnosis, where do we stand?

The 2010's saw demonstration of the power of lung cancer screening to reduce mortality. However, with implementation of lung cancer screening comes the challenge of diagnosing millions of lung nodules every year. When compared to other cancers with widespread screening strategies (breast, colorectal, cervical, prostate, and skin), obtaining a lung nodule tissue biopsy to confirm a positive screening test remains associated with higher morbidity and cost. Therefore, non-invasive diagnostic biomarkers may have a unique opportunity in lung cancer to greatly improve the management of patients at risk. This review covers recent advances in the field of liquid biomarkers and computed tomographic imaging features, with special attention to new methods for combination of biomarkers as well as the use of artificial intelligence for the discrimination of benign from malignant nodules.

A gene expression-based immune signature for lung adenocarcinoma prognosis

BACKGROUND

Lung adenocarcinoma (LUAD) has become the most frequent histologic type of lung cancer in the past several decades. Recent successes with immune checkpoint blockade therapy have demonstrated that the manipulation of the immune system is a very potent treatment for LUAD. This study aims to explore the role of immune-related genes in the development of LUAD and establish a signature that can predict overall survival for LUAD patients.

METHODS

To identify the differential expression genes (DEGs) between normal and tumor tissues, we developed an analysis strategy to combine an independent-sample design and a paired-sample design using RNA-seq transcriptomic profiling data of The Cancer Genome Atlas LUAD samples. Further, we selected prognostic markers from DEGs and evaluated their prognostic value in a prediction model.

RESULTS

We identified and validated PD1, PDL1 and CTLA4 genes as prognostic markers, which are well-known immune checkpoints, and revealed two new potential prognostic immune checkpoints for LUAD, HHLA2 (logFC = 2.55, FDR = 1.89 × 10^-6) and VTCN1 (logFC = -2.86, FDR = 1.72 × 10^-11). Furthermore, we identified an 18-gene LUAD prognostic biomarker panel and observed that the classified high-risk group presented a significantly shorter overall survival time (HR = 3.57, p value = 4.07 × 10^-10). The prediction model was validated in five independent high-throughput gene expression datasets.

CONCLUSIONS

The identified DEG features may serve as potential biomarkers for prognosis prediction of LUAD patients and immunotherapy. Based on that assumption, we identified a gene expression-based immune signature for lung adenocarcinoma prognosis.

TFPI2 and NDRG4 gene promoter methylation analysis in peripheral blood mononuclear cells are novel epigenetic noninvasive biomarkers for colorectal cancer diagnosis

BACKGROUND

As a result of the growing prevalence of colorectal cancer (CRC), new screening and early detection methods are required. Among the novel biomarkers, DNA methylation has emerged as a high-potential diagnosis/screening molecular marker. The present study aimed to assess non-invasive early diagnosis of CRC by examining promoter methylation of TFPI2 and NDRG4 genes in peripheral blood mononuclear cells (PBMCs).

METHODS

Fifty CRC patients and 50 normal controls were recruited to the present study. Quantitative methylation of the promoter region of the TFPI2 and NDRG4 genes was analyzed in DNA extracted from PBMCs of all cases and control subjects using a methylation-quantification endonuclease-resistant DNA (MethyQESD) method.

RESULTS

The sensitivity and specificity of the TFPI2 gene for the diagnosis of CRC was 88% and 92%, respectively, and, for the NDRG4 gene, it was 86% and 92%, respectively. The methylation range for the TFPI2 gene was 43.93% and 11.56% in patients and controls, respectively, and, for the NDRG4 gene, it was 38.8% in CRC patients and 12.23% in healthy controls (p < 0.001). In addition, we observed that a higher percentage of methylation was correlated with the higher stage of CRC.

CONCLUSIONS

The results of the present study reveal that PBMCs are reliable sources of methylation analysis for CRC screening. Furthermore, the TFPI2 and NDRG4 genes provide sufficiently high sensitivity and specificity to be nominated for use in a novel noninvasive CRC screening method in PBMCs.

Identification of human peripheral blood monocyte gene markers for early screening of solid tumors

As cancer mortality is high in most regions of the world, early screening of cancer has become increasingly important. Minimally invasive screening programs that use peripheral blood mononuclear cells (PBMCs) are a new and reliable strategy that can achieve early detection of tumors by identifying marker genes. From 797 datasets, four (GSE12771, GSE24536, GSE27562, and GSE42834) including 428 samples, 236 solid tumor cases, and 192 healthy controls were chosen according to the inclusion criteria. A total of 285 genes from among 440 reported genes were selected by meta-analysis. Among them, 4 of the top significantly differentially expressed genes (ANXA1, IFI44, IFI44L, and OAS1) were identified as marker genes of PBMCs. Pathway enrichment analysis identified, two significant pathways, the 'primary immunodeficiency' pathway and the 'cytokine-cytokine receptor interaction' pathway. Protein- protein interaction (PPI) network analysis revealed the top 27 hubs with a degree centrality greater than 23 to be hub genes. We also identified 3 modules in Molecular Complex Detection (MCODE) analysis: Cluster 1 (related to ANXA1), Cluster 2 (related to IFI44 and IFI44L) and Cluster 3 (related to OAS1). Among the 4 marker genes, IFI44, IFI44L, and OAS1 are potential diagnostic biomarkers, even though their results were not as remarkable as those for ANXA1 in our study. ANXA1 is involved in the immunosuppressive mechanism in tumor-bearing hosts and may be used in a new strategy involving the use of the host's own immunity to achieve tumor suppression.

Biomarkers in Lung Cancer

Biomarkers that focus on lung cancer risk assessment, detection, prognosis, diagnosis, and personalized treatment are in various stages of development. This article provides an overview of lung cancer biomarker development, focusing on clinical utility and highlighting 2 unmet clinical needs: selection of high-risk patients for lung cancer screening and differentiation of early lung cancer from benign pulmonary nodules. The authors highlight biomarkers under development and those lung cancer screening and nodule management biomarkers post-clinical validation. Finally, trends in lung cancer biomarker development that may improve accuracy and accelerate implementation in practice are discussed.

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

Background

The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.

Methods

Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry.

Results

In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis.

Conclusions

This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

Characterization of MicroRNA and Gene Expression Profiles Following Ricin Intoxication

Ricin, derived from the castor bean plant, is a highly potent toxin, classified as a potential bioterror agent. Current methods for early detection of ricin poisoning are limited in selectivity. MicroRNAs (miRNAs), which are naturally occurring, negative gene expression regulators, are known for their tissue specific pattern of expression and their stability in tissues and blood. While various approaches for ricin detection have been investigated, miRNAs remain underexplored. We evaluated the effect of pulmonary exposure to ricin on miRNA expression profiles in mouse lungs and peripheral blood mononuclear cells (PBMCs). Significant changes in lung tissue miRNA expression levels were detected following ricin intoxication, specifically regarding miRNAs known to be involved in innate immunity pathways. Transcriptome analysis of the same lung tissues revealed activation of several immune regulation pathways and immune cell recruitment. Our work contributes to the understanding of the role of miRNAs and gene expression in ricin intoxication.

A Meta-Analysis of the Performance of a Blood-Based Exposure Response Gene Signature Across Clinical Studies on the Tobacco Heating System 2.2 (THS 2.2)

As part of emerging tobacco harm reduction strategies, modified risk tobacco products (MRTP) are being developed to offer alternatives that have the potential to reduce the individual risk and population harm compared with smoking cigarettes for adult smokers who want to continue using tobacco and nicotine products. MRTPs are defined as any tobacco products that are distributed for use to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. One such candidate MRTP is the Tobacco Heating System (THS) 2.2, which does not burn tobacco but instead heats it, thus producing significantly reduced levels of harmful and potentially harmful constituents compared with cigarettes. The clinical assessment of candidate MRTPs requires the development of exposure-response markers to distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Toward this end, a whole blood-derived gene signature was previously developed and reported. Four randomized, controlled, open-label, three-arm parallel group reduced exposure clinical studies have been conducted with subjects randomized to three arms: switching from cigarettes to THS 2.2, continuous use of cigarettes, or smoking abstinence. These clinical studies had an investigational period of 5 days in confinement, which was followed by an 85-day ambulatory period in two studies. Here we tested the previously developed blood-derived signature on the samples derived from those clinical studies. We showed that in all four studies, the signature scores were reduced consistently in subjects who either stopped smoking or switched to THS 2.2 compared with subjects who continued smoking cigarettes.

Inhibition of Ca2+ -activated chloride channel ANO1 suppresses ovarian cancer through inactivating PI3K/Akt signaling

Most common ovarian cancers are epithelial carcinoma in which the etiology for carcinogenesis remains elusive. ANO1/TMEM16A, a member of Ca²⁺ -activated Cl^- channels (CaCCs), has been demonstrated to promote epithelium-originated cancers and whether it plays a role in the pathogenesis of ovarian cancer is unknown. In our study we found that ANO1 proteins were overexpressed in human epithelial ovarian cancer cells and tissue samples. ANO1 protein upregulation was correlated with the clinical FIGO (International Federation of Gynecology and Obstetrics) staging and poor grade in ovarian cancer tissues. Interestingly, the upregulation of ANO1 gene expression was also detected in the peripheral blood mononuclear cells (PBMCs) from preoperative patients with ovarian tumors, and the down-regulation of ANO1 in the PBMCs from postoperative patients. Silencing of ANO1 inhibited proliferation and invasion of ovarian cancer cells. Mechanistically, ANO1 knockdown attenuated phosphorylation of PI3K/Akt, and inhibition of PI3K/Akt signaling by specific inhibitor LY294002 resulted in suppression of ovarian cancer cells growth promoted by ANO1 expression. Furthermore, intratumoral injection of ANO1 siRNA suppressed subcutaneous xenograft tumor growth in nude mice implanted with ovarian cancer SKOV3 cells. Taken together, our findings demonstrate that ANO1 overexpression is involved in the pathogenesis of human epithelial ovarian cancer. Inhibition of ANO1 upregulation or inactivating PI3K/Akt signaling may have therapeutic potential for epithelial ovarian cancer, and the detection of ANO1 expression level in PBMCs from patients may also serve as a biomarker for diagnosis and prognosis of epithelial ovarian cancers.

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 in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.

Gene expression profile of peripheral blood mononuclear cells may contribute to the identification and immunological classification of breast cancer patients

BACKGROUND

It has been reported that the gene expression profile of peripheral blood mononuclear cells (PBMCs) exhibits a unique gene expression signature in several types of cancer. In this study, we aimed to explore the breast cancer patient-specific gene expression profile of PBMCs and discuss immunological insight on host antitumor immune responses.

METHODS

We comprehensively analyzed the gene expression of PBMCs by RNA sequencing in the breast cancer patients as compared to that of healthy volunteers (HVs). Pathway enrichment analysis was performed on MetaCore^tm to search the molecular pathways associated with the gene expression profile of PBMCs in cancer patients compared with HVs.

RESULTS

We found a significant unique gene expression signature, such as the Toll-like receptor (TLR) 3- and TLR4-induced Toll/interleukin-1 receptor domain-containing adapter molecule 1 (TICAM1)-specific signaling pathway in the breast cancer patients as compared to that of healthy volunteers. Distinctive immunological gene expression profiles also showed the possibility of classifying breast cancer patients into subgroups such as T-cell inhibitory and monocyte-activating groups independent of known phenotypes of breast cancer.

CONCLUSIONS

These preliminary findings suggest that evaluation of gene expression patterns of PBMCs might be both a less invasive diagnostic procedure and a useful way to reveal immunological insight of breast cancer, including biomarkers for cancer immunotherapy, such as immune checkpoint inhibitor therapy.

Serum carbohydrate sulfotransferase 7 in lung cancer and non-malignant pulmonary inflammations

BACKGROUND

Carbohydrate sulfotransferases (CHST) were shown to be involved in carcinogenesis. The aim of the study was to assess the diagnostic value of serum CHST7 concentration in differentiation between lung cancer and non-malignant pulmonary inflammations.

METHODS

Clinical case-control study involving 125 participants was conducted: the control group containing cases of pneumonia and chronic obstructive pulmonary disease was compared to the lung cancer group composed of primary and metastatic cancers. Serum concentrations of CHST7 and routinely used markers including carcinoembryonic antigen (CEA), cytokeratin fragment 21-1 (CYFRA 21-1) and neuron-specific enolase (NSE) were determined for each participant using immunochemical methods. Statistical association, receiver operating characteristic (ROC) analysis and cross-validation were used for the evaluation of CHST7 either as a standalone biomarker or as a part of a biomarker panel.

RESULTS

In comparison to the control group, serum CHST7 was elevated in lung cancer (p<0.001), but no differences between the overall stages of primary cancers were detected (p=0.828). The differentiation performance in terms of ROC area under curve (AUC) was 0.848 making CHST7 superior biomarker to the NSE (p=0.031). In comparison to CEA and CYFRA 21-1, the performance differences were not detected. CHST7 was not correlated to other biomarkers, and its addition to the routine biomarker panel significantly improved the cross-validated accuracy (85.6% vs. 75.2%) and ROC AUC (p=0.004) of the differentiation using a machine learning approach.

CONCLUSIONS

Serum CHST7 is a promising biomarker for the differentiation between lung cancer and non-malignant pulmonary inflammations.

Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers

Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.

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.

Whole blood FPR1 mRNA expression predicts both non-small cell and small cell lung cancer

While long‐term survival rates for early‐stage lung cancer are high, most cases are diagnosed in later stages that can negatively impact survival rates. We aim to design a simple, single biomarker blood test for early‐stage lung cancer that is robust to preclinical variables and can be readily implemented in the clinic. Whole blood was collected in PAXgene tubes from a training set of 29 patients, and a validation set of 260 patients, of which samples from 58 patients were prospectively collected in a clinical trial specifically for our study. After RNA was extracted, the expressions of FPR1 and a reference gene were quantified by an automated one‐step Taqman RT‐PCR assay. Elevated levels of FPR1 mRNA in whole blood predicted lung cancer status with a sensitivity of 55% and a specificity of 87% on all validation specimens. The prospectively collected specimens had a significantly higher 68% sensitivity and 89% specificity. Results from patients with benign nodules were similar to healthy volunteers. No meaningful correlation was present between our test results and any clinical characteristic other than lung cancer diagnosis. FPR1 mRNA levels in whole blood can predict the presence of lung cancer. Using this as a reflex test for positive lung cancer screening computed tomography scans has the potential to increase the positive predictive value. This marker can be easily measured in an automated process utilizing off‐the‐shelf equipment and reagents. Further work is justified to explain the source of this biomarker.

What's new?

There have been several lung cancer screening trials evaluating the potential benefit of imaging for improving survival outcomes in lung cancer patients. While low‐dose computed tomography (CT) screening reduces mortality, it yields a 96.4% false‐positive rate. A potential strategy to improve screening may be the identification of additional tools that improve identification of false positives. Using prospectively collected whole blood samples, here the authors show that elevated FPR1 mRNA expression has a 68% sensitivity and 89% specificity. This single biomarker blood test, which can be readily implemented in the clinic, may increase the positive predictive value of detecting lung cancer.

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.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04–0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05–0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only (p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies.

Clinical features of cystatin A expression in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal malignancy known, with an extremely poor prognosis due to the lack of an efficient diagnostic scheme and no radical treatment option, except surgery. Therefore, understanding the pathophysiology of, and finding a novel biomarker to detect, PDAC should be prioritized. We observed an increase in mRNA expression of the cysteine protease inhibitor cystatin A (CSTA) in CD4⁺ T cells in peripheral blood cells of nine patients with PDAC, compared with the expression in seven healthy volunteers. Moreover, we confirmed significantly higher CSTA mRNA expression in a larger cohort of 41 patients with PDAC compared with that in 20 healthy volunteers. Correspondingly, the serum CSTA concentrations in 36 patients with PDAC were higher than those in 37 healthy volunteers, and this increase was correlated with PDAC clinical stage. Furthermore, the expression of CSTA and cathepsin B, which is a lysosomal cysteine protease inhibited by CSTA, was observed in tumor tissues and tumor‐infiltrating immune cells in 20 surgically resected PDAC tissues by immunohistochemical staining. Expression of CSTA was detected in some tumor tissues and many tumor‐infiltrating immune cells. Cathepsin B expression was also observed in most tumor tissues and tumor‐infiltrating immune cells. In conclusion, CSTA and its substrate cathepsin B are involved in PDAC‐related inflammation. The increment of CSTA expression in peripheral blood of patients with PDAC may have a potential role as a PDAC immunopathologic biomarker.

Retinoid X receptor suppresses a metastasis-promoting transcriptional program in myeloid cells via a ligand-insensitive mechanism

Retinoid X receptor (RXR) regulates several key functions in myeloid cells, including inflammatory responses, phagocytosis, chemokine secretion, and proangiogenic activity. Its importance, however, in tumor-associated myeloid cells is unknown. In this study, we demonstrate that deletion of RXR in myeloid cells enhances lung metastasis formation while not affecting primary tumor growth. We show that RXR deficiency leads to transcriptomic changes in the lung myeloid compartment characterized by increased expression of prometastatic genes, including important determinants of premetastatic niche formation. Accordingly, RXR-deficient myeloid cells are more efficient in promoting cancer cell migration and invasion. Our results suggest that the repressive activity of RXR on prometastatic genes is mediated primarily through direct DNA binding of the receptor along with nuclear receptor corepressor (NCoR) and silencing mediator of retinoic acid and thyroid hormone receptor (SMRT) corepressors and is largely unresponsive to ligand activation. In addition, we found that expression and transcriptional activity of RXRα is down-modulated in peripheral blood mononuclear cells of patients with lung cancer, particularly in advanced and metastatic disease. Overall, our results identify RXR as a regulator in the myeloid cell-assisted metastatic process and establish lipid-sensing nuclear receptors in the microenvironmental regulation of tumor progression.

Increased Expression of Plasma-Induced ABCC1 mRNA in Cystic Fibrosis

The ABCC1 gene is structurally and functionally related to the cystic fibrosis transmembrane conductance regulator gene (CFTR). Upregulation of ABCC1 is thought to improve lung function in patients with cystic fibrosis (CF); the mechanism underlying this effect is unknown. We analyzed the ABCC1 promoter single nucleotide polymorphism (SNP rs504348), plasma-induced ABCC1 mRNA expression levels, and ABCC1 methylation status and their correlation with clinical variables among CF subjects with differing CFTR mutations. We assigned 93 CF subjects into disease severity groups and genotyped SNP rs504348. For 23 CF subjects and 7 healthy controls, donor peripheral blood mononuclear cells (PBMCs) stimulated with plasma underwent gene expression analysis via qRT-PCR. ABCC1 promoter methylation was analyzed in the same 23 CF subjects. No significant correlation was observed between rs504348 genotypes and CF disease severity, but pancreatic insufficient CF subjects showed increased colonization with any form of Pseudomonas aeruginosa (OR = 3.125, 95% CI: 1.192-8.190) and mucoid P. aeruginosa (OR = 5.075, 95% CI: 1.307-28.620) compared to the pancreatic sufficient group. A significantly higher expression of ABCC1 mRNA was induced by CF plasma compared to healthy control plasma (p < 0.001). CF subjects with rs504348 (CC/CG) also had higher mRNA expression compared to those with the ancestral GG genotype (p < 0.005). ABCC1 promoter was completely unmethylated; therefore, we did not detect any association between methylation and CF disease severity. In silico predictions suggested that histone modifications are crucial for regulating ABCC1 expression in PBMCs. Our results suggest that ABCC1 expression has a role in CFTR activity thereby increasing our understanding of the molecular underpinnings of the clinical heterogeneity in CF.

Integrating personalized gene expression profiles into predictive disease-associated gene pools

Gene expression data are routinely used to identify genes that on average exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct personalized perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of the molecular manifestations of complex diseases by quantifying the expression-level similarities and differences among patients with the same phenotype. We show that despite the high heterogeneity of the individual perturbation profiles, patients with asthma, Parkinson and Huntington's disease share a broadpool of sporadically disease-associated genes, and that individuals with statistically significant overlap with this pool have a 80-100% chance of being diagnosed with the disease. The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identification, drug development, diagnosis and treatment.

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.

Identification of miRNAs as non-invasive biomarkers for early diagnosis of lung cancers

Current clinical diagnostic methods lack the specificity in detecting lung cancer patients. The issue is particularly critical for stage I and II patients. Considerable evidence showed microRNA plays a very important role in lung carcinogenesis. Here, we identified a panel of 41 miRNAs significantly elevated in patients with lung cancer, of which eight miRNAs were further validated in an independent sample cohort. Classification analysis using the panel of eight miRNAs generated a discriminatory power of 93.3 % sensitivity and 93.8 % specificity in separating non-small-cell lung cancer (NSCLC) patients from normal controls, indicating the miRNAs have a potential clinical utility in discriminating NSCLC. Interestingly, miR-1244 was found significantly elevated in the serum samples of lung cancer patients, and the test characteristics of the single miRNA were area under the curve (AUC) of 0.832 in NSCLC vs healthy controls, and 0.861 in NSCLC vs patients with unidentified pulmonary nodules. This is the first study showing serum miR-1244 could be a biomarker to screen lung cancer patients from the high-risk population.

Identification of gene expression signature for cigarette smoke exposure response--from man to mouse

Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.