Effective Clinical Biomarkers Research Articles

CENPF promotes the proliferation of renal cell carcinoma in vitro.

Metastasis and drug resistance are the main causes of renal cell carcinoma (RCC) mortality. Currently, there are still a limited number of targeted therapies against advanced RCC. It is critical to develop new effective clinical biomarkers and drug targets in RCC. Several studies have shown that centromere protein F (CENPF), a microtubule binding protein, promotes cancer progression in various types of cancer. The purpose of this study was to explore the role of CENPF in RCC. Peripheral blood and corresponding tissue samples of 23 RCC patients and 23 normal physical examination patients who were treated in our hospital from 2018 to 2020 were collected, and CENPF expression was detected by quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemical (IHC) methods. The expression of CENPF was downregulated by small interfering RNA (siRNA) transfection, and the proliferation of the corresponding RCC cells and the corresponding cell cycle were detected. According to The Cancer Genome Atlas (TCGA) data analysis, CENPF is highly expressed in RCC, and its expression level is significantly related to the overall survival (OS) and recurrence-free survival (RFS) of RCC. In addition, high expression of CENPF was found in the tissues of RCC patients in our hospital. Knockdown of CENPF significantly reduced the proliferation of RCC cells in vitro, and knockdown of CENPF regulated the cell cycle by inhibiting the expression of cyclins such as CDK4, CDK6, and CyclinD1. CENPF can be used as an independent prognostic factor of RCC and regulate the proliferation ability and cell cycle of RCC cells. CENPF is a potential oncogene and prognostic marker in RCC.

Infection Biomarkers Based on Metabolomics.

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

MicroRNA Expression in Plasma of Esophageal Squamous Cell Carcinoma Patients.

BackgroundPatients with esophageal squamous cell carcinoma (ESCC) have a poor prognosis and there are no effective clinical biomarkers. Recently, stable microRNAs detected in the blood have been suggested as potential biomarkers in various cancers. Therefore, we investigated whether plasma microRNAs could be feasible biomarkers for ESCC.MethodsPeripheral blood samples were obtained from 16 healthy volunteers and 66 ESCC patients before treatment between May 2016 and April 2021. Plasma miR-18b, miR-21, miR-31, and miR-375 expression levels were measured using reverse transcription-quantitative polymerase chain reaction.ResultsCompared with those in healthy controls, the expression levels of plasma miR-21 were significantly higher (P = 0.022) and those of plasma miR-31 and miR-375 were significantly lower in ESCC patients (both P < 0.001). Plasma miR-18b expression levels increased in ESCC patients, but the difference was not significant (P = 0.164). The sensitivities and specificities of miR-21, miR-31, and miR-375 for differentiating ESCC patients from healthy controls were 87.5% and 61.9%, 87.5% and 98.4%, and 87.5% and 100%, respectively. There was no difference in expression levels of plasma miR-21, miR-31, and miR-375 according to clinicopathological characteristics of sex, age, tumor size and location, histologic grade, and tumor-node-metastasis stage.ConclusionOur study demonstrated that plasma miR-21, miR-31, and miR-375 could be potential biomarkers for the diagnosis of ESCC. Particularly, plasma miR-31 and miR-375 showed high sensitivity and specificity for differentiating ESCC patients from healthy controls.

Immune-Related Adverse Events as Clinical Biomarkers in Patients with Metastatic Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors.

Immune checkpoint inhibitors (ICIs) are an important treatment for metastatic renal cell carcinoma (mRCC). These agents may cause immune-related adverse events (irAEs), and the relationship between irAEs and outcomes is poorly understood. We investigated the association between irAEs and clinical outcomes in patients with mRCC treated with ICIs. We performed a retrospective study of 200 patients with mRCC treated with ICIs at Winship Cancer Institute from 2015 to 2020. Data on irAEs were collected from clinic notes and laboratory values and grades were determined using Common Terminology Criteria in Adverse Events version 5.0. The association with overall survival (OS) and progression-free survival (PFS) was modeled by Cox proportional hazards model. Logistic regression models were used to define odds ratios (ORs) for clinical benefit (CB). Landmark analysis and extended Cox models were used to mitigate lead-time bias by treating irAEs as a time-varying covariate. Most patients (71.0%) were male, and one-third of patients (33.0%) experienced at least one irAE, most commonly involving the endocrine glands (13.0%), gastrointestinal tract (10.5%), or skin (10.0%). Patients who experienced irAEs had significantly longer OS (hazard ratio [HR], 0.52; p=.013), higher chance of CB (OR, 2.10; p=.023) and showed a trend toward longer PFS (HR, 0.71; p=.065) in multivariate analysis. Patients who had endocrine irAEs, particularly thyroid irAEs, had significantly longer OS and PFS and higher chance of CB. In a 14-week landmark analysis, irAEs were significantly associated with prolonged OS (p=.045). Patients who experienced irAEs had significantly longer median OS (44.5 vs. 18.2 months, p=.005) and PFS (7.5 vs. 3.6 months, p=.003) without landmark compared with patients who did not. We found that patients with mRCC treated with ICIs who experienced irAEs, particularly thyroid irAEs, had significantly improved clinical outcomes compared with patients who did not have irAEs. This suggests that irAEs may be effective clinical biomarkers in patients with mRCC treated with ICIs. Future prospective studies are warranted to validate these findings. This study found that early onset immune-related adverse events (irAEs) are associated with significantly improved clinical outcomes in patients with metastatic renal cell carcinoma (mRCC) treated with immune checkpoint inhibitors (ICIs). In this site-specific irAE analysis, endocrine irAEs, particularly thyroid irAEs, were significantly associated with improved clinical outcomes. These results have implications for practicing medical oncologists given the increasing use of ICIs for the treatment of mRCC. Importantly, these results suggest that early irAEs and thyroid irAEs at any time on treatment with ICIs may be clinical biomarkers of clinical outcomes in patients with mRCC treated with ICIs.

Review of novel liquid-based biomarkers for prostate cancer: towards personalised and targeted medicine

Abstract Background: Prostate cancer is the most commonly diagnosed cancer in men and it is responsible for about 10% of all cancer mortalities in both American and Canadian men. At present, serum prostate-specific antigen levels remain the most commonly used test to detect prostate cancer, and the standard and definitive diagnosis of the disease is via prostate biopsy. Conventional tissue biopsies are usually invasive, expensive, painful, time-consuming, and unsuitable for screening and need to be consistently evaluated by expert pathologists and have limited repeatability. Consequently, liquid biopsies are emerging as a favourable alternative to conventional tissue biopsies, providing a non-invasive and cost-effective approach for screening, diagnosis, treatment and monitoring of prostate cancer patients. Materials and methods: We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on liquid-based biomarkers available in detectable quantities in patient bodily fluid samples. In this narrative review paper, we describe seven novel and promising liquid-based biomarkers that potentially account for individual patient variability as well as used in disease risk assessment, screening for early disease detection and diagnosis, identification of patients’ risk for metastatic disease and subsequent relapse, monitoring patient response to specific treatment and providing clinicians the potential to stratify patients likely to benefit from a particular treatment. Conclusions: The concept of precision medicine from prevention to treatment techniques that take individual patient variability into account will depend on the development of effective clinical biomarkers that interrogate key aberrant pathways potentially targetable with molecular targets or immunologic therapies. Liquid-based biomarkers with high sensitivity and specificity for prostate cancer are emerging as minimally invasive, lower risk, readily obtainable and easily repeatable technique for screening for early disease detection and diagnosis, patient stratification at diagnosis into different risk categories, identification of patients’ risk for metastatic disease and subsequent relapse, and real-time monitoring of patient response to specific treatment. Thus, effective liquid-based biomarkers will potentially shift the treatment paradigm of prostate cancer towards more personalised and targeted medicine.

Review of clinical and emerging biomarkers for early diagnosis and treatment management of pancreatic cancer: towards personalised medicine

AbstractBackground:Pancreatic cancer is the 12th most commonly diagnosed cancer and the 3rd leading cause of cancer mortality and accounts for approximately 2·7% of all newly diagnosed cancer cases and 6·4% of all cancer mortalities in Canada. It has a very poor survival rate mainly due to the difficulty of detecting the disease at an early stage. Consequently, in the advancement of disease management towards the concept of precision medicine that takes individual patient variabilities into account, several investigators have focused on the identification of effective clinical biomarkers with high specificity and sensitivity, capable of early diagnosis of symptomatic patients and early detection of the disease in asymptomatic individuals at high risk for developing pancreatic cancer.Materials and methods:We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on biomarkers for the management of pancreatic cancer. In this narrative review paper, we describe 13 clinical and emerging biomarkers for pancreatic cancers used in screening for early detection and diagnosis, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment.Conclusions:Current and emerging biomarkers for pancreatic cancer with high specificity and sensitivity has the potential to account for individual patient variabilities, for early detection of disease before the onset of metastasis to improve treatment outcome and patients’ survival, help screen high-risk populations, predict prognosis, provide accurate information of patient response to specific treatment and improve patients monitoring during treatment. Thus, the future holds promise for the use of effective clinical biomarkers or a panel of biomarkers for personalised patient-specific targeted medicine for pancreatic cancer.

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = –4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

Identification of differentially expressed miRNAs in differentiating benign from malignant pleural effusion

BackgroundTuberculosis pleural effusion (TPE) and malignant pleural effusion (MPE) are very common clinical complications. Considering the totally different prognosis and clinical treatment of TPE and MPE, the accurate and non-invasive diagnosis are very critical for patients with pleural effusion to initiate efficient management and treatment. However, effective clinical biomarkers were rarely explored to distinguish benign from MPE. The purpose of this study is to identify potential miRNAs which can probably be used to differentiate malignant pleural effusion from TPE.ResultsA total of 23 significantly differentially expressed miRNAs were identified in MPE, with 18 up-expressed and 5 down-expressed. And the target genes of the miRNAs mainly involved in the biology process of nervous system, cancer, immune system and metabolic process etc. Three high confident target genes, AGO4, FGF9 and LEF1 can be regulated by miR-195-5p, miR-182-5p and miR-34a-5p respectively. And these genes participate in the canonical pathway of regulation of the Epithelial-Mesenchymal and the biological functions of apoptosis, growth of tumor and cell proliferation of tumor cell lines. Further, RT-PCR validation results based on 64 collected individuals showed that the expression levels of the three miRNAs were 2–5 times higher in MPE samples, which were consistent with the microarray results. In addition, ROC curve analysis demonstrated that the combination of the three miRNAs can achieve higher AUC of 0.93 (p-value< 0.0001) to differentiate MPE from TPE.ConclusionsThe identified miR-195-5p, miR-182-5p and miR-34a-5p can become potential diagnostic biomarkers for MPE with further evidences.

Dramatically changed immune-related molecules as early diagnostic biomarkers of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the main type of lung cancer, with a low 5-year survival rate because of the absence of effective clinical biomarkers for early diagnosis. Based on the immunosurveillance theory, we proposed that changes in the immune system are more pronounced than tumour-associated antigens during the early stage of cancer. Therefore, a new strategy was designed to screen early diagnostic biomarkers from peripheral leukocytes in early-stage NSCLCs with transcriptome sequencing. A total of 358 immune-related differentially expressed genes were identified between early-NSCLC patients and healthy individuals. Orosomucoid-1 (ORM1, a acute phase protein), the total ORM and chitotriosidase-1 (involved in degradation of chitobiose) were selected for further verification in 210 serum samples by western blotting, ELISA and nephelometry immunoassay (based on immuno-scatter turbidmetry). Receiver operating characteristic curve analysis show that ORM1 and total ORM have excellent diagnostic efficacies, with area under the curve of 0.862 and 0.920, respectively, which significantly distinguished very early-NSCLC (IA) from healthy samples. Flow cytometry results showed that CD15+ neutrophils made up 73% of ORM1+ peripheral leukocytes. In mouse lung cancer model, serum ORM1, but not liver ORM1, changed significantly in the early stage of NSCLC. ORM1 expression in peripheral leukocytes was regulated by TGF-β and mediated by the TGF-β/Smad signalling pathway. Our results indicated that combined ORM and TGF-β could be a promising clinical biomarker in the diagnosis of early NSCLC.

Abstract B005: Linking transcriptomic and imaging features of the melanoma tumor microenvironment

Abstract Background: While immunotherapy has demonstrated success in melanoma, a deeper understanding of the heterogeneous tumor microenvironment is needed for stratifying patients for treatment. Technologies such as quantitative multiplex immunofluorescence (qmIF) imaging and transcriptomic profiling both have the potential to provide such insights. Using these tools, we had previously shown that immune cellular compositions and bulk gene expression in tumors are each related to patient survival. However, the connection between these modalities and their impact on prognosis is not well-understood. Here, we investigate the link between the microenvironmental composition of immune cells, such as cytotoxic T lymphocytes (CTLs) and macrophages, with observed transcriptomic signatures. Furthermore, we uncover spatial correlations in cellular positioning, which supports a mechanistic basis underlying these relationships. Methods: QmIF imaging was obtained from 104 stage II-III primary melanomas from Columbia University Irving Medical Center (CUIMC), which included staining for CTLs (CD3 and CD8), macrophages (CD68), and tumor cells (SOX10). HLA-DR and Ki67 were also stained in order to assess activation and proliferation of immune and tumor cells. Visualization, cell segmentation, and phenotyping were performed using inForm software within Mantra workstation. Spatial relationships between cells were quantified via the inhomogeneous pair correlation function (PCF), which compares the observed probability of two cell types being separated by a given distance to that expected by chance. Disease-specific survival status was known for 64 patients, while mRNA expression for 63 immune-related genes was obtained via NanoString for 53 patients. We assessed the similarity of each sample’s gene expression profile to the cellular subtype signatures from LM22, the reference standard for CIBERSORT. Results: The proportion of CTLs observed via qmIF was significantly associated with the CD8 T-cell transcriptomic signature (Spearman’s rho = 0.33, p &amp;lt; 0.02). Likewise, the proportion of HLA-DR+ macrophages was correlated with the M1 macrophage signature (rho = 0.31, p &amp;lt; 0.03). Point-wise discrepancies in PCFs were found in the spatial distribution of cells between disease-specific survivors versus non-survivors. Greater clustering between macrophages and CTLs was negatively associated with survival (Mann-Whitney, p &amp;lt; 6e-5), while greater clustering between macrophages and Ki67- tumor cells was positively associated with survival (p &amp;lt; 6e-4). Conclusions: Bulk RNA profiling reflects the underlying proportions of immune cells within the tumor microenvironment. Additionally, even after accounting for variations in cell density, different patterns of positioning among immune and tumor cells are related to prognosis. The importance of spatial proximity of immune cells to each other and to tumor cells suggests a mechanism by which their infiltration affects disease progression. Future work will investigate the genomic determinants that may lead to these spatial findings. Understanding these connections between the imaging and the genetics of melanoma will bring us closer to forming effective clinical biomarkers for evaluating immunotherapies. Citation Format: Andrew X. Chen, Robyn Gartrell, Douglas K. Marks, Thomas Hart, Emanuelle Rizk, Anthea Monod, Raul Rabadan, Yvonne Saenger. Linking transcriptomic and imaging features of the melanoma tumor microenvironment [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B005.

Ratio of Immune Response to Tumor Burden Predicts Survival Via Regulating Functions of Lymphocytes and Monocytes in Diffuse Large B-Cell Lymphoma

Background/Aims: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, and is the most common type of lymphoma in adults. Although significant progress in treatment has been made using chemotherapy combinations, there exist a large amount of relapse or refractory cases. Thus, effective clinical biomarkers for DLBCL are urgently needed. Our study aims to explore the predictive significance of using the immune response to tumor burden ratio [defined as the lymphocyte to monocyte ratio (LMR)/lactate dehydrogenase (LDH) levels] in 184 DLBCL patients and the potential mechanism underlying the use of the LMR to tumor burden ratio in predicting patient survival. Methods: The correlation between serum LDH levels and tumor levels assessed by PET-CT was determined using Spearman’s correlation analysis. Clinical data from 184 DLBCL patients was assessed using receiver operating characteristic curve analysis and survival analysis. The potential correlation between tumor burden and lymphocytes or monocytes was analyzed by immunohistochemical staining, flow cytometry, and ELISA analysis of patient samples. In addition, we performed in vitro studies to further determine the effects of tumor burden on the anti-tumor activity of T lymphocytes. Results: We observed that serum LDH was an excellent surrogate marker of tumor burden in DLBCL patients, and that the ratio of LMR to LDH was an independent prognostic biomarker capable of predicting survival in DLBCL patients. Further analysis showed that a high tumor burden was correlated with decreased Ki67 expression in T cells, either in the solid tumor tissue or in the circulating blood. In addition, based on an in vitro co-culture study, a higher tumor burden led to the suppression of the anti-tumor response of T cells. Furthermore, we found that a higher tumor burden was correlated with the differentiation of monocytes to tumor associated macrophages in the tumor micro-environment. Both results demonstrate the importance of considering both the immune system and tumor burden for prognostic analysis. Conclusion: Our study has identified a novel clinical biomarker, namely, the immune response to tumor burden ratio, that can be used to distinguish survival outcomes in DLBCL patients, and demonstrated the potential mechanism underlying the use of this biomarker, that incorporates both the immune system and tumor burden, for use in future clinical applications.

A Biophysical Basis for Mucus Solids Concentration as a Candidate Biomarker for Airways Disease

In human airways diseases, including cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD), host defense is compromised and airways inflammation and infection often result. Mucus clearance and trapping of inhaled pathogens constitute key elements of host defense. Clearance rates are governed by mucus viscous and elastic moduli at physiological driving frequencies, whereas transport of trapped pathogens in mucus layers is governed by diffusivity. There is a clear need for simple and effective clinical biomarkers of airways disease that correlate with these properties. We tested the hypothesis that mucus solids concentration, indexed as weight percent solids (wt%), is such a biomarker. Passive microbead rheology was employed to determine both diffusive and viscoelastic properties of mucus harvested from human bronchial epithelial (HBE) cultures. Guided by sputum from healthy (1.5–2.5 wt%) and diseased (COPD, CF; 5 wt%) subjects, mucus samples were generated in vitro to mimic in vivo physiology, including intermediate range wt% to represent disease progression. Analyses of microbead datasets showed mucus diffusive properties and viscoelastic moduli scale robustly with wt%. Importantly, prominent changes in both biophysical properties arose at ∼4 wt%, consistent with a gel transition (from a more viscous-dominated solution to a more elastic-dominated gel). These findings have significant implications for: (1) penetration of cilia into the mucus layer and effectiveness of mucus transport; and (2) diffusion vs. immobilization of micro-scale particles relevant to mucus barrier properties. These data provide compelling evidence for mucus solids concentration as a baseline clinical biomarker of mucus barrier and clearance functions.

Probing gender-specific lipid metabolites and diagnostic biomarkers for lung cancer using Fourier transform ion cyclotron resonance mass spectrometry

BackgroundThere are no effective clinical biomarkers for early and specific detection of lung cancer (LC). The changes in the levels of some serum metabolites of LC patients are associated with patient gender and LC stages. MethodsSerum metabolites of the LC patients (n=58) and healthy controls (n=495) were performed using direct-infusion positive ion electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry in combination with univariate and partial least squares discriminant analyses (PLS-DA). ResultsUnivariate analysis indicated that 141 of the 212 serum metabolites were significantly changed in the LC patients compared with healthy controls. PLS-DA model, based on the 141 metabolites, demonstrated the differential metabolite distribution in single-sex LC patients compared with the corresponding healthy controls, and also in LC females compared with LC males. Several lipids comprising fatty acid derivations, sphingomyelin (SM) and lysophosphatidylcholine (LPC) were associated with the LC progression. Classification using oleamide, long chain acyl carnitines, LPC(18:1), LPC(20:4), LPC(20:3), LPC(22:6), and SM(16:0/1) as a biomarker panel resulted in remarkable separation between the LC patients and healthy controls with sensitivity and specificity of 100% and 91%, respectively. ConclusionThe serum metabolites found in this study may play essential roles in gender-specific LC detection and early diagnosis of cancer.

Molecular Pathology of Pancreatic Cancer: From Bench-to-Bedside Translation

Pancreatic ductal adenocarcinoma (referred here as pancreatic cancer) is a lethal disease with the worst prognosis among all solid tumors. Surgical resection represents the only hope for cure but it is possible only in patients that present with local disease (about 20% of cases). Whether dismal prognosis of pancreatic cancer is a result of late diagnosis or early dissemination to distant organ is still a debate. Moreover, this disease shows an intrinsic chemotherapeutic resistance that has been mainly ascribed to the presence of a dense stromal reaction that significantly impairs drugs delivery. Clinical management of pancreatic cancer patients relies on few molecular markers (e.g., the diagnostic marker CA19-9) that, however, present several limitations to their use. The clinical usefulness of somatic alterations in well-characterized genes (such as KRAS and TP53), whose detection is technically feasible in different biological samples, has been extensively investigated leading to inconsistent results. Furthermore, none of the candidate molecular markers identified in recent years has shown an appropriate clinical performance and therefore none is routinely used. This depicts a scenario where the identification of novel and effective clinical biomarkers is mandatory. Very recent genome-wide comprehensive studies have shed light on the high degree of genetic complexity and heterogeneity of the pancreatic cancers. Although far from being introduced into the clinical settings, results from those studies are expected to change definitively the perspective through which we look at the clinical management of pancreatic cancer patients towards a personalized cancer medicine.