DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.     

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.     

Assessing the contribution of tumor mutational phenotypes to cancer progression risk

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression—overall survival and disease-free survival—can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post-baseline time. A simple solution is the last-value-carry-forward method, but this may result in bias for the risk model estimation and prediction. Another option is to jointly model the longitudinal and survival processes with a shared random effects model. However, when dealing with multiple biomarkers, this approach often results in high-dimensional integrals without a closed-form solution, and thus the computational burden limits its software development and practical use. In this article, we propose to process the longitudinal data by functional principal component analysis techniques, and then use the processed information as predictors in a class of flexible linear transformation models to predict the distribution of residual time-to-event occurrence. The measurement schemes for multiple biomarkers are allowed to be different within subject and across subjects. Dynamic prediction can be performed in a real-time fashion. The advantages of our proposed method are demonstrated by simulation studies. We apply our approach to the African American Study of Kidney Disease and Hypertension, predicting patients' risk of kidney failure or death by using four important longitudinal biomarkers for renal functions.     

Systematic and validated techniques for the detection of ovarian cancer emphasizing the electro-analytical approach

Ovarian cancer refers to a cancer that forms inside or on the surface of an ovary. It results in the abnormal growth of cells that can attack or spread to other parts of the body. According to the reports, early detection can minimize the risk of mortality and can lead to effective management of the disease. Though, recently used methods can only enable to detect the cancer at an advanced stage. Therefore, rapid and effective approaches for detecting ovarian cancer are urgently in demand. Researchers are fascinating towards an effective portable and early detection technique for the diagnosis of ovarian cancer. Biosensors are best alternative to afore mentioned demand as they are designed for detecting a particular biotic analyte by converting a biological entity such as antibody, blood, cell, etc. into an electronic signal that can be measured and analyzed. Biosensor technology can accommodate rapid and precise diagnosis of cancer-related cells and deliver an improved approach for making them sensitive and specific. Furthermore, choice of biomarkers also plays an important role in determining the stage of cancer. This review summarizes the classification of ovarian cancer, biomarkers and techniques employed for the detection of ovarian cancer. Besides, some of the future outlooks of bio sensing technology are also discussed.     

Biomarkers for screening of lung cancer and pre-neoplastic lesions in a high risk Chilean population

Background

The mortality of lung cancer (LC), increases each year in the world, in spite of any advances, in development of new drugs to advance stages of LC. The high incidence of LC has been associated with smoking habit, genetic diversity and environmental pollution. Antofagasta region has been reported to have the highest LC mortality rate in Chile and its inhabitants were exposed to arsenic in their drinking water in concentrations as high as 870 μg/L. Non-invasive techniques such as biomarkers (Automatic Quantitative Cytometry: AQC and DR70) and Auto Fluorescence Bronchoscopy (AFB) might be potentially useful as a supplementary diagnostic approach and early detection. Early detection is one of the most important factors to intervene and prevent cancer progression in LC. This is a work of an ongoing prospective bimodality cancer surveillance study in high risk LC volunteers. Enrolment was done in subjects from Antofagasta and Metropolitan regions. In addition, we enrolled subjects who were suspected of having lung cancer. AQC, DR70 and AFB were used as tools in the detection of pre-neoplastic (PNL) and neoplastic lesions (NL).

Results

Half of the samples, classified as suspicious by AFB, were confirmed as metaplasia or dysplasia by histopathology. For LC, DR70 showed a higher sensitivity (95.8%) and specificity (91.9%) than AQC. However, for PNL AQC showed a higher sensitivity (91.9%) than DR70 (27.3%), although both with low PPV values. As a pre screener, both biomarkers might be employed as complementary tools to detect LC, especially as serially combined tests, with a sensitivity of 60% and a PPV of 65.2%. Additionally, the use of parallel combined tests might support the detection of PNL (sensitivity 91.2%; PPV 49.1%).

Conclusion

This work adds information on cellular and molecular biomarkers to complement imaging techniques for early detection of LC in Latin America that might contribute to formulate policies concerning screening of LC. Supported by INNOVA-CORFO, Chile.     

Low penetrance genetic polymorphisms as potential biomarkers for colorectal cancer predisposition

Colorectal cancer is a leading form of cancer in both males and females. Early detection of individuals at risk of colorectal cancer allows proper treatment and management of the disease to be implemented, which can potentially reduce the burden of colorectal cancer incidence, morbidity and mortality. In recent years, the role of genetic susceptibility factors in mediating predisposition to colorectal cancer has become more and more apparent. Identification of high‐frequency, low‐penetrance genetic polymorphisms associated with the cancer has therefore emerged as an important approach which can potentially aid prediction of colorectal cancer risk. However, the overwhelming amount of genetic epidemiology data generated over the past decades has made it difficult for one to assimilate the information and determine the exact genetic polymorphisms that can potentially be used as biomarkers for colorectal cancer. This review comprehensively consolidates, based primarily on results from meta‐analyses, the recent progresses in the search of colorectal cancer‐associated genetic polymorphisms, and discusses the possible mechanisms involved.     

Current and proposed biomarkers of anthracycline cardiotoxicity in cancer: emerging opportunities in oxidative damage and autophagy

A biomarker is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention". Biomarkers can be utilized to detect disease, evaluate treatment risks, or determine treatment effectiveness. In the case of cancer, anthracyclines such as doxorubicin are front-line therapy to treat a number of different malignancies including breast cancer. However, a significant fraction of patients experience drug-induced cardiotoxicity. This toxicity is dose-limiting and can cause long-term morbidity or mortality. There is an unmet medical need to identify patients who are at risk for doxorubicin-induced cardiotoxicity, to detect cardiac damage early so that patient risk can be minimized, and to monitor the success of cardioprotective strategies. Therefore, doxorubicin treatment of cancer is an excellent example of the need for biomarkers to indicate drug safety in addition to drug efficacy. In this review we will discuss the mechanism of doxorubicinassociated cardiotoxicity, as well as other cancer therapies that induce cardiac toxicity by causing oxidative damage. We will also evaluate established and proposed biomarkers for cardiotoxicity based on our evolving knowledge of oxidative damage and subsequent autophagy. Finally, we will discuss advantages of combining oxidative damage- and autophagy-based protein biomarkers with current biomarkers such as troponins to facilitate early detection and mitigation of cardiotoxicity induced by cancer therapeutic agents.     

Discovery of biomarkers for gastric cancer: a proteomics approach

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.     

Liquid biopsy in pancreatic cancer – Current perspective and future outlook

Pancreatic cancer is a lethal condition with a rising incidence and often presents at an advanced stage, contributing to abysmal five-year survival rates. Unspecific symptoms and the current lack of biomarkers and screening tools hamper early diagnosis. New technologies for liquid biopsies and their respective evaluation in pancreatic cancer patients have emerged over recent years. The term liquid biopsy summarizes the sampling and analysis of circulating tumor cells (CTCs), small extracellular vesicles (sEVs), and tumor DNA (ctDNA) from body fluids. The major advantages of liquid biopsies rely on their minimal invasiveness and repeatability, allowing serial sampling for dynamic insights to aid diagnosis, particularly early detection, risk stratification, and precision medicine in pancreatic cancer. However, liquid biopsies have not yet developed into a new pillar for clinicians' routine armamentarium. Here, we summarize recent findings on the use of liquid biopsy in pancreatic cancer patients. We discuss current challenges and future perspectives of this potentially powerful alternative to conventional tissue biopsies.     

Biomarkers in cancer screening, research and detection: present and future: a review

Biomarkers provide a powerful and dynamic approach to understanding the spectrum of malignancies with applications in observational and analytic epidemiology, randomized clinical trials, screening, diagnosis and prognosis. Defined as alterations in the constituents of tissues or body fluids, these markers offer a means for homogeneous classification of a disease and risk factor, and they can extend one's basic information about the underlying pathogenesis of disease. The goals in cancer research include finding biomarkers that can be used for the early detection of cancers, design individual therapies, and to identify underlying processes involved in the disease. Because so many myriad processes are involved in the diseased states, the goal is similar to 'finding a needle in a haystack'. However, the development of many -omic technologies, such as genomics and proteomics, has allowed us to monitor a large number of key cellular pathways simultaneously. This has enabled the identification of biomarkers and signalling molecules associated with cell growth, cell death and cellular metabolism. These are also facilitating in monitoring the functional disturbance, molecular and cellular damage, and damage response. This brief review describes the development of biomarkers in cancer research and detection with emphasis on different proteomic tools for the identification and discovery of new biomarkers, different clinical assays to detect various biomarkers in different specimens, role of biomarkers in cancer screening and last but not the least, the challenges in this direction of cancer research.     

A panel of noncoding RNAs in non–small-cell lung cancer

Non–small-lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC could pave the way for effective therapies. Analysis of molecular genetic biomarkers in biological fluids has been proposed as a useful tool for cancer diagnosis. Here, we aimed to develop a panel of noncoding RNAs (ncRNAs) in sputum for NSCLC early detection. Expression of 11 ncRNAs were analyzed by real–time polymerase chain reaction in sputum samples of 30 NSCLC patients and 30 sex- and age-matched cancer-free controls. Stability of endogenous microRNAs (miRNAs) in sputum was evaluated after 3 and 6 days at 4°C, 6 months, and 1 year at −80°C. Nine ncRNAs showed significant differences of their expression in sputum between NSCLC patients and controls. A logistic regression model with the best prediction was built based on miR-145, miR-126, and miR-7. The composite of the three miRNAs produced 90% sensitivity and specificity in distinguishing NSCLC patients from the controls. Results indicate that miRNAs could be useful biomarkers based on their stability under various storage conditions and maintain differential changes between cancer and control groups. Moreover, measurement of miRNAs in sputum could be a noninvasive approach for detection of lung cancer.     

Biomarkers in molecular epidemiology studies for health risk prediction

The field of molecular epidemiology is very promising, as sophisticated techniques are being developed to address etiology, genetic susceptibility and mechanisms for induction of disease. The use of biomarkers plays a key role in these investigations because the information can be used to predict the development of disease and to implement disease prevention programs. However, as emphasized by Frederica P. Perera, the field is strewn with studies either that failed to use validated biomarkers or whose designs did not adequately consider the biology of the endpoints, and the availability of validated biomarkers of health risk is still limited. In this review, we have briefly described the usefulness of certain biomarkers for the documentation of exposure and early biological effects, with special concern for the prediction of cancer. An emphasis is placed on understanding the biological and health significance of biomarkers. By building reliable biomarker databases, a promising future is the integration of information from the genome programs to expand the scientific frontiers on etiology, health risk prediction and prevention of environmental disease.     

DNA methylation signatures in circulating cell-free DNA as biomarkers for the early detection of cancer

Detecting cell-free DNA(cfDNA) or circulating tumor DNA(ctDNA) in plasma or serum could serve as a "liquid biopsy", which would be useful for numerous diagnostic applications. cfDNA methylation detection is one of the most promising approaches for cancer risk assessment. Here, we reviewed the literature related to the use of serum or plasma circulating cell-free DNA for cancer diagnosis in the early stage and their power as future biomarkers.     

Search of potential gastric cancer biomarkers using low molecular weight blood plasma proteome profiling by mass spectrometry

Gastric cancer, one of the most widespread malignant tumors, still lacks reliable serum/plasma biomarkers of its early detection. In this study we have developed, unified, and tested a new methodology for search of gastric cancer biomarkers based on profiling of low molecular weight proteome (LMWP) (1–17 kDa). This approach included three main components: sample pre-fractionation, matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), data analysis by a bioinformatics software package. Applicability and perspectives of the developed approach for detection of potential gastric cancer markers during LMWP analysis have been demonstrated using 69 plasma samples from patients with gastric cancer (stages I-IV) and 238 control samples. The study revealed peptides/polypeptides, which may be potentially used for detection of this pathology.     

Biomarkers in cancer screening,research and detection: present and future: a review

Biomarkers provide a powerful and dynamic approach to understanding the spectrum of malignancies with applications in observational and analytic epidemiology, randomized clinical trials, screening, diagnosis and prognosis. Defined as alterations in the constituents of tissues or body fluids, these markers offer a means for homogeneous classification of a disease and risk factor, and they can extend one's basic information about the underlying pathogenesis of disease. The goals in cancer research include finding biomarkers that can be used for the early detection of cancers, design individual therapies, and to identify underlying processes involved in the disease. Because so many myriad processes are involved in the diseased states, the goal is similar to ‘finding a needle in a haystack’. However, the development of many -omic technologies, such as genomics and proteomics, has allowed us to monitor a large number of key cellular pathways simultaneously. This has enabled the identification of biomarkers and signalling molecules associated with cell growth, cell death and cellular metabolism. These are also facilitating in monitoring the functional disturbance, molecular and cellular damage, and damage response. This brief review describes the development of biomarkers in cancer research and detection with emphasis on different proteomic tools for the identification and discovery of new biomarkers, different clinical assays to detect various biomarkers in different specimens, role of biomarkers in cancer screening and last but not the least, the challenges in this direction of cancer research.     

Chiroptical spectroscopy and metabolomics for blood‐based sensing of pancreatic cancer

To enable the early diagnosis of pancreatic cancer, the search for and definition of reliable biomarkers remain a subject of great interest, with the specificity and sensitivity of the currently used biomarkers being below the required values. We tested a novel diagnostic approach for pancreatic cancer based on the specific molecular signature of blood plasma components. To acquire more detailed structural information, structure‐sensitive chiroptical methods (electronic circular dichroism and Raman optical activity) were supplemented by conventional Raman and infrared spectroscopies. The obtained spectra were subsequently processed by linear discriminant analysis yielding high values of specificity and sensitivity. In addition, to monitor not only large biomolecules as potential biomarkers but also those of low molecular weight, we conducted an analysis of blood plasma samples by using metabolomics. The achieved results suggest a panel of promising biomarkers for a reliable detection of pancreatic cancer.