Evaluating markers for the early detection of cancer: overview of study designs and methods

Prospect of extracellular vesicles in tumor immunotherapy

Extracellular vesicles (EVs), as cell-derived small vesicles, facilitate intercellular communication within the tumor microenvironment (TME) by transporting biomolecules. EVs from different sources have varied contents, demonstrating differentiated functions that can either promote or inhibit cancer progression. Thus, regulating the formation, secretion, and intake of EVs becomes a new strategy for cancer intervention. Advancements in EV isolation techniques have spurred interest in EV-based therapies, particularly for tumor immunotherapy. This review explores the multifaceted functions of EVs from various sources in tumor immunotherapy, highlighting their potential in cancer vaccines and adoptive cell therapy. Furthermore, we explore the potential of EVs as nanoparticle delivery systems in tumor immunotherapy. Finally, we discuss the current state of EVs in clinical settings and future directions, aiming to provide crucial information to advance the development and clinical application of EVs for cancer treatment.

Early detection of pancreatic cancer: Study design and analytical considerations in biomarker discovery and early phase validation studies

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation.

METHODS

We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks.

RESULTS

The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described.

CONCLUSIONS

Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.

Designing cancer screening trials for reduction in late-stage cancer incidence

Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

The emerging role of the exosomal proteins in neuroblastoma

Exosomes are a subclass of extracellular vesicles shown to promote the cancer growth and support metastatic progression. The proteomic analysis of neuroblastoma-derived exosomes has revealed proteins involved in cell migration, proliferation, metastasis, and in the modulation of tumor microenvironment - thus contributing to the tumor development and an aggressive metastatic phenotype. This review gives an overview of the current understanding of the exosomal proteins in neuroblastoma and of their potential as diagnostic/prognostic biomarker of disease and therapeutics.

Prediagnostic evaluation of multicancer detection tests: design and analysis considerations

There is growing interest in multicancer detection tests, which identify molecular signals in the blood that indicate a potential preclinical cancer. A key stage in evaluating these tests is a prediagnostic performance study, in which investigators store specimens from asymptomatic individuals and later test stored specimens from patients with cancer and a random sample of controls to determine predictive performance. Performance metrics include rates of cancer-specific true-positive and false-positive findings and a cancer-specific positive predictive value, with the latter compared with a decision-analytic threshold. The sample size trade-off method, which trades imprecise targeting of the true-positive rate for precise targeting of a zero-false-positive rate can substantially reduce sample size while increasing the lower bound of the positive predictive value. For a 1-year follow-up, with ovarian cancer as the rarest cancer considered, the sample size trade-off method yields a sample size of 163 000 compared with a sample size of 720 000, based on standard calculations. These design and analysis recommendations should be considered in planning a specimen repository and in the prediagnostic evaluation of multicancer detection tests.

A comprehensive review on the composition, biogenesis, purification, and multifunctional role of exosome as delivery vehicles for cancer therapy

All forms of life produce nanosized extracellular vesicles called exosomes, which are enclosed in lipid bilayer membranes. Exosomes engage in cell-to-cell communication and participate in a variety of physiological and pathological processes. Exosomes function via their bioactive components, which are delivered to target cells in the form of proteins, nucleic acids, and lipids. Exosomes function as drug delivery vehicles due to their unique properties of innate stability, low immunogenicity, biocompatibility, biodistribution, accumulation in desired tissues, low toxicity in normal tissues, and the stimulation of anti-cancer immune responses, and penetration capacity into distance organs. Exosomes mediate cellular communications by delivering various bioactive molecules including oncogenes, oncomiRs, proteins, specific DNA, messenger RNA (mRNA), microRNA (miRNA), small interfering RNA (siRNA), and circular RNA (circRNA). These bioactive substances can be transferred to change the transcriptome of target cells and influence tumor-related signaling pathways. After considering all of the available literature, in this review we discuss the biogenesis, composition, production, and purification of exosomes. We briefly review exosome isolation and purification techniques. We explore great-length exosomes as a mechanism for delivering a variety of substances, including proteins, nucleic acids, small chemicals, and chemotherapeutic drugs. We also talk about the benefits and drawbacks of exosomes. This review concludes with a discussion future perspective and challenges. We hope that this review will provide us a better understanding of the current state of nanomedicine and exosome applications in biomedicine.

Therapeutic and Diagnostic Potential of Exosomes as Drug Delivery Systems in Brain Cancer

Cancer is designated as one of the principal causes of mortality universally. Among different types of cancer, brain cancer remains the most challenging one due to its aggressiveness, the ineffective permeation ability of drugs through the blood-brain barrier (BBB), and drug resistance. To overcome the aforementioned issues in fighting brain cancer, there is an imperative need for designing novel therapeutic approaches. Exosomes have been proposed as prospective "Trojan horse" nanocarriers of anticancer theranostics owing to their biocompatibility, increased stability, permeability, negligible immunogenicity, prolonged circulation time, and high loading capacity. This review provides a comprehensive discussion on the biological properties, physicochemical characteristics, isolation methods, biogenesis and internalization of exosomes, while it emphasizes their therapeutic and diagnostic potential as drug vehicle systems in brain cancer, highlighting recent advances in the research field. A comparison of the biological activity and therapeutic effectiveness of several exosome-encapsulated cargo including drugs and biomacromolecules underlines their great supremacy over the non-exosomal encapsulated cargo in the delivery, accumulation, and biological potency. Various studies on cell lines and animals give prominence to exosome-based nanoparticles (NPs) as a promising and alternative approach in the management of brain cancer.

Longitudinal Serum Protein Analysis of Women with a High Risk of Developing Breast Cancer Reveals Large Interpatient Versus Small Intrapatient Variations: First Results from the TESTBREAST Study

The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection.

A review on exosomes application in clinical trials: perspective, questions, and challenges

BACKGROUND

Exosomes are progressively known as significant mediators of cell-to-cell communication. They convey active biomolecules to target cells and have vital functions in several physiological and pathological processes, and show substantial promise as novel treatment strategies for diseases.

METHODS

In this review study, we studied numerous articles over the past two decades published on application of exosomes in different diseases as well as on perspective and challenges in this field.

RESULTS

The main clinical application of exosomes are using them as a biomarker, cell-free therapeutic agents, drug delivery carriers, basic analysis for exosome kinetics, and cancer vaccine. Different exosomes from human or plant sources are utilized in various clinical trials. Most researchers used exosomes from the circulatory system for biomarker experiments. Mesenchymal stem cells (MSCs) and dendritic cells (DCs) are two widely held cell sources for exosome use. MSCs-derived exosomes are commonly used for inflammation treatment and drug delivery, while DCs-exosomes are used to induce inflammation response in cancer patients. However, the clinical application of exosomes faces various questions and challenges. In addition, translation of exosome-based clinical trials is required to conform to specific good manufacturing practices (GMP). In this review, we summarize exosomes in the clinical trials according to the type of application and disease. We also address the main questions and challenges regarding exosome kinetics and clinical applications.

CONCLUSIONS

Exosomes are promising platforms for treatment of many diseases in clinical trials. This exciting field is developing hastily, understanding of the underlying mechanisms that direct the various observed roles of exosomes remains far from complete and needs further multidisciplinary research in working with these small vesicles. Video Abstract.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer's disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Comparison of total and endometrial circulating cell-free DNA in women with and without endometriosis

RESEARCH QUESTION

Do women with laparoscopically confirmed endometriosis have higher plasma concentrations of circulating cell-free DNA (cirDNA) than those without endometriosis?

DESIGN

Prospective study of women aged 18-45 years undergoing benign gynaecological laparoscopy at two tertiary hospitals. Venous blood was collected immediately before surgery, and women were allocated to the endometriosis or control groups based on surgical findings. Total plasma cirDNA and cirDNA integrity were measured by quantitative polymerase chain reaction (qPCR) targeting short (115 bases) and long (247 bases) ALU segments. Endometrial-derived cirDNA was measured by qPCR of bisulfite-treated cirDNA using primers selective for a FAM101A sequence uniquely unmethylated in endometrial tissue. Five cirDNA parameters were compared between the control and endometriosis cohorts: total cirDNA concentration, long-stranded cirDNA concentration, integrity ratio, endometrial cirDNA concentration and endometrial cirDNA proportion.

RESULTS

Twenty-eight endometriosis and 15 control samples were included. Women with and without endometriosis had cirDNA concentrations of 2.24 ± 0.89 ng/ml and 2.56 ± 0.92 ng/ml, respectively. Analysis by phenotype of endometriosis revealed a significantly higher endometrial cirDNA concentration in women with superficial disease (n = 10) compared with deep endometriosis (n = 18) (mean difference 0.14 ng/ml; 95% CI 0.15 to 0.26; P = 0.025), but not with controls.

CONCLUSIONS

No significant differences were found in any of the cirDNA parameters between women with and without endometriosis. The low statistical power and heterogenous pelvic pathology in the control group render it difficult to determine whether the negative results reflect a true lack of increase in cirDNA in endometriosis.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer

IMPORTANCE

Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.

OBJECTIVE

To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.

EXPOSURES

Breath testing and SVM algorithm.

MAIN OUTCOMES AND MEASURES

The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).

RESULTS

Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.

CONCLUSIONS AND RELEVANCE

This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

Diagnostic model for pancreatic cancer using a multi-biomarker panel

Purpose

Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.

Methods

Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups).

Results

The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value.

Conclusion

This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Simple Summary

The survival of patients diagnosed with ovarian cancer depends largely on the extent of the disease upon diagnosis. When confined to the ovaries, patients’ 10-year survival is more than 70%. This drastically drops to less than 5% when patients are diagnosed with far-advanced disease. Unfortunately, more than 80% of patients are diagnosed at advanced stage due to the lack of test for early detection. We report the development of a blood test measuring four proteins (macrophage migration inhibitory factor, osteopontin, prolactin and cancer antigen 125), which can distinguish ovarian cancer samples, even early-stage disease, from healthy samples in the population tested. This study is another step towards the application of a useful test for early detection of ovarian cancer that is both highly accurate and specific.

Abstract

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10⁻⁹). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Pancreatic ductal adenocarcinoma can be detected by analysis of volatile organic compounds (VOCs) in alveolar air

Background

In the last decade many studies showed that the exhaled breath of subjects suffering from several pathological conditions has a peculiar volatile organic compound (VOC) profile. The objective of the present work was to analyse the VOCs in alveolar air to build a diagnostic tool able to identify the presence of pancreatic ductal adenocarcinoma in patients with histologically confirmed disease.

Methods

The concentration of 92 compounds was measured in the end-tidal breath of 65 cases and 102 controls. VOCs were measured with an ion-molecule reaction mass spectrometry. To distinguish between subjects with pancreatic adenocarcinomas and controls, an iterated Least Absolute Shrinkage and Selection Operator multivariate Logistic Regression model was elaborated.

Results

The final predictive model, based on 10 VOCs, significantly and independently associated with the outcome had a sensitivity and specificity of 100 and 84% respectively, and an area under the ROC curve of 0.99. For further validation, the model was run on 50 other subjects: 24 cases and 26 controls; 23 patients with histological diagnosis of pancreatic adenocarcinomas and 25 controls were correctly identified by the model.

Conclusions

Pancreatic cancer is able to alter the concentration of some molecules in the blood and hence of VOCs in the alveolar air in equilibrium. The detection and statistical rendering of alveolar VOC composition can be useful for the clinical diagnostic approach of pancreatic neoplasms with excellent sensitivity and specificity.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4452-0) contains supplementary material, which is available to authorized users.

Diagnostic accuracy of serum antibodies to human papillomavirus type 16 early antigens in the detection of human papillomavirus-related oropharyngeal cancer

BACKGROUND

Because of the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV-16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV-16 E antigens in screening for OPC.

METHODS

This case-control study included 378 patients with OPC, 153 patients with nonoropharyngeal head and neck cancer (non-OPC), and 782 healthy control subjects. The tumor HPV status was determined with p16 immunohistochemistry and HPV in situ hybridization. HPV-16 E antibody levels in serum were identified with an enzyme-linked immunosorbent assay. A trained binary logistic regression model based on the combination of all E antigens was predefined and applied to the data set. The sensitivity and specificity of the assay for distinguishing HPV-related OPC from controls were calculated. Logistic regression analysis was used to calculate odds ratios with 95% confidence intervals for the association of head and neck cancer with the antibody status.

RESULTS

Of the 378 patients with OPC, 348 had p16-positive OPC. HPV-16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: 83% sensitivity and 99% specificity for p16-positive OPC).

CONCLUSIONS

A trained binary classification algorithm that incorporates information about multiple E antibodies has high sensitivity and specificity and may be advantageous for risk stratification in future screening trials. Cancer 2017;123:4886-94. © 2017 American Cancer Society.

Photothermal-triggered control of sub-cellular drug accumulation using doxorubicin-loaded single-walled carbon nanotubes for the effective killing of human breast cancer cells

Single-walled carbon nanotubes (SWNTs) are often the subject of investigation as effective photothermal therapy (PTT) agents owing to their unique strong optical absorption. Doxorubicin (DOX)-loaded SWNTs (SWNTs-DOX) can be used as an efficient therapeutic agent for combined near infrared (NIR) cancer photothermal and chemotherapy. However, SWNTs-DOX-mediated induction of cancer cell death has not been fully investigated, particularly the reaction of DOX inside cancer cells by PTT. In this study, we examined how the SWNTs-DOX promoted effective MDA-MB-231 cell death compared to DOX and PTT alone. We successfully synthesized the SWNTs-DOX. The SWNTs-DOX exhibited a slow DOX release, which was accelerated by NIR irradiation. Furthermore, DOX released from the SWNTs-DOX accumulated inside the cells at high concentration and effectively localized into the MDA-MB-231 cell nucleus. A combination of SWNTs-DOX and PTT promoted an effective MDA-MB-231 cell death by mitochondrial disruption and ROS generation. Thus, SWNTs-DOX can be utilized as an excellent anticancer agent for early breast cancer treatment.

Bead-based microarray immunoassay for lung cancer biomarkers using quantum dots as labels

In this study, we developed a multiplex immunoassay system that combines the suspension and planar microarray formats within a single layer of polydimethylsiloxane (PDMS) using soft lithography technology. The suspension format was based on the target proteins forming a sandwich structure between the magnetic beads and the quantum dot (QD) probes through specific antibody-antigen interactions. The planar microarray format was produced by fabricating an array of micro-wells in PDMS. Each micro-well was designed to trap a single microbead and eventually generated a microbead array within the PDMS chamber. The resultant bead-based on-chip assay could be used for simultaneously detecting three lung cancer biomarkers-carcinoembryonic antigen (CEA), fragments of cytokeratin 19 (CYFRA21-1) and neuron-specific enolase (NSE)-in 10 μl of human serum, with a wide linear dynamic range (1.03-111 ng/mL for CEA and CYFRA21-1; 9.26-1000 ng/ml for NSE) and a low detection limit (CEA: 0.19 ng/ml; CYFRA21-1: 0.97 ng/ml; NSE: 0.37 ng/ml; S/N=3). Our micro-well chip does not require complex e-beam lithography or the reactive ion etching process as with existing micro-well systems, which rely on expensive focused ion beam (FIB) milling or optical fiber bundles. Furthermore, the current approach is easy to operate without extra driving equipment such as pumps, and can make parallel detection for multiplexing with rapid binding kinetics, small reagent consumption and low cost. This work has demonstrated the importance of the successful application of on-chip multiplexing sandwich assays for the detection of biomarker proteins.

Direct and multiplex quantification of protein biomarkers in serum samples using an immuno-magnetic platform

A direct and ultrasensitive multiplex assay using an immuno-magnetic platform has been developed for the quantification of trace amounts of circulating cancer-associated antigens in serum.

A direct and ultrasensitive multiplex assay using an immuno-magnetic platform has been developed for the quantification of trace amounts of circulating cancer-associated antigens in serum. The detection is based on the specific immuno-interactions among the target antigen, detection antibody and capture antibody that is immobilized on the surface of magnetic nanoparticles. The sandwiched immuno-assembly is then labelled with turn-on fluorophores and detected with a fluorescence imaging system. To afford a high signal-to-noise ratio, three turn-on fluorophores with unique optical properties have been designed and synthesized to label the target antigens. The developed assay has achieved a remarkable LOD down to the femto-molar regime without sample pre-treatment. This versatile assay can efficiently differentiate the target antigen from a protein matrix and simultaneously quantify multiple cancer-associated antigens, for instance, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), and prostate specific antigen (PSA) using only 6 μL of serum sample in an hour. This novel system has a high applicability to serve as a universal and useful tool for early disease diagnostics.

Potential biomarkers for lung cancer screening

Notwithstanding the encouraging results of the National Lung Screening Trial (NLST) the scientific community still debates on the cost-benefit profile of low dose computed tomography (LDCT) lung cancer screening. Several major concerns regard how to identify subjects at high risk of developing lung cancer, the optimal diagnostic algorithm, the management of lung nodules and the high false positive rates. The use of complementary biomarkers would be a useful strategy for dealing with most of these issues. This short review will focus on candidates' biomarkers circulating in serum or plasma that already reached an advanced validation phase also in LDCT lung cancer screening series. The biomarkers presented below are examples of the value of searching candidates by looking not only to the tumor itself but also to the interplay between the tumor and the host in order to identify early changes related to the biological reactivity of the host to a developing cancer.

On the assessment of the added value of new predictive biomarkers

BACKGROUND

The surge in biomarker development calls for research on statistical evaluation methodology to rigorously assess emerging biomarkers and classification models. Recently, several authors reported the puzzling observation that, in assessing the added value of new biomarkers to existing ones in a logistic regression model, statistical significance of new predictor variables does not necessarily translate into a statistically significant increase in the area under the ROC curve (AUC). Vickers et al. concluded that this inconsistency is because AUC "has vastly inferior statistical properties," i.e., it is extremely conservative. This statement is based on simulations that misuse the DeLong et al. method. Our purpose is to provide a fair comparison of the likelihood ratio (LR) test and the Wald test versus diagnostic accuracy (AUC) tests.

DISCUSSION

We present a test to compare ideal AUCs of nested linear discriminant functions via an F test. We compare it with the LR test and the Wald test for the logistic regression model. The null hypotheses of these three tests are equivalent; however, the F test is an exact test whereas the LR test and the Wald test are asymptotic tests. Our simulation shows that the F test has the nominal type I error even with a small sample size. Our results also indicate that the LR test and the Wald test have inflated type I errors when the sample size is small, while the type I error converges to the nominal value asymptotically with increasing sample size as expected. We further show that the DeLong et al. method tests a different hypothesis and has the nominal type I error when it is used within its designed scope. Finally, we summarize the pros and cons of all four methods we consider in this paper.

SUMMARY

We show that there is nothing inherently less powerful or disagreeable about ROC analysis for showing the usefulness of new biomarkers or characterizing the performance of classification models. Each statistical method for assessing biomarkers and classification models has its own strengths and weaknesses. Investigators need to choose methods based on the assessment purpose, the biomarker development phase at which the assessment is being performed, the available patient data, and the validity of assumptions behind the methodologies.

Molecular biomarkers in esophageal, gastric, and colorectal adenocarcinoma

Cancers of the esophagus, stomach and colon contribute to a major health burden worldwide and over 20% of all cancer deaths. Biomarkers that should indicate pathogenic process and are measureable in an objective manner for these tumors are rare and not established in the clinical setting. In general biomarkers can be very useful for cancer management as they can improve clinical decision-making regarding diagnosis, surveillance, and therapy. Biomarkers can be different types of molecular entities (such as DNA, RNA or proteins), which can be detected, in different tissues or body fluids. However, more important is the type of biomarker itself, which allows diagnostic, prognostic or predictive analyses for different clinical problems. This review aims to systematically summarize the recent findings of genetic and epigenetic markers for gastrointestinal tumors within the last decade. While many biomarkers seem to be very promising, especially if used as panels, further development is urgently needed to address practical considerations of biomarkers in cancer treatment.

Cytokines: monitors of disease severity for the clinic

BACKGROUND

Cytokines communicate between the cells of the immune system and its targets to maintain homeostasis after injury or pathogenic events. They are involved in almost any pathological situation imaginable.

OBJECTIVE

To verify the importance of cytokines as biomarkers in current preclinical (aetiopathogenic, development of new therapies) and clinical studies (diagnosis, disease severity, prognosis and response to therapy).

METHOD/RESULTS

A Medline search with the query 'cytokine' AND 'biomarker' AND a variable for a variety of biomedical fields, followed by deeper-level searches, demonstrated the immense popularity of cytokines as biomarkers in almost any biomedical field.

CONCLUSION

As cytokines are not disease-specific they do not serve as single diagnostic biomarkers. The strength of the cytokines resides in monitoring disease severity, prognosis and response to treatment.

Better cancer biomarker discovery through better study design

BACKGROUND

High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.

METHODS

We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.

RESULT

Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.

CONCLUSIONS

For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.

Personalized medicine using DNA biomarkers: a review

Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers—DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.

Variations in discovery-based preeclampsia candidate genes

Preeclampsia is a common and potentially lethal pregnancy disorder with lifelong increased risk of cardiovascular disease in survivors. Our prior global gene expression microarray analysis led to a novel set of 36 candidates in first trimester placentas of women who subsequently developed preeclampsia. In this report, we present preliminary studies demonstrating biomarkers of genotype and methylation variations in a subset of these candidate genes in maternal leukocyte and fetoplacental DNA of 28 case and 27 control dyads. We tested 84 single nucleotide polymorphisms (SNPs) using MassArray iPLEX and 50 CpG sites using EpiTYPER assays. Promising prediction modeling was identified with 25 SNPs selected using Fisher's exact tests (p ≤ 0.05) and 20 CpG sites selected on fold change. Genotype Distribution Analysis identified SNP variations that differed between nine paired cases versus paired controls. The findings validate the examined candidate genes and support feasibility of methods for further biomarker development. The integrative approach that was implemented begins to translate the 36 candidates toward clinical utility as a screening modality for preeclampsia.

New paradigms in translational science research in cancer biomarkers

Despite significant investments in basic science by the US National Institutes of Health, there is a concern that the return on this investment has been limited in terms of clinical utility. In the field of biomarkers, translational research is used to bridge the gap between the results of basic research that identify biomolecules involved in or the consequence of carcinogenesis and their incorporation into medical application. The cultural separation between different scientific disciplines often makes it difficult to establish the multidisciplinary and multi-skilled teams that are necessary for successful translational research. The field of biomarker research requires extensive interactions between academic researchers and industrial developers, and clinicians are needed to help shape the research direction that can be addressed only by a multidisciplinary, multi-institutional approach. In this article, we provide our perspective on the relatively slow pace of cancer biomarker translation, especially those for early detection and screening.

Establishment of VCA and EBNA1 IgA-based combination by enzyme-linked immunosorbent assay as preferred screening method for nasopharyngeal carcinoma: a two-stage design with a preliminary performance study and a mass screening in southern China

A two-stage study was conducted in southern China to determine and validate an optimal combination of Epstein-Barr virus (EBV)-related seromarkers for nasopharyngeal carcinoma (NPC) screening. In the first stage, six seromarkers [VCA-IgA, EA-IgA, Epstein-Barr virus nuclear antigen 1 (EBNA1-IgA), EBNA1-IgG, Zta-IgA and Rta-IgG] were detected by enzyme-linked immunosorbent assay (ELISA) and two traditional NPC screening seromarkers (VCA-IgA and EA-IgA) were detected by immunofluorescence assay (IFA) in serum samples from 191 NPC patients and 337 controls. An optimal combination of seromarkers for NPC diagnosis was selected using logistic regression models. Results showed that the diagnostic performances of VCA-IgA and EA-IgA tested by ELISA were superior to the performances of the same seromarkers by IFA. VCA-IgA combined with EBNA1-IgA by ELISA was identified as the optimal combination, with an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.97, a sensitivity of 95.3% and a specificity of 94.1% for classification of NPCs vs. controls. In the second stage, 5,481 participants aged 30-59 years and without clinical evidence of NPC were recruited into a population-based NPC screening program from May 2008 to February 2009 in Sihui City, China. Their sera were tested simultaneously by both the new and the traditional screening schemes and eight early stage NPC patients were subsequently histopathologically confirmed. The traditional and the new screening schemes had comparable specificity (estimated as 98.5%), but the sensitivity of the new scheme (75.0%) was significantly higher than that of the traditional one (25.0%). The combination of VCA-IgA and EBNA1-IgA by ELISA outperforms the traditional NPC screening scheme and could become the preferred serodiagnostic strategy for NPC screening in high-incidence areas.

Postoperative biomarkers predict acute kidney injury and poor outcomes after pediatric cardiac surgery

Acute kidney injury (AKI) occurs commonly after pediatric cardiac surgery and associates with poor outcomes. Biomarkers may help the prediction or early identification of AKI, potentially increasing opportunities for therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 311 children undergoing surgery for congenital cardiac lesions to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL), or plasma NGAL could identify which patients would develop AKI and other adverse outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. Severe AKI, defined by dialysis or doubling in serum creatinine during hospital stay, occurred in 53 participants at a median of 2 days after surgery. The first postoperative urine IL-18 and urine NGAL levels strongly associated with severe AKI. After multivariable adjustment, the highest quintiles of urine IL-18 and urine NGAL associated with 6.9- and 4.1-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine NGAL levels associated with longer hospital stay, longer intensive care unit stay, and duration of mechanical ventilation. The accuracy of urine IL-18 and urine NGAL for diagnosis of severe AKI was moderate, with areas under the curve of 0.72 and 0.71, respectively. The addition of these urine biomarkers improved risk prediction over clinical models alone as measured by net reclassification improvement and integrated discrimination improvement. In conclusion, urine IL-18 and urine NGAL, but not plasma NGAL, associate with subsequent AKI and poor outcomes among children undergoing cardiac surgery.

Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery

Acute kidney injury (AKI) is a frequent complication of cardiac surgery and increases morbidity and mortality. The identification of reliable biomarkers that allow earlier diagnosis of AKI in the postoperative period may increase the success of therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 1219 adults undergoing cardiac surgery to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL), or plasma NGAL could identify which patients would develop AKI and other adverse patient outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. After multivariable adjustment, the highest quintiles of urine IL-18 and plasma NGAL associated with 6.8-fold and 5-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine and plasma NGAL levels associated with longer length of hospital stay, longer intensive care unit stay, and higher risk for dialysis or death. The clinical prediction model for AKI had an area under the receiver-operating characteristic curve (AUC) of 0.69. Urine IL-18 and plasma NGAL significantly improved the AUC to 0.76 and 0.75, respectively. Urine IL-18 and plasma NGAL significantly improved risk prediction over the clinical models alone as measured by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In conclusion, urine IL-18, urine NGAL, and plasma NGAL associate with subsequent AKI and poor outcomes among adults undergoing cardiac surgery.

A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial. Using a nested case-control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6 to 8 biomarkers, were evaluated according to a predetermined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (step 1); simultaneous split-sample discovery and validation of models (step 2); and exploratory discovery of new models (step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In step 1, one model showed comparable performance to CA125, with sensitivity, specificity, and AUC at 69.2%, 96.6%, and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In step 2, we observed a similar pattern. In step 3, a model derived from all 28 markers failed to show improvement over CA125. Thus, biomarker panels discovered in diagnostic samples may not validate in prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels.

The role of haematuria in bladder cancer screening among men with former occupational exposure to aromatic amines

Study Type - Diagnostic (validating cohort).

LEVEL OF EVIDENCE

1b. What's known on the subject? and What does the study add? Microscopic haematuria (µH) is frequently detected in elderly adults. The American Urological Association recommends the follow-up of subjects with µH on bladder cancer. Whereas gross haematuria is considered an important sign of the presence of bladder cancer, the disease-predictive value of µH is less clear. No association of µH with the development of bladder tumours in a prospective screening cohort of chemical workers was observed. The positive predictive value of µH for bladder cancer was as low as 1.2%. Haematuria interfered with NMP22 but not with cytology and UroVysion(TM) test results.

OBJECTIVE

• To assess the positive predictive value (PPV) of microhaematuria (µH) and gross haematuria (GH) in bladder cancer screening and the influence of haematuria on tumour tests in a prospective study.

PATIENTS AND METHODS

• From September 2003 to January 2010, 1323 men took part in an annual voluntary bladder cancer screening programme for chemical workers with former exposure to aromatic amines. • In 5315 urine samples haematuria was determined with a dipstick, followed by a microscopic blood cell count in the sediment. Haematuria was categorized into traces, µH and GH. • Urinary leukocytes and other factors were investigated as potential predictors of haematuria using a generalized estimating equation model for repeated urinalysis. The risk of haematuria for positive tumour tests was analysed correspondingly. • The bladder cancer risk was estimated for the highest degree of haematuria occurring during the study with Poisson regression.

RESULTS

• As of July 2010, 15 bladder tumours were detected in 14 participants. • GH was found in four out of nine high-grade tumours and associated with a rate ratio of 3.82, 95% confidence interval (CI) 0.50-29.15 for the development of bladder lesions. • The PPV of GH was 11.4%, but only 1.2% for µH. µH occurred in 18.8% of urine samples and was not associated with bladder cancer [rate ratio (RR) 0.72, 95% CI 0.11-4.78]. • Abundant urinary leukocytes were associated with µH [odds ratio (OR) 8.34, 95% CI 2.26-30.69] and even stronger with GH (OR 22.25, 95% CI 6.42-77.06). • Haematuria and leukocytes influenced NMP22 positivity (µH: OR 1.63, 95% CI 1.06-2.51, abundant leukocytes: OR 8.90, 95% CI 1.58-50.16), but not test results for urine cytology and UroVysion(TM) .

CONCLUSION

• While the PPV of µH for bladder cancer was low, there was a strong influence of haematuria and leukocytes on the protein-based tumour test NMP22®. • Erythrocytes and leukocytes should be determined at least semi-quantitatively for the interpretation of positive NMP22 test results. • In addition, a panel of tumour tests that includes methods not affected by the presence of erythrocytes or leukocytes such as cytology and UroVysion(TM) would improve bladder cancer screening.

Occupational toxicology of asbestos-related malignancies

INTRODUCTION

Asbestos is banned in most Western countries but related malignancies are still of clinical concern because of their long latencies. This review identifies and addresses some controversial occupational and clinical aspects of asbestos-related malignancies.

METHODS

Papers published in English from 1980 to 2009 were retrieved from PubMed. A total of 307 original articles were identified and 159 were included.

ASSESSMENT OF EXPOSURE

The retrospective assessment of exposure is usually performed by using questionnaires and job exposure matrices and by careful collection of medical history. In this way crucial information about manufacturing processes and specific jobs can be obtained. In addition, fibers and asbestos bodies are counted in lung tissue, broncho-alveolar lavage, and sputum, but different techniques and interlaboratory variability hamper the interpretation of reported measurements. SCREENING FOR MALIGNANCIES: The effectiveness of low-dose chest CT screening in exposed workers is debatable. Several biomarkers have also been considered to screen individuals at risk for lung cancer and mesothelioma but reliable signatures are still missing. ATTRIBUTION OF LUNG CANCER: Exposures correlating with lung cancer are high and in the same range where asbestosis occurs. However, the unresolved question is whether the presence of fibrosis is a requirement for the attribution of lung cancer to asbestos. The etiology of lung cancer is difficult to define in cases of low-level asbestos exposure and concurrent smoking habits. MESOTHELIOMA: The diagnosis of malignant mesothelioma may also be difficult, because of procedures in sampling, fixation, and processing, and uses of immunohistochemical probes.

CONCLUSIONS

Assessment of exposure is crucial and requires accurate medical and occupational histories. Quantitative analysis of asbestos body burden is better performed in digested lung tissues by counting asbestos bodies by light microscopy and/or uncoated fibers by transmission electron microscopy. The benefits of screenings for asbestos-related malignancies are equivocal. The attribution of lung cancer to asbestos exposure is difficult in a clinical setting because of the need to assess asbestos body burden and the fact that virtually all these patients are also tobacco smokers or former smokers. Given the premise that asbestosis is necessary to causally link lung cancer to asbestos, it follows that the assessment of both lung fibrosis and asbestos body burden is necessary.

Biomarkers for Early Cancer Detection - Methodological Aspects

The development of a new breast cancer biomarker for early detection is a process that begins with biomarker discovery, followed by a rigorous definition and evaluation of the whole process of biomarker determination (analytical validation). It terminates with the assessment of the impact of the biomarker on clinical practice (clinical validation). A 4-phase scheme for the analytical validation process of the biomarkers for early diagnosis has recently been proposed with the aim of covering the need for standardized operating procedures as well as the need for monitoring and maintaining their quality. As far as clinical validation of biomarkers for early diagnosis is concerned, however, a well established phased approach exists, and guidelines are available for both planning studies and reporting results. Although analytical and clinical validation should be logically linked, often this is not the case in real-word practice, especially in the early phases of biomarker development. This is also the case with breast cancer biomarkers for early detection.

Sources of bias in specimens for research about molecular markers for cancer

Claims about the diagnostic or prognostic accuracy of markers often prove disappointing when "discrimination" found between cancers versus normals is due to bias, a systematic difference between compared groups. This article describes a framework to help simplify and organize current problems in marker research by focusing on the role of specimens as a source of bias in observational research and using that focus to address problems and improve reliability. The central idea is that the "fundamental comparison" in research about markers (ie, the comparison done to assess whether a marker discriminates) involves two distinct processes that are "connected" by specimens. If subject selection (first process) creates baseline inequality between groups being compared, then laboratory analysis of specimens (second process) may erroneously find positive results. Although both processes are important, subject selection more fundamentally influences the quality of marker research, because it can hardwire bias into all comparisons in a way that cannot be corrected by any refinement in laboratory analysis. An appreciation of the separateness of these two processes-and placing investigators with appropriate expertise in charge of each-may increase the reliability of research about cancer biomarkers.

Promoter methylation and the detection of breast cancer

Mammographic screening has been shown to reduce breast cancer mortality in women over the age of 50 years, and to a lesser extent in younger women. The sensitivity of mammography, however, is reduced in some groups of women. There remains a need for a minimally invasive, cost-effective procedure that could be used along side mammography to improve screening sensitivity. Silencing of tumor suppressor genes through promoter hypermethylation is known to be a frequent and early event in carcinogenesis. Further, changes in methylation patterns observed in tumors are also detectable in the circulation of women with breast cancer. This makes these alterations candidate markers for early tumor detection. In this paper, we review the current literature on promoter hypermethylation changes and breast cancer and discuss issues that remain to be addressed in order for the potential of these markers to augment the sensitivity of screening mammography. In general, studies in well-defined populations, including appropriate controls and larger numbers are needed. Further, focus on the optimization of methods of methylation detection in small amounts of DNA is needed.

Proteomics research to discover markers: what can we learn from Netflix?

BACKGROUND

Research in the field of proteomics to discover markers for detection of cancer has produced disappointing results, with few markers gaining US Food and Drug Administration approval, and few claims borne out when subsequently tested in rigorous studies. What is the role of better mathematical or statistical analysis in improving the situation?

CONTENT

This article examines whether a recent successful Netflix-sponsored competition using mathematical analysis to develop a prediction model for movie ratings of individual subscribers can serve to improve studies of markers in the field of proteomics. Netflix developed a database of movie preferences of individual subscribers using a longitudinal cohort research design. Groups of researchers then competed to develop better ways to analyze the data. Against this background, the strengths and weaknesses of research design are reviewed, contrasting the Netflix design with that of studies of biomarkers to detect cancer. Such biomarker studies generally have less-strong design, lower numbers of outcomes, and greater difficulty in even just measuring predictors and outcomes, so the fundamental data that will be used in mathematical analysis tend to be much weaker than in other kinds of research.

CONCLUSIONS

If the fundamental data that will be analyzed are not strong, then better analytic methods have limited use in improving the situation. Recognition of this situation is an important first step toward improving the quality of clinical research about markers to detect cancer.

Improving the biomarker pipeline to develop and evaluate cancer screening tests

The biomarker pipeline to develop and evaluate cancer screening tests has three stages: identification of promising biomarkers for the early detection of cancer, initial evaluation of biomarkers for cancer screening, and definitive evaluation of biomarkers for cancer screening. Statistical and biological issues to improve this pipeline are discussed. Although various recommendations, such as identifying cases based on clinical symptoms, keeping biomarker tests simple, and adjusting for postscreening noise, have been made previously, they are not widely known. New recommendations include more frequent specimen collection to help identify promising biomarkers and the use of the paired availability design with interval cases (symptomatic cancers detected in the interval after screening) for initial evaluation of biomarkers for cancer screening.

The Integrated Oncology Program of the Italian Ministry of Health

In 2007, an Italian cancer research group proposed a specific concerted action aimed at the “analytical and clinical validation of new biomarkers for early diagnosis: Network, resources, methodology, quality control, and data analysis.” The proposal united 37 national operative units involved in different biomarker studies and it created a strong coordinative body with the necessary expertise in methodologies, statistical analysis, quality control, and biological resources to perform ad hoc validation studies for new biomarkers of early cancer diagnosis.

The action, financed by the Italian Ministry of Health within the Integrated Oncology Program (PIO) coordinated by NCI-Istituto Tumori Bari, started in 2007 and activated 7 projects, each of which focused on disease-specific biomarker studies. Overall, the 37 participating units proposed studies on 50 biomarkers, including analytical and clinical validation procedures. Clusters of units were specifically involved in research of early-detection biomarkers for cancers of the lung, digestive tract, prostate/bladder, and nervous system, as well as female cancers. Furthermore, a cluster involved in biomarkers for bioimaging and infection-related cancers was created.

The first investigators' meeting, “Analytical and clinical validation of new biomarkers for early diagnosis,” was held on 9 September 2008 in Bari. During this meeting, methodological aspects, scientific programs and preliminary results were presented and discussed.

Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design

Research methods for biomarker evaluation lag behind those for evaluating therapeutic treatments. Although a phased approach to development of biomarkers exists and guidelines are available for reporting study results, a coherent and comprehensive set of guidelines for study design has not been delineated. We describe a nested case-control study design that involves prospective collection of specimens before outcome ascertainment from a study cohort that is relevant to the clinical application. The biomarker is assayed in a blinded fashion on specimens from randomly selected case patients and control subjects in the study cohort. We separately describe aspects of the design that relate to the clinical context, biomarker performance criteria, the biomarker test, and study size. The design can be applied to studies of biomarkers intended for use in disease diagnosis, screening, or prognosis. Common biases that pervade the biomarker research literature would be eliminated if these rigorous standards were followed.

Sample size for positive and negative predictive value in diagnostic research using case-control designs

Important properties of diagnostic methods are their sensitivity, specificity, and positive and negative predictive values (PPV and NPV). These methods are typically assessed via case-control samples, which include one cohort of cases known to have the disease and a second control cohort of disease-free subjects. Such studies give direct estimates of sensitivity and specificity but only indirect estimates of PPV and NPV, which also depend on the disease prevalence in the tested population. The motivating example arises in assay testing, where usage is contemplated in populations with known prevalences. Further instances include biomarker development, where subjects are selected from a population with known prevalence and assessment of PPV and NPV is crucial, and the assessment of diagnostic imaging procedures for rare diseases, where case-control studies may be the only feasible designs. We develop formulas for optimal allocation of the sample between the case and control cohorts and for computing sample size when the goal of the study is to prove that the test procedure exceeds pre-stated bounds for PPV and/or NPV. Surprisingly, the optimal sampling schemes for many purposes are highly unbalanced, even when information is desired on both PPV and NPV.

Antibody Arrays Identify Potential Diagnostic Markers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. Effective treatment of HCC patients is hampered by the lack of sensitive and specific diagnostic markers of HCC. Alpha-fetoprotein (AFP), the currently used HCC marker, misses 30%-50% of HCC patients, who therefore remain undiagnosed and untreated. In order to identify novel diagnostic markers that can be used individually or in combination with AFP, we used an antibody array platform to detect the levels of candidate proteins in the plasma of HCC patients (n = 48) and patients with chronic hepatitis B or C viral infections (n = 19) (both of which are the major risk factors of HCC). We identified 7 proteins that significantly differentiate HCC patients from hepatitis patients (p < 0.05) (AFP, CTNNB, CSF1, SELL, IGFBP6, IL6R, and VCAM1). Importantly, we also identified 8 proteins that significantly differentiate HCC patients with 'normal' levels of AFP (< 20 ng/ml) from hepatitis patients (p < 0.05) (IL1RN, IFNG, CDKN1A, RETN, CXCL14, CTNNB, FGF2, and SELL). These markers are potentially important complementary markers to AFP. Using an independent immunoassay method in an independent group of 23 HCC patients and 22 hepatitis patients, we validated that plasma levels of CTNNB were significantly higher in the HCC group (p = 0.020). In conclusion, we used an antibody array platform to identify potential circulating diagnostic markers of HCC, some of which may be valuable when used in combination with AFP. The clinical utility of these newly identified HCC diagnostic markers needs to be systematically evaluated.