Phases of biomarker development for early detection of cancer

DNA-Based Biosensors for the Biochemical Analysis: A Review

In recent years, DNA-based biosensors have shown great potential as the candidate of the next generation biomedical detection device due to their robust chemical properties and customizable biosensing functions. Compared with the conventional biosensors, the DNA-based biosensors have advantages such as wider detection targets, more durable lifetime, and lower production cost. Additionally, the ingenious DNA structures can control the signal conduction near the biosensor surface, which could significantly improve the performance of biosensors. In order to show a big picture of the DNA biosensor's advantages, this article reviews the background knowledge and recent advances of DNA-based biosensors, including the functional DNA strands-based biosensors, DNA hybridization-based biosensors, and DNA templated biosensors. Then, the challenges and future directions of DNA-based biosensors are discussed and proposed.

GALAD demonstrates high sensitivity for HCC surveillance in a cohort of patients with cirrhosis

BACKGROUND AND AIMS

Most patients with HCC are diagnosed at a late stage, highlighting the need for more accurate surveillance tests. Although biomarkers for HCC early detection have promising data in Phase 2 case-control studies, evaluation in cohort studies is critical prior to adoption in practice. We leveraged a prospective cohort of patients with Child-Pugh A or B cirrhosis who were followed until incident HCC, liver transplantation, death, or loss to follow-up. We used a prospective specimen collection, retrospective, blinded evaluation design for biomarker evaluation of GALAD (gender × age × log alpha-fetoprotein [AFP] × des-gamma-carboxy prothrombin), longitudinal GALAD, and the HCC Early Detection Screening (HES) algorithm-compared to AFP-using patient-level sensitivity and screening-level specificity.

APPROACH AND RESULTS

Of 397 patients with cirrhosis, 42 developed HCC (57.1% early stage) over a median of 2.0 years. Longitudinal GALAD had the highest c-statistic for HCC detection (0.85; 95% CI, 0.77-0.92) compared to single-time point GALAD (0.79; 95% CI, 0.71-0.87), AFP (0.77; 95% CI, 0.69-0.85), and HES (0.76; 95% CI, 0.67-0.83). When specificity was fixed at 90%, the sensitivity for HCC of single-time point and longitudinal GALAD was 54.8% and 66.7%, respectively, compared to 40.5% for AFP. Sensitivity for HCC detection was higher when restricted to patients with biomarker assessment within 6 months prior to HCC diagnosis, with the highest sensitivities observed for single-time point GALAD (72.0%) and longitudinal GALAD (64.0%), respectively. Sensitivity of single-time point and longitudinal GALAD for early-stage HCC was 53.8% and 69.2%, respectively.

CONCLUSION

GALAD demonstrated high sensitivity for HCC detection in a cohort of patients with cirrhosis. Validation of these results is warranted in large Phase 3 data sets.

Role of Novel Kidney Biomarkers in Patients With Cirrhosis and After Liver Transplantation

Acute kidney injury (AKI) and chronic kidney disease (CKD) are important drivers of morbidity and mortality in patients with cirrhosis before and after liver transplantation (LT). In this review, we examine the role of novel kidney biomarkers for early recognition of kidney injury. Studies are limited by lack of reference standards, heterogeneous definitions of outcomes and biomarker cutoffs, and inconsistent diagnostic performance. Overall, a change in biomarker is more relevant than an absolute cutoff. Cystatin C and urinary neutrophil gelatinase-associated lipocalin (uNGAL) are the most studied candidate biomarkers and identify AKI or progression of AKI earlier than serum creatinine (sCr). Kidney injury molecule 1 and liver-type fatty acid-binding protein (L-FABP) also show potential. NGAL and interleukin 18 may play a role in differentiating acute tubular necrosis from other forms of AKI. Combining novel biomarkers with the Model for End-Stage Liver Disease score may assist prognosis. Persistent elevations in select markers (eg, NGAL) can portend irreversible injury. Several pretransplantation markers (including sCr) predict posttransplantation kidney dysfunction. Pretransplantation assessment of clinical factors (eg, age, diabetes) and novel markers (osteopontin and tissue inhibitor of metalloproteinases 1 [TIMP-1]) may predict renal kidney recovery after LT. Intraoperative changes in biomarkers predict early post-LT AKI. Prediction of CKD remains difficult, although a combination of biomarkers (eg, beta-2 microglobulin, CD40) is promising. Novel biomarkers have yet to replace sCr in guideline-based evaluation and management of kidney dysfunction in patients with cirrhosis. We propose a theoretical framework for practical incorporation of these biomarkers that considers patient characteristics (risk for irreversible injury), markers of functional and structural change, and assessment of the AKI-CKD continuum to identify patients at the highest risk for progressive kidney disease before and after LT.

Personalized statistical learning algorithms to improve the early detection of cancer using longitudinal biomarkers

BACKGROUND

Patients undergoing screening for early detection of cancer have serial biomarker measurements that are not traditionally being incorporated into decision making when evaluating biomarkers.

OBJECTIVE

We discuss statistical learning algorithms that have the ability to learn from patient history to make personalized decision rules to improve the early detection of cancer. These artificial intelligence algorithms are able to learn in real time from data collected on the patient to identify changes in the patient that could signal asymptomatic cancer.

METHODS

We discuss the parametric empirical Bayes (PEB) algorithm for a single biomarker and a Bayesian screening algorithm for multiple biomarkers.

RESULTS

We provide tools to implement these algorithms and discuss their clinical utility for the early detection of hepatocellular carcinoma (HCC). The PEB algorithm is a robust, easily implemented algorithm for defining patient specific thresholds that can improve the patient-level sensitivity of a biomarker in many settings, including HCC. The fully Bayesian algorithm, while more complex, can accommodate multiple biomarkers and further improve the clinical utility of the algorithms.

CONCLUSIONS

These algorithms could be used in many clinical settings and we aim to guide the reader on how these algorithms may improve the detection performance of their biomarkers.

A multivariate parametric empirical Bayes screening approach for early detection of hepatocellular carcinoma using multiple longitudinal biomarkers

The early detection of hepatocellular carcinoma (HCC) is critical to improving outcomes since advanced HCC has limited treatment options. Current guidelines recommend HCC ultrasound surveillance every 6 months in high-risk patients however the sensitivity for detecting early stage HCC in clinical practice is poor. Blood-based biomarkers are a promising direction since they are more easily standardized and less resource intensive. Combining of multiple biomarkers is more likely to achieve the sensitivity required for a clinically useful screening algorithm and the longitudinal trajectory of biomarkers contains valuable information that should be utilized. We propose a multivariate parametric empirical Bayes (mPEB) screening approach that defines personalized thresholds for each patient at each screening visit to identify significant deviations that trigger additional testing with more sensitive imaging. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial provides a valuable source of data to study HCC screening algorithms. We study the performance of the mPEB algorithm applied to serum α -fetoprotein, a widely used HCC surveillance biomarker, and des- γ carboxy prothrombin, an HCC risk biomarker that is FDA approved but not used in practice in the United States. Using cross-validation, we found that the mPEB algorithm demonstrated moderate but improved sensitivity compared to alternative screening approaches. Future research will validate the clinical utility of the approach in larger cohort studies with additional biomarkers.

Development and evaluation of safety and effectiveness of novel cancer screening tests for routine clinical use with applications to multicancer detection technologies

Multicancer screening is a promising approach to improving the detection of preclinical disease, but current technologies have limited ability to identify precursor or early stage lesions, and approaches for developing the evidentiary chain are unclear. Frameworks to enable development and evaluation from discovery through evidence of clinical effectiveness are discussed.

Multicancer early detection test: Preclinical, translational, and clinical evidence-generation plan and provocative questions

Minimally invasive molecular biomarkers have been applied to the early detection of multiple cancers in large scale case-control and cohort studies. These demonstrations of feasibility herald the potential for permanent transformation of current cancer screening paradigms. This commentary discusses the major opportunities and challenges facing the preclinical development and clinical validation of multicancer early detection test strategies. From a diverse set of early detection research perspectives, the authors recommend specific approaches and highlight important questions for future investigation.

A microRNA-based liquid biopsy signature for the early detection of esophageal squamous cell carcinoma: a retrospective, prospective and multicenter study

BACKGROUND

Currently, there is no clinically relevant non-invasive biomarker for early detection of esophageal squamous cell carcinoma (ESCC). Herein, we established and evaluated a circulating microRNA (miRNA)-based signature for the early detection of ESCC using a systematic genome-wide miRNA expression profiling analysis.

METHODS

We performed miRNA candidate discovery using three ESCC tissue miRNA datasets (n = 108, 238, and 216) and the candidate miRNAs were confirmed in tissue specimens (n = 64) by qRT-PCR. Using a serum training cohort (n = 408), we conducted multivariate logistic regression analysis to develop an ESCC circulating miRNA signature and the signature was subsequently validated in two independent retrospective and two prospective cohorts.

RESULTS

We identified eighteen initial miRNA candidates from three miRNA expression datasets (n = 108, 238, and 216) and subsequently validated their expression in ESCC tissues. We thereafter confirmed the overexpression of 8 miRNAs (miR-103, miR-106b, miR-151, miR-17, miR-181a, miR-21, miR-25, and miR-93) in serum specimens. Using a serum training cohort, we developed a circulating miRNA signature (AUC:0.83 [95%CI:0.79-0.87]) and the diagnostic performance of the miRNA signature was confirmed in two independent validation cohorts (n = 126, AUC:0.80 [95%CI:0.69-0.91]; and n = 165, AUC:0.89 [95%CI:0.83-0.94]). Finally, we demonstrated the diagnostic performance of the 8-miRNA signature in two prospective cohorts (n = 185, AUC:0.92, [95%CI:0.87-0.96]); and (n = 188, AUC:0.93, [95%CI:0.88-0.97]). Importantly, the 8-miRNA signature was superior to current clinical serological markers in discriminating early stage ESCC patients from healthy controls (p < 0.001).

CONCLUSIONS

We have developed a novel and robust circulating miRNA-based signature for early detection of ESCC, which was successfully validated in multiple retrospective and prospective multinational, multicenter cohorts.

Extracellular vesicles as a source of prostate cancer biomarkers in liquid biopsies: a decade of research

Prostate cancer is a global cancer burden and considerable effort has been made through the years to identify biomarkers for the disease. Approximately a decade ago, the potential of analysing extracellular vesicles in liquid biopsies started to be envisaged. This was the beginning of a new exciting area of research investigating the rich molecular treasure found in extracellular vesicles to identify biomarkers for a variety of diseases. Vesicles released from prostate cancer cells and cells of the tumour microenvironment carry molecular information about the disease that can be analysed in several biological fluids. Numerous studies document the interest of researchers in this field of research. However, methodological issues such as the isolation of vesicles have been challenging. Remarkably, novel technologies, including those based on nanotechnology, show promise for the further development and clinical use of extracellular vesicles as liquid biomarkers. Development of biomarkers is a long and complicated process, and there are still not many biomarkers based on extracellular vesicles in clinical use. However, the knowledge acquired during the last decade constitutes a solid basis for the future development of liquid biopsy tests for prostate cancer. These are urgently needed to bring prostate cancer treatment to the next level in precision medicine.

The issues and challenges with cancer biomarkers

A biomarker is a measurable indicator used to distinguish precisely/objectively either normal biological state/pathological condition/response to a specific therapeutic intervention. The use of novel molecular biomarkers within evidence-based medicine may improve the diagnosis/treatment of disease, improve health outcomes, and reduce the disease's socio-economic impact. Presently cancer biomarkers are the backbone of therapy, with greater efficacy and better survival rates. Cancer biomarkers are extensively used to treat cancer and monitor the disease's progress, drug response, relapses, and drug resistance. The highest percent of all biomarkers explored are in the domain of cancer. Extensive research using various methods/tissues is carried out for identifying biomarkers for early detection, which has been mostly unsuccessful. The quantitative/qualitative detection of various biomarkers in different tissues should ideally be done in accordance with qualification rules laid down by the Early Detection Research Network (EDRN), Program for the Assessment of Clinical Cancer Tests (PACCT), and National Academy of Clinical Biochemistry. Many biomarkers are presently under investigation, but lacunae lie in the biomarker's sensitivity and specificity. An ideal biomarker should be quantifiable, reliable, of considerable high/low expression, correlate with the outcome progression, cost-effective, and consistent across gender and ethnic groups. Further, we also highlight that these biomarkers' application remains questionable in childhood malignancies due to the lack of reference values in the pediatric population. The development of a cancer biomarker stands very challenging due to its complexity and sensitivity/resistance to the therapy. In past decades, the cross-talks between molecular pathways have been targeted to study the nature of cancer. To generate sensitive and specific biomarkers representing the pathogenesis of specific cancer, predicting the treatment responses and outcomes would necessitate inclusion of multiple biomarkers.

Clinical deployment environments: Five pillars of translational machine learning for health

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development.

Communicating and Using Dementia Risk Evidence

Advances in biomarkers, genetics, and other data used as dementia risk evidence (DRE) are increasingly informing clinical diagnosis and management. The purpose of this Mini-Forum is to provide a solutions-based discussion of the ethical and legal gaps and practical questions about how to use and communicate these data. Investigators often use DRE in research. When participants ask for their personal results, investigators have concerns. Will data that was intended to study groups be valid for individuals? Will sharing data cause distress? Debates around sharing DRE became heated when blood-based amyloid tests and amyloid reducing drugs appeared poised to enable clinicians easily to identify people with elevated brain amyloid and reduce it with a drug. Such an approach would transform the traditional role of DRE from investigational to foundational; however, then the high costs, uncertain clinical benefits and risks of the therapy led to an urgent need for education to support clinical decision making. Further complicating DRE use are direct to consumer genetic testing and increasingly available biomarker testing. Withholding DRE becomes less feasible and public education around responsible use and understanding become vital. A critical answer to these legal and ethical issues is supporting education that clearly delineates known risks, benefits, and gaps in knowledge, and communication to promote understanding among researchers, clinicians, patients, and all stakeholders. This paper provides an overview and identifies general concepts and resource documents that support more informed discussions for individuals and interdisciplinary groups.

Assessment of background levels of autoantibodies as a prognostic marker for severe SARS-CoV-2 infection

Background:

Patients with more severe forms of SARS-CoV-2 exhibit activation of immunological cascades. Participants (current or ex-smokers with at least 20 years pack history) in a trial (Early Diagnosis of Lung Cancer, Scotland [ECLS]) of autoantibody detection to predict lung cancer risk had seven autoantibodies measured 5 years before the pandemic. This study compared the response to Covid infection in study participants who tested positive and negative to antibodies to tumour-associated antigens: p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2.

Methods:

Autoantibody data from the ECLS study was deterministically linked to the EAVE II database, a national, real-time prospective cohort using Scotland’s health data infrastructure, to describe the epidemiology of SARS-CoV-2 infection, patterns of healthcare use and outcomes. The strength of associations was explored using a network algorithm for exact contingency table significance testing by permutation.

Results:

There were no significant differences discerned between SARS-CoV-2 test results and EarlyCDT-Lung test results (p = 0.734). An additional analysis of intensive care unit (ICU) admissions detected no significant differences between those who tested positive and negative. Subgroup analyses showed no difference in COVID-19 positivity or death rates amongst those diagnosed with chronic obstructive pulmonary disease (COPD) with positive and negative EarlyCDT results.

Conclusions:

This hypothesis-generating study demonstrated no clinically valuable or statistically significant associations between EarlyCDT positivity in 2013-15 and the likelihood of SARS-CoV-2 positivity in 2020, ICU admission or death in all participants (current or ex-smokers with at least 20 years pack history) or in those with COPD or lung cancer.

A Novel Blood-Based Panel of Methylated DNA and Protein Markers for Detection of Early-Stage Hepatocellular Carcinoma

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) can be treated effectively if detected at an early stage. Recommended surveillance strategies for at-risk patients include ultrasound with or without α-fetoprotein (AFP), but their sensitivity is suboptimal. We sought to develop a novel, blood-based biomarker panel with improved sensitivity for early-stage HCC detection.

METHODS

In a multicenter, case-control study, we collected blood specimens from patients with HCC and age-matched controls with underlying liver disease but without HCC. Ten previously reported methylated DNA markers (MDMs) associated with HCC, methylated B3GALT6 (reference DNA marker), and 3 candidate proteins, including AFP, were assayed and analyzed by a logistic regression algorithm to predict HCC cases. The accuracy of the multi-target HCC panel was compared with that of other blood-based biomarkers for HCC detection.

RESULTS

The study included 135 HCC cases and 302 controls. We identified a multi-target HCC panel of 3 MDMs (HOXA1, EMX1, and TSPYL5), B3GALT6 and 2 protein markers (AFP and AFP-L3) with a higher sensitivity (71%, 95% CI: 60-81%) at 90% specificity for early-stage HCC than the GALAD score (41%, 95% CI: 30-53%) or AFP ≥7.32 ng/mL (45%, 95% CI: 33-57%). The AUC for the multi-target HCC panel for detecting any stage HCC was 0.92 compared with 0.87 for the GALAD score and 0.81 for AFP alone. The panel performed equally well in important subgroups based on liver disease etiology, presence of cirrhosis, or sex.

CONCLUSIONS

We developed a novel, blood-based biomarker panel that demonstrates high sensitivity for early-stage HCC. These data support the potential for liquid biopsy detection of early-stage HCC to clinically benefit at-risk patients. This study was registered on ClinicalTrials.gov (NCT03628651).

Role of Tumor Biomarkers in the Surveillance of Hepatocellular Carcinoma

Biomarkers are key components of the clinical management of cancer patients because they have contributed to significant survival improvements in these patients. They allow the classification of patients based on common features and facilitate risk stratification, early detection, diagnosis, and prediction of the prognosis or treatment response. In hepatocellular carcinoma (HCC), there are few biomarkers incorporated in clinical practice. Despite this, this has been an extensive area of research in recent years, with increasing efforts to identify the biomarkers across the cancer care continuum from risk stratification to early detection to prognostication and treatment response. The heterogeneous nature of HCCs has restricted the performance of biomarkers. HCC biomarkers have limited roles in risk stratification, diagnosis, and treatment response. Currently, the main role of biomarkers is in the surveillance of HCC to detect it at an earlier stage and reduce mortality, which is the focus of this review.

Limits of serum carcinoembryonic antigen and carbohydrate antigen 19-9 as the diagnosis of gallbladder cancer

Purpose

Although serum CEA and CA 19-9 have been widely utilized for the diagnosis of gallbladder cancer (GBC), few studies have examined the diagnostic performance of them. This study aimed to investigate the diagnostic performance of these 2 biomarkers and demonstrate their clinical usefulness in diagnosing GBC.

Methods

Between January 2000 and March 2020, a total of 751 GBC patients and 2,310 normal controls were included. Serum CEA and CA 19-9 were measured preoperatively. Receiver operating characteristic curves were obtained, and the sensitivity and specificity of each biomarker were evaluated.

Results

In terms of differentiating GBC from the control, the sensitivity and specificity of serum CEA at 5 ng/mL was 12.1% and 99.1%, respectively, and those of serum CA 19-9 at 37 IU/mL were 28.7% and 94.5%, respectively. The optimal cutoff values of CEA and CA 19-9 were set to 2.1 ng/mL and 26 IU/mL in the receiver operating characteristic curves, respectively. The sensitivities of CEA and CA 19-9 at new cutoff values slightly increased but remained low (CEA, 42.9%; CA 19-9, 38.2%). When differentiating early-stage GBC from advanced tumor, the sensitivity and specificity, were 14.2% and 96.1% for CEA (cutoff value, 5 ng/mL) and 33.6% and 90.1% for CA 19-9 (cutoff value, 37 IU/mL), respectively.

Conclusion

Serum CEA and CA 19-9 levels are not suitable for screening GBC patients from controls. New promising biomarkers with higher sensitivity should be explored.

Serum semaphorin 4C as a diagnostic biomarker in breast cancer: A multicenter retrospective study

Background

To date, there is no approved blood‐based biomarker for breast cancer detection. Herein, we aimed to assess semaphorin 4C (SEMA4C), a pivotal protein involved in breast cancer progression, as a serum diagnostic biomarker.

Methods

We included 6,213 consecutive inpatients from Tongji Hospital, Qilu Hospital, and Hubei Cancer Hospital. Training cohort and two validation cohorts were introduced for diagnostic exploration and validation. A pan‐cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors. Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed. We hypothesized that increased pre‐treatment serum SEMA4C levels, measured using optimized in‐house enzyme‐linked immunosorbent assay kits, could detect breast cancer. The endpoints were diagnostic performance, including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Post‐surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification. There was no restriction on disease stage for eligibilities.

Results

We included 2667 inpatients with breast lesions, 2378 patients with other solid tumors, and 1168 healthy participants. Specifically, 118 patients with breast cancer were diagnosed with stage 0 (5.71%), 620 with stage I (30.00%), 966 with stage II (46.73%), 217 with stage III (10.50%), and 8 with stage IV (0.39%). Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls (P < 0.001). Elevated serum SEMA4C levels had AUC of 0.920 (95% confidence interval [CI]: 0.900–0.941) and 0.932 (95%CI: 0.911–0.953) for breast cancer detection in the two validation cohorts. The AUCs for detecting early‐stage breast cancer (n = 366) and ductal carcinoma in situ (n = 85) were 0.931 (95%CI: 0.916–0.946) and 0.879 (95%CI: 0.832–0.925), respectively. Serum SEMA4C levels significantly decreased after surgery, and the reduction was more striking after modified radical mastectomy, compared with mass excision (P < 0.001). The positive rate of enhanced serum SEMA4C levels was 84.77% for breast cancer and below 20.75% for the other 14 solid tumors.

Conclusions

Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis. However, validation in prospective settings and by other study groups is warranted.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Early detection of kidney cancer using urinary proteins: a truly non-invasive strategy

OBJECTIVES

To review urinary protein biomarkers as potential non-invasive, easily obtainable, early diagnostic tools in renal cell carcinoma (RCC).

METHODS

A PubMed database search was performed up to the year 2020 to identify primary studies reporting potential urinary protein biomarkers for RCC. Separate searches were conducted to identify studies describing appropriate methods of developing cancer screening programmes and detection of cancer biomarkers.

RESULTS

Several urinary protein biomarkers are under validation for RCC diagnostics, e.g. aquaporin-1, perilipin-2, carbonic anhydrase-9, Raf-kinase inhibitory protein, nuclear matrix protein-22, 14-3-3 Protein β/α and neutrophil gelatinase-associated lipocalin. However, none has yet been validated or approved for clinical use due to low sensitivity or specificity, inconsistencies in appropriate study design, or lack of external validation.

CONCLUSIONS

Evaluation of biomarkers' feasibility, sample preparation and storage, biomarker validation, and the application of novel technologies may provide a solution that maximises the potential for a truly non-invasive biomarker in early RCC diagnostics.

Functional Characteristics and Regulated Expression of Alternatively Spliced Tissue Factor: An Update

In human and mouse, alternative splicing of tissue factor's primary transcript yields two mRNA species: one features all six TF exons and encodes full-length tissue factor (flTF), and the other lacks exon 5 and encodes alternatively spliced tissue factor (asTF). flTF, which is oftentimes referred to as "TF", is an integral membrane glycoprotein due to the presence of an alpha-helical domain in its C-terminus, while asTF is soluble due to the frameshift resulting from the joining of exon 4 directly to exon 6. In this review, we focus on asTF-the more recently discovered isoform of TF that appears to significantly contribute to the pathobiology of several solid malignancies. There is currently a consensus in the field that asTF, while dispensable to normal hemostasis, can activate a subset of integrins on benign and malignant cells and promote outside-in signaling eliciting angiogenesis; cancer cell proliferation, migration, and invasion; and monocyte recruitment. We provide a general overview of the pioneering, as well as more recent, asTF research; discuss the current concepts of how asTF contributes to cancer progression; and open a conversation about the emerging utility of asTF as a biomarker and a therapeutic target.

Effects of melatonin receptor expression on prognosis and survival in oral squamous cell carcinoma patients

Melatonin receptors can inhibit breast and prostate cancers; however, little is known regarding their effects on oral squamous cell carcinoma. In this study, we collected specimens from 81 patients with oral squamous cell carcinoma and analysed clinicopathological data retrospectively. In addition, the expression of the melatonin receptor was analysed immunohistochemically. Survival rates were calculated using the Kaplan-Meier method and log-rank test. Multivariate analysis was performed based on the Cox proportional-hazards model. Further, an in vitro study was performed using YD15 cells. The cells were transfected with siRNA targeting melatonin receptor 1A and 1B for evaluating the malignancy of melatonin receptors by western blotting, trypan blue-exclusion, colony-forming, wound-healing, and invasion assays. Survival decreased as melatonin receptor expression and clinical and pathological tumour-node-metastasis stages increased. A Cox proportional-hazard model showed that melatonin receptor 1A may serve as a significant predictor of the survival rate of patients with oral squamous cell carcinoma [hazard ratio = 1.423, 95% confidence interval (CI) = 1.019-1.988, p = 0.038]. Melatonin receptor 1A and 1B knockdown significantly suppressed proliferation, migration ability, and invasion ability of YD15 cells in vitro. Our findings reveal that inhibiting melatonin receptor expression may suppress oral squamous cell carcinoma development.

Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Predictive tools, utilising biomarkers, aim to objectively assessthe potentialresponse toa particular clinical intervention in order to direct treatment.Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data andmulti-omicbiomarkers (genomic, transcriptomic and protein expression).Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.

Intraindividual Long-term Immune Marker Stability in Plasma Samples Collected in Median 9.4 Years Apart in 304 Adult Cancer-free Individuals

BACKGROUND

Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm.

METHODS

We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1β, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model.

RESULTS

ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFα, CXCL13, MCP-3, MIP-1α, MIP-1β, fractalkine, VEGF, FGF-2, TGFα, sIL2Rα, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1β, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30.

CONCLUSIONS

IL10, IL13, CXCL13, MCP-3, MIP-1α, MIP-1β, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals.

IMPACT

Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers.

MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

Using cell-free DNA for HCC surveillance and prognosis

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer. Its incidence is rising faster than any other cancer in the United States and it remains one of the leading causes of cancer-related deaths worldwide. While advances in massive parallel sequencing and integration of 'omics information have transformed the field of oncology, tissue access is often limited in HCC and a single biopsy is poorly representative of the known genetic heterogeneity of tumours. Liquid biopsy has emerged as a promising strategy for analysing circulating tumour components including circulating tumour DNA. Cell-free DNA and tumour DNA are derived from necrotic, apoptotic and living eukaryotic cells. The profiling of genetic and epigenetic alterations in circulating cell-free DNA has potential clinical applications including early disease detection, prediction of treatment response and prognostication in real time. Novel biomarker candidates for disease detection and monitoring are under study. Of these, methylation analyses of circulating tumour DNA have shown promising performance for early HCC detection in at-risk patients. Assessments of assay performance in longitudinal validation cohorts are ongoing. Implementation of liquid biopsy for HCC will likely improve upon the current surveillance strategy. This review summarises the most recent developments on the role and utility of circulating cell-free DNA in the detection and management of HCC.

Surveillance and Monitoring of Hepatocellular Carcinoma During the COVID-19 Pandemic

The Coronavirus disease 2019 (COVID-19) pandemic is expected to have a long-lasting impact on the approach to care for patients at risk for and with hepatocellular carcinoma (HCC) due to the risks from potential exposure and resource reallocation. The goal of this document is to provide recommendations on HCC surveillance and monitoring, including strategies to limit unnecessary exposure while continuing to provide high-quality care for patients. Publications and guidelines pertaining to the management of HCC during COVID-19 were reviewed for recommendations related to surveillance and monitoring practices, and any available guidance was referenced to support the authors' recommendations when applicable. Existing HCC risk stratification models should be utilized to prioritize imaging resources to those patients at highest risk of incident HCC and recurrence following therapy though surveillance can likely continue as before in settings where COVID-19 prevalence is low and adequate protections are in place. Waitlisted patients who will benefit from urgent LT should be prioritized for surveillance whereas it would be reasonable to extend surveillance interval by a short period in HCC patients with lower risk tumor features and those more than 2 years since their last treatment. For patients eligible for systemic therapy, the treatment regimen should be dictated by the risk of COVID-19 associated with route of administration, monitoring and treatment of adverse events, within the context of relative treatment efficacy.

Unannotated small RNA clusters associated with circulating extracellular vesicles detect early stage liver cancer

OBJECTIVE

Surveillance tools for early cancer detection are suboptimal, including hepatocellular carcinoma (HCC), and biomarkers are urgently needed. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumour initiation and metastasis; however, most extracellular RNA (exRNA) blood-based biomarker studies are limited to annotated genomic regions.

DESIGN

EVs were isolated with differential ultracentrifugation and integrated nanoscale deterministic lateral displacement arrays (nanoDLD) and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking and deconvolution analysis. Genome-wide sequencing of the largely unexplored small exRNA landscape, including unannotated transcripts, identified and reproducibly quantified small RNA clusters (smRCs). Their key genomic features were delineated across biospecimens and EV isolation techniques in prostate cancer and HCC. Three independent exRNA cancer datasets with a total of 479 samples from 375 patients, including longitudinal samples, were used for this study.

RESULTS

ExRNA smRCs were dominated by uncharacterised, unannotated small RNA with a consensus sequence of 20 nt. An unannotated 3-smRC signature was significantly overexpressed in plasma exRNA of patients with HCC (p<0.01, n=157). An independent validation in a phase 2 biomarker case-control study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (n=209) (area under the receiver operating curve (AUC): 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93.

CONCLUSION

These findings directly lead to the prospect of a minimally invasive, blood-only, operator-independent clinical tool for HCC surveillance, thus highlighting the potential of unannotated smRCs for biomarker research in cancer.

Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network

The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell lines via scratch wound healing assay. An FNN training algorithm model was generated using the MATLAB fitting function in a MATLAB script to accomplish this task. The input parameters were types of cell lines, times, and wound area, and outputs were relative wound area, percentage of wound closure, and wound healing speed. In addition, we tested and compared the initial accuracy of various supervised learning classifier and support vector regression (SVR) algorithms. The proposed ANN model achieved good agreement with the experimental data and minimized error between the estimated and experimental values. The conclusions drawn demonstrate that the developed ANN model is a useful, accurate, fast, and inexpensive method to predict cancerous cell migration characteristics evaluated via scratch wound healing assay.

The PATHFINDER Study: Assessment of the Implementation of an Investigational Multi-Cancer Early Detection Test into Clinical Practice

To examine the extent of the evaluation required to achieve diagnostic resolution and the test performance characteristics of a targeted methylation cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test, ~6200 participants ≥50 years with (cohort A) or without (cohort B) ≥1 of 3 additional specific cancer risk factors will be enrolled in PATHFINDER (NCT04241796), a prospective, longitudinal, interventional, multi-center study. Plasma cfDNA from blood samples will be analyzed to detect abnormally methylated DNA associated with cancer (i.e., cancer "signal") and a cancer signal origin (i.e., tissue of origin). Participants with a "signal detected" will undergo further diagnostic evaluation per guiding physician discretion; those with a "signal not detected" will be advised to continue guideline-recommended screening. The primary objective will be to assess the number and types of subsequent diagnostic tests needed for diagnostic resolution. Based on microsimulations (using estimates of cancer incidence and dwell times) of the typical risk profiles of anticipated participants, the median (95% CI) number of participants with a "signal detected" result is expected to be 106 (87-128). Subsequent diagnostic evaluation is expected to detect 52 (39-67) cancers. The positive predictive value of the MCED test is expected to be 49% (39-58%). PATHFINDER will evaluate the integration of a cfDNA-based MCED test into existing clinical cancer diagnostic pathways. The study design of PATHFINDER is described here.

Diagnostic salivary biomarkers in oral cancer and oral potentially malignant disorders and their relationships to risk factors - A systematic review

INTRODUCTION

Oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) are a significant health burden globally. Smoking, alcohol, and betel quid are the main risk factors. Lack of screening methods has been highlighted as a significant challenge in management. Salivary biomarkers are proposed as noninvasive diagnostic tools. The aim of this systematic review was to study salivary biomarkers reported in OSCC and OPMD. Specific objectives were to select a salivary biomarker panel suitable for early detection of OSCC and OPMD and to assess relationships between salivary biomarkers and risk factors.

METHODS

Electronic literature search was conducted in academic databases (Scopus, Medline, Embase and Web of Science) without any restrictions. Following calibration, two blinded reviewers screened the studies and extracted data. A risk of bias assessment was conducted using Newcastle Ottawa scale. 295 studies were included with descriptive data analysis.

EXPERT OPINION

A salivary biomarker panel including Interleukin (IL) 1β, IL6, and IL8 was selected for OSCC and OPMD. Reported relationships between salivary biomarkers and risk factors are discussed and research gaps are highlighted. Future research should be directed to assess potential salivary biomarkers and their relationships to risk factors in order to understand the biomarker's role in disease initiation.

Clinical validity of increased cortical uptake of [18F]flortaucipir on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase biomarker development framework

PURPOSE

In 2017, the Geneva Alzheimer's disease (AD) Biomarker Roadmap initiative adapted the framework of the systematic validation of oncological diagnostic biomarkers to AD biomarkers, with the aim to accelerate their development and implementation in clinical practice. With this work, we assess the maturity of [18F]flortaucipir PET and define its research priorities.

METHODS

The level of maturity of [18F]flortaucipir was assessed based on the AD Biomarker Roadmap. The framework assesses analytical validity (phases 1-2), clinical validity (phases 3-4), and clinical utility (phase 5).

RESULTS

The main aims of phases 1 (rationale for use) and 2 (discriminative ability) have been achieved. [18F]Flortaucipir binds with high affinity to paired helical filaments of tau and has favorable kinetic properties and excellent discriminative accuracy for AD. The majority of secondary aims of phase 2 were fully achieved. Multiple studies showed high correlations between ante-mortem [18F]flortaucipir PET and post-mortem tau (as assessed by histopathology), and also the effects of covariates on tracer binding are well studied. The aims of phase 3 (early detection ability) were only partially or preliminarily achieved, and the aims of phases 4 and 5 were not achieved.

CONCLUSION

Current literature provides partial evidence for clinical utility of [18F]flortaucipir PET. The aims for phases 1 and 2 were mostly achieved. Phase 3 studies are currently ongoing. Future studies including representative MCI populations and a focus on healthcare outcomes are required to establish full maturity of phases 4 and 5.

The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers

PURPOSE

The development of blood biomarkers that reflect Alzheimer's disease (AD) pathophysiology (phosphorylated tau and amyloid-β) has offered potential as scalable tests for dementia differential diagnosis and early detection. In 2019, the Geneva AD Biomarker Roadmap Initiative included blood biomarkers in the systematic validation of AD biomarkers.

METHODS

A panel of experts convened in November 2019 at a two-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of blood biomarkers was assessed based on the Biomarker Roadmap methodology and discussed fully during the workshop which also evaluated cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers.

RESULTS

Plasma p-tau has shown analytical validity (phase 2 primary aim 1) and first evidence of clinical validity (phase 3 primary aim 1), whereas the maturity level for Aβ remains to be partially achieved. Full and partial achievement has been assigned to p-tau and Aβ, respectively, in their associations to ante-mortem measures (phase 2 secondary aim 2). However, only preliminary evidence exists for the influence of covariates, assay comparison and cut-off criteria.

CONCLUSIONS

Despite the relative infancy of blood biomarkers, in comparison to CSF biomarkers, much has already been achieved for phases 1 through 3 - with p-tau having greater success in detecting AD and predicting disease progression. However, sufficient data about the effect of covariates on the biomarker measurement is lacking. No phase 4 (real-world performance) or phase 5 (assessment of impact/cost) aim has been tested, thus not achieved.

Clinical validity of second-generation tau PET tracers as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework

PURPOSE

In 2017, the Geneva Alzheimer's disease (AD) strategic biomarker roadmap initiative proposed a framework of the systematic validation AD biomarkers to harmonize and accelerate their development and implementation in clinical practice. Here, we use this framework to examine the translatability of the second-generation tau PET tracers into the clinical context.

METHODS

All available literature was systematically searched based on a set of search terms that related independently to analytic validity (phases 1-2), clinical validity (phase 3-4), and clinical utility (phase 5). The progress on each of the phases was determined based on scientific criteria applied for each phase and coded as fully, partially, preliminary achieved or not achieved at all.

RESULTS

The validation of the second-generation tau PET tracers has successfully passed the analytical phase 1 of the strategic biomarker roadmap. Assay definition studies showed evidence on the superiority over first-generation tau PET tracers in terms of off-target binding. Studies have partially achieved the primary aim of the analytical validity stage (phase 2), and preliminary evidence has been provided for the assessment of covariates on PET signal retention. Studies investigating of the clinical validity in phases 3, 4, and 5 are still underway.

CONCLUSION

The current literature provides overall preliminary evidence on the establishment of the second-generation tau PET tracers into the clinical context, thereby successfully addressing some methodological issues from the tau PET tracer of the first generation. Nevertheless, bigger cohort studies, longitudinal follow-up, and examination of diverse disease population are still needed to gauge their clinical validity.

The strategic biomarker roadmap for the validation of Alzheimer's diagnostic biomarkers: methodological update

BACKGROUND

The 2017 Alzheimer's disease (AD) Strategic Biomarker Roadmap (SBR) structured the validation of AD diagnostic biomarkers into 5 phases, systematically assessing analytical validity (Phases 1-2), clinical validity (Phases 3-4), and clinical utility (Phase 5) through primary and secondary Aims. This framework allows to map knowledge gaps and research priorities, accelerating the route towards clinical implementation. Within an initiative aimed to assess the development of biomarkers of tau pathology, we revised this methodology consistently with progress in AD research.

METHODS

We critically appraised the adequacy of the 2017 Biomarker Roadmap within current diagnostic frameworks, discussed updates at a workshop convening the Alzheimer's Association and 8 leading AD biomarker research groups, and detailed the methods to allow consistent assessment of aims achievement for tau and other AD diagnostic biomarkers.

RESULTS

The 2020 update applies to all AD diagnostic biomarkers. In Phases 2-3, we admitted a greater variety of study designs (e.g., cross-sectional in addition to longitudinal) and reference standards (e.g., biomarker confirmation in addition to clinical progression) based on construct (in addition to criterion) validity. We structured a systematic data extraction to enable transparent and formal evidence assessment procedures. Finally, we have clarified issues that need to be addressed to generate data eligible to evidence-to-decision procedures.

DISCUSSION

This revision allows for more versatile and precise assessment of existing evidence, keeps up with theoretical developments, and helps clinical researchers in producing evidence suitable for evidence-to-decision procedures. Compliance with this methodology is essential to implement AD biomarkers efficiently in clinical research and diagnostics.

Building Research Support Capacity across Human Health Biobanks during the COVID-19 Pandemic

Human health biobanks are forms of research infrastructure that supply biospecimens and associated data to researchers, and therefore juxtapose the activities of clinical care and biomedical research. The discipline of biobanking has existed for over 20 years and is supported by several international professional societies and dedicated academic journals. However, despite both rising research demand for human biospecimens, and the growth of biobanking as an academic discipline, many individual biobanks continue to experience sustainability challenges. This commentary will summarize how the COVID-19 pandemic is creating new challenges and opportunities for both the health biobanking sector and the supporting discipline of biobanking. While the challenges for biobanks may be numerous and acute, there are opportunities for both individual biobanks and the discipline of biobanking to embrace change such that biobanks can continue to support and drive biomedical research. We will therefore describe numerous practical steps that individual biobanks and/or the discipline of biobanking can take to survive and possibly thrive in response to the COVID-19 pandemic.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Lower Gastrointestinal Cancers: A Systematic Review and Meta-Analysis

INTRODUCTION

Lower gastrointestinal (GI) cancers are a major cause of cancer deaths worldwide. Prognosis improves with earlier diagnosis, and non-invasive biomarkers have the potential to aid with early detection. Substantial investment has been made into the development of biomarkers; however, studies are often carried out in specialist settings and few have been evaluated for low-prevalence populations.

METHODS

We aimed to identify novel biomarkers for the detection of lower GI cancers that have the potential to be evaluated for use in primary care. MEDLINE, Embase, Emcare and Web of Science were systematically searched for studies published in English from January 2000 to October 2019. Reference lists of included studies were also assessed. Studies had to report on measures of diagnostic performance for biomarkers (single or in panels) used to detect colorectal or anal cancers. We included all designs and excluded studies with fewer than 50 cases/controls. Data were extracted from published studies on types of biomarkers, populations and outcomes. Narrative synthesis was used, and measures of specificity and sensitivity were meta-analysed where possible.

RESULTS

We identified 142 studies reporting on biomarkers for lower GI cancers, for 24,844 cases and 45,374 controls. A total of 378 unique biomarkers were identified. Heterogeneity of study design, population type and sample source precluded meta-analysis for all markers except methylated septin 9 (mSEPT9) and pyruvate kinase type tumour M2 (TuM2-PK). The estimated sensitivity and specificity of mSEPT9 was 80.6% (95% CI 76.6-84.0%) and 88.0% (95% CI 79.1-93.4%) respectively; TuM2-PK had an estimated sensitivity of 81.6% (95% CI 75.2-86.6%) and specificity of 80.1% (95% CI 76.7-83.0%).

CONCLUSION

Two novel biomarkers (mSEPT9 and TuM2-PK) were identified from the literature with potential for use in lower-prevalence populations. Further research is needed to validate these biomarkers in primary care for screening and assessment of symptomatic patients.

COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors—compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient's ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting

This review explains in simple terms, accessible to the non-statistician, general principles regarding the correct research methods to develop and then evaluate imaging biomarkers in a clinical setting, including radiomic biomarkers. The distinction between diagnostic and prognostic biomarkers is made and emphasis placed on the need to assess clinical utility within the context of a multivariable model. Such models should not be restricted to imaging biomarkers and must include relevant disease and patient characteristics likely to be clinically useful. Biomarker utility is based on whether its addition to the basic clinical model improves diagnosis or prediction. Approaches to both model development and evaluation are explained and the need for adequate amounts of representative data stressed so as to avoid underpowering and overfitting. Advice is provided regarding how to report the research correctly. KEY POINTS: • Imaging biomarker research is common but methodological errors are encountered frequently that may mean the research is not clinically useful. • The clinical utility of imaging biomarkers is best assessed by their additive effect on multivariable models based on clinical factors known to be important. • The data used to develop such models should be sufficient for the number of variables investigated and the model should be evaluated, preferably using data unrelated to development.

Cell-free DNA promoter hypermethylation as a diagnostic marker for pancreatic ductal adenocarcinoma - An external validation study

BACKGROUND

We recently identified a diagnostic prediction model based on promoter hypermethylation of eight selected genes in plasma cell-free (cf) DNA, which showed promising results as a diagnostic biomarker for pancreatic ductal adenocarcinoma (PDAC). The aim of the present study was to validate this biomarker profile in an external patient cohort and examine any additional effect of serum CA 19-9.

METHODS

Patients with PDAC (n = 346, stage I-IV) and chronic pancreatitis (n = 25) were included. Methylation-specific PCR of a 28-gene panel was performed on serum cfDNA samples. The previously developed diagnostic prediction model (age>65 years, BMP3, RASSF1A, BNC1, MESTv2, TFPI2, APC, SFRP1 and SFRP2) was validated alone and in combination with serum CA 19-9 in this external patient cohort.

RESULTS

Patients with PDAC had a higher number of hypermethylated genes (mean 8.11, 95% CI 7.70-8.52) than patients with chronic pancreatitis (mean 5.60, 95% CI 4.42-6.78, p = 0.011). Validation of the diagnostic prediction model yielded an AUC of 0.77 (95% CI 0.69-0.84). The combination of serum CA 19-9 and our test had an AUC of 0.93 (95% CI 0.89-0.96) in the primary study and 0.85 (95% CI 0.79-0.91) in the validation study.

CONCLUSION

In this validation study, PDAC was associated with a higher number of hypermethylated genes in serum cfDNA than chronic pancreatitis. Our diagnostic test was superior to the predictive value of serum CA 19-9 alone in both the primary and the validation study. The combination of our test with CA 19-9 may serve as a clinically useful diagnostic biomarker for PDAC.

The assessment of circulating cell-free DNA as a diagnostic tool for breast cancer: an updated systematic review and meta-analysis of quantitative and qualitative ssays

OBJECTIVES

This updated meta-analysis aimed to assess the diagnostic accuracy of circulating cell-free DNA (cfDNA) in breast cancer (BC).

CONTENT

An extensive systematic search was performed in PubMed, Scopus, Embase, and Science Direct databases to retrieve all related literature. Various diagnostic estimates, including sensitivity (SE), specificity (SP), likelihood ratios (LRs), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (sROC) curve, were also calculated using bivariate linear mixed models.

SUMMARY

In this meta-analysis, 57 unique articles (130 assays) on 4246 BC patients and 2,952 controls, were enrolled. For quantitative approaches, pooled SE, SP, PLR, NLR, DOR, and AUC were obtained as 0.80, 0.88, 6.7, 0.23, 29, and 0.91, respectively. Moreover, for qualitative approaches, pooled SE and SP for diagnostic performance were obtained as 0.36 and 0.98, respectively. In addition, PLR was 14.9 and NLR was 0.66. As well, the combined DOR was 23, and the AUC was 0.79.

OUTLOOK

Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.

Identification of a novel non-invasive biological marker to overcome the shortcomings of PSA in diagnosis and risk stratification for prostate cancer: Initial prospective study of developmental endothelial locus-1 protein

Objective

This prospective study sought to clarify the developmental endothelial locus-1 (Del-1) protein as values of diagnosis and risk stratification of prostate cancer (PCa).

Design

From February 2017 to December 2019, a total 458 patients who underwent transrectal ultrasound guided prostate biopsy or surgery of benign prostatic hyperplasia agreed to research of Del-1 protein. We prospectively compared and analyzed the Del-1 protein and prostate specific antigen (PSA) in relation to the patients’ demographic and clinicopathological characteristics.

Results

Mean age was 68.86±8.55 years. Mean PSA and Del-1 protein was 21.72±89.37, 0.099±0.145, respectively. Two hundred seventy-six (60.3%) patients were diagnosed as PCa. Among them, 181 patients underwent radical prostatectomy (RP). There were significant differences in Del-1 protein between benign and PCa group (0.066±0.131 vs 0.121±0.149, respectively, p<0.001). When we set the cut-off value of del-1 protein as 0.120, in patients with 3≤PSA≤8, positive predictive value and specificity of Del-1 protein (≥0.120) for predicting PCa were 88.9% (56/63) and 93.5% (101/108), respectively. Among 181 patients who underwent RP, there were significant differences in Del-1 protein according to stage (pT2 vs pT3a vs ≥pT3b) (0.113±0.078, 0.171±0.121, 0.227±0.161, respectively, p<0.001) and to Gleason score (6 (3+3) or 7 (3+4) vs 7 (4+3) or 8 (4+4) vs 9 or 10) (0.134±0.103, 0.150±0.109, 0.212±0.178, respectively, P = 0.044). Multivariate analysis showed that PSA, Del-1 protein and high Gleason score (≥9) were the independent prognostic factors for predicting higher pT stage (≥3b). Furthermore, age, PSA and Del-1 protein were independent prognostic factors for predicting significant PCa.

Conclusion

Patients with PCa showed higher expression of Del-1 protein than benign patients. Del-1 protein increased with the stage and Gleason score of PCa. Collaboration with PSA, Del-1 protein can be a non-invasive useful marker for diagnosis and risk stratification of PCa.

Hybrid design evaluating new biomarkers when there is an existing screening test

Development of cancer screening biomarkers usually follows the Early Detection Research Network 5-Phase guideline in Pepe et al. A key feature of this guide is that the phased development follows a sequential order, moving to the next phase only when the current phase study is complete and has met its target performance. Motivated by a newly funded Newly onset Diabetes cohort study, we propose a design evaluating new biomarkers to discriminate between cases and controls in the presence of an existing screening test. The proposed design achieves two goals: (1) avoiding bias in estimating sensitivity or specificity in predicting cancer at a given time period prior to clinical diagnosis, using data from both screening detected cancers in Phase IV study and clinically diagnosed cancers in Phase III study; and (2) building a panel with biomarkers for Phase III and IV studies based on all data. A simulation study shows that the proposed design outperforms both a conventional method using data in Phase III arm only and a naive method using data in Phase III and IV arms ignoring the difference between the time of screening the detected cancer and the time of clinical diagnosis. The proposed design yields a smaller standard error of the estimation and increases the statistical power to confirm biomarker performance. This proposed method has the potential to shorten the cancer screening biomarker development process, use resources more effectively, and bring benefits to patients quickly.

Cell-Free DNA Methylation as Blood-Based Biomarkers for Pancreatic Adenocarcinoma—A Literature Update

Pancreatic adenocarcinoma has a horrible prognosis, which is partly due to difficulties in diagnosing the disease in an early stage. Additional blood-born biomarkers for pancreatic adenocarcinoma are needed. Epigenetic modifications, as changes in DNA methylation, is a fundamental part of carcinogenesis. The aim of this paper is to do an update on cell-free DNA methylation as blood-based biomarkers for pancreatic adenocarcinoma. The current literature including our studies clearly indicates that cell-free DNA methylation has the potential as blood-based diagnostic and prognostic biomarkers for pancreatic adenocarcinoma. However, still no clinical applicable biomarker for pancreatic adenocarcinoma based on DNA methylation do exist. Further well-designed validation studies are needed.

Identification of clinically relevant biomarkers of epileptogenesis - a strategic roadmap

Onset of many forms of epilepsy occurs after an initial epileptogenic insult or as a result of an identified genetic defect. Given that the precipitating insult is known, these epilepsies are, in principle, amenable to secondary prevention. However, development of preventive treatments is difficult because only a subset of individuals will develop epilepsy and we cannot currently predict which individuals are at the highest risk. Biomarkers that enable identification of these individuals would facilitate clinical trials of potential anti-epileptogenic treatments, but no such prognostic biomarkers currently exist. Several putative molecular, imaging, electroencephalographic and behavioural biomarkers of epileptogenesis have been identified, but clinical translation has been hampered by fragmented and poorly coordinated efforts, issues with inter-model reproducibility, study design and statistical approaches, and difficulties with validation in patients. These challenges demand a strategic roadmap to facilitate the identification, characterization and clinical validation of biomarkers for epileptogenesis. In this Review, we summarize the state of the art with respect to biomarker research in epileptogenesis and propose a five-phase roadmap, adapted from those developed for cancer and Alzheimer disease, that provides a conceptual structure for biomarker research.

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the "natural" computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumor-triggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the "natural" computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the "brute-force" search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as magnetic nanorobots, a two-dimensional microchannel network as the human vasculature, and a magnetic nanorobotic control system as the external actuating and tracking system. Results from the in silico simulations and in vitro experiments verify the effectiveness of the proposed Se-TS for two representative BGF landscapes.

Fascin actin-bundling protein 1 in human cancer: promising biomarker or therapeutic target?

Fascin actin-bundling protein 1 (FSCN1) is a highly conserved actin-bundling protein that cross links F-actin microfilaments into tight, parallel bundles. Elevated FSCN1 levels have been reported in many types of human cancers and have been correlated with aggressive clinical progression, poor prognosis, and survival outcomes. The overexpression of FSCN1 in cancer cells has been associated with tumor growth, migration, invasion, and metastasis. Currently, FSCN1 is recognized as a candidate biomarker for multiple cancer types and as a potential therapeutic target. The aim of this study was to provide a brief overview of the FSCN1 gene and protein structure and elucidate on its actin-bundling activity and physiological functions. The main focus was on the role of FSCN1 and its upregulatory mechanisms and significance in cancer cells. Up-to-date studies on FSCN1 as a novel biomarker and therapeutic target for human cancers are reviewed. It is shown that FSCN1 is an unusual biomarker and a potential therapeutic target for cancer.