Leveraging biospecimen resources for discovery or validation of markers for early cancer detection

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.

Mock clinical testing in the validation of fluid-phase biomarkers for head and neck carcinoma diagnosis: Scoping review

This review sought to determine the range and nature of prospective-sampling and blinding methods for validating nonviral biofluid markers diagnostic of head and neck carcinomas. Electronic database searching was conducted to identify studies published in English from January 1, 2009 to August 1, 2020. Sixteen studies from 17 articles published between 2011 and 2020 were included in this review. We found that about 3 out of 100 studies utilized at least one of the mock testing approaches for biomarker validation. Protein, mRNA, and metabolomic markers also represented the only groups whose validation has been attempted using these methods. Furthermore, studies that utilized both methods were found to have lower bias concerns on the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. Overall, there is a need to include these protocols in research endeavours verifying diagnostic biomarkers for head and neck carcinomas following the preliminary establishment of their classification accuracy.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

The Early Detection Research Network: A National Infrastructure to Support the Discovery, Development, and Validation of Cancer Biomarkers

In 2000, the NCI (Rockville, MD) established the Early Detection Research Network (EDRN) to identify, develop, and validate biomarkers to improve the detection of early-stage cancers and risk assessment. This consortium of more than 300 investigators at academic institutions and in the private sector is working collaboratively to bring biomarkers and imaging methods to clinical fruition. Although significant roadblocks have hindered the field of biomarker discovery and validation, the EDRN has helped overcome many of them by setting well-defined strategies and milestones focused on solving defined unmet clinical needs. The EDRN has implemented measures to improve biomarker discovery and validation, such as data sharing, use of common data elements, generating multidisciplinary and multi-institutional collaborations within a cohesive and productive team environment, and putting emphasis on quality control and data replication for all candidate biomarkers for reaching a "go" or "no go" decision. A measure of the success of the EDRN is the number of biomarkers tests or devices approved by the FDA to which EDRN investigators have made significant contributions and the number of biomarkers tests developed by EDRN investigators that are available in Clinical Laboratory Improvement Amendments laboratories.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Pitfalls in Cancer Biomarker Discovery and Validation with Emphasis on Circulating Tumor DNA

Despite significant investment of funds and resources, few new cancer biomarkers have been introduced to the clinic in the last few decades. Although many candidates produce promising results in the laboratory, deficiencies in sensitivity, specificity, and predictive value make them less than desirable in a patient setting. This review will analyze these challenges in detail as well as discuss false discovery, problems with reproducibility, and tumor heterogeneity. Circulating tumor DNA (ctDNA), an emerging cancer biomarker, is also analyzed, particularly in the contexts of assay specificity, sensitivity, fragmentation, lead time, mutant allele fraction, and clinical relevance. Emerging artificial intelligence technologies will likely be valuable tools in maximizing the clinical utility of ctDNA which is often found in very small quantities in patients with early-stage tumors. Finally, the implications of challenging false discoveries are examined and some insights about improving cancer biomarker discovery are provided.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Adding the Team into T1 Translational Research: A Case Study of Multidisciplinary Team Science in the Evaluation of Biomarkers of Prostate Cancer Risk and Prognosis

BACKGROUND

Given translational research challenges, multidisciplinary team science is promoted to increase the likelihood of moving from discovery to health effect. We present a case study documenting the utility of multidisciplinary team science in prostate cancer tissue biomarker validation.

METHODS

We used primary data generated by a team consisting of a pathologist, cancer biologists, a biostatistician, and epidemiologists. We examined their contributions by phase of biomarker evaluation to identify when, through the practice of team science, threats to internal validity were recognized and solved. Next, we quantified the extent of bias avoided in evaluating the association of Ki67 (immunohistochemistry), stromal cell telomere length (fluorescence in situ hybridization), and microRNA (miRNA) (miR-21, miR-141, miR-221; quantitative RT-PCR) with prostate cancer risk or recurrence in nested case-control studies.

RESULTS

Threats to validity were tissue storage time (Ki67, miRNA) and laboratory equipment maintenance (telomeres). Solutions were all in the data analysis phase and involved using tissue storage-time specific cutpoints and/or batch-specific cutpoints. Bias in the regression coefficient for quantiles of each biomarker ranged from 24% to 423%, and the coefficient for the test for trend ranged from 15% to 910%. The interpretation of the associations changed as follows: Ki67, null to positive; stromal cell telomere length, null to positive; miR-21 and miR-141 remained null; miR-221, weak to moderate inverse.

CONCLUSIONS

In this case study, we documented the inferential benefits of multidisciplinary team science when the team's collaboration and coordination led to the identification of threats to validity and the implementation of appropriate solutions.

Brain tumor biobanking in the precision medicine era: building a high-quality resource for translational research in neuro-oncology

The growth of precision medicine has made access to biobanks with high-quality, well-annotated neuro-oncology biospecimens critical. Developing and maintaining neuro-oncology biobanks is best accomplished through multidisciplinary collaboration between clinicians and researchers. Balancing the needs and leveraging the skills of all stakeholders in this multidisciplinary effort is of utmost importance. Collaboration with a multidisciplinary team of clinicians, health care team members, and institutions, as well as patients and their families, is essential for access to participants in order to obtain informed consent, collect samples under strict standard operating procedures, and accurate and relevant clinical annotation. Once a neuro-oncology biobank is established, development and implementation of policies related to governance and distribution of biospecimens (both within and outside the institution) is of critical importance for sustainability. Proper implementation of a governance process helps to ensure that the biospecimens and data can be utilized in research with the largest potential benefit. New NIH and peer-reviewed journal policies related to public sharing of 'omic' data generated from stored biospecimens create new ethical challenges that must be addressed in developing informed consents, protocols, and standard operating procedures. In addition, diversification of sources of funding for the biobanks is needed for long-term sustainability.

Evaluating Molecular Biomarkers for the Early Detection of Lung Cancer: When Is a Biomarker Ready for Clinical Use? An Official American Thoracic Society Policy Statement

BACKGROUND

Molecular biomarkers have the potential to improve the current state of early lung cancer detection. The goal of this project was to develop a policy statement that provides guidance about the level of evidence required to determine that a molecular biomarker, used to support early lung cancer detection, is appropriate for clinical use.

METHODS

An ad hoc project steering committee was formed, to include individuals with expertise in the early detection of lung cancer and molecular biomarker development, from inside and outside of the Assembly on Thoracic Oncology. Key questions, generated from the results of a survey of the project steering committee, were discussed at an in-person meeting. Results of the discussion were summarized in a policy statement that was circulated to the steering committee and revised multiple times to achieve consensus.

RESULTS

With a focus on the clinical applications of lung cancer screening and lung nodule evaluation, the policy statement outlines categories of results that should be reported in the early phases of molecular biomarker development, discusses the level of evidence that would support study of the clinical utility, describes the outcomes that should be proven to consider a molecular biomarker clinically useful, and suggests study designs capable of assessing these outcomes.

CONCLUSIONS

The application of molecular biomarkers to assist with the early detection of lung cancer has the potential to substantially improve our ability to select patients for lung cancer screening, and to assist with the characterization of indeterminate lung nodules. We have described relevant considerations and have suggested standards to apply when determining whether a molecular biomarker for the early detection of lung cancer is ready for clinical use.

Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.

A Prospective Evaluation of Early Detection Biomarkers for Ovarian Cancer in the European EPIC Cohort

PURPOSE

About 60% of ovarian cancers are diagnosed at late stage, when 5-year survival is less than 30% in contrast to 90% for local disease. This has prompted search for early detection biomarkers. For initial testing, specimens taken months or years before ovarian cancer diagnosis are the best source of information to evaluate early detection biomarkers. Here we evaluate the most promising ovarian cancer screening biomarkers in prospectively collected samples from the European Prospective Investigation into Cancer and Nutrition study.

EXPERIMENTAL DESIGN

We measured CA125, HE4, CA72.4, and CA15.3 in 810 invasive epithelial ovarian cancer cases and 1,939 controls. We calculated the sensitivity at 95% and 98% specificity as well as area under the receiver operator curve (C-statistic) for each marker individually and in combination. In addition, we evaluated marker performance by stage at diagnosis and time between blood draw and diagnosis.

RESULTS

We observed the best discrimination between cases and controls within 6 months of diagnosis for CA125 (C-statistic = 0.92), then HE4 (0.84), CA72.4 (0.77), and CA15.3 (0.73). Marker performance declined with longer time between blood draw and diagnosis and for earlier staged disease. However, assessment of discriminatory ability at early stage was limited by small numbers. Combinations of markers performed modestly, but significantly better than any single marker.

CONCLUSIONS

CA125 remains the single best marker for the early detection of invasive epithelial ovarian cancer, but can be slightly improved by combining with other markers. Identifying novel markers for ovarian cancer will require studies including larger numbers of early-stage cases. Clin Cancer Res; 22(18); 4664-75. ©2016 AACRSee related commentary by Skates, p. 4542.

Accelerating the Development and Validation of New Value-Based Diagnostics by Leveraging Biobanks

The challenges faced in developing value-based diagnostics has resulted in few of these tests reaching the clinic, leaving many treatment modalities without matching diagnostics to select patients for particular therapies. Many patients receive therapies from which they are unlikely to benefit, resulting in worse outcomes and wasted health care resources. The paucity of value-based diagnostics is a result of the scientific challenges in developing predictive markers, specifically: (1) complex biology, (2) a limited research infrastructure supporting diagnostic development, and (3) the lack of incentives for diagnostic developers to invest the necessary resources. Better access to biospecimens can address some of these challenges. Methodologies developed to evaluate biomarkers from biospecimens archived from patients enrolled in randomized clinical trials offer the greatest opportunity to develop and validate high-value molecular diagnostics. An alternative opportunity is to access high-quality biospecimens collected from large public and private longitudinal observational cohorts such as the UK Biobank, the US Million Veteran Program, the UK 100,000 Genomes Project, or the French E3N cohort. Value-based diagnostics can be developed to work in a range of samples including blood, serum, plasma, urine, and tumour tissue, and better access to these high-quality biospecimens with clinical data can facilitate biomarker research.

An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management

BACKGROUND

Pulmonary nodules are frequently detected during diagnostic chest imaging and as a result of lung cancer screening. Current guidelines for their evaluation are largely based on low-quality evidence, and patients and clinicians could benefit from more research in this area.

METHODS

In this research statement from the American Thoracic Society, a multidisciplinary group of clinicians, researchers, and patient advocates reviewed available evidence for pulmonary nodule evaluation, characterized six focus areas to direct future research efforts, and identified fundamental gaps in knowledge and strategies to address them. We did not use formal mechanisms to prioritize one research area over another or to achieve consensus.

RESULTS

There was widespread agreement that novel tests (including novel imaging tests and biopsy techniques, biomarkers, and prognostic models) may improve diagnostic accuracy for identifying cancerous nodules. Before they are used in clinical practice, however, better evidence is needed to show that they improve more distal outcomes of importance to patients. In addition, the pace of research and the quality of clinical care would be improved by the development of registries that link demographic and nodule characteristics with patient-level outcomes. Methods to share data from registries are also necessary.

CONCLUSIONS

This statement may help researchers to develop impactful and innovative research projects and enable funders to better judge research proposals. We hope that it will accelerate the pace and increase the efficiency of discovery to improve the quality of care for patients with pulmonary nodules.

To Share or Not to Share? A Survey of Biomedical Researchers in the U.S. Southwest, an Ethnically Diverse Region

Background

Cancer health disparities research depends on access to biospecimens from diverse racial/ethnic populations. This multimethodological study, using mixed methods for quantitative and qualitative analysis of survey results, assessed barriers, concerns, and practices for sharing biospecimens/data among researchers working with biospecimens from minority populations in a 5 state region of the United States (Arizona, Colorado, New Mexico, Oklahoma, and Texas). The ultimate goals of this research were to understand data sharing barriers among biomedical researchers; guide strategies to increase participation in biospecimen research; and strengthen collaborative opportunities among researchers.

Methods and Population

Email invitations to anonymous participants (n = 605 individuals identified by the NIH RePORT database), resulted in 112 responses. The survey assessed demographics, specimen collection data, and attitudes about virtual biorepositories. Respondents were primarily principal investigators at PhD granting institutions (91.1%) conducting basic (62.3%) research; most were non-Hispanic White (63.4%) and men (60.6%). The low response rate limited the statistical power of the analyses, further the number of respondents for each survey question was variable.

Results

Findings from this study identified barriers to biospecimen research, including lack of access to sufficient biospecimens, and limited availability of diverse tissue samples. Many of these barriers can be attributed to poor annotation of biospecimens, and researchers’ unwillingness to share existing collections. Addressing these barriers to accessing biospecimens is essential to combating cancer in general and cancer health disparities in particular. This study confirmed researchers’ willingness to participate in a virtual biorepository (n = 50 respondents agreed). However, researchers in this region listed clear specifications for establishing and using such a biorepository: specifications related to standardized procedures, funding, and protections of human subjects and intellectual property. The results help guide strategies to increase data sharing behaviors and to increase participation of researchers with multiethnic biospecimen collections in collaborative research endeavors

Conclusions

Data sharing by researchers is essential to leveraging knowledge and resources needed for the advancement of research on cancer health disparities. Although U.S. funding entities have guidelines for data and resource sharing, future efforts should address researcher preferences in order to promote collaboration to address cancer health disparities.

Cancer biomarker discovery and validation

With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene "signature"-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine.