The PLCO Biorepository: Creating, Maintaining, and Administering a Unique Biospecimen Resource

Machine learning identifies cell-free DNA 5-hydroxymethylation biomarkers that detect occult colorectal cancer in PLCO Screening Trial subjects

Background

Colorectal cancer (CRC) is a leading cause of cancer-related mortality, and CRC detection through screening improves survival rates. A promising avenue to improve patient screening compliance is the development of minimally-invasive liquid biopsy assays that target CRC biomarkers on circulating cell-free DNA (cfDNA) in peripheral plasma. In this report, we identify cfDNA biomarker candidate genes bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that diagnose occult CRC up to 36 months prior to clinical diagnosis using the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial samples.

Methods

Archived PLCO Trial plasma samples containing cfDNA were obtained from the National Cancer Institute (NCI) biorepositories. Study subjects included those who were diagnosed with CRC within 36 months of blood collection (i.e., case, n = 201) and those who were not diagnosed with any cancer during an average of 16.3 years of follow-up (i.e., controls, n = 402). Following the extraction of 3 - 8 ng cfDNA from less than 300 microliters plasma, we employed the sensitive 5hmC-Seal chemical labeling approach, followed by next-generation sequencing (NGS). We then conducted association studies and machine-learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio.

Results

Despite the technical challenges associated with the PLCO samples (e.g., limited plasma volumes, low cfDNA amounts, and long archival times), robust genome-wide 5hmC profiles were successfully obtained from these samples. Association analyses using the Cox proportional hazards models suggested several epigenetic pathways relevant to CRC development distinguishing cases from controls. A weighted Cox model, comprised of 32-associated gene bodies, showed predictive detection value for CRC as early as 24-36 months prior to overt tumor presentation, and a trend for increased predictive power was observed for blood samples collected closer to CRC diagnosis. Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and self-reported race/ethnicity, and significantly outperformed risk factors such as age and obesity according to BMI (body mass index). Additionally, further improvement of predictive performance was achieved by combining the 5hmC-based model and risk factors for CRC.

Conclusions

An assay of 5hmC epigenetic signals on cfDNA revealed candidate biomarkers with the potential to predict CRC occurrence despite the absence of clinical symptoms or the availability of effective predictors. Developing a minimally-invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient survival through higher compliance screening and earlier intervention. Future investigation to expand this strategy to prospectively collected samples is warranted.

Antinuclear Antibodies Are Associated with an Increased Risk of Diffuse Large B-Cell Lymphoma

Immune dysregulation is thought to increase the risk of non-Hodgkin lymphoma (NHL), but the evidence varies by subtype. We evaluated whether antinuclear antibodies (ANA), double-stranded DNA antibodies (anti-dsDNA), and extractable nuclear antigen antibodies (anti-ENA) were associated with the risk of common NHL subtypes in a nested case-control study. The autoantibodies were tested in serum collected years prior to NHL diagnosis in 832 cases and 809 controls from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Logistic regression was used to determine odds ratios (ORs) and 95% confidence intervals (95% CI) for the association with NHL risk. No association was observed between ANA positivity and NHL risk overall (OR: 1.18, 95% CI: 0.88-1.58); however, ANA positivity was associated with an increased risk of diffuse large B-cell lymphoma (DLBCL) (OR: 1.83, 95% CI: 1.15-2.91), with 19.7% of cases and 12.2% of controls testing positive. The presence of either anti-ENA or anti-dsDNA was associated with an increased risk of NHL (OR: 2.93, 95% CI: 1.18-7.28), particularly DLBCL (OR: 3.51, 95% CI: 1.02-12.0) and marginal zone lymphoma (OR: 8.86, 95% CI: 1.26-62.0). Our study demonstrates that autoantibodies are associated with an elevated risk of DLBCL, providing support for autoimmunity as a risk factor.

Mortality Benefit of a Blood-Based Biomarker Panel for Lung Cancer on the Basis of the Prostate, Lung, Colorectal, and Ovarian Cohort

PURPOSE

To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model on the basis of subject characteristics to identify individuals at high risk of harboring a lethal lung cancer.

METHODS

Data from an established logistic regression model that combines four-marker protein panel (4MP) together with the Prostate, Lung, Colorectal, and Ovarian (PLCO) risk model (PLCOm2012) assayed in prediagnostic sera from 552 lung cancer cases and 2,193 noncases from the PLCO cohort were used in this study. Of the 552 lung cancer cases, 387 (70%) died of lung cancer. Cumulative incidence of lung cancer death and subdistributional and cause-specific hazard ratios (HRs) were calculated on the basis of 4MP + PLCOm2012 risk scores at a predefined 1.0% and 1.7% 6-year risk thresholds, which correspond to the current and former US Preventive Services Task Force screening criteria, respectively.

RESULTS

When considering cases diagnosed within 1 year of blood draw and all noncases, the area under receiver operation characteristics curve estimate of the 4MP + PLCOm2012 model for risk prediction of lung cancer death was 0.88 (95% CI, 0.86 to 0.90). The cumulative incidence of lung cancer death was statistically significantly higher in individuals with 4MP + PLCOm2012 scores above the 1.0% 6-year risk threshold (modified χ2, 166.27; P < .0001). Corresponding subdistributional and lung cancer death-specific HRs for test-positive cases were 9.88 (95% CI, 6.44 to 15.18) and 10.65 (95% CI, 6.93 to 16.37), respectively.

CONCLUSION

The blood-based biomarker panel in combination with PLCOm2012 identifies individuals at high risk of a lethal lung cancer.

Banking on a new understanding: translational opportunities from veterinary biobanks

Current advances in geroscience are due in part to the discovery of biomarkers with high predictive ability in short-lived laboratory animals such as flies and mice. These model species, however, do not always adequately reflect human physiology and disease, highlighting the need for a more comprehensive and relevant model of human aging. Domestic dogs offer a solution to this obstacle, as they share many aspects not only of the physiological and pathological trajectories of their human counterpart, but also of their environment. Furthermore, they age at a considerably faster rate. Studying aging in the companion dog provides an opportunity to better understand the biological and environmental determinants of healthy lifespan in our pets, and to translate those findings to human aging. Biobanking, the systematic collection, processing, storage, and distribution of biological material and associated data has contributed to basic, clinical, and translational research by streamlining the management of high-quality biospecimens for biomarker discovery and validation. In this review, we discuss how veterinary biobanks can support research on aging, particularly when integrated into large-scale longitudinal studies. As an example of this concept, we introduce the Dog Aging Project Biobank.

The Availability of Human Biospecimens to Support Biomarker Research

Preserved biospecimens held in biobank inventories and clinical archives are important resources for biomarker research. Recent advances in technologies have led to an increase in use of clinical archives in particular, in order to study retrospective cohorts and to generate data relevant to tissue biomarkers. This raises the question of whether the current sizes of biobank inventories are appropriate to meet the demands of biomarker research. This commentary discusses this question by considering data concerning overall biobank and biospecimen numbers to estimate current biospecimen supply and use. The data suggests that biospecimen supply exceeds current demand. Therefore, it may be important for individual biobanks to reassess the targets for their inventories, consider culling unused portions of these inventories, and shift resources towards providing prospective custom biobanking services.

Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment

PURPOSE

To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics.

METHODS

A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCO_m2012) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera.

RESULTS

The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCO_m2012 model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCO_m2012 model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCO_m2012 model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria.

CONCLUSION

A blood-based biomarker panel in combination with PLCO_m2012 significantly improves lung cancer risk assessment for lung cancer screening.

LncRNA Biomarkers of Inflammation and Cancer

Long noncoding RNAs (lncRNAs) are promising candidates as biomarkers of inflammation and cancer. LncRNAs have several properties that make them well-suited as molecular markers of disease: (1) many lncRNAs are expressed in a tissue-specific manner, (2) distinct lncRNAs are upregulated based on different inflammatory or oncogenic stimuli, (3) lncRNAs released from cells are packaged and protected in extracellular vesicles, and (4) circulating lncRNAs in the blood are detectable using various RNA sequencing approaches. Here we focus on the potential for lncRNA biomarkers to detect inflammation and cancer, highlighting key biological, technological, and analytical considerations that will help advance the development of lncRNA-based liquid biopsies.

Biorepository - A key component of research studies

The last two decades have shown impressive advances in high-throughput assays for gene expression (genomics), proteins (proteomics), and metabolites (metabolomics). As a result, the quest for an equivalent need for human biological samples has increased exponentially. Translational investigations require good quality specimens to guarantee research results' integrity, probity, and reproducibility. A biorepository is a bank of specimens or specimens-derived neosamples (e.g., organoids, nucleic acids) linked to a database containing information related to these specimens. Two requirements must be met to safeguard the authenticity and stability of such a repository. First, the information provided should comprise relevant clinical and therapeutic communication, and second, the chain of custody is assured, guarded, versatile, and accessible. Completing these requirements is crucial for consistency, accuracy, verifiability, and disclosability of scientific and clinical outcomes. This commentary emphasizes that advocacy for standardization of operational workflows is a sine qua non for good science. Safe procedures for clinical trials are crucial to maintaining biorepositories' validity for all researchers.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

ABSTRACT

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

The Utilization of Biospecimens: Impact of the Choice of Biobanking Model

The term research "biobank" is one of multiple names (e.g., bioresource, biorepository,) used to designate an entity that receives, collects, processes, stores, and/or distributes biospecimens or other biospecimen-related products (e.g., data) to support research. There are multiple organizational models of biobanking used by bioresources, but the primary goal of all bioresources should not be simply to collect biospecimens, but ultimately to distribute almost all collected biospecimens and/or data to support scientific research; bioresources should serve as "biodistributors" rather than "biovaults." The appropriate choice of model is the first step in ensuring optimal biospecimen utilization by a bioresource. This article discusses some of the different models that may be used alone or in combination by a bioresource providing biospecimens for research; it describes the factors affecting the choice of the most appropriate model or models, the advantages and disadvantages of the various models, and a discussion of the impact of the choice of the model on biospecimen utilization. Frequently, problems with biospecimen utilization are not caused by any single model, but rather a mismatch between the choice of model and goals of the bioresource, and/or problems with the subsequent design, goals, operations, and management of the bioresource after a model is selected.

Commentary on Improving Biospecimen Utilization by Classic Biobanks: Identifying Past and Minimizing Future Mistakes

Many classic biobanks collect more human tissues than they distribute, leading to increased inventories, unnecessary storage, increased expenses, and reduced chargeback income. This situation is a result of biobanks operating without well-defined goals, having incorrect views of the potential number of investigators who will utilize specimens, and collection of biospecimens without adequately considering the need for specific tissues by investigators. These deficiencies frequently lead to unrealistic plans for biospecimen utilization and biobanks that are larger than necessary. For example, tissue collections usually are not periodically compared with biospecimen distribution and modified accordingly. An ethical issue has arisen as to the acceptability of consenting patients for the use of their tissues in research without a realistic planned approach to distribution of the biospecimens and their ultimate utilization in supporting biomedical research. These issues and how to minimize them are discussed in this commentary focused on how classic biobanks can improve utilization of their biospecimens.

Conserved serum protein biomarkers associated with growing early colorectal adenomas

A major challenge for the reduction of colon cancer is to detect patients carrying high-risk premalignant adenomas with minimally invasive testing. As one step, we have addressed the feasibility of detecting protein signals in the serum of patients carrying an adenoma as small as 6-9 mm in maximum linear dimension. Serum protein biomarkers, discovered in two animal models of early colonic adenomagenesis, were studied in patients using quantitative mass-spectrometric assays. One cohort included patients bearing adenomas known to be growing on the basis of longitudinal computed tomographic colonography. The other cohort, screened by optical colonoscopy, included both patients free of adenomas and patients bearing adenomas whose risk status was judged by histopathology. The markers F5, ITIH4, LRG1, and VTN were each elevated both in this patient study and in the studies of the Pirc rat model. The quantitative study in the Pirc rat model had demonstrated that the elevated level of each of these markers is correlated with the number of colonic adenomas. However, the levels of these markers in patients were not significantly correlated with the total adenoma volume. Postpolypectomy blood samples demonstrated that the elevated levels of these four conserved markers persisted after polypectomy. Two additional serum markers rapidly renormalized after polypectomy: growth-associated CRP levels were enhanced only with high-risk adenomas, while PI16 levels, not associated with growth, were reduced regardless of risk status. We discuss biological hypotheses to account for these observations, and ways for these signals to contribute to the prevention of colon cancer.

Nutritional metabolomics and breast cancer risk in a prospective study

Background: The epidemiologic evidence for associations between dietary factors and breast cancer is weak and etiologic mechanisms are often unclear. Exploring the role of dietary biomarkers with metabolomics can potentially facilitate objective dietary characterization, mitigate errors related to self-reported diet, agnostically test metabolic pathways, and identify mechanistic mediators.Objective: The aim of this study was to evaluate associations of diet-related metabolites with the risk of breast cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.Design: We examined prediagnostic serum concentrations of diet-related metabolites in a nested case-control study in 621 postmenopausal invasive breast cancer cases and 621 matched controls in the multicenter PLCO cohort. We calculated partial Pearson correlations between 617 metabolites and 55 foods, food groups, and vitamin supplements on the basis of the 2015 Dietary Guidelines for Americans and derived from a 137-item self-administered food-frequency questionnaire. Diet-related metabolites (P-correlation < 1.47 × 10^-6) were evaluated in breast cancer analyses. ORs for the 90th compared with the 10th percentile were calculated by using conditional logistic regression, with body mass index, physical inactivity, other breast cancer risk factors, and caloric intake controlled for (false discovery rate <0.2).Results: Of 113 diet-related metabolites, 3 were associated with overall breast cancer risk (621 cases): caprate (10:0), a saturated fatty acid (OR: 1.77; 95% CI = 1.28, 2.43); γ-carboxyethyl hydrochroman (γ-CEHC), a vitamin E (γ-tocopherol) derivative (OR: 1.64; 95% CI: 1.18, 2.28); and 4-androsten-3β,17β-diol-monosulfate (1), an androgen (OR: 1.61; 95% CI: 1.20, 2.16). Nineteen metabolites were significantly associated with estrogen receptor (ER)-positive (ER⁺) breast cancer (418 cases): 12 alcohol-associated metabolites, including 7 androgens and α-hydroxyisovalerate (OR: 2.23; 95% CI: 1.50, 3.32); 3 vitamin E (tocopherol) derivatives (e.g., γ-CEHC; OR: 1.80; 95% CI: 1.20, 2.70); butter-associated caprate (10:0) (OR: 1.81; 95% CI: 1.23, 2.67); and fried food-associated 2-hydroxyoctanoate (OR: 1.46; 95% CI: 1.03, 2.07). No metabolites were significantly associated with ER-negative breast cancer (144 cases).Conclusions: Prediagnostic serum concentrations of metabolites related to alcohol, vitamin E, and animal fats were moderately strongly associated with ER⁺ breast cancer risk. Our findings show how nutritional metabolomics might identify diet-related exposures that modulate cancer risk. This trial was registered at clinicaltrials.gov as NCT00339495.

Impact of freeze-thaw cycles on circulating inflammation marker measurements

BACKGROUND

Circulating inflammation markers are being increasingly measured in prospective cohorts to investigate cancer etiology. However, it is unclear how the measurements are affected by the freeze-thaw cycles of the specimens prior to marker analysis.

METHODS

We compared concentrations of 45 inflammation markers between paired serum vials of 55 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial that have undergone one (T1), two (T2), and three (T3) freeze-thaw cycles at the time of assay. We computed the difference of analyte concentrations across paired vials (T1 vs. T2, T2 vs. T3) and tested whether the difference deviated from zero using the Wilcoxon signed-rank test. We also calculated Spearman rank correlation and weighted kappa statistics for T1 vs. T2 and T2 vs. T3 comparisons to assess agreement in rank ordering of subjects.

RESULTS

Measurements between paired T1 and T2 samples were largely similar, with the difference not statistically deviating from zero for 36 of the 45 markers. In contrast, tests of the difference between paired T2 and T3 samples were statistically significant for 36 markers. However, the rank ordering of participants by marker concentration remained largely consistent across T2 and T3 samples, with Spearman correlation coefficients >0.8 for 42 markers and weighted kappas >0.7 for 37 markers.

CONCLUSION

We recommend that studies measuring inflammation markers use previously unthawed specimens to the extent possible, or match on the number of prior freeze-thaw cycles in nested case-control studies.

The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial Pathology Tissue Resource

BACKGROUND

Pathology tissue specimens with associated epidemiologic and clinical data are valuable for cancer research. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial undertook a large-scale effort to create a public resource of pathology tissues from PLCO participants who developed a cancer during the trial.

METHODS

Formalin-fixed paraffin-embedded tissue blocks were obtained from pathology laboratories on a loan basis for central processing of tissue microarrays, with additional free-standing tissue cores collected for nucleic acid extraction.

RESULTS

Pathology tissue specimens were obtained for prostate cancer (n = 1,052), lung cancer (n = 434), colorectal cancer (n = 675) and adenoma (n = 658), ovarian cancer and borderline tumors (n = 212), breast cancer (n = 870), and bladder cancer (n = 204). The process of creating this resource was complex, involving multidisciplinary teams with expertise in pathology, epidemiology, information technology, project management, and specialized laboratories.

CONCLUSIONS

Creating the PLCO tissue resource required a multistep process, including obtaining medical records and contacting pathology departments where pathology materials were stored after obtaining necessary patient consent and authorization. The potential to link tissue biomarkers to prospectively collected epidemiologic information, screening and clinical data, and matched blood or buccal samples offers valuable opportunities to study etiologic heterogeneity, mechanisms of carcinogenesis, and biomarkers for early detection and prognosis.

IMPACT

The methods and protocols developed for this effort, and the detailed description of this resource provided here, will be useful for those seeking to use PLCO pathology tissue specimens for their research and may also inform future tissue collection efforts in other settings. Cancer Epidemiol Biomarkers Prev; 25(12); 1635-42. ©2016 AACR.

Role of biobanking in urology: a review

In the current era of individualized medicine, a biorepository of human samples is essential to support clinical and translational research. There have been limited efforts in this arena within the field of urology, as cost, logistical and ethical issues represent significant deterrents to biobanking. The Johns Hopkins Brady Urological Institute Biorepository was founded in 1994 as a resource to facilitate discovery. Since its inception, the biorepository has enabled numerous research endeavours including pivotal trials leading to the regulatory approval of four diagnostic tests for prostate cancer. In the present review, we discuss the current state of biobanking within urology, outline the specific ethical and financial challenges to biobanking as well as solutions, and describe the operations of a successful urological biorepository.

PLCO: Evolution of an Epidemiologic Resource and Opportunities for Future Studies

The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), a large-scale, multi-institutional, randomized controlled trial, was launched in 1992 to evaluate the effectiveness of screening modalities for prostate, lung, colorectal, and ovarian cancer. However, PLCO was additionally designed to serve as an epidemiologic resource and the National Cancer Institute has invested substantial resources over the years to accomplish this goal. In this report, we provide a summary of changes to PLCO's follow-up after conclusion of the screening phase of the trial and highlight recent data and biospecimen collections, including ancillary studies, geocoding, administration of a new medication use questionnaire, consent for linkage to Medicare, and additional tissue collection that enhance the richness of the PLCO resource and provide further opportunities for scientific investigation into the prevention, early detection, etiology and treatment of cancer.

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.