Cancer Biomarkers - Volume 33, Number 4

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Editorial
Free accessEditorialFirst published November, 2022pp. 423-424
US-Japan collaboration: Collaboration across international boundary
Sudhir Srivastava
Editorial
Free accessEditorialFirst published November, 2022pp. 425-426
Introduction to the special issue Collaboration between US and Japan for the Early Detection of Cancer
Kazufumi Honda
Research article
Free accessResearch articleFirst published November, 2022pp. 427-436
Targeted proteomics for cancer biomarker verification and validation
Seiryo OgataTakeshi MasudaShingo Ito
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Abstract
Targeted proteomics is a method that measures the amount of target proteins via liquid chromatography-tandem mass spectrometry and is used to verify and validate the candidate cancer biomarker proteins. Compared with antibody-based quantification methods such as ELISA, targeted proteomics enables rapid method development, simultaneous measurement of multiple proteins, and high-specificity detection of modifications. Moreover, by spiking the internal standard peptide, targeted proteomics detects the absolute amounts of marker proteins, which is essential for determining the cut-off values for diagnosis and thus for multi-institutional validation. With these unique features, targeted proteomics can seamlessly transfer cancer biomarker candidate proteins from the discovery phase to the verification and validation phases, thereby resulting in an accelerated cancer biomarker pipeline. Furthermore, understanding the basic principles, advantages, and disadvantages is necessary to effectively utilize targeted proteomics in cancer biomarker pipelines. This review aimed to introduce the technical principles of targeted proteomics for cancer biomarker verification and validation.
Research article
Free accessResearch articleFirst published November, 2022pp. 437-447
Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery
Yasmine AbouleilaAhmed AliKeiko Masuda
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Abstract
Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.
Research article
Free accessResearch articleFirst published November, 2022pp. 449-465
The impact of the lung EDRN-CVC on Phase 1,2,& 3 biomarker validation studies
Michael N. KammerStephen A. DeppenSanja Antic
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Abstract
The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.
Research article
Free accessResearch articleFirst published November, 2022pp. 467-478
A pilot study to troubleshoot quality control metrics when assessing circulating miRNA expression data reproducibility across study sites
Jennifer B. PermuthTania MesaSion L. Williams
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Abstract
BACKGROUND:
Given the growing interest in using microRNAs (miRNAs) as biomarkers of early disease, establishment of robust protocols and platforms for miRNA quantification in biological fluids is critical.
OBJECTIVE:
The goal of this multi-center pilot study was to evaluate the reproducibility of NanoString nCounter™ technology when analyzing the abundance of miRNAs in plasma and cystic fluid from patients with pancreatic lesions.
METHODS:
Using sample triplicates analyzed across three study sites, we assessed potential sources of variability (RNA isolation, sample processing/ligation, hybridization, and lot-to-lot variability) that may contribute to suboptimal reproducibility of miRNA abundance when using nCounter™, and evaluated expression of positive and negative controls, housekeeping genes, spike-in genes, and miRNAs.
RESULTS:
Positive controls showed a high correlation across samples from each site (median correlation coefficient, r > 0.9). Most negative control probes had expression levels below background. Housekeeping and spike-in genes each showed a similar distribution of expression and comparable pairwise correlation coefficients of replicate samples across sites. A total of 804 miRNAs showed a similar distribution of pairwise correlation coefficients between replicate samples ( p = 0.93). After normalization and selecting miRNAs with expression levels above zero in 80% of samples, 55 miRNAs were identified; heatmap and principal component analysis revealed similar expression patterns and clustering in replicate samples.
CONCLUSIONS:
Findings from this pilot investigation suggest the nCounter platform can yield reproducible results across study sites. This study underscores the importance of implementing quality control procedures when designing multi-center evaluations of miRNA abundance.
Research article
Free accessResearch articleFirst published November, 2022pp. 479-488
From space to biomedicine: Enabling biomarker data science in the cloud
D.J. CrichtonL. CinquiniH. Kincaid
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Abstract
NASA’s Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute’s major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as “LabCAS”. As the data increases in size, it is critical that automated approaches be developed to “plug” laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.
Research article
Free accessResearch articleFirst published November, 2022pp. 489-501
Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers
Jaileene Pérez-MoralesHong LuWei Mu
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Abstract
BACKGROUND:
Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes.
METHODS:
Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes.
RESULTS:
Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS).
CONCLUSION:
We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.
Research article
Open accessResearch articleFirst published November, 2022pp. 503-512
Risk stratification of pancreatic cancer by a blood test for apolipoprotein A2-isoforms
Kazufumi Honda
Abstract
Though pancreatic cancer is uncommon, with an age-adjusted annual incidence of 12.9 cases per 100,000 person-years, it is considered a refractory cancer due to the mortality of 11.0 per 100,000 person-years. To efficiently identify patients with potentially surgically-curable pancreatic cancer, high-risk individuals (HRIs) for pancreatic cancer should be identified by easily and minimally invasive methods from the general population. We have identified unique processing patterns in the C-terminal amino acids of apolipoprotein A2 homodimer in the blood of patients with pancreatic cancer and in HRIs, and we called them apoA2-isoforms (apoA2-i). We then established an enzyme-linked immunosorbent assay (ELISA) to measure circulating apoA2-i in the blood stream. The diagnostic accuracy of apoA2-i to distinguish pancreatic cancer HRIs was verified by several retrospective studies, blind testing with the National Cancer Institute (NCI) Early Detection Research Network (EDRN), a prospective study with prediagnostic samples organized by the European Prospective Investigation into Cancer and Nutrition (EPIC) study, and the prospective screening study of pancreatic cancer in Kobe.

The apoA2-i blood test is a potential biomarker to identify HRIs and the curative stage of pancreatic cancer in the general population.

November 2022