China
Africa
Explore chapters and articles related to this topic
Analysis Of Volatile Organic Compounds For Cancer Diagnosis
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Abigail V. Rutter, Josep Sulé-Suso
Although it is evident that it would be ideal to have a VOC or combination of VOCs as biomarker/s for cancer management fulfilling all these criteria, this would be daunting and challenging to achieve, especially in cancer management. In fact, while for some diseases such as P. aeruginosa infection a single VOC might suffice (Smith and Španěl, 2015), in the case of cancer it is believed that a combination of VOCs rather than a single VOC will be needed to screen/diagnose the disease and/or improve the management of cancer patients. Furthermore, there are several steps to be taken before a biomarker or combination of biomarkers can be used in clinical practice. The Early Detection Research Network (EDRN) has identified five phases from the discovery of a biomarker and its clinical implementation: preclinical exploratory studies;clinical assay development for clinical disease;retrospective longitudinal repository studies;prospective screening studies; andcase-control studies (reviewed by Hensing and Salgia, 2013).
Risk Calculators
Published in John Crowley, Antje Hoering, Handbook of Statisticsin Clinical Oncology, 2012
Donna Pauler Ankerst, Yuanyuan Liang
A recent validation of the PCPT risk calculator illustrates discrimination concepts. In 2009 the generalizability of the PCPT risk calculator for potential applicability to other populations than for which it was developed was investigated. The PCPT was developed on a relatively healthy population of predominantly Caucasian men Among the entry requirements were that they be 55 years of age or older, have a normal DRE exam, and a PSA level less than or equal to 3.0 ng/mL. As part of the study they underwent annual screening for 7 years. In contrast, the Early Detection Research Network (EDRN) clinical cohort comprised 645 men who had been referred to multiple urology practices across five states in the northeastern United States and had received a prostate biopsy due to some clinical indication (Eyre et al. 2009). Some of the men in the EDRN cohort were younger than 55 years of age and as Table 1 of Eyre et al. (2009) showed, the cohort differed statistically significantly (p < .0001) in distribution of every risk factor compared to the 5519 manned PCPT cohort used to develop the PCPT risk model. PCPT risks were calculated for each member of the EDRN cohort and compared to the actual clinical outcome on biopsy for assessment of discrimination performance of the PCPT risk calculator. The PCPT risk calculator demonstrated statistically significant superior discrimination for detecting prostate cancer cases compared to PSA (AUC = 69.1% compared to 65.5%, respectively, p-value = .009), and the ROC curve for the PCPT risk calculator consistently fell at or above that for PSA for all FPRs, with the greatest difference for FPRs less than 25%. For example, the thresholds of the PCPT risk calculator and PSA, which obtained a FPR of 20%, were 48.4% and 6.9 ng/mL, respectively (Table 2 of Eyre et al. 2009). One can view these as two competing tests for referral to further intensive diagnostic testing by prostate biopsy, each with equal specificities: the PCPT risk calculator refers a patient to prostate biopsy if his PCPT risk exceeds approximately 50% and the PSA test if his PSA exceeds 6.9 ng/mL. If these two diagnostic tests had been implemented in the EDRN population to “rule in” patients who should undergo prostate biopsy and “rule out” patients who should not, the PCPT risk calculator would have correctly referred 47.1% of the prostate cancer cases (sensitivity) and the PSA test 35 .4% of the prostate cancer cases. Although better than PSA, the PCPT risk calculator would still have missed 50% of the prostate cancer cases. Insisting that 80% of prostate cancer cases get caught for both tests rwould have meant that the thresholds for referral would have had to be lowered, to 38.0% and 4.0 ng/mL, for the PCPT risk calculator and PSA test, respectively (Table 3, Eyre et al. 2009). But this would have approximately halved the specificity of both tests, to 40.3% for the PCPT risk calculator and 44.1% for PSA. In other words, approximately 60% of the men who did not have prostate cancer would have been referred to a prostate biopsy unnecessarily (FPR), an error rate unacceptable from a public health perspective.
Cancer biomarker discovery and translation: proteomics and beyond
Published in Expert Review of Proteomics, 2019
Ventzislava A. Hristova, Daniel W. Chan
Altered protein levels, abnormal structural conformation and impaired function are the most immediate causes of aberrant signaling in tumorigenesis. Protein dynamics are dependent in part on genetic factors, transcriptional regulation and mRNA translation, hence genomic and transcriptome abnormalities are reflected in the proteome [57]. Analyzing the protein composition of tumors has been the focus of immense initiatives such as CPTAC [21]. These studies utilize large cancer and grade-specific cohorts to characterize the proteome of tumor specimens and identify patterns that are indicative of disease onset and progression. Among the challenges with most proteomic analyses are a poor representation of low grade, early stage samples as most patients are diagnosed following the presentation of symptoms that correlate with an advanced disease state. The Early Detection Research Network (EDRN) is another extensive NCI initiative focused on cancer biomarker discovery and validation with the goal of early detection [58]. These multi-institution efforts emphasize the importance of proteomics in understanding disease etiology, but they also recognize the advantage of integrating genomic data thereby establishing proteogenomics as a prominent approach to cancer biomarker discovery [1].
An appraisal of pivotal evaluation designs in validating noninvasive biomarkers for head and neck cancer detection
Published in Acta Oncologica, 2020
John Adeoye, Chi Ching Joan Wan, Peter Thomson
Obtaining consensual molecular markers for contemporary clinical practice to diagnose head and neck cancer subtypes remains an all-important cause in diagnostic oncology. Biofluid specimens obtained noninvasively have seemed the more likely of the lot for clinical application with major advantages that centers on repeatable collection and representativeness of the diverse phenotypic profile of head and neck tumors [1,2]. With many promising biofluid markers being proffered in scientific reports [3–5], concerns exist regarding the scientific rigor utilized in these biomarker validation endeavors as conventional case–control and retrospective methods are laden with systematic biases that reports’ validity. In 2008, the Early Detection Research Network (EDRN) proposed the prospective-specimen-collection retrospective-blinded evaluation (PRoBE) method for pivotal evaluation of biomarker classification accuracy [6,7]. This modality, which may well represent the most-thorough biomarker validation design available (that mimics real-world application), incorporates a nested case–control approach that proposes biofluid sampling before undertaking confirmatory diagnostic tests. Furthermore, the design requires the evaluation of disease-specific or preferential indicators without knowledge of cancer status or otherwise [7]. In essence, evaluating the current practice of the PRoBE method will be tantamount to assessing the state of bedside readiness for the many biofluid markers suggested for head and neck cancer diagnosis. Hence, this brief report aims at mapping the utilization of the PRoBE protocol for research endeavors involving head and neck cancer biomarkers in noninvasive samples.
Trends in biomarker discoveries for the early detection and risk stratification of pancreatic cancer using omics studies
Published in Expert Review of Molecular Diagnostics, 2019
Takashi Kobayashi, Kazufumi Honda
We recently identified plasma/serum apolipoprotein A2 (apoA2) isoforms, which are among the promising proteomic biomarkers for early PDAC and at-risk diseases. ApoA2 is a major component of high-density lipoproteins (HDLs), and plays an important role in directing the fate of lipid metabolism among HDLs. In humans, most circulating apoA2 exists as a homodimer comprising two identical 77-residue polypeptide chains linked by a disulfide bridge between Cys-6 residues. Five isoforms of apoA2 have been identified, showing different amino acid sequences in the C-terminal region: apoA2-ATQ/ATQ, apoA2-ATQ/AT, apoA2-AT/AT, apoA2-AT/A, and apoA2-A/A. In recent years, proteomic approaches have revealed that levels of specific apoA2 isoforms in the serum/plasma of pancreatic cancer patients differ significantly from those seen in healthy individuals, even in the early stages of the disease [24]. A significant reduction in apoA2-ATQ/AT was detected in the plasma of any stage of PDAC. Areas under the curve (AUCs) to differentiate stage I, II, III and IV were 0.939, 0.957, 0.926 and 0.946, respectively. A significant reduction in apoA2-ATQ/AT has also been detected in at-risk diseases for PDAC, such as IPMN and chronic pancreatitis. Those findings were confirmed in a blinded study of a pancreatic cancer reference sample set organized by the National Cancer Institute Early Detection Research Network (NCI EDRN). In this blinded study, AUCs for CA19-9 and apoA2-ATQ/AT as single biomarkers to distinguish patients with early-stage pancreatic cancer (stage-I/II) were 0.783 (95% confidence interval (CI), 0.699–0.855) and 0.809 (95%CI, 0.748–0.867), respectively [25]. In addition, plasma samples collected by the European Prospective Investigation into Cancer and Nutrition study have been investigated, revealing that combination assay with CA19-9 and apoA2-ATQ/AT enabled earlier detection of PDAC than CA19-9 alone up to 18 months before diagnosis [26]. Such findings suggest apoA2 isoforms as potential biomarkers for filtering the general population for individuals at higher risk of PDAC. Although the mechanisms underlying these findings are still unknown, we have previously reported that specific abnormal processing patterns of amino acid in the C-terminal ends of apoA2 homodimer were observed in PDAC or autoimmune pancreatitis (AIP) [27,28]. Hayasaki et al. recently reported that apoA2-ATQ levels seem to reflect pancreatic atrophy and insufficient secretion of circulating pancreatic enzymes, and could provide a biomarker to assess pancreatic exocrine disorder [29].