Assessment of an exhaled breath test using ultraviolet photoionization time-of-flight mass spectrometry for the monitoring of kidney transplant recipients

Continuous monitoring for immunosuppressive status, infection and complications are a must for kidney transplantation (KTx) recipients. Traditional monitoring including blood sampling and kidney biopsy, which caused tremendous medical cost and trauma. Therefore, a cheaper and less invasive approach was urgently needed. We thought that a breath test has the potential to become a feasible tool for KTx monitoring. A prospective-specimen collection, retrospective-blinded assessment strategy was used in this study. Exhaled breath samples from 175 KTx recipients were collected in West China Hospital and tested by online ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS). The classification models based on breath test performed well in classifying normal and abnormal values of creatinine, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and tacrolimus, with AUC values of 0.889, 0.850, 0.849 and 0.889, respectively. Regression analysis also demonstrated the predictive ability of breath test for clinical creatinine, eGFR, BUN, tacrolimus level, as the predicted values obtained from the regression model correlated well with the clinical true values (p < 0.05). The findings of this investigation implied that a breath test by using UVP-TOF-MS for KTx recipient monitoring is possible and accurate, which might be useful for future clinical screenings.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer

IMPORTANCE

Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.

OBJECTIVE

To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.

EXPOSURES

Breath testing and SVM algorithm.

MAIN OUTCOMES AND MEASURES

The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).

RESULTS

Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.

CONCLUSIONS AND RELEVANCE

This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

Planning clinically relevant biomarker validation studies using the "number needed to treat" concept

Purpose

Despite an explosion of translational research to exploit biomarkers in diagnosis, prediction and prognosis, the impact of biomarkers on clinical practice has been limited. The elusiveness of clinical utility may partly originate when validation studies are planned, from a failure to articulate precisely how the biomarker, if successful, will improve clinical decision-making for patients. Clarifying what performance would suffice if the test is to improve medical care makes it possible to design meaningful validation studies. But methods for tackling this part of validation study design are undeveloped, because it demands uncomfortable judgments about the relative values of good and bad outcomes resulting from a medical decision.

Methods

An unconventional use of “number needed to treat” (NNT) can structure communication for the trial design team, to elicit purely value-based outcome tradeoffs, conveyed as the endpoints of an NNT “discomfort range”. The study biostatistician can convert the endpoints into desired predictive values, providing criteria for designing a prospective validation study. Next, a novel “contra-Bayes” theorem converts those predictive values into target sensitivity and specificity criteria, to guide design of a retrospective validation study. Several examples demonstrate the approach.

Conclusion

In practice, NNT-guided dialogues have contributed to validation study planning by tying it closely to specific patient-oriented translational goals. The ultimate payoff comes when the report of the completed study includes motivation in the form of a biomarker test framework directly reflecting the clinical decision challenge to be solved. Then readers will understand better what the biomarker test has to offer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0862-4) contains supplementary material, which is available to authorized users.

Use of semantic workflows to enhance transparency and reproducibility in clinical omics

BACKGROUND

Recent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety.

METHODS

To evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform.

RESULTS

We found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses.

CONCLUSIONS

This is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.

What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment

The cancer stem cell hypothesis is that in human solid cancers, only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of this phenomenon may be more successful if informed by insights into the population dynamics of tumor development. We revive some ideas in tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them.

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

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts.

Prediction of glycan motifs using quantitative analysis of multi-lectin binding: Motifs on MUC1 produced by cultured pancreatic cancer cells

PURPOSE

Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs.

EXPERIMENTAL DESIGN

The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines.

RESULTS

The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates.

CONCLUSIONS AND CLINICAL RELEVANCE

This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.

From differences in means between cases and controls to risk stratification: a business plan for biomarker development

Researchers developing biomarkers for early detection can determine the potential for clinical benefit at early stages of development. We provide the theoretical background showing the quantitative connection between biomarker levels in cases and controls and clinically meaningful risk measures, as well as a spreadsheet for researchers to use in their own research. We provide researchers with tools to decide whether a test is useful, whether it needs technical improvement, whether it may work only in specific populations, or whether any further development is futile. The methods described here apply to any method that aims to estimate risk of disease based on biomarkers, clinical tests, genetics, environment, or behavior.

SIGNIFICANCE

Many efforts go into futile biomarker development and premature clinical testing. In many instances, predictions for translational success or failure can be made early, simply based on critical analysis of case–control data. Our article presents well-established theory in a form that can be appreciated by biomarker researchers. Furthermore, we provide an interactive spreadsheet that links biomarker performance with specific disease characteristics to evaluate the promise of biomarker candidates at an early stage.

Biospecimens, biomarkers, and burgeoning data: the imperative for more rigorous research standards

Knowledge of the altered molecular landscapes in disease offers great promise for developing biomarker-based tests to improve diagnosis and optimize treatment. Progress in biomarker research has been frustratingly slow due to the poor clinical trial design and the lack of standards for specimen collection, biomarker analysis, and data reporting. The ability of high throughput genomics, proteomics, and other 'omics' platforms to profile a large number of analytes in a single assay, together with the pending prospect of rapid expansion of whole exome and whole genome sequencing for clinical use, is increasing the technical and logistical complexity of biomarker validation. Harnessing these new technologies and improved productivity in biomarker validation will depend on adopting systems-based approaches and require major changes in the organization and funding strategies for biomarker research. A systems approach will require new multi-institution collaborations, the integration of diverse technical and clinical activities, greater engagement of industry, and education of regulators, clinicians, and payers about how to use biomarkers for improved patient management and clinical outcomes.

A sample selection strategy to boost the statistical power of signature detection in cancer expression profile studies

In case-control profiling studies, increasing the sample size does not always improve statistical power because the variance may also be increased if samples are highly heterogeneous. For instance, tumor samples used for gene expression assay are often heterogeneous in terms of tissue composition or mechanism of progression, or both; however, such variation is rarely taken into account in expression profiles analysis. We use a prostate cancer prognosis study as an example to demonstrate that solely recruiting more patient samples may not increase power for biomarker detection at all. In response to the heterogeneity due to mixed tissue, we developed a sample selection strategy termed Stepwise Enrichment by which samples are systematically culled based on tumor content and analyzed with t-test to determine an optimal threshold for tissue percentage. The selected tissue-percentage threshold identified the most significant data by balancing the sample size and the sample homogeneity; therefore, the power is substantially increased for identifying the prognostic biomarkers in prostate tumor epithelium cells as well as in prostate stroma cells. This strategy can be generally applied to profiling studies where the level of sample heterogeneity can be measured or estimated.

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.

Sources of bias in specimens for research about molecular markers for cancer

Claims about the diagnostic or prognostic accuracy of markers often prove disappointing when "discrimination" found between cancers versus normals is due to bias, a systematic difference between compared groups. This article describes a framework to help simplify and organize current problems in marker research by focusing on the role of specimens as a source of bias in observational research and using that focus to address problems and improve reliability. The central idea is that the "fundamental comparison" in research about markers (ie, the comparison done to assess whether a marker discriminates) involves two distinct processes that are "connected" by specimens. If subject selection (first process) creates baseline inequality between groups being compared, then laboratory analysis of specimens (second process) may erroneously find positive results. Although both processes are important, subject selection more fundamentally influences the quality of marker research, because it can hardwire bias into all comparisons in a way that cannot be corrected by any refinement in laboratory analysis. An appreciation of the separateness of these two processes-and placing investigators with appropriate expertise in charge of each-may increase the reliability of research about cancer biomarkers.

Randomized clinical trials with biomarkers: design issues

Clinical biomarker tests that aid in making treatment decisions will play an important role in achieving personalized medicine for cancer patients. Definitive evaluation of the clinical utility of these biomarkers requires conducting large randomized clinical trials (RCTs). Efficient RCT design is therefore crucial for timely introduction of these medical advances into clinical practice, and a variety of designs have been proposed for this purpose. To guide design and interpretation of RCTs evaluating biomarkers, we present an in-depth comparison of advantages and disadvantages of the commonly used designs. Key aspects of the discussion include efficiency comparisons and special interim monitoring issues that arise because of the complexity of these RCTs. Important ongoing and completed trials are used as examples. We conclude that, in most settings, randomized biomarker-stratified designs (ie, designs that use the biomarker to guide analysis but not treatment assignment) should be used to obtain a rigorous assessment of biomarker clinical utility.

Current advances in tumor proteomics and candidate biomarkers for hepatic cancer

Tumor proteomics apply proteomics techniques to tumor biological research, mainly by screening candidate biomarkers for early tumor diagnosis, prognosis and treatment. Hepatocellular carcinoma (HCC) is a type of malignant tumor with one of the highest death rates in the world. With the advent of the post-genomic age, tumor biological research developing the technology of proteomics has become a major focus of researchers. The discovery of novel candidate biomarkers is one of crucial problems for the early diagnosis of HCC. In general, there are three distinct types of candidate biomarkers for HCC based on different areas: biochemical biomarkers, antigenic biomarkers and epigenetic biomarkers. This review mainly discusses current advances in the problems and prospects of candidate biomarker for the early diagnosis of HCC, discovered by technologies of tumor proteomics.

Proteomics research to discover markers: what can we learn from Netflix?

BACKGROUND

Research in the field of proteomics to discover markers for detection of cancer has produced disappointing results, with few markers gaining US Food and Drug Administration approval, and few claims borne out when subsequently tested in rigorous studies. What is the role of better mathematical or statistical analysis in improving the situation?

CONTENT

This article examines whether a recent successful Netflix-sponsored competition using mathematical analysis to develop a prediction model for movie ratings of individual subscribers can serve to improve studies of markers in the field of proteomics. Netflix developed a database of movie preferences of individual subscribers using a longitudinal cohort research design. Groups of researchers then competed to develop better ways to analyze the data. Against this background, the strengths and weaknesses of research design are reviewed, contrasting the Netflix design with that of studies of biomarkers to detect cancer. Such biomarker studies generally have less-strong design, lower numbers of outcomes, and greater difficulty in even just measuring predictors and outcomes, so the fundamental data that will be used in mathematical analysis tend to be much weaker than in other kinds of research.

CONCLUSIONS

If the fundamental data that will be analyzed are not strong, then better analytic methods have limited use in improving the situation. Recognition of this situation is an important first step toward improving the quality of clinical research about markers to detect cancer.

Biomarker Validation for Aging: Lessons from mtDNA Heteroplasmy Analyses in Early Cancer Detection

The anticipated biological and clinical utility of biomarkers has attracted significant interest recently. Aging and early cancer detection represent areas active in the search for predictive and prognostic biomarkers. While applications differ, overlapping biological features, analytical technologies and specific biomarker analytes bear comparison. Mitochondrial DNA (mtDNA) as a biomarker in both biological models has been evaluated. However, it remains unclear whether mtDNA changes in aging and cancer represent biological relationships that are causal, incidental, or a combination of both. This article focuses on evaluation of mtDNA-based biomarkers, emerging strategies for quantitating mtDNA admixtures, and how current understanding of mtDNA in aging and cancer evolves with introduction of new technologies. Whether for cancer or aging, lessons from mtDNA based biomarker evaluations are several. Biological systems are inherently dynamic and heterogeneous. Detection limits for mtDNA sequencing technologies differ among methods for low-level DNA sequence admixtures in healthy and diseased states. Performance metrics of analytical mtDNA technology should be validated prior to application in heterogeneous biologically-based systems. Critical in evaluating biomarker performance is the ability to distinguish measurement system variance from inherent biological variance, because it is within the latter that background healthy variability as well as high-value, disease-specific information reside.