Proteomic-based electrochemical non-invasive biosensor for early breast cancer diagnosis

Breast cancer is the second highest cause of cancer-related mortality in women worldwide and in the United States, accounting for around 571,000 deaths per year. Early detection of breast cancer increases treatment results and the possibility of a cure. While existing diagnostic modalities such as mammography, ultrasound, and biopsy exist, some are prohibitively expensive, uncomfortable, time-consuming, and have limited sensitivity, necessitating the development of a cost-effective, rapid, and highly sensitive approach such as an electrochemical biosensor. Our research focuses on detecting breast cancer patients using the ECM1 biomarker, which has higher expression in synthetic urine. Our study has two primary objectives: (i) Diverse ECM1 protein concentrations are measured using electrochemical impedance spectroscopy and ELISA. Establishing a standard curve for the electrochemical biosensor by calibrating ECM-1 protein levels using electrochemical impedance spectroscopy. (ii) Validation of the effectiveness of the electrochemical biosensor. This aim entails testing the unknown concentration of ECM1 in the synthetic urine to ensure the efficiency of the biosensor to detect the biomarker in the early stages. The results show that the synthetic urine solution's ECM-1 detection range ranges from 1 pg/ml to 500 ng/ml. This shows that by detecting changes in ECM-1 protein levels in patient urine, the electrochemical biosensor can consistently diagnose breast cancer in its early stages or during increasing recurrence. Our findings highlight the electrochemical biosensor's efficacy in detecting early-stage breast cancer biomarkers (ECM-1) in patient urine. Further studies will be conducted with patient samples and develop handheld hardware for patient usage.

Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes

BACKGROUND

For medical tests that have a central role in clinical decision-making, current guidelines advocate outcome-based analytical performance specifications. Given that empirical (clinical trial-style) analyses are often impractical or unfeasible in this context, the ability to set such specifications is expected to rely on indirect studies to calculate the impact of test measurement uncertainty on downstream clinical, operational, and economic outcomes. Currently, however, a lack of awareness and guidance concerning available alternative indirect methods is limiting the production of outcome-based specifications. Therefore, our aim was to review available indirect methods and present an analytical framework to inform future outcome-based performance goals.

CONTENT

A methodology review consisting of database searches and extensive citation tracking was conducted to identify studies using indirect methods to incorporate or evaluate the impact of test measurement uncertainty on downstream outcomes (including clinical accuracy, clinical utility, and/or costs). Eighty-two studies were identified, most of which evaluated the impact of imprecision and/or bias on clinical accuracy. A common analytical framework underpinning the various methods was identified, consisting of 3 key steps: (a) calculation of "true" test values; (b) calculation of measured test values (incorporating uncertainty); and (c) calculation of the impact of discrepancies between (a) and (b) on specified outcomes. A summary of the methods adopted is provided, and key considerations are discussed.

CONCLUSIONS

Various approaches are available for conducting indirect assessments to inform outcome-based performance specifications. This study provides an overview of methods and key considerations to inform future studies and research in this area.

Aberrantly Methylated DNA as a Biomarker in Breast Cancer

Aberrant DNA hypermethylation at gene promoters is a frequent event in human breast cancer. Recent genome-wide studies have identified hundreds of genes that exhibit differential methylation between breast cancer cells and normal breast tissue. Due to the tumor-specific nature of DNA hypermethylation events, their use as tumor biomarkers is usually not hampered by analytical signals from normal cells, which is a general problem for existing protein tumor markers used for clinical assessment of breast cancer. There is accumulating evidence that DNA-methylation changes in breast cancer patients occur early during tumorigenesis. This may open up for effective screening, and analysis of blood or nipple aspirate may later help in diagnosing breast cancer. As a more detailed molecular characterization of different types of breast cancer becomes available, the ability to divide patients into subgroups based on DNA biomarkers may improve prognosis. Serial monitoring of DNA-methylation markers in blood during treatment may be useful, particularly when the cancer burden is below the detection level for standard imaging techniques. Overall, aberrant DNA methylation has a great potential as a versatile biomarker tool for screening, diagnosis, prognosis and monitoring of breast cancer. Standardization of methods and biomarker panels will be required to fully exploit this clinical potential.

Monitoring performance of progression assessment criteria for cancer antigen 125 among patients with ovarian cancer compared by computer simulation

BACKGROUND

Cancer antigen 125 (CA125) is used to monitor tumor burden among patients with advanced serous epithelial ovarian cancer. The purpose is to compare the monitoring performance of seven previously proposed criteria.

MATERIALS & METHODS

The CA125 assessment criteria were applied to simulated datasets. We investigated the ability to provide information on CA125 increments as well as their robustness against false positive signals.

RESULTS

For baseline concentrations above cut-off, the best performing criterion was based on a confirmed increment ≥2.5-times the nadir concentration. For baseline concentrations below cut-off, the best performing criterion was based on a confirmed increment from ≤ cut-off to >two-times cut-off.

DISCUSSION

Computer simulation models may be useful for a preclinical validation of criteria to be investigated in clinical trials.

Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model

Background

Reference change values (RCVs) were introduced more than 30 years ago and provide objective tools for assessment of the significance of differences in two consecutive results from an individual. However, in practice, more results are usually available for monitoring, and using the RCV concept on more than two results will increase the number of false-positive results. Therefore, a simple method is needed to interpret the significance of a difference when all available serial biomarker results are considered.

Methods

A computer simulation model using Excel was developed. Based on 10,000 simulated data from healthy individuals, a series of up to 20 results from an individual was generated using different values for the within-subject biological variation plus the analytical variation. Each new result in this series was compared to the initial measurement result. These successive serial relative differences were computed to give limits for significant unidirectional differences with a constant cumulated maximum probability of both 95% ( P < 0.05) and 99% ( P < 0.01).

Results

Factors used to multiply the first result from an individual were calculated to create the limits for constant cumulated significant differences. The factors were shown to become a simple function of the number of results and the total coefficient of variation.

Conclusions

To interpret unidirectional differences in two or more serial results of a biomarker, the limits for significances are easily calculated using the presented factors. The first result is multiplied by the appropriate factor for increase or decrease, which gives the limits for a significant difference.

Design of tumor biomarker-monitoring trials: a proposal by the European Group on Tumor Markers

A major application of tumor biomarkers is in serial monitoring of cancer patients, but there are no published guidelines on how to evaluate biomarkers for this purpose. The European Group on Tumor Markers has convened a multidisciplinary panel of scientists to develop guidance on the design of such monitoring trials. The panel proposes a 4-phase model for biomarker-monitoring trials analogous to that in use for the investigation of new drugs. In phase I, biomarker kinetics and correlation with tumor burden are assessed. Phase II evaluates the ability of the biomarker to identify, exclude, and/or predict a change in disease status. In phase III, the effectiveness of tumor biomarker-guided intervention is assessed by measuring patient outcome in randomized trials. Phase IV consists of an audit of the long-term effects after biomarker monitoring has been included into standard patient care. Systematic well-designed evaluations of biomarkers for monitoring may provide a stronger evidence base that might enable their earlier use in evaluating responses to cancer therapy.