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Toward rapid, high-sensitivity, volume-constrained biomarker quantification and validation using backscattering interferometry.

Olmsted IR, Hassanein M, Kussrow A, Hoeksema M, Li M, Massion PP, Bornhop DJ - Anal. Chem. (2014)

Bottom Line: The two techniques correlated well, ranging from 3-29% difference for Cyfra 21-1 in a blinded patient sample analysis.The label-free and free-solution operation of BSI allowed for a significant improvement in analysis speed, with greater ease, improved LOQ values, and excellent day-to-day reproducibility.The results indicate that the BSI platform can enable rapid, sensitive analytical validation of serum biomarkers and should significantly impact the validation bottleneck of biomarkers.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and the Vanderbilt Institute of Chemical Biology, Vanderbilt University , 4226 Stevenson Center, Nashville, Tennessee 37235, United States.

ABSTRACT
Realizing personalized medicine, which promises to enable early disease detection, efficient diagnostic staging, and therapeutic efficacy monitoring, hinges on biomarker quantification in patient samples. Yet, the lack of a sensitive technology and assay methodology to rapidly validate biomarker candidates continues to be a bottleneck for clinical translation. In our first direct and quantitative comparison of backscattering interferometry (BSI) to fluorescence sensing by ELISA, we show that BSI could aid in overcoming this limitation. The analytical validation study was performed against ELISA for two biomarkers for lung cancer detection: Cyfra 21-1 and Galectin-7. Spiked serum was used for calibration and comparison of analytical figures of merit, followed by analysis of blinded patient samples. Using the ELISA antibody as the probe chemistry in a mix-and-read assay, BSI provided significantly lower detection limits for spiked serum samples with each of the biomarkers. The limit of quantification (LOQ) for Cyrfa-21-1 was measured to be 230 pg/mL for BSI versus 4000 pg/mL for ELISA, and for Galectin-7, it was 13 pg/mL versus 500 pg/mL. The coefficient of variation for 5 day, triplicate determinations was <15% for BSI and <10% for ELISA. The two techniques correlated well, ranging from 3-29% difference for Cyfra 21-1 in a blinded patient sample analysis. The label-free and free-solution operation of BSI allowed for a significant improvement in analysis speed, with greater ease, improved LOQ values, and excellent day-to-day reproducibility. In this unoptimized format, BSI required 5.5-fold less sample quantity needed for ELISA (a 10 point calibration curve measured in triplicate required 36 μL of serum for BSI vs 200 μL for ELISA). The results indicate that the BSI platform can enable rapid, sensitive analytical validation of serum biomarkers and should significantly impact the validation bottleneck of biomarkers.

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Intraclass correlation coefficient (ICC) plots for BSIassays.(A) Raw patient sample data for Cyfra 21-1 measured each day in triplicatefor 5 separate days. The 15 measurements (dots) of the control samples(blue) and the case samples (red) show a clear differentiation betweendisease states seen by the dotted line. (B) Raw patient sample datafor Galectin-7 measured each day in triplicate for 5 days. Althoughthe 15 measurements (dots) of all the samples are well above the BSILOQ (dashed line), the control samples (blue) and the case samples(red) do not show a differentiation. Red and blue arrows point topatient samples that put into question the validity of this biomarker.
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fig5: Intraclass correlation coefficient (ICC) plots for BSIassays.(A) Raw patient sample data for Cyfra 21-1 measured each day in triplicatefor 5 separate days. The 15 measurements (dots) of the control samples(blue) and the case samples (red) show a clear differentiation betweendisease states seen by the dotted line. (B) Raw patient sample datafor Galectin-7 measured each day in triplicate for 5 days. Althoughthe 15 measurements (dots) of all the samples are well above the BSILOQ (dashed line), the control samples (blue) and the case samples(red) do not show a differentiation. Red and blue arrows point topatient samples that put into question the validity of this biomarker.

Mentions: Figure 3A shows BSI and ELISA results plottedon a log-scale versus patient number for the quantification of Cyfra21-1. In this set, there were five samples from patients with disease(cases) and five from patients without (controls). Figure 3B shows the data on an expanded scale, coveringslightly more than 1 decade in concentration. At this scale, the errorbars for the 15 replicate determinations become visible, as well asthe difference in Cyfra 21-1 concentration determined by the two methods.Overall, the BSI correlated with ELISA measurements (3–29%difference), with the cases and controls segregating as expected (seeFigure 5A). In this case, we chose five patientswith relatively advanced disease (four at stage IV and one at stageIIIA-IIIB) and five controls derived from patients that do not currently have detectable disease. Yet, as shown by theLOQ cutoff lines on the plot, the threshold for quantifying the biomarkerwith confidence for the two methodologies is considerably different.This enhanced sensitivity could allow for more accurate stratificationof patients with existing NSCLC. As discussed below, because Cyfra21-1 has relatively low constitutive expression (near the LOQ forELISA), this sensitivity advantage for quantifying Cyfra 21-1 in serumcould allow for earlier detection of disease and/or enable monitoringtherapeutic response in NSCLC.40


Toward rapid, high-sensitivity, volume-constrained biomarker quantification and validation using backscattering interferometry.

Olmsted IR, Hassanein M, Kussrow A, Hoeksema M, Li M, Massion PP, Bornhop DJ - Anal. Chem. (2014)

Intraclass correlation coefficient (ICC) plots for BSIassays.(A) Raw patient sample data for Cyfra 21-1 measured each day in triplicatefor 5 separate days. The 15 measurements (dots) of the control samples(blue) and the case samples (red) show a clear differentiation betweendisease states seen by the dotted line. (B) Raw patient sample datafor Galectin-7 measured each day in triplicate for 5 days. Althoughthe 15 measurements (dots) of all the samples are well above the BSILOQ (dashed line), the control samples (blue) and the case samples(red) do not show a differentiation. Red and blue arrows point topatient samples that put into question the validity of this biomarker.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4215853&req=5

fig5: Intraclass correlation coefficient (ICC) plots for BSIassays.(A) Raw patient sample data for Cyfra 21-1 measured each day in triplicatefor 5 separate days. The 15 measurements (dots) of the control samples(blue) and the case samples (red) show a clear differentiation betweendisease states seen by the dotted line. (B) Raw patient sample datafor Galectin-7 measured each day in triplicate for 5 days. Althoughthe 15 measurements (dots) of all the samples are well above the BSILOQ (dashed line), the control samples (blue) and the case samples(red) do not show a differentiation. Red and blue arrows point topatient samples that put into question the validity of this biomarker.
Mentions: Figure 3A shows BSI and ELISA results plottedon a log-scale versus patient number for the quantification of Cyfra21-1. In this set, there were five samples from patients with disease(cases) and five from patients without (controls). Figure 3B shows the data on an expanded scale, coveringslightly more than 1 decade in concentration. At this scale, the errorbars for the 15 replicate determinations become visible, as well asthe difference in Cyfra 21-1 concentration determined by the two methods.Overall, the BSI correlated with ELISA measurements (3–29%difference), with the cases and controls segregating as expected (seeFigure 5A). In this case, we chose five patientswith relatively advanced disease (four at stage IV and one at stageIIIA-IIIB) and five controls derived from patients that do not currently have detectable disease. Yet, as shown by theLOQ cutoff lines on the plot, the threshold for quantifying the biomarkerwith confidence for the two methodologies is considerably different.This enhanced sensitivity could allow for more accurate stratificationof patients with existing NSCLC. As discussed below, because Cyfra21-1 has relatively low constitutive expression (near the LOQ forELISA), this sensitivity advantage for quantifying Cyfra 21-1 in serumcould allow for earlier detection of disease and/or enable monitoringtherapeutic response in NSCLC.40

Bottom Line: The two techniques correlated well, ranging from 3-29% difference for Cyfra 21-1 in a blinded patient sample analysis.The label-free and free-solution operation of BSI allowed for a significant improvement in analysis speed, with greater ease, improved LOQ values, and excellent day-to-day reproducibility.The results indicate that the BSI platform can enable rapid, sensitive analytical validation of serum biomarkers and should significantly impact the validation bottleneck of biomarkers.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry and the Vanderbilt Institute of Chemical Biology, Vanderbilt University , 4226 Stevenson Center, Nashville, Tennessee 37235, United States.

ABSTRACT
Realizing personalized medicine, which promises to enable early disease detection, efficient diagnostic staging, and therapeutic efficacy monitoring, hinges on biomarker quantification in patient samples. Yet, the lack of a sensitive technology and assay methodology to rapidly validate biomarker candidates continues to be a bottleneck for clinical translation. In our first direct and quantitative comparison of backscattering interferometry (BSI) to fluorescence sensing by ELISA, we show that BSI could aid in overcoming this limitation. The analytical validation study was performed against ELISA for two biomarkers for lung cancer detection: Cyfra 21-1 and Galectin-7. Spiked serum was used for calibration and comparison of analytical figures of merit, followed by analysis of blinded patient samples. Using the ELISA antibody as the probe chemistry in a mix-and-read assay, BSI provided significantly lower detection limits for spiked serum samples with each of the biomarkers. The limit of quantification (LOQ) for Cyrfa-21-1 was measured to be 230 pg/mL for BSI versus 4000 pg/mL for ELISA, and for Galectin-7, it was 13 pg/mL versus 500 pg/mL. The coefficient of variation for 5 day, triplicate determinations was <15% for BSI and <10% for ELISA. The two techniques correlated well, ranging from 3-29% difference for Cyfra 21-1 in a blinded patient sample analysis. The label-free and free-solution operation of BSI allowed for a significant improvement in analysis speed, with greater ease, improved LOQ values, and excellent day-to-day reproducibility. In this unoptimized format, BSI required 5.5-fold less sample quantity needed for ELISA (a 10 point calibration curve measured in triplicate required 36 μL of serum for BSI vs 200 μL for ELISA). The results indicate that the BSI platform can enable rapid, sensitive analytical validation of serum biomarkers and should significantly impact the validation bottleneck of biomarkers.

Show MeSH
Related in: MedlinePlus