Biomarker validation is still the bottleneck in biomarker research

Compensated Interferometry Measures of CYFRA 21-1 Improve Diagnosis of Lung Cancer

Diagnosis of lung cancer patients with indeterminate pulmonary nodules (IPNs) presents a significant clinical challenge, with morbidity and management costs of $28 billion/year. We show that a quantitative free-solution assay (FSA), coupled with a compensated interferometric reader (CIR), improves the diagnostic performance of CYFRA 21-1 as a lung cancer biomarker. FSA-CIR is a rapid, mix-and-read, isothermal, label- and enzyme-free, matrix-insensitive, and target and probe-agnostic assay. Operating FSA-CIR at ∼40, 0.75 μL samples/day delivered a serum CYFRA 21-1 limit of quantification (LOQ) of 81 pg/mL with intra-assay and interassay CVs of 4.9% and 9.6% for four-day replicate determinations. Blinded analysis of a 225 patient cohort, consisting of 75 nonmalignant nodules, 45 adenocarcinomas, 44 squamous cell carcinomas, and 61 small cell lung cancers, gave a clear separation of cases and controls, not observed in the Cobas ECL analysis. The area under the curve (AUC) for the Mayo model increased from 0.595 to 0.923 when combined with the FSA-CIR CYFRA 21-1 measurements. In a population with nodules between 6 and 30 mm, the AUC increased from 0.567 to 0.943. In this subgroup, the positive predictive value (PPV) for all tumors by the CYFRA 21-1 assay was 98.7%. Our results demonstrate increased performance of the CYFRA 21-1 assay using FSA-CIR and represents a proof of concept for redefining the performance characteristics of this important candidate biomarker.

Proteomic Discovery and Development of a Multiplexed Targeted MRM-LC-MS/MS Assay for Urine Biomarkers of Extracellular Matrix Disruption in Mucopolysaccharidoses I, II, and VI

The mucopolysaccharidoses (MPS) are lysosomal storage disorders that result from defects in the catabolism of glycosaminoglycans. Impaired muscle, bone, and connective tissue are typical clinical features of MPS due to disruption of the extracellular matrix. Markers of MPS disease pathology are needed to determine disease severity and monitor effects of existing and emerging new treatments on disease mechanisms. Urine samples from a small cohort of MPS-I, -II, and -VI patients (n = 12) were analyzed using label-free quantative proteomics. Fifty-three proteins including many associated with extracellular matrix organization were differently expressed. A targeted multiplexed peptide MRM LC-MS/MS assay was used on a larger validation cohort of patient samples (MPS-I n = 18, MPS-II n = 12, MPS-VI n = 6, control n = 20). MPS-I and -II groups were further subdivided according to disease severity. None of the markers assessed were altered significantly in the mild disease groups compared to controls. β-galactosidase, a lysosomal protein, was elevated 3.6-5.7-fold significantly (p < 0.05) in all disease groups apart from mild MPS-I and -II. Collagen type Iα, fatty-acid-binding-protein 5, nidogen-1, cartilage oligomeric matrix protein, and insulin-like growth factor binding protein 7 concentrations were elevated in severe MPS I and II groups. Cartilage oligomeric matrix protein, insulin-like growth factor binding protein 7, and β-galactosidase were able to distinguish the severe neurological form of MPS-II from the milder non-neurological form. Protein Heg1 was significantly raised only in MPS-VI. This work describes the discovery of new biomarkers of MPS that represent disease pathology and allows the stratification of MPS-II patients according to disease severity.

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.

Precision medicine in chronic disease management: The multiple sclerosis BioScreen

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine-that is, the application of information technology to medicine-has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.

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

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.