Malti Nikrad

Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy

Significance Duchenne muscular dystrophy (DMD) is a rare and devastating muscle disease caused by mutations in the X-linked DMD gene (which encodes the dystrophin protein). Serum biomarkers hold significant potential as objective phenotypic measures of DMD disease state, as well as potential measures of pharmacological effects of and response to therapeutic interventions. Here we describe a proteomics approach to determine serum levels of 1,125 proteins in 93 DMD patients and 45 controls. The study identified 44 biomarkers that differed significantly between patients and controls. These data are being made available to DMD researchers and clinicians to accelerate the search for new diagnostic, prognostic, and therapeutic approaches. Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy–Cincinnati Children’s Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

Automation of the SomaLogic Proteomics Assay: A Platform for Biomarker Discovery

At SomaLogic, we have embarked on an ambitious program of clinical studies using a novel aptamerbased proteomics technology to discover biomarkers and develop new tools to diagnose, understand, and treat human disease. As part of this program, we designed and implemented an automated assay for its highly multiplexed proteomics discovery platform. The performance of the automated assay was validated in a study that compared the automated assay to the specifications of an established manual method. Results showed that the automated method performed to the required specifications, and that the automation system improved the efficiency, productivity, and economics of our biomarker discovery program.

Detection of rare cancers with aptamer proteomic technology

The need for sensitive, early detection of aggressive, rare malignancies such as pancreatic cancer and mesothelioma is high. Just as importantly, the stringent specificity required of diagnostic tests for these low prevalence diseases creates unique challenges. A diagnostic test which identified these rare diseases early in a significant number of patients without creating a large number of false positive results would be clinically important and would deliver health-economic benefits. However, there is great difficulty in precisely quantifying such signals for large numbers of low abundance proteins. Our group therefore created a highly multiplexed proteomic assay which is continuously expanding in breadth. It currently measures 825 proteins simultaneously from ~15ul blood, with throughput of 300 samples/day. The average dynamic range of each protein in the assay is >3 logs — with nearly seven logs of dynamic range achieved through multiple dilutions — and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is Pancreatic cancer is the fourth leading cause of cancer-related death in the USA. While the 5-year survival is only 5%, this has shown to be increased by early surgical intervention. Plasma samples were analyzed in a prospectively designed case:control study from 143 cases of pancreatic cancer and 116 controls of a similar age and gender distribution. 25% of each group was retained as a blinded verification set. In the training set, 47 markers were significantly different at a false-discovery-rate corrected value of p Other decision thresholds relevant to symptomatic patients enable a sensitivity-driven approach of 90% sensitivity and 75% specificity. The results of this test using the high specificity decision threshold will deliver a positive predictive value of greater than 10% in a population with a disease prevalence of 0.4% or more. Additionally, when the test is used in symptomatic subjects as a differential diagnostic, non-invasive, rapid and sensitive detection of pancreatic cancer enables swift clinical decisions for treatment of this aggressive disease. The second rare cancer analyzed in this clinical series was malignant pleural mesothelioma, which is an aggressive, asbestos-related pulmonary cancer. This disease causes an estimated 15,000 to 20,000 deaths per year worldwide. Between 1940 and 1979, approximately 27.5 million people were occupationally exposed to asbestos in the United States. The incidence of pleural mesothelioma in the US is 3,000 new cases/year and will not peak for another 20 years. Mesothelioma has a latency period of 20-40 years from asbestos exposure, but once diagnosed this aggressive disease is often fatal within 14 months. Because diagnosis is difficult, most patients present at a clinically advanced stage where possibility of cure is minimal. Therefore, we have conducted a broad search for new serum biomarkers with our aptamer-based proteomic platform and defined a classifier for the detection of mesothelioma in asbestos exposed individuals. Serum samples were analyzed with the aptamer proteomics platform in a prospectively designed case:control study of 357 serum samples obtained from patients diagnosed with mesothelioma or lung cancer compared to asbestos exposed controls, high risk smokers and benign lung disease. These samples were divided into a training and test set for classifier development and verification. The objective of the study was to discover proteins which are involved in mesothelioma and to develop algorithms and classifiers for the disease. The initial results are promising. Nineteen significant biomarkers were discovered. Classifiers were built with subsets of these biomarkers resulting in an AUC of 0.95 or better with an overall accuracy of 93%. Applying a 13-plex Random Forest classifier to the blinded test set resulted in a specificity of 100% and sensitivity of 80% for distinction of asbestos exposed controls from mesothelioma. Refinement and confirmation of classifier performance will be established through ongoing validation studies.

New SOMAmer-based assay to discover biomarkers relevant to malignant melanoma

Malignant melanoma is a serious form of skin cancer with approximately 138,000 new cases predicted worldwide in 2010 and an estimated 48,000 deaths. The number is expected to double in less than a decade. There is an unmet need for new molecular technologies that provide for cost-effective, reliable and accurate detection of malignant melanoma at tumor burden levels below those currently observable with standard imaging or histopathological techniques. It would be desirable if a new test could predict a) sentinel lymph node (SLN) positivity in order to avoid SLN biopsy, an invasive and expensive procedure, b) recurrence to help monitor disease and make treatment decisions and c) survival time in metastatic melanoma. Secreted proteins and those released during apoptosis from tumor cells and surrounding tissues undoubtedly contain important biologic information that would theoretically enable early diagnosis and prognostic and therapeutic decisions in oncology. However, there is great difficulty in finding and quantifying such signals for large numbers of low abundance proteins. SomaLogic therefore created a highly multiplexed proteomic assay which is continuously expanding in breadth. It currently measures 825 proteins simultaneously from ~15ul blood, with throughput of 300 samples /day. The average dynamic range of each protein in the assay is >3 logs — with nearly seven logs of dynamic range achieved through multiple dilutions — and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is Retrospective analysis of serum, collected from patients before or after wide local excision / sentinel lymph node removal was done using this aptamer proteomics platform. In patients with metastatic stage IV disease at the time of diagnosis follow-up data was collected for several years to monitor time to survival. A naive Bayesian statistical program was used for analysis of each comparison, negative to positive SLN cases, recurrence and non recurred cases and in case of metastatic melanoma alive vs. dead of disease within a year of blood draw. Of the 39 patients who had SLN biopsy, 25 were negative (stage II) and 14 were positive (stage III) by pathological analysis. 13 proteins were significantly differentially expressed (p Comparison of patients alive with stage IV disease (N=22) verses those dead of disease in 1 year after blood draw (N=15) to yielded 37 proteins significantly different (p= Thus in a preliminary study we have discovered new proteins associated with a regional verses local disease at the time of diagnosis in patients with melanoma lesions >1mm. In metastatic melanoma, we were able to build classifier to predict survival time within a year. These results will be further validated on a larger sample set.