Serum Proteomics
Background:
The etiology and pathogenesis of iMCD and other cytokine storm disorders remain poorly understood, . Diagnosis in iMCD is further complicated by there being no objectiveclinical diagnostic biomarkers and a subjective review of lymph node by a hematopathologist is required, leading to long paths to diagnosis and administration of/ineffective treatments. Anti-IL-6 therapy is effective in ~34% of patients. Recent SOMAlogic proteomic study performed to identify biomarkers and novel therapeutic targets. Comparisons were performed between iMCD and related disorders (Hodgkin Lymphoma, Rheum Arthritis, HIV/HHV8+ MCD) and healthy controls. This has produced a number of valuable discoveries:
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Differentially expressed protein sets in iMCD vs. controls, iMCD vs. related diseases
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up/down regulated pathways for each comparison revealed IL-6-JAK-STAT3
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Also allowed identification of 7 analyte panel that can be used to predict whether a patient would go on to benefit from siltuximab treatment. (not available clinically, limited effectiveness currently)
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CXCL13 is one of most elevated proteins in iMCD
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May be a potential diagnostic biomarker or early indicator of response to anti-IL-6 therapy
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Many shared clinical observations in autoimmune/infectious disease/malignancies may argue for shared proteins/pathways involved in disease pathology. Four overlapping inflammatory conditions are: systemic juvenile idiopathic arthritis (sJIA), a chronic inflammatory disorder of children, sJIA with lung disease (sJIA-LD), macrophage activation syndrome (MAS), a cytokine storm that occurs in patients with sJIA and other autoimmune conditions, and malignancy-associated hemophagocytic lymphohistiocytosis (M-HLH), which is very similar to MAS but occurs due to the presence of a malignancy instead of an autoimmune disease. A recent SOMAlogic study by the Canna Lab of sJIA and MAS found:
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MAS and sJIA proteomic signatures overlapped substantially, including serum amyloid A (SAA) and S100A9
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Heatshock and glycolitic enzymes were also elevated (HSP 70, 90 proteins, S100 genes, GPI, LDH, ENO1)
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ICAM5, MMP7 in sJIA-LD that may be able to distinguish sJIA-LD from sJIA/MAS
Project Plan:
Disparate datasets will be compared using statistical methods to compare across 8 rare disease cohorts (iMCD, RA, HL, HHV8+MCD, sJIA, sJIA-LD, MAS, M-HLH). Z- score transformation compared to healthy donors (to try to address batch effect) will be evaluated as a method to standardize somalogic somascan protein quantification in serum from an external rare disease cohort. This will allow a comparison of the differentially abundant proteins in both published datasets and to draw conclusions about shared/diverse protein architecture in disease pathology. Being able to compare multiple different datasets using somascan technology in various diseases can show overlapping/distinct disease signatures between disorders that share clinical signs/symptoms and may explain disease specific biology.
Research Questions:
Overall goal: To discover insights into the biology of these hyperinflammatory conditions and identify potential biomarkers that could help to distinguish them from one another.
Secondary goal: To serve as promising preliminary data to inform the development of the SPACE study.
Can two (or 3 once we have Jordan data) disparate somascan datasets be combined using Z-scores generated from comparisons to healthy donor datacompared?
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Can we determine if there is a linear relationship between protein analytes from HC cohorts from Canna & Fajgenbaum datasets
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If they are comparable, what is an effective standardization strategy to begin comparing between datasets?
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Can we visualize overall similarities/differences in our dataset using unbiased clustering?
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Perform/Visualize PCA using UMAP or other unbiased clustering to identify overall transcriptomic similarity/differences in 7-8 separate disease states
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Can we recognize subclusters within single disease? Are there a subset of iMCD that are more similar to sJIA-MS? More dissimilar?
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Are any similar/dissimilar findings potential drug targets? Involved in a specific pathway?
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What proteins are most/least abundant in serum for each disease state compared to healthy donors?
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Compare the most significantlytop 25 increased/decreased proteins from each disease
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Are there shared genes? What is their function?
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Are there different genes? Do they explain the different clinical presentations?
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What proteins are most/least abundant in serum for each disease state compared to the other 6-7 disease states?
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Compare the most significantly increased/decreased proteins from each disease
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Are any genes sensitive and specific as diagnostic biomarkers that could distinguish one disease from another?
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Are there shared genes? What is their function?
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Are there different genes? Do they explain the different clinical presentations?
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Targeted analysis of CXCL13
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How does this perform as a differentiator between diseases?
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Targeted analysis of HSP, glycolytic proteins identified in Canna publication
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Thinking heatmap for visualization here?
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Can this z-score transformation strategy be used to add Michael Jordan’s dataset? And potentially beyond, i.e. SPACE
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Need to evaluate only 1300 measured analytes
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Is this data one to one?
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