Proteomic analysis of biomarkers associated with immunotherapy in murine tumour models

Emergence of proteomics and high-throughput technologies has allowed the identification of protein expression patterns of disease that potentially hold clinical importance in predictive medicine. The analysis of complex data generated by these technologies incorporates the use of computer algorithms for data mining and identification of important protein biomarkers. Such candidate biomarkers can potentially be used for diagnosis, prognosis and monitoring a variety of diseases as well as the prediction of therapy response. Mass spectrometry has been used widely, for the discovery and quantitation of disease associated biomarkers using a variety of samples such as serum and tissue. In particular, matrix assisted laser desorption/ionisation time of flight mass spectrometry (MALDI-TOF MS) has been used to generate proteomic profiles or “fingerprints” from serum to distinguish patients at different clinical stages of disease. Currently, early stage disease is difficult to diagnose in most cancers as current cancer markers have limited sensitivity and specificity. In advanced stage metastatic disease, treatment options are limited, although it is recognised that some patients may benefit from immunotherapy and in particular vaccine therapy. The use of animal models is critical to evaluate the efficacy of immunotherapies and to investigate tumour immunity in general and the mechanisms involved in tumour progression. These models provide an in vivo environment which cannot be reproduced in vitro, which results in more accurate and reliable information on the host response to immunotherapy and the mechanisms involved

Rationalising the role of Keratin 9 as a biomarker for Alzheimer’s disease

Keratin 9 was recently identified as an important component of a biomarker panel which demonstrated a high diagnostic accuracy (87%) for Alzheimer’s disease (AD). Understanding how a protein which is predominantly expressed in palmoplantar epidermis is implicated in AD may shed new light on the mechanisms underlying the disease. Here we use immunoassays to examine blood plasma expression patterns of Keratin 9 and its relationship to other AD-associated proteins. We correlate this with the use of an in silico analysis tool VisANT to elucidate possible pathways through which the involvement of Keratin 9 may take place. We identify possible links with Dickkopf-1, a negative regulator of the wnt pathway, and propose that the abnormal expression of Keratin 9 in AD blood and cerebrospinal fluid may be a result of blood brain barrier dysregulation and disruption of the ubiquitin proteasome system. Our findings suggest that dysregulated Keratin 9 expression is a consequence of AD pathology but, as it interacts with a broad range of proteins, it may have other, as yet uncharacterized, downstream effects which could contribute to AD onset and progression

Identification of SPARC-like 1 protein as part of a biomarker panel for Alzheimer's disease in cerebrospinal fluid

We have used proteomic fingerprinting to investigate diagnosis of Alzheimer's disease (AD). Samples of lumbar cerebrospinal fluid (CSF) from clinically-diagnosed AD cases (n = 33), age-matched controls (n = 20), and mild cognitive impairment (MCI) patients (n = 10) were used to obtain proteomic profiles, followed by bioinformatic analysis that generated a set of potential biomarkers in CSF samples that could discriminate AD cases from controls. The identity of the biomarker ions was determined using mass spectroscopy. The panel of seven peptide biomarker ions was able to discriminate AD patients from controls with a median accuracy of 95% (sensitivity 85%, specificity 97%). When this model was applied to an independent blind dataset from MCI patients, the intensity of signals was intermediate between the control and AD patients implying that these markers could potentially predict patients with early neurodegenerative disease. The panel were identified, in order of predictive ability, as SPARC-like 1 protein, fibrinogen alpha chain precursor, amyloid-β, apolipoprotein E precursor, serum albumin precursor, keratin type I cytoskeletal 9, and tetranectin. The 7 ion ANN model was further validated using an independent cohort of samples, where the model was able to classify AD cases from controls with median accuracy of 84.5% (sensitivity 93.3%, specificity 75.7%). Validation by immunoassay was performed on the top three identified markers using the discovery samples and an independent sample cohort which was from postmortem confirmed AD patients (n = 17)