Balancing the robustness and predictive performance of biomarkers.

Recent studies have highlighted the importance of assessing the robustness of putative biomarkers identified from experimental data. This has given rise to the concept of stable biomarkers, which are ones that are consistently identified regardless of small perturbations to the data. Since stability is not by itself a useful objective, we present a number of strategies that combine assessments of stability and predictive performance in order to identify biomarkers that are both robust and diagnostically useful. Moreover, by wrapping these strategies around logistic regression classifiers regularized by the elastic net penalty, we are able to assess the effects of correlations between biomarkers upon their perceived stability. We use a synthetic example to illustrate the properties of our proposed strategies. In this example, we find that: (i) assessments of stability can help to reduce the number of false-positive biomarkers, although potentially at the cost of missing some true positives; (ii) combining assessments of stability with assessments of predictive performance can improve the true positive rate; and (iii) correlations between biomarkers can have adverse effects on their stability and hence must be carefully taken into account when undertaking biomarker discovery. We then apply our strategies in a proteomics context to identify a number of robust candidate biomarkers for the human disease HTLV1-associated myelopathy/tropical spastic paraparesis (HAM/TSP)

Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis.

Background A major cause of disability in secondary progressive multiple sclerosis (SPMS) is progressive brain atrophy, whose pathogenesis is not fully understood. The objective of this study was to identify protein biomarkers of brain atrophy in SPMS. Methods We used surface-enhanced laser desorption-ionization time-of-flight mass spectrometry to carry out an unbiased search for serum proteins whose concentration correlated with the rate of brain atrophy, measured by serial MRI scans over a 2-year period in a well-characterized cohort of 140 patients with SPMS. Protein species were identified by liquid chromatography-electrospray ionization tandem mass spectrometry. Results There was a significant (p<0.004) correlation between the rate of brain atrophy and a rise in the concentration of proteins at 15.1 kDa and 15.9 kDa in the serum. Tandem mass spectrometry identified these proteins as alpha-haemoglobin and beta-haemoglobin, respectively.  The abnormal concentration of free serum haemoglobin was confirmed by ELISA (p<0.001). The serum lactate dehydrogenase activity was also highly significantly raised (p<10-12) in patients with secondary progressive multiple sclerosis. Conclusions An underlying low-grade chronic intravascular haemolysis is a potential source of the iron whose deposition along blood vessels in multiple sclerosis plaques contributes to the neurodegeneration and consequent brain atrophy seen in progressive disease. Chelators of free serum iron will be ineffective in preventing this neurodegeneration, because the iron (Fe2+) is chelated by haemoglobin

Plasma proteome analysis in HTLV-1-associated myelopathy/tropical spastic paraparesis

Background: Human T lymphotropic virus Type 1 (HTLV-1) causes a chronic inflammatory disease of the central nervous system known as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM) which resembles chronic spinal forms of multiple sclerosis (MS). The pathogenesis of HAM remains uncertain. To aid in the differential diagnosis of HAM and to identify pathogenetic mechanisms, we analysed the plasma proteome in asymptomatic HTLV-1 carriers (ACs), patients with HAM, uninfected controls, and patients with MS. We used surface-enhanced laser desorption-ionization (SELDI) mass spectrometry to analyse the plasma proteome in 68 HTLV-1-infected individuals (in two non-overlapping sets, each comprising 17 patients with HAM and 17 ACs), 16 uninfected controls, and 11 patients with secondary progressive MS. Candidate biomarkers were identified by tandem Q-TOF mass spectrometry. Results: The concentrations of three plasma proteins - high [β2-microglobulin], high [Calgranulin B], and low [apolipoprotein A2] - were specifically associated with HAM, independently of proviral load. The plasma [β2- microglobulin] was positively correlated with disease severity. Conclusions: The results indicate that monocytes are activated by contact with activated endothelium in HAM. Using β2-microglobulin and Calgranulin B alone we derive a diagnostic algorithm that correctly classified the disease status (presence or absence of HAM) in 81% of HTLV-1-infected subjects in the cohort

Raw data for SELDI-TOF low range from article: Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis

Raw normalised data from SELDI-TOF at low range following Expression Difference Mapping (ProteinChip Data Manager. Bio-Rad). Columns contain sample name, sample group (corresponds to longitudinal measurements for each patients at 1,3,4 and 5), peak number (all peaks detected at low range at each time point), peak intensity and mass/charge ratio

Nano LC MS-MS peptide matches from article: Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis

Protein peaks associated with brain atrophy: identification by liquid chromatography-electrospray ionization tandem mass spectrometry Relative molecular mass, protein score and identity of the genes with sequence matches to peptide fragments from the protein peaks that were significantly correlated with the rate of brain atrophy. The common contaminant keratin and partial matches to contaminating bacterial sequences were excluded

Raw data for SELDI-TOF high range from article: Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis

Raw normalised data from SELDI-TOF at high range following Expression Difference Mapping (ProteinChip Data Manager. Bio-Rad). Columns contain sample name, sample group (corresponds to longitudinal measurements for each patients at 1,3,4 and 5), peak number (all peaks detected at high range at each time point), peak intensity and mass/charge ratio

Raw data for RBC, Hb and haematocrit from article: Free serum haemoglobin is associated with brain atrophy in secondary progressive multiple sclerosis

Erythrocyte count, total blood haemoglobin and haematocrit levels at the first and fifth clinical visit

Flow sorting of provirus-expressing and non-expressing cells by an independent technique produced similar q4C profiles.

(A) q4C profiles of Tax−(upper panel) and Tax+ (lower panel) cells sorted from clone TBX4B after intracellular staining of Tax. (B) q4C profile of non-expressing (GFP-) (upper panel) and provirus-expressing (GFP+) (lower panel) cells isolated from d2EGFP-TBX4B clones, selected by GFP signal (without Tax staining). (TIFF)</p