Cytokine analysis in the study of disease
Cytokines in cancer diagnostic applications
A first-in-man phase I study conducted in patients with kappa-restricted multiple myeloma to determine the safety of the monoclonal antibody MDX-1097 (KappaMab). MDX-1097 binds to kappa myeloma antigen, which is only present on malignant kappa-type plasma cells and not on normal leukocytes or other human tissue. APAF bioinformatics analysis of 48-plex immunoassays were able to correlate patient immune responses to dose levels of MDX-1097 and by using biomarker analysis was used to demonstrated statistically significant MDX-1097 dose-dependent decreases in serum levels of cytokines involved in cell trafficking, including hepatocyte growth factor, CXCL9, CXCL10, CCL27, granulocyte-colony stimulating factor (G-CSF), and macrophage migration inhibitory factor (MIF). (submitted).
Cytokines in autism research
Autism spectrum disorders (ASDs) are complex, pervasive and heterogeneous neurodevelopmental conditions with varying trajectories, significant male bias and largely unknown etiology. However, an understanding of the biological mechanisms driving pathophysiology is evolving. APAF bioinformatics Immune system aberrations, as identified through cytokine profiles, is believed to have a role in ASD. Altered cytokine levels may facilitate identification of ASD subtypes as well as provide biological markers of response to effective treatments. Multiplex assay techniques were used to measure levels of 27 cytokines obtained from plasma samples. Overall, results showed a significant negative association between platelet-derived growth factor (PDGF)-BB, and the severity of ASD symptoms. Furthermore, a significant interaction with gender suggested a different immune profile for females compared to males. ASD symptom severity was negatively associated with levels of 4 cytokines, IL-1β, IL-8, MIP-1β and VEGF, in females, but not in males.
Analysis of Deviance Tables (Type III Wald chisquare tests) | |||||||||
Combined Gender | Males | Females | |||||||
Effect | Chisq | Df | Pr(>Chisq) | Chisq | Df | Pr(>Chisq) | Chisq | Df | Pr(>Chisq) |
(Intercept) | 387.778 | 1 | < 2.2e-16 *** | 239.732 | 1 | < 2e-16 *** | 105.508 | 1 | < 2.2e-16 *** |
Severity | 3.279 | 1 | 0.070 . | 1.480 | 1 | 0.224 | 0.875 | 1 | 0.350 |
Age | 0.014 | 1 | 0.906 | 0.122 | 1 | 0.726 | 0.442 | 1 | 0.506 |
SRS | 0.134 | 1 | 0.715 | 0.057 | 1 | 0.811 | 1.354 | 1 | 0.245 |
Sleep | 0.077 | 1 | 0.782 | 0.231 | 1 | 0.631 | 0.376 | 1 | 0.540 |
GI | 0.534 | 1 | 0.465 | 0.958 | 1 | 0.328 | 0.246 | 1 | 0.620 |
Analyte | 1079.27 | 26 | < 2.2e-16 *** | 787.121 | 26 | < 2e-16 *** | 247.602 | 26 | < 2.2e-16 *** |
Severity:Analyte | 43.641 | 26 | 0.017 * | 16.423 | 26 | 0.926 | 71.101 | 26 | 4.581e-06 *** |
Age:Analyte | 75.233 | 26 | 1.115e-06 *** | 60.361 | 26 | 0.000 *** | 41.605 | 26 | 0.027 * |
SRS:Analyte | 10.956 | 26 | 0.996 | 19.094 | 26 | 0.832 | 40.283 | 26 | 0.037* |
Sleep:Analyte | 17.093 | 26 | 0.906 | 12.471 | 26 | 0.988 | 28.197 | 26 | 0.349 |
GI:Analyte | 9.653 | 26 | 0.999 | 13.108 | 26 | 0.983 | 23.971 | 26 | 0.578 |
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 |