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).

See 2015 newsletter

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