Prediction Of TNF Inhibitor Response In Rheumatoid Arthritis Patients Using Single Cell Network Profiling Of Intracellular Immune Signaling

Jason Ptacek¹, Rachael Hawtin², Brent Louie¹, Erik Evensen¹, James Cordeiro¹, Barbara Mittleman¹, Michelle Atallah¹, Alessandra Cesano², Clifton O. Bingham III³, Stacey Cofield⁴, Jeffrey R. Curtis⁵, Maria I. Danila⁴, Richard A. Furie⁶, MC Genovese⁷, Marc C. Levesque⁸, Larry W. Moreland⁸, Peter A. Nigrovic⁹, James R. O'Dell¹⁰, William H. Robinson¹¹, Nancy A. Shadick⁹, E. William St Clair¹², Christopher C. Striebich¹³, Geoffrey M Thiele¹⁴, Peter K. Gregersen⁶ and S. Louis Bridges Jr.⁴, ¹Nodality, Inc., South San Francisco, CA, ²Nodality Inc., South San Francisco, CA, ³Rheumatology, Johns Hopkins University, Baltimore, MD, ⁴University of Alabama at Birmingham, Birmingham, AL, ⁵Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, ⁶The Feinstein Institute for Medical Research and North Shore-Long Island Jewish Health System, Lake Success, NY, ⁷Stanford University, Palo Alto, CA, ⁸University of Pittsburgh, Pittsburgh, PA, ⁹Brigham and Women's Hospital/Harvard University, Cambridge, MA, ¹⁰Veteran Affairs Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Omaha, NE, ¹¹VA Palo Alto Health Care System and Stanford University, Palo Alto, CA, ¹²Medicine, Duke Unversity Medical Center, Durham, NC, ¹³University of Colorado Denver, Aurora, CO, ¹⁴Omaha VA and the University of Nebraska Medical Center, Omaha, NE

Meeting: 2013 ACR/ARHP Annual Meeting

Keywords: anti-TNF therapy, Biomarkers, Clinical Response, Diagnosis and rheumatoid arthritis (RA)

Session Information

Title: Rheumatoid Arthritis Treatment - Small Molecules, Biologics and Gene Therapy: Efficacy of Approved Biologics II

Session Type: Abstract Submissions (ACR)

Background/Purpose: Biomarkers predictive of drug efficacy are lacking in rheumatoid arthritis (RA) and would be useful in clinical practice and clinical trials. Single cell network profiling (SCNP) is a multiparametric flow cytometry-based assay that measures induced changes in intracellular signaling proteins, providing a functional measure of pathway activity and immune networking in multiple cell subsets without physical separation. Induced signaling was measured in specific subsets of monocytes, B and T cells from RA patients (pts) initiating new treatment, and analyzed to build models to predict treatment response.

Methods: PBMCs from RA pts (n=87) starting TNF inhibitors (TNFi) were examined by SCNP of 42 nodes (combinations of modulator and intracellular readout) within 21 immune cell subsets. RA pts were a subset of ~200 from the Treatment Efficacy and Toxicity in Rheumatoid Arthritis Database and Repository (TETRAD). Blood samples were collected before initiating treatment with TNFi (adalimumab, etanercept, infliximab, golimumab). Clinical data included disease activity (DAS28) and EULAR response criteria at baseline, 3, 6, and 12 months. For the 53 evaluable patients, statistical analyses, including ordinal logistic regression and multivariate modeling, were performed to identify signaling profiles associated with response to TNFi after controlling for baseline DAS28 and age.

Results: Immune cell subsets from RA pts collected before initiating TNFi treatment exhibited heterogeneity in their induced intracellular signaling. Of note, T cell receptor (TCR) and IFNa modulation produced cell subset-specific signaling profiles that were associated with EULAR response at 3 months. Specifically, TCR→p-CD3z in CD4^–CD45RA⁺ T cells was weakest in pts that had a good EULAR response to TNFi (p=0.04). Similarly, donors with weak IFNa→p-STAT5 in B cells were more likely to have a good response to TNFi (p=0.007). In contrast, IL-6→p-STAT3 in naïve CD4⁺ T cells was weakest in autoantibody (RF/ACPA)-positive (autoAb+) pts with no response (p=0.01). Although TNFa→p-p38 in monocytes was associated with baseline DAS28, this signaling was not predictive of TNFi response. Combining signaling nodes produced a model of TNFi response in autoAb+ donors defined by IL-6→p-STAT3 in naïve CD4⁺ T cells and IFNa→p-STAT1 in monocytes with an area under receiver operating characteristic curve (AUC) of 0.91 in the full dataset, or 0.64 cross-validated.

Conclusion: These data provide the first evidence that measurement of peripheral blood immune cell function can: 1) identify patients likely to respond to TNFi, and 2) reveal the biology associated with TNFi response or lack thereof, thus providing information on therapeutic strategies. SCNP has revealed predictive biomarkers that, once replicated in future studies, may enable patient stratification in clinical practice and clinical trials.

Disclosure:

J. Ptacek,

Nodality, Inc.,

R. Hawtin,

Nodality, Inc.,

B. Louie,

Nodality, Inc.,

E. Evensen,

Nodality, Inc.,

J. Cordeiro,

Nodality, Inc.,

B. Mittleman,

Nodality, Inc.,

M. Atallah,

Nodality, Inc.,

A. Cesano,

Nodality, Inc.,

C. O. Bingham III,
None;

S. Cofield,
None;

J. R. Curtis,
None;

M. I. Danila,
None;

R. A. Furie,
None;

M. Genovese,
None;

M. C. Levesque,
None;

L. W. Moreland,
None;

P. A. Nigrovic,
None;

J. R. O’Dell,
None;

W. H. Robinson,
None;

N. A. Shadick,

Crescendo Biosciences, Medimmune, BMS, Genentech, Abbott and AMGEN,

E. W. St Clair,
None;

C. C. Striebich,
None;

G. M. Thiele,
None;

P. K. Gregersen,
None;

S. L. Bridges Jr.,
None.

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