Genome-Wide Gene Expression Profiling of Patients With ITP Receiving Thrombopoietin Mimetics

Genome-Wide Gene Expression Profiling of Patients With ITP Receiving Thrombopoietin Mimetics


Lead Sponsor: Stanford University

Collaborator: Weill Medical College of Cornell University

Source Stanford University
Brief Summary


Ineffective platelet production has been proven to play a role in the etiology of Immune Thrombocytopenia (ITP) in addition to increased platelet destruction. The second-generation thrombopoietin (TPO) mimetics have shown good efficacy in boosting platelet counts in the great majority of patients with chronic ITP in several clinical trials.1, 2 Nevertheless, about 20% of patients with ITP fail to respond to the TPO mimetic treatment. Those treatment-resistant patients are un-characterized and the reasons for the lack of response have not been studied. The identification of predictive blood biomarkers of patients' response to treatment will be useful in reducing both cost and potential side effects; and it will be of equal importance and interest to investigate the molecular mechanisms underlying the patients' heterogeneous responses to TPO mimetic treatment.

Specific Aims:

1. To identify blood classifier genes which correlate with patients' response to TPO mimetic treatment.

2. To compare the blood gene expression changes in responders and non-responders after TPO mimetic treatment and explore the possible molecular mechanisms accounting for the non-responsiveness to the treatment.

Detailed Description

1. Identification and validation of response-predictive genes. The normalized pre-treatment microarray data of the training set is retrieved from SMD for statistical analysis. The supervised analysis SAM (Significance Analysis of Microarrays, two class unpaired) is performed to identify genes whose expression is significantly different between responders and non-responders. Then a Leave-one-out cross-validated gene-expression predictor for the 2 response classes is devised by the PAM (Predication Analysis of Microarrays) method based on nearest shrunken centroids. The unsupervised clustering of the independent test set is performed using the predictive genes and the prediction accuracy is calculated. Quantitative real-time PCR is performed as further validation using the un-amplified RNA samples and Taqman gene expression assays (Applied Biosciences).

2. Gene expression changes correlated with TPO mimetic treatment and pathway analysis.

2.1. Hypothesis: The transcriptional profile of patients who respond to TPO agonists is different than those who do not respond.

Plan: The expression data of pre-treatment as well as the 1-week and 1-month after initiation of treatment samples is retrieved from SMD. The two class paired SAM analysis is performed to compare pre-treatment samples with samples collected at either 1-week or 1-month after initiation of treatment in responders and non-responders. The two class unpaired SAM analysis is also used to compare post-treatment samples of responders and non-responders at the same time point. The significant genes (q value<0.05, fold change>2.5) are subsequently analyzed by IPA (Ingenuity Pathway Analysis) system to be transformed into a set of relevant networks based on the extensive records maintained in the Ingenuity Pathway Knowledge Base. The statistically significant networks, molecular and cellular functions, top canonical pathways and toxicity lists associated with each pair of dataset will be recognized through this analysis. Hypothesis on non-response to TPO mimetics can be generated based on the different functional subsets of significant genes. Genes involved in important pathways identified by IPA analysis will be validated by QRT-PCR as in our recent publication on oxidative stress pathways in ITP4. Our goal is to develop biomarkers which predict likelihood of response to therapy and identify pathways associated with resistance to therapy which could be targeted.

2.2 Hypothesis: Since available TPO agonists have different mechanisms of action, there may be differences in responders and non-responders between the different drugs.

Plan: We recognize that TPO agonists have different mechanisms of action which could affect downstream signaling pathways and transcriptional responses. For this reason in addition to evaluating the TPO agonists as a group in 2.1 above, patients will also be analyzed by type of agonist. The conclusions of this type of analysis will be limited by the numbers of individuals treated with a particular drug but could be useful for hypothesis generation and confirmation in a larger cohort.

Overall Status Completed
Start Date July 2012
Completion Date February 2017
Primary Completion Date February 2017
Study Type Observational
Primary Outcome
Measure Time Frame
1. To identify blood classifier genes which correlate with patients' response to TPO mimetic treatment. 2 years
Enrollment 75

Sampling Method: Non-Probability Sample


Inclusion Criteria:

- clinical diagnosis of ITP TPO treatment

Exclusion Criteria:

- thrombocytopenia not due to ITP

Gender: All

Minimum Age: N/A

Maximum Age: N/A

Healthy Volunteers: No

Overall Official
Last Name Role Affiliation
James L Zehnder, MD Principal Investigator Stanford University
Stanford University | Stanford, California, 94305, United States
Weill Medical College, Cornell University | New York, New York, 10065, United States
Location Countries

United States

Verification Date

April 2017

Responsible Party

Type: Principal Investigator

Investigator Affiliation: Stanford University

Investigator Full Name: James L Zehnder

Investigator Title: Professor

Has Expanded Access No
Condition Browse
Arm Group

Label: TPO responder

Description: Patients with therapeutic response to TPO

Label: TPO non-responder

Description: Patients not responding to TPO agonists

Patient Data No
Study Design Info

Observational Model: Cohort

Time Perspective: Prospective