ID: 26. Issue 4 (April 2016)

• Record Type: Journal Article
• Authors: Nath, A; Wang, F; Lenkala, D; LaCroix, B; Glavin, N; Kipping-Johnson, K; Geeleher, P; Rich, E; Thirman, M; Godley, L; Raca, G; Larson, R; Huang, R
• Is Part Of: Journal of investigative medicine. Volume 64:Issue 4(2016)
• Publication Date: 2016-04
• Subjects: Clinical medicine -- Periodicals; Medicine -- Research -- Periodicals; Medicine; Research -- United States; Clinical medicine; Medicine -- Research; Periodicals; 616.075
• DOI: 10.1136/jim-2016-000120.50
• Languages: English
• ISSNs: 1081-5589

Abstract

Abstract : The interpretation and implementation of large-scale genetic profiles into clinical practice remains a challenge despite substantial growth in our understanding of genetic contributors to drug response. Most current omic studies focus on identifying genetic features that are distinct between normal and tumor samples, but fail to capture the dynamics of association between omic profiles, treatment response and disease progression over time. The focus of this research is to analyze the longitudinal transcriptomic profile of chronic myeloid leukemia patients (CML) in context of tyrosine kinase inhibitor (TKI) treatment and clinical status. The main objectives were to compare a series of post-TKI treatment transcriptome profiles to their baseline levels, and characterize the impact of TKI treatment and CML disease status on the individual's transcriptome over time. Our ultimate goal is to develop TKI response predictors using the longitudinal expression data collected over the treatment course. Peripheral blood samples, buccal swabs and detailed clinical data were collected from each study participant (screened for BCR-ABL1 translocation) for a period of 6 months, in addition to pre-therapy baseline. RNA was extracted from granulocytes isolated from peripheral blood samples, and profiled using RNA sequencing. RNAseq profiles over TKI treatment course were compared to baseline, as well as against hematologic response (complete blood count), cytogenetic response (FISH), and clinical disease progression. We investigated dynamic trends in RNAseq profiles associated TKI response, as well as with the clinical status of the patient over time. We identified genetic features that were either 1) Differentially expressed between baseline and post-TKI time points; 2) Showed non-random spikes in expression levels at specific time points; 3) Associated with hematological and clinical phenotypes, including white blood cell count, percentage granulocytes and percentage cells with BCR-ABL1 translocation; 4) Demonstrated highly correlated patterns of expression over time. Through clustering and enrichment analysis of the selected transcripts, we identified several pathways and molecular features associated with TKI-response, and altered disease state. Of note, we found mTOR signaling, and pro-apoptotic pathways to be significantly altered between baseline and TKI-responding individuals. In addition, we observed significant changes in transcription regulatory network of several transcription factors, notably AP-1, over the treatment time course. To our knowledge, this is the first study to establish the utility of comprehensive longitudinal multiple transcriptome profile analysis of TKI-response in CML. We believe this study will pave way for future large-scale longitudinal omic profiling of CML and other cancer-types.