We have assembled a multidisciplinary team which is highly accomplished in clinical research, genomics, and bioinformatics/biostatistics, with the goal to identify prognostic biomarkers to better predict and understand progression of chronic lymphocytic leukemia (CLL). The course of CLL is variable and its pathogenesis is poorly understood: while some patients have long-term indolent disease prior to progression, others progress rapidly requiring therapy within a relatively short time after diagnosis. Although existing biomarkers and clinical factors can stratify patients into high and low risk groups, there remains a need for longitudinal biomarkers which can signal development of progressive disease after a variable indolent period. We have a large number of clinically well-annotated CLL tissue samples with long term follow-up on outcome, available from the UCSD site of the CLL Clinical Research Consortium (CRC). Samples include viably-frozen leukemia cells, leukemia-cell DNA, RNA, and germline DNA. We propose a retrospective matched case-control study comparing an early-progressing group of CLL patients (n=12) with a later-progressing (n=12) and a long-term indolent group (n=12). The later-progressing and long-term indolent groups are individually matched on gender and time to progression or time to last follow-up, respectively. All three groups have two blood draws within the first two years;the later-progressing and long-term progression-free patients also have a matched third blood draw between 3 and 8 years after diagnosis. We will use second-generation sequencing to generate transcriptome data for these 96 CLL tumor samples (12 early-progressing, sampled at 2 time points;24 later-progressing and long-term indolent, sampled at 3 time points). We will quantify mRNA transcript levels for genes and for alternative splice isoforms, and identify recurrent mutations. We will use these data to identify candidate biomarkers at baseline, and then identify candidate longitudinal biomarkers by finding differences between early-progressing and the other two groups in the change score during the first two years. We will validate these candidate longitudinal biomarkers by assessing them for significant differences between later progressing and long-term indolent groups after three years. Finally, we will integrate putative longitudinal biomarkers with existing known prognostic factors in the clinical database. If successful, our candidate biomarkers will be well poised to carry forward for further validation in the full CRC biorepository, and to propose for prospective clinical studies.

Public Health Relevance

Project Narrative Chronic Lymphocytic Leukemia (CLL) is the most common adult leukemia in the United States. Here we seek to improve the health care of CLL patients by developing biomarkers to better predict and understand disease progression.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21CA152613-02
Application #
8094505
Study Section
Special Emphasis Panel (ZRG1-CBSS-J (08))
Program Officer
Jessup, John M
Project Start
2010-06-01
Project End
2012-05-31
Budget Start
2011-06-01
Budget End
2012-05-31
Support Year
2
Fiscal Year
2011
Total Cost
$97,787
Indirect Cost
Name
University of California San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
D'Antonio, Matteo; Tamayo, Pablo; Mesirov, Jill P et al. (2016) Kataegis Expression Signature in Breast Cancer Is Associated with Late Onset, Better Prognosis, and Higher HER2 Levels. Cell Rep 16:672-83
Smith, E N; Ghia, E M; DeBoever, C M et al. (2015) Genetic and epigenetic profiling of CLL disease progression reveals limited somatic evolution and suggests a relationship to memory-cell development. Blood Cancer J 5:e303
DeBoever, Christopher; Ghia, Emanuela M; Shepard, Peter J et al. (2015) Transcriptome sequencing reveals potential mechanism of cryptic 3' splice site selection in SF3B1-mutated cancers. PLoS Comput Biol 11:e1004105
Smith, Erin N; Jepsen, Kristen; Khosroheidari, Mahdieh et al. (2014) Biased estimates of clonal evolution and subclonal heterogeneity can arise from PCR duplicates in deep sequencing experiments. Genome Biol 15:420
Mraz, Marek; Chen, Liguang; Rassenti, Laura Z et al. (2014) miR-150 influences B-cell receptor signaling in chronic lymphocytic leukemia by regulating expression of GAB1 and FOXP1. Blood 124:84-95
Shlush, Liran I; Zandi, Sasan; Mitchell, Amanda et al. (2014) Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506:328-33
Barrett, Christian L; Schwab, Richard B; Jung, HyunChul et al. (2013) Transcriptome sequencing of tumor subpopulations reveals a spectrum of therapeutic options for squamous cell lung cancer. PLoS One 8:e58714
Harismendy, Olivier; Schwab, Richard B; Alakus, Hakan et al. (2013) Evaluation of ultra-deep targeted sequencing for personalized breast cancer care. Breast Cancer Res 15:R115
Yost, Shawn E; Alakus, Hakan; Matsui, Hiroko et al. (2013) Mutascope: sensitive detection of somatic mutations from deep amplicon sequencing. Bioinformatics 29:1908-9
Yost, Shawn E; Pastorino, Sandra; Rozenzhak, Sophie et al. (2013) High-resolution mutational profiling suggests the genetic validity of glioblastoma patient-derived pre-clinical models. PLoS One 8:e56185

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