Longitudinal Genome Wide Analysis of Chronic Lymphocytic Leukemia Reveals Complex Evolution of Clonal Architecture at Disease Progression and at the Time of Relapse
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE30217 | GEO | 2012/06/30
SECONDARY ACCESSION(S): PRJNA143983
REPOSITORIES: GEO
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2012-06-29 | E-GEOD-30217 | biostudies-arrayexpress
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Project description:Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.
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Project description:In this study we addressed subclonal evolutionary process after treatment and subsequent relapse in multiple myeloma (MM) in a cohort of 24 MM patients treated either with conventional chemotherapy or with the proteasome inhibitor, bortezomib. Because MM is a highly heterogeneous disease coupled with a large number of DNA copy number alterations (CNAs) and loss of heterozygosity (LOH), we focused our study on the secondary genetic events: 1q21 gain, NF-kB activating mutations, RB1 and TP53 deletions, that seem to reflect progression. By using genome-wide high resolution SNP arrays we identified subclones with nonlinear complex evolutionary histories in a third of patients with myeloma, the relapse clone apparently derived from a minor subclone at diagnosis. Such reordering of the spectrum of genetic lesions during therapy is likely to reflect selection of genetically distinct subclones not initially competitive against the dominant population that survived chemotherapy, thrived and acquired new anomalies. In addition we found that emergence of minor subclones at relapse was significantly associated with bortezomib treatment. Altogether, these data support the idea of new strategy of future clinical trials in MM that would combine targeted therapy and subpopulations control to eradicate all myeloma subclones in order to obtain long-term remission. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
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Project description:B cell chronic lymphocytic leukemia - A model with immune response
Seema Nanda 1, , Lisette dePillis 2, and Ami Radunskaya 3,
1.
Tata Institute of Fundamental Research, Centre for Applicable Mathematics, Bangalore 560065, India
2.
Department of Mathematics, Harvey Mudd College, Claremont, CA 91711
3.
Department of Mathematics, Pomona College, Claremont, CA, 91711, United States
Abstract
B cell chronic lymphocytic leukemia (B-CLL) is known to have substantial clinical heterogeneity. There is no cure, but treatments allow for disease management. However, the wide range of clinical courses experienced by B-CLL patients makes prognosis and hence treatment a significant challenge. In an attempt to study disease progression across different patients via a unified yet flexible approach, we present a mathematical model of B-CLL with immune response, that can capture both rapid and slow disease progression. This model includes four different cell populations in the peripheral blood of humans: B-CLL cells, NK cells, cytotoxic T cells and helper T cells. We analyze existing data in the medical literature, determine ranges of values for parameters of the model, and compare our model outcomes to clinical patient data. The goal of this work is to provide a tool that may shed light on factors affecting the course of disease progression in patients. This modeling tool can serve as a foundation upon which future treatments can be based.
Keywords: NK cell, chronic lymphocytic leukemia, mathematical model, T cell., B-CLL.
2020-01-09 | MODEL2001090002 | BioModels