Project description:Micro-RNA expression data of CD19 selected B-cells from previously treated and relapsed chronic lymphocytic leukemia patients. We aimed to correlate miR-34a with TP53 mutation status and del17p status. CD19 B-cells from previously treated and relapsed chronic lymphocytic leukemia patients were selected for RNA extraction and hybridization on Affymetrix microarrays.
Project description:Interventions: Group 1: Patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL) and patients with relapsed or refractory acute lymphocytic leukemia (ALL) who have been selected for treatment with CD19-targeting CAR-T cells will be recruited. Stool samples are collected at multiple timepoints (before lymphodepletion, before T-cell infusion, and after T-cell infusion).
Primary outcome(s): The main outcome is bacterial alpha diversity according to Simpson (AD) at the time of CAR-T cell therapy, which should be associated with survival within 24 months.
Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: treatment
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.
Project description:Fresh blood samples were collected from 10 CLL (chronic lymphocytic leukemia) previously untreated patients before and after 2 weeks of the first cycle of CCR (cladribine, cyclophosphamide, rituximab) treatment. Peripheral blood mononuclear cells (PBMNCs) were separated from EDTA blood by layering on the Histopaque 1077 and centrifuging on a density gradient. Mean B-cell CD19+ purity was ≥95% as measured by flow cytometry (FC). Microarray analysis was performed using 0.5 microgram of RNA transcribed to cDNA. Prepared cDNA samples were subjected to RT-PCR in duplicate in the TaqMan 7900HT Sequence Detection System (Applied Biosystems).
Project description:Fresh blood samples were collected from 8 CLL (chronic lymphocytic leukemia) previously untreated patients before and after 2 weeks of the first cycle of CC (cladribine, cyclophosphamide) treatment. Peripheral blood mononuclear cells (PBMNCs) were separated from EDTA blood by layering on the Histopaque 1077 and centrifuging on a density gradient. Mean B-cell CD19+ purity was ≥95% as measured by flow cytometry (FC). Microarray analysis was performed using 0.5 microgram of RNA transcribed to cDNA. Prepared cDNA samples were subjected to RT-PCR in duplicate in the TaqMan 7900HT Sequence Detection System (Applied Biosystems).
Project description:Micro-RNA expression data of CD19 selected B-cells from previously treated and relapsed chronic lymphocytic leukemia patients. We aimed to correlate miR-34a with TP53 mutation status and del17p status.
