Gene expression workflow to analyze residual leukemic cells in Chronic Lymphocytic Leukemia.
RNA
cDNA
chronic lymphocytic leukemia
microarrays
minimal residual disease
Journal
International journal of laboratory hematology
ISSN: 1751-553X
Titre abrégé: Int J Lab Hematol
Pays: England
ID NLM: 101300213
Informations de publication
Date de publication:
Aug 2020
Aug 2020
Historique:
received:
11
02
2020
revised:
30
03
2020
accepted:
01
04
2020
pubmed:
26
4
2020
medline:
6
1
2021
entrez:
26
4
2020
Statut:
ppublish
Résumé
In chronic lymphocytic leukemia, a better understanding of leukemic cell characteristics after treatment would help to design specific therapeutic approaches aimed at preventing clinical relapse. Gene arrays have become a powerful approach to perform gene expression profiling; nevertheless, to work with residual cells entails an intensive labor. The aim of this study was to set forth an effective gene expression approach to analyze residual leukemic cells. Leukocytes from CLL patient's samples were sorted by flow cytometry using a 6-color panel. The quality and quantity of RNA isolated from different inputs of cells were compared by two silica column protocols: RNeasy Micro and RNeasy Mini. RNA amplifications were carried out according to two manufacturer's protocols: Ovation Pico SL and Ovation Pico WTA. A total of 3.5 μg of cDNA was labeled and hybridized to Human Gene 2.0 ST arrays. RNA extracted from low number of input cells by RNeasy Micro showed similar RNA integrity number to that obtained from RNeasy Mini; however, the RNA quantity was higher using the RNeasy Micro Kit. In addition, those RNA samples obtained with RNeasy Micro and amplified with Ovation Pico WTA showed good quality to proceed for a gene array study, independently of the number of input cells (range: 1 × 10 We observed that this workflow is a feasible approach to obtain genomic material extracted from leukemic cells as little as 1 × 10
Sections du résumé
BACKGROUND
BACKGROUND
In chronic lymphocytic leukemia, a better understanding of leukemic cell characteristics after treatment would help to design specific therapeutic approaches aimed at preventing clinical relapse. Gene arrays have become a powerful approach to perform gene expression profiling; nevertheless, to work with residual cells entails an intensive labor. The aim of this study was to set forth an effective gene expression approach to analyze residual leukemic cells.
METHODS
METHODS
Leukocytes from CLL patient's samples were sorted by flow cytometry using a 6-color panel. The quality and quantity of RNA isolated from different inputs of cells were compared by two silica column protocols: RNeasy Micro and RNeasy Mini. RNA amplifications were carried out according to two manufacturer's protocols: Ovation Pico SL and Ovation Pico WTA. A total of 3.5 μg of cDNA was labeled and hybridized to Human Gene 2.0 ST arrays.
RESULTS
RESULTS
RNA extracted from low number of input cells by RNeasy Micro showed similar RNA integrity number to that obtained from RNeasy Mini; however, the RNA quantity was higher using the RNeasy Micro Kit. In addition, those RNA samples obtained with RNeasy Micro and amplified with Ovation Pico WTA showed good quality to proceed for a gene array study, independently of the number of input cells (range: 1 × 10
CONCLUSIONS
CONCLUSIONS
We observed that this workflow is a feasible approach to obtain genomic material extracted from leukemic cells as little as 1 × 10
Types de publication
Clinical Trial
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
423-430Subventions
Organisme : Catalan Government AGAUR
ID : 2014 SGR 1281
Organisme : Catalan Government AGAUR
ID : 2017 SGR 1395
Organisme : Cellex Foundation Barcelona
Organisme : Red Temática de Investigación Cooperativa en Cáncer
ID : RD12/0036/0071
Organisme : Asociación Española contra el Cáncer (AECC)
Organisme : Instituto de Salud Carlos III
ID : FIS PI11/01740
Organisme : Instituto de Salud Carlos III
ID : FIS PI19/00753
Organisme : Obra Social La Caixa Barcelona
Organisme : Catalan Government AGAUR
Informations de copyright
© 2020 John Wiley & Sons Ltd.
Références
Varghese AM, Howard DR, Pocock C, et al. Eradication of minimal residual disease improves overall and progression-free survival in patients with chronic lymphocytic leukaemia, evidence from NCRN CLL207: a phase II trial assessing alemtuzumab consolidation. Br J Haematol. 2017;176:573-582.
Scarfo L, Ferreri AJ, Ghia P. Chronic lymphocytic leukaemia. Crit Rev Oncol Hematol. 2016;104:169-182.
Howard DR, Munir T, McParland L, et al. Clinical effectiveness and cost-effectiveness results from the randomised, Phase IIB trial in previously untreated patients with chronic lymphocytic leukaemia to compare fludarabine, cyclophosphamide and rituximab with fludarabine, cyclophosphamide, mitoxantrone and low-dose rituximab: the Attenuated dose Rituximab with ChemoTherapy In Chronic lymphocytic leukaemia (ARCTIC) trial. Health Technol Assess. 2017;21:1-374.
Fischer K, Bahlo J, Fink AM, et al. Long-term remissions after FCR chemoimmunotherapy in previously untreated patients with CLL: updated results of the CLL8 trial. Blood. 2016;127:208-215.
Ahn IE, Farooqui MZH, Tian X, et al. Depth and durability of response to ibrutinib in CLL: 5-year follow-up of a phase II study. Blood. 2018;131:2357-2366.
Roberts AW, Davids MS, Pagel JM, et al. Targeting BCL2 with venetoclax in relapsed chronic lymphocytic leukemia. N Engl J Med. 2016;374:311-322.
Owen C, Christofides A, Johnson N, Lawrence T, MacDonald D, Ward C. Use of minimal residual disease assessment in the treatment of chronic lymphocytic leukemia. Leuk Lymphoma. 2017;58:2777-2785.
Bottcher S, Ritgen M, Fischer K, et al. Minimal residual disease quantification is an independent predictor of progression-free and overall survival in chronic lymphocytic leukemia: a multivariate analysis from the randomized GCLLSG CLL8 trial. J Clin Oncol. 2012;30:980-988.
Dimier N, Delmar P, Ward C, et al. A model for predicting effect of treatment on progression-free survival using MRD as a surrogate end point in CLL. Blood. 2018;131:955-962.
Thompson PA, Wierda WG. Eliminating minimal residual disease as a therapeutic end point: working toward cure for patients with CLL. Blood. 2016;127:279-286.
Kwok M, Rawstron AC, Varghese A, et al. Minimal residual disease is an independent predictor for 10-year survival in CLL. Blood. 2016;128:2770-2773.
Logan AC, Zhang B, Narasimhan B, et al. Minimal residual disease quantification using consensus primers and high-throughput IGH sequencing predicts post-transplant relapse in chronic lymphocytic leukemia. Leukemia. 2013;27:1659-1665.
Bottcher S, Ritgen M, Dreger P. Allogeneic stem cell transplantation for chronic lymphocytic leukemia: lessons to be learned from minimal residual disease studies. Blood Rev. 2011;25:91-96.
Rawstron AC, Fazi C, Agathangelidis A, et al. A complementary role of multiparameter flow cytometry and high-throughput sequencing for minimal residual disease detection in chronic lymphocytic leukemia: an European Research Initiative on CLL study. Leukemia. 2016;30:929-936.
Rawstron AC, Villamor N, Ritgen M, et al. International standardized approach for flow cytometric residual disease monitoring in chronic lymphocytic leukaemia. Leukemia. 2007;21:956-964.
Shivarov V, Bullinger L. Expression profiling of leukemia patients: key lessons and future directions. Exp Hematol. 2014;42:651-660.
Ruiz-Lafuente N, Alcaraz-Garcia MJ, Sebastian-Ruiz S, et al. The gene expression response of chronic lymphocytic leukemia cells to IL-4 is specific, depends on ZAP-70 status and is differentially affected by an NFkappaB inhibitor. PLoS ONE. 2014;9:e109533.
Wiestner A, Rosenwald A, Barry TS, et al. ZAP-70 expression identifies a chronic lymphocytic leukemia subtype with unmutated immunoglobulin genes, inferior clinical outcome, and distinct gene expression profile. Blood. 2003;101:4944-4951.
Stratowa C, Loffler G, Lichter P, et al. CDNA microarray gene expression analysis of B-cell chronic lymphocytic leukemia proposes potential new prognostic markers involved in lymphocyte trafficking. Int J Cancer. 2001;91:474-480.
Durig J, Nuckel H, Huttmann A, et al. Expression of ribosomal and translation-associated genes is correlated with a favorable clinical course in chronic lymphocytic leukemia. Blood. 2003;101:2748-2755.
Carlucci F, Marinello E, Tommassini V, Pisano B, Rosi F, Tabucchi A. A 57-gene expression signature in B-cell chronic lymphocytic leukemia. Biomed Pharmacother. 2009;63:663-671.
Frezzato F, Accordi B, Trimarco V, et al. Profiling B cell chronic lymphocytic leukemia by reverse phase protein array: focus on apoptotic proteins. J Leukoc Biol. 2016;100:1061-1070.
Song JH, Kim HJ, Lee CH, Kim SJ, Hwang SY, Kim TS. Identification of gene expression signatures for molecular classification in human leukemia cells. Int J Oncol. 2006;29:57-64.
Patel VM, Balakrishnan K, Douglas M, et al. Duvelisib treatment is associated with altered expression of apoptotic regulators that helps in sensitization of chronic lymphocytic leukemia cells to venetoclax (ABT-199). Leukemia. 2017;31:1872-1881.
Oberg JA, Glade Bender JL, Sulis ML, et al. Implementation of next generation sequencing into pediatric hematology-oncology practice: moving beyond actionable alterations. Genome Med. 2016;8:133.
Marks LJ, Oberg JA, Pendrick D, et al. Precision medicine in children and young adults with hematologic malignancies and blood disorders: the Columbia University Experience. Front Pediatr. 2017;5:265.
Ghia P, Stamatopoulos K, Belessi C, et al. ERIC recommendations on IGHV gene mutational status analysis in chronic lymphocytic leukemia. Leukemia. 2007;21:1-3.
Ardjmand A, de Bock CE, Shahrokhi S, et al. Fat1 cadherin provides a novel minimal residual disease marker in acute lymphoblastic leukemia. Hematology. 2013;18:315-322.
Sitthi-Amorn J, Herrington B, Megason G, et al. Transcriptome analysis of minimal residual disease in subtypes of pediatric B cell acute lymphoblastic leukemia. Clin Med Insights Oncol. 2015;9:51-60.
Marjanovic I, Karan-Djurasevic T, Ugrin M, et al. Use of Wilms tumor 1 gene expression as a reliable marker for prognosis and minimal residual disease monitoring in acute myeloid leukemia with normal karyotype patients. Clin Lymphoma Myeloma Leuk. 2017;17:312-319.
Paiva B, Corchete LA, Vidriales MB, et al. Phenotypic and genomic analysis of multiple myeloma minimal residual disease tumor cells: a new model to understand chemoresistance. Blood. 2016;127:1896-1906.
Mack E, Neubauer A, Brendel C. Comparison of RNA yield from small cell populations sorted by flow cytometry applying different isolation procedures. Cytometry A. 2007;71:404-409.
Belder N, Coskun O, Doganay Erdogan B, et al. From RNA isolation to microarray analysis: comparison of methods in FFPE tissues. Pathol Res Pract. 2016;212:678-685.
Desjardins P, Conklin D. NanoDrop microvolume quantitation of nucleic acids. J Vis Exp. 2010;45:2565.
Diez C, Bertsch G, Simm A. Isolation of full-size mRNA from cells sorted by flow cytometry. J Biochem Biophys Methods. 1991;40:69-80.
Iglesias-Ussel M, Marchionni L, Romerio F. Isolation of microarray-quality RNA from primary human cells after intracellular immunostaining and fluorescence-activated cell sorting. J Immunol Methods. 2013;391:22-30.
Clement-Ziza M, Gentien D, Lyonnet S, Thiery JP, Besmond C, Decraene C. Evaluation of methods for amplification of picogram amounts of total RNA for whole genome expression profiling. BMC Genom. 2009;10:246.
Turner L, Heath JD, Kurn N. Gene expression profiling of RNA extracted from FFPE tissues: NuGEN technologies' whole-transcriptome amplification system. Methods Mol Biol. 2011;724:269-280.
Chen P, Lepikhova T, Hu Y, Monni O, Hautaniemi S. Comprehensive exon array data processing method for quantitative analysis of alternative spliced variants. Nucleic Acids Res. 2011;39:e123.