Literature DB >> 30531723

Immunoglobulin Gene Sequence Analysis In Chronic Lymphocytic Leukemia: From Patient Material To Sequence Interpretation.

Andreas Agathangelidis1, Lesley Ann Sutton2, Anastasia Hadzidimitriou3, Cristina Tresoldi4, Anton W Langerak5, Chrysoula Belessi6, Frederic Davi7, Richard Rosenquist8, Kostas Stamatopoulos9, Paolo Ghia10.   

Abstract

During B cell maturation, the complex process of immunoglobulin (IG) gene V(D)J recombination coupled with somatic hypermutation (SHM) gives rise to a unique DNA sequence within each individual B cell. Since B cell malignancies result from the clonal expansion of a single cell, IG genes represent a unique molecular signature common to all the malignant cells within an individual patient; thus, IG gene rearrangements can be used as clonal markers. In addition to serving as an important clonal identifier, the IG gene sequence can act as a 'molecular timeline' since it is associated with specific developmental stages and hence reflects the history of the B cell involved in the neoplastic transformation. Moreover, for certain malignancies, in particular chronic lymphocytic leukemia (CLL), the IG gene sequence holds prognostic and potentially predictive capabilities. That said, extrapolating meaningful conclusions from IG gene sequence analysis would be impossible if robust methods and tools were not available to aid in their analysis. This article, drawing on the vast experience of the European Research Initiative on CLL (ERIC), details the technical aspects and essential requirements necessary to ensure reliable and reproducible IG gene sequence analysis in CLL, a test that is now recommended for all CLL patients prior to treatment. More specifically, the various analytical stages are described ranging from the identification of the clonotypic IG gene rearrangement and the determination of the nucleotide sequence to the accurate clinical interpretation of the IG gene sequence data.

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Year:  2018        PMID: 30531723     DOI: 10.3791/57787

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  2 in total

1.  Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Authors:  Fan Cao; Yu Zhang; Yichao Cai; Sambhavi Animesh; Ying Zhang; Semih Can Akincilar; Yan Ping Loh; Xinya Li; Wee Joo Chng; Vinay Tergaonkar; Chee Keong Kwoh; Melissa J Fullwood
Journal:  Genome Biol       Date:  2021-08-16       Impact factor: 13.583

2.  Expression of the Chemokine Receptor CCR1 in Burkitt Lymphoma Cell Lines Is Linked to the CD10-Negative Cell Phenotype and Co-Expression of the EBV Latent Genes EBNA2, LMP1, and LMP2.

Authors:  Laura Zvejniece; Svetlana Kozireva; Zanna Rudevica; Ainars Leonciks; Barbro Ehlin-Henriksson; Elena Kashuba; Irina Kholodnyuk
Journal:  Int J Mol Sci       Date:  2022-03-22       Impact factor: 5.923

  2 in total

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