Dataset Information

Genomic profiling for clinical decision making in lymphoid neoplasms.

ABSTRACT: With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

SUBMITTER: de Leval L

PROVIDER: S-EPMC9837456 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomic profiling for clinical decision making in lymphoid neoplasms.

de Leval Laurence L Alizadeh Ash A AA Bergsagel P Leif PL Campo Elias E Davies Andrew A Dogan Ahmet A Fitzgibbon Jude J Horwitz Steven M SM Melnick Ari M AM Morice William G WG Morin Ryan D RD Nadel Bertrand B Pileri Stefano A SA Rosenquist Richard R Rossi Davide D Salaverria Itziar I Steidl Christian C Treon Steven P SP Zelenetz Andrew D AD Advani Ranjana H RH Allen Carl E CE Ansell Stephen M SM Chan Wing C WC Cook James R JR Cook Lucy B LB d'Amore Francesco F Dirnhofer Stefan S Dreyling Martin M Dunleavy Kieron K Feldman Andrew L AL Fend Falko F Gaulard Philippe P Ghia Paolo P Gribben John G JG Hermine Olivier O Hodson Daniel J DJ Hsi Eric D ED Inghirami Giorgio G Jaffe Elaine S ES Karube Kennosuke K Kataoka Keisuke K Klapper Wolfram W Kim Won Seog WS King Rebecca L RL Ko Young H YH LaCasce Ann S AS Lenz Georg G Martin-Subero José I JI Piris Miguel A MA Pittaluga Stefania S Pasqualucci Laura L Quintanilla-Martinez Leticia L Rodig Scott J SJ Rosenwald Andreas A Salles Gilles A GA San-Miguel Jesus J Savage Kerry J KJ Sehn Laurie H LH Semenzato Gianpietro G Staudt Louis M LM Swerdlow Steven H SH Tam Constantine S CS Trotman Judith J Vose Julie M JM Weigert Oliver O Wilson Wyndham H WH Winter Jane N JN Wu Catherine J CJ Zinzani Pier L PL Zucca Emanuele E Bagg Adam A Scott David W DW

Blood 20221101 21

With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the descr ...[more]

PMID: 36001803

Similar Datasets

Project description:BackgroundScientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.ResultsSidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.ConclusionsSidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.

Dataset Information

Genomic profiling for clinical decision making in lymphoid neoplasms.

Publications

Genomic profiling for clinical decision making in lymphoid neoplasms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets