Cargando…

Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology

SIMPLE SUMMARY: The present work comes up after detecting stakeholders’ need to test for manifold molecular biomarkers in each sample from an individual diagnosed with myeloid neoplasm or acute leukemia. The development of gene panels based on NGS technology is considered a potentially effective tes...

Descripción completa

Detalles Bibliográficos
Autores principales: Gargallo, Pablo, Molero, Merche, Bilbao, Cristina, Stuckey, Ruth, Carrillo-Cruz, Estrella, Hermosín, Lourdes, Pérez-López, Olga, Jiménez-Velasco, Antonio, Soria, Elena, Lázaro, Marián, Carbonell, Paula, Yáñez, Yania, Gómez, Iria, Izquierdo-García, Marta, Valero-García, Jennifer, Ruiz, Carlos, Such, Esperanza, Calabria, Inés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030630/
https://www.ncbi.nlm.nih.gov/pubmed/35454892
http://dx.doi.org/10.3390/cancers14081986
_version_ 1784692189208510464
author Gargallo, Pablo
Molero, Merche
Bilbao, Cristina
Stuckey, Ruth
Carrillo-Cruz, Estrella
Hermosín, Lourdes
Pérez-López, Olga
Jiménez-Velasco, Antonio
Soria, Elena
Lázaro, Marián
Carbonell, Paula
Yáñez, Yania
Gómez, Iria
Izquierdo-García, Marta
Valero-García, Jennifer
Ruiz, Carlos
Such, Esperanza
Calabria, Inés
author_facet Gargallo, Pablo
Molero, Merche
Bilbao, Cristina
Stuckey, Ruth
Carrillo-Cruz, Estrella
Hermosín, Lourdes
Pérez-López, Olga
Jiménez-Velasco, Antonio
Soria, Elena
Lázaro, Marián
Carbonell, Paula
Yáñez, Yania
Gómez, Iria
Izquierdo-García, Marta
Valero-García, Jennifer
Ruiz, Carlos
Such, Esperanza
Calabria, Inés
author_sort Gargallo, Pablo
collection PubMed
description SIMPLE SUMMARY: The present work comes up after detecting stakeholders’ need to test for manifold molecular biomarkers in each sample from an individual diagnosed with myeloid neoplasm or acute leukemia. The development of gene panels based on NGS technology is considered a potentially effective testing alternative with less human effort, compared to that required by other conventional techniques (PCR, FISH, conventional karyotype, etc.). The validation of this panel aims to propose a new solution for hospitals to face the challenges posed by the molecular study of this group of onco-hematological diseases. ABSTRACT: A suitable diagnostic classification of myeloid neoplasms and acute leukemias requires testing for a large number of molecular biomarkers. Next-generation sequencing is a technology able to integrate identification of the vast majority of them in a single test. This manuscript includes the design, analytical validation and clinical feasibility evaluation of a molecular diagnostic kit for onco-hematological diseases. It is based on sequencing of the coding regions of 76 genes (seeking single-nucleotide variants, small insertions or deletions and CNVs), as well as the search for fusions in 27 target genes. The kit has also been designed to detect large CNVs throughout the genome by including specific probes and employing a custom bioinformatics approach. The analytical and clinical feasibility validation of the Haematology OncoKitDx panel has been carried out from the sequencing of 170 patient samples from 6 hospitals (in addition to the use of commercial reference samples). The analytical validation showed sensitivity and specificity close to 100% for all the parameters evaluated, with a detection limit of 2% for SNVs and SVs, and 20% for CNVs. Clinically relevant mutations were detected in 94% of all patients. An analysis of the correlation between the genetic risk classification of AML (according to ELN 2017) established by the hospitals and that obtained by the Haematology OncoKitDx panel showed an almost perfect correlation (K = 0.94). Among the AML samples with a molecular diagnosis, established by the centers according to the WHO, the Haematology OncoKitDx analysis showed the same result in 97% of them. The panel was able to adequately differentiate between MPN subtypes and also detected alterations that modified the diagnosis (FIP1L1-PDGFRA). Likewise, the cytogenetic risk derived from the CNV plot generated by the NGS panel correlated substantially with the results of the conventional karyotype (K = 0.71) among MDS samples. In addition, the panel detected the main biomarkers of prognostic value among patients with ALL. This validated solution enables a reliable analysis of a large number of molecular biomarkers from a DNA sample in a single assay.
format Online
Article
Text
id pubmed-9030630
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90306302022-04-23 Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology Gargallo, Pablo Molero, Merche Bilbao, Cristina Stuckey, Ruth Carrillo-Cruz, Estrella Hermosín, Lourdes Pérez-López, Olga Jiménez-Velasco, Antonio Soria, Elena Lázaro, Marián Carbonell, Paula Yáñez, Yania Gómez, Iria Izquierdo-García, Marta Valero-García, Jennifer Ruiz, Carlos Such, Esperanza Calabria, Inés Cancers (Basel) Article SIMPLE SUMMARY: The present work comes up after detecting stakeholders’ need to test for manifold molecular biomarkers in each sample from an individual diagnosed with myeloid neoplasm or acute leukemia. The development of gene panels based on NGS technology is considered a potentially effective testing alternative with less human effort, compared to that required by other conventional techniques (PCR, FISH, conventional karyotype, etc.). The validation of this panel aims to propose a new solution for hospitals to face the challenges posed by the molecular study of this group of onco-hematological diseases. ABSTRACT: A suitable diagnostic classification of myeloid neoplasms and acute leukemias requires testing for a large number of molecular biomarkers. Next-generation sequencing is a technology able to integrate identification of the vast majority of them in a single test. This manuscript includes the design, analytical validation and clinical feasibility evaluation of a molecular diagnostic kit for onco-hematological diseases. It is based on sequencing of the coding regions of 76 genes (seeking single-nucleotide variants, small insertions or deletions and CNVs), as well as the search for fusions in 27 target genes. The kit has also been designed to detect large CNVs throughout the genome by including specific probes and employing a custom bioinformatics approach. The analytical and clinical feasibility validation of the Haematology OncoKitDx panel has been carried out from the sequencing of 170 patient samples from 6 hospitals (in addition to the use of commercial reference samples). The analytical validation showed sensitivity and specificity close to 100% for all the parameters evaluated, with a detection limit of 2% for SNVs and SVs, and 20% for CNVs. Clinically relevant mutations were detected in 94% of all patients. An analysis of the correlation between the genetic risk classification of AML (according to ELN 2017) established by the hospitals and that obtained by the Haematology OncoKitDx panel showed an almost perfect correlation (K = 0.94). Among the AML samples with a molecular diagnosis, established by the centers according to the WHO, the Haematology OncoKitDx analysis showed the same result in 97% of them. The panel was able to adequately differentiate between MPN subtypes and also detected alterations that modified the diagnosis (FIP1L1-PDGFRA). Likewise, the cytogenetic risk derived from the CNV plot generated by the NGS panel correlated substantially with the results of the conventional karyotype (K = 0.71) among MDS samples. In addition, the panel detected the main biomarkers of prognostic value among patients with ALL. This validated solution enables a reliable analysis of a large number of molecular biomarkers from a DNA sample in a single assay. MDPI 2022-04-14 /pmc/articles/PMC9030630/ /pubmed/35454892 http://dx.doi.org/10.3390/cancers14081986 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gargallo, Pablo
Molero, Merche
Bilbao, Cristina
Stuckey, Ruth
Carrillo-Cruz, Estrella
Hermosín, Lourdes
Pérez-López, Olga
Jiménez-Velasco, Antonio
Soria, Elena
Lázaro, Marián
Carbonell, Paula
Yáñez, Yania
Gómez, Iria
Izquierdo-García, Marta
Valero-García, Jennifer
Ruiz, Carlos
Such, Esperanza
Calabria, Inés
Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title_full Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title_fullStr Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title_full_unstemmed Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title_short Next-Generation DNA Sequencing-Based Gene Panel for Diagnosis and Genetic Risk Stratification in Onco-Hematology
title_sort next-generation dna sequencing-based gene panel for diagnosis and genetic risk stratification in onco-hematology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030630/
https://www.ncbi.nlm.nih.gov/pubmed/35454892
http://dx.doi.org/10.3390/cancers14081986
work_keys_str_mv AT gargallopablo nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT moleromerche nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT bilbaocristina nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT stuckeyruth nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT carrillocruzestrella nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT hermosinlourdes nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT perezlopezolga nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT jimenezvelascoantonio nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT soriaelena nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT lazaromarian nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT carbonellpaula nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT yanezyania nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT gomeziria nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT izquierdogarciamarta nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT valerogarciajennifer nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT ruizcarlos nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT suchesperanza nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology
AT calabriaines nextgenerationdnasequencingbasedgenepanelfordiagnosisandgeneticriskstratificationinoncohematology