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Enregistrement W3214724472 · doi:10.1182/blood-2021-154005

High-Throughput Characterization of Cytogenomic Heterogeneity of MDS Using High-Resolution Optical Genome Mapping

2021· article· en· W3214724472 sur OpenAlexaboutno aff
Hui Yang, Guillermo Garcia‐Manero, Guillermo Montalban‐Bravo, Kelly S. Chien, Awdesh Kalia, Zhenya Tang, Yue Wei, Manjunath Nimmakayalu, Diana Rush, Saradhi Mallampati, Hagop M. Kantarjian, Rajyalakshmi Luthra, L. Jeffrey Medeiros, Rashmi Kanagal‐Shamanna

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Genomics and Diagnostics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCopy number analysisBiologyComputational biologyDNA sequencingGeneticsGenomeMolecular Inversion ProbeCopy-number variationGeneSingle-nucleotide polymorphismGenotype

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Introduction of next-generation sequencing has defined the somatic mutational landscape in MDS. Comprehensive high-throughput structural variant profiling (SVP) is as important as mutation profiling in characterizing MDS clonal architecture since these large genomic aberrations have already shown to be critical for diagnosis and risk-stratification of MDS. A subset (MECOM, KMT2A rearrangements) are therapeutic targets in clinical trials. At this time, technical advances in SVP for copy number alterations (CNAs) and fusions have not been congruent with mutation profiling due to the inability of short-read (150bp) NGS to detect SVs. Currently available long-read (10-20Kbp) and whole genome sequencing cannot detect all SVs due to the presence of repeat sequences. Hence, conventional karyotyping (CK) remains the gold standard. Optical genome mapping (OGM) is a novel single-platform technique that measures ultra-long-range sequence patterns (>300Kbp), thereby unaffected by repeat sequences, enabling unbiased evaluation of all types of SVs at a high resolution. Here, we performed comprehensive SVP and mutation profiling in a large well-characterized cohort of MDS. Methods We selected samples with available fresh/frozen BM cells from consecutive treatment-naïve MDS pts who also underwent standard-of-care tests (CK, FISH, targeted 81-gene NGS for mutations). For OGM, ultra-high-molecular-weight-DNA was extracted, followed by labeling, linearization and imaging of DNA (Saphyr, Bionano) [median coverage:>300X]. The results were analyzed using de novo (>500 bp), rare variant (>5000 bp) and copy number (>500,000 bp) pipelines. The data was compared against 200 healthy controls to exclude common germline SVs. Clinical significance of the SVs was determined based on the location/overlap with the coding region of myeloid malignancy associated genes. The detection sensitivity was 10%. Results There were 76 treatment naïve MDS patients. Baseline characteristics, comprehensive cytogenetic scoring system (CCSS) and R-IPPS risk categories and somatic mutations are in Fig 1. OGM identified all clonal abnormalities detected by CK [CNAs, inversions, inter/intra-chromosomal translocations, dicentric, complex derivative chromosomes]. Precise mapping of SVs by OGM at gene-level allowed determining the status of clinically informative biomarkers such as TET2, MECOM, TP53 and KMT2A, without the need for confirmatory assays. Detailed gene-level characterization of different SVs included KMT2A-ELL [t(11;19)] in MDS with WT1 mut, t(9;11) with SYTL2 fusion (and not KMT2A), der(1;7) leading to del(7q) in MDS with GATA2 mut/IDH2 mutand t(1;3)(p36;q21) rearrangements with potential PRDM16 disruption in SF3B1 mut/RUNX1 mutMDS, among others. Using OGM, we mapped the sequence patterns in both samples with IM with high level of confidence. Additionally, OGM identified 23 cryptic, clinically significant SVs in 14 (18%) of 76 pts. These included deletions of TET2, KMT2A, and del(5q), KMT2A amplification in MDS with FLT3-ITD/DNMT3A mut/RAS mut, NUP98-PRRX2, MECOM rearrangement in TET mut mutated NK-MDS. In addition, there were SVs of uncertain significance: duplications of chr1 (PDE41P), deletions of chr21 (involving RUNX1), chr2 (DNMT3A, ASXL2), chr12 (ETV6) and chr22 (EP300) and der(16)t(12;16)(q21.1;q12.1). These cryptic SVs were noted across all R-IPSS risk categories (highest yield in very-low and low R-IPSS) and across all cytogenetic risk-groups (very-good to very-poor). In complex karyotype setting, OGM could resolve the markers and additional genetic material, and in most cases, showed a much higher the degree of complexity within the genome than was apparent by CK. Four pts showed SV patterns typical of chromothripsis/chromoplexy. The median number of mutations per pt was 1 (0-6). When compared to mutation subsets, cryptic SVs were only identified in pts with ≤3 mutations. Majority represented either MDS with TP53 mut (6, 29%) or SF3B1 mut/TET mut (deletions of TET2, KMT2A, NOTCH1 and EP300 genes). Conclusions Unbiased, high-throughput whole genome SVP revealed cryptic, clinically significant SVs in ~18% of MDS pts. OGM is a single-platform cytogenomic tool that can facilitate SVP at a gene-level resolution. This study provides strong support for further validation in expanded cohorts to guide clinical implementation and integration of SVP for routine work-up. Figure 1 Figure 1. Disclosures Wei: Daiichi Sanko: Research Funding. Kantarjian: Ipsen Pharmaceuticals: Honoraria; Amgen: Honoraria, Research Funding; Astellas Health: Honoraria; Astra Zeneca: Honoraria; AbbVie: Honoraria, Research Funding; KAHR Medical Ltd: Honoraria; NOVA Research: Honoraria; Ascentage: Research Funding; Aptitude Health: Honoraria; Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Jazz: Research Funding; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; BMS: Research Funding; Precision Biosciences: Honoraria; Taiho Pharmaceutical Canada: Honoraria.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,234
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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