Single Nucleotide Polymorphism Array (SNP-A) Genomic Profiling of Mantle Cell Lymphoma (MCL) Against a Large Control Database Reveals Recurring Copy Number Alterations (CNAs) and Copy Neutral Loss of Heterozygosity (CN-LOH)
Notice bibliographique
Résumé
Abstract Abstract 2001 Background: MCL is characterized by extreme genomic instability. Conventional techniques such as metaphase cytogenetics are unable to detect small deletions, amplifications, or uniparental disomy (UPD) in the MCL tumor genome. SNP-A analysis permits high resolution karyotyping and detection of unbalanced DNA defects, including somatic UPD. We performed SNP-A analysis on MCL tumor samples, excluded CNA present in normal controls, and assessed our results in context of clinical outcome and Ki-67 index. Methods: With IRB approval, available frozen tissue from 18 patients diagnosed with cyclin D1-positive MCL between 1997–2006 was analyzed using high-resolution genome-wide human SNP Array 6.0 (Affymetrix). Signal intensity and SNP calls were determined using the Gene Chip Genotyping Analysis Software Version 4.0 (GTYPE) (Affymetrix). Copy number was also determined. Somatic MCL CNAs and CN-LOH were discerned from germline variants (CNV) by comparing to a database of 1535 normal controls subjected to 250K and/or SNP Array 6.0 analysis. Clinical data was available for all patients, and 15/18 samples were subject to immunohistochemistry (IHC) for Ki67 using an automated immunostainer (Discovery; Ventana Medical Systems). Kaplan-Meier survival analysis was performed. Results: Analysis of 18 MCL patient samples revealed an average 13 CNAs (6.2 gains, 6.4 losses) and 0.4 CN-LOH per patient (Figure 1). Gains were frequently observed in 3q (33%), 8q (22%), 12q (17%), and 18q (11%). A unique 12q micro-gain in one patient narrowed the minimal common region (MCR) to linear region 130.4 – 131.3 Mbp, overlapping with an area of CN-LOH, and including candidate genes MMP17 (upregulated in invasive breast cancer), ULK1 (regulator of autophagy), and EP400 (regulator of chromatin remodeling, proliferation and apoptosis). Recurring deletions were observed at 11q (50%), 1p, 6q (39%), 9p (33%), 13 q (28%), 9q (22%), 7q, and 17p (17%). Similar to prior studies, losses at 1p encompassing CDKN2C and FAF were seen though we further narrowed a common MCR, spanning 93.1–99.7 Mpb and including ARHGAP29 (PARG1)—previously identified in MCL by aCGH/gene expression, whose promoter is a frequent target of MCL methylation. As previously reported, losses in components of the Hippo tumor suppressor pathway were frequently affected by these recurring deletions (6q: LATS1, 9p: MOBKL2B) and by one deletion on 19p (MOBK2LA). Other high-frequency losses encompassed CDKN2A, CDKN2B, and MTAP (on 9p), RB1 and DLEU1/2/miR15a/16-1 (13q), and TP53 (17p). Unique homozygous losses were detected at 9p (3.2-3.3 Mbp; involving only RFX3), 11q (94.5-111.7 Mbp; spanning the ATM region), and 13q (82.7-99.5 Mbp; including the miR17-92 region), and micro-deletions at 6q (121.1-121.9; GJA1/Cx43), 12p (7.5-7.6; CD163), and 13q (73.3-73.4; KLF12, and 75.2–75.3; LMO7). CN-LOH was observed at 6p, similar to prior studies, though we found novel regions of UPD at 4p, 8q, 18q, 19q, and 22q. An overall survival of 3.6 years and relapse-free survival of 1.3 years was observed. Survival was significantly worse among 8 pts with Ki67 >75% (OS 1.4 years, p=.003), but was unaffected by del 11q, del 9p, gain 3q, or gain 8q. Conclusions: SNP-A analysis of 18 primary samples confirms that gains in 3q and 8q and losses in 11q, 6q, and 9p represent common secondary genetic lesions in MCL, and are not frequent in normal controls. We narrowed the MCR of several deletions, potential targets for gene sequencing, and confirm the presence of deletions of potential relevance to the Hippo pathway. Further analysis of our findings in light of tissue micro-array and fluorescence in-situ hybridization studies is underway, to assess pathobiologic consequences of genomic lesions as well as potential therapeutic targets. Disclosures: No relevant conflicts of interest to declare.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».