Characterization of Genomic Rearrangements Involving CIITA and SOCS1 Using Targeted Capture Sequencing of Archival Tissue Specimens
Notice bibliographique
Résumé
Abstract Introduction: Malignant lymphomas account for 5% of all newly diagnosed cancer cases per year and affect patients of all ages. Genomic rearrangements represent a pathogenic hallmark of most B-cell lymphoma entities and some are associated with an unfavorable clinical outcome. Recurrent structural genomic aberrations involving the MHC class II transactivator CIITA (located on chromosome 16p13) have been identified in multiple lymphoma subtypes in which they contribute to an immune escape phenotype. Moreover, inactivating mutations of the tumor suppressor gene SOCS1, located in close proximity to CIITA on chromosome 16, result in constitutively active JAK-STAT-signaling across a spectrum of lymphomas. Preliminary data from our group indicate that SOCS1 is also involved in recurrent rearrangements. However, more detailed study of these rearrangements was hampered due to the lack of analytic methods and platforms applicable to archival formalin-fixed and paraffin-embedded tissue (FFPET) specimens. Here, using FFPET biopsies, we sought to characterize the comprehensive landscape of CIITA and SOCS1rearrangement partner genes, determine the exact breakpoint anatomy, and study the functional impact of individual alterations. Methods: In order to select cases for DNA extraction and subsequent sequencing analysis we revisited the results of previously conducted fluorescence in-situ hybridization (FISH) experiments performed on lymphoma cases arranged on tissue microarrays. Cases were included based on the presence of rearrangements in CIITA, SOCS1 and the 9p24.1 locus. 92 specimens met these criteria and DNA was extracted using the Qiagen AllPrep DNA/RNA FFPE kit. An optimized 96-well-plate protocol was used for whole genome library construction (FFPET small gap protocol) and 16 to 18 libraries were multiplexed and pooled prior to capture using a custom Agilent SureSelect design targeting the aforementioned loci. Sequencing was performed on an Illumina HiSeq 2500. Predicted structural variants (SV) were generated with the computational tools DELLY and deStruct, and subsequently filtered based on read support. High confidence predictions were validated by either PCR-amplification of the genomic DNA spanning the breakpoint sequence followed by Sanger Sequencing, or customized FISH assays. Results: Library construction was successful in 68 cases (74%) and the mean coverage across the target regions for these libraries was above 600x with 97.7% of bases having an average depth of > 100x. After filtering, a list of 82 rearrangement events was generated consisting of 16 translocations (12 affecting CIITA and 4 affecting SOCS1), 44 deletions, 19 inversions and 3 duplications. Mapping of the translocation breakpoints revealed two distinct breakpoint cluster regions within intron 1 of CIITA and exon 2 of the SOCS1 gene. None of the exact breakpoints were recurrent and identification of rearrangement partner genes confirmed that CIITA and SOCS1 rearrange promiscuously. Furthermore, we demonstrate that some translocation events result in the generation of novel in-frame fusion transcripts (e.g. CIITA-PRDM16), warranting further functional characterization. Consistent with previous studies, intra-chromosomal alterations were detected in intron 1 of CIITA in48% of the cases while SOCS1 was shown to be frequently deleted (20% of cases).Overall, the concordance between FISH and targeted capture sequencing was 63%, reflecting the differences in sensitivity to detect large- and small-scale genomic events. Conclusions: Targeted capture sequencing, in conjunction with FISH, represents a valuable tool to explore the spectrum of genomic structural variants and rearrangement partner genes in archival lymphoma FFPET specimens. Future studies are required to address the functional impact of specific alterations and the utility of capture-sequencing-based assays for clinical decision-making. Disclosures Scott: NanoString Technologies: Patents & Royalties: named inventor on a patent for molecular subtyping of DLBCL that has been licensed to NanoString Technologies.
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 | 0,002 | 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 | 0,001 |
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 ».