Abstract 939: Genomic characterization of PMBCL, cHL and DLBCL utilizing tissue and liquid biopsies
Notice bibliographique
Résumé
Abstract Background: Primary mediastinal large B-cell lymphoma (PMBCL), arising from thymic medullary B cells, shares molecular features with classic Hodgkin lymphoma (cHL), including activation of JAK/STAT and Nuclear Factor Kappa B (NF-ƙB) pathways, as well as PD-L1 mediated immune evasion. Much of the molecular characterization relied on availability of tissue samples. Cell-free DNA (cfDNA) has emerged as a promising non-invasive approach for molecular profiling in lymphomas. In this study, we characterized the molecular features of PMBCL and cHL in the Chinese population utilizing both tissue-based and liquid biopsies. Methods: Tissue and/or plasma samples from a total of 35 PMBCL patients, 52 cHL patients and 81 DLBCL patients that were subjected to targeted next-generation sequencing (NGS) using a 475 cancer-related gene panel were included in the analysis. Results: Analysis of matched tumor and plasma samples from 38 cHL patients revealed 152 (28.10%) overlapping genetic alterations, with a higher number of mutations detected in the plasma compared to tissue (Total=472 vs. 221; Median=11 vs. 3 per patient, P<0.01). Of these, 320 (59.15%) were unique to the plasma, whereas 69 (12.15%) were only found in the tissue. Mutant allele frequencies (MAFs) were also lower in the tissue than in the plasma, with a median of 1.71% vs 2.99% (P<0.01). In 18 PMBCL patients, who had paired plasma and tumor samples, similar number of genetic alterations were detected (Total=347 vs. 411; Median=19.5 vs. 21 per patient, P=0.09). Of these, 273 (56.29%) alterations were shared variants, 74 (15.28%) were unique to the plasma sample and 138 (28.45%) were unique to the tissue. MAFs were higher in the tissue compared with the plasma (23.35% vs. 8.78%, P<0.01). Comparisons of mutational profiles among PMBCL, cHL, and DLBCL showed similar mutational profiles between PMBCL and cHL. The most frequently detected genetic alterations in PMBCL and cHL were STAT6 (68.57% vs. 40.38%), SOCS1 (57.14% vs. 57.69%), ACTB (48.57% vs. 23.08%), B2M (48.57% vs. 42.31%), and TNFAIP3 (34.29% vs. 40.38%). On the contrary, the frequencies of STAT6 (68.57% vs. 6.17%, P <0.01), SOCS1 (57.14% vs. 18.52%, P<0.05), and ACTB (48.57% vs. 9.88%, P<0.05) were markedly different comparing PMBCL and DLBCL. Meanwhile, the more common genetic alterations in DLBCL, such as MYD88 (33.33% vs. 0.00%, P<0.01), BCL6 (33.33% vs. 11.43%, P=0.06), BCL2 (20.99% vs. 2.86%, P<0.05), and CDKN2A (24.69% vs. 2.86%, P<0.05) were rarely detected in PMBCL patients. Conclusion: Our findings support that ctDNA in PMBCL and cHL may be an attractive source of genetic materials to assess tumor genomics and guide treatment decisions, especially in cHL with only 1% Hodgkin and Reed/Sternberg (HRS) cells. We also demonstrate for the first time in the Chinese population that PMBCL and cHL shared comparable mutational profiles, which were different from that of DLBCL. Citation Format: Xiaonan Wang, Lu Shen, Liuqing Zhu, Jiani C. Yin, Haimeng Tang, Yang Shao. Genomic characterization of PMBCL, cHL and DLBCL utilizing tissue and liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 939.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 ».