Abstract IA42: Detecting and quantifying mutations associated with treatment resistance in aggressive lymphomas using ctDNA
Notice bibliographique
Résumé
Abstract A significant proportion of diffuse large B-cell lymphoma (DLBCL) patients treated with immunochemotherapy containing rituximab (R-CHOP) exhibit either primary or acquired treatment resistance. The advancement of therapeutics in the relapse setting has likely been encumbered by our limited understanding of the molecular features that underlie resistance to R-CHOP. Unfortunately, our knowledge of DLBCL genetics is mostly limited to analyses conducted on diagnostic tissue biopsies, which have not been exposed to the selective pressures imposed by therapy. Identifying genetic alterations that contribute to treatment resistance may reveal additional treatment options and lead to biomarkers allowing patients to be paired with appropriate treatments. Genetic subgroups are gaining popularity as a new strategy to implement precision medicine in DLBCL (1). The relevance of these and other biomarkers in the relapse setting remains unclear due to limited genetic exploration of relapsed and refractory DLBCL (rrDLBCL). Progress has been limited, in part, by the requirement of tissue biopsies collected after relapse. It is well established that quantitative genomic techniques such as digital PCR and targeted sequencing can be used to determine the proportion of tumor DNA in plasma from lymphoma patients (2). With a sufficiently broad panel, sequencing affords additional opportunities including the ability to identify subclonal structure and population dynamics over time. This presentation will discuss our recent analysis of a large collection of ctDNA primarily comprising DLBCL patients on various clinical trials (3). Targeted sequencing of these samples and comparison to exome data from a meta-cohort of previously characterized untreated DLBCL biopsies revealed six genes significantly enriched for mutations upon relapse. We found both TP53 and KMT2D were mutated in the majority of rrDLBCLs, and these mutations persisted in the dominant clone following relapse, suggesting a role in primary treatment resistance. By inferring subclonal dynamics, we observed recurrent patterns of clonal expansion and contraction following rituximab-based therapy, with MS4A1 mutations representing the only example of consistent clonal expansion. MS4A1 missense mutations within the transmembrane domains led to loss of CD20 expression in vitro, and patient tumors harboring these mutations lacked CD20 protein expression. Our analysis nominates TP53 and KMT2D mutation status as novel prognostic factors that may facilitate the identification of high-risk patients prior to therapy. Moreover, we have demonstrated the potential to identify tumors with loss of CD20 surface expression stemming from MS4A1 mutations. Implementation of noninvasive assays to detect such features of acquired treatment resistance may allow timely transition to more effective treatment regimens. In certain scenarios whole-exome sequencing (WES) or whole-genome sequencing (WGS) can be successfully applied to ctDNA, thereby allowing the identification of mutations, structural variation, and copy number changes. Low-pass sequencing of shotgun libraries can also be used to ascertain course estimates of ctDNA levels as well as the copy number landscape (4). Given the importance of copy number and structural alterations in the inference of genetic subgroups, these methods may allow the exploration of these groups and their stability over time. Through a series of illustrative examples, this presentation will explore the benefits of each of these techniques in the study of tumor evolution and acquired treatment resistance in DLBCL. References: 1. Morin RD, Scott DW. DLBCL subclassification: Divide and conquer? Blood 2020;135:1722–4. 2. Rossi D et al. The development of liquid biopsy for research and clinical practice in lymphomas: Report of the 15-ICML workshop on ctDNA. Hematol Oncol 2020;38:34–7. 3. Rushton CK et al. Genetic and evolutionary patterns of treatment resistance in relapsed B-cell lymphoma. Blood Adv 2020;4:2886–98. 4. Adalsteinsson VA et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 2017;8:1324. Citation Format: Nicole Thomas, Laura K. Hilton, Neil Michaud, Kevin Bushell, Ryan Rys, Michael Jain, Lois Shepherd, Marco A. Marra, John Kuruvilla, Michael Crump, Koren Mann, Sarit Assouline, Christian Steidl, Mark S. Cragg, David W. Scott, Nathalie Johnson, Ryan D. Morin, Christopher K. Rushton, Sarah E. Arthur, Miguel Alcaide, Matthew Cheung, Aixiang Jiang, Krysta M. Coyle, Kirstie L. S. Cleary. Detecting and quantifying mutations associated with treatment resistance in aggressive lymphomas using ctDNA [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr IA42.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».