Abstract IA42: Detecting and quantifying mutations associated with treatment resistance in aggressive lymphomas using ctDNA
Authors: Thomas Nicole , Hilton Laura K. , Michaud Neil , Bushell Kevin , Rys Ryan , Jain Michael , Shepherd Lois , Marra Marco A. , Kuruvilla John , Crump Michael , Mann Koren , Assouline Sarit , Steidl Christian , Cragg Mark S. , Scott David W. , Johnson Nathalie , Morin Ryan D. , Rushton Christopher K. , Arthur Sarah E. , Alcaide Miguel , Cheung Matthew , Jiang Aixiang , Coyle Krysta M. , Cleary Kirstie L. S. 
Published: December 2020

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.