-Author name in bold denotes the presenting author
-Asterisk * with author name denotes a Non-ASH member
Clinically Relevant Abstract denotes an abstract that is clinically relevant.

PhD Trainee denotes that this is a recommended PHD Trainee Session.

Ticketed Session denotes that this is a ticketed session.

278 Analysis of Genomic Landscape of Large Granular Lymphocyte Leukemia Reveals Etiologic Insights

Program: Oral and Poster Abstracts
Type: Oral
Session: 621. Lymphoma—Genetic/Epigenetic Biology: Genetic and epigenetic profiling of malignant lymphomas
Hematology Disease Topics & Pathways:
Leukemia, Diseases, LGLL, Biological Processes, epigenetics, Lymphoid Malignancies, genomics
Saturday, December 5, 2020: 2:00 PM

Heejin Cheon, BSc1,2, Jeffrey C Xing, BS1,2,3,4, David S Chung, BS2,3*, Mariella F Toro, BA2,3*, Cait E Hamele, BS2,3*, Thomas L. Olson, PhD2,5*, David J Feith, PhD2,3*, Aakrosh Ratan, PhD6* and Thomas P. Loughran Jr., MD7,8

1Medical Scientist Training Program, University of Virginia School of Medicine, Charlottesville, VA
2University of Virginia Cancer Center, Charlottesville, VA
3Department of Medicine, Division of Hematology/Oncology, University of Virginia Health System, Charlottesville, VA
4Contributed equally, Co-first
5Department of Medicine, Division of Hematology/Oncology, University of Virginia, Charlottesville, VA
6Center for Public Health Genomics, University of Virginia, Charlottesville, VA
7Division of Hematology-Oncology; Program for T-Cell Lymphoma Research, University of Virginia, Charlottesville, VA
8Department of Medicine, Division of Hematology/Oncology, University of Virginia Cancer Center, Charlottesville, VA

Introduction: Large Granular Lymphocyte (LGL) leukemia is a rare lymphoproliferative disorder characterized by clonal expansion of either CD3+ cytotoxic T cells expressing T cell receptor (TCR) alpha beta or CD3- natural killer (NK) cells. A less frequent CD3+ T cell subtype expresses TCR gamma delta (GD). Here, we present the molecular landscape of known LGL types (T, NK, GD) from analysis of the largest patient cohort assembled to date. An integrative analysis of genomic datasets from all LGL subtypes is necessary to more precisely define the shared and unique etiology of this rare disorder.

Methods: We collected paired saliva and PBMC samples and related clinical information from 116 LGL leukemia patients after informed consent. The assembled cohort consisted of 93 T-LGL, 11 NK-LGL, and 12 GD T-LGL patients. Genomic analyses were performed on the leukemic (PBMC) and germline (saliva) samples after whole exome sequencing (WES) and transcriptome sequencing (RNAseq, PBMC only).

Results: Somatic mutations were detected in the previously described potential drivers STAT3 (n=56), TNFAIP3 (n=9), and PIK3R1 (n=4). We also identified somatic mutations in CDH8 (n=3) and CCL22 (n=4), which we postulate as putative drivers based on mutational clustering. CDH8 was mutated in all three LGL subtypes, but CCL22 somatic mutations were only observed in NK-LGL patients. We observed that STAT3 and CCL22 together account for 64% (7/11) of NK-LGL cases.

STAT3 is the most recurrently mutated gene in LGL leukemia, yet concurrent molecular and clinical features are incompletely defined. Interestingly, patients with STAT3 mutations have a higher mutational burden (P=0.0006) compared to those with wild-type (WT) STAT3. This effect is independent of the age of the patient, which correlates with the mutational burden (R=0.26, P=0.0039) and agrees with the finding that the dominant mutational signatures in this cohort exhibit clock-like properties. We also observed that patients with STAT3 mutations are enriched (P=0.0273) for additional mutations in chromatin modifier enzymes such as KMT2D, TET2, DNMT3A, and SETD1B (Figure 1). We found that ~10% of the samples exhibit broad somatic copy-number aberrations, and a patient with somatic mutations in STAT3 and KMT2D displayed high-level microsatellite instability. STAT3 mutations were also significantly associated with increased expression of genes involved in apoptosis, complement activation, and interferon cytokine signaling compared to STAT3 WT (FDR < 0.05).

Early-onset LGL patients with age 51 years or less (n=28), as defined by the bottom quartile of the cohort, displayed no differential enrichment of the somatic driver genes. Interestingly, the age of the patient was significantly associated with absolute neutrophil counts (ANC) (P = 0.0068), with younger patients exhibiting lower neutrophil counts, even after adjusting for the presence of STAT3 mutation, as it is associated with lower ANC. As neutropenia is a hallmark feature of LGL leukemia and often a trigger for initiating therapy, the association of young age with lower neutrophil counts and lower somatic mutational burden suggests other mechanisms may be involved.

Focusing on the germline variants, we found that 17 of the patients (14.6%) had at least one pathogenic or likely pathogenic germline variant with known oncogenic association as annotated using CharGer. 5 patients had pathogenic mutations in known tumor suppressors including FANCC (n=1), BRCA1 (n=1), PALB2 (n=1), MUTYH (n=1), and SDHA (n=1), while 1 patient had pathogenic mutations in ALK, a known oncogene.

Conclusions: We report on the genomic analyses done on whole exome and RNA-seq data from the largest cohort assembled for LGL leukemia to date. We show that the presence of STAT3 mutation is significantly associated with an increase in mutation burden and additional somatic mutations in chromatin modifiers, hinting at potential pathogenic mechanisms within STAT3 mutated patients. By combining LGL subtypes in our analysis, we were able to identify CDH8 as a putative driver that is present in T, NK, and GD subtypes. Additionally, we report CCL22 mutations specific to NK-LGL leukemia, however did not detect any subtype specific mutations in GD T-LGL. We found that about 15% of the patients carry at least one pathogenic germline variant with known oncogenic associations. These findings highlight emerging etiologic insights into this rare disorder.

Disclosures: Feith: Kymera Therapeutics: Membership on an entity's Board of Directors or advisory committees. Loughran: Keystone Nano: Membership on an entity's Board of Directors or advisory committees; Bioniz Therapeutics: Membership on an entity's Board of Directors or advisory committees; Kymera Therapeutics: Membership on an entity's Board of Directors or advisory committees; Dren Bio: Membership on an entity's Board of Directors or advisory committees.

Previous Abstract | Next Abstract >>
*signifies non-member of ASH