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3195 A Single Next Generation Sequencing Assay for Detection of Driver Mutations, Translocations and Copy Number Alterations in Patients with Multiple Myeloma

Program: Oral and Poster Abstracts
Session: 652. Multiple Myeloma and Plasma Cell Dyscrasias: Clinical and Epidemiological: Poster II
Hematology Disease Topics & Pathways:
Plasma Cell Disorders, Diseases, Lymphoid Malignancies, Technology and Procedures, omics technologies
Sunday, December 11, 2022, 6:00 PM-8:00 PM

Cecilia Bonolo De Campos, PhD1*, James B Smadbeck, PhD2*, Mariano Arribas1*, Neeraj Sharma, PhD3, Gregory J Ahmann, BS4, Shaji K Kumar, MD5, A. Keith Stewart, MBChB6, Rafael Fonseca, MD7, P. Leif Bergsagel, MD1, Yan Asmann, PhD8, Linda B. Baughn, Ph.D9 and Esteban Braggio, PhD10

1Division of Hematology and Medical Oncology, Mayo Clinic, Scottsdale, AZ
2Center for Individualized Medicine, Biomarker Discovery, Mayo Clinic, Rochester, MN
3Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
4Division of Hematology/Oncology, Mayo Clinic Arizona, Scottsdale, AZ
5Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN
6University of Toronto, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
7Division of Hematology and Medical Oncology, Mayo Clinic, Phoenix, AZ
8Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
9Division of Laboratory Genetics, Mayo Clinic, Rochester, MN
10Division of Hematology and Medical Oncology, Mayo Clinic Arizona, Scottsdale, AZ

Multiple myeloma (MM) is a plasma cell malignancy characterized by several genetic abnormalities that impact risk stratification, outcome, and response to therapy; with fluorescence in situ hybridization (FISH) considered the gold-standard assay to detect the presence of chromosomal translocations and copy-number variations (CNV). Here, we evaluate the performance of a single clinically actionable targeted sequencing (NGS) assay for detecting cytogenetic abnormalities in addition to recurrent driver mutations in MM. Patient-derived bone marrow aspirates were obtained and enriched for CD138+ plasma cells. Targeted sequencing was performed using a 2.3 Mb custom capture panel covering 139 genes and regions surrounding MYC, immunoglobulin heavy chain (IgH), and immunoglobulin light chain kappa (IgK) and lambda (IgL) loci. Chromosomal translocations were identified using an in-house algorithm designed specifically for targeted capture DNA sequencing data analyses. Structural variants were called based on discordant read pairs as well as junction split reads, solving the challenges from both the overlapping paired-end reads due to the longer read lengths (150 bp) and the mapping ambiguities in the Ig regions. CNV were assessed using both on and off target reads, using a 200 kb bin size for segmentation covering the entire genome. Clinical annotations and FISH panel reports were abstracted. We sequenced 264 MM patient samples, 94% of which had available FISH information. A median of two somatic mutated genes per sample (range 0-13) was found. The most commonly mutated genes per sample were: KRAS (35%), NRAS (25%), FAM46C (15%), DIS3 (13%), BRAF (13%), ATM (11%), and TP53 (11%). Comparing NGS with FISH, we found a perfect concordance for the identification of t(4;14), t(6;14), t(14;16), and t(14;20); a 100% specificity and 97% sensitivity for t(11;14); and sensitivity >90% and an average specificity of 85% for trisomies of chromosomes 3, 7, 9, 11, and 15. The NGS approach was also able to detect gain(1q) and mono(13) with a sensitivity and specificity >90%, and del(17p) with a 60% sensitivity and 100% specificity. The reduced sensitivity for del(17p) detection was mostly due to focal deletions and low tumor purity. Because the TP53 gene is included in the custom capture panel, we increased the sensitivity by determining six separate regions of coverage using on-target probes, which enabled the detection of focal del(17p), increasing the sensitivity to 78%. The NGS approach also allowed the characterization of 16 (6%) samples with unavailable FISH. Of the 27 samples with del(17p) detected by FISH, NGS was able to identify TP53 mutations (i.e., biallelic inactivation) in 14 samples (52%). Due to the existence of multiple and promiscuous chromosomal partners involved in IgH translocation, the identification of the chromosomal partners is limited to the clinical FISH probe sets; in fact, 19 study samples presented abnormal IgH without a known partner by FISH. Our NGS assay was able to identify these partners, confirming MYC-IGH translocations in seven (37%) of these cases. Additionally, we detected MYC translocations with immunoglobulin light chains: 14 (5%) cases with IGK, and eight (3%) with IGL. Although IgL translocations, particularly IgL-MYC translocations, have been recently associated to poor prognosis, they are not routinely evaluated in FISH assays. The risk stratification and clinical utility of the NGS assay was demonstrated by the shorter overall survival observed in samples classified as high risk (mSMART 3.0; hazard ratio [HR]= 2.81; CI:1.89-4.17; p<0.0001), which was also observed with risk assessment by FISH (HR = 2.48; CI:1.72-3.58; p<0.0001). Therefore, in addition to the detection of clinically relevant somatic mutations, the targeted NGS approach showed robust detection of recurrent MM specific IgH translocations and CNV. Furthermore, the detection of relevant abnormalities not routinely assessed in most clinical labs, such as TP53 bi-allelic inactivation, IgL and MYC rearrangements, supports the use of this small, targeted panel as a clinically relevant, single molecular assay, for the comprehensive profiling of MM.

Disclosures: Kumar: Roche: Research Funding; Novartis,: Research Funding; Merck,: Research Funding; MedImmune/Astra Zeneca,: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE,: Research Funding; Adaptive,: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda,: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen,: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie,: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Oncopeptides: Other: Independent review committee. Stewart: GlaxoSmithKline: Consultancy; Sanofi: Consultancy; Tempus Health: Consultancy, Other: Stock ownership (not including stocks owned in a managed portfolio; Amgen: Consultancy; Janssen: Consultancy. Fonseca: AbbVie: Consultancy; Amgen: Consultancy; Bayer: Consultancy; BMS/Celgene: Consultancy; GSK: Consultancy; H3 Therapeutics: Consultancy; Janssen: Consultancy; Juno: Consultancy; Karyopharm: Consultancy; Kite: Consultancy; Merck: Consultancy; Novartis: Consultancy; Oncopeptides: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Pharmacyclics: Consultancy; Regeneron: Consultancy; Sanofi: Consultancy; Takeda: Consultancy; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; OncoMyx: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Oncopeptides: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Consultancy; Pfizer: Consultancy. Baughn: Roche-Genentech: Consultancy.

*signifies non-member of ASH