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4807 Accurate Detection of Clinically Actionable Copy Number Variants in Diverse Hematological Neoplasms By Routine Targeted Sequencing: A Comparative Performance Study

Program: Oral and Poster Abstracts
Session: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Poster III
Hematology Disease Topics & Pathways:
Research, Lymphoid Leukemias, MDS, CLL, Plasma Cell Disorders, bioinformatics, Chronic Myeloid Malignancies, Diseases, Lymphoid Malignancies, computational biology, Myeloid Malignancies, emerging technologies, Technology and Procedures, profiling
Monday, December 12, 2022, 6:00 PM-8:00 PM

Alicia Palomino Mosquera, MD1, Hitomi Hosoya, MD, PhD2, Michael C. Jin2*, Mohammad Shahrokh Esfahani, PhD2*, Joseph Schroers-Martin, MD2, Brian Sworder, MD, PhD2, Chih Long Liu, PhD2*, Elizabeth Spiteri3*, Yasodha Natkunam, MD, PhD4, James L Zehnder, MD3, Henning Stehr, PhD3*, David M. Kurtz, MD, PhD2 and Ash A. Alizadeh, MD, PhD5

1Divisions of Oncology and Hematology, Department of Medicine, Stanford University School of Medicine, Emerald Hills, CA
2Divisions of Oncology and Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, CA
3Department of Pathology, Stanford University School of Medicine, Stanford, CA
4Stanford University School of Medicine, Stanford, CA
5Divisions of Oncology and Hematology, Department of Medicine, Stanford University School of Medicine, San Mateo, CA

Background: Accurate genotyping of Copy Number Variations (CNVs) is an essential part of the clinical evaluation of diverse hematologic malignancies. Conventional karyotyping and FISH are current gold standards to assess CNVs, but both have some limitations. Next-generation targeted sequencing panels are commonly incorporated into current clinical practice in most cancer centers. We examined the utility of CANARy (Copy number ANomaly Assessment and Recovery), a CNV profiling method for three hematological malignancies using targeted panel sequencing data.

Patients and Methods: We studied patients with myelodysplastic syndrome (MDS), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), and plasma cell disorders (PCD) identified from a cohort of 4752 hematological specimens profiled at Stanford University by conventional cytogenetics and next-generation sequencing (NGS) between 2018 and 2021. We curated a cohort of 200 patients diagnosed with MDS (n=72), CLL (n=46), and PCDs including MM, AL amyloidosis, and MGUS (PCD, n=82), along with 50 cytogenetically normal controls. Specimens were bone marrow aspirates, peripheral blood, or FFPE tissue. All samples were characterized by conventional metaphase karyotyping and/or FISH and we focused on the analysis of clinically relevant CNV (-5q, -7/-7q, -20q, +8 for MDS; -11q22, -13q14, -17p13, +12 for CLL; and +1q21, -1p32, -13q14, -17p.13 for PCD). The specimens separately had clinical NGS (164 genes, Heme-STAMP) performed no more than four weeks from standard cytogenetic analysis. We applied multiple CNV enumeration tools to sequencing data for CNV genotyping at these loci, including our recently developed method CANARy (Chabon et al, Nature 2020). CANARy is a read depth-based, genome-wide CNV caller from targeted sequencing data that combines information from on-target and off-target fragments to assign a final Z-score and copy number index to a set of pre-defined genomic regions. We benchmarked CANARy alongside three others publicly available CNV callers: CNVkit (Talevich et al, PLoS Comput Biol. 2016), ichorCNA (Adalsteinsson et al, Nat Commun. 2017), and WISECONDOR (Straver et al, Nucleic Acids Res. 2014). All tools were run using the default parameters and following author recommendations. We calculated the accuracy and other statistics parameters for each CNV caller, considering karyotype/FISH as the true reference. We estimated tumor fraction using two strategies: the variant allele fraction (VAF) of disease-related driver mutations and using the ichorCNA estimate.

Results: A total of 211 CNVs were identified by karyotype and/or FISH, with disease-specific distribution summarized in Fig. A. CANARy detected 148/211 (70.1%), CNVkit 88/211 (41.7%), ichorCNA 116/211 (55%) and WISECONDOR 158/211 (74.9%). The overall accuracy for CANARy, CNVkit, ichorCNA and WISECONDOR was 86.3%, 81.8%, 84.3% and 83.1% respectively. CANARy had both the highest overall accuracy (86.3%) and overall F1-score (0.73) (Fig.B). WISECONDOR had the highest number of false positives (83 vs. CANARy = 48). Interestingly, all CNV tools performed better in MDS specimens, with 92% accuracy for CANARy in MDS samples, likely related to the higher neoplastic purity of these samples. The median ichorCNA tumor fraction estimates for MDS, CLL and PCD were 22% (0-99%), 14% (0-70%), and 7% (0-94%), respectively. In addition, CNV calling was inferior in PCD tumors than other tumor types. Within CLL, -11q22 and -13q14 lesions were more challenging to detect with any of the four methods, with CANARy sensitivity of 50% and 31.3% respectively, likely related to the smaller minimal deletion regions of these lesions. The 17p deletion in CLL cases, known for its poor prognostic impact, was best identified by CANARy; with a 100% sensitivity and a 97.6% specificity. The only non-concordant result for -17p was a false positive that was detected by all 4 NGS CNV genotypers but not FISH, possibly suggesting a false negative FISH result.

Conclusions: CANARy accurately identifies clinically significant CNVs in common hematologic neoplasms and could thus serve as a complementary approach to the current cytogenetic evaluation. CANARy uses data already generated from targeted sequencing panels and therefore does not impose additional work or cost. Low tumor fraction could be a limitation for CNV calling.

Disclosures: Palomino Mosquera: Janssen: Research Funding. Shahrokh Esfahani: Foresight Diagnostics: Consultancy. Sworder: Foresight Diagnostics: Consultancy. Kurtz: Roche: Consultancy; Adaptive Biotechnologies: Consultancy; Foresight Diagnostics: Consultancy, Current equity holder in private company, Patents & Royalties; Genentech: Consultancy. Alizadeh: Cibermed Inc: Consultancy, Current equity holder in private company, Patents & Royalties; Roche: Consultancy; Syncopation: Current equity holder in private company, Patents & Royalties; Gilead: Consultancy, Divested equity in a private or publicly-traded company in the past 24 months, Patents & Royalties; Karyopharm: Consultancy; Adaptive Biotechnologies: Consultancy; BMS: Consultancy, Research Funding; Genentech: Consultancy; Foresight Diagnostics: Consultancy, Current equity holder in private company, Patents & Royalties.

*signifies non-member of ASH