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3654 Using Next Generation Sequencing of Flow Cytometry CD Markers and Machine Learning As a Replacement to Flow Cytometry Analysis for the Diagnosis of Hematologic Neoplasms

Program: Oral and Poster Abstracts
Session: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Poster II
Hematology Disease Topics & Pathways:
Technology and Procedures, Pathology
Sunday, December 10, 2023, 6:00 PM-8:00 PM

Maher Albitar, MD1, Hong Zhang, MD1*, Andrew Ip, MD MSc2, Wanlong Ma, MT(ASCP)1*, James McCloskey, MD3, Katherine Linder, MD2*, Jeffrey Justin Estella1*, Jamie Koprivnikar, MD2*, Noa Biran, MD4, David S. Siegel, MD, PhD5, Ahmad Charifa, MD1*, Arash Mohtashamian, MD1, Andrew L Pecora, MD2 and Andre Goy, MD6

1Genomic Testing Cooperative, Irvine, CA
2Hackensack University Medical Center, Hackensack, NJ
3The John Theurer Cancer Center at Hackensack Meridian Health, Hackensack, NJ
4Multiple Myeloma Division, John Theurer Cancer Center, Hackensack Meridian Health, Hackensack, NJ
5Multiple Myeloma Division, Hackensack Univ. Med. Ctr., Hackensack, NJ
6Lymphoma Division, John Theurer Cancer Center, Hackensack Meridian Health, Hackensack, NJ

Introduction: Flow cytometry performs multi-parameter analysis of cells and analyzes surface and intracellular markers for accurate phenotypic characterization of a cell population. Flow cytometry is used extensively in the diagnosis and classification of various hematologic neoplasms. However, analysis of the generated data is time consuming and remains subjective, requiring special skill and experience. Furthermore, some diagnostic classes, such as myeloproliferative neoplasms (MPN) and myelodysplastic syndrome (MDS), are difficult to diagnose using flow cytometry. The RNA levels of the CD markers used in flow cytometry can be reliably quantified using next generation sequencing (NGS). However, when all cells are jointly sequenced, studying subpopulation of cells is lost, which hinders accurate diagnosis. However, machine learning algorithms are capable of multi-marker normalizing and compensate for the loss of subclonal analysis. To validate this assumption, we explored the potential of using the RNA levels of 30 CD markers along with a machine learning algorithm in the differential diagnosis between various types of hematologic neoplasms.

Methods: RNA was extracted from fresh bone marrow and peripheral blood samples from 172 acute myeloid leukemia (AML), 369 normal control, 68 MPN, 218 MDS, 93 acute lymphoblastic leukemia (ALL), 74 chronic lymphocytic leukemia (CLL), 38 mantle cell lymphoma, and 83 multiple myeloma cases. The samples were consecutive and collected without selection. RNA sequencing was performed using a targeted hybrid capture panel that included CD1A, CD2, CD3D, CD3E, CD3G, CD4, CD5, CD7, CD8A, CD8B, CD10, CD14, CD19, CD20, CD22, CD33, CD34, CD38, CD40, CD44, CD47, CD68, CD70, CD74, CD79A, CD79B, CD81, CD138, CD200, and CD274 genes. Salmon v1.4.0 software was used for expression quantification (TPM). Machine learning algorithm (random forest) was used for classifying diseases. Two thirds of samples were used for training the random forest algorithm and one third was used for testing.

Results:

While frequently a diagnosis can be made by simply inspecting the RNA levels of various CD markers, machine learning is needed when the fraction of the neoplastic cells is low. Using machine learning (random forest), diagnosis of most hematologic neoplasms was achieved with high sensitivity and specificity in the testing set. Area under the curve (AUC) was at 0.972 (95% CI: 0.950-0.994) for AML vs. normal, 0.936 (95% CI: 0.898-0.974) for normal vs. MM, 0.965 (95% CI: 0.909-1.00) for mantle vs. CLL, 0.962 (95% CI: 0.907-1.00) for CLL vs. ALL, 0.935 (95% CI: 0.866-1.00) for CLL vs. normal, and 0.964 (95% CI: 0.927-1.00) for AML vs. ALL. Diseases that are difficult to diagnose by routine flow cytometry were diagnosed by RNA expression and machine learning at acceptable accuracy. For example, AUC was at 0.761 (95% CI: 0.689-0.834) for MDS vs. normal, 0.831 (95% CI: 0.762-0.901) for MDS vs. AML, 0.888 (95% CI: 0.822-0.954) for MDS vs. MPN, and 0.785 (95% CI: 0.698-0.872) for MPN vs. normal.

Conclusions:

This data demonstrates that NGS quantification of RNA from 30 CD markers when combined with machine learning is adequate for reliable diagnosis of various types of hematologic neoplasms. This approach can provide valuable information to distinguish between MPN, MDS, and normal bone marrow that flow cytometry cannot provide. Furthermore, this technology can be automated and less susceptible to human error and practically can be used as a replacement to routine flow cytometry analysis.

Disclosures: McCloskey: Incyte: Speakers Bureau; Blueprint Medicines: Consultancy; Amgen: Speakers Bureau; BluePrint Health: Speakers Bureau; BluPrint Oncology: Honoraria; Bristol-Myers Squibb/Pfizer: Consultancy, Honoraria, Speakers Bureau; Jazz Pharmaceuticals: Speakers Bureau; Novartis: Consultancy; Takeda: Speakers Bureau; Stemline Therapeutics: Speakers Bureau. Koprivnikar: Alexion: Consultancy; GSK: Consultancy; Novatis: Consultancy; Apellis: Consultancy. Biran: Abbvie: Honoraria; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genomic Testing Cooperative: Divested equity in a private or publicly-traded company in the past 24 months; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Other: spouse of employee; GSK: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Siegel: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celularity Scientific: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Goy: AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Other: Steering Committee, Research Funding; Medscape: Consultancy; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Steering Committee, Research Funding; MorphoSys: Research Funding; Clinical Advances in Hematology & Oncology: Consultancy; Xcenda: Consultancy, Honoraria; Karyopharm: Research Funding; Genentech: Research Funding; Michael J. Hennessey: Consultancy, Honoraria; Acerta: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; OncLive Peer Exchange: Honoraria; Alloplex: Current holder of stock options in a privately-held company, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie/ Pharmacyclics LLC: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Steering Committee, Research Funding; COTA Healthcare: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership Role; Genomics Testing Cooperative LLC: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership Role; OMI: Current Employment; Bristol Myers Squibb/Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Infinity: Research Funding; Hoffman la Roche: Consultancy, Honoraria, Research Funding; Resilience: Current holder of stock options in a privately-held company; Seagen: Research Funding; Physicians Education Resource, LLC: Consultancy, Honoraria, Other: travel, accommodations, and expenses; Constellation: Research Funding; Verastem: Research Funding; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other; Novartis: Consultancy, Honoraria; Regional Cancer Care Associates, OMI: Current Employment, Research Funding; Pharmacyclics LLC, an AbbVie Company: Other: Steering Committee, Research Funding; Practice Update Oncology: Consultancy, Honoraria.

*signifies non-member of ASH