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3680 A Next-Generation Sequencing-Based Prediction Model to Rule out Hematological Malignancy without a Bone Marrow Biopsy in Patients with CytopeniaClinically Relevant Abstract

Program: Oral and Poster Abstracts
Session: 637. Myelodysplastic Syndromes — Clinical and Epidemiological: Poster III
Hematology Disease Topics & Pathways:
Adults, Diseases, Myeloid Malignancies, Technology and Procedures, Study Population, molecular testing
Monday, December 13, 2021, 6:00 PM-8:00 PM

Dicte Træden, BA1,2,3*, Morten Tulstrup, MD1,2,3*, Jack Bernard Cowland, MS, PhD4*, Kirsten Grønbæk, Prof, MD, DMSc1,2,3, Mette Klarskov Andersen, MD, DMSc4 and Jakob Werner Hansen, MD, PhD1,2,3*

1Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
2Department of Hematology, Rigshospitalet, Copenhagen, Denmark
3The Danish Stem Cell Center (Danstem), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
4Department of Clinical Genetics, Rigshospitalet, Copenhagen, Denmark

Introduction

Peripheral blood cytopenias are often found in the elderly population, and establishing a diagnosis in patients with cytopenias can be challenging. Bone marrow biopsies are the core of the diagnostic work-up of patients with cytopenias, and next-generation sequencing (NGS) is mostly used as an add-on to assist in prognostication and subclassification once a diagnosis has been established. However, as somatic mutations are found in a variety of individuals, ranging from healthy to patients with malignant disease, the diagnostic utility of NGS is not yet clearly defined. Aiming to explore whether NGS could be used to rule out hematological malignancies prior to performing a bone marrow biopsy, we incorporated NGS in a model to predict presence of disease in the bone marrow of patients with cytopenia.

Methods

We analyzed the occurrence of mutations in 508 patients with cytopenias, referred for primary work-up for a suspected hematological malignancy from 2015-2020. NGS of genes associated with myeloid malignancies, bone marrow biopsy and complete blood count (CBC) were performed in all patients. Diagnoses were defined according to the WHO 2016 classification of myeloid neoplasms. We created a model utilizing only data that are obtainable without a bone marrow biopsy, including mutational status, age, sex, and hemoglobin, platelet and neutrophil levels. The patients were divided into a discovery (n = 340) and validation (n = 168) cohort by random sampling.

Results

Overall, mutations were identified in 267 (53%) patients, and abnormal bone marrow morphology was found in 188 (37%) patients. Idiopathic cytopenia of undetermined significance (ICUS) was the most frequent diagnosis (36%), followed by clonal cytopenia of undetermined significance (CCUS) (26%) and myelodysplastic syndrome (MDS) (18%). Among patients > 70 years, CCUS was most prevalent, accounting for 41% of patients. The most frequently mutated genes were TET2, DNMT3A, ASXL1 and SRSF2, each being mutated in at least 10% of patients. Patients presenting with pancytopenia showed the highest frequency of abnormal bone marrow morphology (66%), while patients with isolated neutropenia had the lowest frequency of both abnormal bone marrow morphology (5%) and mutations (21%). None of the patients with isolated neutropenia progressed to a hematological malignancy during the follow up period (median 2.5 years). The number of mutations per patient was significantly higher in patients with abnormal bone marrow morphology than in patients with a non-diagnostic bone marrow (P < 0.001), with the median number of mutations per patient being zero (IQR: 0-1) in patients with a non-diagnostic bone marrow and two (IQR: 1-3) in patients with abnormal bone marrow morphology. In a multivariable logistic regression, mutations in TET2, SF3B1, U2AF1, TP53 and RUNX1 were significantly associated with abnormal bone marrow morphology. In the validation cohort, the model combining mutational status and clinical data identified 34 patients without abnormal bone marrow morphology with a sensitivity of 100% (95%-CI: 93%-100%) and a negative predictive value of 100% (95%-CI: 90%-100%). Of the 34 patients identified by the model, 28 (82%) had ICUS and 6 (18%) had CCUS. No patients presenting with isolated anemia, pancytopenia or the combination of anemia and neutropenia were found among the 34 patients.

Conclusions

We show that NGS combined with clinical data can predict the presence of abnormal bone marrow morphology in patients with cytopenia with high sensitivity and thus that NGS can be used to assess the need of a bone marrow biopsy, which can potentially spare patients an unnecessary procedure. The findings of this study indicate that using NGS systematically in diagnostics can optimize the hematological work-up of patients with cytopenias.

Disclosures: Hansen: Bristol Myers Squibb: Consultancy.

*signifies non-member of ASH