A predictive model for bone marrow disease in cytopenia based on noninvasive procedures

Abstract

Bone marrow specimens are the core of the diagnostic workup of patients with cytopenia. To explore whether next-generation sequencing (NGS) could be used to rule out malignancy without bone marrow specimens, we incorporated NGS in a model to predict presence of disease in the bone marrow of patients with unexplained cytopenia. We analyzed the occurrence of mutations in 508 patients with cytopenia, referred for primary workup of a suspected hematologic malignancy from 2015 to 2020. We divided patients into a discovery (n = 340) and validation (n = 168) cohort. Targeted sequencing, bone marrow biopsy, and complete blood count were performed in all patients. Mutations were identified in 267 (53%) and abnormal bone marrow morphology in 188 (37%) patients. Patients with isolated neutropenia had the lowest frequency of both mutations (21%) and abnormal bone marrow morphology (5%). The median number of mutations per patient was 2 in patients with abnormal bone marrow morphology compared with 0 in patients with a nondiagnostic bone marrow morphology (P < .001). 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, a model combining mutational status and clinical data identified 34 patients (20%) without abnormal bone marrow morphology with a sensitivity of 100% (95% confidence interval: 93%-100%). Overall, we show that NGS combined with clinical data can predict the presence of abnormal bone marrow morphology in patients with unexplained cytopenia and thus can be used to assess the need of a bone marrow biopsy.

Graphical abstract

Figure 1.

Mutational landscape of 508 patients with cytopenia. (A) Distribution of mutations in 20 genes associated with myeloid malignancies in 508 cases of patients with cytopenia. (B) Frequency of mutations identified in the cohort with diagnosis indicated by the respective colors.

Figure 2.

Distribution of mutations and diagnoses in different age groups. (A) Proportion of patients with 1 or more mutations detected in the indicated age groups. (B) Proportion of patients with each of the shown diagnoses, stratified into 6 age groups.

Figure 3.

Distribution of the types of cytopenia that patients presented with. For each of the indicated diagnoses, the number of patients with 1, 2, or 3 cytopenias (ie, an isolated cytopenia, bicytopenia, or pancytopenia, respectively) is shown, with the colors of the bars representing the type of cytopenia.

Figure 4.

Odds ratios for the association between abnormal bone marrow morphology and the depicted variables derived from multivariable logistic regression. P value and OR with corresponding 95% CI for the association with abnormal bone marrow morphology is shown for each variable. Severe anemia: Hgb < 10 g/dL. Mild anemia: Hgb ≥ 10 - < 11.8 g/dL for females and Hgb ≥ 10 - < 13.4 g/dL for males. Severe thrombocytopenia: Platelets (plts) < 30 × 10⁹/L. Moderate thrombocytopenia: plts ≥ 30 × 10⁹/L - < 90 × 10⁹/L. Mild thrombocytopenia: plts ≥ 90 × 10⁹/L - < 150 × 10⁹/L. Severe neutropenia: Absolute neutrophil count (ANC) < 0.8 × 10⁹/L. Mild neutropenia: ANC ≥ 0.8 × 10⁹/L - < 1.8 × 10⁹/L.