<i>SH2B3</i> Mutations in Patients with <i>JAK2</i>-Negative Erythrocytosis
Bibliographic record
Abstract
Introduction: Erythrocytosis is a common reason for referral to Hematology clinics. Molecular testing for JAK2 mutations, often by next-generation sequencing (NGS), has become part of the diagnostic workup of patients with erythrocytosis, increasing the molecular characterization of this previously undifferentiated patient population. A recent study (Bhai et al. 2022) found that 4% of patients referred for JAK2-negative erythrocytosis harboured variants in the SH2B3 gene, which encodes the LNK protein, a known negative regulator of JAK2/STAT5 signaling. The objective of this study was to characterize a cohort of patients with JAK2-negative erythrocytosis with SH2B3 mutations, evaluating clinical presentation, hematologic parameters, as well as cardiovascular and thrombotic outcomes. Methods: All adult patients referred to the London Health Sciences Centre, a tertiary care centre in Southwestern Ontario, for assessment of erythrocytosis (hemoglobin >160g/L in women; >165g/L in men) between January 1, 2018 and December 31, 2022 and who underwent NGS testing were reviewed. NGS testing was performed using the Oncomine Myeloid Research Assay (Thermofisher Scientific, MA, USA). Patients with JAK2, CALR, MPL, or BCR::ABL1 mutations were excluded. Patients without JAK2 mutations ( JAK2-negative) with variants in SH2B3 were further characterized. Clinical variables included risk factors for secondary erythrocytosis and occurrence of arterial/venous thrombotic events. Laboratory variables included complete blood count and white cell differential. Results: Sixteen patients with JAK2-negative erythrocytosis met inclusion criteria with mutations in SH2B3. All mutations in SH2B3 were characterized as Tier III variants (Variants of Uncertain Significance). Laboratory and clinical parameters in this patient population are presented in Table 1. Mean hemoglobin was 181 g/L, hematocrit 0.54 L/L, leukocyte count 7.2 x10 9 cells/L, and platelets 229 x10 9 cells/L. Secondary causes of erythrocytosis were identified in 13 (81%) patients: 10 (63%) reported active smoking or had a smoking history, 4 (25%) had obstructive sleep apnea, 2 (13%) had chronic obstructive pulmonary disease, 1 (6%) had a right-to-left cardiac shunt, 4 (25%) were on an SGLT2 inhibitor, and 1 (6%) reported testosterone use. Of note, one patient had a history of stroke and another had a myocardial infarction both of which predated the development of erythrocytosis and NGS testing. One patient had a recurrent deep vein thrombosis (DVT) at the time of NGS testing. Full molecular data and comparative analysis with other cases of JAK2-negative erythrocytosis will be presented at ASH. Discussion: SH2B3 mutations have been identified in rare cases of congenital erythrocytosis and implicated in the pathogenesis of erythropoiesis. To our knowledge, this is the largest cohort of patients with JAK2-negative erythrocytosis with variants in SH2B3 reported to date. This cohort showed similar laboratory parameters and prevalence of secondary risk factors for erythrocytosis as previously reported in the general population of patients with JAK2-negative erythrocytosis at our centre (Chin-Yee et al. 2022). Thrombotic outcomes were also similar, and lower than patients with polycythemia vera. Given the high prevalence of potential secondary causes of erythrocytosis in our cohort we cannot confirm that SH2B3 mutations are causally implicated in elevated hemoglobin in these cases. Nonetheless, we cannot exclude that SH2B3 variants may confer an additional risk factor for erythrocytosis in this patient population. Further research is required to elucidate the potential impact of SH2B3 variants in patients with JAK2-negative erythrocytosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".