<i>SH2B3</i> Mutations in Patients with <i>JAK2</i>-Negative Erythrocytosis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».