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Enregistrement W3211995673 · doi:10.1182/blood-2021-151458

Dynamic Stromal Changes in Myelofibrosis Patients Pre/Post JAK Inhibition Is Revealed in Clinically Archived Bone Marrow Biopsies By Smooth Muscle Actin (SMA)-CD34 Dual Immunohistochemistry

2021· article· en· W3211995673 sur OpenAlexaff
Katelyn Wang, Iran Rashedi, James T. England, Rashmi S. Goswami, Larissa Liontos, Raheem Peerani, Anne Tierens, Michael J. Rauh, Vikas Gupta, Hubert Tsui

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueMyeloproliferative Neoplasms: Diagnosis and Treatment
Établissements canadiensPrincess Margaret Cancer CentreQueen's UniversityUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineMyelofibrosisPathologyStromal cellCD34Bone marrowPulmonary fibrosisFibrosisCancer researchInternal medicineOncologyStem cellBiology

Résumé

récupéré en direct d'OpenAlex

Abstract The natural history of BCR-ABL1 negative myeloproliferative neoplasms (MPNs) is progression towards an overt myelofibrotic (MF) phase with variable risk to develop secondary acute myeloid leukemia. Current treatments include Janus kinase inhibitors (JAKi) which can temporarily alleviate MF-related symptoms but are non-curative and most patients eventually progress to a more advanced stage. Given the negative prognostic impact of bone marrow fibrosis in MPNs and generally poor outcome post JAKi failure, it would be important to identify in situ biomarkers that address the initiation, perpetuation and early reversal of the fibrotic reaction. The current clinical standard for bone marrow fibrosis assessment involves reticulin/trichrome stains that detect relatively static extracellular matrix products rather than the fibrosis driving cells directly. To address this, we have developed a smooth muscle actin stromal-vascular (SMA-CD34) dual immunohistochemical (IHC) technique amenable to morphologic scoring and complemented with a CellProfiler image analysis pipeline. SMA was prioritized over other validated stromal IHC markers given work by others in experimental models demonstrating SMA+ myofibroblasts to be the differentiated output of critical fibrosis inducing Gli1+ 'driver' mesenchymal stem/progenitor cells in MPN. Herein, we demonstrate the feasibility of our translational approach using a clinically annotated cohort of MF patients from the Princess Margaret Cancer Centre MPN Registry. After selecting for high quality (>1.0 cm) paired pre and post JAKi biopsies amenable to image and transcriptome-based analysis, the pilot cohort was comprised of 13 cases with 38% high-risk, 54% intermediate-2 and 8% intermediate-1 risk by DIPSS. Driver mutations were JAK2 V617F (77%), CALR (15%) and other (8%). JAKi therapies included ruxolitinib (31%) + pelabresib (23%), momelotinib (15%), itacitinib (15%) and pacritinib (8%). The SMA-CD34 stromal assessment at baseline revealed distinct interstitial myofibroblast patterns and vascular perturbations not captured by conventional clinical hematopathology assessment (e.g. SMA+ dilated sinusoids). A SMA-CD34 scoring system was developed using a 4-point scale representing normal (0 pts), increased vascularity (1 pt), focal interstitial SMA (2 pts), multifocal interstitial SMA (3 pts) and diffuse SMA (4 pts). Scoring was then performed by blinded hematopathologists. A trend towards JAK2 mutated MF cases demonstrating higher SMA grade at baseline was noted. Interestingly, variable trajectories in SMA scores emerged following treatment with JAKi. Specifically, SMA signals had increased in 15%, decreased in 46% and were stable in 38% post-JAKi when using a morphologic SMA grading scheme. When compared to reticulin fibrosis, the severity of SMA signals had diverged in 1/3 of the cases (e.g. SMA grade decreased, reticulin grade stable). To further complement the SMA-CD34 morphologic grading, a CellProfiler image analysis pipeline was developed yielding a non-vessel associated normalized SMA area metric as a supervised correlate of the clinical SMA scoring system (R 2 = 0.68). Additional supervised and unsupervised bioinformatic approaches for clustering of relevant SMA-CD34 features including an algorithm that informs SMA spatial patterns with respect to niche elements such as arterioles (CD34+SMA+), sinusoids (CD34+) and adipocytes is in development. Lastly, Nanostring Fibrosis V2 panel was employed on a subset that met RNA concentration and quality metrics. Exploratory interpretation showed significant differentially expressed genes in pre vs. post JAKi specimens related to lipid metabolism such as ADIPOR1, SCD, ELOVL6 as well as the chemokine CXCL16. This may suggest a link between fatty acid metabolism and inflammatory differentiation along the SMA-vascular axis in the bone marrow modulated by JAKi treatment. SMA-CD34 IHC stratifies MF bone marrow biopsies differentially from standard WHO reticulin/trichome grading providing a practical formalin-fixed paraffin embedded (FFPE) tissue-based biomarker for assessing fibrosis related bone marrow niche elements from archived clinical samples. While our pilot numbers precluded statistical evaluation by JAKi-type, clinical response and NGS mutational profile at this time, further studies are underway to validate the SMA-CD34 signature on a larger MF cohort. Figure 1 Figure 1. Disclosures Gupta: Sierra Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS-Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Incyte: Honoraria, Research Funding; Constellation Pharma: Consultancy, Honoraria; Pfizer: Consultancy.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,259
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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