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Enregistrement W2489660615 · doi:10.1182/blood.v126.23.3252.3252

Proxe: A Public Repository of Xenografts to Facilitate Studies of Biology and Expedite Preclinical Drug Development in Leukemia and Lymphoma

2015· article· en· W2489660615 sur OpenAlexaff
Mark A. Murakami, Alexandra Christodoulou, Amanda L. Christie, Tiffany DeSouza, Abner Louissaint, Una Vojinović, Raphael Koch, Loretta S. Li, Scott P. Kallgren, Prakash K. Rao, Johannes Köster, Raga Vadhi, Eilene Duberow, Elizabeth A. Morgan, Hongjun Wang, Samia S. Ahmed, Katharine L. Majewski, Marina Konopleva, Jérôme Tamburini, Alejandro Gutiérrez, Michelle A. Kelliher, Julia Etchin, Irmela Jeremias, Andrew P. Weng, Andrew L. Kung, Andrew A. Lane, Francine Garnache‐Ottou, Shai Izraeli, Eric D. Jacobsen, Ilene Galinsky, Richard M. Stone, Marian H. Harris, David M. Dorfman, Jon C. Aster, Henry W. Long, Lewis B. Silverman, David M. Weinstock

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésLymphomaContext (archaeology)Cancer researchBiologyImmunophenotypingTransplantationLeukemiaComputational biologyMedicineImmunologyFlow cytometryInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract To expedite the translation of biologic discoveries into novel therapeutics, there is a pressing need for panels of in vivo models that capture the molecular complexity of human disease. While traditional cell lines and genetically engineered mouse models are useful tools, they are insufficient to assess the broad diversity of human tumors within a context that recapitulates in situ biology. Patient-derived xenografts (PDXs), generated by transplanting primary human tumor cells into immune-deficient NOD.Cg-Prkdcscid/Il2rgtm1Wjl/SzJ (NSG) mice, surmount some of the limitations of these traditional platforms and have been increasingly utilized as tools for preclinical investigation. However, the infrastructure required to generate, bank, and characterize PDX models limits their availability to only a few investigators. To address this issue, we established a repository of PDX models of leukemia and lymphoma, which we have named the Public Repository of Xenografts (PRoXe). At the time of this writing, PRoXe contains 213 independent lines that have been passaged through mice once (P0), 123 of which have been repassaged in a second generation (P1) or further repassaged. The repository encompasses AML, B- and T-ALL, and B- and T-cell non-Hodgkin lymphoma (NHL) across a range of cytogenetic- and molecularly-defined subtypes (Table 1). PRoXe is extensively annotated with patient-level information, including demographics, phase of treatment, prior therapies, tumor immunophenotye, cytogenetics, and molecular diagnostics. PDX lines available for distribution are characterized by immunophenotyping, whole transcriptome sequencing (RNAseq), and targeted exon sequencing of ~300 genes. To confirm fidelity of engrafted tumors to their corresponding clinical samples, lymphomas were morphologically assessed in P0 mice by H&E and, when pathologic adjudication was required, by immunohistochemistry. Xenografted leukemias were compared to their original tumors immunophenotypically. Unsupervised hierarchical clustering was performed on 132 of these lines based on transcriptome sequencing data and demonstrated 94% concordance between classification of the PDX lines by RNA expression and by the annotated clinical-pathologic diagnoses. Discordant cases highlighted unusual variants, such as B-ALL with aberrant expression of myeloid markers and a follicular lymphoma that underwent blastic transformation in the mouse. Multiple lines have been luciferized and confirmed to home to bone marrow, spleen, and liver. Existing lines from PRoXe have already been shared with more than ten academic laboratories and multiple industrial partners. All of the data referenced here are freely available through a customized web-based search application at http://proxe.org, and lines can be requested for in vitro or in vivo experiments. We are actively expanding the size of PRoXe to allow for large pre-clinical studies that are powered to detect differences across genetically defined subsets. Thus, we are happy to host additional lines from outside investigators on PRoXe and thereby expand the availability of these valuable reagents. Finally, we have made the source code for PRoXe (in R Shiny) open-access, so that other investigators can establish their own portals. Table 1. WHO diagnostic entities encompassed within PRoXe at P1 or later, or P0 or later for B-ALLs. WHO Classification - number of lines per diagnostic entity AML, Other Myeloid, and Ambiguous Lineage [n=32] ALL [n=107] AML - recurrent gene mutations 6 B-ALL - NOS 44 AML - MDS-related changes 5 B-ALL - MLL-rearranged 11 AML - NOS 4 B-ALL - BCR-ABL 10 AML - MLLT3-MLL 2 B-ALL - hyperdiploidy 9 Acute myelomonocytic leukemia 1 B-ALL - TEL-AML1 8 Acute monocytic leukemia 1 B-ALL - E2A-PBX1 3 AML unable to classify 2 B-ALL unable to classify 1 Blastic plasmacytoid dendritic cell neoplasm 8 T-ALL 21 Mixed phenotype, MLL rearranged 1 B/myeloid acute leukemia 1 Myelodysplastic syndrome 1 Mature B cell neoplasms[n=11] Mature T and NK cell neoplasms [n=4] DBLCL - NOS 4 Angioimmunoblastic T-cell lymphoma 1 Mantle cell lymphoma 3 Adult T-cell leukemia/lymphoma 1 Extranodal marginal zone lymphoma 1 Extranodal NK/T-cell lymphoma 1 B-cell lymphoma, unclassifiable, with features intermediate between DLBCL and BL 3 SŽzary syndrome 1 Disclosures Konopleva: Novartis: Research Funding; AbbVie: Research Funding; Stemline: Research Funding; Calithera: Research Funding; Threshold: Research Funding. Etchin:Karyopharm: Research Funding. Lane:Stemline Therapeutics, Inc.: Research Funding. Stone:Abbvie: Consultancy; Novartis: Research Funding; Celator: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Agios: Consultancy; Sunesis: Consultancy, Other: DSMB for clinical trial; Merck: Consultancy; Karyopharm: Consultancy; Roche/Genetech: Consultancy; Pfizer: Consultancy; AROG: Consultancy; Juno: 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)0,007
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,003
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0030,004
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0230,013

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 Opus0,147
Tête enseignante GPT0,378
Écart entre enseignants0,231 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations3
Publié2015
Routes d'admission1
Résumé présentoui

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