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Record W4399318880 · doi:10.1016/j.celrep.2024.114260

Immunotherapeutic targeting of surfaceome heterogeneity in AML

2024· article· en· W4399318880 on OpenAlexafffund
Marie-Ève Bordeleau, Éric Audemard, Arnaud Métois, Louis Thérêt, Véronique Lisi, Azer Farah, Jean-François Spinella, Jalila Chagraoui, Ossama Moujaber, Léo Aubert, Banafsheh Khakipoor, Laure Mallinger, Isabel Boivin, Nadine Mayotte, Azadeh Hajmirza, Éric Bonneil, François Béliveau, Albert Feghaly, Geneviève Boucher, Patrick Gendron, Pierre Thibault, Frédéric Barabé, Sébastien Lemieux, Guillaume Richard‐Carpentier, Josée Hébert, Vincent‐Philippe Lavallée, Philippe P. Roux, Guy Sauvageau

Bibliographic record

VenueCell Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of TorontoUniversity Health NetworkCentre Hospitalier Universitaire Sainte-JustinePrincess Margaret Cancer CentreCentre hospitalier universitaire de QuébecHôpital Maisonneuve-RosemontUniversité LavalUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersFonds de Recherche du Québec - SantéInstitut de Valorisation des DonnéesCanada First Research Excellence FundUniversité de MontréalGovernment of CanadaGénome QuébecCanadian Institutes of Health ResearchAlliance de recherche numérique du CanadaGenome Canada
KeywordsImmunotherapyMyeloid leukemiaAntigenBiologyComputational biologyMyeloidPopulationCancer immunotherapyAntibodyImmunologyCancer researchMedicineImmune system

Abstract

fetched live from OpenAlex

Immunotherapy remains underexploited in acute myeloid leukemia (AML) compared to other hematological malignancies. Currently, gemtuzumab ozogamicin is the only therapeutic antibody approved for this disease. Here, to identify potential targets for immunotherapeutic intervention, we analyze the surface proteome of 100 genetically diverse primary human AML specimens for the identification of cell surface proteins and conduct single-cell transcriptome analyses on a subset of these specimens to assess antigen expression at the sub-population level. Through this comprehensive effort, we successfully identify numerous antigens and markers preferentially expressed by primitive AML cells. Many identified antigens are targeted by therapeutic antibodies currently under clinical evaluation for various cancer types, highlighting the potential therapeutic value of the approach. Importantly, this initiative uncovers AML heterogeneity at the surfaceome level, identifies several antigens and potential primitive cell markers characterizing AML subgroups, and positions immunotherapy as a promising approach to target AML subgroup specificities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2024
Admission routes2
Has abstractyes

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