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Record W2126194508 · doi:10.18632/oncotarget.4746

A core of kinase-regulated interactomes defines the neoplastic MDSC lineage

2015· article· en· W2126194508 on OpenAlex
María Gato, Xabier Martínez de Morentin, Idoia Blanco‐Luquin, Joaquín Fernández‐Irigoyen, Isabel Zudaire, Thérèse Liechtenstein, Hugo Arasanz, Teresa Lozano, Noëlia Casares, A. Chaikuad, Stefan Knapp, David Guerrero‐Setas, David Escors, Grazyna Kochan, Enrique Santamaría

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOncotarget · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsInstitute of Infection and Immunity
FundersInstituto de Salud Carlos IIIGobierno de NavarraEusko JaurlaritzaBerrikuntza + Ikerketa + Osasuna Eusko FundazioaWellcome Trust
KeywordsKinaseCancer researchMAPK/ERK pathwayMyeloid-derived Suppressor CellMyeloidProtein kinase BPI3K/AKT/mTOR pathwayBiologyCancerImmunologyCell biologySignal transductionSuppressorGenetics

Abstract

fetched live from OpenAlex

// Maria Gato-Cañas 1, 2, * , Xabier Martinez de Morentin 3, * , Idoia Blanco-Luquin 1, 2, * , Joaquin Fernandez-Irigoyen 3, * , Isabel Zudaire 1 , Therese Liechtenstein 1, 2 , Hugo Arasanz 1, 4 , Teresa Lozano 5 , Noelia Casares 5 , Apirat Chaikuad 6 , Stefan Knapp 6, 7 , David Guerrero-Setas 8 , David Escors 1, 2 , Grazyna Kochan 9 , Enrique Santamaría 3 1 Immunomodulation group, Navarrabiomed-FMS, IdiSNA, Pamplona, Spain 2 Immunomodulation group, Division of Infection and Immunity, University College London, UK 3 Proteomics Unit, Navarrabiomed-FMS, Proteored-ISCIII IdiSNA, Pamplona, Spain 4 Hospital de Navarra, Department of Oncology, IdiSNA, Pamplona, Spain 5 Immunology and Immunotherapy Program, Center for Applied Medical Research, University of Navarra, IdiSNA, Pamplona, Spain 6 Structural Genomics Consortium (SGC), University of Oxford, Headington, UK 7 Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe-University, Frankfurt, Germany 8 Cancer Epigenetics group, Navarrabiomed-FMS, IdiSNA, Pamplona, Spain 9 Protein Production Unit, Navarrabiomed-FMS, IdiSNA, Pamplona, Spain * These authors have contributed equally to this work Correspondence to: David Escors, e-mail: descorsm@navarra.es Grazyna Kochan, e-mail: grazyna.kochan@navarra.es Enrique Santamaría, e-mail: esantamma@navarra.es Keywords: MDSC, proteomics, interactomes, kinases, therapeutic targets Received: May 25, 2015      Accepted: July 13, 2015      Published: July 23, 2015 ABSTRACT Myeloid-derived suppressor cells (MDSCs) differentiate from bone marrow precursors, expand in cancer-bearing hosts and accelerate tumor progression. MDSCs have become attractive therapeutic targets, as their elimination strongly enhances anti-neoplastic treatments. Here, immature myeloid dendritic cells (DCs), MDSCs modeling tumor-infiltrating subsets or modeling non-cancerous (NC)-MDSCs were compared by in-depth quantitative proteomics. We found that neoplastic MDSCs differentially expressed a core of kinases which controlled lineage-specific (PI3K-AKT and SRC kinases) and cancer-induced (ERK and PKC kinases) protein interaction networks (interactomes). These kinases contributed to some extent to myeloid differentiation. However, only AKT and ERK specifically drove MDSC differentiation from myeloid precursors. Interfering with AKT and ERK with selective small molecule inhibitors or shRNAs selectively hampered MDSC differentiation and viability. Thus, we provide compelling evidence that MDSCs constitute a distinct myeloid lineage distinguished by a “kinase signature” and well-defined interactomes. Our results define new opportunities for the development of anti-cancer treatments targeting these tumor-promoting immune cells.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.055
GPT teacher head0.287
Teacher spread0.232 · 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