A core of kinase-regulated interactomes defines the neoplastic MDSC lineage
Bibliographic record
Abstract
// 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".