ArticleScientific reports2017
Logic programming reveals alteration of key transcription factors in multiple myeloma.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00907452 (Pharmacogenomic Study to Predict Survival, Best Response and Toxicity in Newly Diagnosed Myeloma Patients Above the Age of 65 Treated With Either a Combination of Melphalan-prednisone-thalidomide or Lenalidomide-dexamethasone), which is not on this map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pharmacogenomic Study to Predict Survival, Best Response and Toxicity in Newly Diagnosed Myeloma Patients Above the Age of 65 Treated With Either a Combination of Melphalan-prednisone-thalidomide or Lenalidomide-dexamethasone
Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- The Genetic and Molecular Drivers of Multiple Myeloma: Current Insights, Clinical Implications, and the Path Forward.Pharmacogenomics and personalized medicine · 2024Review
- Article
- Current perspectives on interethnic variability in multiple myeloma: Single cell technology, population pharmacogenetics and molecular signal transduction.Translational oncology · 2022Article
- The Role of AP-1 Transcription Factors in Plasma Cell Biology and Multiple Myeloma Pathophysiology.Cancers · 2021Review
- Clinical prognostic implications of EPB41L4A expression in multiple myeloma.Journal of Cancer · 2020Article
- Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma.Disease markers · 2020Article
- High expression of CHML predicts poor prognosis of multiple myeloma.Journal of Cancer · 2019Article
- Prediction and prognostic significance of BCAR3 expression in patients with multiple myeloma.Journal of translational medicine · 2018Article
- Article
- Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.Journal of medical systems · 2018Article
- Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.BMC systems biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 8 institutions in 2 countries.
Funding
Abstract
Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.