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Record W3097593597 · doi:10.1182/blood-2020-140651

Plasmablastic Lymphoma: Synergism between<i>Wnt/B-Catenin</i>and the<i>RAS S</i>ignalling Molecules May Identify Potential Targets for Therapy

2020· article· en· W3097593597 on OpenAlexaff
Hamza Kamran, Ariz Akhter, Hassan Rizwan, Meer-Taher Shahbani-Rad, Ghaleb Elyamany, Douglas A. Stewart, Adnan Mansoor

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWnt signaling pathwayCancer researchLymphomaDiffuse large B-cell lymphomaBiologyCateninCarcinogenesisB-cell lymphomaCancerGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Background:Plasmablastic lymphoma (PBL), is a rare aggressive B-cell lymphoma that shares many overlapping characteristics with activated B-cell type diffuse large B-cell lymphoma (ABC-DLBCL) and multiple myeloma (MM). High expression ofWnt/β-cateninpathway molecules has been linked with several aspects of tumour biology in ABC-DLBCL and MM. In MMWnt/β-cateninplay critical role in chemoresistance, while high FOXP1 in ABC-DLBCL exert poor prognosis through up-regulation of theWnt/β-cateninsignalling pathway. There is strong evidence that enhanced crosstalk between the Wnt/β-catenin andRASpathways impact tumorigenesis and metastasis of cancerous stem cells in various cancers. In breast cancer; targeting theWnt/β-cateninand RAS pathways with small molecular inhibitors have shown effective results. The role of the Wnt/β-catenin signalling pathway and its corresponding linkage toRASsignalling molecules remained unknown in PBL. This pilot study provides preliminary data in relation to expressionof Wnt/β-cateninandRASpathways molecules in PBL in contrast to ABC-DLBCL. Method:FFPE RNA from diagnostic tissue samples in PBL patients (n=31) were compared with ABC-DLBCLs (n=18) patients for keyWnt/β-cateninandRASpathway molecules expression, utilizing nCounter (NanoString Technologies) platform. Qlucore Omics Explorer software was employed with defined criteria (fold change >2.0; p<0.01 and q <0.05) for statistical analysis. Gene Set Enrichment Analysis (GSEA) from 5 publicly available gene data sets was used to analyze the expression of other accompanying pathways. Result:We identified significant differential expression of mRNA related toWnt/β-cateninsignalling between ABC-DLBCL and PBL (Figure 1). Expression ofWnt/β-cateninsignalling inhibitors (CXXC4, SFRP2, and DKK1) were significantly higher among PBL compared to ABC-DLBCL (8.12-3.22 log fold difference). In divergence, molecules linked withWnt/β-cateninsignalling activation were elevated in PBL when compared to ABC-DLBCL (FZD3andWNT10B). The GESA analysis proved that the RAS pathway was significantly up-regulated in PBL patients compared to ABC-DLBCL. In particular, the expression of crucial RAS pathway genes such asNRAS, RAF1, SHC1, andSOS1was significantly up-regulated in PBL patients when compared to ABC-DLBCL patients (Figure 2). Conclusion:Our data suggest that the expression ofWnt/B-catenintarget genes and ligands are enhanced in PBL patients along with the up-regulation of theRASsignalling pathway molecules as compared to ABC-DLBCL. The heightened expression of crucialWnt/B-catenininhibitors does not down-regulate the Wnt/β-catenin signalling. We anticipate that the combined down-regulation of theWnt/ β-cateninandRASpathways by targeting its key members (RAF1, NRAS, FZD3) may serve to contain tumor progression in PBL, hence impacting prognosis. Disclosures Stewart: Gilead:Honoraria;Sandoz:Honoraria;Teva:Honoraria;Amgen:Honoraria;Celgene:Honoraria;Abbvie:Honoraria;Roche:Honoraria;Janssen:Honoraria;Novartis:Honoraria;AstraZeneca:Honoraria.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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