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2016· article· en· W4234150380 on OpenAlexaff
Ana Lopez-Campistrous, Esther Ekpe Adewuyi, Martin Benesch, Yi‐An Ko, Raymond Lai, Aducio Thiesen, James Dewald, P Wang, Karen Chu, Sunita Ghosh, David Williams, Larissa J. Vos, David N. Brindley, Tara McMullen, Guodong Fu, Olena Polyakova, Adam H. Hsieh, Christine Macmillan, Ranju Ralhan, P. G. Walfish, Rozita Bagheri‐Yarmand, Michelle D. Williams, Elizabeth G. Grubbs, Robert F. Gagel, Allan Carlé, Peter Laurberg, Rudi Steffensen, Jens Faber, Birte Nygaard, Naoko Arata, Shinya Ito, Eisuke Inoue, Yuko Ohashi, H Onose, S. Kubota, Kenjiro Kosaki, Rika Kosaki, Junichi Tajiri, Yoh Hidaka, Shuji Fukata, Naoko Momotani, Hiroyuki Yoshikawa, Atsuko Murashima, M. Schlumberger, Isabelle Borget, Bogdan Catargi, Désirèe Deandreis, Slimane Zerdoud, Stéphane Bardet, Darian Rusu, Yann Godbert, Laurence Leenhardt, Claire Schvartz, Pierre Véra, Olivier Morel, D. Benisvy, Claire Bournaud, M-E Toubert, A. Kelly, Sophie Leboulleux, Shiping Chen, Xiaoyang Zhou, Huijuan Zhu, Hongbo Yang, Fengying Gong, Lei Wang, M. Zhang, Ying Jiang, Yan Chen, J Li, Qing Wang, S Zhang, Hui Pan

Bibliographic record

VenueThyroid · 2016
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

Dedifferentiation of follicular cells is a central event in resistance to radioactive iodine and patient mortality in papillary thyroid carcinoma (PTC).We used six different thyroid cell lines, human primary cultures and SCID mouse xenograft models to explore how PDGFRa alters follicular cell differentiation as defined by TTF1, Pax8 expression and iodide transport.Confocal microscopy, invasion assays and 3D culture defined TTF1 subcellular targeting and cell phenotype.Immunohistochemistry on patient tissue arrays (n = 287) with matching clinical data (follow-up period 11 years) were used to map out recurrence rates as a function of PDGFRa and cytoplasmic TTF1 expression.We reveal that PDGFRa has a unique role in driving aggressive disease in PTC through a double-hit on follicular cells.First, PDGFRa specifically drives dedifferentiation by disrupting the transcriptional activity of TTF1.PDGFRa activation dephosphorylates TTF1 consequently shifting the localization of this transcription factor from the nucleus to the cytoplasm.Disrupted TTF1 function creates an invasive phenotype that lacks thyroglobulin production and sodium iodide symporter function.Patients exhibiting PDGFRa at time of diagnosis are three times more likely to exhibit nodal metastases and are 18 times more likely to recur within 5 years than those patients lacking PDGFRa expression.Moreover, high levels of PDGFRa and low levels of nuclear TTF1 predict resistance to radioactive iodine therapy.Secondly, PDGFRa transforms PTC cell lines as documented by cytoskeletal rearrangement, increased migratory potential, the formation of invadopodia and through the epithelial-mesenchymal transition by impressive augmentation of Snail and Slug expression.Crenolanib, a small molecule inhibitor of PDGFRa reverses these phenotypic changes.Moreover, we demonstrate in SCID xenografts that crenolanib treatment restores iodide transport and decreases tumor burden by more than 50%.We demonstrate that PDGFRa drives PTC metastases by disrupting TTF1 function and mediating the epithelial-mesenchymal transition.Focused inhibition of PDGFRa, combined with radioactive iodine, represents a new avenue for treating patients with aggressive variants of PTC.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.082
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.9180.873

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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2016
Admission routes1
Has abstractyes

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