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Record W4213012668 · doi:10.15252/emmm.202114552

A clinically compatible drug‐screening platform based on organotypic cultures identifies vulnerabilities to prevent and treat brain metastasis

2022· article· en· W4213012668 on OpenAlexaff
Lucía Zhu, Diana Retana, Pedro García‐Gómez, Laura Álvaro‐Espinosa, Neibla Priego, Mariam Masmudi‐Martín, Natalia Yebra, Lauritz Miarka, Elena Hernández‐Encinas, Carmen Blanco‐Aparicio, Sonia Martı́nez, Cecilia Sobrino, Nuria Ajenjo, María-Jesús Artiga, Eva Ortega‐Paino, Raúl Torres, Sandra Rodríguez, Riccardo Soffietti, Luca Bertero, Paola Cassoni, Tobias Weiß, Javier Muñoz, Juan Manuel Sepúlveda-Sánchez, P. González, Luis Jiménez‐Roldán, Luis Miguel Moreno-Gómez, Olga Esteban, Ángel Pérez‐Núñez, Aurelio Hernández‐Laín, Óscar Toldos, Yolanda Ruano, Lucía Alcázar, Guillermo Blasco, J.F. Alén, Eduardo Caleiras, Miguel Lafarga, Diego Megı́as, Osvaldo Graña‐Castro, Carolina Nör, Michael D. Taylor, Leonie S. Young, Damir Varešlija, Nicola Cosgrove, Fergus J. Couch, Lorena Cussó, Manuel Desco, Silvana Mourón, Miguel Quintela-Fandiño, Michael Weller, Joaquı́n Pastor, Manuel Valiente, Adolfo de la Lama‐Zaragoza, Lourdes Calero‐Felix, Concepcion Fiaño‐Valverde, Pedro David Delgado‐López, Antonio Montalvo‐Afonso, Mar Pascual‐Llorente, Ángela Díaz‐Piqueras, SH Nam‐Cha, Gerard Plans Ahicart, Elena Martínez‐Sáez, Santiago Ramón y Cajal, Pilar Nicolás

Bibliographic record

VenueEMBO Molecular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersH2020 European Research CouncilInstituto de Salud Carlos IIIHorizon 2020 Framework ProgrammeUniversität ZürichCentro Nacional de Investigaciones CardiovascularesDipartimenti di EccellenzaBristol-Myers SquibbScience Foundation IrelandFundació la Marató de TV3Worldwide Cancer ResearchFundación Científica Asociación Española Contra el CáncerFundación Ramón ArecesPromedica StiftungComunidad de MadridNational Cancer InstituteMinistero dell’Istruzione, dell’Università e della RicercaMelanoma Research AllianceBoehringer Ingelheim FondsMemorial Sloan-Kettering Cancer CenterMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaCancer Research Institute
KeywordsDrugBrain metastasisMetastasisDrug discoveryMedicineDrug traffickingComputational biologyCancer researchBiologyBioinformaticsPharmacologyInternal medicineCancerPsychology

Abstract

fetched live from OpenAlex

We report a medium-throughput drug-screening platform (METPlatform) based on organotypic cultures that allows to evaluate inhibitors against metastases growing in situ. By applying this approach to the unmet clinical need of brain metastasis, we identified several vulnerabilities. Among them, a blood-brain barrier permeable HSP90 inhibitor showed high potency against mouse and human brain metastases at clinically relevant stages of the disease, including a novel model of local relapse after neurosurgery. Furthermore, in situ proteomic analysis applied to metastases treated with the chaperone inhibitor uncovered a novel molecular program in brain metastasis, which includes biomarkers of poor prognosis and actionable mechanisms of resistance. Our work validates METPlatform as a potent resource for metastasis research integrating drug-screening and unbiased omic approaches that is compatible with human samples. Thus, this clinically relevant strategy is aimed to personalize the management of metastatic disease in the brain and elsewhere.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.024
GPT teacher head0.322
Teacher spread0.299 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations36
Published2022
Admission routes1
Has abstractyes

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