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Enregistrement W4408346796 · doi:10.18632/oncotarget.28703

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients

2025· article· en· W4408346796 sur OpenAlexaffabout
Wafik S. El‐Deiry, Catherine Bresson, Fanny Wunder, Benedito A. Carneiro, Don S. Dizon, Jeremy L. Warner, Stephanie L. Graff, Christopher G. Azzoli, Eric T. Wong, Liang Cheng, Sendurai A. Mani, Howard Safran, Casey Williams, Tobias Meißner, Benjamin Solomon, Eitan Rubin, Angel Porgador, Guy Berchem, Pierre Saintigny, Amir Onn, Jair Bar, Raanan Berger, Manon Gantenbein, Zhen Chen, Cristiano de Pádua Souza, Rui Manuel Reis, Marina Sekacheva, Andrés Cervantes Valarezo, William L. Dahut, Christina M. Annunziata, Kerri Gober, Khaled M. Musallam, Humaid O. Al‐Shamsi, Ibrahim Abu-Gheida, Ramón Salazar, Sewanti Limaye, Adel T. Aref, Roger R. Reddel, Mohammed Ussama Al Homsi, Abdulrouf Pallivalapila, Said Dermime, Jassim Al Suwaidi, Cătălin Vlad, Rareş Buiga, Amal Al‐Omari, Hikmat Abdel‐Razeq, Luis F. Oñate‐Ocaña, Finn Cilius Nielsen, Leah Graham, Jens Rueter, Anthony M. Joshua, Eugenia Girda, Steven K. Libutti, Gregory Riedlinger, Mohamed E. Salem, Carol Farhangfar, Ruben A. Mesa, Bishoy M. Faltas, Olivier Elemento, C.S. Pramesh, Manju Sengar, Satoru Aoyama, Sadakatsu Ikeda, Ioana Berindan‐Neagoe, Himabindu Gaddipati, Mandar Kulkarni, Elisabeth Auzias, Maria Gerogianni, Nicolas Wolikow, Simon Istolainen, Pessie Schlafrig, Naftali Z. Frankel, Jim Palma, Alejandro Piris Gimenez, Alberto Hernando‐Calvo, Enriqueta Felip, Apostolia M. Tsimberidou, Roy S. Herbst, Josep Tabernero, Richard L. Schilsky, Jia Liu, Yves A. Lussier, Jacques Raynaud, Gerald Batist, Shai Magidi, Razelle Kurzrock

Notice bibliographique

RevueOncotarget · 2025
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Genomics and Diagnostics
Établissements canadiensMcGill UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicinePrecision medicinePersonalized medicineCancer MedicineOncologyCancerInternal medicineMedical physicsBioinformaticsPathology

Résumé

récupéré en direct d'OpenAlex

// Wafik S. El-Deiry 1 , 2 , Catherine Bresson 1 , Fanny Wunder 1 , Benedito A. Carneiro 2 , Don S. Dizon 2 , Jeremy L. Warner 2 , Stephanie L. Graff 2 , Christopher G. Azzoli 2 , Eric T. Wong 2 , Liang Cheng 2 , Sendurai A. Mani 2 , Howard P. Safran 2 , Casey Williams 3 , Tobias Meissner 3 , Benjamin Solomon 3 , Eitan Rubin 4 , Angel Porgador 4 , Guy Berchem 5 , 6 , 7 , Pierre Saintigny 8 , 9 , Amir Onn 10 , Jair Bar 10 , 11 , Raanan Berger 10 , Manon Gantenbein 7 , Zhen Chen 12 , Cristiano de Pádua Souza 13 , Rui Manuel Vieira Reis 13 , 14 , Marina Sekacheva 15 , Andrés Cervantes 16 , William L. Dahut 17 , Christina M. Annunziata 17 , Kerri Gober 17 , Khaled M. Musallam 18 , Humaid O. Al-Shamsi 18 , Ibrahim Abu-Gheida 18 , Ramon Salazar 19 , Sewanti Limaye 20 , Adel T. Aref 21 , Roger R. Reddel 21 , Mohammed Ussama Al Homsi 22 , Abdul Rouf 22 , Said Dermime 22 , Jassim Al Suwaidi 22 , Catalin Vlad 23 , Rares Buiga 23 , Amal Al Omari 24 , Hikmat Abdel-Razeq 24 , Luis F. Oñate-Ocaña 25 , Finn Cilius Nielsen 26 , Leah Graham 27 , Jens Rueter 27 , Anthony M. Joshua 28 , 29 , Eugenia Girda 30 , Steven Libutti 30 , Gregory Riedlinger 30 , Mohammed E. Salem 31 , Carol J. Farhangfar 31 , Ruben A. Mesa 31 , Bishoy M. Faltas 32 , Olivier Elemento 32 , C.S. Pramesh 33 , Manju Sengar 33 , Satoru Aoyama 34 , Sadakatsu Ikeda 34 , Ioana Berindan-Neagoe 35 , 36 , Himabindu Gaddipati 37 , Mandar Kulkarni 37 , Elisabeth Auzias 38 , Maria Gerogianni 38 , Nicolas Wolikow 38 , Simon Istolainen 38 , Pessie Schlafrig 39 , Naftali Z. Frankel 39 , Amanda R. Ferraro 40 , Jim Palma 41 , Alejandro Piris Gimenez 42 , Alberto Hernando-Calvo 42 , Enriqueta Felip 42 , Apostolia M. Tsimberidou 43 , Roy S. Herbst 44 , Josep Tabernero 42 , Richard L. Schilsky 45 , Jia Liu 21 , 28 , 29 , Yves Lussier 1 , 46 , Jacques Raynaud 1 , Gerald Batist 47 , Shai Magidi 1 and Razelle Kurzrock 1 , 48 1 Worldwide Innovative Network (WIN) Association – WIN Consortium, Chevilly-Larue, France 2 Legorreta Cancer Center at Brown University, Providence, RI 02912, USA 3 Avera Cancer Institute, Sioux Falls, SD 57105, USA 4 Ben-Gurion University of the Negev, Be'er Sheva, Israel 5 Centre Hospitalier du Luxembourg, Luxembourg 6 University of Luxembourg, Esch-sur-Alzette, Luxembourg 7 Luxembourg Institute of Health, Luxembourg 8 Department of Medical Oncology, Centre Léon Bérard, Lyon, France 9 University of Lyon, Claude Bernard Lyon 1 University, INSERM 1052, CNRS 5286, Centre Léon Bérard, Cancer Research Center of Lyon, Lyon, France 10 Jusidman Cancer Center, Sheba Medical Center, Ramat Gan, Israel 11 Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel 12 Fudan University Shanghai Cancer Center, Shanghai, China 13 Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil 14 Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal 15 I.M Sechenov First Medical State University, Moscow, Russian Federation 16 INCLIVA Instituto de Investigación Sanitaria, Valencia, Spain 17 American Cancer Society, Atlanta, Georgia, MD 21742, USA 18 Burjeel Medical City (BMC), Mohamed Bin Zayed City, Abu Dhabi, UAE 19 Medical Oncology Deparment. Institut Català d'Oncologia. Oncobell Program (IDIBELL), Universitat de Barcelona (Campus Bellvitge), CIBERONC, Barcelona, Spain 20 Sir H.N. Reliance Foundation Hospital and Research Centre, Mumbai, India 21 ProCan, Children’s Medical Research Institute, The University of Sydney, Australia 22 National Center for Cancer Care and Research Hamad Medical Corporation, Doha, Qatar 23 Oncology Institute Ion Chiricuta, Cluj, Romania 24 King Hussein Cancer Center, Amman, Jordan 25 Instituto Nacional de Cancerología (INCan), Mexico City, Mexico 26 Rigshospitalet, Copenhagen, Denmark 27 The Jackson Laboratory, The Maine Cancer Genomics Initiative, Bar Harbor, ME 04609, USA 28 The Kinghorn Cancer Centre, St Vincent’s Hospital, Darlinghurst, Australia 29 School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia 30 Rutgers Cancer Institute, New Brunswick, NJ 08901, USA 31 Wake Forest University Health Sciences/Atrium Health (WFUHS), Winston-Salem, NC 27157, USA 32 Weill Cornell Medical College, NY 10065, USA 33 Tata Memorial Centre, Affiliated to Homi Bhabha National Institute, Mumbai, India 34 Institute of Science Tokyo Hospital, Tokyo, Japan 35 University of Medicine and Pharmacy Iuliu Hatieganu, Cluj, Romania 36 Academy of Medical Sciences, Bucharest, Romania 37 Vyas Cancer Research (VCR Park), Maharanipeta, Visakhpatnam, Andhra Pradesh, India 38 Cure51, Paris, France 39 CHAIM Medical Resource Organization, NY 10950, USA 40 Cancer is an A*, LLC, Manalapan Township, NJ 07726, USA 41 TargetCancer Foundation, Cambridge, MA 02139, USA 42 Vall d’Hebron Hospital Campus and Institute of Oncology (VHIO), Barcelona, Spain 43 The University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030, USA 44 Yale School of Medicine, New Haven, CT 06510, USA 45 The University of Chicago, Chicago, IL 60637, USA 46 The University of Utah, Salt Lake City, UT 84112, USA 47 Segal Cancer Centre, Jewish Hospital, McGill University, Montreal, Quebec, Canada 48 Medical College of Wisconsin, Milwaukee, WI 53226, USA Correspondence to: Wafik S. El-Deiry, email: wafik@brown.edu Keywords: precision oncology; N-of-1 basket trials; AI algorithms; digital pathology; drug access Received: December 30, 2024     Accepted: February 27, 2025     Published: March 12, 2025 Copyright: © 2025 El-Deiry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity—including financial and time—are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN’s collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,575
Score d'incertitude au seuil0,636

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,316
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2025
Routes d'admission2
Résumé présentoui

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