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Enregistrement W2385366530 · doi:10.1158/1538-8514.tumang15-ia22

Abstract IA22: Preclinical modeling of adjuvant and metastatic antiangiogenic therapy: Relevance for better predicting clinical outcomes

2015· article· en· W2385366530 sur OpenAlexaff
Robert S. Kerbel

Notice bibliographique

RevueMolecular Cancer Therapeutics · 2015
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAngiogenesis and VEGF in Cancer
Établissements canadiensSunnybrook HospitalSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineSunitinibMetastatic breast cancerBevacizumabOncologyInternal medicineBreast cancerAdjuvant therapyPrimary tumorChemotherapyPazopanibClinical trialAdjuvantMetastasisCancer

Résumé

récupéré en direct d'OpenAlex

Abstract Over the last decade my lab has developed a number of models involving treatment of mice with either early stage microscopic metastatic disease for adjuvant therapy or late stage overt metastatic disease for metastatic therapy studies1-3. More recently, models of neoadjuvant therapy have been developed as well with the lab of Dr. John Ebos4. The rationale for utilizing the first two models is that they may be superior in predicting future activity in patients enrolled in randomized phase III adjuvant or metastatic therapy clinical trials, in comparison to conventional treatment models involving mice with unresected established primary tumors, and evaluating the effect on the primary tumor growth only1. As an example, we reported that sunitinib (or pazopanib) or DC101, the VEGFR-2 antibody were all devoid of anti-tumor activity when treating mice with advanced metastatic breast cancer after primary tumor resection, whereas in contrast, all showed efficacy when treating orthotopic primary tumors in control experiments5. Combining chemotherapy with sunitinib also did not improve outcomes in the metastatic setting. In contrast, combining chemotherapy with DC101 caused a small but statistically significant benefit in survival. These results retrospectively correlated with outcomes of four metastatic breast cancer phase III trials evaluating sunitinib alone or in combination with chemotherapy in the metastatic setting (all were negative) or multiple phase III trials evaluating the VEGF antibody, bevacizumab, with chemotherapy, which showed variable benefits in improving PFS in metastatic breast cancer5. With respect to adjuvant therapy modelling, we reported in 2009 that adjuvant sunitinib therapy of mice with microscopic metastases after resection of orthotopic primary human breast cancer xenografts resulted in a worsened survival outcome, with accelerated progression of metastatic disease2. On the basis of the results we raised a cautionary “flag” about the rationale of antiangiogenic drugs in the clinic for adjuvant setting6. As is now well known, there have been multiple adjuvant trials evaluating bevacizumab plus chemotherapy in postsurgical early stage colorectal or breast cancer, as well as sorafenib in hepatocellular carcinoma, and all of these trials have failed to meet their primary endpoint of a benefit in disease free survival7. This has provoked considerable discussion and debate about the basis for such failures in contrast to the same drugs/therapies showing efficacy in the more advanced metastatic settings of the same malignancies. One basis for the modest positive effects noted of such therapies in the metastatic setting, and for the failure in the adjuvant setting, concerns the impact that “vessel co-option”, especially in distant metastases, may have on therapeutic outcomes. Evidence is growing that a variety of tumors and especially overt metastases in certain sites such as the lungs, liver, and brain are minimally or non-angiogenic and instead “hijack” the existing vasculature in such organ sites8. The same may be the case for microscopic metastases. Consequently, there will be growing interest in evaluating whether vessel co-option can be therapeutically targeted (and also what the implications may be for drug-induced vascular normalization). In this regard there are a number of strategies being evaluated such as the impact of metronomic chemotherapy and targeting other pro-angiogenic factors/pathways beyond VEGF such as ang2/tie29, which may be effective as an adjuvant therapy strategy9. References: 1. Francia G, Cruz-Munoz W, Man S, Xu P, Kerbel RS. Perspective: Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nature Reviews Cancer 2011; 11:135-41. 2. Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 2009; 15:232-9. 3. Jedeszko C, Paez-Ribes M, Di Desidero T, Bocci G, Man S, Lee CR, et al. Orthotopic primary and postsurgical adjuvant or metastatic renal cell carcinoma therapy models reveal potent anti-tumor activity of minimally toxic metronomic oral topotecan with pazopanib. Sci Transl Med 2015; in press. 4. Ebos JM, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med 2014; 6:1561-76. 5. Guerin E, Man S, Xu P, Kerbel RS. A model of postsurgical advanced metastatic breast cancer more accurately replicates the clinical efficacy of antiangiogenic drugs. Cancer Res 2013; 73:2743-8. 6. Ebos JML and Kerbel RS. Impact of antiangiogenic therapy on invasion, disease progression, and metastasis. Nat Rev Clin Oncol 2011; 8:210-21. 7. Sledge GW. Anti-vascular endothelial growth factor therapy in breast cancer: game over? J Clin Oncol 2015; 33:133-5. 8. Donnem T, Hu J, Ferguson M, Adighibe O, Snell C, Harris AL, et al. Vessel co-option in primary human tumors and metastases: an obstacle to effective anti-angiogenic treatment? Cancer Med 2013; 2:427-36. 9. Srivastava K, Hu J, Korn C, Savant S, Teichert M, Kapel SS, et al. Postsurgical adjuvant tumor therapy by combining anti-angiopoietin-2 and metronomic chemotherapy limits metastatic growth. Cancer Cell 2014; 26:880-95. Citation Format: Robert S. Kerbel. Preclinical modeling of adjuvant and metastatic antiangiogenic therapy: Relevance for better predicting clinical outcomes. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Angiogenesis and Vascular Normalization: Bench to Bedside to Biomarkers; Mar 5-8, 2015; Orlando, FL. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl):Abstract nr IA22.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,007

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,129
Tête enseignante GPT0,398
Écart entre enseignants0,269 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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