Abstract 3099: <i>KRAS</i> and clinical context: Differential dynamic signaling output of <i>KRAS</i> mutant lung, colorectal and pancreatic cancer cell lines when exposed to targeted anticancer drugs
Notice bibliographique
Résumé
Abstract Background Clinical trials have shown that cancers originating from different tissues driven by the same oncogene respond differently to targeted anticancer drugs. We aimed to understand different signaling patterns in KRAS mutant cells derived from non-small cell lung cancer (NSCLC), colorectal cancer (CRC) and pancreatic cancer. Materials and methods We optimized a 50 phosphoprotein antibody-based assay on the Luminex 200 platform. We then exposed a panel of 15 KRAS mutant cell lines (5 cell lines each originating in the lung, pancreas and colon) to a DMSO control (n = 3) and clinically significant concentrations (Cmax achieved in humans adjusted for protein binding in culture medium) of a PI3K (GDC-0941), AKT (AZD5363), m-TOR (everolimus), BRAF (vemurafenib), EGFR (gefitinib), MEK (trametinib) and an HSP90 inhibitor (luminespib) for 1 hr. We quantified the change in phosphorylation of proteins for each drug compared to control. Logistic regression analysis was used to analyse differences between KRAS-driven cell lines originating from different anatomical sites. Results There were changes in phosphorylation related to the pharmacodynamic effects of the drug independent of cell line of origin; however, there were interesting differences between KRAS mutant cells originating from different anatomical sites. In NSCLC cell lines, p-EGFR levels changed significantly less when exposed to PI3K, AKT and m-TOR inhibitors (p = 0.047, 0.022 and 0.047, respectively) when compared to cells originating from CRC and pancreatic cancer. CRC cell lines, when compared to NSCLC and pancreatic cancer cell lines, showed significantly less changes in phosphorylation of key cell cycle regulators such as CHK1 when exposed to PI3K, AKT and m-TOR inhibitors, (p = 0.001, 0.047 and 0.047, respectively) and RB when exposed to an AKT and m-TOR inhibitor (p = 0.047 and 0.047, respectively). Interestingly, pancreatic cell lines showed significantly more changes in p-m-TOR compared to CRC and NSCLC cell lines following exposure to PI3K and AKT inhibitors (p = 0.0095 and 0.022, respectively). Of note, drugs not directly targeting the PI3K pathway differentially regulated different nodes in the PI3K pathway, for example, BRAF inhibitors significantly differentially changed levels of phosphorylation at different nodes in the PI3K pathway such as AKT in NSCLC cell lines, p = 0.047, p70S6K in CRC cell lines, p = 0.0472 and PRAS40 in the pancreatic cancer cell lines, p = 0.022. Conclusion These results suggest that there are significant differences in signaling patterns caused by PI3K pathway inhibitors in KRAS mutant NSCLC, CRC and pancreatic cancer cell lines. Our findings shed light on the putative use of PI3K pathway inhibitors in KRAS mutant cancers. They also question the universal application of solely using genetic mutations to stratify patients in ‘basket’ clinical studies. Citation Format: Adam Stewart, Elizabeth Coker, Anna Minchom, Parames Thavasu, Alexandros Georgiou, Anguraj Sadanandam, Timothy A. Yap, Johann S. de Bono, Bissan Al-Lazikani, Udai Banerji. KRAS and clinical context: Differential dynamic signaling output of KRAS mutant lung, colorectal and pancreatic cancer cell lines when exposed to targeted anticancer drugs. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3099.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».