Abstract B009: Exploring the putative Kras-p53 mutational interface for vulnerability
Notice bibliographique
Résumé
Abstract Introduction: Kras Gain of Function mutations are frequently detected in lung, colorectal and pancreatic cancers, in addition to others. Activating Kras mutations results in a constantly active protein, which we reasoned is quite unphysiological, yet it is tolerated by cells since this mutation is seen in hyperplasia. Activating this oncogene is insufficient alone for malignant transformation. Mutations in the tumor suppressor gene (TSG) p53 (the most frequently mutated gene in human cancer), cooperate with mutant Kras and is sufficient to permit full display of the actions of oncogenic Ras (as confirmed in Genetically Engineered Mouse Models and in clinical cases). The phenomenon of oncogene addiction may in fact be the result of obligatory requirements (cellular metabolism or otherwise) brought on by the constant oncogene signaling. This required adjustment in cellular circuitry can be afforded by specific cooperating TSG and since it too is a DNA constant change, becomes an new rigid reality for a cell with a given set of oncogene-TSG pair. p53 mutational spectrum was described as ‘enigmatic’ presumably because they almost never completely abrogate the function of this major regulator suggesting to us a possible essential mechanistic role for the retained transcriptional targets within the (well annotated) p53 transcriptional network. This could represent a putative synthetic lethality opportunity against activated Kras, an oncogene that has proven difficult to drug. We hypothesized that specific mutations in p53 with their respective transcriptional lesions cooperate with unique mutant Kras in tissue specific manner identifying a short list of gene targets for synthetic lethality experiments. Methods: To examine this hypothesis, we analyzed the TCGA database to determine a conserved pair cooperation between common Kras 12C; D or V and the top 6 reported p53 (hot spot) mutations [on residues 175; 245;248;249;273 and 282] in a stage-agnostic manner. To examine whether those putative interactions are cell-type specific, we performed this analysis in 3 different histologies (lung, colon, and pancreas). Results: The results suggested a non-random distribution of p53 mutants among the Kras driven cancers. KRAS (12 C, 12 V, 12 D were reported in 80% of Colon, Pancreas, and Lung Cancer. p53 (175 - 30%) (245 - 30%) (248 - 15%) (249 - 12%) (273 - 6.5%) (282 - 6.5%). More detailed analysis is planned for the poster session. Conclusion: Despite having a single activated oncogene, the distribution of the cooperating p53 mutations is nonrandom so examining the transcriptionally retained gene list represents a novel approach to explore for gene editing experiments. This provides and approach to drug the addiction of cancers to their oncogenes in a cancer specific, and occasionally to target essential proto-oncogene such as Myc where direct inhibition is highly undesirable due to its physiologic roles. Citation Format: Nishanth Thalambedu, Shallya Anand, Haya Safar, Farah Mazahreh, Ahmad Mazin M. Safar. Exploring the putative Kras-p53 mutational interface for vulnerability [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B009.
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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 ».