Abstract IA19: Regulation of signaling interactomes in cancer
Notice bibliographique
Résumé
Abstract With the advent of next generation DNA sequencing technologies, the pace of discovery of cancer-associated sequence variants has greatly accelerated, leading to the realization that tumors (and especially solid tumors) harbor hundreds of mutations. Ongoing statistical analysis across multiple laboratories worldwide is progressing rapidly, helping to identify which of these mutations are likely drivers of the cancer phenotype. However, in spite of rapid progress in mapping of cancer related signaling and interaction networks, there has been an increasing disconnect between the identification of a cancer variant, and the mechanistic understanding of transformation induced by the mutation. Such molecular understanding is crucial for developing therapeutic interventions. In particular, frequent consequences of cancer-related mutations are specific alterations of protein-protein interactions affecting downstream signaling networks: an analysis of these altered interactions offers novel therapeutic avenues. At the same time, we still need to gain more knowledge regarding the protein-protein interactions targeted by anticancer drugs, in order to validate on-target effects and identify potential off-target modulation. For both of these objectives, we require the development of approaches that can quantify both gain and loss of interactions, in a sensitive manner, and as much as possible, under conditions that recapitulate the cellular context. Our research team has coupled affinity purification of a protein to quantitative mass spectrometry approaches. Key to our success has been the expression at near endogenous levels of a recombinant bait protein (as wild type or sequence variants) in human cells, followed by affinity purification using an antibody directed against the epitope tag and simultaneous identification and quantification by mass spectrometry. In particular, we are reporting here on our efforts to apply the novel data-independent mass spectrometric acquisition (DIA) method known as SWATH to these questions (see Lambert et al., Nature Methods, 2013; Tsou et al., Nature Methods, 2015). We are demonstrating how the approach can be used to profile the differential interactomes of cancer-associated mutants of kinase and phosphatase proteins (CDK4, PPP2R1A, PPP6C), and identify potentially actionable interactions. We are further demonstrating, using JQ1, an inhibitor of the interaction between acetyl-lysine modified histones and the bromodomain and extra-terminal (BET) protein family, (BRD2, BRD3, BRD4 and BRDT) that our approach permits to globally study the modulation of interactions following exposure to an anticancer agent. We reveal that JQ1 induces a rapid rewiring of the interactome of each BET protein, both decreasing interactions with acetylated histones as expected, but also modulating BET association with numerous interaction partners. Furthermore, multiple new interactions were induced upon JQ1 treatment that may alter the compound's potency. Taken together, our results demonstrate that the AP-SWATH-MS allows for the characterization of dynamically modulated interactomes for a wide array of cancer-associated proteins. Citation Format: Jean-Philippe Lambert, Yiwang Zhou, Amber Couzens, Chih-Chiang Tsou, Sarah Picaud, Gordana Ivosev, Stephen Tate, Alexey Nesvizhskii, Panagis Filippakopoulos, Anne-Claude Gingras. Regulation of signaling interactomes in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA19.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| 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) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».