Abstract IA19: Regulation of signaling interactomes in cancer
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".