Abstract P6-05-09: Unravelling the global effect of estrogen on breast cancer cell proteome using quantitative proteomics.
Bibliographic record
Abstract
Abstract Estrogens exert their function through genomic and non-genomic pathways mediated mainly by estrogen receptors. Estrogen signalling is highly complex and once activated initiates cancer cell proliferation and survival, playing a pivotal role in breast cancer development and progression. The identification of estrogen regulated genes is the first step towards elucidating the mechanisms of estrogen function. Although numerous studies have investigated the effect of estradiol at the mRNA level, assessment of global changes at the protein level has been limited mainly due to technological limitations. Here, we present the most extensive proteomic study in the quest of proteins whose expression is regulated or associated with estradiol action. To facilitate accurate quantification of proteins, we first developed and optimized a proteomic protocol for cell lysis and sample preparation, based on a mass spectrometry-compatible detergent. Following our protocol coupled to stable isotope labelling with amino acids in cell culture (SILAC) of MCF-7 breast cancer cells, we quantified approximately 4,000 proteins and identified 153 proteins differentially expressed upon estradiol stimulation. Notably, known estrogen regulated proteins such as trefoil 1(TFF1) and progesterone receptor (PGR) showed a four- and three-fold increase respectively, at 48 hours after estradiol stimulation. Forty eight of 153 differentially expressed proteins have been found to contain estrogen receptor elements (EREs) indicating direct regulation by estradiol. Protein-protein network analysis (STRING) revealed an extensive protein network related to cell proliferation. Differential expression of proteins was verified in three estrogen receptor positive breast cancer cell lines (MCF-7, HCC-1428, BT-483) using a targeted mass spectrometry-based quantitative approach; selected reaction monitoring (SRM). A multiplex SRM assay was developed for the simultaneous quantification of 56 proteins. The same assay was utilized to study the kinetics of these proteins in time and in presence of inhibitors of estrogen receptor facilitating the distinction between direct and indirect protein targets. To our knowledge, such an extensive network of estrogen-regulated genes has never been previously studied at the protein level. Interestingly, numerous differentially expressed proteins identified in the present study have not been connected to estrogen signalling or breast cancer before. The role of these proteins in breast carcinogenesis and their potential as therapeutic targets warrants further investigation. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P6-05-09.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".