Abstract P2-05-11: Proteomic identification and in-silico verification of subtype-specific signatures in breast cancer.
Bibliographic record
Abstract
Abstract Breast cancer is a highly heterogeneous disease with different subtypes presenting distinct clinical characteristics. During the last decade has been shown that molecular classification of breast cancer holds the promise for the development of novel prognostic tools and the identification of novel treatment targets. However, proteins are the main mediators of biological processes and the molecular targets of the majority of drugs. Moreover, the proteome integrates the cellular genetic information and environmental influences. Hence coupling proteomic and transcriptomic studies may augment the development of targeted therapies and the identification of disease-relevant proteins networks. Here, we report an extensive mass spectrometry-based (MS) secretome analysis of eight breast cancer cell lines representing, the three main breast cancer subtypes (luminal, basal and HER2-neu amplified) in search of subtype-specific proteomic signatures. Following a bottom-up proteomic approach coupled to tandem mass spectrometry the conditioned media of 8 breast cancer cell lines were analyzed. More than 5,200 non-redundant proteins were identified in the conditioned media of the 8 cell lines with a false positive rate less than 1%. The mass spectrometry-based identification of 5 proteins was successfully verified by ELISA. For the generation of subtype-specific proteomic signatures, proteins common to all cell lines of the same subtype but not present in the other two subtypes were identified. Twenty-three, four and four proteins were found uniquely in basal, HER2-neu amplified and luminal breast cancer cells. Notably, ERBB2 was one of the proteins uniquely identified in the HER2-neu amplified subtype verifying the validity of our approach. Given that most of these proteins have no quantitative methods available at present, we opted to preliminarily examine the relationship of these candidates with breast cancer subtypes by using an in silico approach, based on transcriptomic data. We performed an in silico mRNA expression analysis using publicly available data from four independent experiments containing a total of 1039 patients with primary breast cancer. The expression profiles of 15 of 31 proteins investigated showed significant correlation with estrogen receptor expression and power to distinguish subtypes. The development of multiplex quantitative mass spectrometry-based assays for the quantification of the proteomic signatures in relevant biological specimens along with immunohistochemical studies for two of the 31 candidates are currently ongoing. The significance of these subtype-specific proteins in breast cancer prognosis and disease progression warrants further investigation. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-05-11.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".