Abstract 4574: Mining the proteome of breast cancer cell lines and nipple aspirate fluid in the quest for novel breast cancer biomarkers
Bibliographic record
Abstract
Abstract Approximately 200,000 new cases of breast cancer are estimated in the United States for 2009, rendering breast cancer the most frequently diagnosed cancer in women. Patients diagnosed with early stage disease have significantly improved survival rates compared to late stage patients, underlining the need for identification of biomarkers for early detection. Breast cancer is highly heterogeneous and it can be categorized into five subtypes with distinct clinical outcome and different treatment modalities. In this study the secretome of breast cancer cell lines and nipple aspirate fluid (NAF) were analyzed by tandem mass spectrometry to identify novel breast cancer biomarkers. To reflect disease heterogeneity, three cell lines for each of three breast cancer types [estrogen (ER)/ progesterone (PR) receptor positive, triple negative, HER2/neu amplified] were selected (HCC-1428, BT483, MCF-7, MDA-MB-231, HCC-1143, HCC-38, SK-BR-3, HCC-202, UACC-812). NAF samples were obtained from 3 patients with ER positive breast cancer. Proteins of NAF samples and conditioned media of the cell lines were denatured, reduced and trypsin-digested. The peptides were separated by two-dimensional liquid chromatography and the fractions were analyzed in a linear ion-trap coupled to an orbitrap mass analyser. Spectra were searched with Mascot and X!Tandem engines using the IPI 3.46 human database. Scaffold software was used to cross-validate Mascot and X!Tandem results. Spectra were exported from Scaffold and uploaded into an in-house-program for further data analysis. Over 1,000 unique proteins were identified in the conditioned media of each cell line, resulting in more than 4,000 proteins from the 9 breast cancer cell lines. Additionally, 780 proteins were identified in the three NAF samples generating the most extensive NAF cancer proteome so far. Using an in-house program, we annotated the cellular localization and the biological function for each protein. Proteins identified in the three cell lines of each subtype were combined to generate non-redundant, subtype-specific proteomes. The proteomes of different subtypes were then compared, to distinguish proteins that may reveal subtype-specific signatures. The comparison between the ER-positive cancer cell line secretome and the NAF proteome revealed 400 common proteins which were selected for further investigation. A set of selection criteria were applied to generate a panel of the 30 most promising candidates for ER-positive breast cancer. Multiple reaction monitoring (MRM) assays for each of these proteins are being developed to verify their utility as potential biomarkers in serum. In conclusion, proteomic analysis of NAF and tissue culture supernatants of breast cancer cell lines holds promise for breast cancer-specific biomarker discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4574.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".