Developing a Proteomic Prognostic Signature for Breast Cancer Patients
Bibliographic record
Abstract
Breast cancer is a major health issue, affecting annually approximately 1.4 million women worldwide. It is a highly heterogeneous disease with the different subtypes having distinct clinical outcomes and different sensitivity to various treatment modalities. The focus of the present dissertation was the identification of novel proteomic prognostic markers for patients with early stage breast cancer. Three different approaches to identify potential prognostic markers were undertaken. First, we hypothesized that since different breast cancer subtypes have distinct clinical outcomes, breast cancer subtype-specific proteins may retain prognostic potential. Second, given the central role of estrogen signaling in breast epithelial cell biology, we hypothesized that estrogen-regulated proteins may be useful in predicting patient outcome. Finally, we hypothesized that genes related to survival based on meta-analyses of publicly available breast cancer tissue microarray data, may also demonstrate prognostic potential at the proteome level. As such, a variety of mass spectrometry-based approaches and biological samples were utilized for the discovery of these potential prognostic protein markers resulting in twenty-four candidates. Upon the identification of candidate biomarkers, a mass spectrometry-based assay for the simultaneous quantification of these proteins in breast cancer tissue samples was established. The developed assay was used for measuring the relative expression levels of the potential biomarkers in a cohort of 96 breast cancer tissue samples from untreated patients with early stage breast cancer. This exercise uncovered two proteins that showed the potential to discriminate between ER-positive patients at high and low risk of disease recurrence, namely KPNA2 and CDK1. In conclusion, the present dissertation describes the development of a preclinical exploratory study, from the discovery to the preliminary verification of potential prognostic biomarkers for breast cancer patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".