Differential proteomic comparison of breast cancer secretome using a quantitative paired analysis workflow
Bibliographic record
Abstract
BACKGROUND: Worldwide, breast cancer is the main cause of cancer mortality in women. Most cases originate in mammary ductal cells that produce the nipple aspirate fluid (NAF). In cancer patients, this secretome contains proteins associated with the tumor microenvironment. NAF studies are challenging because of inter-individual variability. We introduced a paired-proteomic shotgun strategy that relies on NAF analysis from both breasts of patients with unilateral breast cancer and extended PatternLab for Proteomics software to take advantage of this setup. METHODS: The software is based on a peptide-centric approach and uses the binomial distribution to attribute a probability for each peptide as being linked to the disease; these probabilities are propagated to a final protein p-value according to the Stouffer's Z-score method. RESULTS: A total of 1227 proteins were identified and quantified, of which 87 were differentially abundant, being mainly involved in glycolysis (Warburg effect) and immune system activation (activated stroma). Additionally, in the estrogen receptor-positive subgroup, proteins related to the regulation of insulin-like growth factor transport and platelet degranulation displayed higher abundance, confirming the presence of a proliferative microenvironment. CONCLUSIONS: We debuted a differential bioinformatics workflow for the proteomic analysis of NAF, validating this secretome as a treasure-trove for studying a paired-organ cancer type.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".