Analysis of BRCA1-related functional associations in sporadic triple negative breast cancer: A network-based approach.
Bibliographic record
Abstract
1070 Background: Sporadic triple negative breast cancers (TNBC) are aggressive malignancies that present a yet unaddressed clinical challenge. They are refractory to hormonal and HER2-targeted therapy, leaving chemotherapy as the mainstay of systemic treatment. Their striking resemblance to BRCA1-mutated hereditary breast cancers has led to the speculation that BRCA1 pathway derangement underlies sporadic TNBC and could be exploited therapeutically. BRCA1 has been implicated in multiple cellular processes via interactions with diverse partner proteins. Methods: To search for TNBC-specific aberrations within the complex array of BRCA1-related functional associations, we devised a strategy that takes into account the versatility of the BRCA1 protein and combines database mining, literature curation, network modeling, and transcriptome analysis. Results: We used database- and literature-derived data on protein-protein interactions, co-complex memberships, and biochemical modifications involving BRCA1 to build a BRCA1-centered Interaction network of 157 known BRCA1 partners. We applied functional linkage analysis to add predicted functional links and generate a highly-connected Shell network of 1137 nodes and 2941 edges, directly or indirectly associated with BRCA1. We next used a curated compendium of 14 datasets comprising 2022 uniformly subtyped sporadic primary breast cancers to compute, meta-analytically, the relative expression of the network components in TNBC vs non-TNBC subtypes. Lastly, we derived TNBC-specific subnetworks of significantly hyperactive or suppressed BRCA1-functionally associated genes. Conclusions: Via network analysis, we have identified presumptive signatures of BRCA1 pathway deregulation in sporadic TNBC. We will assess their prognostic/predictive value and potential use for patient stratification. Where relevant, we will also employ them as hypothesis-generating tools to prioritize BRCA1-related genes/proteins and pathways for in-depth analyses and to search for therapeutically exploitable vulnerabilities. These studies can help to inform therapy selection and tailored treatment and, hence, guide clinical trial design.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".