Glycoproteomics Identifies Plexin-B3 as Targetable Cell Surface Protein Required for Growth and Invasion of Triple Negative Breast Cancer Cells
Bibliographic record
Abstract
Summary Driven by the lack of targeted therapies, triple negative breast cancers (TNBC) have the worst overall survival of all breast cancer subtypes. Considering cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBC. Here, we performed N -glycoproteomics on six TNBC and five normal control (NC) cell lines using hydrazide-based enrichment. Quantitative proteomics and integrative data mining led to the discovery of Plexin-B3 (PLXNB3), a previously undescribed TNBC-enriched cell surface protein. Furthermore, siRNA knock-down and CRISPR-Cas9 editing of in vitro and in vivo models show that PLXNB3 is required for TNBC cell line growth, invasion, and migration. Altogether, we provide insight into N -glycoproteome remodeling associated with TNBC and functional evaluation of an extracted target, which indicate the surface protein PLXNB3 as a potential therapeutic target for TNBC. Highlights In-depth N -glycoproteomic profiles of six TNBC and five NC cell line models Identification of PLXNB3 as a novel TNBC-enriched cell surface protein PLXNB3 affects growth, invasion, and migration in TNBC models PLXNB3 inhibition represents a targeted treatment option for TNBC
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".