Candidate prognostic markers in breast cancer: focus on extracellular proteases and their inhibitors
Bibliographic record
Abstract
The extracellular matrix (ECM) is the complex network of proteins that surrounds cells in multicellular organisms. Due to its diverse nature and composition, the ECM has a multifaceted role in both normal tissue homeostasis and pathophysiology. It provides structural support, segregates tissues from one another, and regulates intercellular communication. Furthermore, the ECM sequesters a wide range of growth factors and cytokines that may be released upon specific and well-coordinated cues. Regulation of the ECM is performed by the extracellular proteases, which are tasked with cleaving and remodeling this intricate and diverse protein matrix. Accordingly, extracellular proteases are differentially expressed in various tissue types and in many diseases such as cancer. In fact, metastatic dissemination of tumor cells requires degradation of extracellular matrices by several families of proteases, including metalloproteinases and serine proteases, among others. Extracellular proteases are emerging as strong candidate cancer biomarkers for aiding and predicting patient outcome. Not surprisingly, inhibition of these protumorigenic enzymes in animal models of metastasis has shown impressive therapeutic effects. As such, many of these proteolytic inhibitors are currently in various phases of clinical investigation. In addition to direct approaches, aberrant expression of extracellular proteases in disease states may also facilitate the selective delivery of other therapeutic or imaging agents. Herein, we outline extracellular proteases that are either bona fide or probable prognostic markers in breast cancer. Furthermore, using existing patient data and multiple robust statistical analyses, we highlight several extracellular proteases and associated inhibitors (eg, uPA, ADAMs, MMPs, TIMPs, RECK) that hold the greatest potential as clinical biomarkers. With the recent advances in high-throughput technology and targeted therapies, the incorporation of extracellular protease status in breast cancer patient management may have a profound effect on improving outcomes in this deadly disease.
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".