Circulating extracellular vesicles containing S100A9 reflect histopathology, immunophenotype and therapeutic responses of liver metastasis in colorectal cancer patients
Bibliographic record
Abstract
BACKGROUND: Metastasis is the principal cause of cancer treatment failure and an area of dire diagnostic needs. Colorectal cancer metastases to the liver (CRCLMs) are predominantly classified into desmoplastic and replacement based on their histological growth patterns (HGPs). Desmoplastic responds well to current treatments, while replacement HGP has a poor prognosis with low overall survival rates. METHODS: We hypothesised that complex cellular response underlying HGPs may be reflected in the proteome of circulating extracellular vesicles (EVs). EV proteomics data was generated through LC-MS/MS and analysed with Maxquant and Perseus. To validate the S100A9 signature, ELISA was performed, and IHC and IF were conducted on tissue for marker detection and colocalization study. RESULTS: Plasma EV proteome signature distinguished desmoplastic from the replacement in patients with 22 differentially expressed proteins, including immune related markers. Unsupervised PCA analysis revealed clear separation of the two lesions. The marker with the highest confidence level to stratify the two HGPs was S100A9, which was traced in CRCLM lesions and found to colocalize with macrophages and neutrophils. EV-associated S100A9 in plasma may reflect the innate immunity status of metastatic lesions and their differential therapeutic responses. CONCLUSION: Plasma EV-derived S100A9 could be useful in personalising therapy in patients with CRCLM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".