Platelets increase survival of adenocarcinoma cells challenged with anticancer drugs: mechanisms and implications for chemoresistance
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cancer cells grow without the restraints of feedback control mechanisms, leading to increased cancer cell survival. The treatment of cancer is often complicated by the lack of response to chemotherapy leading to chemoresistance and persistent survival of tumour cells. In this work we studied the role of platelets in chemotherapy-induced cancer cell death and survival. EXPERIMENTAL APPROACH: Human adenocarcinoma cells, colonic (Caco-2) and ovarian (59 M) cells, were incubated with 5-fluorouracil (1-300 µg·mL(-1) ) or paclitaxel (1-200 µg·mL(-1) ) in the presence or absence of platelets (1.5 × 10(8) mL(-1) ) for 1, 24 or 72 h. Following incubation, cancer cells were harvested and cell survival/death was assayed using flow cytometry, Western blotting, real-time PCR, TaqMan® Gene Expression Assays and proteomics. KEY RESULTS: Human platelets increased the survival of colonic and ovarian adenocarcinoma cells treated with two standard anticancer drugs, 5-fluorouracil and paclitaxel. In the presence of platelets, cancer cells up-regulated anti-apoptotic and down-regulated pro-apoptotic genes, increased the number of cells in the synthesis of DNA and decreased the number in the quiescent phase, increased expression of cyclins, DNA repair proteins and MAPKs. The analysis of platelet-Caco-2 secretome demonstrated the release of the chemokine RANTES, thrombospondin-1, TGF-β and clusterin. Finally, human recombinant RANTES and thrombospondin-1 improved survival of Caco-2 cells challenged with paclitaxel. CONCLUSIONS AND IMPLICATIONS: These data demonstrate that platelets increase adenocarcinoma cells survival, proliferation and chemoresistance to standard anticancer drugs. Modulating cancer cell-platelet interactions may offer a new strategy to improve the efficacy of chemotherapy.
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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 | 0.001 | 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) | 0.000 | 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".