Investigating Therapeutic Potential of Targeting Platelets in Cancer Treatment: A Literature Review
Bibliographic record
Abstract
Cancer is one of the leading causes of global mortality and continues to persist as a significant healthcare burden. Patient non-responsiveness or relapse following first-line cancer treatments necessitates the development of novel treatments. Platelets are small blood cells that are essential to stop bleeding. Platelets are increasingly recognized for their roles in supporting tumorigenesis. They can aid cancer cells to evade immune cells, promote immune suppression, and release molecules that protect the stability of tumor microenvironments. Conversely, platelets can also secrete molecules that recruit leukocytes to the site of tissue injury and coordinate immune cell responses and crosstalk. Anti-platelet therapies, including aspirin, have been advocated as preventive measures in some cancers, but the long-term risks for bleeding, thrombotic events, ineffective immune responses, and overall efficacy towards cancer recovery remain uncertain. To address these gaps, this literature review reports pre-clinical studies and clinical trials from 2017-2023 that explore (1) novel molecules and pathways participating in platelet and cancer cell crosstalk, (2) overall efficacy of cancer treatment or effects on cancer cell survival, proliferation, and metastasis, and (3) safety of anti-platelet therapy. This study’s findings reveal that anti-platelet therapies have an improved benefit for the prevention and treatment of some cancers, such as colorectal, breast, prostate, or lung cancers. There has been some success using anti-platelet treatments, such as ticagrelor to inhibit P2Y12 pathway, or low molecular weight heparin. While aspirin usage was successful in some cancers, such as colorectal cancer, it was not effective in others, such as ovarian cancer. Finally, the safety of using anti-platelet medications was explored; these medications may increase the risk of bleeding and other side effects, even if they have demonstrated promise in lowering the risk of cancer and increasing patient survival. Overall, this review highlights the complex interactions between platelets and different cancers, with considerations for cancer treatment efficacy and safety.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".