Targeting Colorectal Cancer: Unravelling the Transcriptomic Impact of Cisplatin and High-THC Cannabis Extract
Bibliographic record
Abstract
Cisplatin and other platinum-derived chemotherapy drugs have been used for the treatment of cancer for a long time and are often combined with other medications. Unfortunately, tumours often develop resistance to cisplatin, forcing scientists to look for alternatives or synergistic combinations with other drugs. In this work, we attempted to find a potential synergistic effect between cisplatin and cannabinoid delta-9-THC, as well as the high-THC Cannabis sativa extract, for the treatment of HT-29, HCT-116, and LS-174T colorectal cancer cell lines. However, we found that combinations of the high-THC cannabis extract with cisplatin worked antagonistically on the tested colorectal cancer cell lines. To elucidate the mechanisms of drug interactions and the distinct impacts of individual treatments, we conducted a comprehensive transcriptomic analysis of affected pathways within the colorectal cancer cell line HT-29. Our primary objective was to gain a deeper understanding of the underlying molecular mechanisms associated with each treatment modality and their potential interactions. Our findings revealed an antagonistic interaction between cisplatin and high-THC cannabis extract, which could be linked to alterations in gene transcription associated with cell death (BCL2, BAD, caspase 10), DNA repair pathways (Rad52), and cancer pathways related to drug resistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".