Effectiveness of sulforaphane in increasing drug mediated cytotoxicity toward cancer stem cells in head and neck squamous cell carcinoma
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada with the head and neck cancer (HNC) ranked seventh in the rate of incidence.More than 90% of HNCs are squamous cell carcinomas (HNSCC) with an overall survival rate of 64.5%.One suggested cause for cancer treatment failure is the limitation of chemotherapy (CT) efficacy by its severe toxic side effects as the conventional treatment will cause damage to non-cancerous cells along with the cancer cells.Thus, reducing the dose of used CT while maintaining its efficiency is critical for improving the treatment outcome of HNSCC.Another possible cause for treatment failure is the presence of a subpopulation of cells inside the tumor that is highly treatment-resistant and able to cause recurrence termed cancer stem cells (CSCs).A plausible way to improve HNSCC treatment is to identify, isolate and target these CSCs.The first aim of this thesis was to find if we can improve the efficacy of conventional chemotherapy; Cisplatin (CIS) and 5-Fluorouracil (5-FU), using a natural product Sulforaphane (SF), which is extracted from broccoli.We combined low doses of CT with SF in a dose concentration that can be achievable by oral ingestion of broccoli sprouts.This combined treatment was tested in-vitro on HNSCC cell lines SCC12 and SCC38 and on non-cancerous human cell line and primary cells for 3 days.Our results demonstrated that SF increased the cytotoxicity of CIS and 5-FU significantly by decreasing viability, proliferation, DNA repair after treatment and increasing apoptosis through activation of Caspase-dependent apoptosis pathway with no effect on non-cancerous cells.In conclusion, SF combined treatment can be a safe method to enhance chemotherapy and improve the patient's life quality. VIThe first step to target CSCs with any new treatment modality is to identify these cells and characterize them.The second aim of this thesis was to analyze the expression of CSCs cell markers CD44 and CD271 in HNSCC.The results showed that CD271+ cells are a subpopulation of CD44+ cells.In addition, CD44+/CD271+ cells have higher proliferation and growth rate, more treatment resistance and more tumorigenic in-vitro and in-vivo compared to CD44+/CD271-cells or the total cells population.These results suggest that CD271 is a more precise marker to identify HNSCC-CSCs compared to the widely used CD44.Utilizing the data we collected from the first two parts of our project, we targeted the HNSCC-CSCs using SF.The third part of this thesis examined if the combination of SF with conventional CT, such as CIS and 5-FU, would increase its efficacy on CSCs as was reported for the total cellular population in HNSCC.The results demonstrated that SF increased the cytotoxicity of CIS and 5-FU toward HNSCC-CSCs both in-vitro and in-vivo, inhibited proliferation and tumorigenicity and prevented the elevation of the expression of stem cell (SC) related genes, such as BMI-1 and ALDh1A1, with conventional chemotherapy.In summary, SF has a strong anti-cancerous effect.SF can augment the effect of CIS and 5-FU against HNSCC.Combining CD44 and CD271 cell markers are more reliable to isolate CSCs from HNSCC as compared to CD44 alone.Finally, SF proved to be a very promising anti-cancer stem cells therapy either alone or as a combination with conventional chemotherapy.This naturally derived chemical has great potential for future clinical applications.VII
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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) | 0.000 | 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) | 0.004 | 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".