Therapeutic potential of stem cells expressing suicide genes that selectively target human breast cancer cells: Evidence that they exert tumoricidal effects via tumor tropism
Bibliographic record
Abstract
Breast cancer is the most prevalent cancer in women worldwide and is classified into ductal and lobular carcinoma. Breast cancer as well as lobular carcinoma is associated with various risk factors such as gender, age, female hormone exposure, ethnicity, family history and genetic risk factor-associated genes. Genes associated with a high risk of developing breast cancer include BRCA1, BRCA2, p53, PTEN, CHEK2 and ATM. Surgery, chemotherapy, radiotherapy and hormone therapy are used to treat breast cancer but these therapies, except for surgery, have many side-effects such as alopecia, anesthesia, diarrhea and arthralgia. Gene-directed enzyme/prodrug therapy (GEPT) or suicide gene therapy, may improve the therapeutic efficacy of conventional cancer radiotherapy and chemotherapy without side-effects. GEPT most often involves the use of a viral vector to deliver a gene not found in mammalian cells and that produces enzymes which can convert a relatively non-toxic prodrug into a toxic agent. Examples of these systems include cytosine deaminase/5-fluorocytosine (CD/5-FC), carboxyl esterase/irinotecan (CE/CPT-11), and thymidine kinase/ganciclovir (TK/GCV). Recently, therapies based on genetically engineered stem cells (GESTECs) using a GEPT system have received a great deal of attention for their clinical and therapeutic potential to treat breast cancer. In this review, we discuss the potential of GESTECs via tumor tropism effects and therapeutic efficacy against several different types of cancer cells. GESTECs represent a useful tool for treating breast cancer without inducing injuries associated with conventional therapeutic modalities.
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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) | 0.001 | 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".