Targeting tumour-initiating cells to improve the cure rates for triple-negative breast cancer
Bibliographic record
Abstract
Tumour recurrence is one of the biggest challenges in breast cancer management because it affects 25-30% of women with breast cancer and the tumours are often incurable. Women with triple-negative breast cancer (TNBC--lacking expression of the oestrogen receptor, progesterone receptor and the receptor HER2/ERBB2) have the highest rates of early recurrence relative to other breast cancer subtypes. Early recurrence might be due to tumour-initiating cells (TICs), which are resistant to conventional therapies, can remain dormant and can subsequently give rise to secondary tumours. In breast cancer, TICs are identified by the cell-surface markers CD44+/CD24-/EpCAM+ and/or possess ALDH1 enzyme activity. This subpopulation has the ability to self-renew, grow as mammospheres and initiate tumour formation. Fuelling the problem of relapse is the fact that chemotherapy and radiation can induce or select for TICs; this was reported in preclinical models and more recently in women being treated for breast cancer. Thus, new therapeutic agents for TNBC are presently being sought to overcome this problem. Here we review the roles of receptor tyrosine kinases, signalling intermediates and transcription factors in sustaining the TIC subpopulation. Particular emphasis is placed on targeting these molecules in order to eliminate and/or prevent the induction of TICs and ultimately reduce the frequency of TNBC recurrence.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".