Lapatinib inhibits stem/progenitor proliferation in preclinical in vitro models of ductal carcinoma in situ (DCIS)
Bibliographic record
Abstract
Breast-conserving surgery for ductal carcinoma in situ (DCIS) is often combined with irradiation, reducing recurrence rates to 20% within 10 years; however, there is no change in overall survival. Evidence in the invasive breast indicates that breast cancer stem cells (CSCs) are radiotherapy-resistant and are capable of re-initiating a tumor recurrence; hence, targeting CSCs in high risk DCIS patient may improve survival. HER2 is overexpressed in 20% of DCIS and is known to be highly active in breast CSCs; we therefore investigated the effect of Lapatinib on DCIS CSC activity using 2 in vitro culture systems. Two DCIS cell lines DCIS.com (HER2 normal) and SUM225 (HER2 overexpressed) as well as DCIS cells from patient samples (n = 18) were cultured as mammospheres to assess CSC activity and in differentiated 3D-matrigel culture to determine effects within the non-CSCs. Mammosphere formation was reduced regardless of HER2 status, although this was more marked within the HER2-positive samples. When grown as differentiated DCIS acini in 3D-matrigel culture, Lapatinib only reduced acini size in the HER2-positive samples via decreased proliferation. Further investigation revealed lapatinib did not reduce self-renewal activity in the CSC population, but their proliferation was decreased regardless of HER2 status. In conclusion we show Lapatinib can reduce DCIS CSC activity, suggesting that the use of Lapatinib in high-risk DCIS patients has the potential to reduce recurrence and the progression of DCIS to invasive disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".