Lapatinib for breast cancer: a review of the current literature
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: The identification of HER-2 expression as a predictive and prognostic marker revolutionized breast cancer. Trastuzumab, a humanized mAb, improves survival in both early and advanced HER-2 overexpressing breast cancer. However, many cancers either do not respond or develop resistance to this agent. Lapatinib is an oral tyrosine kinase inhibitor which has both HER-1 and -2 activities and has been licensed for use in recurrent breast cancer that overexpresses HER-2. Studies of lapatinib in early breast cancer are ongoing. AREAS COVERED IN THIS REVIEW: A PubMed search was conducted using 'lapatinib' and 'breast cancer' as the key words. All published works up to July 2010 were reviewed. A manual review of abstracts presented at the ASCO Annual meeting and the San Antonio Breast Cancer Symposium was conducted for the last 2 years. In this review, we summarize the current knowledge of lapatinib and pose questions which need to be addressed as we further expand our knowledge of the HER-2 subtypes of breast cancer. WHAT THE READER WILL GAIN: The reader will gain an up-to-date and comprehensive review of the current literature as it pertains to the safety and efficacy of lapatinib in the treatment of breast cancer. TAKE HOME MESSAGE: Lapatinib has provided an alternative for the treatment of advanced HER-2 overexpressing breast cancer and is currently being assessed in early disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".