Abstract ES7-2: Tumor Dormancy from an Experimental Biologist's Perspective
Bibliographic record
Abstract
Abstract Survival from breast cancer has improved steadily over the past decades. However, most breast cancer deaths are due to metastasis, and considerably less progress has been made against metastatic disease. We still know remarkably little about preventing or delaying metastatic recurrence or effectively treating the cancer if it does recur. Making breast cancer treatment even more challenging is the fact that breast cancer patients may appear to be cured of their disease, only to have it return later. These recurrences can happen even decades after apparently successful primary treatment. This creates years of uncertainty for breast cancer patients, who do not know if they have been cured or if the cancer will return, and makes clinical management difficult. We have used experimental models, both in vitro and in vivo, to try to understand the process of breast cancer metastasis. We have found that a population of cancer cells can remain as dormant cells, and have shown that these cells can resist being killed by cytotoxic chemotherapy that successfully kills actively diving cells. Experimental studies raise questions that must be addressed clinically. For example, what is the prevalence of dormant cells in breast cancer patients – are they common or rare? Are there factors inherent to the cancer cells that influence the likelihood of dormant cells persisting and subsequently re-awakening? Conversely, are there host microenvironmental factors, such as diet, exercise or stress, that can influence whether dormant cells persist and re-awaken? This information will be important in learning better how to prevent, delay and treat metastatic breast cancer. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr ES7-2.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".