Abstract IA13: Clonal dynamics of normal and malignant human mammary cell growth in xenografts
Bibliographic record
Abstract
Abstract Most human breast cancers have diversified genomically and biologically by the time they become clinically evident and little is known about their origin from normal human mammary cells, or the cellular and molecular mechanisms that lead to their genesis and evolution. We have developed methods to quantify, purify and characterize different subsets of normal human mammary cells and have used these to identify properties that may influence their propensity for transformation. We have also developed methods for inducing the rapid transformation in vivo of these purified subsets following their transplantation into immunodeficient mice. The results demonstrate the ability of a single oncogene (KRASG12D) to induce the formation of serially transplantable, polyclonal, invasive ductal carcinomas within 8 weeks of being introduced either subrenally or subcutaneously into immunodeficient mice. Both primary and secondary tumors are phenotypically heterogeneous and transcriptome analyses of primary tumors assign them to a “normal-like” category. DNA barcoding of the cells at the time of their initial transduction with KRASG12D has revealed a dramatic change in the numbers and sizes of clones they generate after 2 weeks in vivo. DNA barcoding also showed the unexpected appearance of many “new” clones in tumors generated upon passage into secondary recipients, thus recapitulating some features of in vivo passaged human breast cancer cell lines and patients’ tumor xenografts. This system challenges previous concepts about the process of human mammary oncogenesis and provides a new system for analyzing factors that can influence its speed, efficiency and heterogeneity of outcomes. Citation Format: Connie J. Eaves, Long Nguyen, Davide Pellacani, Nagarajan Kannan, Sylvan Lefort, Sneha Balani, Claire Cox, Tomo Osako, Samuel Aparicio, Martin Hirst. Clonal dynamics of normal and malignant human mammary cell growth in xenografts. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr IA13.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".