Abstract IA16: Clonal evolution in breast cancer patients and patient derived xenografts
Bibliographic record
Abstract
Abstract The notion that most cancers are ecosystems of evolving clones has implications for biological understanding and clinical application. The evolution of clonal composition has particular significance when evidence of positive or negative selection can be associated with the clonal genotype or epigenotype. Over the last 4 years next generation sequencing of tumors and methods for single cell analysis have opened up this approach for solid epithelial malignancies. I will discuss the implications of clonal evolution for cancer medicine and biological studies of cancer with reference to breast cancers. We have developed informatics approaches to population level clonal analysis and extended these to single cell measurements of genotypes. I shall discuss our more recent data from single cell sequencing and clonal analysis applied to clonal evolution of patient derived tumor xenografts, to illustrate the impact of clonal evolution on biological studies of cancer in model systems. Citation Format: Samuel Aparicio. Clonal evolution in breast cancer patients and patient derived 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 IA16.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".