Abstract ES2-3: The Clonal and Mutational Composition of Triple Negative Breast Cancers
Bibliographic record
Abstract
Abstract The notion that cancers are mosaics, composed of clones of cells has been fundamental to cancer biology for several decades. Clonal evolution may underpin clinical features of cancer, such as emergence of treatment resistance, the heterogeneous responses of metastatic disease within individual patients and divergence between primary and metastatic disease. Spatial and temporal variation in clonal composition poses significant challenges for diagnostic sampling and disease monitoring. We have recently developed next generation sequencing approaches to these questions and applied them to the understanding of mutational and clonal composition of primary triple negative breast cancers (TNBC). The notion that triple negative breast cancers form a distinct disease entity is already strongly challenged by expression analysis and small scale mutational analysis. Our results show that both the mutational composition and clonal structure of primary triple negative breast cancers is in fact a complete spectrum, exploding the notion of TNBC (currently defined), as a distinct disease. Clonal analysis suggests a means by which the genetic complexity might be reduced by following patient evolution over time and space. I will discuss the specific implications of the mutational and transcriptome landscapes of TNBC in relation to possible disease biologies. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr ES2-3.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".