Bibliographic record
Abstract
Abstract Breast cancers exhibit inter-patient and intra-tumoural genomic variability which underpins our understanding of intrinsic drivers of the disease. The advent of next generation sequencing methods and informatics approaches has redefined the landscape of primary breast cancer subtypes into many more molecularly defined patient subgroups than had been previously appreciated, thus redefining our understanding of inter-patient variation. These studies have implications for the understanding of driver mutation context in primary cancers and should pattern future clinical and molecular studies of primary breast cancers. Closely linked to inter-patient genomic variability is the notion that most cancers are ecosystems of evolving cellular 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 5 years next generation sequencing of tumours 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 tumour xenografts, to illustrate the impact of clonal evolution on biological studies of breast cancer in model systems. Citation Format: Aparicio S. Clonal Dynamics and Breast Cancer Subtypes. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr PL2.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".