The breast cancer genome and the complexity of different subgroups: what does it all mean?
Bibliographic record
Abstract
Recent progress in understanding breast cancer has come from identifying the various different molecular subgroups that exist within this heterogeneous disease.The authors in this Nature paper studied the genomic and transcriptional alterations that exist in two large series of breast cancers from UK and Canada tumour banks that had prolonged clinical follow-up, using one set as a discovery cohort (n=997) which was then tested in a second independent validation cohort (n=995). 1 Their unsupervised analysis of DNA-RNA profiles in the breast cancer genome specifically looked at copy number alterations (CNAs) which are a frequent acquisition in somatic breast cancers, in addition to loss of gene expression transcripts that may indicate gene deletions, somatic mutations or gene silencing by methylation.Using this approach, the authors identified ten subgroups with different and distinct clinical outcomes which were then validated in the second cohort.They discovered at least two novel subgroups.One was a high risk ER+ group with amplification of the 11q 13/14 cis-activating region which may contain some known amplicons that code for driver genes such as CCND1, as well as others such as EMSY, PAK1 and RSF1.Another subgroup with an excellent prognosis was marked by a paucity of CNAs, but had a strong immune/inflammatory signature with trans-acting deletion hotspots associated with a lymphocytic infiltrate and mature T lymphocytes with rearranged TCR loci.Yet another group of the so-called basal cancers harboured chromosome 5 deletions that were associated with alterations in the transcriptional control of cell cycle regulation and genomic/chromosomal instability that promote aneuploidy.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".