High Mutation Burden in ER-Positive/HER2-Negative/Luminal Breast Cancers
Bibliographic record
Abstract
BACKGROUND: Tumor mutation burden (TMB) is arising as a useful marker of checkpoint inhibitors' effectiveness in cancer patients in general and has been proposed as predictive in breast cancers. Despite the initial success of checkpoint inhibitors in triple-negative breast cancer, ER-positive breast cancers are less amenable to immunotherapy treatments due to the lower immunogenicity of this subset, associated with lower TMB and less pronounced inflammatory cell infiltration. However, a minority of ER-positive breast cancers do have a higher TMB and could be targets of immune checkpoint inhibitors. METHODS: This investigation uses publicly available genomic data to examine ER-positive/HER2-negative or luminal breast cancers with high mutation numbers and compare them with cancers of the same subtype and low mutation numbers. Clinical characteristics and molecular correlates according to mutation numbers are described. RESULTS: . A significant proportion of cancers with high mutation numbers carry mutations in microsatellite instability genes and genes involved in DNA damage response. Despite these differences, the prognosis of ER-positive/HER2-negative and luminal breast cancers with high mutation numbers is not significantly different compared to counterparts with lower mutation counts. CONCLUSIONS: These data may inform the potential suitability of these cancers for immunotherapy and could guide the development of rational combination therapies based on immune checkpoint inhibitors with other targeted drugs.
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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 | 0.001 |
| 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) | 0.002 | 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".