Expression of MHC class I, HLA-A and HLA-B identifies immune-activated breast tumors with favorable outcome
Why this work is in the frame
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Bibliographic record
Abstract
Antigen recognition by MHC class I molecules is a key step for the initiation of the immune response. We hypothesized that expression of these molecules could be a marker of immune-activated breast cancers. Data from KM Plotter were extracted to develop an exploratory cohort. Information from Cancer Genome Atlas (TCGA) and METABRIC was used to create two validation cohorts. Raw data were re-processed and analyzed using plyr R and Bioconductor. We predicted epitope-HLA binding to MHC I molecules by using NetMHC 4.0. Cox proportional hazards regression was computed to correlate gene expression and survival outcome. There was a weak but positive correlation between mutational burden and the expression of most MHC class I molecules. In the exploratory cohort, expression of HLA-A and HLA-B was associated with favorable relapse-free survival (RFS) and overall survival (OS) in the basal-like subgroup. This was confirmed in the METABRIC and TCGA dataset. Expression of HLA-A and HLA-B was associated with biomarkers of T cell activation (GZMA, GZMB, and PRF1) and improved the predictive capacity of known immunologic signatures. Several neopeptides expressed in breast cancer were also identified including FUK, SNAPC3, GC, ANO8, DOT1L, HIST1H3F, MYBPH, STX2, FRMD6, CPSF1, or SMTN, among others. Expression of HLA A and B is associated with T cell activation and identifies immune activated, basal-like breast cancers with favorable prognosis. Antigen recognition markers should be incorporated into the assessment of the tumor immune state of basal-like breast patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it