Expression of MHC class I, HLA-A and HLA-B identifies immune-activated breast tumors with favorable outcome
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.
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 | 0.001 |
| 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) | 0.003 | 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".