Increased expression of the immunoproteasome subunits PSMB8 and PSMB9 by cancer cells correlate with better outcomes for triple-negative breast cancers
Bibliographic record
Abstract
Proteasome dependency is a feature of many cancers that can be targeted by proteasome inhibitors. For some cancer types, notably breast cancer and triple-negative breast cancer (TNBC), high mRNA expression of a modified form of the proteasome, called the immunoproteasome (ImP), correlates with better outcomes and higher expression of one ImP subunit was associated with slower tumor growth in a small patient cohort. While these findings are in line with an anti-tumoral role of the ImP in breast cancer, studies investigating ImP expression at the protein level in large patient cohorts are lacking. Furthermore, while ImPs can be found in both immune and non-immune cells, the cellular source is often ignored in correlative studies. In order to determine the impact of ImP expression on breast cancer outcomes, we assessed the protein expression and cellular source of the ImP subunits PSMB8 and PSMB9 in a cohort of 2070 patients. Our data show a clear correlation between high ImP expression and better outcomes, most notably for TNBC patients and when tumor cells rather than stromal or immune cells express PSMB8 or PSMB9. Our results therefore suggest that ImP expression by tumor cells could be used as prognostic markers of TNBC outcomes.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".