Mutational Analysis of Gene Fusions Predicts Novel MHC Class I–Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: Gene fusions are frequently found in prostate cancer and may result in the formation of unique chimeric amino acid sequences (CASQ) that span the breakpoint of two fused gene products. This study evaluated the potential for fusion-derived CASQs to be a source of tumor neoepitopes, and determined their relationship to patterns of immune signatures in prostate cancer patients. Experimental Design: A computational strategy was used to identify CASQs and their corresponding predicted MHC class I epitopes using RNA-Seq data from The Cancer Genome Atlas of prostate tumors. In vitro peptide-specific T-cell expansion was performed to identify CASQ-reactive T cells. A multivariate analysis was used to relate patterns of in silico–predicted tumor-infiltrating immune cells with prostate tumors harboring these mutational events. Results: Eighty-seven percent of tumors contained gene fusions with a mean of 12 per tumor. In total, 41% of fusion-positive tumors were found to encode CASQs. Within these tumors, 87% gave rise to predicted MHC class I–binding epitopes. This observation was more prominent when patients were stratified into low- and intermediate/high-risk categories. One of the identified CASQ from the recurrent TMPRSS2:ERG type VI fusion contained several high-affinity HLA-restricted epitopes. These peptides bound HLA-A*02:01 in vitro and were recognized by CD8+ T cells. Finally, the presence of fusions and CASQs were associated with expression of immune cell infiltration. Conclusions: Mutanome analysis of gene fusion-derived CASQs can give rise to patient-specific predicted neoepitopes. Moreover, these fusions predicted patterns of immune cell infiltration within a subgroup of prostate cancer patients. Clin Cancer Res; 23(24); 7596–607. ©2017 AACR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".