Multifactorial remodeling of the cancer immunopeptidome by interferon gamma
Bibliographic record
Abstract
Abstract IFNγ alters the immunopeptidome presented on HLA class I (HLA-I), and its activity on cancer cells is known to be important for effective immunotherapy responses. We performed proteomic analyses of untreated and IFNγ-treated colorectal cancer patient-derived organoids (PDOs) and combined this with transcriptomic and HLA-I immunopeptidomics data to dissect mechanisms that lead to remodeling of the immunopeptidome through IFNγ. IFNγ-induced changes in the abundance of source proteins, switching from the constitutive- to the immunoproteasome, and differential upregulation of different HLA alleles explained some, but not all, observed peptide abundance changes. By selecting for peptides which increased or decreased the most in abundance, but originated from proteins with limited abundance changes, we discovered that the amino acid composition of presented peptides also influences whether a peptide is up- or downregulated on HLA-I through IFNγ. The presence of proline within the peptide core was most strongly associated with peptide downregulation. This was validated in an independent dataset. Proline substitution in relevant core positions did not influence the predicted HLA-I binding affinity or stability, indicating that proline effects on peptide processing may be most relevant. Understanding the multiple factors that influence the abundance of peptides presented on HLA-I in the absence or presence of IFNγ is important to identify the best targets for antigen-specific cancer immunotherapies such as vaccines or T-cell receptor engineered therapeutics.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".