Human Leukocyte Antigen–Presented Macrophage Migration Inhibitory Factor Is a Surface Biomarker and Potential Therapeutic Target for Ovarian Cancer
Bibliographic record
Abstract
T cells recognize cancer cells via HLA/peptide complexes, and when disease overtakes these immune mechanisms, immunotherapy can exogenously target these same HLA/peptide surface markers. We previously identified an HLA-A2-presented peptide derived from macrophage migration inhibitory factor (MIF) and generated antibody RL21A against this HLA-A2/MIF complex. The objective of the current study was to assess the potential for targeting the HLA-A2/MIF complex in ovarian cancer. First, MIF peptide FLSELTQQL was eluted from the HLA-A2 of the human cancerous ovarian cell lines SKOV3, A2780, OV90, and FHIOSE118hi and detected by mass spectrometry. By flow cytometry, RL21A was shown to specifically stain these four cell lines in the context of HLA-A2. Next, partially matched HLA-A*02:01+ ovarian cancer (n = 27) and normal fallopian tube (n = 24) tissues were stained with RL21A by immunohistochemistry to assess differential HLA-A2/MIF complex expression. Ovarian tumor tissues revealed significantly increased RL21A staining compared with normal fallopian tube epithelium (P < 0.0001), with minimal staining of normal stroma and blood vessels (P < 0.0001 and P < 0.001 compared with tumor cells) suggesting a therapeutic window. We then demonstrated the anticancer activity of toxin-bound RL21A via the dose-dependent killing of ovarian cancer cells. In summary, MIF-derived peptide FLSELTQQL is HLA-A2-presented and recognized by RL21A on ovarian cancer cell lines and patient tumor tissues, and targeting of this HLA-A2/MIF complex with toxin-bound RL21A can induce ovarian cancer cell death. These results suggest that the HLA-A2/MIF complex should be further explored as a cell-surface target for ovarian cancer immunotherapy.
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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 | 0.000 | 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".