Recurrent Immunogenic Neoantigens and Their Cognate T-cell Receptors in Treatment-Resistant Metastatic Prostate Cancer
Bibliographic record
Abstract
New approaches that generate long-lasting therapeutic responses in patients with therapy-resistant metastatic cancer are urgently needed. To address this challenge, we developed Spot Neoantigens in Metastases (SpotNeoMet), a novel data-driven pipeline that systematically identifies recurrently presented neopeptides in treatment-resistant patients. We identified seven therapy resistance mutations predicted to produce neopeptides presented by common HLAs. Using HLA immunopeptidomics, we discovered three novel neopeptides derived from androgen receptor (AR) H875Y, a common metastatic castration-resistant prostate cancer (mCRPC) mutation. We validated these neoantigens as highly immunogenic and then isolated and characterized cognate T-cell receptors (TCR) from healthy donor peripheral blood mononuclear cells. We demonstrated that AR H875Y-specific TCRs are highly specific and kill prostate cancer cells presenting AR neopeptides in vitro and in vivo. Our new pipeline identifies novel immunotherapy targets and potential treatment options for patients with mCRPC. Moreover, SpotNeoMet offers a systematic route to identify "HLA-peptide" pairs and their cognate TCRs across treatment-resistant cancers. SIGNIFICANCE: As the emergence of resistance to targeted treatments in patients with metastatic cancer, there is an urgent need for innovative therapeutic approaches for this population. Our study provides a new analytic framework to identify neoantigens from treatment-resistant mutations and a proof-of-concept T cell-based immunotherapy treatment for mCRPC.
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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".