A library-based screening method identifies neoantigen-reactive T cells in peripheral blood prior to relapse of ovarian cancer
Bibliographic record
Abstract
Mutated cancer antigens, or neoantigens, represent compelling immunological targets and appear to underlie the success of several forms of immunotherapy. While there are anecdotal reports of neoantigen-specific T cells being present in the peripheral blood and/or tumors of cancer patients, effective adoptive cell therapy (ACT) against neoantigens will require reliable methods to isolate and expand rare, neoantigen-specific T cells from clinically available biospecimens, ideally prior to clinical relapse. Here, we addressed this need using “mini-lines”, large libraries of parallel T cell cultures, each originating from only 2,000 T cells. Using small quantities of peripheral blood from multiple time points in an ovarian cancer patient, we screened over 3.3 × 106 CD8+ T cells by ELISPOT for recognition of peptides corresponding to the full complement of somatic mutations (n = 37) from the patient's tumor. We identified ten T cell lines which collectively recognized peptides encoding five distinct mutations. Six of the ten T cell lines recognized a previously described neoantigen from this patient (HSDL1L25V), whereas the remaining four lines recognized peptides corresponding to four other mutations. Only the HSDL1L25V-specific T cell lines recognized autologous tumor. HSDL1L25V-specific T cells comprised at least three distinct clonotypes and could be identified and expanded from peripheral blood 3–9 months prior to the first tumor recurrence. These T cells became undetectable at later time points, underscoring the dynamic nature of the response. Thus, neoantigen-specific T cells can be expanded from small volumes of blood during tumor remission, making pre-emptive ACT a plausible clinical strategy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".