Toward Personalized Lymphoma Immunotherapy: Identification of Common Driver Mutations Recognized by Patient CD8+ T Cells
Bibliographic record
Abstract
PURPOSE: A fundamental challenge in the era of next-generation sequencing (NGS) is to design effective treatments tailored to the mutational profiles of tumors. Many newly discovered cancer mutations are difficult to target pharmacologically; however, T-cell-based therapies may provide a valuable alternative owing to the exquisite sensitivity and specificity of antigen recognition. To explore this concept, we assessed the immunogenicity of a panel of genes that are common sites of driver mutations in follicular lymphoma, an immunologically sensitive yet currently incurable disease. EXPERIMENTAL DESIGN: Exon capture and NGS were used to interrogate tumor samples from 53 patients with follicular lymphoma for mutations in 10 frequently mutated genes. For 13 patients, predicted mutant peptides and proteins were evaluated for recognition by autologous peripheral blood T cells after in vitro priming. RESULTS: Mutations were identified in 1-5 genes in 81% (43/53) of tumor samples. Autologous, mutation-specific CD8(+) T cells were identified in 23% (3/13) of evaluated cases. T-cell responses were directed toward putative driver mutations in CREBBP and MEF2B. Responding T cells showed exquisite specificity for mutant versus wild-type proteins and recognized lymphoma cells expressing the appropriate mutations. Responding T cells appeared to be from the naïve repertoire, as they were found at low frequencies and only at single time points in each patient. CONCLUSIONS: Patients with follicular lymphoma harbor rare yet functionally competent CD8(+) T cells specific for recurrent mutations. Our results support the concept of using NGS to design individualized immunotherapies targeting common driver mutations in follicular lymphoma and other malignancies. Clin Cancer Res; 22(9); 2226-36. ©2015 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 | 0.001 |
| 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".