Abstract 608: Augmenting and broadening T cell responses against mutated tumour neo-antigens
Bibliographic record
Abstract
Abstract Cytotoxic T lymphocytes (CTLs) recognize mutated tumor proteins (neo-antigens) and are important for anti-tumor immunity, especially in the context of immune checkpoint blockade immunotherapy (ICPB). Positive outcomes to ICPB are associated with high neo-antigen loads and with neo-antigen specific CTL responses. However only around 20% of patients respond to ICPB. In order to examine ways in which the non-responders might become responders we examined several strategies to improve response rates, using anti-CTLA4 initially as the ICPB therapy in BALB/c mice in which Uqcrc2 has been defined as a DNA/RNAseq-identified neo-antigen in AB1 tumor lines induced by a relevant human carcinogen(1). a) We first examined therapy-induced changes in T-cell responses against Uqcrc2. Anti-CTLA4 alone increased the magnitude of responses against Uqcrc2. Anti-CTLA4 combined with anti-GITR induced determinant spreading, unmasked responses against a new neo-antigen UNC45A that was undetectable during normal tumour growth. Immunogenic chemotherapy also unmasked responses against UNC45A, suggesting that subdominant neo-antigens can be unmasked by the appropriate immunotherapy. b) We then evaluated neo-antigen vaccination strategies. Uqcrc2 vaccination only protected against tumor growth when administered in combination with partial Treg depletion (Foxp3.DTR mice), suggesting that neo-antigen vaccination will only be maximally effective when administered in combination with therapies that modulate existing immune restraints, including Tregs. c) We determined optimal anatomical location for tracking neo-antigen CTL responses and identified the draining lymph node as an optimal, though not exclusive, location for response testing compared to blood or tumor. We are currently examining these observations using sequencing-defined neo-antigens in our patients and will present data on changes in human neo-antigen responses to therapy. These observations have important translational implications for identification of key neo-antigens, choice of therapy and monitoring of anti-tumor responses. 1. J. Creaney et al., Strong spontaneous tumor neoantigen responses induced by a natural human carcinogen. Oncoimmunology 4, e1011492 (2015). Citation Format: Bruce W. Robinson, Shaokang Ma, Jonathan Chee, Craig Rive, Paula Van Miert, Rob A. Holt, Jenette Creaney. Augmenting and broadening T cell responses against mutated tumour neo-antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 608. doi:10.1158/1538-7445.AM2017-608
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".