Abstract 1520: Targetable immunogenic tumor specific antigens can be identified in non-coding regions of the genome
Bibliographic record
Abstract
Abstract CD8+ cytotoxic T cells are the main mediators of immune responses during cancer immunotherapy. Effective T cell functionality depends on the specific interaction with major histocompatibility (MHC) class I-bound peptide antigens. Significant efforts are being dedicated to the identification of novel tumor specific antigens (TSAs), investigating not only the known proteome, but also non-coding regions of the genome, that would allow for improved discrimination between cancer cells and healthy tissues. Through extensive comparisons of tumor and healthy tissues at the transcriptional and MHC-presented peptidome levels, TSAs were identified that derived from the translation in canonical and non-canonical reading frames of non-mutated non-coding genomic regions, including 5'- and 3'-untranslated regions (UTRs), introns and intergenic regions. A remarkable feature of these TSAs is that they are shared among patients and solid tumor types, thus representing ideal targets for cancer immunotherapies, including vaccines and adoptive cell therapies. To identify TSAs that can elicit T cell responses, a high throughput screening procedure was used to investigate the immunogenicity of 47 TSAs in the context of five common HLA types. Constructs harboring the TSA sequences were developed and transfected into HLA-matched monocyte-derived dendritic cells (mDCs) that were used to stimulate autologous CD8+ T cells. TSA-reactive T cells were enriched upon stimulation with antigen-positive and -negative cells using the T cell activation marker CD137 and sorted as single cells. Reactivity of individual T cell clones towards specific TSAs was confirmed by measuring cytokine release upon co-culture with HLA-matched TSA-positive and negative cell lines. Ten immunogenic TSAs were identified with this procedure, including at least one immunogenic TSA for each of the five analyzed HLAs. For some of these antigens, specific T cells were found in multiple healthy donors. The identified immunogenic TSAs derive from a variety of non-coding regions, such as introns, 5'-UTRs and non-coding RNAs. The T cell receptor (TCR) α and β chain sequences of TSA-reactive T cell clones were identified by NGS, engineered into a retroviral expression construct and transduced into CD8+ T cells. The reactivity of TCR-transgenic T cells against TSA-positive target cells was confirmed by recognition of TSA-peptide-loaded cell lines and target cells internally processing and presenting the TSAs. In conclusion, our high throughput screening approach successfully detected immunogenic TSAs. Furthermore, it can be used for the identification of TSA-reactive TCRs, thus representing a key tool in the development of novel TCR-based cancer immunotherapies targeting this novel class of TSAs. Citation Format: Tiziana Franceschetti, Qingchuan Zhao, Krystel Vincent, Claude Perreault, Slavoljub Milosevic, Daniel Sommermeyer. Targetable immunogenic tumor specific antigens can be identified in non-coding regions of the genome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1520.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.002 |
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".