Abstract B19: Tumor-specific structural rearrangements as potential targets for anticancer vaccines.
Bibliographic record
Abstract
Abstract A hallmark of cancer is the accumulation of mutations that allow cells to proliferate uncontrollably. Every tumor has a unique set of somatic mutations, which can be identified with next generation sequencing. In theory, CD8+ T cells can recognize mutated proteins present in human cancers if the mutations are presented as peptides bound to MHC class I molecules. We hypothesize that personalized, tumor-specific peptide vaccines will activate CD8+ T cells and induce tumor regression. To test this concept, we subjected four mouse mammary tumors to RNA sequencing using the Illumina sequencing platform. We identified 14 somatic point mutations and one fusion transcript unique to these tumors. In addition, we have optimized a vaccination strategy involving long peptides and the adjuvant poly (I:C) that results in massive proliferation of antigen-specific CD8+ T cells. When we used this optimized vaccination protocol to target tumor specific mutations, a strong T cell response was elicited towards the fusion protein, but only weak responses or no responses were elicited towards the point mutations. Currently, we are vaccinating mice with immunogenic mutated-peptides, and assessing whether the T cell response elicited by the vaccines causes regression or rejection of established tumors that harbor the same mutations. As the cost of sequencing the human genome continues to decrease, personalized vaccines that target tumor-specific mutations may become a treatment strategy that is clinically feasible. Citation Format: Spencer David Martin, Darin A. Wick, John R. Webb, Robert A. Holt, Brad H. Nelson. Tumor-specific structural rearrangements as potential targets for anticancer vaccines. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology: Multidisciplinary Science Driving Basic and Clinical Advances; Dec 2-5, 2012; Miami, FL. Philadelphia (PA): AACR; Cancer Res 2013;73(1 Suppl):Abstract nr B19.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".