Abstract B56: Endogenous retrovirus transcript levels are associated with immunogenic signatures in multiple metastatic cancer types
Bibliographic record
Abstract
Abstract Variability in the immunogenic landscape of metastatic cancer lesions has revealed insight into detection of potential immunotherapy-responsive patients, though underlying mechanisms driving such variation are not fully understood. Endogenous retrovirus (ERV)-containing transcripts have recently emerged as a potential source of tumor-associated antigen that are orthogonal to somatic mutation-derived neoantigens. To characterize the intersection between ERV levels and predicted immunogenicity in metastatic cancer, we comprehensively profiled the transcript abundance of 702,533 ERV loci in 199 metastatic tumors from breast, colorectal, and pancreatic cancer patients. In all three cancer types, overall ERV transcript load was associated with upregulation of genes involved in innate antiviral response pathways as well as genes involved in both adaptive and innate immune signaling. In colorectal and pancreatic tumors, samples with concomitant increases in ERV load and antiviral response gene expression, termed viral mimicry tumors, showed high expression of the DNA demethylation gene TET2, a gene previously described to promote transcription of ERV-containing transcripts. Collectively, these data are compatible with the notion that an increased level of ERV-containing transcripts may account for increased immunogenicity in a subset of metastatic tumors and support the relationship between DNA demethylation and ERV load in colorectal and pancreatic tumors. Citation Format: James T. Topham, Emma Titmuss, Erin Pleasance, Laura M. Williamson, Joanna M. Karasinska, Luka Culibrk, Michael K.C. Lee, Steve E. Kalloger, Shehara Mendis, Richard A. Moore, Andrew J. Mungall, Janessa Laskin, Jonathan M. Loree, Dixie L. Mager, Marco A. Marra, Steven J.M. Jones, David F. Schaeffer, Daniel J. Renouf. Endogenous retrovirus transcript levels are associated with immunogenic signatures in multiple metastatic cancer types [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr B56.
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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 | 0.001 | 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) | 0.005 | 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".