Comprehensive transcriptome analysis reveals link between epigenetic dysregulation, endogenous retrovirus expression and immunogenicity in metastatic colorectal carcinoma.
Bibliographic record
Abstract
3535 Background: Endogenous retrovirus (ERV) elements represent genomic footprints of ancestral retroviral infections within the human genome. Previous studies have demonstrated increases in ERV mRNA as a result of DNA hypomethylation, and ERV transcription has been associated with increased immunogenicity in metastatic renal cell carcinoma. We performed comprehensive bioinformatics analysis of ERV transcription in metastatic colorectal carcinoma (mCRC), to identify novel links between ERV transcription, epigenetic dysregulation and immunogenicity in metastatic colorectal carcinoma (mCRC). Methods: Tumour samples from 63 patients with mCRC were subjected to RNA sequencing as part of the Personalized OncoGenomics program (POG; NCT02155621) at BC Cancer. Patients were enrolled between 07/2012-07/2017. ERV transcription was quantified across 702,533 distinct loci. Tumors were classified ERV-hi if their total ERV expression (RPKM) was greater than the mean across all samples. High antiviral gene expression tumors (AVG-hi) were designated as having a mean expression of IFIH1, DDX58, TLR3, TANK, TBKBP1, TBK1, IRF3 and IRF7 that was greater than the mean across all samples. All pairwise comparisons of gene expression were subjected to multiple hypothesis correction. Results: Median age was 59 years, with 34 (54%) male and 1 tumor microsatellite unstable. ERV-hi tumors showed increased expression of DNA demethylators TET2 ( q=0.0045) and TET3 ( q<0.0001). Significant overlap existed between ERV-hi and AVG-hi tumors (18/27, p=0.016). Tumors both ERV-hi and AVG-hi trended towards increased PD-L1 expression (p=0.055) and showed a significant increase in survival compared to tumors with high antiviral expression in the absence of high ERV transcription (p=0.0043). Conclusions: Our results suggest DNA demethylation drives increased ERV transcription and ERV-associated immunogenicity in mCRC. Moreover, we provide novel insight into the impact of ERV transcription on the biology of mCRC, highlighting ERV transcription as a potential biomarker and target for precision immunotherapy. Clinical trial information: NCT02155621.
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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 | 0.000 | 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".