Specificity of Aberrant Exons in a Saturated Background of Differentially Expressed Exons
Bibliographic record
Abstract
Abstract Determining the maximal specificity of aberrant exons from multifactorial disease tissues in a whole body background could provide insights into the exon origin as well as potential targets of diagnosis or therapy, but it remains a challenging task. For this purpose, we obtained a saturated list of differentially expressed exons (DEXs) from our independent reference human exome extracted from 56 normal tissues by DEXSeq. We found that the DEXs comprised the majority of the reference exome and that 99.4% of cancer-specific exons relative to paired normal tissues fell within the DEX list, consistent with the ectopic expression of the majority of aberrant exons. We then screened over seven thousand pathogenic single nucleotide variants (SNVs) in the TCGA and COSMIC databases by SpliceAI. The analysis identified over three hundreds of highly confident, mostly somatic SNV-specific splicing events and/or novel exonic fragments in five types of adenocarcinomas. Interestingly, these events are all associated with cis -acting mutations in tumor suppressor genes, particularly in TP53 with an antigenic novel peptide. This revelation of the DEX exome dominance and non-specificity of nearly all aberrant exons not only reshapes our understanding of the normal human exome by highlighting its mainly variable over constitutive exons, but also illuminates the path through cis -acting somatic and pathogenic SNVs to identify genuine cancer-specific exonic fragments that are absent in any normal tissues, beyond neo-exon junctions. This path holds promise for identifying novel peptide fragments for antigens in holistic cancer treatments including vaccination and immunotherapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".