Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes
Why this work is in the frame
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Bibliographic record
Abstract
Introduction The human genome is peppered with mobile repetitive elements called long interspersed nuclear element–1 (L1) retrotransposons. Propagating through RNA and cDNA intermediates, these molecular parasites copy and insert themselves throughout the genome, with potentially disruptive effects on neighboring genes or regulatory sequences. In the germ line, unique sequence downstream of L1 elements can also be retrotransposed if transcription continues beyond the repeat, a process known as 3′ transduction. There has been growing interest in retrotransposition and 3′ transduction as a possible source of somatic mutations during tumorigenesis. Rationale To explore whether 3′ transductions are frequent in cancer, we developed a bioinformatic algorithm for identifying somatically acquired retrotranspositions in cancer genomes. We applied our algorithm to 290 cancer samples from 244 patients across 12 tumor types. The unique downstream sequence mobilized with 3′ transductions effectively fingerprints the L1 source element, providing insights into the activity of individual L1 loci across the genome. Results Across the 290 samples, we identified 2756 somatic L1 retrotranspositions. Tumors from 53% of patients had at least one such event, with colorectal and lung cancers being most frequently affected (93% and 75% of patients, respectively). Somatic 3′ transductions comprised 24% of events, half of which represented mobilizations of unique sequence alone, without any accompanying L1 sequence. Overall, 95% of 3′ transductions identified derived from only 72 germline L1 source elements, with as few as four loci accounting for 50% of events. In a given sample, the same source element could generate 50 or more somatic transductions, scattered extensively across the genome. About 5% of somatic transductions arose from L1 source elements that were themselves somatic retrotranspositions. In three of the cases in which we sequenced more than one sample from a patient’s tumor, we were able to place 3′ transductions on the phylogenetic tree. We found that the activity of individual source elements fluctuated during tumor evolution, with different subclones exhibiting much variability in which elements were “on” and which were “off.” The ability to identify the individual L1 source elements active in a given tumor enabled us to study the promoter methylation of those elements specifically. We found that 3′ transduction activity in a patient’s tumor was always associated with hypomethylation of that element. Overall, 2.3% of transductions distributed exons or entire genes to other sites in the genome, and many more mobilized deoxyribonuclease I (DNAse-I) hypersensitive sites or transcription factor binding sites identified by the ENCODE project. Occasionally, somatic L1 insertions inserted near coding sequence and redistributed these exons elsewhere in the genome. However, we found no general effects of retrotranspositions on transcription levels of genes at the insertion points and no evidence for aberrant RNA species resulting from somatically acquired transposable elements. Indeed, as with germline retrotranspositions, somatic insertions exhibited a strong enrichment in heterochromatic, gene-poor regions of the genome. Conclusion Somatic 3′ transduction occurs frequently in human tumors, and in some cases transduction events can scatter exons, genes, and regulatory elements widely across the genome. Dissemination of these sequences appears to be due to a small number of highly active L1 elements, whose activity can wax and wane during tumor evolution. The majority of the retrotransposition events are likely to be harmless “passenger” mutations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it