DNA amplification is a common mechanism of oncogene activation in lung cancer
Bibliographic record
Abstract
1719 Background: Genetic aberration and the corresponding activation of oncogenes is an important event in cancer development. This can occur through mechanisms such as mutation of DNA sequences, chromosomal translocations and segmental copy number changes, all which lead to deregulation of gene activity. The activation of oncogenes through gene amplification has been reported for many cancers. These mutations lead to an increase in gene dosage and in turn, can induce overexpression in cancer cells as a consequence. However, few genes have been shown to undergo this process as a mechanism of activation. The low incidence of oncogene amplification may be attributed to the failure of detection rather than governed by tumor biology. It is difficult to comprehensively identify genetic alterations using current molecular cytogenetic techniques due to their limited resolution, as small segmental amplifications may escape detection. Consequently their contribution to the oncogenic process may be grossly underestimated. Objective: To determine the contribution of gene amplification to the activation of oncogenes in cancer genomes by high-resolution tiling array comparative genomic hybridization (CGH). Methods: DNA was isolated from 104 cancer cell lines of multiple tissue origins. Cancer samples and normal reference DNA were differentially labeled for hybridization analysis using an array CGH platform that spans the human genome with a tiling set of BAC clones. Array data were assessed using SeeGH software and regions of genomic amplification were defined and compiled for each sample. Results: Over 100 tumor genome profiles of diverse tissue origins were generated. In addition to identifying known oncogenes previously shown to be activated by other genetic mechanisms that are frequently amplified, the delineation of amplification “hotspots” in the genome showed differences between tumor types and allowed the discovery of novel genes potentially involved in tumorigenesis. These hotspots were enriched for genes related to cell proliferation, apoptosis and linage dependency, reflecting functions advantageous to tumor growth. Integration of parallel copy number and expression data highlighted the downstream impact of these amplifications on gene transcription levels in lung cancer cell lines and clinical tumors. For example, multiple downstream components of the EGFR family signaling pathway, including CDK5, AKT1, and SHC1, are overexpressed as a direct result of gene amplification in lung cancer. Conclusions: Our findings suggest that DNA amplification is far more common a mechanism of oncogene activation than previously believed and that specific regions of the cancer genome are hotspots of amplification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".