Molecular genetic alterations in egfr CA-SSR-1 microsatellite and egfr copy number changes are associated with aggressiveness in thymoma
Bibliographic record
Abstract
BACKGROUND: The key role of egfr in thymoma pathogenesis has been questioned following the failure in identifying recurrent genetic alterations of egfr coding sequences and relevant egfr amplification rate. We investigated the role of the non-coding egfr CA simple sequence repeat 1 (CA-SSR-1) in a thymoma case series. METHODS: We used sequencing and egfr-fluorescence in situ hybridization (FISH) to genotype 43 thymomas; (I) for polymorphisms and somatic loss of heterozygosity of the non-coding egfr CA-SSR-1 microsatellite and (II) for egfr gene copy number changes. RESULTS: We found two prevalent CA-SSR-1 genotypes: a homozygous 16 CA repeat and a heterozygous genotype, bearing alleles with 16 and 20 CA repeats. The average combined allele length was correlated with tumor subtype: shorter sequences were significantly associated with the more aggressive WHO thymoma subtype group including B2/B3, B3 and B3/C histotypes. Four out of 29 informative cases analysed for somatic CA-SSR-1 loss of heterozygosity showed allelic imbalance (AI), 3/4 with loss of the longer allele. By egfr-FISH analysis, 9 out of 33 cases were FISH positive. Moreover, the two integrated techniques demonstrated that 3 out of 4 CA-SSR-1-AI positive cases with short allele relative prevalence showed significantly low or high chromosome 7 "polysomy"/increased gene copy number by egfr-FISH. CONCLUSIONS: Our molecular and genetic and follow up data indicated that CA-SSR-1-allelic imbalance with short allele relative prevalence significantly correlated with EGFR 3+ immunohistochemical score, increased egfr Gene Copy Number, advanced stage and with relapsing/metastatic behaviour in thymomas.
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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 | 0.001 | 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) | 0.001 | 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".