Distinguishing Genetic Alterations Versus (Epi)Mutations in Silver–Russell Syndrome and Focus on the <i>IGF1R</i> Gene
Bibliographic record
Abstract
CONTEXT: Silver-Russell Syndrome (SRS) is a growth retardation disorder characterized by pre- and postnatal growth failure, relative macrocephaly at birth, prominent forehead, body asymmetry, and feeding difficulties. The main molecular mechanisms are imprinting alterations at multiple loci, though a small number of pathogenic variants have been reported in the SRS genes IGF2-PLAG1-HMGA2 and CDKN1C. However, around 40% of clinically suspected SRS cases do not achieve a molecular diagnosis, highlighting the necessity to uncover the underlying mechanism in unsolved cases. OBJECTIVE: Evaluate the frequency of genetic variants in undiagnosed SRS patients [Netchine-Harbison Clinical Scoring System (NH-CSS) ≥ 4], and investigate whether (epi)genetic patients may be distinguished from genetic patients. METHODS: One hundred thirty-two clinically SRS patients without (epi)genetic deregulations were investigated by whole-exome (n = 15) and targeted (n = 117) Sequencing. Clinical data from our cohort and from an extensive revision of the literature were compared. RESULTS: Pathogenic variants were identified in 9.1% of this cohort: 3% in IGF2, PLAG1, and HMGA2 genes and 3% in the IGF1R gene, associated with IGF-1 resistance (IGF1RES), an SRS differential diagnosis. Overall, IGF2-PLAG1-HMGA2 and IGF1R account for 3.6% of SRS with NH-CSS score ≥ 4. A clinical cross-comparison of (epi)genetic vs genetic SRS underlined (epi)genotype-phenotype correlation highlighted the prevalence of body asymmetry and relative macrocephaly in mosaic (epi)genetic SRS and recurrence of genetic familial cases. Furthermore, overlapping features were evidenced in (epi)genetic SRS and IGF1RES patients. CONCLUSION: Our study explores the frequency of genetic SRS, underscores body asymmetry as a distinctive phenotype in (epi)genetic SRS and suggests IGF1R sequencing in a SRS diagnostic flowchart.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".