Molecular characterization of 13 patients with <scp><i>PIK3CA</i></scp>‐related overgrowth spectrum using a targeted deep sequencing approach
Bibliographic record
Abstract
Activating variants in the PIK3CA gene cause a heterogeneous spectrum of disorders that involve congenital or early-onset segmental/focal overgrowth, now referred to as PIK3CA-related overgrowth spectrum (PROS). Historically, the clinical diagnoses of patients with PROS included a range of distinct syndromes, including CLOVES syndrome, dysplastic megalencephaly, hemimegalencephaly, focal cortical dysplasia, Klippel-Trenaunay syndrome, CLAPO syndrome, fibroadipose hyperplasia or overgrowth, hemihyperplasia multiple lipomatosis, and megalencephaly capillary malformation-polymicrogyria (MCAP) syndrome. MCAP is a sporadic overgrowth disorder that exhibits core features of progressive megalencephaly, vascular malformations, distal limb malformations, cortical brain malformations, and connective tissue dysplasia. In 2012, our research group contributed to the identification of predominantly mosaic, gain-of-function variants in PIK3CA as an underlying genetic cause of the syndrome. Mosaic variants are technically more difficult to detect and require implementation of more sensitive sequencing technologies and less stringent variant calling algorithms. In this study, we demonstrated the utility of deep sequencing using the Illumina TruSight Oncology 500 (TSO500) sequencing panel in identifying variants with low allele fractions in a series of patients with PROS and suspected mosaicism: pathogenic, mosaic PIK3CA variants were identified in all 13 individuals, including 6 positive controls. This study highlights the importance of screening for low-level mosaic variants in PROS patients. The use of targeted panels with deep sequencing in clinical genetic testing laboratories would improve diagnostic yield and accuracy within this patient population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".