High frequency of mismatch repair deficiency among pediatric high grade gliomas in <scp>J</scp>ordan
Bibliographic record
Abstract
Biallelic mismatch repair deficiency (bMMRD) is a cancer predisposition syndrome affecting primarily individuals from consanguinous families resulting in multiple childhood cancers including high grade gliomas (HGG). This is the first study to assess the prevalence of bMMRD among patients with HGG in countries where consanguinity is high. We collected molecular and clinical information on all children diagnosed with HGG and supratentorial primitive neuroectodermal tumors (sPNET) between 2003 and 2013 at King Hussein Cancer Center, Jordan. Comparison was made to a similar cohort from Toronto. Clinical data regarding presence of café au lait macules(CAL), family history of cancer, consanguinity, pathology and treatment were collected. Tumors were centrally reviewed and tested for MMRD by immunohistochemistry of the corresponding proteins. Forty-two patients fulfilled the inclusion criteria, including 36 with HGG. MMRD was observed in 39% of HGG of whom 79% also lost MMR staining in the corresponding normal cells suggestive of bMMRD. P53 dysfunction was highly enriched in MMR deficient tumors (p = 0.0003).The frequency of MMRD was significantly lower in Toronto cohort (23%, p = 0.03). Both evidence of CAL and consanguinity correlated with bMMRD (p = 0.005 and 0.05,respectively) but family history of cancer didn't. HGG with all three bMMRD risk factors had evidence of MMRD and all children affected by multiple bMMRD related cancers had identical gene loss by immunohistochemical staining. In Jordan, the frequency of clinical and immunohistochemical alterations suggestive of bMMRD in pediatric HGG is high. Genetic testing will enable appropriate counseling and cancer screening to improve survival of these patients.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".