Genomic analysis of a Palestinian family with inherited cancer syndrome: a next-generation sequencing study
Bibliographic record
Abstract
Familial predisposition is a strong risk factor for different types of cancer and accounts for around 10% of the cases. In this study, we investigated cancer predisposition in a Palestinian family using whole-exome sequencing (WES) technologies. In this study, we focused more on cutaneous melanoma (CM). Our analysis identified three heterozygous rare missense variants, WRN (p.L383F and p.A995T) and TYRP1 (p.T262M) and a pathogenic homozygous missense mutation in ERCC2 (p.R683Q). Although WRN and TYRP1 genes and their variations were correlated with different types of cancer, including melanoma, the currently identified WRN and TYRP1 variants were not reported previously in melanoma cases. The pathogenic mutation was segregated with the clinical phenotypes and found in the two affected brothers, one with CM and the other with brain tumor, and was confirmed by Sanger sequencing analysis. Segregation analysis of this mutation revealed that family members are either heterozygous or wild type. Our findings confirm that the homozygous ERCC2 (p.R683Q) mutation was responsible for causing melanoma and other cancer types in the family. Our work highlights the value to decipher the mutational background of familial cancers, especially CM, in the Palestinian population to guide diagnosis, prevention, and treatment of affected patients and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".