Unveiling Mysteries: Novel Germline Mutations in Children with Bone Marrow Failure Syndrome and Hematologic Malignancy of Arab Ancestry
Bibliographic record
Abstract
Introduction : Inherited predisposition to Hematologic malignancies, due to deleterious germline variants in a variety of genes, is an important clinical entity with implications for the health and management of patients and their family members. We sought to study genetic of Cancer predisposition and or bone marrow failure genes in children treated at King Hussein Cancer Center. Methods: This is a retrospective review of patients who were referred to our Cancer Predisposition clinic that was established in Jan 2020. Referred patients were screened using Jongmans' and McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) for cancer predisposition. Next-generation sequencing of germline DNA was performed using panels of genes suited for patients' diagnosis and family history. Results: 103 patients were screened; all were under age of 18-years .Most of patients fulfilled Jongmans' and/or MIPOGG criteria; sixty patients (58%) had family history of cancer, congenital or other phenotypic anomalies were detected in 45 patients with inherited bone marrow failure syndrome (IBMFS), five patients were referred due the diagnosis of two malignancies, excessive toxicity related to cancer treatment was reported in 10 patients. Primary Hematological malignancies were diagnosed in 58 patients : ALL (n=17), AML (n=11), Myelodysplastic (MDS)/ myeloproliferative diseases (MPD) (n=8) and lymphomas (n=22 ). Pathogenic/Likely pathogenic germline mutations were identified in 25 patients (43%), VUS /likely causative in 27 patients (47% ) with hematologic malignancy across the cancer predisposition (CPS) genes as following: CPS genes in ALL( MSH6,PTCH1,TP53, MET, CDH1, POLE, ERCC6L2, RUNX1, RAD50, ATM, SDHA, RABGAP1, POT1, VHL). CPS genes in AML( ACD, GATA2, MUTYH, BPTF,FANCA, FANCG, HAX1,ELANE and mitochondrial DNA deletion ~5kb in Pearson syndrome). CPS genes in MDS/MPD ( NF1, PTPN11, VPS45, GATA2, ACD,DUT, FANCG, ELANE). CPS genes in Lymphoma(RASGRP1, BRCA1, CHEK2, ATM, MSH3,TNFRSF9, PTCH1, MSH6, NAF1, CASR, DCLRE1C, RASGRP1, RIPK1, ATM, BRCA2, ERBIN, STK4, DOCK8). Pathogenic/Likely pathogenic germline mutations were identified in 36 patients (82%), VUS /likely causative in 8 patients (18%) with IBMFS as following: Fanconi Anemia (FANCA,FANCD2,FANCF,FANCG) Diamond-Blackfan anemia(RPL11, RPS19, RPL5, RPL35A), Dyskeratosis congenita (TINF2, PARN), Shwachman-Diamond Syndrome (SBDS), Severe congenital neutropenia(CSF3R), ERCC6L2 Syndrome (ERCC6L2), Adenosine Deaminase 2 deficiency( ADA2),CBL Syndrome(CBL), Epimerase deficiency galactosemia (GALE), Ghosalhemato diaphyseal dysplasia (TBXAS1),Idiopathic Bone marrow failure ( ALPK3), Bone Marrow Failure Syndrome 4 (MYSM1),THPO-Hereditary Thrombocytopenia (THPO),TUBB1-related Macrothrombocytopenia ( TUBB1), X-linked recessive Wiskott-Aldrich syndrome (WAS). Conclusion: By screening our patients carefully, we were able to identify a significant number of patients with cancer predisposition Syndrome. The early recognition of genetic predispositions for childhood cancers may provide an opportunity of therapy adjustment and specific screening for different syndrome-related malignancies as well as genetic counseling. Further refinement of our testing may require WES to detect structural variations and collaboration with research centers to perform functional testing for patients with VUS.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".