Molecular landscape of non-driver genes in myeloproliferative neoplasms through 333 cancer genes panel: Insights to reveal in Pakistan
Bibliographic record
Abstract
BACKGROUND: Discoveries of driver mutations in myeloproliferative neoplasms (MPNs) have filled the diagnostic gap however there are non-driver genes which play an important role in the phenotype of the disease. This study is the first to evaluate the molecular landscape of non-driver genes in MPNs patients from Pakistan. METHODS: A sample of fourteen MPNs patients (eight essential thrombocythemia, five primary myelofibrosis and one polycythemia vera) was investigated by the next generation sequencing, using 333 cancer genes panel. Chi square test was run in SPSS 22.0 to check association of non-driver genes with sub categories of MPNs. RESULT: Among 333 oncology related genes, possible pathogenic variations were identified in 2.1% of analyzed genes (7/333). TP53 and KIT were the only known frequent non-driver genes in MPNs which were found mutated in this study. The highest frequency (85.7%) was found of UGT1 A1 gene variant *28 with 71.4% heterozygous (*1/*28) and 14.2% homozygous genotype (*28/*28). Second most common (64.2%) detected gene variants were of MTHFR with WT/c.1298A > C, c.1298A > C/c.1298A > C and c.677C > T/c.1298A > C genotypes 28.5%, 28.5% and 7.1%, respectively. Frequency of TP53 substitution c.215C > G was 57.1% and XRCC1 c.1196 A > G was 42.8%. KIT CNV was 42.8% whereas KIT substitution c.1924A > G was 7.1%. The frequency of DPYD *9A/c.496A > G/ IVS1 0-15 T > C and *2A/*9A/c.496A > G was 21.4%. The lowest frequency (7.1%) was observed of CYP2D6 *4/*41. KIT was significantly (P = 0.026) frequently mutated in primary myelofibrosis patients (4/5). CONCLUSION: A distinct molecular landscape of non-driver genes was observed in MPNs from Pakistan and most of the genes detected belonged to drug metabolizing pathways.
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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 | 0.000 | 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".