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Prognostic Impact of TS, MTHFR and XRCC1 Genetic Variants in 113 Patients with Myelodysplastic Syndromes

2015· article· en· W2556091347 on OpenAlexaff
Federica Loscocco, Giuseppe Visani, Elisa Giacomini, Annamaria Ruzzo, Sara Galimberti, Maria Teresa Voso, Carlo Finelli, Elena Ciabatti, Emiliano Fabiani, Francesco Graziano, Sara Barulli, Antonio Volpe, Domenico Magro, Pier Paolo Piccaluga, Fabio Fuligni, Marco Vignetti, Paola Fazi, Alfonso Piciocchi, Cristina Clissa, Elisa Gabucci, Marco Rocchi, Mauro Magnani, Alessandro Isidori

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMethylenetetrahydrofolate reductaseXRCC1Myelodysplastic syndromesSingle-nucleotide polymorphismGenotypeInternal medicineInternational Prognostic Scoring SystemXRCC3BiologyOncologyHazard ratioGeneticsProportional hazards modelGastroenterologyMedicineGeneBone marrowConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Several studies have suggested that genetic variability related with single nucleotide polymorphisms (SNPs) of the BER system, DNA synthesis and folate-metabolizing pathway genes could modulate DNA repair capacity. Moreover, these genes are supposed to be related to cancer risk. However, the prognostic impact of the association of individual and/or combined genetic variants in patients with myelodysplastic syndromes (MDS) remains undetermined. Methods: We genotyped 113 MDS patients, 54 with IPSS low/int-1 receiving only best supportive care (BSC group) and 59 with IPSS int-2/high treated with azacitidine (AZA-group), for the following polymorphisms: XRCC1 194 and 399, APE1 148, XRCC3 241, TS5'-UTR (2R/3R and G/C) and 3'-UTR (6bp+/6bp-), MTHFR 677 and 1298. Genomic DNA was analyzed by High Resolution Melting assay and restriction digests of PCR products. Overall survival (OS) was calculated using the Kaplan-Meier estimate probabilities, and differences between survival curves were analyzed by the log-rank test. Multivariate analyses were performed using the Cox method. Results: For all the target genes, the distribution of genotypes was consistent with the Hardy-Weinberg equilibrium. Among the baseline characteristics analyzed (age, sex, diagnosis according to WHO, hemoglobin) there was no statistically significant difference in the genotype distribution of studied polymorphisms. In the BSC group, the variants XRCC1 399 GG [Hazard ratio (HR)=7.07; p=0.02], -6/-6 of TS3'-UTR (HR=4.65; p=0.05), 2R/3G, 3C/3G, 3G/3G of TS5'-UTR (HR=11.44; p=0.02) and TT of MTHFR 677 (HR=67.12; p<0.001), were associated with a statistically significant adverse clinical outcome compared to variant alleles (Table 1). This is consistent with the enzymatic activity reduction attributed to these genetic variants. Multivariable regression model analysis was also performed in the AZA group for the same genetic variants. We found similar results for the association between XRCC1 399 GG(HR=5.71 p=0.002), TS3'-UTR +6/+6(HR=0.097 p=0.004), MTHFR 677 TT (HR=8.58 p<0.001) and survival, but not for SNPs in TS5'-UTR (Table 2). Finally, we performed an exploratory analysis to investigate the combined effect of the unfavorable genotypes on survival. In the BSC group, the 3-year OS was 33% for those patients with ≥2 variant alleles, as compared to 62.5%, and 100%, respectively, for those with 2 or 0/1 variant alleles. The predictive role of the adverse genotypes combination on survival was confirmed also in the AZA group, suggesting that patients with a higher number of genetic variants had a shorter survival. Interestingly, when we compared survival of patients with adverse genotypes between BSC and AZA groups, we did not find any statistically significant difference between the 2 groups (Kaplan-Meyer and Log-rank test). Therefore, we speculated that azacitidine could give a survival advantage to patients with unfavorable genetic variants, independently from IPSS at diagnosis. Conclusion: Our study reveals, for the first time, an associations between genetic variants in TS, MTHFR and XRCC1 genes, BSC, azacitidine and survival in MDS patients. If confirmed, they could represent new prognostic markers able to provide guidance for clinical management of MDS patients. In particular, the presence of adverse genotypes could represent a biomarker to treat patients with low-risk IPSS with azacitidine, if confirmed on larger series. Further studies with larger population are needed to validate these associations, especially in SNPs with low variant allele frequency. Table 1. Gene Genotype Hazard risk 95,0% CI forHazard Risk Lower 95,0% CI forHazard Risk Upper p value XRCC1 399 [G/G] versus [A/G-A/A] 7,072 1,295 38,619 0,024 TS5'-UTR [3G/3G, 3G/3C, 2R/3G] versus [2R/2R, 2R/3C, 3C/3C] 11,447 1,330 98,544 0,026 TS3'-UTR [Del/Del] versus [Del/Ins, Ins/Ins] 4,653 0,946 22,874 0,058 MTHFR 677 [T/T] versus [C/T-C/C] 67,125 6,409 703,081 <0,001 Table 2. Gene Genotype Hazard risk 95,0% CI forHazard Risk Lower 95,0% CI forHazard Risk Upper p value XRCC1 399 [G/G] versus [A/G-A/A] 5,713 1,904 17,142 0,002 TS3'-UTR [Ins/Ins] versus [Ins6/del6, del6/del6] 0,097 0,019 0,479 0,004 MTHFR 677 [T/T] versus [C/T-C/C] 8,587 2,749 26,828 <0,001 Disclosures Finelli: Celgene: Other: Speaker, Research Funding; Novartis: Other: Speaker; Janssen: Other: Speaker.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.271
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
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