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. 2023 Mar 28;7(6):1040-1044.
doi: 10.1182/bloodadvances.2022007988.

Germline SNPs previously implicated as prognostic biomarkers do not associate with outcomes in intensively treated AML

Aarif M N Batcha  1   2 , Nele Buckup  3 , Stefanos A Bamopoulos  3   4 , Vindi Jurinovic  1   3 , Maja Rothenberg-Thurley  3 , Hanna Gittinger  3 , Bianka Ksienzyk  3 , Annika Dufour  3 , Stephanie Schneider  3   5 , Mika Kontro  6   7   8 , Joseph Saad  7 , Caroline A Heckmann  7 , Cristina Sauerland  9 , Dennis Görlich  9 , Wolfgang E Berdel  10 , Bernhard J Wörmann  11 , Utz Krug  12 , Jan Braess  13 , Ulrich Mansmann  1   2   14   15 , Wolfgang Hiddemann  3   14   15 , Karsten Spiekermann  3   14   15 , Klaus H Metzeler  16 , Tobias Herold  3   14   15
Affiliations

Germline SNPs previously implicated as prognostic biomarkers do not associate with outcomes in intensively treated AML

Aarif M N Batcha et al. Blood Adv. .
No abstract available

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Conflict of interest statement

Conflict-of-interest disclosure: H.G. is a current employee of Roche Pharma AG, Grenzach-Wyhlen, Germany. The remaining authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Flow diagram of cohorts and statistical analysis. (A) Flow diagram of cohorts under study. The difference in the number of variants called is due to the difference in sequencing types and the region of coverage. Sample exclusion comprise those (1) with missing phenotype information, (2) with M3 sub-types: t(15;17), and (3) exclusion of pretreated patients limiting the analysis to patients receiving AML-directed induction treatment. Variant exclusion consist of those (1) that are not annotated in the dbSNP database; (2) with major allele frequency (MAF) < 1% in European population based on 1000 Genomes project (excluding rare SNPs in European population); (3) with MAF <5% in our primary cohort (excluding rare SNPs in our cohort); (4) which failed with Hardy-Weinberg equilibrium P-value threshold of 10−6; and (5) which are missing in more than 70% of samples. (B) Statistical analyses applied. Cox proportional hazards models were applied for detecting differences in OS and RFS, whereas logistic models were used for CR, RD, and other genetic factors. In the case of ELN2017 association with SNPs, we used the Jonckheere-Terpstra test to identify ordered differences among different groups. SNPs significant in the univariate analysis (unadjusted P < .01) were included in a multivariable model and stratified for age and the ELN2017 classification. We also added cohort as covariate for those SNPs that were present in both AMLCG and AMLSG data sets. For comparison, we also performed multivariable models excluding the stratification for ELN2017. The P values from the multivariable models were corrected for multiple testing using the Benjamini-Hochberg method. CR, complete remission; RD, refractory disease; SNV, single nucleotide variant; TGCA, The Cancer Genome Atlas.
Figure 2.
Figure 2.
Multivariable analysis of common SNPs stratified for age and cohort. The colors represent if the analysis was also accounted for the ELN2017 classification. The Cox proportional hazard model was used for both OS and RFS, whereas logistic regression was used for CR. The numbers represent odds ratio/hazard ratio in its respective analysis. The red line indicates the adjusted P value cutoff of .01 after multiple testing correction. In the case of the validation cohort, we did not adjust the P values for multiple testing correction.
See this image and copyright information in PMC

References

    1. Megías-Vericat JE, Rojas L, Herrero MJ, et al. Influence of ABCB1 polymorphisms upon the effectiveness of standard treatment for acute myeloid leukemia: a systematic review and meta-analysis of observational studies. Pharmacogenomics J. 2015;15(2):109–118. - PubMed
    1. Falk IJ, Fyrberg A, Paul E, et al. Decreased survival in normal karyotype AML with single-nucleotide polymorphisms in genes encoding the AraC metabolizing enzymes cytidine deaminase and 5′-nucleotidase. Am J Hematol. 2013;88(12):1001–1006. - PubMed
    1. Kutny MA, Alonzo AL, Gamazon ER, et al. Ethnic variation of TET2 SNP rs2454206 and association with clinical outcome in childhood AML: a report from the Children’s Oncology Group. Leukemia. 2015;29(12):2424–2426. - PMC - PubMed
    1. Perez-Andreu V, Roberts Kathryn G, Heng Xu, et al. A genome-wide association study of susceptibility to acute lymphoblastic leukemia in adolescents and young adults. Blood. 2015;125(4):680–686. - PMC - PubMed
    1. Walker CJ, Oakes CC, Genutis LK, et al. Genome-wide association study identifies an acute myeloid leukemia susceptibility locus near BICRA. Leukemia. 2019;33(3):771–775. - PMC - PubMed

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