Associated Papers
Establishment and validation of a gene mutation-based risk model for predicting prognosis and therapy response in acute myeloid leukemia.
Liu Y et al.
Heliyon, 2024
38831838 (PubMed )
Supplement: mmc3.xlsx › Sheet1
Broad Next-Generation Integrated Sequencing of Myelofibrosis Identifies Disease-Specific and Age-Related Genomic Alterations.
Kandarpa M et al.
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research, 2024
38386293 (PubMed )
Supplement: ccr-23-0372_supplemental_table_2_suppst2.xlsx › Suppl._Table_2
Inferring the initiation and development of myeloproliferative neoplasms.
Hermange G et al.
Proceedings of the National Academy of Sciences of the United States of America, 2022
36083966 (PubMed )
Supplement: pnas.2120374119.sapp.pdf
ASXL1 mutation as a surrogate marker in acute myeloid leukemia with myelodysplasia-related changes and normal karyotype.
Prats-Martín C et al.
Cancer Medicine, 2020
Prediction of acute myeloid leukaemia risk in healthy individuals.
Abelson S et al.
Nature, 2018
29988082 (PubMed )
Supplement: NIHMS77608-supplement-Supplementary_Table_2.xlsx › 2.2_VC_ARCH-PD
Distinct age-associated molecular profiles in acute myeloid leukemia defined by comprehensive clinical genomic profiling.
Tarlock K et al.
Oncotarget, 2018
29899868 (PubMed )
Supplement: oncotarget-09-26417-s005.xlsx › genomics
Cellular stressors contribute to the expansion of hematopoietic clones of varying leukemic potential.
Wong TN et al.
Nature Communications, 2018
29386642 (PubMed )
Supplement: 41467_2018_2858_MOESM8_ESM.xlsx › TAML-TMDS
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