Varaico: Literature-Extracted Genetic Variants

Gene:RUNX1
Variant:p.D198G
Back to variants list

RUNX1

NM_001754.5:p.D198G

Associated Papers

Comparative outcome and prognostic factor analysis among MDS/AML patients with TP53 mutations, snps, and wild type.

Yang X et al.

Human Genomics, 2025

40877983 (PubMed )
Supplement: 40246_2025_792_MOESM2_ESM.xlsx › Table_S4

Single-cell Transcriptional Atlas of Human Hematopoiesis Reveals Genetic and Hierarchy-Based Determinants of Aberrant AML Differentiation.

Zeng AGX et al.

Blood Cancer Discovery, 2025

40294241 (PubMed )
Supplement: bcd-24-0342_supplementary_tables_s1-s21_suppst1.xlsx › S18_-_Tapestri_CO131_Panel

Clinical and analytical validation of a combined RNA and DNA exome assay across a large tumor cohort.

Yudina A et al.

Communications Medicine, 2025

40523952 (PubMed )
Supplement: 43856_2025_934_MOESM3_ESM.xlsx › Table_S22

Impact of myelodysplasia-related gene mutations and residual mutations at remission in venetoclax/azacitidine for AML.

Ikoma Y et al.

Leukemia, 2025

40259101 (PubMed )
Supplement: 41375_2025_2625_MOESM2_ESM.xlsx › Sheet5

Persistent postremission clonal hematopoiesis shapes the relapse trajectories of acute myeloid leukemia.

Chow RD et al.

Blood Advances, 2025

39938015 (PubMed )
Supplement: BLOODA_ADV-2024-015149-mmc2.xlsx › S2

Emergence and Cytogenetic Clonal Evolution of Chromosome 7 Abnormalities in Myeloid Malignancies: Investigating the Role of Telomere Dysfunction.

Baldazzi C et al.

International Journal of Molecular Sciences, 2025

39940930 (PubMed )
Supplement: ijms-26-01162-s001.zip › Supplementary_Table_S1.xlsx › Supplementary_Table_1

Early drivers of clonal hematopoiesis shape the evolutionary trajectories of de novo acute myeloid leukemia.

Chow RD et al.

Medrxiv : the Preprint Server for Health Sciences, 2024

39252918 (PubMed )
Supplement: media-1.xlsx › S1

Whole Exome Sequencing of Intermediate-Risk Acute Myeloid Leukemia without Recurrent Genetic Abnormalities Offers Deeper Insights into New Diagnostic Classifications.

Guijarro F et al.

International Journal of Molecular Sciences, 2024

39201354 (PubMed )
Supplement: ijms-25-08669-s001.zip › ijms-3087068-supplementary › Supplementary_Tables_S1-S5.xlsx › Table_S1

Molecular and clinical analyses of PHF6 mutant myeloid neoplasia provide their pathogenesis and therapeutic targeting.

Kubota Y et al.

Nature Communications, 2024

38418452 (PubMed )
Supplement: 41467_2024_46134_MOESM5_ESM.xlsx › Supplementary_Data_2

Spontaneous remission and loss of monosomy 7: a window of opportunity for young children with SAMD9L syndrome.

Erlacher M et al.

Haematologica, 2024

Mining cancer genomes for change-of-metabolic-function mutations.

Tu KJ et al.

Communications Biology, 2023

37950065 (PubMed )
Supplement: 42003_2023_5475_MOESM2_ESM.pdf

Mutational Profile Enables the Identification of a High-Risk Subgroup in Myelodysplastic Syndromes with Isolated Trisomy 8.

Toribio-Castelló S et al.

Cancers, 2023

37568638 (PubMed )
Supplement: cancers-15-03822-s001.zip › Supplementary_tables.xlsx › Table_S2

RAB27B controls palmitoylation-dependent NRAS trafficking and signaling in myeloid leukemia.

Ren JG et al.

The Journal of Clinical Investigation, 2023

37317963 (PubMed )
Supplement: jci-133-165510-s058.xlsx › Sheet2

Distinct gene expression pattern of RUNX1 mutations coordinated by target repression and promoter hypermethylation in acute myeloid leukemia.

Li J et al.

Frontiers of Medicine, 2022

Acquired somatic variants in inherited myeloid malignancies.

Armes H et al.

Leukemia, 2022

35140362 (PubMed )
Supplement: 41375_2022_1515_MOESM4_ESM.xlsx › Sheet1

RUNX1 and cancer.

Lin TC

Biochimica Et Biophysica Acta. Reviews on Cancer, 2022

SmMIP-tools: a computational toolset for processing and analysis of single-molecule molecular inversion probes-derived data.

Medeiros JJF et al.

Bioinformatics (oxford, England), 2022

35150236 (PubMed )
Supplement: btac081_supplementary_data.zip › Table_S4.xlsx › Clinically_reported_SNVs

Distinct genomic landscape of Chinese pediatric acute myeloid leukemia impacts clinical risk classification.

Liu T et al.

Nature Communications, 2022

35347147 (PubMed )
Supplement: 41467_2022_29336_MOESM8_ESM.xlsx › Sheet1

A Nationwide Study of GATA2 Deficiency in Norway-the Majority of Patients Have Undergone Allo-HSCT.

Jørgensen SF et al.

Journal of Clinical Immunology, 2022

Networking for advanced molecular diagnosis in acute myeloid leukemia patients is possible: the PETHEMA NGS-AML project

Sargas C et al.

Haematologica, 2021

33179471 (PubMed )
Supplement: 2020_263806_SARGAS_SUPPL.pdf

The Mutational Landscape of Acute Myeloid Leukaemia Predicts Responses and Outcomes in Elderly Patients from the PETHEMA-FLUGAZA Phase 3 Clinical Trial.

Ayala R et al.

Cancers, 2021

34070172 (PubMed )
Supplement: cancers-13-02458-s001.zip › cancers-1203671-supplementary › Table_S8.xlsx › Hoja2

RUNX1 mutations in blast-phase chronic myeloid leukemia associate with distinct phenotypes, transcriptional profiles, and drug responses.

Adnan Awad S et al.

Leukemia, 2021

Expression of transgenes enriched in rare codons is enhanced by the MAPK pathway.

Peterson J et al.

Scientific Reports, 2020

33335127 (PubMed )
Supplement: 41598_2020_78453_MOESM1_ESM.pdf

Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing.

Abelson S et al.

Science Advances, 2020

33298453 (PubMed )
Supplement: abe3722_Table_S3.xlsx › tab2

Clinico-pathologic characteristics and outcomes of the World Health Organization (WHO) provisional entity de novo acute myeloid leukemia with mutated RUNX1.

Quesada AE et al.

Modern Pathology : an Official Journal of the United States and Canadian Academy of Pathology, Inc, 2020

High frequency of germline RUNX1 mutations in patients with RUNX1-mutated AML.

Simon L et al.

Blood, 2020

The assessment of minimal residual disease versus that of somatic mutations for predicting the outcome of acute myeloid leukemia patients.

Salehzadeh S et al.

Cancer Cell International, 2019

Integrative genomic analysis of adult mixed phenotype acute leukemia delineates lineage associated molecular subtypes.

Takahashi K et al.

Nature Communications, 2018

29991687 (PubMed )
Supplement: 41467_2018_4924_MOESM1_ESM.pdf

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

Chemogenomic Landscape of RUNX1-mutated AML Reveals Importance of RUNX1 Allele Dosage in Genetics and Glucocorticoid Sensitivity.

Simon L et al.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research, 2017

Clinical Outcomes and Co-Occurring Mutations in Patients with RUNX1-Mutated Acute Myeloid Leukemia.

Khan M et al.

International Journal of Molecular Sciences, 2017

Frequency and Clinicopathologic Features of RUNX1 Mutations in Patients With Acute Myeloid Leukemia Not Otherwise Specified.

You E et al.

American Journal of Clinical Pathology, 2017

Mutational analysis of disease relapse in patients allografted for acute myeloid leukemia.

Quek L et al.

Blood Advances, 2016

Evaluation of Patients and Families With Concern for Predispositions to Hematologic Malignancies Within the Hereditary Hematologic Malignancy Clinic (HHMC).

DiNardo CD et al.

Clinical Lymphoma, Myeloma & Leukemia, 2016

RUNX1 prevents oestrogen-mediated AXIN1 suppression and β-catenin activation in ER-positive breast cancer.

Chimge NO et al.

Nature Communications, 2016

Targeted resequencing analysis of 31 genes commonly mutated in myeloid disorders in serial samples from myelodysplastic syndrome patients showing disease progression.

Pellagatti A et al.

Leukemia, 2016

25991409 (PubMed )
Supplement: leu2015129x2.xls › Sheet1

Role of TP53 mutations in the origin and evolution of therapy-related acute myeloid leukaemia.

Wong TN et al.

Nature, 2015

25487151 (PubMed )
Supplement: NIHMS635306-supplement-5.xlsx › taml.extension.all.tier1.2013.0

Assessment of computational methods for predicting the effects of missense mutations in human cancers.

Gnad F et al.

Bmc Genomics, 2013

23819521 (PubMed )
Supplement: 1471-2164-14-S3-S7-S1.xlsx › COSMIC
Select a paper to view details
UCSC Genome Browser·Imprint·Privacy·Terms·Feedback·Email Updates·

Regarding the variants extracted from papers and shown text snippets from research papers, the following holds: "Some rights reserved. This work permits non-commercial use, distribution and reproduction in any medium, provided the original author and source are credited."