Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
Howard Hughes Medical Institute - United States
P50 CA254838
NCI NIH HHS - United States
P30 CA008748
NCI NIH HHS - United States
29685
Cancer Research UK - United Kingdom
K12 CA184746
NCI NIH HHS - United States
PubMed
32747829
PubMed Central
PMC8381722
DOI
10.1038/s41591-020-1008-z
PII: 10.1038/s41591-020-1008-z
Knihovny.cz E-resources
- MeSH
- Alleles MeSH
- Survival Analysis MeSH
- Phenotype MeSH
- Gene Frequency MeSH
- Cohort Studies MeSH
- Humans MeSH
- Mutation MeSH
- DNA Mutational Analysis MeSH
- Myelodysplastic Syndromes diagnosis genetics mortality therapy MeSH
- Tumor Suppressor Protein p53 genetics MeSH
- Genomic Instability genetics MeSH
- Prognosis MeSH
- DNA Copy Number Variations genetics MeSH
- Treatment Outcome MeSH
- Loss of Heterozygosity genetics MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Tumor Suppressor Protein p53 MeSH
- TP53 protein, human MeSH Browser
Tumor protein p53 (TP53) is the most frequently mutated gene in cancer1,2. In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease3,4, rapid transformation to acute myeloid leukemia (AML)5, resistance to conventional therapies6-8 and dismal outcomes9. Consistent with the tumor-suppressive role of TP53, patients harbor both mono- and biallelic mutations10. However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53-mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R)11. Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response.
Center for Hematologic Malignancies Memorial Sloan Kettering Cancer Center New York NY USA
Chang Gung Memorial Hospital at Linkou Chang Gung University Taoyuan City Taiwan
CIBERONC Instituto de Salud Carlos 3 Madrid Spain
Clinics of Hematology and Medical Oncology University Medical Center Göttingen Germany
Department of Biomedical and Neuromotor Sciences University of Bologna Bologna Italy
Department of Biomedical Sciences Humanitas University Milan Italy
Department of Data Sciences Dana Farber Cancer Institute Boston MA USA
Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA
Department of Genomics Institute of Hematology and Blood Transfusion Prague Czech Republic
Department of Health Sciences University of York York UK
Department of Hematology and Genetics Unit University Hospital La Fe Valencia Spain
Department of Hematology and Oncology Graduate School of Medicine Kyoto University Kyoto Japan
Department of Hematology Atomic Bomb Disease Institute Nagasaki University Nagasaki Japan
Department of Hematology Chugoku Central Hospital Fukuyama Japan
Department of Hematology Democritus University of Thrace Medical School Alexandroupolis Greece
Department of Hematology Faculty of Medicine University of Tsukuba Tsukuba Japan
Department of Hematology Gifu Municipal Hospital Gifu Japan
Department of Hematology Gifu University Graduate School of Medicine Gifu Japan
Department of Hematology Hôpital St Louis and Paris University Paris France
Department of Hematology Hospital Universitario y Politécnico La Fe Valencia Spain
Department of Hematology IRCCS Fondazione Policlinico S Matteo Pavia Italy
Department of Hematology Kobe City Medical Center General Hospital Kobe Japan
Department of Hematology VU University Medical Center Amsterdam Amsterdam the Netherlands
Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA
Department of Molecular Medicine University of Pavia Pavia Italy
Department of Pathology and Tumor Biology Kyoto University Kyoto Japan
Department of Pathology Massachusetts General Hospital Boston MA USA
Department of Pathology Memorial Sloan Kettering Cancer Center New York NY USA
Drug Research and Development Center Federal University of Ceara Ceara Brazil
Haematological Malignancy Diagnostic Service St James's University Hospital Leeds UK
Humanitas Clinical and Research Center IRCCS Humanitas Cancer Center Milan Italy
Institute of Hematology S Orsola Malpighi University Hospital Bologna Italy
Integrated Genomics Operation Memorial Sloan Kettering Cancer Center New York NY USA
Japanese Data Center for Hematopoietic Cell Transplantation Nagoya Japan
Laboratory Hematology Department LABGK Radboud University Medical Centre Nijmegen the Netherlands
MDS Group Institut de Recerca Contra la Leucèmia Josep Carreras Barcelona Spain
MDS Unit Hematology AOU Careggi University of Florence Florence Italy
Oncology Hematology Center Hospital Israelita Albert Einstein São Paulo Brazil
Radcliffe Department of Medicine University of Oxford and Oxford BRC Haematology Theme Oxford UK
Stanford University Cancer Institute Stanford CA USA
See more in PubMed
Kandoth C et al.Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013). PubMed PMC
Zehir A et al.Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017). PubMed PMC
Haase D et al.TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups. Leukemia 33, 1747–1758 (2019). PubMed PMC
Bejar R et al.Clinical effect of point mutations in myelodysplastic syndromes. N. Engl. J. Med. 364, 2496–2506 (2011). PubMed PMC
Kitagawa M, Yoshida S, Kuwata T, Tanizawa T & Kamiyama R p53 expression in myeloid cells of myelodysplastic syndromes. Association with evolution of overt leukemia. Am. J. Pathol. 145, 338–344 (1994). PubMed PMC
Lindsley RC et al.Prognostic mutations in myelodysplastic syndrome after stem-cell transplantation. N. Engl. J. Med. 376, 536–547 (2017). PubMed PMC
Yoshizato T et al.Genetic abnormalities in myelodysplasia and secondary acute myeloid leukemia: impact on outcome of stem cell transplantation. Blood 129, 2347–2358 (2017). PubMed PMC
Jädersten M et al.TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict disease progression. J. Clin. Oncol. 29, 1971–1979 (2011). PubMed
Haferlach T et al.Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28, 241–247 (2014). PubMed PMC
Kastenhuber ER & Lowe SW Putting p53 in context. Cell 170, 1062–1078 (2017). PubMed PMC
Greenberg PL et al.Revised international prognostic scoring system for myelodysplastic syndromes. Blood 120, 2454–2465 (2012). PubMed PMC
Schanz J et al.New comprehensive cytogenetic scoring system for primary myelodysplastic syndromes (MDS) and oligoblastic acute myeloid leukemia after MDS derived from an international database merge. J. Clin. Oncol. 30, 820–829 (2012). PubMed PMC
Breems DA et al.Monosomal karyotype in acute myeloid leukemia: a better indicator of poor prognosis than a complex karyotype. J. Clin. Oncol. 26, 4791–4797 (2008). PubMed
Donehower LA et al.Integrated analysis of TP53 gene and pathway alterations in the cancer genome atlas. Cell Rep. 28, 1370–1384 (2019). PubMed PMC
Rucker FG et al.TP53 alterations in acute myeloid leukemia with complex karyotype correlate with specific copy number alterations, monosomal karyotype, and dismal outcome. Blood 119, 2114–2121 (2012). PubMed
Papaemmanuil E et al.Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016). PubMed PMC
Sallman DA et al.Impact of TP53 mutation variant allele frequency on phenotype and outcomes in myelodysplastic syndromes. Leukemia 30, 666–673 (2016). PubMed PMC
Goel S et al.High prevalence and allele burden-independent prognostic importance of p53 mutations in an inner-city MDS/AML cohort. Leukemia 30, 1793–1795 (2016). PubMed
Montalban-Bravo G et al.Genomic context and TP53 allele frequency define clinical outcomes in TP53-mutated myelodysplastic syndromes. Blood Adv. 4, 482–495 (2020). PubMed PMC
Lausen B & Schumacher M Maximally selected rank statistics. Biometrics 48, 73–85 (1992).
Boettcher S et al.A dominant-negative effect drives selection of TP53 missense mutations in myeloid malignancies. Science 365, 599–604 (2019). PubMed PMC
Levine AJ The many faces of p53: something for everyone. J. Mol. Cell Biol. 11, 524–530 (2019). PubMed PMC
Lang GA et al.Gain of function of a p53 hot spot mutation in a mouse model of li-Fraumeni syndrome. Cell 119, 861–872 (2004). PubMed
Olive KP et al.Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 119, 847–860 (2004). PubMed
Loizou E et al.A gain-of-function p53-mutant oncogene promotes cell fate plasticity and myeloid leukemia through the pluripotency factor FOXH1. Cancer Discov. 9, 962–979 (2019). PubMed PMC
Wong TN et al.Role of TP53 mutations in the origin and evolution of therapy-related acute myeloid leukaemia. Nature 518, 552–555 (2015). PubMed PMC
Platzbecker U Treatment of MDS. Blood 133, 1096–1107 (2019). PubMed
Roman E et al.Myeloid malignancies in the real-world: occurrence, progression and survival in the UK’s population-based Haematological Malignancy Research Network 2004–15. Cancer Epidemiol. 42, 186–198 (2016). PubMed PMC
Smith A et al.Cohort profile: the Haematological Malignancy Research Network (HMRN); a UK population-based patient cohort. Int. J. Epidemiol. 47, 700–700g (2018). PubMed PMC
Welch JS et al.TP53 and decitabine in acute myeloid leukemia and myelodysplastic syndromes. N. Engl. J. Med. 375, 2023–2036 (2016). PubMed PMC
Malcovati L et al.Clinical significance of SF3B1 mutations in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Blood 118, 6239–6246 (2011). PubMed PMC
Papaemmanuil E et al.Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122, 3616–3627 (2013). quiz 3699. PubMed PMC
International Standing Committee on Human Cytogenetic Nomenclature. ISCN 2013: An International System for Human Cytogenetic Nomenclature (Karger, 2013).
Li H & Durbin R Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010). PubMed PMC
Cibulskis K et al.Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013). PubMed PMC
Saunders CT et al.Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012). PubMed
Ye K, Schulz MH, Long Q, Apweiler R & Ning Z Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009). PubMed PMC
Karczewski KJ et al.Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Nature 581, 434–443 (2020). PubMed
Thorvaldsdóttir H, Robinson JT & Mesirov JP Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinformatics 14, 178–192 (2013). PubMed PMC
Tate JG et al.COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019). PubMed PMC
Cerami E et al.The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012). PubMed PMC
Chang MT et al.Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016). PubMed PMC
Chang MT et al.Accelerating discovery of functional mutant alleles in cancer. Cancer Discov. 8, 174–183 (2018). PubMed PMC
Landrum MJ et al.ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014). PubMed PMC
Chakravarty D et al.OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, 10.1200 (2017). PubMed PMC
Grinfeld J et al.Classification and personalized prognosis in myeloproliferative neoplasms. N. Engl. J. Med. 379, 1416–1430 (2018). PubMed PMC
Bouaoun L et al.TP53 variations in human cancers: new lessons from the iarc tp53 database and genomics data. Hum. Mutat. 37, 865–876 (2016). PubMed
Giacomelli AO et al.Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50, 1381–1387 (2018). PubMed PMC
Talevich E, Shain AH, Botton T & Bastian BC CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016). PubMed PMC
Molecular taxonomy of myelodysplastic syndromes and its clinical implications
Molecular and clinical presentation of UBA1-mutated myelodysplastic syndromes
Genomic abnormalities of TP53 define distinct risk groups of paediatric B-cell non-Hodgkin lymphoma