Clinical trial NCT04889729: GENOMED4ALL: Improving MDS Classification and Prognosis by AI
Official title: Genomic and Personalized Medicine for All (GENOMED4ALL): Application of Artificial Intelligence to Improve Disease Classification and Prognosis in Myelodysplastic Syndrome.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Myelodysplastic Syndromes
Keywords: ARTIFICIAL INTELLIGENCE
HEMATOLOGICAL DISEASE
GENOMICS
PROGNOSIS
DISEASE CLASSIFICATION
Lead sponsor: Istituto Clinico Humanitas
Sponsor class: OTHER
First posted: 2021-05-17
Enrollment: 13284
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients affected by MDS. 13284 patients with clinical and genomic information availability

Brief summary: Myelodysplastic syndromes (MDS) typically occur in elderly people. Current disese classifcation system and prognostic scores (International Prognostic Scoring System, IPSS) present limitations and in most cases fail to capture reliable prognostic information at individual level. Study of MDS has been rapidly transformed by genome characterization and there is increasing evidence that mutation screening may add significant information to currently available prognostic scores. The project will aim to develop artificial intelligence (AI)-based solutions to improve MDS classification and prognostication, through the implementation of a personalized medicine approach. In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, FPA 739541), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform (ENROL, GA 947670).

Detailed description: Myelodysplastic syndromes (MDS) typically occur in elderly people. Patients present peripheral blood cytopenia, and with time a portion of these subjects evolve into acute myeloid leukaemia (AML). The natural history of MDS is heterogeneous ranging from conditions with a near-normal life expectancy to forms close to AML, and therefore a risk-adapted treatment strategy is mandatory. Current prognostic scores (Revised International Prognostic Scoring System, IPSS-R) present limitations, and in most cases fail to capture reliable prognostic information at individual level.

Study of MDS has been rapidly transformed by genome characterization. Somatic mutations occur in the genomes of hematopoietic stem cells at a low, but detectable frequency during normal DNA replication. Any genetic alteration that causes a selective advantage relative to other self-renewing cells will lead to clonal dominance (clonal haematopoiesis, CH). The consequence of CH is genomic instability leading to increased risk of acquiring additional mutations and to develop MDS, solid cancer and other illnesses. The time and place of individual mutations and their clonal emergence during the course of the disease are central issues for a better comprehension of MDS pathogenesis and phenotype and for the development of cancer preventive strategies.

Important steps forward have been made in defining the molecular architecture of MDS. The MDS associated with 5q deletion derives from the haploinsufficiency of RPS14 gene. Genes encoding for spliceosome components were identified in a high proportion of subjects with MDS. There is a close relationship between ring sideroblasts and SF3B1 mutations, which is consistent with a causal relationship. In addition, an increasing number of genes have been found to carry recurrent mutations in MDS, involved in DNA methylation (DNMT3A, TET2, IDH1/2), chromatin modification (EZH2, ASXL1), transcriptional regulation (RUNX1), signal transduction (KRAS, CBL).

Gene mutations have been reported to influence survival and risk of disease progression in MDS, and the evaluation of the mutation status may add significant information to currently used prognostic scores. For instance, we found that SF3B1 mutations were independent predictors of favorable prognosis, while driver mutations of ASXL1, SRSF2, RUNX1, TP53 and EZH2 genes were associated with a reduced probability of survival. MDS with ring sideroblasts provide the best evidence that the identification of the mutant gene responsible for the initial clone is relevant to clinical outcome. In fact, ring sideroblasts may be found not only in patients with a founding mutation in SF3B1, but also in those with an initiating oncogenic lesion in SRSF2. However, the median leukemia-free survival is \>10 years in the former vs \<2 years in the latter.

Moreover, mutation screening may affect clinical decision making : a) in MDS with 5q-, subjects carrying TP53 mutations have a higher risk of leukemic progression and a lower probability of response to lenalidomide; b) in patients receiving HSCT, TP53 mutations predict high probability of relapse; c) SF3B1 mutations are associated with increased probability of erythroid response to TGFb inhibitors (luspatercept), and d) TET2 mutations might be associated with response to HMA.

Despite these findings, caution is needed against immediately adopting such mutational testing in clinical practice. First, the presence of mutations in a given individual has only limited predictive power, as conversion to MDS is rare regardless of mutation status. In addition, in patients with overt MDS, genetic abnormalities explain only a proportion of the total hazard for survival associated with specific treatments, meaning that a large percentage is still associated with clinical and non-mutational factors. Comprehensive analyses of large patient population and new methods to study gene-gene interactions and genoptype-phenotype correlations are warranted to correctly estimate the independent effect of each genomic abnormality on clinical outcome and response to treatment.

By combining an already available, large amount of sequenced genomic data and clinical information, the authors hypothesize that AI will allow to understand better MDS biology and classification, enhance prognostic/predictive capacity of currently available tools and apply treatments in a more targeted way, thus facilitating the implementation of personalized medicine program across EU.

Eligibility criteria: Inclusion Criteria:

* Patients affected by MDS according WHO criteria \> 18 years old
* Avaliability of clinical and hematological information
* Availability of information on targeted mutation screening

Exclusion Criteria:

* none of the above

Primary outcomes:
measure: Improving MDS classification
timeFrame: through study completion, an average of 2 years
measure: Prediction of probability of overall survival (months between diagnosis and death or end of follow up) for patients with MDS
timeFrame: through study completion, an average of 2 years
measure: Prediction of probability of leukemia free surivival (months from diagnosis to progression to acute leukemia or end of follow up) for patients with MDS
timeFrame: through study completion, an average of 2 years
Secondary outcomes:
Other outcomes:
Locations:
city: Milano
country: Italy
facility: Istituto Clinico Humanitas
Study officials:
affiliation: UNIVERSIDAD POLITECNICA DE MADRID SPAIN
name: Federico Alvarez
role: PRINCIPAL_INVESTIGATOR
affiliation: DATAWIZARD SRL ITALY
name: Lucia Comnes
role: PRINCIPAL_INVESTIGATOR
affiliation: FUNDACIO HOSPITAL UNIVERSITARI VALL D'HEBRON - INSTITUT DE RECERCA SPAIN
name: Mar Manu Pereira
role: PRINCIPAL_INVESTIGATOR
affiliation: ASSISTANCE PUBLIQUE HOPITAUX DE PARIS FRANCE
name: Pierre Fenaux
role: PRINCIPAL_INVESTIGATOR
affiliation: MLL MUNCHNER LEUKAMIELABOR GMBH GERMANY
name: Torsten Haferlach
role: PRINCIPAL_INVESTIGATOR
affiliation: Instituto de investigacion biomedica de Salamanca, IBSAL SPAIN
name: Maria Diez Campelo
role: PRINCIPAL_INVESTIGATOR
affiliation: UNIVERSITAET LEIPZIG GERMANY
name: Uwe Platzbecker
role: PRINCIPAL_INVESTIGATOR
affiliation: ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA ITALY
name: Gastone Castellani
role: PRINCIPAL_INVESTIGATOR
affiliation: KOBENHAVNS UNIVERSITET DENMARK
name: Andres Krogh
role: PRINCIPAL_INVESTIGATOR
affiliation: FUNDACIO CENTRE DE REGULACIO GENOMICA SPAIN
name: Babita Singh
role: PRINCIPAL_INVESTIGATOR
affiliation: UNIVERSITA DEGLI STUDI DI TORINO ITALY
name: Piero Fariselli
role: PRINCIPAL_INVESTIGATOR
affiliation: IDRYMA TECHNOLOGIAS KAI EREVNAS GREECE
name: Kostantinos Marias
role: PRINCIPAL_INVESTIGATOR
affiliation: European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet)
name: Mar Mañu Pereira
role: PRINCIPAL_INVESTIGATOR
References:
citation: Bersanelli M, Travaglino E, Meggendorfer M, Matteuzzi T, Sala C, Mosca E, Chiereghin C, Di Nanni N, Gnocchi M, Zampini M, Rossi M, Maggioni G, Termanini A, Angelucci E, Bernardi M, Borin L, Bruno B, Bonifazi F, Santini V, Bacigalupo A, Voso MT, Oliva E, Riva M, Ubezio M, Morabito L, Campagna A, Saitta C, Savevski V, Giampieri E, Remondini D, Passamonti F, Ciceri F, Bolli N, Rambaldi A, Kern W, Kordasti S, Sole F, Palomo L, Sanz G, Santoro A, Platzbecker U, Fenaux P, Milanesi L, Haferlach T, Castellani G, Della Porta MG. Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. J Clin Oncol. 2021 Apr 10;39(11):1223-1233. doi: 10.1200/JCO.20.01659. Epub 2021 Feb 4.
citation: Ades L, Itzykson R, Fenaux P. Myelodysplastic syndromes. Lancet. 2014 Jun 28;383(9936):2239-52. doi: 10.1016/S0140-6736(13)61901-7. Epub 2014 Mar 21.
citation: Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, Bloomfield CD, Cazzola M, Vardiman JW. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016 May 19;127(20):2391-405. doi: 10.1182/blood-2016-03-643544. Epub 2016 Apr 11.
citation: Della Porta MG, Travaglino E, Boveri E, Ponzoni M, Malcovati L, Papaemmanuil E, Rigolin GM, Pascutto C, Croci G, Gianelli U, Milani R, Ambaglio I, Elena C, Ubezio M, Da Via' MC, Bono E, Pietra D, Quaglia F, Bastia R, Ferretti V, Cuneo A, Morra E, Campbell PJ, Orazi A, Invernizzi R, Cazzola M; Rete Ematologica Lombarda (REL) Clinical Network. Minimal morphological criteria for defining bone marrow dysplasia: a basis for clinical implementation of WHO classification of myelodysplastic syndromes. Leukemia. 2015 Jan;29(1):66-75. doi: 10.1038/leu.2014.161. Epub 2014 May 20.
citation: Senent L, Arenillas L, Luno E, Ruiz JC, Sanz G, Florensa L. Reproducibility of the World Health Organization 2008 criteria for myelodysplastic syndromes. Haematologica. 2013 Apr;98(4):568-75. doi: 10.3324/haematol.2012.071449. Epub 2012 Oct 12.
citation: Malcovati L, Porta MG, Pascutto C, Invernizzi R, Boni M, Travaglino E, Passamonti F, Arcaini L, Maffioli M, Bernasconi P, Lazzarino M, Cazzola M. Prognostic factors and life expectancy in myelodysplastic syndromes classified according to WHO criteria: a basis for clinical decision making. J Clin Oncol. 2005 Oct 20;23(30):7594-603. doi: 10.1200/JCO.2005.01.7038. Epub 2005 Sep 26.
citation: Malcovati L, Hellstrom-Lindberg E, Bowen D, Ades L, Cermak J, Del Canizo C, Della Porta MG, Fenaux P, Gattermann N, Germing U, Jansen JH, Mittelman M, Mufti G, Platzbecker U, Sanz GF, Selleslag D, Skov-Holm M, Stauder R, Symeonidis A, van de Loosdrecht AA, de Witte T, Cazzola M; European Leukemia Net. Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet. Blood. 2013 Oct 24;122(17):2943-64. doi: 10.1182/blood-2013-03-492884. Epub 2013 Aug 26.
citation: Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Malcovati L, Cazzola M, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilstocker M, Sekeres M, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012 Sep 20;120(12):2454-65. doi: 10.1182/blood-2012-03-420489. Epub 2012 Jun 27.
citation: Della Porta MG, Tuechler H, Malcovati L, Schanz J, Sanz G, Garcia-Manero G, Sole F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilstocker M, Sekeres MA, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D, Greenberg PL, Cazzola M. Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM). Leukemia. 2015 Jul;29(7):1502-13. doi: 10.1038/leu.2015.55. Epub 2015 Feb 27.
citation: Cazzola M, Della Porta MG, Malcovati L. The genetic basis of myelodysplasia and its clinical relevance. Blood. 2013 Dec 12;122(25):4021-34. doi: 10.1182/blood-2013-09-381665. Epub 2013 Oct 17.
citation: Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D, Pellagatti A, Wainscoat JS, Hellstrom-Lindberg E, Gambacorti-Passerini C, Godfrey AL, Rapado I, Cvejic A, Rance R, McGee C, Ellis P, Mudie LJ, Stephens PJ, McLaren S, Massie CE, Tarpey PS, Varela I, Nik-Zainal S, Davies HR, Shlien A, Jones D, Raine K, Hinton J, Butler AP, Teague JW, Baxter EJ, Score J, Galli A, Della Porta MG, Travaglino E, Groves M, Tauro S, Munshi NC, Anderson KC, El-Naggar A, Fischer A, Mustonen V, Warren AJ, Cross NC, Green AR, Futreal PA, Stratton MR, Campbell PJ; Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med. 2011 Oct 13;365(15):1384-95. doi: 10.1056/NEJMoa1103283. Epub 2011 Sep 26.
citation: Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, Sato Y, Sato-Otsubo A, Kon A, Nagasaki M, Chalkidis G, Suzuki Y, Shiosaka M, Kawahata R, Yamaguchi T, Otsu M, Obara N, Sakata-Yanagimoto M, Ishiyama K, Mori H, Nolte F, Hofmann WK, Miyawaki S, Sugano S, Haferlach C, Koeffler HP, Shih LY, Haferlach T, Chiba S, Nakauchi H, Miyano S, Ogawa S. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011 Sep 11;478(7367):64-9. doi: 10.1038/nature10496.
citation: Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, Van Loo P, Yoon CJ, Ellis P, Wedge DC, Pellagatti A, Shlien A, Groves MJ, Forbes SA, Raine K, Hinton J, Mudie LJ, McLaren S, Hardy C, Latimer C, Della Porta MG, O'Meara S, Ambaglio I, Galli A, Butler AP, Walldin G, Teague JW, Quek L, Sternberg A, Gambacorti-Passerini C, Cross NC, Green AR, Boultwood J, Vyas P, Hellstrom-Lindberg E, Bowen D, Cazzola M, Stratton MR, Campbell PJ; Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013 Nov 21;122(22):3616-27; quiz 3699. doi: 10.1182/blood-2013-08-518886. Epub 2013 Sep 12.
citation: Schanz J, Tuchler H, Sole F, Mallo M, Luno E, Cervera J, Granada I, Hildebrandt B, Slovak ML, Ohyashiki K, Steidl C, Fonatsch C, Pfeilstocker M, Nosslinger T, Valent P, Giagounidis A, Aul C, Lubbert M, Stauder R, Krieger O, Garcia-Manero G, Faderl S, Pierce S, Le Beau MM, Bennett JM, Greenberg P, Germing U, Haase D. 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. 2012 Mar 10;30(8):820-9. doi: 10.1200/JCO.2011.35.6394. Epub 2012 Feb 13.
citation: Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, Potter NE, Heuser M, Thol F, Bolli N, Gundem G, Van Loo P, Martincorena I, Ganly P, Mudie L, McLaren S, O'Meara S, Raine K, Jones DR, Teague JW, Butler AP, Greaves MF, Ganser A, Dohner K, Schlenk RF, Dohner H, Campbell PJ. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med. 2016 Jun 9;374(23):2209-2221. doi: 10.1056/NEJMoa1516192.
citation: Gerstung M, Papaemmanuil E, Martincorena I, Bullinger L, Gaidzik VI, Paschka P, Heuser M, Thol F, Bolli N, Ganly P, Ganser A, McDermott U, Dohner K, Schlenk RF, Dohner H, Campbell PJ. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017 Mar;49(3):332-340. doi: 10.1038/ng.3756. Epub 2017 Jan 16.
citation: Grinfeld J, Nangalia J, Baxter EJ, Wedge DC, Angelopoulos N, Cantrill R, Godfrey AL, Papaemmanuil E, Gundem G, MacLean C, Cook J, O'Neil L, O'Meara S, Teague JW, Butler AP, Massie CE, Williams N, Nice FL, Andersen CL, Hasselbalch HC, Guglielmelli P, McMullin MF, Vannucchi AM, Harrison CN, Gerstung M, Green AR, Campbell PJ. Classification and Personalized Prognosis in Myeloproliferative Neoplasms. N Engl J Med. 2018 Oct 11;379(15):1416-1430. doi: 10.1056/NEJMoa1716614.
citation: Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996 Feb 28;15(4):361-87. doi: 10.1002/(SICI)1097-0258(19960229)15:43.0.CO;2-4.
citation: Perperoglou A. Cox models with dynamic ridge penalties on time-varying effects of the covariates. Stat Med. 2014 Jan 15;33(1):170-80. doi: 10.1002/sim.5921. Epub 2013 Aug 2.
citation: Pellagatti A, Armstrong RN, Steeples V, Sharma E, Repapi E, Singh S, Sanchi A, Radujkovic A, Horn P, Dolatshad H, Roy S, Broxholme J, Lockstone H, Taylor S, Giagounidis A, Vyas P, Schuh A, Hamblin A, Papaemmanuil E, Killick S, Malcovati L, Hennrich ML, Gavin AC, Ho AD, Luft T, Hellstrom-Lindberg E, Cazzola M, Smith CWJ, Smith S, Boultwood J. Impact of spliceosome mutations on RNA splicing in myelodysplasia: dysregulated genes/pathways and clinical associations. Blood. 2018 Sep 20;132(12):1225-1240. doi: 10.1182/blood-2018-04-843771. Epub 2018 Jun 21.
citation: Malcovati L, Papaemmanuil E, Bowen DT, Boultwood J, Della Porta MG, Pascutto C, Travaglino E, Groves MJ, Godfrey AL, Ambaglio I, Galli A, Da Via MC, Conte S, Tauro S, Keenan N, Hyslop A, Hinton J, Mudie LJ, Wainscoat JS, Futreal PA, Stratton MR, Campbell PJ, Hellstrom-Lindberg E, Cazzola M; Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium and of the Associazione Italiana per la Ricerca sul Cancro Gruppo Italiano Malattie Mieloproliferative. Clinical significance of SF3B1 mutations in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Blood. 2011 Dec 8;118(24):6239-46. doi: 10.1182/blood-2011-09-377275. Epub 2011 Oct 12.
citation: Klampfl T, Gisslinger H, Harutyunyan AS, Nivarthi H, Rumi E, Milosevic JD, Them NC, Berg T, Gisslinger B, Pietra D, Chen D, Vladimer GI, Bagienski K, Milanesi C, Casetti IC, Sant'Antonio E, Ferretti V, Elena C, Schischlik F, Cleary C, Six M, Schalling M, Schonegger A, Bock C, Malcovati L, Pascutto C, Superti-Furga G, Cazzola M, Kralovics R. Somatic mutations of calreticulin in myeloproliferative neoplasms. N Engl J Med. 2013 Dec 19;369(25):2379-90. doi: 10.1056/NEJMoa1311347. Epub 2013 Dec 10.
citation: Reilly B, Tanaka TN, Diep D, Yeerna H, Tamayo P, Zhang K, Bejar R. DNA methylation identifies genetically and prognostically distinct subtypes of myelodysplastic syndromes. Blood Adv. 2019 Oct 8;3(19):2845-2858. doi: 10.1182/bloodadvances.2019000192.
citation: Masaki S, Ikeda S, Hata A, Shiozawa Y, Kon A, Ogawa S, Suzuki K, Hakuno F, Takahashi SI, Kataoka N. Myelodysplastic Syndrome-Associated SRSF2 Mutations Cause Splicing Changes by Altering Binding Motif Sequences. Front Genet. 2019 Apr 16;10:338. doi: 10.3389/fgene.2019.00338. eCollection 2019.
citation: Liang Y, Tebaldi T, Rejeski K, Joshi P, Stefani G, Taylor A, Song Y, Vasic R, Maziarz J, Balasubramanian K, Ardasheva A, Ding A, Quattrone A, Halene S. SRSF2 mutations drive oncogenesis by activating a global program of aberrant alternative splicing in hematopoietic cells. Leukemia. 2018 Dec;32(12):2659-2671. doi: 10.1038/s41375-018-0152-7. Epub 2018 Jun 1.
citation: Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, Schnittger S, Sanada M, Kon A, Alpermann T, Yoshida K, Roller A, Nadarajah N, Shiraishi Y, Shiozawa Y, Chiba K, Tanaka H, Koeffler HP, Klein HU, Dugas M, Aburatani H, Kohlmann A, Miyano S, Haferlach C, Kern W, Ogawa S. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014 Feb;28(2):241-7. doi: 10.1038/leu.2013.336. Epub 2013 Nov 13.
citation: Bacher U, Haferlach T, Schnittger S, Zenger M, Meggendorfer M, Jeromin S, Roller A, Grossmann V, Krauth MT, Alpermann T, Kern W, Haferlach C. Investigation of 305 patients with myelodysplastic syndromes and 20q deletion for associated cytogenetic and molecular genetic lesions and their prognostic impact. Br J Haematol. 2014 Mar;164(6):822-33. doi: 10.1111/bjh.12710. Epub 2013 Dec 26.
citation: Haase D, Stevenson KE, Neuberg D, Maciejewski JP, Nazha A, Sekeres MA, Ebert BL, Garcia-Manero G, Haferlach C, Haferlach T, Kern W, Ogawa S, Nagata Y, Yoshida K, Graubert TA, Walter MJ, List AF, Komrokji RS, Padron E, Sallman D, Papaemmanuil E, Campbell PJ, Savona MR, Seegmiller A, Ades L, Fenaux P, Shih LY, Bowen D, Groves MJ, Tauro S, Fontenay M, Kosmider O, Bar-Natan M, Steensma D, Stone R, Heuser M, Thol F, Cazzola M, Malcovati L, Karsan A, Ganster C, Hellstrom-Lindberg E, Boultwood J, Pellagatti A, Santini V, Quek L, Vyas P, Tuchler H, Greenberg PL, Bejar R; International Working Group for MDS Molecular Prognostic Committee. TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups. Leukemia. 2019 Jul;33(7):1747-1758. doi: 10.1038/s41375-018-0351-2. Epub 2019 Jan 11.
citation: Kulasekararaj AG, Jiang J, Smith AE, Mohamedali AM, Mian S, Gandhi S, Gaken J, Czepulkowski B, Marsh JC, Mufti GJ. Somatic mutations identify a subgroup of aplastic anemia patients who progress to myelodysplastic syndrome. Blood. 2014 Oct 23;124(17):2698-704. doi: 10.1182/blood-2014-05-574889. Epub 2014 Aug 18.
citation: Della Porta MG, Galli A, Bacigalupo A, Zibellini S, Bernardi M, Rizzo E, Allione B, van Lint MT, Pioltelli P, Marenco P, Bosi A, Voso MT, Sica S, Cuzzola M, Angelucci E, Rossi M, Ubezio M, Malovini A, Limongelli I, Ferretti VV, Spinelli O, Tresoldi C, Pozzi S, Luchetti S, Pezzetti L, Catricala S, Milanesi C, Riva A, Bruno B, Ciceri F, Bonifazi F, Bellazzi R, Papaemmanuil E, Santoro A, Alessandrino EP, Rambaldi A, Cazzola M. Clinical Effects of Driver Somatic Mutations on the Outcomes of Patients With Myelodysplastic Syndromes Treated With Allogeneic Hematopoietic Stem-Cell Transplantation. J Clin Oncol. 2016 Oct 20;34(30):3627-3637. doi: 10.1200/JCO.2016.67.3616.
citation: Lindsley RC, Saber W, Mar BG, Redd R, Wang T, Haagenson MD, Grauman PV, Hu ZH, Spellman SR, Lee SJ, Verneris MR, Hsu K, Fleischhauer K, Cutler C, Antin JH, Neuberg D, Ebert BL. Prognostic Mutations in Myelodysplastic Syndrome after Stem-Cell Transplantation. N Engl J Med. 2017 Feb 9;376(6):536-547. doi: 10.1056/NEJMoa1611604.