Classification and Personalized Prognosis in Myeloproliferative Neoplasms

Jacob Grinfeld; Jyoti Nangalia; E Joanna Baxter; David C Wedge; Nicos Angelopoulos; Robert Cantrill; Anna L Godfrey; Elli Papaemmanuil; Gunes Gundem; Cathy MacLean; Julia Cook; Laura O'Neil; Sarah O'Meara; Jon W Teague; Adam P Butler; Charlie E Massie; Nicholas Williams; Francesca L Nice; Christen L Andersen; Hans C Hasselbalch; Paola Guglielmelli; Mary F McMullin; Alessandro M Vannucchi; Claire N Harrison; Moritz Gerstung; Anthony R Green; Peter J Campbell

doi:10.1056/nejmoa1716614

Classification and Personalized Prognosis in Myeloproliferative Neoplasms

Jacob Grinfeld, Jyoti Nangalia, E Joanna Baxter, David C Wedge, Nicos Angelopoulos, Robert Cantrill, Anna L Godfrey, Elli Papaemmanuil, Gunes Gundem, Cathy MacLean, Julia Cook, Laura O'Neil, Sarah O'Meara, Jon W Teague, Adam P Butler, Charlie E Massie, Nicholas Williams, Francesca L Nice, Christen L Andersen, Hans C HasselbalchPaola Guglielmelli, Mary F McMullin, Alessandro M Vannucchi, Claire N Harrison, Moritz Gerstung, Anthony R Green, Peter J Campbell

Institut for Klinisk Medicin

174 Citationer (Scopus)

Abstract

BACKGROUND: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.

METHODS: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.

RESULTS: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.

CONCLUSIONS: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

Originalsprog	Engelsk
Tidsskrift	The New England Journal of Medicine
Vol/bind	379
Udgave nummer	15
Sider (fra-til)	1416-1430
ISSN	0028-4793
DOI	https://doi.org/10.1056/nejmoa1716614
Status	Udgivet - 11 okt. 2018

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10.1056/nejmoa1716614

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Grinfeld, J., Nangalia, J., Baxter, E. J., Wedge, D. C., Angelopoulos, N., Cantrill, R., Godfrey, A. L., Papaemmanuil, E., Gundem, G., MacLean, C., Cook, J., O'Neil, L., O'Meara, S., Teague, J. W., Butler, A. P., Massie, C. E., Williams, N., Nice, F. L., Andersen, C. L., ... Campbell, P. J. (2018). Classification and Personalized Prognosis in Myeloproliferative Neoplasms. The New England Journal of Medicine, 379(15), 1416-1430. https://doi.org/10.1056/nejmoa1716614

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 2018, 'Classification and Personalized Prognosis in Myeloproliferative Neoplasms', The New England Journal of Medicine, bind 379, nr. 15, s. 1416-1430. https://doi.org/10.1056/nejmoa1716614

@article{3f52a41e4a9f45a7b9b9d6bc6b9316be,

title = "Classification and Personalized Prognosis in Myeloproliferative Neoplasms",

abstract = "BACKGROUND: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.METHODS: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.RESULTS: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.CONCLUSIONS: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).",

keywords = "Bayes Theorem, Calreticulin/genetics, DNA, Neoplasm/analysis, Disease Progression, Disease-Free Survival, Humans, Janus Kinase 2/genetics, Multivariate Analysis, Mutation, Myeloproliferative Disorders/classification, Phenotype, Precision Medicine, Prognosis, Proportional Hazards Models, Receptors, Thrombopoietin/genetics, Sequence Analysis, DNA",

author = "Jacob Grinfeld and Jyoti Nangalia and Baxter, {E Joanna} and Wedge, {David C} and Nicos Angelopoulos and Robert Cantrill and Godfrey, {Anna L} and Elli Papaemmanuil and Gunes Gundem and Cathy MacLean and Julia Cook and Laura O'Neil and Sarah O'Meara and Teague, {Jon W} and Butler, {Adam P} and Massie, {Charlie E} and Nicholas Williams and Nice, {Francesca L} and Andersen, {Christen L} and Hasselbalch, {Hans C} and Paola Guglielmelli and McMullin, {Mary F} and Vannucchi, {Alessandro M} and Harrison, {Claire N} and Moritz Gerstung and Green, {Anthony R} and Campbell, {Peter J}",

year = "2018",

month = oct,

day = "11",

doi = "10.1056/nejmoa1716614",

language = "English",

volume = "379",

pages = "1416--1430",

journal = "New England Journal of Medicine",

issn = "0028-4793",

publisher = "Massachusetts Medical Society",

number = "15",

}

TY - JOUR

T1 - Classification and Personalized Prognosis in Myeloproliferative Neoplasms

AU - Grinfeld, Jacob

AU - Nangalia, Jyoti

AU - Baxter, E Joanna

AU - Wedge, David C

AU - Angelopoulos, Nicos

AU - Cantrill, Robert

AU - Godfrey, Anna L

AU - Papaemmanuil, Elli

AU - Gundem, Gunes

AU - MacLean, Cathy

AU - Cook, Julia

AU - O'Neil, Laura

AU - O'Meara, Sarah

AU - Teague, Jon W

AU - Butler, Adam P

AU - Massie, Charlie E

AU - Williams, Nicholas

AU - Nice, Francesca L

AU - Andersen, Christen L

AU - Hasselbalch, Hans C

AU - Guglielmelli, Paola

AU - McMullin, Mary F

AU - Vannucchi, Alessandro M

AU - Harrison, Claire N

AU - Gerstung, Moritz

AU - Green, Anthony R

AU - Campbell, Peter J

PY - 2018/10/11

Y1 - 2018/10/11

N2 - BACKGROUND: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.METHODS: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.RESULTS: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.CONCLUSIONS: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

AB - BACKGROUND: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.METHODS: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.RESULTS: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.CONCLUSIONS: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).

KW - Bayes Theorem

KW - Calreticulin/genetics

KW - DNA, Neoplasm/analysis

KW - Disease Progression

KW - Disease-Free Survival

KW - Humans

KW - Janus Kinase 2/genetics

KW - Multivariate Analysis

KW - Mutation

KW - Myeloproliferative Disorders/classification

KW - Phenotype

KW - Precision Medicine

KW - Prognosis

KW - Proportional Hazards Models

KW - Receptors, Thrombopoietin/genetics

KW - Sequence Analysis, DNA

U2 - 10.1056/nejmoa1716614

DO - 10.1056/nejmoa1716614

M3 - Journal article

C2 - 30304655

SN - 0028-4793

VL - 379

SP - 1416

EP - 1430

JO - New England Journal of Medicine

JF - New England Journal of Medicine

IS - 15

ER -

Classification and Personalized Prognosis in Myeloproliferative Neoplasms

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