Genomic profiling predicts outcomes in patients with MPN

Genomic characteristics of patients with myeloproliferative neoplasms (MPN) can predict clinical outcomes, a recent study found.

Gio_tto/Thinkstock

Eight genomic subgroups of MPN were recognized, each with distinct clinical features, including event-free survival, risk of leukemic transformation, and blood counts, according to Jacob Grinfeld, MD, of the Wellcome-MRC Cambridge (England) Stem Cell Institute and Cambridge Institute for Medical Research and his colleagues.

“Current classification schemes distinguish among the subtypes of myeloproliferative neoplasms according to clinical and laboratory features, but uncertainty clouds where and how to draw dividing lines among them,” the investigators wrote in the New England Journal of Medicine. “In blood cancers, a progressive shift is under way, from clinical and morphologic classification schemes to those that are based on genomics.”

MPNs are often driven by mutations in CALR, MPL, or JAK2 genes, but classification is not confined to just three genomic types; many patients have additional driver mutations throughout a variety of cancer genes, and it is these additional mutations that are responsible for the wide range of disease phenotypes and clinical outcomes.

This study included 2,035 patients with MPNs, including essential thrombocythemia, polycythemia vera, myelofibrosis, and other MPN diagnoses. The investigators performed targeted sequencing for the full coding sequence of 69 genes and genomewide copy-number information in 1,887 patients. Another 148 patients underwent whole-exome sequencing.

By sequencing coding exons from 69 myeloid cancer genes, the investigators were able to survey the diversity of mutations across a population of patients with MPNs and identify mutation-associated clinical outcomes.

The results showed that slightly less than half (45%) of the patients had a solitary abnormality in CALR, MPL, or JAK2, while the remaining patients had additional driver mutations. In some instances, additional mutations were numerous, particularly in older patients with advanced disease. In at least five cases, 33 genes had driver mutations.

Further analysis identified eight genomic subgroups that could predict clinical outcomes based on shared chromosomal abnormalities and mutations. For example, one subgroup included patients with TP53 mutations; these individuals had a “dismal prognosis” and were 15.5 times more likely to transform to acute myeloid leukemia (AML), compared with the JAK2-heterozygous subgroup (P less than .001).

Because prognosis is “a key determinant of the treatment of patients with MPNs,” genomic subgrouping may one day guide clinical decision making, the investigators concluded.

To further this cause, the investigators have made available an online calculator of individualized patient outcomes, which can be accessed at https://cancer.sanger.ac.uk/mpn-multistage/.

The study was funded by the Wellcome Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cancer Research UK, and others. Some study authors reported fees from Celgene, Novartis, Gilead, Shire, and others outside of the study.

SOURCE: Grinfeld J et al. N Engl J Med. 2018;379:1416-30.

Genomic characteristics of patients with myeloproliferative neoplasms (MPN) can predict clinical outcomes, a recent study found.

Gio_tto/Thinkstock

Eight genomic subgroups of MPN were recognized, each with distinct clinical features, including event-free survival, risk of leukemic transformation, and blood counts, according to Jacob Grinfeld, MD, of the Wellcome-MRC Cambridge (England) Stem Cell Institute and Cambridge Institute for Medical Research and his colleagues.

“Current classification schemes distinguish among the subtypes of myeloproliferative neoplasms according to clinical and laboratory features, but uncertainty clouds where and how to draw dividing lines among them,” the investigators wrote in the New England Journal of Medicine. “In blood cancers, a progressive shift is under way, from clinical and morphologic classification schemes to those that are based on genomics.”

MPNs are often driven by mutations in CALR, MPL, or JAK2 genes, but classification is not confined to just three genomic types; many patients have additional driver mutations throughout a variety of cancer genes, and it is these additional mutations that are responsible for the wide range of disease phenotypes and clinical outcomes.

This study included 2,035 patients with MPNs, including essential thrombocythemia, polycythemia vera, myelofibrosis, and other MPN diagnoses. The investigators performed targeted sequencing for the full coding sequence of 69 genes and genomewide copy-number information in 1,887 patients. Another 148 patients underwent whole-exome sequencing.

By sequencing coding exons from 69 myeloid cancer genes, the investigators were able to survey the diversity of mutations across a population of patients with MPNs and identify mutation-associated clinical outcomes.

The results showed that slightly less than half (45%) of the patients had a solitary abnormality in CALR, MPL, or JAK2, while the remaining patients had additional driver mutations. In some instances, additional mutations were numerous, particularly in older patients with advanced disease. In at least five cases, 33 genes had driver mutations.

Further analysis identified eight genomic subgroups that could predict clinical outcomes based on shared chromosomal abnormalities and mutations. For example, one subgroup included patients with TP53 mutations; these individuals had a “dismal prognosis” and were 15.5 times more likely to transform to acute myeloid leukemia (AML), compared with the JAK2-heterozygous subgroup (P less than .001).

Because prognosis is “a key determinant of the treatment of patients with MPNs,” genomic subgrouping may one day guide clinical decision making, the investigators concluded.

To further this cause, the investigators have made available an online calculator of individualized patient outcomes, which can be accessed at https://cancer.sanger.ac.uk/mpn-multistage/.

The study was funded by the Wellcome Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cancer Research UK, and others. Some study authors reported fees from Celgene, Novartis, Gilead, Shire, and others outside of the study.

SOURCE: Grinfeld J et al. N Engl J Med. 2018;379:1416-30.

Genomic characteristics of patients with myeloproliferative neoplasms (MPN) can predict clinical outcomes, a recent study found.

Gio_tto/Thinkstock

Eight genomic subgroups of MPN were recognized, each with distinct clinical features, including event-free survival, risk of leukemic transformation, and blood counts, according to Jacob Grinfeld, MD, of the Wellcome-MRC Cambridge (England) Stem Cell Institute and Cambridge Institute for Medical Research and his colleagues.

“Current classification schemes distinguish among the subtypes of myeloproliferative neoplasms according to clinical and laboratory features, but uncertainty clouds where and how to draw dividing lines among them,” the investigators wrote in the New England Journal of Medicine. “In blood cancers, a progressive shift is under way, from clinical and morphologic classification schemes to those that are based on genomics.”

MPNs are often driven by mutations in CALR, MPL, or JAK2 genes, but classification is not confined to just three genomic types; many patients have additional driver mutations throughout a variety of cancer genes, and it is these additional mutations that are responsible for the wide range of disease phenotypes and clinical outcomes.

This study included 2,035 patients with MPNs, including essential thrombocythemia, polycythemia vera, myelofibrosis, and other MPN diagnoses. The investigators performed targeted sequencing for the full coding sequence of 69 genes and genomewide copy-number information in 1,887 patients. Another 148 patients underwent whole-exome sequencing.

By sequencing coding exons from 69 myeloid cancer genes, the investigators were able to survey the diversity of mutations across a population of patients with MPNs and identify mutation-associated clinical outcomes.

The results showed that slightly less than half (45%) of the patients had a solitary abnormality in CALR, MPL, or JAK2, while the remaining patients had additional driver mutations. In some instances, additional mutations were numerous, particularly in older patients with advanced disease. In at least five cases, 33 genes had driver mutations.

Further analysis identified eight genomic subgroups that could predict clinical outcomes based on shared chromosomal abnormalities and mutations. For example, one subgroup included patients with TP53 mutations; these individuals had a “dismal prognosis” and were 15.5 times more likely to transform to acute myeloid leukemia (AML), compared with the JAK2-heterozygous subgroup (P less than .001).

Because prognosis is “a key determinant of the treatment of patients with MPNs,” genomic subgrouping may one day guide clinical decision making, the investigators concluded.

To further this cause, the investigators have made available an online calculator of individualized patient outcomes, which can be accessed at https://cancer.sanger.ac.uk/mpn-multistage/.

The study was funded by the Wellcome Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cancer Research UK, and others. Some study authors reported fees from Celgene, Novartis, Gilead, Shire, and others outside of the study.

SOURCE: Grinfeld J et al. N Engl J Med. 2018;379:1416-30.