Dr Melissa Call, Associate Professor Matthew Call, and Dr Jessica Bridgford led the study at the Walter and Eliza Hall Institute.
Institute scientists have revealed seven new genetic mutations that cause myeloproliferative diseases – chronic disorders of the blood that can lead to bone marrow failure, stroke and leukaemia.
The study uncovered seven genetic changes that drive myeloproliferative diseases, as well as 90 mutations with the potential to make the disease worse in existing patients.
The findings could help clinicians to accurately diagnose patients and potentially lead to new targeted treatments.
Dr Jessica Bridgford, Dr Melissa Call and Associate Professor Matthew Call from the Walter and Eliza Hall Institute of Medical Research led the study in collaboration with Institute computational biologist Dr Alan Rubin, Dr Andrew Brooks from the University of Queensland, and haematologists based in Italy. The study was published in the journal Blood.
At a glance
Researchers have revealed seven new drivers of myeloproliferative diseases – chronic bone marrow disorders that can lead to cancer.
The collaboration used advanced genomics and computational biology to fast-track genetic testing and rank mutations from the least to most active in driving disease.
The findings will help haematologists more accurately diagnose patients with myeloproliferative diseases.
An efficient new testing approach
Myeloproliferative diseases are caused by excess production of mature blood cells in the bone marrow. This abnormal growth can lead to chronic pain and illnesses including bone marrow failure, stroke, heart attack and can progress to become blood cancers such as leukaemia. Myeloproliferative diseases, sometimes called myeloproliferative neoplasms, include disorders such as essential thrombocytopenia, polycythaemia vera and primary myelofibrosis.
The research team focused on a region of the protein MPL, which is known to be a hotspot for mutations that cause uncontrolled blood cell growth. Using an advanced new technique called deep mutational scanning (DMS), the researchers tested 600 variants of MPL for their potential to contribute to myeloproliferative diseases.
Dr Call said the study was only feasible as a result of the new DMS technology.
“Older methods require testing the activity of each mutation individually. For our study, this would have taken about three years, with variables that would have been impossible to control,” she said.
“Instead, the novel DMS approach took a matter of weeks and allowed us to test all 600 protein variants at the same time, under the same conditions. This produced an unbiased view of how every mutation in the region of interest might affect MPL function in a disease context.”
Offering confidence in diagnosis
Collaborating with haematologists in Italy, the researchers confirmed that many of the newly identified mutations were present in the DNA of patients with myeloproliferative disease.
Dr Bridgford said it was exciting to validate that the mutations could be useful in accurately diagnosing patients.
“The seven new mutations highlighted in our research have not previously been used in diagnosing myeloproliferative disease.”
“The fact that this information is now available along with a ‘catalogue’ of all known mutations in this region of the MPL protein offers haematologists confidence in their diagnosis,” she said.
Dr Call said identifying the mutations could, in the future, lead to the development of ‘precision medicine’ approaches to treating myeloproliferative diseases.
“By understanding the disease-causing mutations in individual patients, researchers could in the future develop therapies that target those mutations, shutting down their harmful effects.
“‘Precision medicine’ holds the promise of transforming how we treat diseases, and it would be wonderful for this to be used in the context of myeloproliferative diseases,” Dr Call said.
Powerful computing drives research
Dr Rubin said the study demonstrated the significant power of bioinformatics methods to fast-track fundamental research.
“New statistical methods, which are available through the Enrich2 software package developed at the Institute, enabled the team to turn the raw DNA sequencing data into scores for each mutation that could then be visualised and interpreted.
“This meant it was possible to rank mutations from least to most active in driving disease,” he said.
Data from the study is also available through the genomic database MaveDB, developed by Dr Rubin as part of an international collaboration. Data obtained from deep mutational scans and similar assays is enabling researchers to better understand gene and protein function, measure the involvement of genetic variants in a disease, and investigate how the function of proteins can be enhanced synthetically for the development of new treatments.
This study was supported by the Australian National Health and Medical Research Council and Victorian Government.
