User login
Researchers have developed a new method for detecting single-nucleotide variants (SNVs) in a single cell, and they believe it could have applications for cancer diagnosis and treatment.
The team said the method, known as Monovar, improves upon current single-cell sequencing (SCS) by more accurately detecting SNVs.
During testing, Monovar identified 28 new somatic SNVs in cells from a patient with acute lymphoblastic leukemia (ALL).
The researchers described Monovar in Nature Methods.
“To improve the SNVs in SCS datasets, we developed Monovar,” said study author Nicholas Navin, PhD, of the University of Texas MD Anderson Cancer Center in Houston.
“Monovar is a novel statistical method able to leverage data from multiple single cells to discover SNVs and provides highly detailed genetic data.”
Dr Navin and his colleagues found Monovar superior to standard algorithms for analyzing cells from previously studied patients with 3 different cancer types.
The team analyzed single cells from a patient with triple-negative breast cancer, a patient with muscle-invasive bladder cancer, and a child with ALL.
In cells from the ALL patient, Monovar discovered 57 somatic mutations, including 28 new somatic SNVs.
The researchers said Monovar identified significant mutations in OR4C3 and GPR107 (all subclones); LRFN5, PKD2L1, and ZNF781 (in subs 2, 4 and 5); DNAH7 (sub 1); LYAR and FMNL1 (sub 2); RGS3 (subs 4 and 5); and ADAMTS13, PRSS3, and PKD2L1 (subs 2-5).
The clonal mutations in OR4C3 and GPR107 and the subclonal mutations in PKD2L1, ADAMTS13, PRSS3, and RGS3 were not identified in the original study of the ALL patient (C Gawad et al. PNAS 2014).
Dr Navin and his colleagues said Monovar could have significant translational applications in cancer diagnosis and treatment, personalized medicine, and prenatal genetic diagnosis, where the accurate detection of SNVs is critical for patient care.
The researchers also believe Monovar could be used for studies in a range of biomedical fields.
“With the recent innovations in SCS methods to analyze thousands of single cells in parallel with RNA analysis, which will soon be extended to DNA analysis, the need for accurate DNA variant detection will continue to grow,” said Ken Chen, PhD, also of MD Anderson Cancer Center.
“Monovar is capable of analyzing large-scale datasets and handling different whole-genome protocols. Therefore, it is well-suited for many types of studies.” 
Researchers have developed a new method for detecting single-nucleotide variants (SNVs) in a single cell, and they believe it could have applications for cancer diagnosis and treatment.
The team said the method, known as Monovar, improves upon current single-cell sequencing (SCS) by more accurately detecting SNVs.
During testing, Monovar identified 28 new somatic SNVs in cells from a patient with acute lymphoblastic leukemia (ALL).
The researchers described Monovar in Nature Methods.
“To improve the SNVs in SCS datasets, we developed Monovar,” said study author Nicholas Navin, PhD, of the University of Texas MD Anderson Cancer Center in Houston.
“Monovar is a novel statistical method able to leverage data from multiple single cells to discover SNVs and provides highly detailed genetic data.”
Dr Navin and his colleagues found Monovar superior to standard algorithms for analyzing cells from previously studied patients with 3 different cancer types.
The team analyzed single cells from a patient with triple-negative breast cancer, a patient with muscle-invasive bladder cancer, and a child with ALL.
In cells from the ALL patient, Monovar discovered 57 somatic mutations, including 28 new somatic SNVs.
The researchers said Monovar identified significant mutations in OR4C3 and GPR107 (all subclones); LRFN5, PKD2L1, and ZNF781 (in subs 2, 4 and 5); DNAH7 (sub 1); LYAR and FMNL1 (sub 2); RGS3 (subs 4 and 5); and ADAMTS13, PRSS3, and PKD2L1 (subs 2-5).
The clonal mutations in OR4C3 and GPR107 and the subclonal mutations in PKD2L1, ADAMTS13, PRSS3, and RGS3 were not identified in the original study of the ALL patient (C Gawad et al. PNAS 2014).
Dr Navin and his colleagues said Monovar could have significant translational applications in cancer diagnosis and treatment, personalized medicine, and prenatal genetic diagnosis, where the accurate detection of SNVs is critical for patient care.
The researchers also believe Monovar could be used for studies in a range of biomedical fields.
“With the recent innovations in SCS methods to analyze thousands of single cells in parallel with RNA analysis, which will soon be extended to DNA analysis, the need for accurate DNA variant detection will continue to grow,” said Ken Chen, PhD, also of MD Anderson Cancer Center.
“Monovar is capable of analyzing large-scale datasets and handling different whole-genome protocols. Therefore, it is well-suited for many types of studies.”
Researchers have developed a new method for detecting single-nucleotide variants (SNVs) in a single cell, and they believe it could have applications for cancer diagnosis and treatment.
The team said the method, known as Monovar, improves upon current single-cell sequencing (SCS) by more accurately detecting SNVs.
During testing, Monovar identified 28 new somatic SNVs in cells from a patient with acute lymphoblastic leukemia (ALL).
The researchers described Monovar in Nature Methods.
“To improve the SNVs in SCS datasets, we developed Monovar,” said study author Nicholas Navin, PhD, of the University of Texas MD Anderson Cancer Center in Houston.
“Monovar is a novel statistical method able to leverage data from multiple single cells to discover SNVs and provides highly detailed genetic data.”
Dr Navin and his colleagues found Monovar superior to standard algorithms for analyzing cells from previously studied patients with 3 different cancer types.
The team analyzed single cells from a patient with triple-negative breast cancer, a patient with muscle-invasive bladder cancer, and a child with ALL.
In cells from the ALL patient, Monovar discovered 57 somatic mutations, including 28 new somatic SNVs.
The researchers said Monovar identified significant mutations in OR4C3 and GPR107 (all subclones); LRFN5, PKD2L1, and ZNF781 (in subs 2, 4 and 5); DNAH7 (sub 1); LYAR and FMNL1 (sub 2); RGS3 (subs 4 and 5); and ADAMTS13, PRSS3, and PKD2L1 (subs 2-5).
The clonal mutations in OR4C3 and GPR107 and the subclonal mutations in PKD2L1, ADAMTS13, PRSS3, and RGS3 were not identified in the original study of the ALL patient (C Gawad et al. PNAS 2014).
Dr Navin and his colleagues said Monovar could have significant translational applications in cancer diagnosis and treatment, personalized medicine, and prenatal genetic diagnosis, where the accurate detection of SNVs is critical for patient care.
The researchers also believe Monovar could be used for studies in a range of biomedical fields.
“With the recent innovations in SCS methods to analyze thousands of single cells in parallel with RNA analysis, which will soon be extended to DNA analysis, the need for accurate DNA variant detection will continue to grow,” said Ken Chen, PhD, also of MD Anderson Cancer Center.
“Monovar is capable of analyzing large-scale datasets and handling different whole-genome protocols. Therefore, it is well-suited for many types of studies.”