Dataset Information

Genome-wide segregation of single nucleotide and structural variants into single cancer cells.

ABSTRACT: Single-cell genome sequencing provides high-resolution details of the clonal genomic modifications that occur during cancer initiation, progression, and ongoing evolution as patients undergo treatment. One limitation of current single-cell sequencing strategies is a suboptimal capacity to detect all classes of single-nucleotide and structural variants in the same cells.Here we present a new approach for determining comprehensive variant profiles of single cells using a microfluidic amplicon-based strategy to detect structural variant breakpoint sequences instead of using relative read depth to infer copy number changes. This method can reconstruct the clonal architecture and mutational history of a malignancy using all classes and sizes of somatic variants, providing more complete details of the temporal changes in mutational classes and processes that led to the development of a malignant neoplasm. Using this approach, we interrogated cells from a patient with leukemia, determining that processes producing structural variation preceded single nucleotide changes in the development of that malignancy.All classes and sizes of genomic variants can be efficiently detected in single cancer cells using our new method, enabling the ordering of distinct classes of mutations during tumor evolution.

SUBMITTER: Easton J

PROVIDER: S-EPMC5702214 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Genome-wide segregation of single nucleotide and structural variants into single cancer cells.

Easton John J Gonzalez-Pena Veronica V Yergeau Donald D Ma Xiaotu X Gawad Charles C

BMC genomics 20171125 1

<h4>Background</h4>Single-cell genome sequencing provides high-resolution details of the clonal genomic modifications that occur during cancer initiation, progression, and ongoing evolution as patients undergo treatment. One limitation of current single-cell sequencing strategies is a suboptimal capacity to detect all classes of single-nucleotide and structural variants in the same cells.<h4>Results</h4>Here we present a new approach for determining comprehensive variant profiles of single cells ...[more]

PMID: 29178827

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Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Dataset Information

Genome-wide segregation of single nucleotide and structural variants into single cancer cells.

Publications

Genome-wide segregation of single nucleotide and structural variants into single cancer cells.

Similar Datasets

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