A punctuated model is consistent with the mechanisms that underlie CNAs, including chromosome missegregation 32 , cytokinesis defects and breakage-fusion-bridge 33 , which can generate complex rearrangements in just a few cell divisions.
In contrast, point mutations occur through defects in DNA repair or replication machinery 34 , which accumulate more gradually over many cell divisions. Our data are consistent with these mechanisms, and further show that two distinct molecular clocks were operating at different stages of tumor growth Extended Data Fig. A pervasive problem in the field is the inability to validate mutations that are detected in single cells.
To address this problem, we combined single cell sequencing with targeted single-molecule deep-sequencing. This approach not only validates mutations, but also measures the precise mutation frequencies in the bulk population.
These rare mutations may play an important role in diversifying the phenotypes of cancer cells, allowing them to surive selective pressures in the tumor microenvironment, including the immune system, hypoxia and chemotherapy 35 , A question in the field of chemotherapy is whether resistance mutations are pre-existing in rare cells in the tumor, or alternatively, emerge spontaneously in response to being challenged by the therapeutic agent.
While this question has been studied for decades in bacteria 37 it remains poorly understood in human cancers. Our data suggest that a large number of diverse mutations are likely to be pre-existing in the tumor mass, prior to chemotherapy.
Our data also has important implications for the mutator phenotype, which posits that tumor evolution is driven by increased mutation rates 34 , While TCGA studies 39 — 41 report increased mutation frequencies , it remains unclear whether these mutations accumulate over many cell divisions at a normal error rate or through an increased mutation rate. Our TNBC data suggest an increased mutation rate On a final note, we expect that single cell genome sequencing will open up new avenues of investigation in many diverse fields of biology.
In cancer research there will be immediate applications for studying cancer stem cells and circulating tumor cells. In the clinic these tools will have important applications in early detection and non-invasive monitoring. Beyond cancer, these tools will have important applications in microbiology, development, immunology and neuroscience and will lead to vast improvements in our fundamental understanding of human diseases.
Sequence libraries were prepared using one of two methods: c, Tn5 tagmentation, or d, low-input TA ligation cloning see methods section. For each cell, 22 reactions were performed using primer pairs that target each autosome and the resulting bp PCR product were separated by gel electrophoresis methods.
Single cell segmented copy number profiles were clustered and used to build heatmaps, showing amplifications in red and deletions in blue. We thank Ralf Krahe and Mei Rui for reviewing the manuscript.
This research was supported by grants to N. Hsu and the Alice-Reynolds Kleberg Foundation. National Center for Biotechnology Information , U. Author manuscript; available in PMC Feb Navin 1, 2, 3. Marco L. Nicholas E. Author information Copyright and License information Disclaimer. Copyright notice. The publisher's final edited version of this article is available at Nature. See other articles in PMC that cite the published article.
Associated Data Supplementary Materials 1. SUMMARY Sequencing studies of breast tumor cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumors. Open in a separate window. Figure 1. Validation in a Monoclonal Cancer Cell Line To validate our method we used a breast cancer cell line SK-BR-3 that was previously shown to be genetically monoclonal 11 , Figure 2.
Figure 3. Single-Molecule Targeted Deep Sequencing To validate the mutations detected by single cell sequencing and determine their frequencies in the bulk tumor, we performed targeted single-molecule deep-sequencing. Figure 4. Duplex Mutation Frequencies and Mutation Rates a, ER duplex mutation frequencies from targeted deep-sequencing of the bulk tumor tissue b, TNBC duplex mutation frequencies from deep-sequencing of the bulk tumor tissue. Extended Data Extended Data Figure 1.
Extended Data Figure 2. Extended Data Figure 3. Clustered Heatmaps of Single Cell Copy Number Profiles Single cell segmented copy number profiles were clustered and used to build heatmaps, showing amplifications in red and deletions in blue. Extended Data Figure 4. Duplex Single-Molecule Targeted Deep-Sequencing a, Experimental protocol for generating duplex libraries from bulk tumor DNA for custom capture and targeted ultra-deep sequencing.
Extended Data Figure 5. Extended Data Figure 6. Models of Clonal Evolution in Breast Cancer a, Clonal evolution in the ER breast tumor inferred from single cell exome and copy number data.
Supplementary Material 1 Click here to view. Funding Agencies N. Carcinogen-specific induction of genetic instability.
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