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Clonal evolution in breast cancer revealed by single nucleus genome sequencing

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Abstract

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER+) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER+ tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

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Figure 1: Method performance in a monoclonal cell line.
Figure 2: Single cell and population sequencing of an ER tumour.
Figure 3: Single cell and population sequencing of a triple-negative breast cancer.
Figure 4: Duplex mutation frequencies and mutation rates.

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Acknowledgements

We thank L. Ramagli, H. Tang, E. Thompson, K. Khanna, W. Schober and J. Tyler. We are grateful to S. Kennedy and L. Loeb for help with the duplex protocols. We thank M. Edgerton, J. Hicks, M. Wigler and J. Kendall for discussions. We thank R. Krahe and M. Rui for reviewing the manuscript. N.E.N. is a Nadia’s Gift Foundation Damon Runyon-Rachleff Innovator (DRR-25-13). This research was supported by grants to N.E.N. from NIH (R21CA174397-01) and NCI (1RO1CA169244-01). N.E.N. was supported by T.C. Hsu and the Alice-Reynolds Kleberg Foundation. N.E.N. and P.S. were supported by the Center for Genetics & Genomics. F.M.-B was supported by an NIH UL1 (TR000371) and Susan Komen (SAC10006). K.C. was supported by the NCI (RO1CA172652). H.L. was supported by the NIH (U24CA143883). F.M. was supported by PS-OC (U54CA143798). K.C. and H.L. were supported by the Dell Foundation. M.L.L. is a CPRIT scholar and is supported by ALA. This work was also supported by an NCI center grant (CA016672). A.U. is a Rosalie B. Hite Fellow.

Author information

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Authors

Contributions

Y.W. performed experiments and data analysis. M.L.L., J.W., A.M. and X.S. performed experiments. A.U., W.R., K.C., H.L., P.S. and S.V. performed data and statistical analyses. H.Z. and F.M.-B. obtained clinical samples. R.Z. and F.M. performed modelling. N.E.N. performed experiments, analysed data and wrote the manuscript.

Corresponding author

Correspondence to Nicholas E. Navin.

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Competing interests

The authors declare no competing financial interests.

Additional information

The data from this study has been deposited into the Sequence Read Archive (SRA053195).

Extended data figures and tables

Extended Data Figure 1 Nuc-seq method.

a, Nuclear suspensions were prepared and stained with DAPI for flow-sorting, showing distributions of ploidy. The G2/M distribution was gated and single nuclei were deposited into wells. b, Cells were lysed and incubated with the Φ29 polymerase to perform multiple-displacement-amplification for a limited isothermal time-frame. c, d, Sequence libraries were prepared using one of two methods: Tn5 tagmentation (c), or low-input TA ligation cloning (d) (see Methods). e, Exome capture was optionally performed to isolate gDNA in exonic regions. f, Libraries were sequenced on the Illumina HiSeq2000 system. g, Somatic mutations were detected using a custom processing pipeline (Methods).

Extended Data Figure 2 Evaluation of WGA efficiency using chromosome-specific primers.

Whole genome amplified DNA from each single cell was used to perform PCR quality control experiments to determine WGA efficiency. For each cell, 22 reactions were performed using primer pairs that target each autosome and the resulting 200 bp PCR product were separated by gel electrophoresis (Methods). a, Two single nuclei were flow-sorted from the G2/M gate and amplified to WGA followed by PCR using 22 primer pairs. b, Two single nuclei were flow-sorted from the G1/0 gate and subject to WGA followed by PCR using 22 primer pairs. PCR products that failed to amplify are marked with an ‘x’ on the gel.

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. a, Copy number profiles of 50 single cells from the ERBC. b, Copy number profiles of 50 single cells from the TNBC patient.

Extended Data Figure 4 Duplex single-molecule targeted deep-sequencing.

a, Experimental protocol for generating duplex libraries from bulk tumour DNA for custom capture and targeted ultra-deep sequencing. b, Data processing pipeline for duplex data to generate single-molecule data and detect mutation frequencies. c, Distribution of unique molecule tag duplicates for the ER breast cancer patient d, Distribution of unique molecule tag duplicates for the TNBC. e, Single-molecule coverage depth distribution for the ER+ tumour data. f, Single-molecule coverage depth distribution for the TNBC data.

Extended Data Figure 5 TNBC Multi-dimensional scaling and protein prediction plots.

a, Multi-dimensional scaling plot of the nonsynonymous mutations from the single-nuclei exome sequencing data in the TNBC b, Polyphen and SIFT protein impact prediction scores for the subclonal mutations in the TNBC patient.

Extended Data Figure 6 Models of clonal evolution in breast cancer.

a, Clonal evolution in the ERBC inferred from single cell exome and copy number data. b, Clonal evolution in the TNBC inferred from single cell exome and copy number data.

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Wang, Y., Waters, J., Leung, M. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014). https://doi.org/10.1038/nature13600

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