Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing

Jake Leighton; Min Hu; Emi Sei; Funda Meric-Bernstam; Nicholas E Navin

doi:10.1016/j.xgen.2022.100215

Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing

Cell Genom. 2022 Nov 9;3(1):100215. doi: 10.1016/j.xgen.2022.100215. eCollection 2023 Jan 11.

Authors

Jake Leighton^{1

2

3}, Min Hu^{1

2}, Emi Sei^{1

2}, Funda Meric-Bernstam^{3

4}, Nicholas E Navin^{1

2

3

5}

Affiliations

¹ Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
² Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
³ Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Precision Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
⁵ Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

Keywords: breast cancer; intratumor heterogeneity; mutational evolution; single-cell genomics; triple-negative breast cancer.

Abstract

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