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
Affiliations
Jake Leighton
Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
Min Hu
Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
Emi Sei
Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
Funda Meric-Bernstam
Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Precision Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
Nicholas E. Navin
Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate School of Biological Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Corresponding author
Summary: 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.