Spatial Intratumor Genomic Heterogeneity within Localized Prostate Cancer Revealed by Single-nucleus Sequencing

2018 
Abstract Background Prostate adenocarcinoma (PCa) is a complex genetic disease, and the implementation of personalized treatment in PCa faces challenges due to significant inter- and intrapatient tumor heterogeneities. Objective To systematically explore the genomic complexity of tumor cells with different Gleason scores (GSs) in PCa. Design, setting, and participants We performed single-cell whole genome sequencing of 17 tumor cells from localized lesions with distinct GS and matched four normal samples from two prostatectomy patients. Outcome measurements and statistical analysis All classes of genomic alterations were identified, including substitutions, insertions/deletions, copy number alterations, and rearrangements. Results and limitations Significant spatial, intra- and intertumoral heterogeneities were observed at the cellular level. In the patient 1, all cells shared the same TP53 driver mutation, implying a monoclonal origin of PCa. In the patient 2, only a subpopulation of cells contained the TP53 driver mutation, whereas other cells carried different driver mutations, indicating a typical polyclonal model with separate clonal cell expansions. The tumor cells from different sides of prostate owned various mutation patterns. Considerable neoantigens were predicted among different cells, implying unknown immune editing components helping prostate tumor cells escaping from immune surveillance. Conclusions There is a significant spatial genomic heterogeneity even in the same PCa patient. Our study also provides the first genome-wide evidence at single-cell level, supporting that the origin of PCa could be either polyclonal or monoclonal, which has implications for treatment decisions for prostate cancer. Patient summary We reported the first single-cell whole genomic data of prostate adenocarcinoma (PCa) from different Gleason scores. Identification of these genetic alterations may help understand PCa tumor progression and clonal evolution.
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