bioRxivCancer Biology
Islands of genomic stability in the face of genetically unstable metastatic cancer
K. Bowland, J. Lai, A. Skaist, Y. Zhang, S. S. K. Teh, N. J. Roberts, E. Thompson, S. J. Wheelan, R. H. Hruban, R. Karchin, C. A. Iacobuzio-Donahue, J. R. Eshleman
Abstract
AbstractO_ST_ABSIntroductionC_ST_ABSMetastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because [~]10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9. MethodsWhole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing. ResultsWe found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases. ConclusionsRegions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
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