Genomic characterization of preclinical models derived from primary and metastatic sites from rapid autopsy patients in PDMR.

Abstract

e13506 Background: The National Cancer Institute has developed a repository of preclinical models [Patient-Derived Models Repository (NCI PDMR, https://pdmr.cancer.gov )] including patient derived xenografts (PDXs), organoids (PDOrgs) and in vitro tumor cultures (PDCs) from patients with solid tumor cancer histologies. A subset of these preclinical models is derived from post-mortem collections from rapid autopsies representing the end point in disease progression. Clinical annotations and genomic datasets associated with these models provide a unique opportunity to study tumor evolution, mechanistic insights into the metastatic process, and treatment resistance. Methods: To date, 43 PDXs, 21 PDCs, and 23 PDOrgs using rapid autopsy specimens from 8 primary and 35 metastatic sites of 18 patients have been developed by the Biological Testing Branch (DTP, DCTD, NCI Frederick, MD) for the PDMR. Whole exome (WES) and total transcriptome (RNASeq) data were processed to generate mutation, copy number alteration (CNA) and gene expression data. Multi-model lineage trees were reconstructed based on putative somatic variants for all the models derived from all patients. The fraction of the genome affected by CNA was compared both within and across PDX models. Results: Most of the rapid autopsy PDX models (32/43) are derived from pancreatic adenocarcinoma (PAAD) patients (13/18), with metastatic specimens originating from sites including liver, colon, omentum, and lung. Driver mutations are present in all preclinical model specimens derived from the same patient. For instance, KRAS p.G12D is present in all patient-derived model specimens derived from PAAD patient 521955. The fraction of the genome affected by CNA remains stable within a PDX model across passages (n = 24, mean = 6.39%, sd = 5.90%). However, we found that this increased when comparing PDX models derived from metastatic sites versus the primary site (n = 19, mean = 16.92%, sd = 10.46%). This indicates presence of tumor heterogeneity between metastatic and primary sites. The lineage tree for models from patient 521955 indicates that one liver metastasis has a unique seeding event compared to the other 4 metastatic sites. Unsupervised clustering analysis on gene expression data also confirms the observed tumor site relationships. Conclusions: Our data demonstrate the potential use of these preclinical models available from the NCI PDMR. These models provide a unique resource for preclinical studies in tumor evolution, metastatic spread mediators, and drug resistance.