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To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and adjacent normal tissue under a rapid autopsy program and performed whole exome sequencing (WES) on tumors as well as adjacent normal tissue. Patient-derived xenograft (PDX) models were also generated, sequenced and compared to primary tumor tissue.

Identifiers: SRA: ERP010378
BioProject: PRJEB9296
PFIZER: ena-STUDY-PFIZER-05-05-2015-01:04:16:751-170
Study Type: 
Other
Abstract: Pancreatic Ductal Adenocarcinoma (PDAC) is a highly lethal malignancy due to its propensity to invade and rapidly metastasize and remains very difficult to manage clinically. One major hindrance towards a better understanding of PDAC is the lack of molecular data sets and models representative of end stage disease. Moreover, it remains unclear how molecularly similar patient-derived xenograft models are to the primary tumor from which they were derived. To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and adjacent normal tissue under a rapid autopsy program and performed whole exome sequencing (WES) on tumors as well as adjacent normal tissue. Patient-derived xenograft (PDX) models were also generated, sequenced and compared to primary tumor tissue. Across the matched data sets generated for three patients, there were on average approximately 160 somatic mutations in each sample. The majority of the mutations in each patient were shared among the primary and metastatic samples and, importantly, were largely retained in the xenograft models. In two of the tumor sets, we reconstructed a parsimonious clonal evolution history of KRAS, TP53, SMAD4 and MUC16 that may play an important role in tumor initiation, progression and metastasis. These results add to our understanding of pancreatic tumor biology, and demonstrate that PDX models closely approximate the genetics of the primary tumors from which they were derived. As these PDX models were derived from metastatic end stage disease they represent a biologically and clinically relevant pre-clinical platform that may enable the development of effective targeted therapies for PDAC.
Description: Pancreatic Ductal Adenocarcinoma (PDAC) is a highly lethal malignancy due to its propensity to invade and rapidly metastasize and remains very difficult to manage clinically. One major hindrance towards a better understanding of PDAC is the lack of molecular data sets and models representative of end stage disease. Moreover, it remains unclear how molecularly similar patient-derived xenograft models are to the primary tumor from which they were derived. To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and adjacent normal tissue under a rapid autopsy program and performed whole exome sequencing (WES) on tumors as well as adjacent normal tissue. Patient-derived xenograft (PDX) models were also generated, sequenced and compared to primary tumor tissue. Across the matched data sets generated for three patients, there were on average approximately 160 somatic mutations in each sample. The majority of the mutations in each patient were shared among the primary and metastatic samples and, importantly, were largely retained in the xenograft models. In two of the tumor sets, we reconstructed a parsimonious clonal evolution history of KRAS, TP53, SMAD4 and MUC16 that may play an important role in tumor initiation, progression and metastasis. These results add to our understanding of pancreatic tumor biology, and demonstrate that PDX models closely approximate the genetics of the primary tumors from which they were derived. As these PDX models were derived from metastatic end stage disease they represent a biologically and clinically relevant pre-clinical platform that may enable the development of effective targeted therapies for PDAC.

Related SRA data

Experiments:
13 ( 13 samples )
Runs:
13 (70.3Gbp; 32.0Gb)
Additional objects:
File type count
bam 13