Defining the Comprehensive Genomic Landscapes of Pancreatic Ductal Adenocarcinoma Using Real-World Endoscopic Aspiration Samples

Most patients with pancreatic ductal adenocarcinoma (PDAC) present with surgically unresectable cancer. As a result, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the most common biospecimen source available for diagnosis in treatment-naïve patients. Unfortunately, these limited s...

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Veröffentlicht in:Clinical cancer research 2021-02, Vol.27 (4), p.1082-1093
Hauptverfasser: Semaan, Alexander, Bernard, Vincent, Lee, Jaewon J, Wong, Justin W, Huang, Jonathan, Swartzlander, Daniel B, Stephens, Bret M, Monberg, Maria E, Weston, Brian R, Bhutani, Manoop S, Chang, Kyle, Scheet, Paul A, Maitra, Anirban, Jakubek, Yasminka A, Guerrero, Paola A
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Sprache:eng
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Zusammenfassung:Most patients with pancreatic ductal adenocarcinoma (PDAC) present with surgically unresectable cancer. As a result, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the most common biospecimen source available for diagnosis in treatment-naïve patients. Unfortunately, these limited samples are often not considered adequate for genomic analysis, precluding the opportunity for enrollment on precision medicine trials. Applying an epithelial cell adhesion molecule (EpCAM)-enrichment strategy, we show the feasibility of using real-world EUS-FNA for in-depth, molecular-barcoded, whole-exome sequencing (WES) and somatic copy-number alteration (SCNA) analysis in 23 patients with PDAC. Potentially actionable mutations were identified in >20% of patients. Further, an increased mutational burden and higher aneuploidy in WES data were associated with an adverse prognosis. To identify predictive biomarkers for first-line chemotherapy, we developed an SCNA-based complexity score that was associated with response to platinum-based regimens in this cohort. Collectively, these results emphasize the feasibility of real-world cytology samples for in-depth genomic characterization of PDAC and show the prognostic potential of SCNA for PDAC diagnosis.
ISSN:1078-0432
1557-3265
DOI:10.1158/1078-0432.ccr-20-2667