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Hepato-pancreato-biliary

E200 Single Cell Multiomic Sequencing with Bioinformatic Analysis Identifies Dynamic Transcriptional and Epigenetic Changes During Metastasis of Pancreatic Cancer

E200: Single Cell Multiomic Sequencing with Bioinformatic Analysis Identifies Dynamic Transcriptional and Epigenetic Changes During Metastasis of Pancreatic Cancer

ePoster Abstract Author(s)

Michaela R. Cunningham, MD (she/her/hers)

Resident
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA, United States

Author(s)

Michaela R. Cunningham, MD (she/her/hers)

Resident
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA, United States
HZ

Hongpan Zhang, MS

Graduate Student
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA, United States
SA

Sara J. Adair, MS

Lab Manager
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA, United States
XW

Xiaolong Wei, PhD

Research Assistant Professor
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA, United States
SH

Sheng'en Hu, PhD

Research Scientist
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA, United States
VR

Victoria A. Remley, BS

Graduate Student
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA, United States
CZ

Chongzhi Zang, PhD

Associate Professor
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA; UVA Comprehensive Cancer Center, Charlottesville, VA, USA, United States
TB

Todd Bauer, MD

Professor of Surgery
Department of Surgery, University of Virginia Health System, Charlottesville, VA, USA; UVA Comprehensive Cancer Center, Charlottesville, VA, USA, United States

Introduction: Identification of transcriptional and epigenetic changes that drive pancreatic cancer metastasis may yield novel targets for therapy. We used a patient derived xenograft (PDX) model of pancreatic cancer to identify dynamic transcriptional and epigenetic changes that occur during metastasis.

Methods: Cells from PDX lines 395 and 366 were injected into the pancreas of separate immunocompromised mice. Tumors were allowed to grow and generate spontaneous liver metastases. A primary pancreatic tumor and matching liver metastasis were excised for the two PDXs then RNA and ATAC sequencing were performed using 10X Genomics Chromium Single Cell Multiome ATAC + Gene Expression kit. Tumor cells were clustered based on RNA and ATAC sequencing using Seurat 4.0. Pearson correlation and Jaccard Index of cluster signature genes were used to identify similarities between clusters. Semi-supervised Slingshot was used to evaluate dynamic gene expression over pseudotime from primary to metastatic cell clusters. Lineages were compared to find similarities and signature genes were identified.

Results: 9 clusters were generated from primary and metastatic tumor cells in PDX366. 12 clusters were generated from primary and metastatic tumor cells in PDX395. Comparison of these clusters identified similarities between PDX366 cluster 7 and PDX395 cluster 7 (Pearson correlation 0.52, Jaccard Index 0.37), PDX366 cluster 3 and PDX395 cluster 5 (Pearson correlation 0.33, Jaccard Index 0.25) and PDX366 cluster 2 and PDX395 cluster 1 (Pearson Correlation 0.03, Jaccard Index 0.19). For semi-supervised Slingshot analysis, PDX366 cluster 2 and PDX395 cluster 1 were chosen as start points given their composition of primary tumor cells and similarity. Four lineages were identified for each PDX tumor/metastasis pair. PDX366 lineage 1 and PDX395 lineage 2 were identified as potentially interesting, as each of these lineages ended with their respective metastatic cluster 7. From association test, 158 genes in PDX366 lineage 1 and 307 genes in PDX395 lineage 2 were implicated as changing significantly. 86 of these genes were shared between both lineages. These are potential high priority targets for treatment.

Conclusions: Single cell multiomic sequencing and integrative bioinformatic analysis can be used to identify key dynamic transcriptional and epigenetic changes that occur during metastasis of pancreatic cancer. Ongoing experiments are interrogating high priority targets to identify novel drivers of progression and metastasis.

Learning Objectives:

Upon completion, participants will be able to discuss the use of multiomic sequencing and bioinformatics methods to identify drivers of pancreatic cancer metastasis.
Upon completion, participants will be able to identify novel methods in the study of transcriptional and epigenetic changes in cancer metastasis.
Upon completion, participants will be able to define multiomic sequencing as it relates to cancer metastasis.