In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 24_Supplement ( 2019-12-15), p. A22-A22
Kurzfassung:
Objective: Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease as existing molecular subtypes are insufficient and do not currently inform clinical decisions. Rare cell types, including those responsible for resistance, are difficult to detect with bulk transcriptomic profiling. Indeed, several previously identified transcriptomic subtypes of PDAC are unintentionally driven by “contaminating” stromal components. Single-cell transcriptomics provides an unprecedented degree of resolution into the properties of individual cells. However, RNA extraction from RNase- and stroma-rich pancreatic tissue is difficult and prior single-cell efforts have been limited by suboptimal dissociation/RNA quality. We developed a robust single-nucleus RNA-seq (sNuc-seq) technique compatible with frozen archival PDAC specimens and computational techniques to identify the transcriptomic programs driving tumor subtypes and therapeutic resistance. Methods: Patients with localized PDAC undergoing surgical resection with or without neoadjuvant chemoradiotherapy were consented for this IRB-approved study. Specimens were screened for RNA Integrity Number & gt;6. Single nuclei suspensions were extracted from flash-frozen primary PDAC specimens and organoids. Approximately 8,000 nuclei were loaded on the 10x Genomics Chromium platform per sample to generate and sequence 3’ gene expression libraries (Illumina HiSeq 2500, 125 bp paired-end reads). sNuc-seq derived reads were processed using the 10X CellRanger v3.0.2 pipeline. Unsupervised clustering was utilized to identify different cell populations and known marker genes from literature were used to label cell types. Results: Both treatment-naïve (n=12) and treatment-resistant (n=11) specimens yielded high-quality sNuc-seq data ( & gt;1,000 nuclei per sample, & gt;1,000 median genes per nucleus). In each tumor, distinct clusters with gene expression profiles consistent with ductal, fibroblast, endothelial, endocrine, lymphocyte, and myeloid cell populations were identified. Malignant cells were confirmed by inferred copy number variation analysis (InferCNV v3.9) and segregated into several distinct clusters for each individual patient highlighting intratumoral heterogeneity. While some malignant clusters corresponded to previously identified basal-squamous and classical-progenitor bulk subtypes, others featured expression profiles distinct from known subtypes, including cells with upregulation of hypoxia-associated or cytoskeletal genes. Conclusions: Applying sNuc-seq to treatment-naïve and pretreated PDAC specimens, we uncovered significant intratumoral heterogeneity in the malignant and stromal compartments and identified malignant cells featuring transcriptomic programs that do not fit previously identified bulk subtypes. Characterization of therapeutic resistance programs, spatial relationships among cell types, and association with clinical outcomes is ongoing. Citation Format: William L. Hwang, Karthik A. Jagadeesh, Orr Ashenberg, Eugene Drokhlyansky, George Eng, Nicholas Van Wittenberghe, William Freed-Pastor, Clifton Rodriguez, Danielle Dionne, Julia Waldman, Michael Cuoco, Alexander Tsankov, Connor Lambden, Caroline Porter, Jason Schenkel, Laurens Lambert, Debora Ciprani, Andrew J. Aguirre, Mari Mino-Kenudson, Theodore S. Hong, Orit Rozenblatt-Rosen, Carlos Fernandez-del Castillo, Andrew S. Liss, Aviv Regev, Tyler E. Jacks. Molecular subtypes and resistance programs in pancreatic ductal adenocarcinoma elucidated with single-nucleus RNA-seq [abstract] . In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A22.
Materialart:
Online-Ressource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.PANCA19-A22
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2019
ZDB Id:
2036785-5
ZDB Id:
1432-1
ZDB Id:
410466-3
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