Refining pancreatic ductal adenocarcinoma molecular subtype and precision therapeutics with single-nucleus RNA-seq

Pancreatic cancer is predicted to become the second leading cause of cancer-related deaths in the USA by 2030 with a 5-year survival rate of only 9% [1, 2]. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic malignancy and remains a treatment-refractory disease. So far, apart from surgical resection, conventional chemotherapies, radiotherapies, few therapeutic strategies, adjuvant or neoadjuvant, are available for improved PDAC patient benefit. In recent years, more research efforts have been devoted to developing targeted therapeutics via discovery of novel molecular vulnerabilities and molecular subtyping of various diseases. For PDAC, despite many attempts to refine its molecular taxonomy over the years, the current molecular subtyping still does not efficiently inform novel molecular vulnerabilities for the development of targeted therapies. Thus, fine-tuning the resolution of PDAC molecular subtyping has become a pivotal need.

Here, we discuss a manuscript from Hwang et al. which focused on the optimization of a single-nucleus RNA-sequencing (snRNA-seq) technique to understand how preoperative treatment may impact residual tumors. Moreover, we examine how this technique can further contribute to the field by identifying additional molecular vulnerabilities that can be harnessed for informative stratification during PDAC patient clinical management and targeted combinations with neoadjuvant therapies [3].

Use of snRNA-seq for frozen archival PDAC specimen
Single-cell technologies, especially single-cell RNA-seq, have been regularly used in elucidating intertumoral tumor microenvironment (TME) and heterogeneity as well as expanding upon data from traditional bulk RNA profiling of various tumors. However, the use of scRNA-seq is not as prevalent in PDAC as in other cancers due to high intrinsic nuclease content and dense desmoplastic stroma. High-resolution transcriptional networks and any resulting patient stratification from bulk RNA analyses also tend to be obscured because of lower-quality sample collection due to the complicated PDAC TME. Hwang and colleagues propose an optimized snRNA-seq technique for better recovery of PDAC cancer cell profile without compromising the spectrum of cell states. The authors demonstrate, with frozen archival specimens not commonly considered for analysis, the potential of snRNA-seq to capture various cell types with comparable quality to that obtained from gold standard multiplex profiling in situ. This technique also enables processing of banked frozen tissue samples dating back at least 7 years, while bypassing some of the challenges involved in sample preparation for traditional single-cell RNA-seq, such as balancing sample viability and accurate cell type representation. Thus, the use of snRNA-seq provides an exciting avenue to further resolve PDAC molecular subtypes and gain novel insights for precision medicine approaches based on tumor reprogramming profile.

Refined molecular taxonomy informing prognostic patient stratification
Apart from accurately capturing the malignant and non-malignant compartments of human PDAC tumors, the snRNA-seq analyses, combined with spatially resolved transcriptomics, also revealed a novel, clinically relevant molecular taxonomy with better patient stratification potential than that from the two previously identified consensus molecular subtypes, basal-pancreatic and classical-pancreatic [4]. Under the refined molecular taxonomy of PDAC proposed in the preprint, patients can be further stratified into prognostic risk groups based on malignant cell and cancer-associated fibroblast programs beyond just their conventional, consensus tumor profile.