Hwang and his team will optimize two high-resolution techniques they developed to leverage single-nucleus RNA sequencing and digital spatial profiling data in order to gain novel insights into the diverse pathways by which pancreatic NETS develop, self-organize and respond to environmental stresses, including treatment.
What question will the researchers try to answer?
A clinically-relevant molecular classification for pancreatic NETs (pNETS) to predict patient outcomes and guide therapeutic decision-making has been elusive. The reason for this is, in part, because prior studies have primarily looked at the entire tumor in aggregate, leading to an unknown mixture of cancer cells, immune cells, and other components of connective tissue. By separately dissecting out the properties and behaviors of each cell type in the complex tumor ecosystem, as well as preserving the spatial relationships among different cell types, he will identify critical features, multicellular communities, and intercellular interactions that underlie specific molecular subtypes and mediate therapeutic resistance.
Why is this important?
Some pNETs secrete excess hormones and are termed “functional” whereas others do not and are referred to as “non-functional.” The clinical behavior of pNETs varies widely but approximately half of cases of pNETs progress to metastases and cancer-related death after surgery. Having a reliable way to predict and therapeutically target this clinical heterogeneity is therefore critical to improving patient outcomes.
What will researchers do?
Their team will optimize innovative technologies such as single-nucleus RNA sequencing and spatially-resolved proteotranscriptomics for pNETs and then apply them to a diverse cohort of tumors (e.g., primary vs. metastasis, functional vs. non-functional, treated vs. untreated).
How might this improve the treatment of NETs?
The insights gained from this study will provide a molecular blueprint to enhance prognosis and therapeutic strategies in pNETs, ultimately leading to improved clinical outcomes for patients.
What is the next step?
This study will highlight the molecular features, multicellular communities, and intercellular interactions that underlie the poorest prognostic subtypes and treatment-resistant phenotypes in pNETs. Next steps include validating these specific findings through targeted panels applied to a large independent validation cohort as well as identifying key regulators of these critical subtypes/phenotypes and testing novel therapeutic strategies in preclinical models.