Systematic Characterization of Small Nucleolar RNAs in Cancer

Abstract Despite recent advancements in treatment options for cancer, a majority of cancer types continue to lack fully characterized and effective targeted therapies. This insufficiency has resulted in the demand for alternative, previously unconsidered treatment approaches to improve disease diagnostics, prognoses, and patient

survival outcomes. Recently, we performed integrative analysis for small nucleolar RNA (snoRNAs) in a large number of patient samples and identified 46 snoRNAs that exhibit broad-spectrum clinical significance with 12 or more types of cancer. We developed a data portal, snoRNA in cancers (SNORic), which allows researchers

to explore the significance of individual snoRNAs in cancer. SNORic has been accessed >100,000 times since its release in 2017, suggesting its broad impact in the biomedical research community. We provided initial genetic evidence indicating that elevated expression of snoRNAs facilitated the tumorigenesis of mammary

gland malignancies. Mechanistically we demonstrated that SNORD46 plays important roles in promoting the initiation, growth, invasion and progression of TNBC. Therefore, elucidation of the roles of snoRNAs in promoting tumorigenesis serves as the first step in the development of a novel class of snoRNA-based

biomarkers and therapeutic targets. Our central hypothesis is that snoRNAs serve as essential RNA targets in promoting cancer initiation, progression, and drug resistance, which could be attenuated in vivo by an antisense oligonucleotide-based targeted therapy. In specific aim 1, we will delineate the diagnostic and prognostic values of these snoRNAs in

triple-negative breast cancer (Aim 1.1). We will demonstrate the molecular mechanism that these snoRNAs promote TNBC cell proliferation, mobility and invasion (Aim 1.2). We will demonstrate that antisense oligonucleotide-based snoRNA targeted therapy effectively inhibits TNBC growth in vivo (Aim 1.3). We will

evaluate and interpret causal effects through molQTL analysis (Aim 1.4). In specific aim 2, we will determine the role of three snoRNAs in breast cancer drug resistance (Aim 2.1). We will understand the molecular mechanisms for drug resistance through multi-omics data (Aim 2.2). To expand our perspective on drug

resistance, we will predict the drug response from individual snoRNA expression with the augmentation of deep learning (Aim 2.3). We will study the drug responses effects among snoRNA-based subtypes (Aim 2.4). We will build a user-friendly data portal for releasing the date generated through integrative analysis (Aim 2.5).

This study will significantly advance the prognostic, diagnostic, and therapeutic potential of snoRNAs; the absence of this research work will greatly hinder the realization of snoRNA-based therapeutic considerations for cancer patients.