Although alternative splicing is one of the major drivers of cellular diversity and growth during development,
the splicing machinery can be hijacked in cancer to promote metastasis, immune escape, invasion and anti-
apoptotic actions. While splicing factor mutations occur in 1-15% of patients, depending on the cancer,
emerging data suggest that commonly dysregulated oncogenes such as MYC indirectly regulate mRNA
processing pathways leading to cancer promoting alternative splice isoforms in distinct malignancies. Using a
series of recently developed unsupervised splicing detection and candidate splicing regulatory prediction
techniques, we discovered that splicing is broadly disrupted in adult and pediatric cancers independent of
obvious splicing factor mutations. These data suggest a potentially paradigm shifting model, in which
widespread coordinated splicing dysfunction occurs across cancers, likely via imbalances in splicing factor
expression, signaling or genetic alternations. If true, spliceosome directed and upstream therapies may be
broadly repurposed across cancers, focused on specific splicing signatures and implicated regulatory
pathways rather than on specific mutations alone. To test these hypotheses and develop reusable analytical
resources for the cancer community, we propose the following aims.
Aim 1: Implicate key splicing pathway vulnerabilities with observed oncogenic events across
diverse cancers. We will characterize alternative splicing on a global-level with our existing integrative multi-
omics computational workflow across dozens of cancers and thousands of samples. Splicing events identified
using novel unsupervised or supervised analyses will be compared within and between distinct cancers as well
as normal cells of different origins to define reproducible tumor intrinsic vs. differentiation associated programs.
Aim 2: Define and validate the core splicing regulatory networks in pediatric AML and diverse
human cancers. We will build and validate a novel learning model to define the splicing regulatory network in
pediatric AML and ultimately across diverse adult and pediatric cancers. We will adapt current best practices
for multi-evidence transcriptional regulatory network inference to splicing and rigorously test our models with
validation data. A large library of experimental splicing factor binding datasets will be used to improve our
predictions. These analyses will identify novel splicing regulators and RNA recognition elements.
Aim 3: Build a discovery platform for precision splicing biomarker detection and selective splicing
target inhibition. We will develop an interactive computational interface to identify specific RNA isoforms
associated with poor prognosis splicing subtypes in diverse cancers obtained in Aim 1. By integrating splicing,
gene expression, proteomics and methylation data on the same patients, we will enable the discovery of
splicing events linked to diverse modes of gene regulation, that potentially manifest at the protein level.
Associated isoform interactions and weighted coexpression networks will be built to prioritize specific splicing
events in known cancer pathways.
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