# Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing Data

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $326,848

## Abstract

Limitless replicative potential is a key hallmark of cancer and critically depends on telomere maintenance. Many
cancers thus aberrantly reactivate the telomerase reverse transcriptase (TERT), a catalytic subunit of the
telomerase complex that elongates telomere. It has been recently discovered that this common path to
immortality in multiple cancers is through two activating point mutations in the TERT promoter (TERTp), found
in more than 50 different cancer types, often at strikingly high frequencies, e.g. roughly 83% in glioblastomas
(GBM) and 71% in melanomas. In the previous funding period, the PI has identified the molecular function of
these highly recurrent mutations, demonstrating that the transcription factor (TF) GABP binds the mutant TERTp
with exquisite specificity, but not the wild-type TERTp. The high prevalence of TERTp mutations across multiple
cancer types and the selectivity of GABP recruitment to mutant TERTp thus provide an unprecedented
opportunity for treating a large number of cancer patients with minimal toxicity to healthy cells. Despite the clear
significance of this opportunity, however, several important questions surrounding the molecular functions and
modulators of TERTp mutations remain poorly understood, hindering the development of effective and safe
therapeutic strategies.
 Our long-term goal is to establish a rigorous computational framework for understanding the aberrant
transcriptional and epigenetic networks in cancers and to apply the resulting knowledge to devise novel
therapeutic strategies that account for the genetic background of individual patients and that can a priori predict
and avoid potential resistance mechanisms. The objective of our current renewal proposal is to develop powerful
computational methods for transforming our knowledge about the non-coding TERTp mutations into an effective
and safe molecular target. At the same time, the resulting methods will help resolve several outstanding
challenges in the field of transcriptional gene regulation and have broad applications in cancer genomics. We
will accomplish our objective my pursuing the following Aims: (1) Develop and test a computational framework
for inferring sequence features that determine the distinct and shared binding patterns of paralogous TFs; (2)
Develop and validate integrative tools for discovering the molecular basis of genetic interactions between
germline variations and oncogenic mutations; (3) Develop and apply computational methods for studying the
role of DNA helical phase between adjacent binding motifs in recruiting ETS factors to chromatin; (4) Perform a
systematic genomic characterization of the effects of knocking out GABPB1L in TERTp-mutant cancer cells and
healthy cells.
The results of this proposal will have a broad impact on cancer research by providing powerful tools for studying
paralogous oncogenic TFs and revealing novel insights into a highly promising therapeutic strategy.

## Key facts

- **NIH application ID:** 9831048
- **Project number:** 5R01CA163336-08
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Jun S Song
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $326,848
- **Award type:** 5
- **Project period:** 2011-12-16 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9831048

## Citation

> US National Institutes of Health, RePORTER application 9831048, Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing Data (5R01CA163336-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9831048. Licensed CC0.

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