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.

Public Health Relevance

The proposed research will provide computational and bioinformatic resources for studying the binding pattern of paralogous oncogenic transcription factors. It will provide a computational framework for inferring the function of non-coding regulatory mutations and studying their interaction with common genetic variants. As an important application, we will systematically analyze a novel therapeutic strategy that has the potential to treat effectively a large number of patients across multiple cancer types harboring the recently discovered TERT promoter mutations.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA163336-09
Application #
10054960
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Li, Jerry
Project Start
2011-12-16
Project End
2022-11-30
Budget Start
2020-12-01
Budget End
2021-11-30
Support Year
9
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Illinois Urbana-Champaign
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
041544081
City
Champaign
State
IL
Country
United States
Zip Code
61820
Mancini, Andrew; Xavier-Magalhães, Ana; Woods, Wendy S et al. (2018) Disruption of the ?1L Isoform of GABP Reverses Glioblastoma Replicative Immortality in a TERT Promoter Mutation-Dependent Manner. Cancer Cell 34:513-528.e8
Jin, Hu; Finnegan, Alex I; Song, Jun S (2018) A unified computational framework for modeling genome-wide nucleosome landscape. Phys Biol 15:066011
Hejna, Miroslav; Moon, Wooyoung M; Cheng, Jeffrey et al. (2018) Local genomic features predict the distinct and overlapping binding patterns of the bHLH-Zip family oncoproteins MITF and MYC-MAX. Pigment Cell Melanoma Res :
Gapinske, Michael; Luu, Alan; Winter, Jackson et al. (2018) CRISPR-SKIP: programmable gene splicing with single base editors. Genome Biol 19:107
Zhang, Yi; Manjunath, Mohith; Zhang, Shilu et al. (2018) Integrative Genomic Analysis Predicts Causative Cis-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084. Cancer Res 78:1579-1591
Ye, Julia; Jin, Hu; Pankov, Aleksandr et al. (2017) NF45 and NF90/NF110 coordinately regulate ESC pluripotency and differentiation. RNA 23:1270-1284
Finnegan, Alex; Song, Jun S (2017) Maximum entropy methods for extracting the learned features of deep neural networks. PLoS Comput Biol 13:e1005836
Salma, Nunciada; Song, Jun S; Kawakami, Akinori et al. (2017) Tfe3 and Tfeb Transcriptionally Regulate Peroxisome Proliferator-Activated Receptor ?2 Expression in Adipocytes and Mediate Adiponectin and Glucose Levels in Mice. Mol Cell Biol 37:
Hejna, Miroslav; Jorapur, Aparna; Song, Jun S et al. (2017) High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells. Sci Rep 7:11943
Qin, Han; Hejna, Miroslav; Liu, Yanxia et al. (2016) YAP Induces Human Naive Pluripotency. Cell Rep 14:2301-12

Showing the most recent 10 out of 31 publications