Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer worldwide, with only a 50% cure rate in spite of combined treatment modalities. Therapeutic targeting of the epidermal growth factor receptor (EGFR) improves the survival in a subset of patients, although molecular predictors of sensitivity are currently elusive. Moreover, responsive patients often acquire resistance and ultimately succumb to their disease. Distinguishing the specific molecular processes that drive such therapeutic resistance amid complex cross-talk in cell signaling processes and stochastic evolutionary pressures requires dynamical models built from serial data. Therefore, in this application, we develop novel computational algorithms to infer the molecular mechanisms underlying cetuximab resistance from in vitro and in vivo model of cetuximab resistant HNSCC. Specifically, we will investigate the hypotheses that: (1) short-term time course data improve the ability of in silicon modeling techniques to infer both on- and off-target signaling responses to cetuximab;(2) combined epigenetic, post-transcriptional, and genomic changes in HNSCC cells upon chronic exposure to cetuximab result in acquired resistance;and (3) modeling inter and intra-individual heterogeneity will discern the specific cellular signaling processes that are activated to drive in vivo acquired cetuximab resistance in cell- line xenograft models of HNSCC. The results from this project will ultimately contribute to the selection of patients for cetuximab treatment and alternative molecular targets to overcome acquired cetuximab resistance. The algorithms developed will also be directly applicable to inference of molecular drivers of therapeutic resistance in additional cancers.
This proposed study will develop novel computational algorithms to discern the molecular mechanisms associated with cetuximab resistance in biological models of head and neck squamous cell carcinoma (HNSCC). Measuring the genomic landscape serially as these biological models develop cetuximab resistance will facilitate the development of computational models that can distinguish dominant molecular changes results in acquired resistance. Although focused on cetuximab resistance in HNSCC, the computational algorithms developed will be generally applicable to discovery of therapeutic resistance mechanisms in molecularly targeted agents and facilitate development of biomarkers that will allow appropriate selection of patients with potential clinical benefits.
|Stein-O'Brien, Genevieve L; Arora, Raman; Culhane, Aedin C et al. (2018) Enter the Matrix: Factorization Uncovers Knowledge from Omics. Trends Genet 34:790-805|
|Dinalankara, Wikum; Ke, Qian; Xu, Yiran et al. (2018) Digitizing omics profiles by divergence from a baseline. Proc Natl Acad Sci U S A 115:4545-4552|
|Kagohara, Luciane T; Stein-O'Brien, Genevieve L; Kelley, Dylan et al. (2018) Epigenetic regulation of gene expression in cancer: techniques, resources and analysis. Brief Funct Genomics 17:49-63|
|Stein-O'Brien, Genevieve; Kagohara, Luciane T; Li, Sijia et al. (2018) Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance. Genome Med 10:37|
|Afsari, Bahman; Guo, Theresa; Considine, Michael et al. (2018) Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer. Bioinformatics 34:1859-1867|
|Guo, Theresa; Sakai, Akihiro; Afsari, Bahman et al. (2017) A Novel Functional Splice Variant of AKT3 Defined by Analysis of Alternative Splice Expression in HPV-Positive Oropharyngeal Cancers. Cancer Res 77:5248-5258|
|Finegersh, Andrey; Kulich, Scott; Guo, Theresa et al. (2017) DNA methylation regulates TMEM16A/ANO1 expression through multiple CpG islands in head and neck squamous cell carcinoma. Sci Rep 7:15173|
|Stein-O'Brien, Genevieve L; Carey, Jacob L; Lee, Wai Shing et al. (2017) PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF. Bioinformatics 33:1892-1894|
|Stavrovskaya, Elena D; Niranjan, Tejasvi; Fertig, Elana J et al. (2017) StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data. Bioinformatics 33:3158-3165|
|Kelley, Dylan Z; Flam, Emily L; Izumchenko, Evgeny et al. (2017) Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks. Cancer Res 77:6538-6550|
Showing the most recent 10 out of 23 publications