Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

Genevieve Stein-O’Brien; Luciane T. Kagohara; Sijia Li; Manjusha Thakar; Ruchira Ranaweera; Hiroyuki Ozawa; Haixia Cheng; Michael Considine; Sandra Schmitz; Alexander V. Favorov; Ludmila V. Danilova; Joseph A. Califano; Evgeny Izumchenko; Daria A. Gaykalova; Christine H. Chung; Elana J. Fertig

doi:10.1101/136564

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

Genevieve Stein-O’Brien, Luciane T. Kagohara, Sijia Li, Manjusha Thakar, Ruchira Ranaweera, Hiroyuki Ozawa, Haixia Cheng, Michael Considine, Sandra Schmitz, Alexander V. Favorov, Ludmila V. Danilova, Joseph A. Califano, Evgeny Izumchenko, Daria A. Gaykalova, Christine H. Chung, Elana J. Fertig

School of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

BACKGROUND Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

Original language	English (US)
Journal	Unknown Journal
DOIs	https://doi.org/10.1101/136564
State	Published - May 10 2017

Keywords

Acquired resistance
data integration
epigenetics
genomics
time course analysis

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
Immunology and Microbiology(all)
Neuroscience(all)
Pharmacology, Toxicology and Pharmaceutics(all)

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10.1101/136564

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Stein-O’Brien, G., Kagohara, L. T., Li, S., Thakar, M., Ranaweera, R., Ozawa, H., Cheng, H., Considine, M., Schmitz, S., Favorov, A. V., Danilova, L. V., Califano, J. A., Izumchenko, E., Gaykalova, D. A., Chung, C. H., & Fertig, E. J. (2017). Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance. Unknown Journal. https://doi.org/10.1101/136564

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance. / Stein-O’Brien, Genevieve; Kagohara, Luciane T.; Li, Sijia; Thakar, Manjusha; Ranaweera, Ruchira; Ozawa, Hiroyuki; Cheng, Haixia; Considine, Michael; Schmitz, Sandra; Favorov, Alexander V.; Danilova, Ludmila V.; Califano, Joseph A.; Izumchenko, Evgeny; Gaykalova, Daria A.; Chung, Christine H.; Fertig, Elana J.

In: Unknown Journal, 10.05.2017.

Research output: Contribution to journal › Article › peer-review

Stein-O’Brien, G, Kagohara, LT, Li, S, Thakar, M, Ranaweera, R, Ozawa, H, Cheng, H, Considine, M, Schmitz, S, Favorov, AV , Danilova, LV, Califano, JA, Izumchenko, E, Gaykalova, DA, Chung, CH & Fertig, EJ 2017, 'Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance', Unknown Journal. https://doi.org/10.1101/136564

Stein-O’Brien, Genevieve ; Kagohara, Luciane T. ; Li, Sijia ; Thakar, Manjusha ; Ranaweera, Ruchira ; Ozawa, Hiroyuki ; Cheng, Haixia ; Considine, Michael ; Schmitz, Sandra ; Favorov, Alexander V. ; Danilova, Ludmila V. ; Califano, Joseph A. ; Izumchenko, Evgeny ; Gaykalova, Daria A. ; Chung, Christine H. ; Fertig, Elana J. / Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance. In: Unknown Journal. 2017.

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title = "Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance",

abstract = " BACKGROUND Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients{\textquoteright} treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.",

keywords = "Acquired resistance, data integration, epigenetics, genomics, time course analysis",

author = "Genevieve Stein-O{\textquoteright}Brien and Kagohara, {Luciane T.} and Sijia Li and Manjusha Thakar and Ruchira Ranaweera and Hiroyuki Ozawa and Haixia Cheng and Michael Considine and Sandra Schmitz and Favorov, {Alexander V.} and Danilova, {Ludmila V.} and Califano, {Joseph A.} and Evgeny Izumchenko and Gaykalova, {Daria A.} and Chung, {Christine H.} and Fertig, {Elana J.}",

note = "Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2017",

month = may,

day = "10",

doi = "10.1101/136564",

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AU - Stein-O’Brien, Genevieve

AU - Kagohara, Luciane T.

AU - Li, Sijia

AU - Thakar, Manjusha

AU - Ranaweera, Ruchira

AU - Ozawa, Hiroyuki

AU - Cheng, Haixia

AU - Considine, Michael

AU - Schmitz, Sandra

AU - Favorov, Alexander V.

AU - Danilova, Ludmila V.

AU - Califano, Joseph A.

AU - Izumchenko, Evgeny

AU - Gaykalova, Daria A.

AU - Chung, Christine H.

AU - Fertig, Elana J.

N1 - Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2017/5/10

Y1 - 2017/5/10

N2 - BACKGROUND Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

AB - BACKGROUND Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

KW - Acquired resistance

KW - data integration

KW - epigenetics

KW - genomics

KW - time course analysis

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