Predicting master transcription factors from pan-cancer expression data

Jessica Reddy; Marcos A.S. Fonseca; Rosario I. Corona; Robbin Nameki; Felipe Segato Dezem; Isaac A. Klein; Heidi Chang; Daniele Chaves-Moreira; Lena K. Afeyan; Tathiane M. Malta; Xianzhi Lin; Forough Abbasi; Alba Font-Tello; Thais Sabedot; Paloma Cejas; Norma Rodríguez-Malavé; Ji Heui Seo; De Chen Lin; Ursula Matulonis; Beth Y. Karlan; Simon A. Gayther; Bogdan Pasaniuc; Alexander Gusev; Houtan Noushmehr; Henry Long; Matthew L. Freedman; Ronny Drapkin; Richard A. Young; Brian J. Abraham; Kate Lawrenson

doi:10.1126/sciadv.abf6123

Predicting master transcription factors from pan-cancer expression data

Jessica Reddy, Marcos A.S. Fonseca, Rosario I. Corona, Robbin Nameki, Felipe Segato Dezem, Isaac A. Klein, Heidi Chang, Daniele Chaves-Moreira, Lena K. Afeyan, Tathiane M. Malta, Xianzhi Lin, Forough Abbasi, Alba Font-Tello, Thais Sabedot, Paloma Cejas, Norma Rodríguez-Malavé, Ji Heui Seo, De Chen Lin, Ursula Matulonis, Beth Y. KarlanSimon A. Gayther, Bogdan Pasaniuc, Alexander Gusev, Houtan Noushmehr, Henry Long, Matthew L. Freedman, Ronny Drapkin, Richard A. Young, Brian J. Abraham, Kate Lawrenson

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

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Abstract

Critical developmental "master transcription factors" (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

Original language	English (US)
Article number	eabf6123
Journal	Science Advances
Volume	7
Issue number	48
DOIs	https://doi.org/10.1126/sciadv.abf6123
State	Published - Nov 2021
Externally published	Yes

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Reddy, J., Fonseca, M. A. S., Corona, R. I., Nameki, R., Dezem, F. S., Klein, I. A., Chang, H., Chaves-Moreira, D., Afeyan, L. K., Malta, T. M., Lin, X., Abbasi, F., Font-Tello, A., Sabedot, T., Cejas, P., Rodríguez-Malavé, N., Seo, J. H., Lin, D. C., Matulonis, U., ... Lawrenson, K. (2021). Predicting master transcription factors from pan-cancer expression data. Science Advances, 7(48), Article eabf6123. https://doi.org/10.1126/sciadv.abf6123

Reddy, J, Fonseca, MAS, Corona, RI, Nameki, R, Dezem, FS, Klein, IA, Chang, H, Chaves-Moreira, D, Afeyan, LK, Malta, TM, Lin, X, Abbasi, F, Font-Tello, A, Sabedot, T, Cejas, P, Rodríguez-Malavé, N, Seo, JH, Lin, DC, Matulonis, U, Karlan, BY, Gayther, SA, Pasaniuc, B, Gusev, A, Noushmehr, H, Long, H, Freedman, ML, Drapkin, R, Young, RA, Abraham, BJ & Lawrenson, K 2021, 'Predicting master transcription factors from pan-cancer expression data', Science Advances, vol. 7, no. 48, eabf6123. https://doi.org/10.1126/sciadv.abf6123

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title = "Predicting master transcription factors from pan-cancer expression data",

abstract = "Critical developmental {"}master transcription factors{"} (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.",

author = "Jessica Reddy and Fonseca, {Marcos A.S.} and Corona, {Rosario I.} and Robbin Nameki and Dezem, {Felipe Segato} and Klein, {Isaac A.} and Heidi Chang and Daniele Chaves-Moreira and Afeyan, {Lena K.} and Malta, {Tathiane M.} and Xianzhi Lin and Forough Abbasi and Alba Font-Tello and Thais Sabedot and Paloma Cejas and Norma Rodr{\'i}guez-Malav{\'e} and Seo, {Ji Heui} and Lin, {De Chen} and Ursula Matulonis and Karlan, {Beth Y.} and Gayther, {Simon A.} and Bogdan Pasaniuc and Alexander Gusev and Houtan Noushmehr and Henry Long and Freedman, {Matthew L.} and Ronny Drapkin and Young, {Richard A.} and Abraham, {Brian J.} and Kate Lawrenson",

year = "2021",

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doi = "10.1126/sciadv.abf6123",

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AU - Reddy, Jessica

AU - Fonseca, Marcos A.S.

AU - Corona, Rosario I.

AU - Nameki, Robbin

AU - Dezem, Felipe Segato

AU - Klein, Isaac A.

AU - Chang, Heidi

AU - Chaves-Moreira, Daniele

AU - Afeyan, Lena K.

AU - Malta, Tathiane M.

AU - Lin, Xianzhi

AU - Abbasi, Forough

AU - Font-Tello, Alba

AU - Sabedot, Thais

AU - Cejas, Paloma

AU - Rodríguez-Malavé, Norma

AU - Seo, Ji Heui

AU - Lin, De Chen

AU - Matulonis, Ursula

AU - Karlan, Beth Y.

AU - Gayther, Simon A.

AU - Pasaniuc, Bogdan

AU - Gusev, Alexander

AU - Noushmehr, Houtan

AU - Long, Henry

AU - Freedman, Matthew L.

AU - Drapkin, Ronny

AU - Young, Richard A.

AU - Abraham, Brian J.

AU - Lawrenson, Kate

PY - 2021/11

Y1 - 2021/11

N2 - Critical developmental "master transcription factors" (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

AB - Critical developmental "master transcription factors" (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

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Predicting master transcription factors from pan-cancer expression data

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