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1. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

2. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

3. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

4. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

5. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

6. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses.

Author

Broyde, Joshua, Broyde, Joshua, Simpson, David R, Murray, Diana, Paull, Evan O, Chu, Brennan W, Tagore, Somnath, Jones, Sunny J, Griffin, Aaron T, Giorgi, Federico M, Lachmann, Alexander, Jackson, Peter, Sweet-Cordero, E Alejandro, Honig, Barry, Califano, Andrea, Broyde, Joshua, Broyde, Joshua, Simpson, David R, Murray, Diana, Paull, Evan O, Chu, Brennan W, Tagore, Somnath, Jones, Sunny J, Griffin, Aaron T, Giorgi, Federico M, Lachmann, Alexander, Jackson, Peter, Sweet-Cordero, E Alejandro, Honig, Barry, and Califano, Andrea

Abstract

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

Published
2021

7. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Yang, Tai-Hsien Ou, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Thorsson, Vésteinn, Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Yang, Tai-Hsien Ou, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, and Broom, Bradley M

Abstract

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

Published
2019

8. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, The Cancer Genome Atlas Research Network, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, and The Cancer Genome Atlas Research Network

Published
2018

9. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, The Cancer Genome Atlas Research Network, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, and The Cancer Genome Atlas Research Network

Published
2018

10. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, The Cancer Genome Atlas Research Network, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, and The Cancer Genome Atlas Research Network

Published
2018

11. The Immune Landscape of Cancer

Author

Onderzoeksgroep 6, Pathologie Pathologen staf, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, The Cancer Genome Atlas Research Network, Onderzoeksgroep 6, Pathologie Pathologen staf, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, and The Cancer Genome Atlas Research Network

Published
2018

12. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

Author

Niepel, Mario, Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, Sorger, Peter K, Niepel, Mario, Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, and Sorger, Peter K

Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Published
2017

13. Prophetic Granger Causality to infer gene regulatory networks.

Author

Carlin, Daniel E, Carlin, Daniel E, Paull, Evan O, Graim, Kiley, Wong, Christopher K, Bivol, Adrian, Ryabinin, Peter, Ellrott, Kyle, Sokolov, Artem, Stuart, Joshua M, Carlin, Daniel E, Carlin, Daniel E, Paull, Evan O, Graim, Kiley, Wong, Christopher K, Bivol, Adrian, Ryabinin, Peter, Ellrott, Kyle, Sokolov, Artem, and Stuart, Joshua M

Abstract

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

Published
2017

14. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

Author

Graim, Kiley, Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, Griffith R, Cordero, Sergio P, Rubin, Daniel L, Stuart, Joshua M, Graim, Kiley, Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, Griffith R, Cordero, Sergio P, Rubin, Daniel L, and Stuart, Joshua M

Abstract

BackgroundPatient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.MethodsHere, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.ResultsIn an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.ConclusionsSubtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Published
2017

15. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

Author

Niepel, Mario, Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, Sorger, Peter K, Niepel, Mario, Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, and Sorger, Peter K

Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Published
2017

16. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

Author

Graim, Kiley, Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, Griffith R, Cordero, Sergio P, Rubin, Daniel L, Stuart, Joshua M, Graim, Kiley, Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, Griffith R, Cordero, Sergio P, Rubin, Daniel L, and Stuart, Joshua M

Abstract

BackgroundPatient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.MethodsHere, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.ResultsIn an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.ConclusionsSubtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Published
2017

17. Prophetic Granger Causality to infer gene regulatory networks.

Author

Carlin, Daniel E, Emmert-Streib, Frank1, Carlin, Daniel E, Paull, Evan O, Graim, Kiley, Wong, Christopher K, Bivol, Adrian, Ryabinin, Peter, Ellrott, Kyle, Sokolov, Artem, Stuart, Joshua M, Carlin, Daniel E, Emmert-Streib, Frank1, Carlin, Daniel E, Paull, Evan O, Graim, Kiley, Wong, Christopher K, Bivol, Adrian, Ryabinin, Peter, Ellrott, Kyle, Sokolov, Artem, and Stuart, Joshua M

Abstract

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

Published
2017

18. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE).

Author

Paull, Evan O, Paull, Evan O, Carlin, Daniel E, Niepel, Mario, Sorger, Peter K, Haussler, David, Stuart, Joshua M, Paull, Evan O, Paull, Evan O, Carlin, Daniel E, Niepel, Mario, Sorger, Peter K, Haussler, David, and Stuart, Joshua M

Abstract

MotivationIdentifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations.ResultsApplication of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.AvailabilitySoftware is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie.Contactjstuart@ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Published
2013

19. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer.

Author

Drake, Justin M, Drake, Justin M, Paull, Evan O, Graham, Nicholas A, Lee, John K, Smith, Bryan A, Titz, Bjoern, Stoyanova, Tanya, Faltermeier, Claire M, Uzunangelov, Vladislav, Carlin, Daniel E, Fleming, Daniel Teo, Wong, Christopher K, Newton, Yulia, Sudha, Sud, Vashisht, Ajay A, Huang, Jiaoti, Wohlschlegel, James A, Graeber, Thomas G, Witte, Owen N, Stuart, Joshua M, Drake, Justin M, Drake, Justin M, Paull, Evan O, Graham, Nicholas A, Lee, John K, Smith, Bryan A, Titz, Bjoern, Stoyanova, Tanya, Faltermeier, Claire M, Uzunangelov, Vladislav, Carlin, Daniel E, Fleming, Daniel Teo, Wong, Christopher K, Newton, Yulia, Sudha, Sud, Vashisht, Ajay A, Huang, Jiaoti, Wohlschlegel, James A, Graeber, Thomas G, Witte, Owen N, and Stuart, Joshua M

Abstract

We used clinical tissue from lethal metastatic castration-resistant prostate cancer (CRPC) patients obtained at rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues to synthesize a robust signaling network consisting of druggable kinase pathways. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Individual autopsy profiles developed using these hallmarks revealed clinically relevant pathway information potentially suitable for patient stratification and targeted therapies in late stage prostate cancer. Here, we describe phosphorylation-based cancer hallmarks using integrated personalized signatures (pCHIPS) that shed light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Published
2016

20. Pathway-Based Genomics Prediction using Generalized Elastic Net.

Author

Sokolov, Artem, Sokolov, Artem, Carlin, Daniel E, Paull, Evan O, Baertsch, Robert, Stuart, Joshua M, Sokolov, Artem, Sokolov, Artem, Carlin, Daniel E, Paull, Evan O, Baertsch, Robert, and Stuart, Joshua M

Abstract

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

Published
2016

21. Inferring causal molecular networks: empirical assessment through a community-based effort.

Author

Hill, Steven M, Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, HPN-DREAM Consortium, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M, Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, HPN-DREAM Consortium, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2016

22. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer.

Author

Drake, Justin M, Drake, Justin M, Paull, Evan O, Graham, Nicholas A, Lee, John K, Smith, Bryan A, Titz, Bjoern, Stoyanova, Tanya, Faltermeier, Claire M, Uzunangelov, Vladislav, Carlin, Daniel E, Fleming, Daniel Teo, Wong, Christopher K, Newton, Yulia, Sudha, Sud, Vashisht, Ajay A, Huang, Jiaoti, Wohlschlegel, James A, Graeber, Thomas G, Witte, Owen N, Stuart, Joshua M, Drake, Justin M, Drake, Justin M, Paull, Evan O, Graham, Nicholas A, Lee, John K, Smith, Bryan A, Titz, Bjoern, Stoyanova, Tanya, Faltermeier, Claire M, Uzunangelov, Vladislav, Carlin, Daniel E, Fleming, Daniel Teo, Wong, Christopher K, Newton, Yulia, Sudha, Sud, Vashisht, Ajay A, Huang, Jiaoti, Wohlschlegel, James A, Graeber, Thomas G, Witte, Owen N, and Stuart, Joshua M

Abstract

We used clinical tissue from lethal metastatic castration-resistant prostate cancer (CRPC) patients obtained at rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues to synthesize a robust signaling network consisting of druggable kinase pathways. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Individual autopsy profiles developed using these hallmarks revealed clinically relevant pathway information potentially suitable for patient stratification and targeted therapies in late stage prostate cancer. Here, we describe phosphorylation-based cancer hallmarks using integrated personalized signatures (pCHIPS) that shed light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Published
2016

23. Inferring causal molecular networks: empirical assessment through a community-based effort.

Author

Hill, Steven M, Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, HPN-DREAM Consortium, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M, Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, HPN-DREAM Consortium, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2016

24. Pathway-Based Genomics Prediction using Generalized Elastic Net.

Author

Sokolov, Artem, Przytycka, Teresa M1, Sokolov, Artem, Carlin, Daniel E, Paull, Evan O, Baertsch, Robert, Stuart, Joshua M, Sokolov, Artem, Przytycka, Teresa M1, Sokolov, Artem, Carlin, Daniel E, Paull, Evan O, Baertsch, Robert, and Stuart, Joshua M

Abstract

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

Published
2016

25. ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.

Author

Sokolov, Artem, Sokolov, Artem, Paull, Evan O, Stuart, Joshua M, Sokolov, Artem, Sokolov, Artem, Paull, Evan O, and Stuart, Joshua M

Abstract

The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.

Published
2016

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