ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Hannah E. Bergom; Ashraf Shabaneh; Abderrahman Day; Atef Ali; Ella Boytim; Sydney Tape; John R. Lozada; Xiaolei Shi; Carlos Perez Kerkvliet; Sean McSweeney; Samuel P. Pitzen; Megan Ludwig; Emmanuel S. Antonarakis; Justin M. Drake; Scott M. Dehm; Charles J. Ryan; Jinhua Wang; Justin Hwang

doi:10.1038/s42003-023-04795-1

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Hannah E. Bergom, Ashraf Shabaneh, Abderrahman Day, Atef Ali, Ella Boytim, Sydney Tape, John R. Lozada, Xiaolei Shi, Carlos Perez Kerkvliet, Sean McSweeney, Samuel P. Pitzen, Megan Ludwig, Emmanuel S. Antonarakis, Justin M. Drake, Scott M. Dehm, Charles J. Ryan, Jinhua Wang, Justin Hwang

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Abstract

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

Original language	English (US)
Article number	417
Journal	Communications biology
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s42003-023-04795-1
State	Published - Dec 2023

Bibliographical note

Funding Information:
We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Funding Information:
We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/ . The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga .

Publisher Copyright:
© 2023, The Author(s).

PubMed: MeSH publication types

Journal Article
Research Support, Non-U.S. Gov't

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Bergom, H. E., Shabaneh, A., Day, A., Ali, A., Boytim, E., Tape, S., Lozada, J. R., Shi, X., Kerkvliet, C. P., McSweeney, S., Pitzen, S. P., Ludwig, M., Antonarakis, E. S., Drake, J. M., Dehm, S. M., Ryan, C. J., Wang, J., & Hwang, J. (2023). ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems. Communications biology, 6(1), Article 417. https://doi.org/10.1038/s42003-023-04795-1

Bergom, HE , Shabaneh, A, Day, A, Ali, A, Boytim, E, Tape, S, Lozada, JR, Shi, X, Kerkvliet, CP, McSweeney, S, Pitzen, SP, Ludwig, M , Antonarakis, ES , Drake, JM , Dehm, SM , Ryan, CJ , Wang, J & Hwang, J 2023, 'ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems', Communications biology, vol. 6, no. 1, 417. https://doi.org/10.1038/s42003-023-04795-1

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title = "ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems",

abstract = "Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.",

author = "Bergom, {Hannah E.} and Ashraf Shabaneh and Abderrahman Day and Atef Ali and Ella Boytim and Sydney Tape and Lozada, {John R.} and Xiaolei Shi and Kerkvliet, {Carlos Perez} and Sean McSweeney and Pitzen, {Samuel P.} and Megan Ludwig and Antonarakis, {Emmanuel S.} and Drake, {Justin M.} and Dehm, {Scott M.} and Ryan, {Charles J.} and Jinhua Wang and Justin Hwang",

note = "Funding Information: We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Funding Information: We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/ . The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga . Publisher Copyright: {\textcopyright} 2023, The Author(s).",

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month = dec,

doi = "10.1038/s42003-023-04795-1",

language = "English (US)",

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AU - Bergom, Hannah E.

AU - Shabaneh, Ashraf

AU - Day, Abderrahman

AU - Ali, Atef

AU - Boytim, Ella

AU - Tape, Sydney

AU - Lozada, John R.

AU - Shi, Xiaolei

AU - Kerkvliet, Carlos Perez

AU - McSweeney, Sean

AU - Pitzen, Samuel P.

AU - Ludwig, Megan

AU - Antonarakis, Emmanuel S.

AU - Drake, Justin M.

AU - Dehm, Scott M.

AU - Ryan, Charles J.

AU - Wang, Jinhua

AU - Hwang, Justin

N1 - Funding Information: We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Funding Information: We thank our funding sources Ray of Light Foundation (University of Minnesota) and American Cancer Society in supporting this work. Ray of Light Foundation award from Division of Hematology, Oncology, and Transplantation, University of Minnesota was allocated to the research efforts of J.H. and H.E.B. American Cancer Society (IRG-21-049-61-IRG) was allocated to the research efforts of J.H. and S.T. The figures and workflows shown here were created with https://biorender.com/ . The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga . Publisher Copyright: © 2023, The Author(s).

PY - 2023/12

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N2 - Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

AB - Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

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ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Abstract

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UN SDGs

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