Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

Ying Zhu; Sammy Ferri-Borgogno; Jianting Sheng; Tsz Lun Yeung; Jared K. Burks; Paola Cappello; Amir A. Jazaeri; Jae Hoon Kim; Gwan Hee Han; Michael J. Birrer; Samuel C. Mok; Stephen T.C. Wong

doi:10.3390/cancers13081777

Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

Ying Zhu, Sammy Ferri-Borgogno, Jianting Sheng, Tsz Lun Yeung, Jared K. Burks, Paola Cappello, Amir A. Jazaeri, Jae Hoon Kim, Gwan Hee Han, Michael J. Birrer, Samuel C. Mok, Stephen T.C. Wong

Department of Obstetrics and Gynecology

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

9 Citations (Scopus)

Abstract

Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

Original language	English
Article number	1777
Journal	Cancers
Volume	13
Issue number	8
DOIs	https://doi.org/10.3390/cancers13081777
Publication status	Published - 2021 Apr 2

Bibliographical note

Funding Information:
Funding: This study was supported in part by W81XWH-17-1-0126 and W81XWH-16-1-0038 from the Ovarian Cancer Research Program, US Department of Defense; the MD Anderson Cancer Center Support Grant P30CA016672 from the National Institutes of Health; the Sister Institution Network Fund from the University of Texas MD Anderson Cancer Center; the US Department of Health and Human Services; the T.T. and W.F. Chao Foundation, Stephanie C. Stelter Foundation, John S. Dunn Research Foundation; and Carole Walter Looke Fund. Y.Z. was supported by a Computational Cancer Biology Training Program Fellowship from the Gulf Coast Consortia (CPRIT Grant No. RP170593) with S.C.M. and S.T.C.W. as mentors. This study was supported in part by the National Institutes of Health through M. D. Anderson’s Cancer Center Support Grant CA016672, the NCI’s Research Specialist 1 R50 CA243707-01A1, and a Shared Instrumentation Award from the Cancer Prevention Research Institution of Texas (CPRIT), RP121010.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

Oncology
Cancer Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/cancers13081777

Cite this

@article{bd54bb5ff6ab4ea9bda9bc31d772df1b,

title = "Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment",

abstract = "Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients{\textquoteright} survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.",

author = "Ying Zhu and Sammy Ferri-Borgogno and Jianting Sheng and Yeung, {Tsz Lun} and Burks, {Jared K.} and Paola Cappello and Jazaeri, {Amir A.} and Kim, {Jae Hoon} and Han, {Gwan Hee} and Birrer, {Michael J.} and Mok, {Samuel C.} and Wong, {Stephen T.C.}",

note = "Funding Information: Funding: This study was supported in part by W81XWH-17-1-0126 and W81XWH-16-1-0038 from the Ovarian Cancer Research Program, US Department of Defense; the MD Anderson Cancer Center Support Grant P30CA016672 from the National Institutes of Health; the Sister Institution Network Fund from the University of Texas MD Anderson Cancer Center; the US Department of Health and Human Services; the T.T. and W.F. Chao Foundation, Stephanie C. Stelter Foundation, John S. Dunn Research Foundation; and Carole Walter Looke Fund. Y.Z. was supported by a Computational Cancer Biology Training Program Fellowship from the Gulf Coast Consortia (CPRIT Grant No. RP170593) with S.C.M. and S.T.C.W. as mentors. This study was supported in part by the National Institutes of Health through M. D. Anderson{\textquoteright}s Cancer Center Support Grant CA016672, the NCI{\textquoteright}s Research Specialist 1 R50 CA243707-01A1, and a Shared Instrumentation Award from the Cancer Prevention Research Institution of Texas (CPRIT), RP121010. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = apr,

day = "2",

doi = "10.3390/cancers13081777",

language = "English",

volume = "13",

journal = "Cancers",

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T2 - A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

AU - Zhu, Ying

AU - Ferri-Borgogno, Sammy

AU - Sheng, Jianting

AU - Yeung, Tsz Lun

AU - Burks, Jared K.

AU - Cappello, Paola

AU - Jazaeri, Amir A.

AU - Kim, Jae Hoon

AU - Han, Gwan Hee

AU - Birrer, Michael J.

AU - Mok, Samuel C.

AU - Wong, Stephen T.C.

N1 - Funding Information: Funding: This study was supported in part by W81XWH-17-1-0126 and W81XWH-16-1-0038 from the Ovarian Cancer Research Program, US Department of Defense; the MD Anderson Cancer Center Support Grant P30CA016672 from the National Institutes of Health; the Sister Institution Network Fund from the University of Texas MD Anderson Cancer Center; the US Department of Health and Human Services; the T.T. and W.F. Chao Foundation, Stephanie C. Stelter Foundation, John S. Dunn Research Foundation; and Carole Walter Looke Fund. Y.Z. was supported by a Computational Cancer Biology Training Program Fellowship from the Gulf Coast Consortia (CPRIT Grant No. RP170593) with S.C.M. and S.T.C.W. as mentors. This study was supported in part by the National Institutes of Health through M. D. Anderson’s Cancer Center Support Grant CA016672, the NCI’s Research Specialist 1 R50 CA243707-01A1, and a Shared Instrumentation Award from the Cancer Prevention Research Institution of Texas (CPRIT), RP121010. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/4/2

Y1 - 2021/4/2

N2 - Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

AB - Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

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Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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