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December 2021 Joint and individual analysis of breast cancer histologic images and genomic covariates
Iain Carmichael, Benjamin C. Calhoun, Katherine A. Hoadley, Melissa A. Troester, Joseph Geradts, Heather D. Couture, Linnea Olsson, Charles M. Perou, Marc Niethammer, Jan Hannig, J. S. Marron
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Ann. Appl. Stat. 15(4): 1697-1722 (December 2021). DOI: 10.1214/20-AOAS1433

Abstract

The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fields have been relatively superficial. We bridge this gap by investigating the Carolina Breast Cancer Study through the development of an integrative, exploratory analysis framework. Our analysis gives insights—some known, some novel—that are engaging to both pathologists and geneticists. Our analysis framework is based on angle-based joint and individual variation explained (AJIVE) for statistical data integration and exploits convolutional neural networks (CNNs) as a powerful, automatic method for image feature extraction. CNNs raise interpretability issues that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g., PCA or AJIVE) applied to CNN features.

Funding Statement

Research reported in this publication was supported by a Specialized Program of Research Excellence (SPORE) in breast cancer (P50 CA058223), an award from the Susan G. Komen Foundation (OGUNC1202), the North Carolina University Cancer Research Fund and a Cancer Center Support Grant (P30 CA016086). Iain Carmichael and J. S. Marron were partially supported by NSF Grant IIS-1633074, BIG DATA 2016–2019. Iain Carmichael is currently supported by NSF MSPRF DMS-1902440. Katherine Hoadley was supported by Komen Career Catalyst Grant (CCR16376756).

Acknowledgments

We thank the Carolina Breast Cancer Study participants and staff. We also want to acknowledge Robert C. Millikan, founder of the Carolina Breast Cancer Study Phase 3.

Funding Statement

Research reported in this publication was supported by a Specialized Program of Research Excellence (SPORE) in breast cancer (P50 CA058223), an award from the Susan G. Komen Foundation (OGUNC1202), the North Carolina University Cancer Research Fund and a Cancer Center Support Grant (P30 CA016086). Iain Carmichael and J. S. Marron were partially supported by NSF Grant IIS-1633074, BIG DATA 2016–2019. Iain Carmichael is currently supported by NSF MSPRF DMS-1902440. Katherine Hoadley was supported by Komen Career Catalyst Grant (CCR16376756).

Acknowledgments

We thank the Carolina Breast Cancer Study participants and staff. We also want to acknowledge Robert C. Millikan, founder of the Carolina Breast Cancer Study Phase 3.

Citation

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Iain Carmichael. Benjamin C. Calhoun. Katherine A. Hoadley. Melissa A. Troester. Joseph Geradts. Heather D. Couture. Linnea Olsson. Charles M. Perou. Marc Niethammer. Jan Hannig. J. S. Marron. "Joint and individual analysis of breast cancer histologic images and genomic covariates." Ann. Appl. Stat. 15 (4) 1697 - 1722, December 2021. https://doi.org/10.1214/20-AOAS1433

Information

Received: 1 April 2020; Revised: 1 October 2020; Published: December 2021
First available in Project Euclid: 21 December 2021

Digital Object Identifier: 10.1214/20-AOAS1433

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.15 • No. 4 • December 2021
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