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Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer*

  • Xiaohui Zhan
    Affiliations
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202
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  • Jun Cheng
    Affiliations
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202
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  • Zhi Huang
    Affiliations
    School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, 47907
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  • Zhi Han
    Affiliations
    Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202

    Regenstrief Institute, Indianapolis, 46202
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  • Bryan Helm
    Affiliations
    Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202
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  • Xiaowen Liu
    Affiliations
    ‡School of Informatics and Computing, Indiana University Purdue University at Indianapolis, Indiana, 46202
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  • Jie Zhang
    Affiliations
    Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202
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  • Tian-Fu Wang
    Affiliations
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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  • Dong Ni
    Correspondence
    To whom correspondence may be addressed:National-Regional Key Technology Engineering Laboratory for Medical, Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China. Tel: 0755-86671920.
    Affiliations
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
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  • Kun Huang
    Correspondence
    To whom correspondence may be addressed:Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202. Tel.:(317) 278-7722;
    Affiliations
    Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, 46202

    Regenstrief Institute, Indianapolis, 46202
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  • Author Footnotes
    * This work was partially supported by the Shenzhen Peacock Plan (KQTD2016053112051497) to X.Z., J.C., T.-F.W., and D.N., NCI ITCR U01CA188547 to J.Z. and K.H., and Indiana University Precision Health Initiative to K.H., J.Z., Z. Han, B.H., and Z. Huang.
    This article contains supplemental Figures and Tables. No potential conflicts of interest were disclosed.
Open AccessPublished:July 08, 2019DOI:https://doi.org/10.1074/mcp.RA118.001232
      Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.

      Graphical Abstract

      The aggregation of large amount of trans-omics data including high-throughput genetic, transcriptomic, proteomic and clinical information has revolutionized disease research in the past decade but also led to a series of new analytical challenges, calling for new approaches and solutions that aim at improving diagnosis, prognosis, and treatment of complex diseases (
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      ). To date, many studies have addressed the close relationship between molecular events and morphological features of tumor tissues. For instance, Baba et al. (
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      ) systematically discussed the association between mitoses and metabolism with nuclear changes and Wang et al. (
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      Identifying survival associated morphological features of triple negative breast cancer using multiple datasets.
      ) identified genes whose expression levels are associated with multiple morphological features of tumor cells in triple negative breast cancer. Although remarkable achievements have been made, there are still many important questions to be answered. For example, what is the underlying molecular basis for the cellular and tissue heterogeneity in the tumor (
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      )? How are the transcriptional and proteomic aberrations reflected on cellular morphology? Therefore, studying the correlations between nuclear morphology and molecular data, especially functional data including both transcriptomic and proteomic data will shed light on the molecular basis of various morphological features of cells and tissues, addressing important questions in cancer development.
      Pathological diagnosis is critical for clinical oncology where morphological features are extensively used for diagnostics and prognosis (
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      ). Histopathology images derived from hematoxylin and eosin (H&E)-stained cancer tissue slide contain information regarding morphology (e.g. nuclear area, nuclear shape) and spatial context (e.g. cell density) of diverse types of cells coexisting in the tumor microenvironment (
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      ) have successfully demonstrated a connection between cellular and tissue morphology and clinical outcome for cancers, the underlying molecular basis especially key biological processes associated with these morphological features have not been well understood. Therefore, investigating the biological processes underlying the prognostic morphological features is an important issue in cancer biology and outcome prediction.
      To address these issues, matched histopathology images and multi-omics datasets for cancers are required. Fortunately, large consortium endeavors, such as The Cancer Genome Atlas (TCGA)
      The abbreviations used are: TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; RPPA, reverse phase protein array; TME, tumor microenvironment; GO, Gene ontology; BP, biological process; ECM, extracellular matrix; MRPs, mitochondrial ribosomal proteins.
      1The abbreviations used are: TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; RPPA, reverse phase protein array; TME, tumor microenvironment; GO, Gene ontology; BP, biological process; ECM, extracellular matrix; MRPs, mitochondrial ribosomal proteins.
      have accumulated many large datasets to enable such analyses. TCGA aggregates an extensive collection of omics and clinical datasets from large cohorts of patients for more than 30 types of cancers (
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      In this study we aim to systematic explore the relationship among molecular, morphological, and clinical data for differential cell types in breast cancer. Previously, we developed a quantitative image analysis pipeline that automatically extracts quantitative cellular morphological features such as nuclear size, nuclear shape, and cell density from H&E-stained whole-slide images (
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      ) are associated with different morphological feature based on the correlated proteomic data. Furthermore, we examined the relationship between nuclear morphology and patient outcome (i.e. survival time). Both prognostically favorable and unfavorable morphological features have been identified. The biological processes associated with these prognostic morphological features were also identified based on proteomic data. The biological processes such as immune responses, cell cycle, and extracellular matrix development have been previously associated with cancer patient outcome. In summary, our work linked molecular data, morphology, and clinical outcome, which led to new insights and hypotheses into the relationships between cancer tissue development and molecular events, thus contributing to a more comprehensive understanding of breast cancer. The entire process and workflow can be applied to other cancers.

      DISCUSSION

      Solid tumors such as breast cancer are highly heterogeneous, with multiple types of cells such as epithelial cells, immune cells and other stromal cells. Given the importance of tumor morphological features in diagnosis and prognosis, investigating the relationship between the molecular data and morphology can lead to potential new insight on the molecular basis underlying cancer development and prognosis. Taking advantage of the computational pathology workflow we established for processing whole slide images, we were able to extract quantitative morphological features from histopathology slides of breast cancer tissues, thus enabling investigating relationships between tumor tissue morphology and omics data. In addition, because mRNA and protein data contain related but different levels of molecular information, integrating both data with morphological features can lead to discovery of different biological events associated with cancer tissue morphology.
      Based on the correlation analysis between morphological features derived from whole slide images of tissue samples and molecular data (mRNA or proteomic data), four major types of biology processes, namely metabolic, cell cycle, immune, and ECM development processes have been identified. These processes have all been strongly associated with cancer hallmarks (
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      ). We found that these stromal cells related features are most strongly associated with tumor microenvironment (TME) development (e.g. ECM, cell adhesion, cell migration). Previous studies have demonstrated that the interaction between stromal cells (such as cancer-associated fibroblasts, a typical stroma cell) and ECM has a crucial role in tumor initiation, progression, and metastasis (
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      Extracellular matrix-dependent regulation of Fas ligand expression in human endometrial stromal cells.
      ,
      • Valkenburg K.C.
      • de Groot A.E.
      • Pienta K.J.
      Targeting the tumour stroma to improve cancer therapy.
      ,
      • Desmouliere A.
      • Guyot C.
      • Gabbiani C.
      The stroma reaction myofibroblast: a key player in the control of tumor cell behavior.
      ), which is an important hallmark of cancers. Beck et al. previously demonstrated the importance of TME related morphological features in breast cancer prognosis (
      • Beck A.H.
      • Sangoi A.R.
      • Leung S.
      • Marinelli R.J.
      • Nielsen T.O.
      • van de Vijver M.J.
      • West R.B.
      • van de Rijn M.
      • Koller D.
      Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.
      ) and our results linked related features to the potential underlying genes. In addition, cancer-associated fibroblast (CAF) is a typical stromal cell and can recruit and bind collagen fibers (key components of ECM) thus convert a loose stroma into a dense stromal network (
      • Valkenburg K.C.
      • de Groot A.E.
      • Pienta K.J.
      Targeting the tumour stroma to improve cancer therapy.
      ,
      • Desmouliere A.
      • Guyot C.
      • Gabbiani C.
      The stroma reaction myofibroblast: a key player in the control of tumor cell behavior.
      ), this network acts as a barrier to drug flow, thereby increasing chemoresistance. Lastly, Yuan et al. also identified that immune related pathways were correlated with the lymphocyte morphologic features (
      • Yuan Y.
      • Failmezger H.
      • Rueda O.M.
      • Ali H.R.
      • Gräf S.
      • Chin S.F.
      • Schwarz R.F.
      • Curtis C.
      • Dunning M.J.
      • Bardwell H.
      • Johnson N.
      • Doyle S.
      • Turashvili G.
      • Provenzano E.
      • Aparicio S.
      • Caldas C.
      • Markowetz F.
      Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.
      ), which is consistent with our observation. Taken together, our approach can identify the specific biological process associated with individual morphological features. These results not only confirm our understanding of the molecular basis of morphology, but also offer new insights and hypotheses regarding the development of cancer tissues for future investigation.
      When comparing the significantly enriched biological processes associated with morphological features based on mRNA and protein, we found that although most of the significantly enriched biological process categories were similar, some unique biological processes associated with morphological features were identified only based on proteomics data (e.g. posttranscriptional related biological processes). In addition, the mitochondria-related metabolism processes also stood out based on proteomic data. Latonen et al. recently showed that post-transcriptional events take important roles in the mitochondria during cancer progression (
      • Latonen L.
      • Afyounian E.
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      • Tammela T.T.L.
      • Beuerman R.W.
      • Uusitalo H.
      • Nykter M.
      • Visakorpi T.
      Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression.
      ). These results strongly suggest that proteomic data are important in fully characterizing the molecular events associated with morphological changes at cellular and tissue levels and are important for understand the development of cancers.
      Because histopathology images are essential for cancer diagnosis and prognosis, we also identified favorable and unfavorable prognostic morphological features and the corresponding biological process associated with them. Among these unfavorable predictors, large values of long distance to adjacent nuclei imply a high percentage of stromal components in the in whole-slide images. Yuan et al. and Beck et al. both demonstrated that stromal morphologic structure is an important prognostic factor in breast cancer, patients with higher stromal proportions had worse prognosis than other patients (
      • Yuan Y.
      • Failmezger H.
      • Rueda O.M.
      • Ali H.R.
      • Gräf S.
      • Chin S.F.
      • Schwarz R.F.
      • Curtis C.
      • Dunning M.J.
      • Bardwell H.
      • Johnson N.
      • Doyle S.
      • Turashvili G.
      • Provenzano E.
      • Aparicio S.
      • Caldas C.
      • Markowetz F.
      Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.
      ,
      • Beck A.H.
      • Sangoi A.R.
      • Leung S.
      • Marinelli R.J.
      • Nielsen T.O.
      • van de Vijver M.J.
      • West R.B.
      • van de Rijn M.
      • Koller D.
      Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.
      ). In addition, we also observed that large nuclear area is associated with poor survival. Previous studies have highlighted that cancer cells with enlarged nuclei almost always indicate more aggressive outcomes (
      • Edens L.J.
      • White K.H.
      • Jevtic P.
      • Li X.Y.
      • Levy D.L.
      Nuclear size regulation: from single cells to development and disease.
      ). Currently, anti-estrogen therapy to decreased nuclear size in tumors are used for preoperative treatment of breast cancer patients (
      • Edens L.J.
      • White K.H.
      • Jevtic P.
      • Li X.Y.
      • Levy D.L.
      Nuclear size regulation: from single cells to development and disease.
      ). As for favorable predictors, most of them were related to immune responses, suggesting that activation of immune system plays critical roles in fighting cancer, which are consistent with many recent studies on cancer immunology and immunotherapy (
      • Yuan Y.
      • Failmezger H.
      • Rueda O.M.
      • Ali H.R.
      • Gräf S.
      • Chin S.F.
      • Schwarz R.F.
      • Curtis C.
      • Dunning M.J.
      • Bardwell H.
      • Johnson N.
      • Doyle S.
      • Turashvili G.
      • Provenzano E.
      • Aparicio S.
      • Caldas C.
      • Markowetz F.
      Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.
      ,
      • Emens L.A.
      Breast cancer immunotherapy: facts and hopes.
      ,
      • Schmid P.
      • Adams S.
      • Rugo H.S.
      • Schneeweiss A.
      • Barrios C.H.
      • Iwata H.
      • Dieras V.
      • Hegg R.
      • Im S.A.
      • Shaw Wright G.
      • Henschel V.
      • Molinero L.
      • Chui S.Y.
      • Funke R.
      • Husain A.
      • Winer E.P.
      • Loi S.
      • Emens L.A.
      Atezolizumab and Nab-Paclitaxel in advanced triple-negative breast cancer.
      ).
      Despite the extensive observations and results generated from our analysis, some limitations of this study should be noticed as well. First, the key molecular regulators for the cell type morphology features were still unknown, even though the associated biological processes were inferred because our current study focuses on correlation analysis instead of causal analysis. Deeper analysis for the regulatory and driver genes and proteins using more sophisticated statistical methods combined with experimental validation will be carried out soon. Second, we only included 73 breast cancer patients for the correlation analysis between molecular data and morphology phenotypes in this study because of the limitation of available data. The image-protein and image-mRNA relationships identified here may not represent all breast cancer subtypes. Despite that the correlative relationships between proteomic data and morphology were validated using matched RPPA data, further confirmation using independent datasets is still needed despite the lack of such data at the meantime. Last but not the least, even though we showed that the cell nucleic features suggested stromal or tumor cells, it is difficult to distinguish different cell types accurately just based on the nucleic morphology alone.
      In summary, we carried out a unique systematic study on the relationship between tumor tissue morphology and transcriptomic as well as proteomic data in breast cancer. We observed concordant distribution patterns of correlation coefficients between image-mRNA and image-protein at the genome scale. Four major types of important biological processes related to cancers have been associated with various morphological features. Importantly, proteomic data are critical in identifying protein related biological processes associated with morphological features, which cannot be captured by transcriptomic data. In addition, morphological features associated with patient survival have been identified and their underlying molecular processes based on the associated proteins can link these morphological features to different hallmarks of cancers.
      In conclusion, our analysis demonstrated the potential for integrating morphological information and molecular data for generating new biological hypothesis for cancer research. The algorithmic development for computational pathology unleashes the potential for similar large-scale studies for different cancers. More sophisticated modeling and integration methods will lead to deeper understanding of the regulation of the tissue morphology and importance of protein in this process, contributing to the generation of new insights for cancer biology and outcome prediction.

      Data Availability

      The data used for this study are downloaded from various public sources. Proteomic data were accessed from the NCI CPTAC Data Portal. Histopathology images were downloaded directly through the NCI GDC TCGA Data Portal, whereas transcriptomic data were downloaded from the UCSC Xena data portal (https://xena.ucsc.edu/public-hubs/). Matched RPPA proteomic data were obtained from the Broad GDAC Firehose (https://gdac.broadinstitute.org). cell morphological features and patient survival outcomes, 1,057 BRCA-type breast patients with matched 1057 H&E-stained tissue images and corresponding clinical survival information were used. The patient clinical data were obtained from UCSC Xena. The analysis scripts that we used for this manuscript are available at GitHub: https://github.com/xiaohuizhan/cor_image_omics_BRCA.

      Acknowledgments

      We thank Mrs. Megan Metzger for editing the manuscript.

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