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Phenomics – An Emerging Concept in Digital Pathology
A Bridge Between Digital Pathology and Genomics
Phenomics is a term coined by a vendor to describe data mining of an hematoxylin and eosin stain (H&E) digital slide. Literature is appearing that emphasizes the importance of tumor context; ie, the reaction of stroma to the invading malignant cells, the type of inflammatory response, and the morphologic characteristics of the neoplasm itself. It is a “big data” approach to clinical oncology in which all of the visual information in a tissue image is catalogued and evaluated. Each tumor has its own signature. Most of that is revealed by molecular and genomic studies, but there may well be morphologic features that are not readily apparent to the human eye. The goal of phenomics is to complement genomics in diagnostics by making those distinctive histologic characteristics available for clinical study and analysis.
Discoveries in genomics has made great advances in our understanding of cancer biology and enabled higher diagnostics abilities; however, to date it yet remains unable to carry patient care alone. Correlation of quantitative morphometric data from tissue with RNA and DNA molecular genomics carry the real potential for improving patient outcomes in the era of increasing need for personalized medicine. Tissue phenomics enables data in tissue images to be quantified in a context where several standard pathologic tissue biomarkers used plus new complex tissue genetic signatures are taken in consideration. This approach is often difficult to assess with the human eye and opens new door for bioinformatic analysis and discovery.
Beck and colleagues recently described the first truly objective machine learning-based quantitative system for cancer cells and its surrounding stroma. This computational pathology system used 6,642 features to synthesize a scoring system that can predict outcome in breast cancer patients. The system defined features as both standard morphometric descriptors of image objects as well as contextual, relational, and global image features that were collected for both tumor and stroma. The authors were able to show a prognostic model score that was highly associated with overall survival in two independent cohorts. Interestingly, three stromal features were significantly associated with survival. Tissue phonemics is an area of major gap in our age. Phenomics will help identify prognostic and predictive markers based on morphology and contextual information within tissue. This will result into faster transition to standardized processes in clinical practice and research. It also will increase the statistical significance of results in clinical validation studies
Suggested Articles and Resources
- A) Novel Genotype-Phenotype Associations in Human Cancers Enabled by Advanced Molecular Platforms and Computational Analysis of Whole Slide Images
Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest. 2015; 95(4): 366-376. doi: 10.1038/labinvest.2014.153.
Summary: Technological advances in computing, imaging, and genomics have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper, we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue-based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations, and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.
Free full text available from PubMed
PMID: 25599536
- B) Mining Genome Sequencing Data to Identify the Genomic Features Linked to Breast Cancer Histopathology
Ping Z, Siegal GP, Almeida JS, Schnitt SJ, Shen D. Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology. J Pathol Inform. 2014;5(1):3. doi: 10.4103/2153-3539.126147.
Summary: BACKGROUND: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. RESULTS: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. CONCLUSIONS: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.
Free full text available from PubMed
PMID: 24672738
- C) Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011; 3(108): 108ra113. doi: 10.1126/scitranslmed.3002564.
Free full text available from Science Translational Medicine (subscription required)
PMID: 22072638
NOTE: Also cited in Section 2.
- D) SIVQ-Aided Laser Capture Microdissection: A Tool for High-Throughput Expression Profiling
Hipp J, Cheng J, Hanson JC, et al. SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling. J Pathol Inform. 2011; 2:19. doi: 10.4103/2153-3539.78500.
Free full text available from PubMed
PMID: 21572509
- E) Multi-field-of-view Strategy for Image-based Outcome Prediction of Multi-parametric Estrogen Receptor-positive Breast Cancer Histopathology: Comparison to Oncotype DX
Basavanhally A, Feldman M, Shih N, et al. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX. J Pathol Inform. 2011;2:S1. doi: 10.4103/2153-3539.92027.
Free full text available from PubMed
PMID: 22811953
- F) Morphological Signatures and Genomic Correlates in Glioblastoma
Cooper LA, Kong J, Wang F, et al. Morphological Signatures and Genomic Correlates in Glioblastoma. Proc IEEE Int Symp Biomed Imaging. 2011; 1624-1627.
Free full text available from PubMed
PMID: 22183148
Phenomics – An Emerging Concept in Digital Pathology
A Bridge Between Digital Pathology and Genomics
Phenomics is a term coined by a vendor to describe data mining of an hematoxylin and eosin stain (H&E) digital slide. Literature is appearing that emphasizes the importance of tumor context; ie, the reaction of stroma to the invading malignant cells, the type of inflammatory response, and the morphologic characteristics of the neoplasm itself. It is a “big data” approach to clinical oncology in which all of the visual information in a tissue image is catalogued and evaluated. Each tumor has its own signature. Most of that is revealed by molecular and genomic studies, but there may well be morphologic features that are not readily apparent to the human eye. The goal of phenomics is to complement genomics in diagnostics by making those distinctive histologic characteristics available for clinical study and analysis.
Discoveries in genomics has made great advances in our understanding of cancer biology and enabled higher diagnostics abilities; however, to date it yet remains unable to carry patient care alone. Correlation of quantitative morphometric data from tissue with RNA and DNA molecular genomics carry the real potential for improving patient outcomes in the era of increasing need for personalized medicine. Tissue phenomics enables data in tissue images to be quantified in a context where several standard pathologic tissue biomarkers used plus new complex tissue genetic signatures are taken in consideration. This approach is often difficult to assess with the human eye and opens new door for bioinformatic analysis and discovery.
Beck and colleagues recently described the first truly objective machine learning-based quantitative system for cancer cells and its surrounding stroma. This computational pathology system used 6,642 features to synthesize a scoring system that can predict outcome in breast cancer patients. The system defined features as both standard morphometric descriptors of image objects as well as contextual, relational, and global image features that were collected for both tumor and stroma. The authors were able to show a prognostic model score that was highly associated with overall survival in two independent cohorts. Interestingly, three stromal features were significantly associated with survival. Tissue phonemics is an area of major gap in our age. Phenomics will help identify prognostic and predictive markers based on morphology and contextual information within tissue. This will result into faster transition to standardized processes in clinical practice and research. It also will increase the statistical significance of results in clinical validation studies
Suggested Articles and Resources
- A) Novel Genotype-Phenotype Associations in Human Cancers Enabled by Advanced Molecular Platforms and Computational Analysis of Whole Slide Images
Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest. 2015; 95(4): 366-376. doi: 10.1038/labinvest.2014.153.
Summary: Technological advances in computing, imaging, and genomics have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper, we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue-based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations, and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.
Free full text available from PubMed
PMID: 25599536
- B) Mining Genome Sequencing Data to Identify the Genomic Features Linked to Breast Cancer Histopathology
Ping Z, Siegal GP, Almeida JS, Schnitt SJ, Shen D. Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology. J Pathol Inform. 2014;5(1):3. doi: 10.4103/2153-3539.126147.
Summary: BACKGROUND: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. RESULTS: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. CONCLUSIONS: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.
Free full text available from PubMed
PMID: 24672738
- C) Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011; 3(108): 108ra113. doi: 10.1126/scitranslmed.3002564.
Free full text available from Science Translational Medicine (subscription required)
PMID: 22072638
NOTE: Also cited in Section 2.
- D) SIVQ-Aided Laser Capture Microdissection: A Tool for High-Throughput Expression Profiling
Hipp J, Cheng J, Hanson JC, et al. SIVQ-aided laser capture microdissection: A tool for high-throughput expression profiling. J Pathol Inform. 2011; 2:19. doi: 10.4103/2153-3539.78500.
Free full text available from PubMed
PMID: 21572509
- E) Multi-field-of-view Strategy for Image-based Outcome Prediction of Multi-parametric Estrogen Receptor-positive Breast Cancer Histopathology: Comparison to Oncotype DX
Basavanhally A, Feldman M, Shih N, et al. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX. J Pathol Inform. 2011;2:S1. doi: 10.4103/2153-3539.92027.
Free full text available from PubMed
PMID: 22811953
- F) Morphological Signatures and Genomic Correlates in Glioblastoma
Cooper LA, Kong J, Wang F, et al. Morphological Signatures and Genomic Correlates in Glioblastoma. Proc IEEE Int Symp Biomed Imaging. 2011; 1624-1627.
Free full text available from PubMed
PMID: 22183148