1
|
Li Y, Van Alsten SC, Lee DN, Kim T, Calhoun BC, Perou CM, Wobker SE, Marron JS, Hoadley KA, Troester MA. Visual Intratumor Heterogeneity and Breast Tumor Progression. Cancers (Basel) 2024; 16:2294. [PMID: 39001357 PMCID: PMC11240824 DOI: 10.3390/cancers16132294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024] Open
Abstract
High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or 'visual' heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22-2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression.
Collapse
Affiliation(s)
- Yao Li
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sarah C Van Alsten
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dong Neuck Lee
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Taebin Kim
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Benjamin C Calhoun
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Charles M Perou
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sara E Wobker
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - J S Marron
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Katherine A Hoadley
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Melissa A Troester
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
2
|
Sánchez J, Martín-Landrove M. Multifractal Analysis of Brain Tumor Interface in Glioblastoma. ADVANCES IN NEUROBIOLOGY 2024; 36:487-499. [PMID: 38468049 DOI: 10.1007/978-3-031-47606-8_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The dynamics of tumor growth is a very complex process, generally accompanied by numerous chromosomal aberrations that determine its genetic and dynamical heterogeneity. Consequently, the tumor interface exhibits a non-regular and heterogeneous behavior often described by a single fractal dimension. A more suitable approach is to consider the tumor interface as a multifractal object that can be described by a set of generalized fractal dimensions. In the present work, detrended fluctuation and multifractal analysis are used to characterize the complexity of glioblastoma.
Collapse
Affiliation(s)
- Jacksson Sánchez
- Faculty of Science and Technology, Physics Department, Universidad Nacional Pedro Henríquez Ureña, Santo Domingo, Dominican Republic
| | - Miguel Martín-Landrove
- Centre for Medical Visualization, National Institute for Bioengineering, INABIO, Universidad Central de Venezuela and Centro de Diagnóstico Docente Las Mercedes, Caracas, Venezuela.
| |
Collapse
|
3
|
Li F, Zhou C, Li S, Wang J, Li M, Mu H. Bioinformatic analysis of differentially expressed profiles of lncRNAs and miRNAs with their related ceRNA network in endometrial cancer. Medicine (Baltimore) 2023; 102:e32573. [PMID: 36701720 PMCID: PMC9857477 DOI: 10.1097/md.0000000000032573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Increasing evidence suggests that long non-coding riboneucleic acids (lncRNAs), as competing endogenous RNA (ceRNA), play a key role in the initiation, invasion, and metastasis of cancer. As a new hypothesis, the lncRNA-micro RNA (miRNA)-messenger RNA (mRNA), ceRNA regulatory network has been successfully constructed in a variety of cancers. However, lncRNA, which plays a ceRNA function in endometrial cancer (EC), is still poorly understood. In this study, we downloaded EC expression profiling from The Cancer Genome Atlas database and used the R software "edgeR" package to analyze the differentially expressed genes between EC and normal endometrium samples. Then, differentially expressed (DE) lncRNAs, miRNAs and mRNAs were selected to construct a lncRNA-miRNA-mRNA prognosis-related regulatory network based on interaction information. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed on the genes in the network to predict the potential underlying mechanisms and functions of lncRNAs in EC. Kaplan-Meier method and the log-rank test were used for survival analysis. Based on the "ceRNA hypothesis," we constructed a co-expression network of mRNA and lncRNA genes mediated by miRNA in the process of tumor genesis. Furthermore, we successfully constructed a dysregulated lncRNA-associated ceRNA network containing 96 DElncRNAs, 27 DEmiRNAs, and 74 DEmRNAs. Through Kaplan-Meier curve analysis, we found that 9 lncRNAs, 3 miRNAs, and 12 mRNAs were significantly correlated with the overall survival rate of patients among all lncRNAs, miRNAs, and mRNAs involved in ceRNA (P < .05). Our research provides a new perspective for the interaction among lncRNAs, miRNAs, and mRNA and lays the foundation for further research on the mechanism of lncRNAs in the occurrence of EC.
Collapse
Affiliation(s)
- Fengfan Li
- The First Central Clinical College of Tianjin Medical University, Tianjin, China
| | - Chunlei Zhou
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Shuxuan Li
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Jingyu Wang
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Ming Li
- Department of Gynecology, Peking University Second Hospital, Beijing, China
| | - Hong Mu
- Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
- * Correspondence: Hong Mu, Tianjin First Central Hospital, No. 24 Fukang Road, Nankai District, Tianjin 300190, China (e-mail: )
| |
Collapse
|
4
|
Mao XY, Perez-Losada J, Abad M, Rodríguez-González M, Rodríguez CA, Mao JH, Chang H. iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers. World J Clin Oncol 2022; 13:616-629. [PMID: 36157157 PMCID: PMC9346422 DOI: 10.5306/wjco.v13.i7.616] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/24/2022] [Accepted: 06/03/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies.
AIM To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients.
METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort.
RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone.
CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.
Collapse
Affiliation(s)
- Xuan-Yu Mao
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, 94720, United States
| | - Jesus Perez-Losada
- Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca, Salamanca 37007, Spain
| | - Mar Abad
- Department of Pathology, Universidad de Salamanca, Salamanca 37007, Spain
| | | | - Cesar A Rodríguez
- Department of Medical Oncology, Universidad de Salamanca, Salamanca 37007, Spain
| | - Jian-Hua Mao
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, 94720, United States
| | - Hang Chang
- Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, 94720, United States
| |
Collapse
|
5
|
Sánchez J, Martín-Landrove M. Morphological and Fractal Properties of Brain Tumors. Front Physiol 2022; 13:878391. [PMID: 35832478 PMCID: PMC9271830 DOI: 10.3389/fphys.2022.878391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Tumor interface dynamics is a complex process determined by cell proliferation and invasion to neighboring tissues. Parameters extracted from the tumor interface fluctuations allow for the characterization of the particular growth model, which could be relevant for an appropriate diagnosis and the correspondent therapeutic strategy. Previous work, based on scaling analysis of the tumor interface, demonstrated that gliomas strictly behave as it is proposed by the Family-Vicsek ansatz, which corresponds to a proliferative-invasive growth model, while for meningiomas and acoustic schwannomas, a proliferative growth model is more suitable. In the present work, other morphological and dynamical descriptors are used as a complementary view, such as surface regularity, one-dimensional fluctuations represented as ordered series and bi-dimensional fluctuations of the tumor interface. These fluctuations were analyzed by Detrended Fluctuation Analysis to determine generalized fractal dimensions. Results indicate that tumor interface fractal dimension, local roughness exponent and surface regularity are parameters that discriminate between gliomas and meningiomas/schwannomas.
Collapse
Affiliation(s)
- Jacksson Sánchez
- Faculty of Science and Technology, Physics Department, Universidad Nacional Pedro Henríquez Ureña, Santo Domingo, Dominican Republic
| | - Miguel Martín-Landrove
- Centre for Medical Visualization, National Institute for Bioengineering, INABIO, Universidad Central de Venezuela, Caracas, Venezuela
- Centro de Diagnóstico Docente Las Mercedes, Caracas, Venezuela
- *Correspondence: Miguel Martín-Landrove,
| |
Collapse
|
6
|
Liu XP, Jin X, Seyed Ahmadian S, Yang X, Tian SF, Cai YX, Chawla K, Snijders AM, Xia Y, van Diest PJ, Weiss WA, Mao JH, Li ZQ, Vogel H, Chang H. Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning. Neuro Oncol 2022; 25:68-81. [PMID: 35716369 PMCID: PMC9825346 DOI: 10.1093/neuonc/noac154] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Lower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes. METHODS Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The double-blinded validation for important immunotherapy-related biomarkers was executed using immunohistochemistry (IHC). RESULTS We developed a machine learning (ML) pipeline to extract CMBs from whole-slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multicenter cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by IHC staining. In addition, the subtypes learned from LGG demonstrate translational impact on glioblastoma (GBM). CONCLUSIONS We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune microenvironment, prognosis, and treatment response.
Collapse
Affiliation(s)
| | | | - Saman Seyed Ahmadian
- Department of Pathology, Stanford University Medical Center, Stanford, California, USA
| | - Xu Yang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Su-Fang Tian
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yu-Xiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Kuldeep Chawla
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Antoine M Snijders
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Yankai Xia
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - William A Weiss
- Departments of Neurology, Neurological Surgery, and Pediatrics, University of California, San Francisco, San Francisco, California, USA
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA,Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Zhi-Qiang Li
- Corresponding Authors: Zhi-Qiang Li, MD, PhD, Department of Neurosurgery, Zhongnan Hospital of Wuhan University, 169 East Lake Road, Wuchang District, Wuhan, Hubei 430071 China (); Hang Chang, PhD, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA ()
| | | | - Hang Chang
- Corresponding Authors: Zhi-Qiang Li, MD, PhD, Department of Neurosurgery, Zhongnan Hospital of Wuhan University, 169 East Lake Road, Wuchang District, Wuhan, Hubei 430071 China (); Hang Chang, PhD, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA ()
| |
Collapse
|
7
|
Corvo A, Caballero HSG, Westenberg MA, van Driel MA, van Wijk JJ. Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:3851-3866. [PMID: 32340951 DOI: 10.1109/tvcg.2020.2990336] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.
Collapse
|
8
|
Ariey-Bonnet J, Carrasco K, Le Grand M, Hoffer L, Betzi S, Feracci M, Tsvetkov P, Devred F, Collette Y, Morelli X, Ballester P, Pasquier E. In silico molecular target prediction unveils mebendazole as a potent MAPK14 inhibitor. Mol Oncol 2020; 14:3083-3099. [PMID: 33021050 PMCID: PMC7718943 DOI: 10.1002/1878-0261.12810] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 08/27/2020] [Accepted: 09/29/2020] [Indexed: 12/15/2022] Open
Abstract
The concept of polypharmacology involves the interaction of drug molecules with multiple molecular targets. It provides a unique opportunity for the repurposing of already-approved drugs to target key factors involved in human diseases. Herein, we used an in silico target prediction algorithm to investigate the mechanism of action of mebendazole, an antihelminthic drug, currently repurposed in the treatment of brain tumors. First, we confirmed that mebendazole decreased the viability of glioblastoma cells in vitro (IC50 values ranging from 288 nm to 2.1 µm). Our in silico approach unveiled 21 putative molecular targets for mebendazole, including 12 proteins significantly upregulated at the gene level in glioblastoma as compared to normal brain tissue (fold change > 1.5; P < 0.0001). Validation experiments were performed on three major kinases involved in cancer biology: ABL1, MAPK1/ERK2, and MAPK14/p38α. Mebendazole could inhibit the activity of these kinases in vitro in a dose-dependent manner, with a high potency against MAPK14 (IC50 = 104 ± 46 nm). Its direct binding to MAPK14 was further validated in vitro, and inhibition of MAPK14 kinase activity was confirmed in live glioblastoma cells. Consistent with biophysical data, molecular modeling suggested that mebendazole was able to bind to the catalytic site of MAPK14. Finally, gene silencing demonstrated that MAPK14 is involved in glioblastoma tumor spheroid growth and response to mebendazole treatment. This study thus highlighted the role of MAPK14 in the anticancer mechanism of action of mebendazole and provides further rationale for the pharmacological targeting of MAPK14 in brain tumors. It also opens new avenues for the development of novel MAPK14/p38α inhibitors to treat human diseases.
Collapse
Affiliation(s)
- Jeremy Ariey-Bonnet
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Kendall Carrasco
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Marion Le Grand
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Laurent Hoffer
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Stéphane Betzi
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Mikael Feracci
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Philipp Tsvetkov
- CNRS, UMR 7051, INP, Inst Neurophysiopathol, Fac Pharm, Aix Marseille Université, France
| | - Francois Devred
- CNRS, UMR 7051, INP, Inst Neurophysiopathol, Fac Pharm, Aix Marseille Université, France
| | - Yves Collette
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Xavier Morelli
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Pedro Ballester
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| | - Eddy Pasquier
- Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université, France
| |
Collapse
|
9
|
Barsoum I, Tawedrous E, Faragalla H, Yousef GM. Histo-genomics: digital pathology at the forefront of precision medicine. ACTA ACUST UNITED AC 2020; 6:203-212. [PMID: 30827078 DOI: 10.1515/dx-2018-0064] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/28/2018] [Indexed: 12/26/2022]
Abstract
The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.
Collapse
Affiliation(s)
- Ivraym Barsoum
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Eriny Tawedrous
- Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Hala Faragalla
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - George M Yousef
- Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.,Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada
| |
Collapse
|
10
|
Crawford L, Monod A, Chen AX, Mukherjee S, Rabadán R. Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2019.1671198] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Lorin Crawford
- Department of Biostatistics, Brown University, Providence, RI
- Center for Statistical Sciences, Brown University, Providence, RI
- Center for Computational Molecular Biology, Brown University, Providence, RI
| | - Anthea Monod
- Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel
| | - Andrew X. Chen
- Department of Systems Biology, Columbia University, New York, NY
| | - Sayan Mukherjee
- Department of Statistical Science, Duke University, Durham, NC
- Department of Computer Science, Duke University, Durham, NC
- Department of Mathematics, Duke University, Durham, NC
- Department of Bioinformatics & Biostatistics, Duke University, Durham, NC
| | - Raúl Rabadán
- Department of Systems Biology, Columbia University, New York, NY
| |
Collapse
|
11
|
Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS, Perou CM, Troester MA, Niethammer M. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer 2018; 4:30. [PMID: 30182055 PMCID: PMC6120869 DOI: 10.1038/s41523-018-0079-1] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 07/17/2018] [Accepted: 07/23/2018] [Indexed: 11/09/2022] Open
Abstract
RNA-based, multi-gene molecular assays are available and widely used for patients with ER-positive/HER2-negative breast cancers. However, RNA-based genomic tests can be costly and are not available in many countries. Methods for inferring molecular subtype from histologic images may identify patients most likely to benefit from further genomic testing. To identify patients who could benefit from molecular testing based on H&E stained histologic images, we developed an image analysis approach using deep learning. A training set of 571 breast tumors was used to create image-based classifiers for tumor grade, ER status, PAM50 intrinsic subtype, histologic subtype, and risk of recurrence score (ROR-PT). The resulting classifiers were applied to an independent test set (n = 288), and accuracy, sensitivity, and specificity of each was assessed on the test set. Histologic image analysis with deep learning distinguished low-intermediate vs. high tumor grade (82% accuracy), ER status (84% accuracy), Basal-like vs. non-Basal-like (77% accuracy), Ductal vs. Lobular (94% accuracy), and high vs. low-medium ROR-PT score (75% accuracy). Sampling considerations in the training set minimized bias in the test set. Incorrect classification of ER status was significantly more common for Luminal B tumors. These data provide proof of principle that molecular marker status, including a critical clinical biomarker (i.e., ER status), can be predicted with accuracy >75% based on H&E features. Image-based methods could be promising for identifying patients with a greater need for further genomic testing, or in place of classically scored variables typically accomplished using human-based scoring.
Collapse
Affiliation(s)
- Heather D Couture
- 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Lindsay A Williams
- 2Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Joseph Geradts
- 3Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02115 USA
| | - Sarah J Nyante
- 4Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Ebonee N Butler
- 2Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - J S Marron
- 5Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.,6Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Charles M Perou
- 5Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.,7Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Melissa A Troester
- 2Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.,5Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Marc Niethammer
- 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.,8Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| |
Collapse
|
12
|
Popovici V, Budinská E, Dušek L, Kozubek M, Bosman F. Image-based surrogate biomarkers for molecular subtypes of colorectal cancer. Bioinformatics 2018; 33:2002-2009. [PMID: 28158480 DOI: 10.1093/bioinformatics/btx027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Accepted: 01/17/2017] [Indexed: 12/15/2022] Open
Abstract
Motivation Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes. Results A hierarchical decomposition of the multi-class problem was obtained with an overall accuracy of 0.84 (95%CI=0.79-0.88). The predictions from the image-based classifier showed significant prognostic value similar to their molecular counterparts. Contact popovici@iba.muni.cz. Availability and Implementation Source code used for the image analysis is freely available from https://github.com/higex/qpath . Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Vlad Popovici
- Faculty of Medicine, Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Eva Budinská
- Faculty of Science, Research Centre for Toxic Compounds in the Environment, Masaryk University, Brno, Czech Republic
| | - Ladislav Dušek
- Faculty of Medicine, Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
| | - Michal Kozubek
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Fred Bosman
- University Institute of Pathology, University of Lausanne, Switzerland
| |
Collapse
|
13
|
Mercan E, Aksoy S, Shapiro LG, Weaver DL, Brunyé TT, Elmore JG. Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study. J Digit Imaging 2018; 29:496-506. [PMID: 26961982 DOI: 10.1007/s10278-016-9873-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.
Collapse
Affiliation(s)
- Ezgi Mercan
- Department of Computer Science & Engineering, Paul G. Allen Center for Computing, University of Washington, 185 Stevens Way, Seattle, WA, 98195, USA.
| | - Selim Aksoy
- Department of Computer Engineering, Bilkent University, Bilkent, 06800, Ankara, Turkey
| | - Linda G Shapiro
- Department of Computer Science & Engineering, Paul G. Allen Center for Computing, University of Washington, 185 Stevens Way, Seattle, WA, 98195, USA
| | - Donald L Weaver
- Department of Pathology, University of Vermont, Burlington, VT, 05405, USA
| | - Tad T Brunyé
- Department of Psychology, Tufts University, Medford, MA, 02155, USA
| | - Joann G Elmore
- Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
14
|
Saltz J, Sharma A, Iyer G, Bremer E, Wang F, Jasniewski A, DiPrima T, Almeida JS, Gao Y, Zhao T, Saltz M, Kurc T. A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Res 2017; 77:e79-e82. [PMID: 29092946 DOI: 10.1158/0008-5472.can-17-0316] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/17/2017] [Accepted: 09/01/2017] [Indexed: 11/16/2022]
Abstract
Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. Cancer Res; 77(21); e79-82. ©2017 AACR.
Collapse
Affiliation(s)
- Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Ganesh Iyer
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Alina Jasniewski
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Jonas S Almeida
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Yi Gao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Department of Pathology, Stony Brook University, Stony Brook, New York
| | - Mary Saltz
- Department of Radiology, Stony Brook University, Stony Brook, New York
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.,Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| |
Collapse
|
15
|
Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A. A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry A 2017; 91:566-573. [PMID: 28192639 PMCID: PMC6124660 DOI: 10.1002/cyto.a.23065] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 11/05/2016] [Accepted: 01/18/2017] [Indexed: 11/10/2022]
Abstract
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P < 0.01). Comparisons of other risk groups, using both ODX score and histological grade, were also found to present significantly different automated mitoses distributions. Additionally, a support vector machine classifier trained to separate low/high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy. © 2017 International Society for Advancement of Cytometry.
Collapse
Affiliation(s)
- David Romo-Bucheli
- Engineering Faculty, Universidad Nacional de Colombia, Bogotá, DC Colombia
| | - Andrew Janowczyk
- Biomedical Engineering Department, Case Western Reserve University, Cleveland, Ohio
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Eduardo Romero
- Engineering Faculty, Universidad Nacional de Colombia, Bogotá, DC Colombia
| | - Anant Madabhushi
- Biomedical Engineering Department, Case Western Reserve University, Cleveland, Ohio
| |
Collapse
|
16
|
Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A. Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images. Sci Rep 2016; 6:32706. [PMID: 27599752 PMCID: PMC5013328 DOI: 10.1038/srep32706] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 08/15/2016] [Indexed: 12/22/2022] Open
Abstract
Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these tests are expensive, tissue destructive and require specialized facilities. Interestingly BCa grade has been shown to be correlated with the ODX risk score. Unfortunately Bloom-Richardson (BR) grade determined by pathologists can be variable. A constituent category in BR grading is tubule formation. This study aims to develop a deep learning classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei (a tubule formation indicator - TFI) correlates with the corresponding ODX risk categories. This correlation was assessed in 7513 fields extracted from 174 WSI. The results suggests that low ODX/BR cases have a larger TFI than high ODX/BR cases (p < 0.01). The low ODX/BR cases also presented a larger TFI than that obtained for the rest of cases (p < 0.05). Finally, the high ODX/BR cases have a significantly smaller TFI than that obtained for the rest of cases (p < 0.01).
Collapse
Affiliation(s)
- David Romo-Bucheli
- Universidad Nacional de Colombia, Engineering Faculty, Bogotá D.C, Colombia
| | - Andrew Janowczyk
- Case Western Reserve University, Biomedical Engineering department, Cleveland, OH, USA
| | - Hannah Gilmore
- University Hospitals, School of Medicine, Cleveland, OH, USA
| | - Eduardo Romero
- Universidad Nacional de Colombia, Engineering Faculty, Bogotá D.C, Colombia
| | - Anant Madabhushi
- Case Western Reserve University, Biomedical Engineering department, Cleveland, OH, USA
| |
Collapse
|
17
|
Zhou Y, Chang H, Barner KE, Parvin B. NUCLEI SEGMENTATION VIA SPARSITY CONSTRAINED CONVOLUTIONAL REGRESSION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2015; 2015:1284-1287. [PMID: 28101301 PMCID: PMC5239217 DOI: 10.1109/isbi.2015.7164109] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge.
Collapse
Affiliation(s)
- Yin Zhou
- Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A
- University of Delaware, Newark, DE, U.S.A
| | - Hang Chang
- Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A
- Department of Electrical and Computer Engineering, University of California, Riverside, U.S.A
| | | | - Bahram Parvin
- Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A
- Biomedical Engineering Department, University of Nevada, Reno, NV, U.S.A
- Department of Electrical and Computer Engineering, University of California, Riverside, U.S.A
| |
Collapse
|
18
|
Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. J Transl Med 2015; 95:377-84. [PMID: 25599534 DOI: 10.1038/labinvest.2014.155] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 10/22/2014] [Indexed: 12/13/2022] Open
Abstract
The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data- the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.
Collapse
|
19
|
Mousavi HS, Monga V, Rao G, Rao AUK. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis. J Pathol Inform 2015; 6:15. [PMID: 25838967 PMCID: PMC4382761 DOI: 10.4103/2153-3539.153914] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2014] [Accepted: 01/05/2015] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. RESULTS We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. CONCLUSION The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.
Collapse
Affiliation(s)
- Hojjat Seyed Mousavi
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Vishal Monga
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Ganesh Rao
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind U K Rao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
20
|
Irshad H, Montaser-Kouhsari L, Waltz G, Bucur O, Nowak J, Dong F, Knoblauch N, Beck AH. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2015:294-305. [PMID: 25592590 PMCID: PMC4299942 DOI: 10.1142/9789814644730_0029] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The development of tools in computational pathology to assist physicians and biomedical scientists in the diagnosis of disease requires access to high-quality annotated images for algorithm learning and evaluation. Generating high-quality expert-derived annotations is time-consuming and expensive. We explore the use of crowdsourcing for rapidly obtaining annotations for two core tasks in com- putational pathology: nucleus detection and nucleus segmentation. We designed and implemented crowdsourcing experiments using the CrowdFlower platform, which provides access to a large set of labor channel partners that accesses and manages millions of contributors worldwide. We obtained annotations from four types of annotators and compared concordance across these groups. We obtained: crowdsourced annotations for nucleus detection and segmentation on a total of 810 images; annotations using automated methods on 810 images; annotations from research fellows for detection and segmentation on 477 and 455 images, respectively; and expert pathologist-derived annotations for detection and segmentation on 80 and 63 images, respectively. For the crowdsourced annotations, we evaluated performance across a range of contributor skill levels (1, 2, or 3). The crowdsourced annotations (4,860 images in total) were completed in only a fraction of the time and cost required for obtaining annotations using traditional methods. For the nucleus detection task, the research fellow-derived annotations showed the strongest concordance with the expert pathologist- derived annotations (F-M =93.68%), followed by the crowd-sourced contributor levels 1,2, and 3 and the automated method, which showed relatively similar performance (F-M = 87.84%, 88.49%, 87.26%, and 86.99%, respectively). For the nucleus segmentation task, the crowdsourced contributor level 3-derived annotations, research fellow-derived annotations, and automated method showed the strongest concordance with the expert pathologist-derived annotations (F-M = 66.41%, 65.93%, and 65.36%, respectively), followed by the contributor levels 2 and 1 (60.89% and 60.87%, respectively). When the research fellows were used as a gold-standard for the segmentation task, all three con- tributor levels of the crowdsourced annotations significantly outperformed the automated method (F-M = 62.21%, 62.47%, and 65.15% vs. 51.92%). Aggregating multiple annotations from the crowd to obtain a consensus annotation resulted in the strongest performance for the crowd-sourced segmentation. For both detection and segmentation, crowd-sourced performance is strongest with small images (400 × 400 pixels) and degrades significantly with the use of larger images (600 × 600 and 800 × 800 pixels). We conclude that crowdsourcing to non-experts can be used for large-scale labeling microtasks in computational pathology and offers a new approach for the rapid generation of labeled images for algorithm development and evaluation.
Collapse
Affiliation(s)
- H. Irshad
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | | | - G. Waltz
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | - O. Bucur
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | - J.A. Nowak
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | - F. Dong
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | - N.W. Knoblauch
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| | - A. H. Beck
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston USA
| |
Collapse
|
21
|
Ying H, Lv J, Ying T, Jin S, Shao J, Wang L, Xu H, Yuan B, Yang Q. Gene-gene interaction network analysis of ovarian cancer using TCGA data. J Ovarian Res 2013; 6:88. [PMID: 24314048 PMCID: PMC4029308 DOI: 10.1186/1757-2215-6-88] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 11/14/2013] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis. METHODS Microarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs. Next, NetBox software was used to for the gene-gene interaction (GGI) network construction and the corresponding modules identification, and functions of genes in the modules were screened using DAVID. RESULTS Our studies identified 332 DEGs, including 146 up-regulated genes which mainly involved in the cell cycle related functions and cell cycle pathway, and 186 down-regulated genes which were enriched in extracellular region par function, and Ether lipid metabolism pathway. GGI network was constructed by 127 DEGs and their significantly interacted 209 genes (LINKERs). In the top 10 nodes ranked by degrees in the network, 5 were LINKERs. Totally, 7 functional modules in the network were selected, and they were enriched in different functions and pathways, such as mitosis process, DNA replication and DNA double-strand synthesis, lipid synthesis processes and metabolic pathways. AR, BRCA1, TFDP1, FOXM1, CDK2, and DBF4 were identified as the transcript factors of the 7 modules. CONCLUSION our data provides a comprehensive bioinformatics analysis of genes, functions, and pathways which may be involved in the pathogenesis of ovarian cancer.
Collapse
Affiliation(s)
- Huanchun Ying
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| | - Jing Lv
- Department of Oncology, The fifth Hospital of Shenyang, Shenyang 110023, China
| | - Tianshu Ying
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| | - Shanshan Jin
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| | - Jingru Shao
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| | - Lili Wang
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| | - Hongying Xu
- Department of Gynecology and Obstetrics, The ninth Hospital of Shenyang, Shenyang 110024, China
| | - Bin Yuan
- Department of Oncology, The fifth Hospital of Shenyang, Shenyang 110023, China
| | - Qing Yang
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, Liaoning Province 110004, China
| |
Collapse
|
22
|
Gutman DA, Cobb J, Somanna D, Park Y, Wang F, Kurc T, Saltz JH, Brat DJ, Cooper LAD. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J Am Med Inform Assoc 2013; 20:1091-8. [PMID: 23893318 PMCID: PMC3822112 DOI: 10.1136/amiajnl-2012-001469] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Revised: 05/29/2013] [Accepted: 07/05/2013] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types. OBJECTIVE To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. MATERIALS AND METHODS All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites. RESULTS The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types. DISCUSSION The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics. CONCLUSIONS With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.
Collapse
Affiliation(s)
- David A Gutman
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
- Winship Cancer Institute, Atlanta, Georgia, USA
| | - Jake Cobb
- College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Dhananjaya Somanna
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
| | - Yuna Park
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Fusheng Wang
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
- Department of Pathology and Laboratory Medicine, Atlanta, Georgia, USA
| | - Daniel J Brat
- Winship Cancer Institute, Atlanta, Georgia, USA
- Department of Pathology and Laboratory Medicine, Atlanta, Georgia, USA
| | - Lee A D Cooper
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
- Center for Comprehensive Informatics, Emory University,Atlanta, Georgia, USA
- Winship Cancer Institute, Atlanta, Georgia, USA
| |
Collapse
|
23
|
Kothari S, Phan JH, Wang MD. Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade. J Pathol Inform 2013; 4:22. [PMID: 24083057 PMCID: PMC3779385 DOI: 10.4103/2153-3539.117448] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 07/03/2013] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Analysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. MATERIALS AND METHODS We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. RESULTS When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. CONCLUSION The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.
Collapse
Affiliation(s)
- Sonal Kothari
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - John H. Phan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - May D. Wang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Winship Cancer Institute, Parker H. Petit Institute of Bioengineering and Biosciences, Institute of People and Technology, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| |
Collapse
|
24
|
Kothari S, Phan JH, Stokes TH, Wang MD. Pathology imaging informatics for quantitative analysis of whole-slide images. J Am Med Inform Assoc 2013; 20:1099-108. [PMID: 23959844 PMCID: PMC3822114 DOI: 10.1136/amiajnl-2012-001540] [Citation(s) in RCA: 182] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.
Collapse
Affiliation(s)
- Sonal Kothari
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | | | | | | |
Collapse
|
25
|
Chang H, Borowsky A, Spellman P, Parvin B. Classification of Tumor Histology via Morphometric Context. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2013; 2013:10.1109/CVPR.2013.286. [PMID: 24319324 PMCID: PMC3850786 DOI: 10.1109/cvpr.2013.286] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types; (ii) robust in the presence of wide technical and biological variations; (iii) invariant to different nuclear segmentation strategies; and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system.
Collapse
Affiliation(s)
- Hang Chang
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, U.S.A
| | | | - Paul Spellman
- Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, Oregon, U.S.A
| | - Bahram Parvin
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, U.S.A
| |
Collapse
|
26
|
Wang C, Pécot T, Zynger DL, Machiraju R, Shapiro CL, Huang K. Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc 2013; 20:680-7. [PMID: 23585272 PMCID: PMC3721170 DOI: 10.1136/amiajnl-2012-001538] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Background and objective Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we propose a workflow for analyzing histopathological images and integrate them with genomic data for discovering biomarkers for TNBC. Materials and methods We developed an image analysis workflow for extracting a large collection of morphological features and deployed the same on histological images from The Cancer Genome Atlas (TCGA) TNBC samples during the discovery phase (n=44). Strong correlations between salient morphological features and gene expression profiles from the same patients were identified. We then evaluated the same morphological features in predicting survival using a local TNBC cohort (n=143). We further tested the predictive power on patient prognosis of correlated gene clusters using two other public gene expression datasets. Results and conclusion Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.
Collapse
Affiliation(s)
- Chao Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA
| | | | | | | | | | | |
Collapse
|
27
|
Nayak N, Chang H, Borowsky A, Spellman P, Parvin B. CLASSIFICATION OF TUMOR HISTOPATHOLOGY VIA SPARSE FEATURE LEARNING. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2013; 2013. [PMID: 24319533 DOI: 10.1109/isbi.2013.6556499] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Our goal is to decompose whole slide images (WSI) of histology sections into distinct patches (e.g., viable tumor, necrosis) so that statistics of distinct histopathology can be linked with the outcome. Such an analysis requires a large cohort of histology sections that may originate from different laboratories, which may not use the same protocol in sample preparation. We have evaluated a method based on a variation of the restricted Boltzmann machine (RBM) that learns intrinsic features of the image signature in an unsupervised fashion. Computed code, from the learned representation, is then utilized to classify patches from a curated library of images. The system has been evaluated against a dataset of small image blocks of 1k-by-1k that have been extracted from glioblastoma multiforme (GBM) and clear cell kidney carcinoma (KIRC) from the cancer genome atlas (TCGA) archive. The learned model is then projected on each whole slide image (e.g., of size 20k-by-20k pixels or larger) for characterizing and visualizing tumor architecture. In the case of GBM, each WSI is decomposed into necrotic, transition into necrosis, and viable. In the case of the KIRC, each WSI is decomposed into tumor types, stroma, normal, and others. Evaluation of 1400 and 2500 samples of GBM and KIRC indicates a performance of 84% and 81%, respectively.
Collapse
Affiliation(s)
- Nandita Nayak
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, U.S.A
| | | | | | | | | |
Collapse
|
28
|
Chang H, Han J, Borowsky A, Loss L, Gray JW, Spellman PT, Parvin B. Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:670-82. [PMID: 23221815 PMCID: PMC3728287 DOI: 10.1109/tmi.2012.2231420] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Automated analysis of whole mount tissue sections can provide insights into tumor subtypes and the underlying molecular basis of neoplasm. However, since tumor sections are collected from different laboratories, inherent technical and biological variations impede analysis for very large datasets such as The Cancer Genome Atlas (TCGA). Our objective is to characterize tumor histopathology, through the delineation of the nuclear regions, from hematoxylin and eosin (H&E) stained tissue sections. Such a representation can then be mined for intrinsic subtypes across a large dataset for prediction and molecular association. Furthermore, nuclear segmentation is formulated within a multi-reference graph framework with geodesic constraints, which enables computation of multidimensional representations, on a cell-by-cell basis, for functional enrichment and bioinformatics analysis. Here, we present a novel method, multi-reference graph cut (MRGC), for nuclear segmentation that overcomes technical variations associated with sample preparation by incorporating prior knowledge from manually annotated reference images and local image features. The proposed approach has been validated on manually annotated samples and then applied to a dataset of 377 Glioblastoma Multiforme (GBM) whole slide images from 146 patients. For the GBM cohort, multidimensional representation of the nuclear features and their organization have identified 1) statistically significant subtypes based on several morphometric indexes, 2) whether each subtype can be predictive or not, and 3) that the molecular correlates of predictive subtypes are consistent with the literature. Data and intermediaries for a number of tumor types (GBM, low grade glial, and kidney renal clear carcinoma) are available at: http://tcga.lbl.gov for correlation with TCGA molecular data. The website also provides an interface for panning and zooming of whole mount tissue sections with/without overlaid segmentation results for quality control.
Collapse
Affiliation(s)
- Hang Chang
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 U.S.A
| | - Ju Han
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 U.S.A
| | - Alexander Borowsky
- Center for Comparative Medicine, University of California, Davis, California, 95616 U.S.A
| | - Leandro Loss
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 U.S.A
| | - Joe W. Gray
- Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, Oregon, 97239 U.S.A
| | - Paul T. Spellman
- Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, Oregon, 97239 U.S.A
| | - Bahram Parvin
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 U.S.A
| |
Collapse
|
29
|
Chang H, Han J, Spellman PT, Parvin B. Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme. IEEE Trans Biomed Eng 2012; 59:3460-7. [PMID: 22987497 PMCID: PMC3728286 DOI: 10.1109/tbme.2012.2218107] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20 × or 40 × objective, in which, each tissue section varies in size from 40k × 40k pixels to 100k × 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.
Collapse
Affiliation(s)
- Hang Chang
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA ()
| | - Ju Han
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA ()
| | - Paul T. Spellman
- Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, OR 97239 USA ()
| | - Bahram Parvin
- Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA ()
| |
Collapse
|
30
|
Kothari S, Phan JH, Osunkoya AO, Wang MD. Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2012; 2012:218-225. [PMID: 29568817 PMCID: PMC5859578 DOI: 10.1145/2382936.2382964] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.
Collapse
|
31
|
Orr BA, Eberhart CG. Nature versus nurture in glioblastoma: microenvironment and genetics can both drive mesenchymal transcriptional signature. THE AMERICAN JOURNAL OF PATHOLOGY 2012; 180:1768-71. [PMID: 22449951 PMCID: PMC3349836 DOI: 10.1016/j.ajpath.2012.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2012] [Revised: 03/09/2012] [Accepted: 03/11/2012] [Indexed: 11/15/2022]
|