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Liu Q, Huang S, Desautels D, McManus KJ, Murphy L, Hu P. Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer. Comput Struct Biotechnol J 2023; 21:2940-2949. [PMID: 37216014 PMCID: PMC10196919 DOI: 10.1016/j.csbj.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/24/2023] Open
Abstract
Background Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs. Methods In this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts. Results We identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups. Conclusion This is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shujun Huang
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Danielle Desautels
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Winnipeg, Manitoba, Canada
| | - Kirk J. McManus
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
| | - Leigh Murphy
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
| | - Pingzhao Hu
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
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Al Fryan LH, Shomo MI, Alazzam MB. Application of Deep Learning System Technology in Identification of Women’s Breast Cancer. Medicina (B Aires) 2023; 59:medicina59030487. [PMID: 36984487 PMCID: PMC10052988 DOI: 10.3390/medicina59030487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/03/2023] [Indexed: 03/06/2023] Open
Abstract
Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.
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Affiliation(s)
- Latefa Hamad Al Fryan
- Department of Educational Technology, College of Education, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Mahasin Ibrahim Shomo
- Applied College, Curriculum and Instruction, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Malik Bader Alazzam
- Information Technology College, Ajloun National University, Ajloun 26873, Jordan
- Research Center, The University of Mashreq, Baghdad 11001, Iraq
- Correspondence:
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Leng D, Zheng L, Wen Y, Zhang Y, Wu L, Wang J, Wang M, Zhang Z, He S, Bo X. A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol 2022; 23:171. [PMID: 35945544 PMCID: PMC9361561 DOI: 10.1186/s13059-022-02739-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .
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Affiliation(s)
- Dongjin Leng
- Institute of Health Service and Transfusion Medicine, Beijing, People’s Republic of China
| | - Linyi Zheng
- School of Informatics, Xiamen University, Xiamen, People’s Republic of China
| | - Yuqi Wen
- Institute of Health Service and Transfusion Medicine, Beijing, People’s Republic of China
| | - Yunhao Zhang
- School of Informatics, Xiamen University, Xiamen, People’s Republic of China
| | - Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People’s Republic of China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, People’s Republic of China
| | - Meihong Wang
- School of Informatics, Xiamen University, Xiamen, People’s Republic of China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen, People’s Republic of China
| | - Song He
- Institute of Health Service and Transfusion Medicine, Beijing, People’s Republic of China
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing, People’s Republic of China
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Liñares-Blanco J, Pazos A, Fernandez-Lozano C. Machine learning analysis of TCGA cancer data. PeerJ Comput Sci 2021; 7:e584. [PMID: 34322589 PMCID: PMC8293929 DOI: 10.7717/peerj-cs.584] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study.
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Affiliation(s)
- Jose Liñares-Blanco
- CITIC-Research Center of Information and Communication Technologies, University of A Coruna, A Coruña, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruna, A Coruña, Spain
| | - Alejandro Pazos
- CITIC-Research Center of Information and Communication Technologies, University of A Coruna, A Coruña, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruna, A Coruña, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR). Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- CITIC-Research Center of Information and Communication Technologies, University of A Coruna, A Coruña, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruna, A Coruña, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR). Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021; 84:129-143. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/29/2021] [Accepted: 02/16/2021] [Indexed: 02/07/2023]
Abstract
The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.
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Affiliation(s)
- Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany; German Center for Lung Research (DZL), Partner Site Heidelberg, Heidelberg, Germany.
| | - Maximilian Alber
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Aignostics GmbH, Schumannstr. 17, Berlin, 10117, Germany
| | - Michael Allgäuer
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany
| | - Philipp Jurmeister
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Research Institute, Children's Cancer Center Hamburg, Hamburg, Germany
| | - Jan Budczies
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Johannes Eschrich
- Department of Hepatology & Gastroenterology, Charité University Medical Center, Berlin, Germany
| | - Daniel Kazdal
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Center for Lung Research (DZL), Partner Site Heidelberg, Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, Heidelberg, 69120, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, 43205, USA; Department of Pediatrics, The Ohio State University, Columbus, OH, 43210, USA
| | - Frank Tacke
- Department of Hepatology & Gastroenterology, Charité University Medical Center, Berlin, Germany
| | - David Capper
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Berlin, 10587, Germany; Department of Artificial Intelligence, Korea University, Seoul, 136-713, South Korea; Max-Planck-Institute for Informatics, Saarland Informatics Campus E1 4, Saarbrücken, 66123, Germany; Google Research, Brain Team, Berlin, Germany.
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pathology, Ludwig-Maximilians-Universität München, Thalkirchner Strasse 36, München, 80337, Germany.
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Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun 2021; 12:124. [PMID: 33402734 PMCID: PMC7785750 DOI: 10.1038/s41467-020-20430-7] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 12/02/2020] [Indexed: 01/08/2023] Open
Abstract
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook—multi-omics mix (momix)—to foster reproducibility, and support users and future developers. Advances in omics technology have resulted in the generation of multi-view data for cancer samples. Here, the authors compare dimensionality reduction techniques using simulated and TCGA data and identify the features of the methods with superior performance.
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Kim AA, Rachid Zaim S, Subbian V. Assessing reproducibility and veracity across machine learning techniques in biomedicine: A case study using TCGA data. Int J Med Inform 2020; 141:104148. [DOI: 10.1016/j.ijmedinf.2020.104148] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/22/2020] [Accepted: 04/16/2020] [Indexed: 11/28/2022]
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Mourragui S, Loog M, van de Wiel MA, Reinders MJT, Wessels LFA. PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinformatics 2020; 35:i510-i519. [PMID: 31510654 PMCID: PMC6612899 DOI: 10.1093/bioinformatics/btz372] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting. Results We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors. Availability and implementation PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Soufiane Mourragui
- Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.,Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands
| | - Marco Loog
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Mark A van de Wiel
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Marcel J T Reinders
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands.,Computational Biology Center, Leiden University Medical Center, Leiden ZC, The Netherlands
| | - Lodewyk F A Wessels
- Computational Cancer Biology, Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam CX, The Netherlands.,Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft CD, The Netherlands
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Are contralateral parenchymal enhancement on dynamic contrast-enhanced MRI and genomic ER-pathway activity in ER-positive/HER2-negative breast cancer related? Eur J Radiol 2019; 121:108705. [PMID: 31655316 DOI: 10.1016/j.ejrad.2019.108705] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/07/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE To retrospectively explore the relation between parenchymal enhancement of the healthy contralateral breast on dynamic contrast-enhanced magnetic resonance imaging (MRI) and genomic tests for estrogen receptor (ER)-pathway activity in patients with ER-positive/HER2-negative cancer. METHODS A subset of 227 consecutively included patients with unilateral invasive ER-positive/HER2-negative breast cancer underwent dynamic contrast-enhanced MRI prior to breast-conserving therapy between 2000 and 2008. Perfusion of the parenchyma in the healthy breast was assessed using a previously reported measure of contralateral parenchymal enhancement (CPE), consisting of the mean of the top-10% late enhancement. ER-pathway activity was assessed from the surgical resection specimen by the previously reported sensitivity to endocrine therapy (SET)-index and ER-factor. The SET-index is a genetic test to estimate survival benefit from endocrine therapy, consisting of genes related to the ESR1 gene. The ER-factor examines other factors as well including protein expression. The relation between CPE and ER-pathway activity was modeled using linear regression. RESULTS Patients had a median age of 59 years. CPE was not significantly associated with the SET-index (R-squared = 0.005) nor the ER-factor (R-squared = 0.0002). The only variable significantly different between low and high CPE was age at diagnosis (P < 0.001). CONCLUSIONS Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI was not associated with tumor-derived estrogen receptor pathway activity.
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Jiang J, Xing F, Zeng X, Zou Q. Investigating Maize Yield-Related Genes in Multiple Omics Interaction Network Data. IEEE Trans Nanobioscience 2019; 19:142-151. [PMID: 31170079 DOI: 10.1109/tnb.2019.2920419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Zea mays (maize) is the highest yielding food crop globally, feeding large numbers of people across the planet. It is thus especially important to explore the key genes that affect maize production with prior knowledge. Merging multiple datasets of different types can improve the accuracy of candidate genes prediction results, so we constructed interaction networks using gene, mRNA, protein, and expression profile datasets. A network propagation schedule was used considering combined scores obtained by integrating both network scores and significance scores for each candidate gene based on the guilt-by-association principle. An SVM model was used to optimize the weighted parameters to achieve more reliable results, according to the accuracy of label classification. We found that integrating multiple omics data with more data types improves the reliability of the results. We investigated the GO terms particularly associated with the top 100 candidate genes and the known genes, and analyzed the roles that these genes play in determining the phenotype of maize. We hope that the candidate genes identified here will provide a biological perspective and contribute to maize breeding research.
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