Imaging and the completion of the omics paradigm in breast cancer

Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.ZusammenfassungDie „Omics-Strategien“ – „genomics, transcriptomics, proteomics, metabolomics“ – haben viele potenzielle Anwendungsgebiete innerhalb der Onkologie und könnten das Verständnis der Krebsentstehung und des Fortschreitens der Erkrankung deutlich verbessern. Mit „Omics-Strategien“ können große Datenmengen verschiedenster biologischer Informationen schnell ausgewertet werden, mit dem Ziel, sinnvolle Biomarker für Brustkrebs und hiermit ein integratives Porträt dieser Erkrankung zu entwickeln. Diese neue Richtung in der medizinischen Wissenschaft wurde durch verschiedenste Fortschritte in Bildgebungs- und Bildanalysemethoden sowie die Entwicklung von Techniken zur Extraktion und Korrelation verschiedenster Bildgebungsparameter mit „Omics-Daten“ eingeläutet. Radiogenomics haben zum Ziel, Bildgebungscharakteristika (Phänotypen) mit Genexpressionsmustern, Genmutationen und weiteren genomassoziierten Eigenschaften zu korrelieren. Hiermit repräsentieren Radiogenomics die Evolution der Korrelation von Radiologie und Pathologie von der anatomisch-histologischen hin zur molekularen Ebene und stellen einen zentralen Schritt des „Omics-Paradigmas“ bei Brustkrebs dar. In Verbindung mit modernen Softwareanalysetechniken können quantitative und qualitative Bildgebungsbiomarker bisher beispiellose Erkenntnisse über komplexe Tumorbiologie liefern und ein besseres Verständnis der Krebsentstehung und -progression bewirken. Radiogenomics stellen ein sich rasch entwickelndes Forschungsfeld dar, und Resultate aus ersten Studien sind vielversprechend. Es ist zu erwarten, dass Radiogenomics zukünftig eine wichtige Rolle spielen werden, da sie das Potenzial haben, Diagnose, Behandlung und Prognose von Brustkrebs zu beeinflussen. Im vorliegenden Artikel wird ein Überblick über Radiogenomics der Brust, ihre aktuelle Rolle, zukünftige Anwendungen und Herausforderungen gegeben.

[1]  Harini Veeraraghavan,et al.  Breast cancer molecular subtype classifier that incorporates MRI features , 2016, Journal of magnetic resonance imaging : JMRI.

[2]  Paul M. Parizel,et al.  European Society of Radiology (ESR) , 2015 .

[3]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[4]  Yoav Benjamini,et al.  Identifying differentially expressed genes using false discovery rate controlling procedures , 2003, Bioinform..

[5]  M. Giger,et al.  Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma , 2015, Scientific Reports.

[6]  L. V. van't Veer,et al.  Imaging Features of HER2 Overexpression in Breast Cancer: A Systematic Review and Meta-analysis , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[7]  S Michiels,et al.  Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[9]  M. Thakur,et al.  Diffusion magnetic resonance imaging: A molecular imaging tool caught between hope, hype and the real world of “personalized oncology” , 2017, World journal of radiology.

[10]  R. Gelber,et al.  Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011 , 2011, Annals of oncology : official journal of the European Society for Medical Oncology.

[11]  M. Kuo,et al.  Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations. , 2014, Radiology.

[12]  C. Perou,et al.  Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013 , 2013, Annals of oncology : official journal of the European Society for Medical Oncology.

[13]  Wolfgang Bogner,et al.  Quantitative Sodium MR Imaging at 7 T: Initial Results and Comparison with Diffusion-weighted Imaging in Patients with Breast Tumors. , 2016, Radiology.

[14]  Ravinder Reddy,et al.  Chemical Exchange Saturation Transfer (CEST) Imaging: Description of Technique and Potential Clinical Applications , 2013, Current Radiology Reports.

[15]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[16]  L. Carey,et al.  Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. , 2009, Seminars in radiation oncology.

[17]  Christine B. Peterson,et al.  Many Phenotypes Without Many False Discoveries: Error Controlling Strategies for Multitrait Association Studies , 2015, Genetic epidemiology.

[18]  Lars J. Grimm,et al.  Can breast cancer molecular subtype help to select patients for preoperative MR imaging? , 2015, Radiology.

[19]  H. Hricak,et al.  Background, current role, and potential applications of radiogenomics , 2018, Journal of magnetic resonance imaging : JMRI.

[20]  Neema Jamshidi,et al.  Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis. , 2015, Radiology.

[21]  Brian Leyland-Jones,et al.  A systems approach to clinical oncology: Focus on breast cancer , 2006, Proteome Science.

[22]  Lars J. Grimm,et al.  Breast MRI radiogenomics: Current status and research implications , 2016, Journal of magnetic resonance imaging : JMRI.

[23]  Kimberly M Ray,et al.  Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features. , 2018, Radiology.

[24]  S. Lakhani,et al.  ‘Omics Approaches in Breast Cancer Research and Clinical Practice , 2016, Advances in anatomic pathology.

[25]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[26]  T. Sørlie,et al.  Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. , 2012 .

[27]  Christos Davatzikos,et al.  Imaging genomics in cancer research: limitations and promises. , 2016, The British journal of radiology.

[28]  European Society of Radiology White paper on imaging biomarkers , 2010, Insights into imaging.

[29]  Ahmed Bilal Ashraf,et al.  Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. , 2014, Radiology.

[30]  W. D. den Dunnen,et al.  Optimizing targeted cancer therapy: towards clinical application of systems biology approaches. , 2012, Critical reviews in oncology/hematology.

[31]  J. Thrall Moreton Lecture: Imaging in the Age of Precision Medicine. , 2015, Journal of the American College of Radiology : JACR.

[32]  James H Thrall,et al.  Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. , 2016, Radiology.

[33]  E. Stickeler,et al.  HER2-Orientated Therapy in Early and Metastatic Breast Cancer , 2016, Breast Care.

[34]  Lars J. Grimm,et al.  Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms , 2015, Journal of magnetic resonance imaging : JMRI.

[35]  R. Grimm,et al.  Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma , 2015, Journal of magnetic resonance imaging : JMRI.

[36]  Roderick W McColl,et al.  Blood oxygenation level‐dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast cancer chemotherapy response: A pilot study , 2013, Journal of magnetic resonance imaging : JMRI.

[37]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[38]  Erich P Huang,et al.  MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. , 2016, Radiology.

[39]  Jong-Hyeon Jeong,et al.  Breast cancer adjuvant therapy: time to consider its time-dependent effects. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  K. Pinker,et al.  Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “Breast Imaging Reporting and Data System” for multiparametric 3-T imaging of breast lesions , 2013, European Radiology.

[41]  Erich P Huang,et al.  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set , 2016, npj Breast Cancer.

[42]  R. Gelber,et al.  Patterns of Recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international breast cancer study group trials VIII and IX. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[43]  S. Hahn,et al.  Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion‐weighted MRI at 3.0 Tesla , 2015, Journal of magnetic resonance imaging : JMRI.

[44]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[45]  R. Ponzone,et al.  Correlations between diffusion-weighted imaging and breast cancer biomarkers , 2012, European Radiology.

[46]  Lars J. Grimm,et al.  Relationships Between MRI Breast Imaging‐Reporting and Data System (BI‐RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype , 2017, The breast journal.

[47]  Greg Zaharchuk,et al.  Arterial spin labeling MRI: Clinical applications in the brain , 2015, Journal of magnetic resonance imaging : JMRI.

[48]  Stephen R. Piccolo,et al.  Systems Approaches to Cancer Biology. , 2016, Cancer research.

[49]  M. Mazurowski Radiogenomics: what it is and why it is important. , 2015, Journal of the American College of Radiology : JACR.

[50]  R. A. Lerski,et al.  Magnetic resonance imaging texture analysis classification of primary breast cancer , 2016, European Radiology.

[51]  D. D. Maki,et al.  Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. , 2012, AJR. American journal of roentgenology.

[52]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[53]  T. Uematsu MR imaging of triple-negative breast cancer , 2011, Breast cancer.