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Wang T, Guo W, Zhang X, Ma J, Li F, Zheng S, Zhu M, Dong Y, Bai M. Correlation between conventional ultrasound features combined with contrast-enhanced ultrasound patterns and pathological prognostic factors in malignant non-mass breast lesions. Clin Hemorheol Microcirc 2023; 85:433-445. [PMID: 37781796 DOI: 10.3233/ch-231936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
OBJECTIVE To investigate the correlation between ultrasound performance and prognostic factors in malignant non-mass breast lesions (NMLs). MATERIALS AND METHODS This study included 106 malignant NMLs in 104 patients. Different US features and contrast enhancement patterns were evaluated. Prognostic factors, including histological types and grades, axillary lymph node and peritumoral lymphovascular status, estrogen and progesterone receptor status and the expression of HER-2 and Ki-67 were determined. A chi-square test and logistic regression analysis were used to analyse possible associations. RESULTS Lesion size (OR: 3.08, p = 0.033) and posterior echo attenuation (OR: 8.38, p < 0.001) were useful in reflecting malignant NMLs containing an invasive carcinoma component. Posterior echo attenuation (OR: 7.51, p = 0.003) and unclear enhancement margin (OR: 6.50, p = 0.018) were often found in tumors with axillary lymph node metastases. Peritumoural lymphovascular invasion mostly exhibited posterior echo attenuation (OR: 3.84, p = 0.049) and unclear enhancement margin (OR: 8.68, p = 0.042) on ultrasound images. Perfusion defect was a comparatively accurate enhancement indicator for negative ER (OR: 2.57, p = 0.041) and PR (OR: 3.04, p = 0.008) expression. Calcifications (OR: 3.03, p = 0.025) and enlarged enhancement area (OR: 5.36, p = 0.033) imply an increased risk of positive HER-2 expression. Similarly, Calcifications (OR: 4.13, p = 0.003) and enlarged enhancement area (OR: 11.05, p < 0.001) were valid predictors of high Ki-67 proliferation index. CONCLUSION Ultrasound performance is valuable for non-invasive prediction of prognostic factors in malignant NMLs.
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Affiliation(s)
- Tong Wang
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjuan Guo
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xuemei Zhang
- Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ji Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Siqi Zheng
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Miao Zhu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Min Bai
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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2
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Casasent AK, Almekinders MM, Mulder C, Bhattacharjee P, Collyar D, Thompson AM, Jonkers J, Lips EH, van Rheenen J, Hwang ES, Nik-Zainal S, Navin NE, Wesseling J. Learning to distinguish progressive and non-progressive ductal carcinoma in situ. Nat Rev Cancer 2022; 22:663-678. [PMID: 36261705 DOI: 10.1038/s41568-022-00512-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/07/2022] [Indexed: 02/07/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a non-invasive breast neoplasia that accounts for 25% of all screen-detected breast cancers diagnosed annually. Neoplastic cells in DCIS are confined to the ductal system of the breast, although they can escape and progress to invasive breast cancer in a subset of patients. A key concern of DCIS is overtreatment, as most patients screened for DCIS and in whom DCIS is diagnosed will not go on to exhibit symptoms or die of breast cancer, even if left untreated. However, differentiating low-risk, indolent DCIS from potentially progressive DCIS remains challenging. In this Review, we summarize our current knowledge of DCIS and explore open questions about the basic biology of DCIS, including those regarding how genomic events in neoplastic cells and the surrounding microenvironment contribute to the progression of DCIS to invasive breast cancer. Further, we discuss what information will be needed to prevent overtreatment of indolent DCIS lesions without compromising adequate treatment for high-risk patients.
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Affiliation(s)
- Anna K Casasent
- Department of Genetics, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Charlotta Mulder
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | | | - Jos Jonkers
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Jacco van Rheenen
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | - Serena Nik-Zainal
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Nicholas E Navin
- Department of Genetics, MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics, MD Anderson Cancer Center, Houston, TX, USA
| | - Jelle Wesseling
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands.
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3
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Gil Del Alcazar CR, Trinh A, Alečković M, Rojas Jimenez E, Harper NW, Oliphant MU, Xie S, Krop ED, Lulseged B, Murphy KC, Keenan TE, Van Allen EM, Tolaney SM, Freeman GJ, Dillon DA, Muthuswamy SK, Polyak K. Insights into Immune Escape During Tumor Evolution and Response to Immunotherapy Using a Rat Model of Breast Cancer. Cancer Immunol Res 2022; 10:680-697. [PMID: 35446942 PMCID: PMC9177779 DOI: 10.1158/2326-6066.cir-21-0804] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/25/2022] [Accepted: 04/20/2022] [Indexed: 11/16/2022]
Abstract
Animal models are critical for the preclinical validation of cancer immunotherapies. Unfortunately, mouse breast cancer models do not faithfully reproduce the molecular subtypes and immune environment of the human disease. In particular, there are no good murine models of estrogen receptor-positive (ER+) breast cancer, the predominant subtype in patients. Here, we show that Nitroso-N-methylurea-induced mammary tumors in outbred Sprague-Dawley rats recapitulate the heterogeneity for mutational profiles, ER expression, and immune evasive mechanisms observed in human breast cancer. We demonstrate the utility of this model for preclinical studies by dissecting mechanisms of response to immunotherapy using combination TGFBR inhibition and PD-L1 blockade. Short-term treatment of early-stage tumors induced durable responses. Gene expression profiling and spatial mapping classified tumors as inflammatory and noninflammatory, and identified IFNγ, T-cell receptor (TCR), and B-cell receptor (BCR) signaling, CD74/MHC II, and epithelium-interacting CD8+ T cells as markers of response, whereas the complement system, M2 macrophage phenotype, and translation in mitochondria were associated with resistance. We found that the expression of CD74 correlated with leukocyte fraction and TCR diversity in human breast cancer. We identified a subset of rat ER+ tumors marked by expression of antigen-processing genes that had an active immune environment and responded to treatment. A gene signature characteristic of these tumors predicted disease-free survival in patients with ER+ Luminal A breast cancer and overall survival in patients with metastatic breast cancer receiving anti-PD-L1 therapy. We demonstrate the usefulness of this preclinical model for immunotherapy and suggest examination to expand immunotherapy to a subset of patients with ER+ disease. See related Spotlight by Roussos Torres, p. 672.
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Affiliation(s)
- Carlos R. Gil Del Alcazar
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Anne Trinh
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Maša Alečković
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Ernesto Rojas Jimenez
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Nicholas W. Harper
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Michael U.J. Oliphant
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Shanshan Xie
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Ethan D. Krop
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Bethlehem Lulseged
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Katherine C. Murphy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Tanya E. Keenan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- The Broad Institute, Cambridge, Massachusetts
| | - Eliezer M. Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- The Broad Institute, Cambridge, Massachusetts
| | - Sara M. Tolaney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- The Broad Institute, Cambridge, Massachusetts
| | - Gordon J. Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Deborah A. Dillon
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Pathology, Harvard Medical School, Boston, Massachusetts
| | - Senthil K. Muthuswamy
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- The Broad Institute, Cambridge, Massachusetts
- Harvard Stem Cell Institute, Cambridge, Massachusetts
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4
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Solek J, Chrzanowski J, Cieslak A, Zielinska A, Piasecka D, Braun M, Sadej R, Romanska HM. Subtype-Specific Tumour Immune Microenvironment in Risk of Recurrence of Ductal Carcinoma In Situ: Prognostic Value of HER2. Biomedicines 2022; 10:biomedicines10051061. [PMID: 35625798 PMCID: PMC9138378 DOI: 10.3390/biomedicines10051061] [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: 03/22/2022] [Revised: 04/25/2022] [Accepted: 04/30/2022] [Indexed: 11/16/2022] Open
Abstract
Increasing evidence suggests that the significance of the tumour immune microenvironment (TIME) for disease prognostication in invasive breast carcinoma is subtype-specific but equivalent studies in ductal carcinoma in situ (DCIS) are limited. The purpose of this paper is to review the existing data on immune cell composition in DCIS in relation to the clinicopathological features and molecular subtype of the lesion. We discuss the value of infiltration by various types of immune cells and the PD-1/PD-L1 axis as potential markers of the risk of recurrence. Analysis of the literature available in PubMed and Medline databases overwhelmingly supports an association between densities of infiltrating immune cells, traits of immune exhaustion, the foci of microinvasion, and overexpression of HER2. Moreover, in several studies, the density of immune infiltration was found to be predictive of local recurrence as either in situ or invasive cancer in HER2-positive or ER-negative DCIS. In light of the recently reported first randomized DCIS trial, relating recurrence risk with overexpression of HER2, we also include a closing paragraph compiling the latest mechanistic data on a functional link between HER2 and the density/composition of TIME in relation to its potential value in the prognostication of the risk of recurrence.
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Affiliation(s)
- Julia Solek
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 92-213 Lodz, Poland; (J.S.); (A.Z.); (M.B.)
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419 Lodz, Poland; (J.C.); (A.C.)
| | - Jedrzej Chrzanowski
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419 Lodz, Poland; (J.C.); (A.C.)
| | - Adrianna Cieslak
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419 Lodz, Poland; (J.C.); (A.C.)
| | - Aleksandra Zielinska
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 92-213 Lodz, Poland; (J.S.); (A.Z.); (M.B.)
| | - Dominika Piasecka
- Department of Molecular Enzymology and Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdansk, 80-210 Gdansk, Poland;
| | - Marcin Braun
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 92-213 Lodz, Poland; (J.S.); (A.Z.); (M.B.)
| | - Rafal Sadej
- Department of Molecular Enzymology and Oncology, Intercollegiate Faculty of Biotechnology, Medical University of Gdansk, 80-210 Gdansk, Poland;
- Correspondence: (R.S.); (H.M.R.); Tel.: +48-58-349-14-69 (R.S.); +48-42-272-56-05 (H.M.R.)
| | - Hanna M. Romanska
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 92-213 Lodz, Poland; (J.S.); (A.Z.); (M.B.)
- Correspondence: (R.S.); (H.M.R.); Tel.: +48-58-349-14-69 (R.S.); +48-42-272-56-05 (H.M.R.)
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5
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Wetstein SC, Stathonikos N, Pluim JPW, Heng YJ, Ter Hoeve ND, Vreuls CPH, van Diest PJ, Veta M. Deep learning-based grading of ductal carcinoma in situ in breast histopathology images. J Transl Med 2021; 101:525-533. [PMID: 33608619 PMCID: PMC7985025 DOI: 10.1038/s41374-021-00540-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 01/05/2021] [Accepted: 01/07/2021] [Indexed: 11/08/2022] Open
Abstract
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.
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Affiliation(s)
- Suzanne C Wetstein
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yujing J Heng
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Natalie D Ter Hoeve
- Department of Pathology, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Celien P H Vreuls
- Department of Pathology, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Mitko Veta
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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6
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Narayanan PL, Raza SEA, Hall AH, Marks JR, King L, West RB, Hernandez L, Guppy N, Dowsett M, Gusterson B, Maley C, Hwang ES, Yuan Y. Unmasking the immune microecology of ductal carcinoma in situ with deep learning. NPJ Breast Cancer 2021; 7:19. [PMID: 33649333 PMCID: PMC7921670 DOI: 10.1038/s41523-020-00205-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
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Affiliation(s)
- Priya Lakshmi Narayanan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
| | - Shan E Ahmed Raza
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
- Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Allison H Hall
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Lorraine King
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Robert B West
- Department of Pathology, Surgical Pathology, Stanford, CA, USA
| | - Lucia Hernandez
- Department of Anatomic Pathology, Hospital Universitario, 12 de Octubre, Madrid, Spain
| | - Naomi Guppy
- Breast Cancer Now Histopathology Core, Institute of Cancer Research, London, UK
- UCL Advanced Diagnostics, University College London, London, UK
| | - Mitch Dowsett
- The Breast Cancer Now Toby Robins Research Centre, Institute of Cancer Research, London, UK
- Academic Department of Biochemistry, Royal Marsden Hospital, London, UK
| | - Barry Gusterson
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Carlo Maley
- Biodesign Center for Personalized Diagnostics and School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Yinyin Yuan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
- Division of Molecular Pathology, Institute of Cancer Research, London, UK.
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