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1,966 results on '"Ellis, Ian O."'

1. What factors influence cellular pathologists’ confidence in case reporting?

Author

Evans, Harriet, Kimani, Peter K., Hiller, Louise, Tsang, Yee Wah, Sah, Shatrughan, Gopalakrishnan, Kishore, Boyd, Clinton, Loughrey, Maurice B., Kelly, Paul J., Boyle, David P., Clark, David, Ellis, Ian O., Ilyas, Mohammad, Rakha, Emad, Bickers, Adam, Roberts, Ian S. D., Soares, Maria F., Neil, Desley A. H., Dunn, Janet A., Azam, Ayesha, and Snead, David

Published
2024
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3. The presentation, management and outcome of patients with ductal carcinoma in situ (DCIS) with microinvasion (invasion ≤1 mm in size)—results from the UK Sloane Project

Author

Shaaban, Abeer M., Hilton, Bridget, Clements, Karen, Dodwell, David, Sharma, Nisha, Kirwan, Cliona, Sawyer, Elinor, Maxwell, Anthony, Wallis, Matthew, Stobart, Hilary, Mylvaganam, Senthurun, Litherland, Janet, Brace-McDonnell, Samantha, Dulson-Cox, Joanne, Kearins, Olive, Provenzano, Elena, Ellis, Ian O., Pinder, Sarah E., and Thompson, Alastair M.

Published
2022
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4. Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification

Author

Xu, Bolei, Liu, Jingxin, Hou, Xianxu, Liu, Bozhi, Garibaldi, Jon, Ellis, Ian O., Green, Andy, Shen, Linlin, and Qiu, Guoping

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

One issue with computer based histopathology image analysis is that the size of the raw image is usually very large. Taking the raw image as input to the deep learning model would be computationally expensive while resizing the raw image to low resolution would incur information loss. In this paper, we present a novel deep hybrid attention approach to breast cancer classification. It first adaptively selects a sequence of coarse regions from the raw image by a hard visual attention algorithm, and then for each such region it is able to investigate the abnormal parts based on a soft-attention mechanism. A recurrent network is then built to make decisions to classify the image region and also to predict the location of the image region to be investigated at the next time step. As the region selection process is non-differentiable, we optimize the whole network through a reinforcement approach to learn an optimal policy to classify the regions. Based on this novel Look, Investigate and Classify approach, we only need to process a fraction of the pixels in the raw image resulting in significant saving in computational resources without sacrificing performances. Our approach is evaluated on a public breast cancer histopathology database, where it demonstrates superior performance to the state-of-the-art deep learning approaches, achieving around 96\% classification accuracy while only 15% of raw pixels are used., Comment: Accepted to ISBI'19

Published
2019

8. An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray

Author

Liu, Jingxin, Xu, Bolei, Zheng, Chi, Gong, Yuanhao, Garibaldi, Jon, Soria, Daniele, Green, Andew, Ellis, Ian O., Zou, Wenbin, and Qiu, Guoping

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

Published
2018
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9. Epigenome erosion and SOX10 drive neural crest phenotypic mimicry in triple-negative breast cancer

Author

Saunus, Jodi M., De Luca, Xavier M., Northwood, Korinne, Raghavendra, Ashwini, Hasson, Alexander, McCart Reed, Amy E., Lim, Malcolm, Lal, Samir, Vargas, A. Cristina, Kutasovic, Jamie R., Dalley, Andrew J., Miranda, Mariska, Kalaw, Emarene, Kalita-de Croft, Priyakshi, Gresshoff, Irma, Al-Ejeh, Fares, Gee, Julia M. W., Ormandy, Chris, Khanna, Kum Kum, Beesley, Jonathan, Chenevix-Trench, Georgia, Green, Andrew R., Rakha, Emad A., Ellis, Ian O., Nicolau, Jr, Dan V., Simpson, Peter T., and Lakhani, Sunil R.

Published
2022
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11. Identification of Glandular (Acinar)/Tubule Formation in Invasive Carcinoma of the Breast: A Study to Determine Concordance Using the World Health Organization Definition

Author

Lo, Yungtai, primary, Lester, Susan C., additional, Ellis, Ian O., additional, Lanjewar, Sonali, additional, Laurini, Javier, additional, Patel, Ami, additional, Bhattarai, Ava, additional, Ustun, Berrin, additional, Harmon, Bryan, additional, Kleer, Celina G., additional, Ross, Dara, additional, Amin, Ali, additional, Wang, Yihong, additional, Bradley, Robert, additional, Turashvili, Gulisa, additional, Zeng, Jennifer, additional, Baum, Jordan, additional, Singh, Kamaljeet, additional, Hakima, Laleh, additional, Harigopal, Malini, additional, Komforti, Miglena, additional, Shin, Sandra J., additional, Abbott, Sara E., additional, Jaffer, Shabnam, additional, Badve, Sunil Shankar, additional, Khoury, Thaer, additional, D’Alfonso, Timothy M., additional, Ginter, Paula S., additional, Collins, Victoria, additional, Towne, William, additional, Gan, Yujun, additional, Nassar, Aziza, additional, Sahin, Aysegul A., additional, Flieder, Andrea, additional, Aldrees, Rana, additional, Ngo, Marie-Helene, additional, Edema, Ukuemi, additional, Sapna, FNU, additional, Schnitt, Stuart J., additional, and Fineberg, Susan A., additional

Published
2024
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12. Digital pathology for reporting histopathology samples, including cancer screening samples – definitive evidence from a multisite study

Author

Azam, Ayesha S, primary, Tsang, Yee‐Wah, additional, Thirlwall, Jenny, additional, Kimani, Peter K, additional, Sah, Shatrughan, additional, Gopalakrishnan, Kishore, additional, Boyd, Clinton, additional, Loughrey, Maurice B, additional, Kelly, Paul J, additional, Boyle, David P, additional, Salto‐Tellez, Manuel, additional, Clark, David, additional, Ellis, Ian O, additional, Ilyas, Mohammad, additional, Rakha, Emad, additional, Bickers, Adam, additional, Roberts, Ian S D, additional, Soares, Maria F, additional, Neil, Desley A H, additional, Takyi, Abi, additional, Raveendran, Sinthuri, additional, Hero, Emily, additional, Evans, Harriet, additional, Osman, Rania, additional, Fatima, Khunsha, additional, Hughes, Rhian W, additional, McIntosh, Stuart A, additional, Moran, Gordon W, additional, Ortiz‐Fernandez‐Sordo, Jacobo, additional, Rajpoot, Nasir M, additional, Storey, Ben, additional, Ahmed, Imtiaz, additional, Dunn, Janet A, additional, Hiller, Louise, additional, and Snead, David R J, additional

Published
2024
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14. Retrospective observational study of HER2 immunohistochemistry in borderline breast cancer patients undergoing neoadjuvant therapy, with an emphasis on Group 2 (HER2/CEP17 ratio ≥2.0, HER2 copy number <4.0 signals/cell) cases

Author

Rakha, Emad A., Miligy, Islam M., Quinn, Cecily M., Provenzano, Elena, Shaaban, Abeer M., Marchiò, Caterina, Toss, Michael S., Gallagy, Grace, Murray, Ciara, Walshe, Janice, Katayama, Ayaka, Eldib, Karim, Badr, Nahla, Tanchel, Bruce, Millican-Slater, Rebecca, Purdie, Colin, Purnell, Dave, Pinder, Sarah E., Ellis, Ian O., and Lee, Andrew H. S.

Published
2021
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16. The androgen receptor is a tumor suppressor in estrogen receptor–positive breast cancer

Author

Hickey, Theresa E., Selth, Luke A., Chia, Kee Ming, Laven-Law, Geraldine, Milioli, Heloisa H., Roden, Daniel, Jindal, Shalini, Hui, Mun, Finlay-Schultz, Jessica, Ebrahimie, Esmaeil, Birrell, Stephen N., Stelloo, Suzan, Iggo, Richard, Alexandrou, Sarah, Caldon, C. Elizabeth, Abdel-Fatah, Tarek M., Ellis, Ian O., Zwart, Wilbert, Palmieri, Carlo, Sartorius, Carol A., Swarbrick, Alex, Lim, Elgene, Carroll, Jason S., and Tilley, Wayne D.

Published
2021
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26. Genomic and protein expression analysis reveals flap endonuclease 1 (FEN1) as a key biomarker in breast and ovarian cancer

Author

Abdel-Fatah, Tarek MA, Russell, Roslin, Albarakati, Nada, Maloney, David J, Dorjsuren, Dorjbal, Rueda, Oscar M, Moseley, Paul, Mohan, Vivek, Sun, Hongmao, Abbotts, Rachel, Mukherjee, Abhik, Agarwal, Devika, Illuzzi, Jennifer L, Jadhav, Ajit, Simeonov, Anton, Ball, Graham, Chan, Stephen, Caldas, Carlos, Ellis, Ian O, Wilson, David M, and Madhusudan, Srinivasan

Subjects
Rare Diseases, Genetics, Ovarian Cancer, Cancer, Breast Cancer, Aged, Biomarkers, Tumor, Breast, Breast Neoplasms, Carcinoma, Ovarian Epithelial, Female, Flap Endonucleases, Gene Expression Regulation, Neoplastic, Humans, Neoplasms, Glandular and Epithelial, Ovarian Neoplasms, Ovary, Prognosis, Breast cancer, Drug target, FEN1, Predictive factor, Prognostic factor, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

FEN1 has key roles in Okazaki fragment maturation during replication, long patch base excision repair, rescue of stalled replication forks, maintenance of telomere stability and apoptosis. FEN1 may be dysregulated in breast and ovarian cancers and have clinicopathological significance in patients. We comprehensively investigated FEN1 mRNA expression in multiple cohorts of breast cancer [training set (128), test set (249), external validation (1952)]. FEN1 protein expression was evaluated in 568 oestrogen receptor (ER) negative breast cancers, 894 ER positive breast cancers and 156 ovarian epithelial cancers. FEN1 mRNA overexpression was highly significantly associated with high grade (p = 4.89 × 10(-57)), high mitotic index (p = 5.25 × 10(-28)), pleomorphism (p = 6.31 × 10(-19)), ER negative (p = 9.02 × 10(-35)), PR negative (p = 9.24 × 10(-24)), triple negative phenotype (p = 6.67 × 10(-21)), PAM50.Her2 (p = 5.19 × 10(-13)), PAM50. Basal (p = 2.7 × 10(-41)), PAM50.LumB (p = 1.56 × 10(-26)), integrative molecular cluster 1 (intClust.1) (p = 7.47 × 10(-12)), intClust.5 (p = 4.05 × 10(-12)) and intClust. 10 (p = 7.59 × 10(-38)) breast cancers. FEN1 mRNA overexpression is associated with poor breast cancer specific survival in univariate (p = 4.4 × 10(-16)) and multivariate analysis (p = 9.19 × 10(-7)). At the protein level, in ER positive tumours, FEN1 overexpression remains significantly linked to high grade, high mitotic index and pleomorphism (ps < 0.01). In ER negative tumours, high FEN1 is significantly associated with pleomorphism, tumour type, lymphovascular invasion, triple negative phenotype, EGFR and HER2 expression (ps < 0.05). In ER positive as well as in ER negative tumours, FEN1 protein overexpression is associated with poor survival in univariate and multivariate analysis (ps < 0.01). In ovarian epithelial cancers, similarly, FEN1 overexpression is associated with high grade, high stage and poor survival (ps < 0.05). We conclude that FEN1 is a promising biomarker in breast and ovarian epithelial cancer.

Published
2014

31. The amino acid transporter SLC7A11 expression in breast cancer.

Author

Nath, Preyanka, Alfarsi, Lutfi H., El-Ansari, Rokaya, Masisi, Brendah K., Erkan, Busra, Fakroun, Ali, Ellis, Ian O., Rakha, Emad A., and Green, Andrew R.

Subjects
GLUTAMATE transporters, GENE expression, BRCA genes, AMINO acids, METABOLIC reprogramming
Abstract

Breast cancer (BC), characterized by its diverse molecular profiles and clinical outcomes, presents a significant challenge in the development of effective therapeutic strategies. Metabolic reprogramming, a defining characteristic of cancer, has emerged as a promising target for novel therapies. SLC7A11, an amino acid transporter that facilitates cysteine uptake in exchange for glutamate, plays a crucial role in sustaining the altered metabolism of cancer cells. This study delves into the comprehensive analysis of SLC7A11 at the genomic, transcriptomic, and protein levels in extensive BC datasets to elucidate its potential role in different BC subtypes. SLC7A11 gene copy number and mRNA expression were evaluated using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort (n = 1,980) and Breast Cancer Gene Expression Miner (n = 4,712). SLC7A11 protein was assessed using immunohistochemistry in a large BC cohort (n = 1,981). Additionally, The Cancer Genome Atlas (TCGA) dataset was used to explore SLC7A11 DNA methylation patterns using MethSurv (n = 782) and association of SLC7A11 mRNA expression with immune infiltrates using TIMER (n = 1,100). High SLC7A11 mRNA and SLC7A11 protein expression were significantly associated with high tumor grade (p = .02), indicating a potential role in cancer progression. Interestingly, SLC7A11 copy number gain was observed in HER2+ tumors (p = .01), suggesting a subtype-specific association. In contrast, SLC7A11 mRNA expression was higher in the basal-like/triple-negative (TN; p < .001) and luminal B tumors (p = .02), highlighting its differential expression across BC subtypes. Notably, high SLC7A11 protein expression was predominantly observed in Estrogen Receptor (ER)-negative and Triple Negative (TN) BC, suggesting a role in these aggressive subtypes. Further analysis revealed that SLC7A11 was positively correlated with other amino acid transporters and enzymes associated with glutamine metabolism, implying a coordinated role in metabolic regulation. Additionally, SLC7A11 gene expression was positively associated with neutrophil and macrophage infiltration, suggesting a potential link between SLC7A11 and tumor immunity. Our findings suggest that SLC7A11 plays a significant role in BC metabolism, demonstrating differential expression across subtypes and associations with poor patient outcomes. Further functional studies are warranted to elucidate the precise mechanisms by which SLC7A11 contributes to BC progression and to explore its potential as a therapeutic target. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Molecular Pathology of Breast Cancer Metastasis

Author

Aleskandarany, Mohammed A., Ellis, Ian O., Rakha, Emad A., Cagle, Philip T., Series editor, Khan, Ashraf, editor, Ellis, Ian O., editor, Hanby, Andrew M., editor, Cosar, Ediz F., editor, Rakha, Emad A., editor, and Kandil, Dina, editor

Published
2015
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40. Molecular Classification of Breast Cancer

Author

Aleskandarany, Mohammed A., Ellis, Ian O., Rakha, Emad A., Cagle, Philip T., Series editor, Khan, Ashraf, editor, Ellis, Ian O., editor, Hanby, Andrew M., editor, Cosar, Ediz F., editor, Rakha, Emad A., editor, and Kandil, Dina, editor

Published
2015
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41. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update

Author

Wolff, Antonio C., Hammond, M. Elizabeth Hale, Allison, Kimberly H., Harvey, Brittany E., Mangu, Pamela B., Bartlett, John M.S., Bilous, Michael, Ellis, Ian O., Fitzgibbons, Patrick, Hanna, Wedad, Jenkins, Robert B., Press, Michael F., Spears, Patricia A., Vance, Gail H., Viale, Giuseppe, McShane, Lisa M., and Dowsett, Mitchell

Subjects
Practice guidelines (Medicine) -- Evaluation, Molecular diagnostic techniques -- Laws, regulations and rules, Breast cancer -- Genetic aspects -- Diagnosis -- Care and treatment, Government regulation, Health
Abstract

* Purpose.--To update key recommendations of the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) human epidermal growth factor receptor 2 (HER2) testing in breast cancer guideline. Methods.--Based on the signals approach, an Expert Panel reviewed published literature and research survey results on the observed frequency of less common in situ hybridization (ISH) patterns to update the recommendations. Recommendations.--Two recommendations addressed via correspondence in 2015 are included. First, immunohistochemistry (IHC) 2+ is defined as invasive breast cancer with weak to moderate complete membrane staining observed in >10% of tumor cells. Second, if the initial HER2 test result in a core needle biopsy specimen of a primary breast cancer is negative, a new HER2 test may (not 'must') be ordered on the excision specimen based on specific clinical criteria. The HER2 testing algorithm for breast cancer is updated to address the recommended workup for less common clinical scenarios (approximately 5% of cases) observed when using a dual-probe ISH assay. These scenarios are described as ISH group 2 (HER2/chromosome enumeration probe 17 [CEP17] ratio [greater than or equal to] 2.0; average HER2 copy number (Arch Pathol Lab Med. 2018;142:1364-1382; doi: 10.5858/arpa.2018-0902-SA), First released in 2007 and updated in 2013, the recommendations by the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) human epidermal growth factor receptor 2 (HER2) testing [...]

Published
2018
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43. Utility of ankyrin 3 as a prognostic marker in androgen-receptor-positive breast cancer

Author

Kurozumi, Sasagu, Joseph, Chitra, Raafat, Sara, Sonbul, Sultan, Kariri, Yousif, Alsaeed, Sami, Pigera, Marian, Alsaleem, Mansour, Nolan, Christopher C., Johnston, Simon J., Aleskandarany, Mohammed A., Ogden, Angela, Fujii, Takaaki, Shirabe, Ken, Martin, Stewart G., Alshankyty, Ibraheem, Mongan, Nigel P., Ellis, Ian O., Green, Andrew R., and Rakha, Emad A.

Published
2019
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44. A key genomic subtype associated with lymphovascular invasion in invasive breast cancer

Author

Kurozumi, Sasagu, Joseph, Chitra, Sonbul, Sultan, Alsaeed, Sami, Kariri, Yousif, Aljohani, Abrar, Raafat, Sara, Alsaleem, Mansour, Ogden, Angela, Johnston, Simon J, Aleskandarany, Mohammed A, Fujii, Takaaki, Shirabe, Ken, Caldas, Carlos, Ashankyty, Ibraheem, Dalton, Leslie, Ellis, Ian O, Desmedt, Christine, Green, Andrew R, Mongan, Nigel P, and Rakha, Emad A

Published
2019
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47. SHON expression predicts response and relapse risk of breast cancer patients after anthracycline-based combination chemotherapy or tamoxifen treatment

Author

Abdel-Fatah, Tarek M. A., Broom, Reuben J., Lu, Jun, Moseley, Paul M., Huang, Baiqu, Li, Lili, Liu, Suling, Chen, Longxin, Ma, Runlin Z., Cao, Wenming, Wang, Xiaojia, Li, Yan, Perry, Jo K., Aleskandarany, Mohammed, Nolan, Christopher C., Rakha, Emad A., Lobie, Peter E., Chan, Stephen Y. T., Ellis, Ian O., Hwang, Le-Ann, Lane, David P., Green, Andrew R., and Liu, Dong-Xu

Published
2019
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49. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

Author

Rueda, Oscar M., Sammut, Stephen-John, Seoane, Jose A., Chin, Suet-Feung, Caswell-Jin, Jennifer L., Callari, Maurizio, Batra, Rajbir, Pereira, Bernard, Bruna, Alejandra, Ali, H. Raza, Provenzano, Elena, Liu, Bin, Parisien, Michelle, Gillett, Cheryl, McKinney, Steven, Green, Andrew R., Murphy, Leigh, Purushotham, Arnie, Ellis, Ian O., Pharoah, Paul D., Rueda, Cristina, Aparicio, Samuel, Caldas, Carlos, and Curtis, Christina

Published
2019
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