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Rao S, Verrill C, Cerundolo L, Alham NK, Kaya Z, O'Hanlon M, Hayes A, Lambert A, James M, Tullis IDC, Niederer J, Lovell S, Omer A, Lopez F, Leslie T, Buffa F, Bryant RJ, Lamb AD, Vojnovic B, Wedge DC, Mills IG, Woodcock DJ, Tomlinson I, Hamdy FC. Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features. Genome Med 2024; 16:35. [PMID: 38374116 PMCID: PMC10877771 DOI: 10.1186/s13073-024-01302-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification. METHODS We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants. RESULTS In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees. CONCLUSIONS Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.
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Affiliation(s)
- Srinivasa Rao
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
- Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK.
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lucia Cerundolo
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Zeynep Kaya
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Miriam O'Hanlon
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alicia Hayes
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Adam Lambert
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Martha James
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Jane Niederer
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Shelagh Lovell
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Altan Omer
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Francisco Lopez
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Tom Leslie
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | | | - Richard J Bryant
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alastair D Lamb
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Boris Vojnovic
- Department of Oncology, University of Oxford, Oxford, UK
| | - David C Wedge
- Manchester Cancer Research Centre, University of Manchester, Manchester, UK
| | - Ian G Mills
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Dan J Woodcock
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ian Tomlinson
- Department of Oncology, University of Oxford, Oxford, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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Haghighat M, Browning L, Sirinukunwattana K, Malacrino S, Khalid Alham N, Colling R, Cui Y, Rakha E, Hamdy FC, Verrill C, Rittscher J. Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci Rep 2022; 12:5002. [PMID: 35322056 PMCID: PMC8943120 DOI: 10.1038/s41598-022-08351-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/03/2022] [Indexed: 02/07/2023] Open
Abstract
Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at \documentclass[12pt]{minimal}
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\begin{document}$$5\times$$\end{document}5× magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall ‘usability’ (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86–90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.
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Affiliation(s)
- Maryam Haghighat
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,CSIRO, Brisbane, QLD, Australia.
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Korsuk Sirinukunwattana
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Nasullah Khalid Alham
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Ying Cui
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Emad Rakha
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Freddie C Hamdy
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jens Rittscher
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
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3
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Chatrian A, Colling RT, Browning L, Alham NK, Sirinukunwattana K, Malacrino S, Haghighat M, Aberdeen A, Monks A, Moxley-Wyles B, Rakha E, Snead DRJ, Rittscher J, Verrill C. Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies. Mod Pathol 2021; 34:1780-1794. [PMID: 34017063 PMCID: PMC8376647 DOI: 10.1038/s41379-021-00826-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/29/2022]
Abstract
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.
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Affiliation(s)
- Andrea Chatrian
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK.
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.
| | - Richard T Colling
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK
| | - Nasullah Khalid Alham
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Korsuk Sirinukunwattana
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Stefano Malacrino
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Maryam Haghighat
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
| | - Alan Aberdeen
- Ground Truth Labs, 9400 Garsington Road, Oxford Business Park, Oxford, UK
| | - Amelia Monks
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK
| | - Benjamin Moxley-Wyles
- Department of Cellular Pathology, Buckinghamshire Healthcare NHS Trust, Amersham, UK
| | - Emad Rakha
- School of Medicine, University of Nottingham, Nottingham, Nottinghamshire, UK
| | - David R J Snead
- Department of Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, West Midlands, UK
| | - Jens Rittscher
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building Oxford, Oxford, UK.
- Oxford Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK.
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK.
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford, UK.
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Ghosh A, Sirinukunwattana K, Khalid Alham N, Browning L, Colling R, Protheroe A, Protheroe E, Jones S, Aberdeen A, Rittscher J, Verrill C. The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer. Cancers (Basel) 2021; 13:cancers13061325. [PMID: 33809521 PMCID: PMC7998792 DOI: 10.3390/cancers13061325] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/08/2021] [Accepted: 03/12/2021] [Indexed: 11/16/2022] Open
Abstract
Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.
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Affiliation(s)
- Abhisek Ghosh
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (L.B.); (R.C.); (C.V.)
- Nuffield Department of Clinical and Laboratory Sciences, Oxford University, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Correspondence:
| | - Korsuk Sirinukunwattana
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK; (K.S.); (N.K.A.); (J.R.)
- Oxford NIHR Biomedical Research Centre, Oxford University, Oxford OX3 9DU, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
- Ground Truth Labs, Oxford OX4 2HN, UK;
| | - Nasullah Khalid Alham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK; (K.S.); (N.K.A.); (J.R.)
- Oxford NIHR Biomedical Research Centre, Oxford University, Oxford OX3 9DU, UK
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (L.B.); (R.C.); (C.V.)
- Oxford NIHR Biomedical Research Centre, Oxford University, Oxford OX3 9DU, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (L.B.); (R.C.); (C.V.)
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK;
| | - Andrew Protheroe
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (A.P.); (E.P.)
| | - Emily Protheroe
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (A.P.); (E.P.)
| | - Stephanie Jones
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK;
| | | | - Jens Rittscher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK; (K.S.); (N.K.A.); (J.R.)
- Oxford NIHR Biomedical Research Centre, Oxford University, Oxford OX3 9DU, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK; (L.B.); (R.C.); (C.V.)
- Oxford NIHR Biomedical Research Centre, Oxford University, Oxford OX3 9DU, UK
- Nuffield Department of Surgical Sciences, Oxford University, Oxford OX3 9DU, UK;
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Fan S, Kroeger B, Marie PP, Bridges EM, Mason JD, McCormick K, Zois CE, Sheldon H, Khalid Alham N, Johnson E, Ellis M, Stefana MI, Mendes CC, Wainwright SM, Cunningham C, Hamdy FC, Morris JF, Harris AL, Wilson C, Goberdhan DCI. Glutamine deprivation alters the origin and function of cancer cell exosomes. EMBO J 2020; 39:e103009. [PMID: 32720716 PMCID: PMC7429491 DOI: 10.15252/embj.2019103009] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 02/09/2020] [Accepted: 02/10/2020] [Indexed: 12/18/2022] Open
Abstract
Exosomes are secreted extracellular vesicles carrying diverse molecular cargos, which can modulate recipient cell behaviour. They are thought to derive from intraluminal vesicles formed in late endosomal multivesicular bodies (MVBs). An alternate exosome formation mechanism, which is conserved from fly to human, is described here, with exosomes carrying unique cargos, including the GTPase Rab11, generated in Rab11-positive recycling endosomal MVBs. Release of Rab11-positive exosomes from cancer cells is increased relative to late endosomal exosomes by reducing growth regulatory Akt/mechanistic Target of Rapamycin Complex 1 (mTORC1) signalling or depleting the key metabolic substrate glutamine, which diverts membrane flux through recycling endosomes. Vesicles produced under these conditions promote tumour cell proliferation and turnover and modulate blood vessel networks in xenograft mouse models in vivo. Their growth-promoting activity, which is also observed in vitro, is Rab11a-dependent, involves ERK-MAPK-signalling and is inhibited by antibodies against amphiregulin, an EGFR ligand concentrated on these vesicles. Therefore, glutamine depletion or mTORC1 inhibition stimulates release from Rab11a compartments of exosomes with pro-tumorigenic functions, which we propose promote stress-induced tumour adaptation.
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Affiliation(s)
- Shih‐Jung Fan
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Benjamin Kroeger
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Pauline P Marie
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Esther M Bridges
- Department of OncologyWeatherall Institute of Molecular MedicineUniversity of OxfordOxfordUK
| | - John D Mason
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Kristie McCormick
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Christos E Zois
- Department of OncologyWeatherall Institute of Molecular MedicineUniversity of OxfordOxfordUK
| | - Helen Sheldon
- Department of OncologyWeatherall Institute of Molecular MedicineUniversity of OxfordOxfordUK
| | - Nasullah Khalid Alham
- Institute of Biomedical EngineeringDepartment of Engineering ScienceUniversity of OxfordOxfordUK
- Nuffield Department of Surgical SciencesOxford NIHR Biomedical Research Centre (BRC)John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Errin Johnson
- Sir William Dunn School of PathologyUniversity of OxfordOxfordUK
| | - Matthew Ellis
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | | | - Cláudia C Mendes
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | | | - Christopher Cunningham
- Nuffield Department of Surgical SciencesJohn Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Freddie C Hamdy
- Nuffield Department of Surgical SciencesJohn Radcliffe HospitalUniversity of OxfordOxfordUK
| | - John F Morris
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
| | - Adrian L Harris
- Department of OncologyWeatherall Institute of Molecular MedicineUniversity of OxfordOxfordUK
| | - Clive Wilson
- Department of Physiology, Anatomy and GeneticsUniversity of OxfordOxfordUK
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