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1. Association of pre-existing cardiovascular disease with administration of fluoropyrimidine chemotherapy in patients with gastrointestinal malignancies

Author

Chris Gale, Li Wei, Adam Timmis, Michael D Peake, Lucy Elliss-Brookes, Peter Ludman, Alison Fielding, David Adlam, Francis Murgatroyd, Clive Weston, Theresa McDonagh, Lizz Paley, Alistair Ring, Charlotte Manisty, Mike Hawkins, Raoul Reulen, Abbas Khushnood, Sally Vernon, John Deanfield, Nadeem Fazal, Jem Rashbass, Andrew Goodwin, Chengsheng Ju, Sarah Slater, Brian Shand, Mark De Belder, Paul Lambert, Catherine A Welch, Andrew Harrison, Michael Sweeting, Jennifer Lai, Mick Peake, Paul C Lambert, Mark de Belder, Paul Charlton, Alexander Lyon, Sarah Darby, Freya Tyrer, Mark Rutherford, Aderonke Temilade Abiodun, Pinkie Chambers, Kai Keen Shiu, Sally Jeans, Andy Deutsch, James Chal, Akosua Donkor, and Anil Gunesh

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Objective Fluoropyrimidine chemotherapy is a first-line treatment for many gastrointestinal (GI) cancers, however, cardiotoxicity concerns may limit administration in patients with pre-existing cardiovascular disease (CVD). This study investigated the association of pre-existing CVD with use of fluoropyrimidine chemotherapy in tumour-eligible GI cancer patients.Methods and analysis National cancer registry data from the Virtual Cardio-Oncology Research Initiative from England between 2014 and 2018 was used to identify GI cancer patients eligible to receive fluoropyrimidine chemotherapy. Linkage to Hospital Episode Statistics and CVD registry data were used to ascertain prior CVD and outcomes. Primary outcome was first administration of fluoropyrimidine chemotherapy following cancer diagnosis. Cox proportional hazard models determined HR and 95% CIs for the association between initiation of fluoropyrimidine treatment and prior CVD.Results 112 726 eligible patients were identified (median age 71 years (IQR 62–80), 39.7% female). 33 026 (29.3%) had pre-existing CVD. 73 392 (65.1%) patients had a diagnosis of colorectal, 23 208 (20.6%) oesophageal, 14 788 (13.1%) gastric and 1338 (1.2%) small bowel cancer. Individuals with pre-existing CVD had a 27% reduced rate of receiving fluoropyrimidine chemotherapy (HR, 0.73; 95% CI 0.70 to 0.75) on multivariable analysis. Significantly reduced rates of fluoropyrimidine administration were found across all subtypes of pre-existing CVD.Conclusions GI cancer patients with all types of pre-existing CVD are less likely to receive fluoropyrimidine chemotherapy despite eligibility. This suggests widespread caution regarding administration of fluoropyrimidines across this population; further research is needed to assess whether such conservatism is justified.

Published
2024
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2. Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Author

Qian, Zhaozhi, Alaa, Ahmed M., Bellot, Alexis, Rashbass, Jem, and van der Schaar, Mihaela

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial for understanding disease pathology and predicting prognoses. To this end, we develop deep diffusion processes (DDP) to model "dynamic comorbidity networks", i.e., the temporal relationships between comorbid disease onsets expressed through a dynamic graph. A DDP comprises events modelled as a multi-dimensional point process, with an intensity function parameterized by the edges of a dynamic weighted graph. The graph structure is modulated by a neural network that maps patient history to edge weights, enabling rich temporal representations for disease trajectories. The DDP parameters decouple into clinically meaningful components, which enables serving the dual purpose of accurate risk prediction and intelligible representation of disease pathology. We illustrate these features in experiments using cancer registry data.

Published
2020

3. BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2.

Author

Cline, Melissa S, Liao, Rachel G, Parsons, Michael T, Paten, Benedict, Alquaddoomi, Faisal, Antoniou, Antonis, Baxter, Samantha, Brody, Larry, Cook-Deegan, Robert, Coffin, Amy, Couch, Fergus J, Craft, Brian, Currie, Robert, Dlott, Chloe C, Dolman, Lena, den Dunnen, Johan T, Dyke, Stephanie OM, Domchek, Susan M, Easton, Douglas, Fischmann, Zachary, Foulkes, William D, Garber, Judy, Goldgar, David, Goldman, Mary J, Goodhand, Peter, Harrison, Steven, Haussler, David, Kato, Kazuto, Knoppers, Bartha, Markello, Charles, Nussbaum, Robert, Offit, Kenneth, Plon, Sharon E, Rashbass, Jem, Rehm, Heidi L, Robson, Mark, Rubinstein, Wendy S, Stoppa-Lyonnet, Dominique, Tavtigian, Sean, Thorogood, Adrian, Zhang, Can, Zimmermann, Marc, BRCA Challenge Authors, Burn, John, Chanock, Stephen, Rätsch, Gunnar, and Spurdle, Amanda B

Subjects
BRCA Challenge Authors, Humans, Breast Neoplasms, Ovarian Neoplasms, Genetic Predisposition to Disease, Risk Factors, Information Dissemination, Gene Frequency, Phenotype, Penetrance, Mutation, Alleles, Genes, BRCA1, Genes, BRCA2, Databases, Genetic, Female, Male, Genetic Variation, Breast Cancer, Cancer, Genetics, Human Genome, 2.6 Resources and infrastructure (aetiology), Developmental Biology
Abstract

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project's outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases-Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)-as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.

Published
2018

5. Clear Improvement in Real-World Chronic Myeloid Leukemia Survival: A Comparison With Randomized Controlled Trials

Author

Claudia Vener, Silvia Rossi, Pamela Minicozzi, Rafael Marcos-Gragera, Hélène A. Poirel, Marc Maynadié, Xavier Troussard, Gabriella Pravettoni, Roberta De Angelis, Milena Sant, the EUROCARE-6 Working Group, M. Hackl, E. Van Eycken, Z. Valerianova, M. Sekerija, P. Pavlou, L. Dušek, H. Storm, M. Mägi, K. Innos, N. Malila, J. Pitkäniemi, M. Velten, X. Troussard, A.M. Bouvier, V. Jooste, A.V. Guizard, G. Launoy, S. Dabakuyo Yonli, M. Maynadié, A.S. Woronoff, J.B. Nousbaum, G. Coureau, A. Monnereau, I. Baldi, K. Hammas, B. Tretarre, M. Colonna, S. Plouvier, T. D’Almeida, F. Molinié, A. Cowppli-Bony, S. Bara, C. Schvartz, G. Defossez, B. Lapôtre-Ledoux, P. Grosclaude, S. Luttmann, R. Stabenow, A. Nennecke, J. Kieschke, S. Zeissig, B. Holleczek, A. Katalinic, H. Birgisson, D. Murray, P.M. Walsh, G. Mazzoleni, F. Vittadello, F. Cuccaro, R. Galasso, G. Sampietro, S. Rosso, M. Magoni, M. Ferrante, A. Sutera Sardo, M.L. Gambino, P. Ballotari, E. Giacomazzi, S. Ferretti, A. Caldarella, G. Manneschi, G. Gatta, M. Sant, P. Baili, F. Berrino, L. Botta, A. Trama, R. Lillini, A. Bernasconi, S. Bonfarnuzzo, C. Vener, F. Didonè, P. Lasalvia, G. Del Monego, M.C. Magri, L. Buratti, D. Serraino, L. Dal Maso, R. Capocaccia, R. De Angelis, E. Demuru, C. Di Benedetto, S. Rossi, M. Santaquilani, S. Venanzi, R.A. Filiberti, S. Iacovacci, V. Gennaro, A.G. Russo, G. Spagnoli, L. Cavalieri d’Oro, M. Fusco, M.F. Vitale, M. Usala, F. Vitale, M. Michiara, G. Chiranda, G. Cascone, E. Spata, L. Mangone, F. Falcini, R. Cavallo, D. Piras, A. Madeddu, F. Bella, A.C. Fanetti, S. Minerba, G. Candela, T. Scuderi, R.V. Rizzello, F. Stracci, G. Tagliabue, M. Rugge, A. Brustolin, S. Pildava, G. Smailyte, M. Azzopardi, T.B. Johannesen, J. Didkowska, U. Wojciechowska, M. Bielska-Lasota, A. Pais, J.L. Pontes, A. Miranda, C. Safaei Diba, V. Zadnik, T. Zagar, C. Sánchez-Contador Escudero, P. Franch Sureda, A. Lopez de Munain, M. De-La-Cruz, M.D. Rojas, A. Aleman, A. Vizcaino, R. Marcos-Gragera, M.J. Sanchez, M.D. Chirlaque, M. Guevara Eslava, E. Ardanaz, J. Galceran, M. Carulla, Y. Bergeron, C. Bouchardy, S. Mohsen Mousavi, A. Bordoni, O. Visser, J. Rashbass, A. Gavin, D. Morrison, and D. W. Huws

Subjects
cancer registries, chronic myeloid leukemia (CML), randomized controlled trials (RCTs), real-world data, survival, Europe, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Tyrosine kinase inhibitors (TKIs) have been improving the prognosis of patients with chronic myeloid leukemia (CML), but there are still large differences in survival among European countries. This raises questions on the added value of results from population-based studies, which use real-world data, compared to results of randomized controlled trials (RCTs) involving patients with CML. There are also questions about the extent of the findings on RCTs effectiveness for patients in the general population. We compare survival data extracted from our previous systematic review and meta-analysis of CML RCTs with the latest updated population-based survival data of EUROCARE-6, the widest collaborative study on cancer survival in Europe. The EUROCARE-6 CML survival estimated in patients (15–64 years) diagnosed in 2000–2006 vs. 2007–2013 revealed that the prognostic improvement highlighted by RCTs was confirmed in real-world settings, too. The study shows, evaluating for the first time all European regions, that the optimal outcome figures obtained in controlled settings for CML are also achievable (and indeed achieved) in real-world settings with prompt introduction of TKIs in daily clinical practice. However, some differences still persist, particularly in Eastern European countries, where overall survival values are lower than elsewhere, probably due to a delayed introduction of TKIs. Our results suggest an insufficient adoption of adequate protocols in daily clinical practice in those countries where CML survival values remain lower in real life than the values obtained in RCTs. New high-resolution population-based studies may help to identify failures in the clinical pathways followed there.

Published
2022
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6. Retrospective cohort study of admission timing and mortality following COVID-19 infection in England

Author

Jonathan Benger, Ahmed Alaa, Zhaozhi Qian, Jem Rashbass, and Mihaela van der Schaar

Subjects
Medicine
Abstract

Objectives We investigated whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission.Design Retrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis.Setting Acute hospital trusts in England that submit data to CHESS routinely.Participants Of 14 150 patients included in CHESS until 13 May 2020, 401 lacked a confirmed diagnosis of COVID-19 and 7666 lacked a recorded date of symptom onset. This left 6083 individuals, of whom 15 were excluded because the time between symptom onset and hospital admission exceeded 3 months. The study cohort therefore comprised 6068 unique individuals.Main outcome measures All-cause mortality during the study period.Results Timing of hospital admission was an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Each additional day between symptom onset and hospital admission was associated with a 1% increase in mortality risk (HR 1.01; p

Published
2020
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7. Crumbs2 mediates ventricular layer remodelling to form the spinal cord central canal.

Author

Christine M Tait, Kavitha Chinnaiya, Elizabeth Manning, Mariyam Murtaza, John-Paul Ashton, Nicholas Furley, Chris J Hill, C Henrique Alves, Jan Wijnholds, Kai S Erdmann, Andrew Furley, Penny Rashbass, Raman M Das, Kate G Storey, and Marysia Placzek

Subjects
Biology (General), QH301-705.5
Abstract

In the spinal cord, the central canal forms through a poorly understood process termed dorsal collapse that involves attrition and remodelling of pseudostratified ventricular layer (VL) cells. Here, we use mouse and chick models to show that dorsal ventricular layer (dVL) cells adjacent to dorsal midline Nestin(+) radial glia (dmNes+RG) down-regulate apical polarity proteins, including Crumbs2 (CRB2) and delaminate in a stepwise manner; live imaging shows that as one cell delaminates, the next cell ratchets up, the dmNes+RG endfoot ratchets down, and the process repeats. We show that dmNes+RG secrete a factor that promotes loss of cell polarity and delamination. This activity is mimicked by a secreted variant of Crumbs2 (CRB2S) which is specifically expressed by dmNes+RG. In cultured MDCK cells, CRB2S associates with apical membranes and decreases cell cohesion. Analysis of Crb2F/F/Nestin-Cre+/- mice, and targeted reduction of Crb2/CRB2S in slice cultures reveal essential roles for transmembrane CRB2 (CRB2TM) and CRB2S on VL cells and dmNes+RG, respectively. We propose a model in which a CRB2S-CRB2TM interaction promotes the progressive attrition of the dVL without loss of overall VL integrity. This novel mechanism may operate more widely to promote orderly progenitor delamination.

Published
2020
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8. Data resource description: National Cancer Registration Dataset in England

Author

Katherine Henson, Lucy Elliss-Brookes, Victoria Coupland, Elsita Payne, Sally Vernon, Brian Rous, and Jem Rashbass

Subjects
Demography. Population. Vital events, HB848-3697
Abstract

Background The National Cancer Registration and Analysis Service (NCRAS), in Public Health England (PHE), collects and quality assures clinical information on all cases of cancer that occur in people living in England. The data collected supports the delivery of effective NHS cancer services. Aim NCRAS aims to curate a clinically rich data resource, which this abstract describes. Approach The data collected by NCRAS comes from several sources including Multi-Disciplinary Team (MDT) meetings and pathology reports. Data is reviewed by Cancer Registration Officers with the assistance of automated tools for data linkage and de-duplication of identical data sources. The data collected includes: patient identifiers; patient characteristics (e.g. sex, ethnicity); cancer specific fields (e.g. diagnosis date, cancer site, morphology, laterality, stage at diagnosis); cancer treatment fields; death information (e.g. date and cause of death); health care system information (e.g. hospital of diagnosis); and geography at diagnosis (based on patient’s postcode of residence). Results Data is submitted by 162 healthcare providers, covering over 1,700 MDTs. Population-based official statistics are available for cancers diagnosed since 1971. The data is routinely used to inform and allow evidence-based decisions about NHS service provision and patient care and to longitudinally track cancer incidence rates and survival statistics. In recent years, completeness of specific items such as stage has greatly improved. However, non-death outcomes, such as quality of life assessments and recurrence, are currently difficult to identify. Conclusion Detailed clinical information on cancer across the patient pathway enables the measurement of the diagnosis, treatment and survival outcome of all patients diagnosed with cancer. This population-based national data resource is critical to support service provision, clinical audit, commissioning, planning of services, public health and epidemiological research; all of which contribute to improved outcomes for people diagnosed with cancer.

Published
2019
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9. Data resource description: Systemic Anti-Cancer Therapy (SACT) Dataset

Author

Chloe Bright, Sarah Lawton, Stephen Benson, Martine Bomb, David Dodwell, Katherine Henson, Sean McPhail, Louise Miller, Jem Rashbass, Alice Turnball, and Rebecca Smittenaar

Subjects
Demography. Population. Vital events, HB848-3697
Abstract

Background The Systemic Anti-Cancer Therapy (SACT) dataset is a unique source of national, real-world data reporting chemotherapy activity delivered by NHS treatment providers in England. SACT data are collected with the intent of improving disease-free or progression-free survival. Main Aim The SACT dataset aims to provide a detailed resource on systemic treatments delivered in secondary and tertiary settings in England for patients with cancer. Approach The SACT dataset is collected and curated by the National Cancer Registration and Analysis Service (NCRAS) at Public Health England. The dataset was launched in a phased implementation from April 2012. From April 2014 submission of SACT data became mandatory for all NHS treatment providers. Data are collected on the patient, tumour, regimen, treatment cycle, drug details, and treatment outcomes. The SACT dataset can be linked to other data held by NCRAS, for example the cancer registry1, Hospital Episodes Statistics2, and the national Radiotherapy Dataset to provide a complete picture of the cancer patient pathway. Results The SACT dataset is being used: for the appraisal of drugs funded through the Cancer Drugs Fund; to review the quality of cancer care as part of the National Cancer Audits; and for observational research purposes, including investigating 30-day mortality following chemotherapy3, and sociodemographic variation in the use of chemotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer4. The data are also released to external researchers via the Office for Data Release. Conclusion The SACT dataset is a unique resource in terms of the breadth and depth of information on chemotherapy prescribed to cancer patients. The key strengths are: the national coverage; real-world data; detailed clinical information beyond cancer registration data; and the ability to link to other cancer datasets. References • Henson KE, Elliss-Brookes L, Coupland VH, Payne E, Vernon S, Rous B et al. Data Resource Profile: National Cancer Registration Dataset in England. Int J Epidemiol 2019; doi 10.1093/ije/dyz076. • Herbert A, Wijlaars L, Zylbersztejn A, Cromwell D, Hardelid P. Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol 2017; 46(4): 1093-1093i; e-pub ahead of print 2017/03/25; doi 10.1093/ije/dyx015. • Wallington M, Saxon EB, Bomb M, Smittenaar R, Wickenden M, McPhail S et al. 30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study. The Lancet Oncology 2016; 17(9): 1203-1216; doi 10.1016/s1470-2045(16)30383-7. • Henson KE, Fry A, Lyratzopoulos G, Peake M, Roberts KJ, McPhail S. Sociodemographic variation in the use of chemotherapy and radiotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer: evidence from population-based data in England during 2013-2014. Br J Cancer 2018; doi 10.1038/s41416-018-0028-7.

Published
2019
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10. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

Author

Francisco J. Candido dos Reis, Gordon C. Wishart, Ed M. Dicks, David Greenberg, Jem Rashbass, Marjanka K. Schmidt, Alexandra J. van den Broek, Ian O. Ellis, Andrew Green, Emad Rakha, Tom Maishman, Diana M. Eccles, and Paul D. P. Pharoah

Subjects
Breast cancer, Prognosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Methods Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. Results In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. Conclusions The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Published
2017
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11. BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2.

Author

Melissa S Cline, Rachel G Liao, Michael T Parsons, Benedict Paten, Faisal Alquaddoomi, Antonis Antoniou, Samantha Baxter, Larry Brody, Robert Cook-Deegan, Amy Coffin, Fergus J Couch, Brian Craft, Robert Currie, Chloe C Dlott, Lena Dolman, Johan T den Dunnen, Stephanie O M Dyke, Susan M Domchek, Douglas Easton, Zachary Fischmann, William D Foulkes, Judy Garber, David Goldgar, Mary J Goldman, Peter Goodhand, Steven Harrison, David Haussler, Kazuto Kato, Bartha Knoppers, Charles Markello, Robert Nussbaum, Kenneth Offit, Sharon E Plon, Jem Rashbass, Heidi L Rehm, Mark Robson, Wendy S Rubinstein, Dominique Stoppa-Lyonnet, Sean Tavtigian, Adrian Thorogood, Can Zhang, Marc Zimmermann, BRCA Challenge Authors, John Burn, Stephen Chanock, Gunnar Rätsch, and Amanda B Spurdle

Subjects
Genetics, QH426-470
Abstract

The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project's outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases-Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)-as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.

Published
2018
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12. Crumbs2 mediates ventricular layer remodelling to form the spinal cord central canal

Author

Tait, C.M., Chinnaiya, K., Manning, E., Murtaza, M., Ashton, J-P., Furley, N., Hill, C.J., Alves, C.H., Wijnholds, J., Erdmann, K.S., Furley, A., Rashbass, P., Das, R.M., Storey, K.G., and Placzek, M.

Abstract

In the spinal cord, the central canal forms through a poorly understood process termed dorsal collapse that involves attrition and remodelling of pseudostratified ventricular layer (VL) cells. Here, we use mouse and chick models to show that dorsal ventricular layer (dVL) cells adjacent to dorsal midline Nestin(+) radial glia (dmNes+RG) down-regulate apical polarity proteins, including Crumbs2 (CRB2) and delaminate in a stepwise manner; live imaging shows that as one cell delaminates, the next cell ratchets up, the dmNes+RG endfoot ratchets down, and the process repeats. We show that dmNes+RG secrete a factor that promotes loss of cell polarity and delamination. This activity is mimicked by a secreted variant of Crumbs2 (CRB2S) which is specifically expressed by dmNes+RG. In cultured MDCK cells, CRB2S associates with apical membranes and decreases cell cohesion. Analysis of Crb2F/F/Nestin-Cre+/− mice, and targeted reduction of Crb2/CRB2S in slice cultures reveal essential roles for transmembrane CRB2 (CRB2TM) and CRB2S on VL cells and dmNes+RG, respectively. We propose a model in which a CRB2S–CRB2TM interaction promotes the progressive attrition of the dVL without loss of overall VL integrity. This novel mechanism may operate more widely to promote orderly progenitor delamination.

Published
2020

14. Worldwide trends in population-based survival for children, adolescents, and young adults diagnosed with leukaemia, by subtype, during 2000–14 (CONCORD-3): analysis of individual data from 258 cancer registries in 61 countries

Author

Ssenyonga, Naomi, Stiller, Charles, Nakata, Kayo, Shalkow, Jaime, Redmond, Shelagh, Bulliard, Jean-Luc, Girardi, Fabio, Fowler, Christine, Marcos-Gragera, Rafael, Bonaventure, Audrey, Saint-Jacques, Nathalie, Minicozzi, Pamela, De, Prithwish, Rodríguez-Barranco, Miguel, Larønningen, Siri, Di Carlo, Veronica, Mägi, Margit, Valkov, Mikhail, Seppä, Karri, Wyn Huws, Dyfed, Coleman, Michel P, Allemani, Claudia, Bouzbid, Sabiha, Hamdi-Chérif, Mokhtar, Kara, Lamia, Meguenni, Kaouel, Regagba, Derbali, Bayo, Sine, Cheick Bougadari, Traore, Manraj, Shyam Shunker, Bendahhou, Karima, Ladipo, Akinade, Ogunbiyi, Olufemi, Somdyala, Nontuthuzelo, Chaplin, María Agustina, Moreno, Florencia, Calabrano, Gladis, Espinola, Silvia, Carballo Quintero, Beatriz, Fita, Rosalba, Laspada, Walter, Ibañez, Susana, Lima, Carlos, Mafra Da Costa, Allini, De Souza, Paulo César, Chaves, Juliana, Laporte, Cyntia, Curado, Maria Paula, de Oliveira, Jose Carlos, Veneziano, Claudia, Veneziano, Donaldo, Almeida, Ana Beatriz, Latorre, Maria, Azevedo e Silva, Gulnar, Rebelo, Marise, Santos, Marceli, Galaz, Juan, Aparicio Aravena, Mackarena, Sanhueza Monsalve, Jocelyn, Herrmann, Denise, Vargas, Solange, Herrera, Victor, Uribe, Claudia, Bravo, Luis Eduardo, Garcia, Luz Stella, Arias-Ortiz, Nelson, Morantes, Daniela, Jurado, Daniel, Yépez Chamorro, María, Delgado, Sandra, Ramirez, Melissa, Galán Alvarez, Yaima, Torres, Priscila, Martínez-Reyes, Fray, Jaramillo, Leyda, Quinto, Rina, Castillo, Jhoanna, Mendoza, Mariela, Cueva, Patricia, Yépez, José, Bhakkan, Bernard, Deloumeaux, Jacqueline, Joachim, Clarisse, Macni, Jonathan, Carrillo, Rodolfo, Shalkow Klincovstein, Jaime, Rivera Gomez, Rebeca, Perez, Patricia, Poquioma, Ebert, Tortolero-Luna, Guillermo, Zavala, Diego, Alonso, Rafael, Barrios, Enrique, Eckstrand, Angela, Nikiforuk, Cindy, Woods, Ryan, Noonan, Gail, Turner, Donna, Kumar, Eshwar, Zhang, Bin, Dowden, Jeff, Doyle, Gregory, Saint-Jacques, Nathalie, Walsh, Gordon, Anam, Aniq, De, Prithwish, McClure, Carol, Vriends, Kim, Bertrand, Christine, Ramanakumar, Agnihotram, Kozie, Serena, Stuart-Panko, Heather, Freeman, Tara, George, Justin, Avila, Rosa, O'Brien, David, Holt, Abby, Almon, Lyn, Ward, Kevin, Kwong, Sandy, Morris, Cyllene, Rycroft, Randi, Mueller, Lloyd, Phillips, Cathryn, Brown, Heather, Cromartie, Betsy, Ruterbusch, Julie, Schwartz, Ann, Levin, Gary, Wohler, Brad, Bayakly, Rana, Gomez, Scarlett, McKinley, Meg, Cress, Rosemary, Davis, Joni, Hernandez, Brenda, Johnson, Christopher, Morawski, Bozena, Ruppert, Laura, Bentler, Suzanne, Charlton, Mary, Huang, Bin, Tucker, Thomas, Deapen, Dennis, Liu, Lihua, Hsieh, Mei-Chin, Wu, Xiao-Cheng, Schwenn, Molly, Stern, Kimberly, Gershman, Susan, Knowlton, Richard, Alverson, Georgetta, Weaver, Tamara, Desai, Jay, Rogers, Deirdre, Jackson-Thompson, Jeanette, Lemons, Debbi, Zimmerman, Heather, Hood, Michelle, Roberts-Johnson, Jenifer, Hammond, Whitney, Rees, Judith, Pawlish, Karen, Stroup, Antoinette, Key, Charles, Wiggins, Charles, Kahn, Amy, Schymura, Maria, Radhakrishnan, Soundarya, Rao, Chandrika, Giljahn, Lynn, Slocumb, Roberta, Dabbs, Christy, Espinoza, Raffaella, Aird, Karen, Beran, Todd, Rubertone, Jim, Slack, Stephen, Oh, Junhie, Janes, Tiffany, Schwartz, Stephen, Chiodini, Stephanie, Hurley, Deborah, Whiteside, Martin, Rai, Saroj, Williams, Melanie, Herget, Kim, Sweeney, Carol, Johnson, Alison, Keitheri Cheteri, Mahesh, Migliore Santiago, Patti, Blankenship, Steven, Farley, Shawn, Borchers, Robert, Malicki, Robin, Espinoza, Julia, Grandpre, Joseph, Edwards, Brenda, Mariotto, Angela, Weir, Hannah, Wilson, Reda, Wang, Ning, Yang, Lei, Chen, Jian-Shun, He, Yu-Tong, Song, Guo-Hui, Gu, Xiao-Ping, Mei, Dan, Ge, Heng-Ming, Wu, Tong-Hao, Li, Yan-Yan, Zhao, De-Li, Jin, Feng, Zhang, Jian-Hua, Zhu, Feng-Dong, Junhua, Qian, Yang, Yan Lei, Jiang, Chun-Xiao, Biao, Wang, Wang, Jian, Li, Qi-Long, Yi, He, Zhou, Xin, Dong, JianMei, Li, WeiWei, Fu, Fang-Xian, Chen, Jian-Guo, Zhu, Jian, Li, Yan-Hua, Lu, Yu-Qiang, Fan, Min, Huang, Su-Qin, Guo, Guo-Ping, Zhaolai, Hua, Wei, Kuangrong, Chen, Wan-Qing, Wei, Wenqiang, Zeng, Hongmei, Demetriou, Anna, Mang, Wai Kong, Ngan, Kai Cheong, Kataki, Amal, Krishnatreya, Manigreeva, Jayalekshmi, Padmavathi Amma, Sebastian, Paul, George, Preethi, Mathew, Aleyamma, Nandakumar, Ambakumar, Malekzadeh, Reza, Roshandel, Gholamreza, Keinan-Boker, Lital, Silverman, Barbara, Ito, Hidemi, Koyanagi, Yuriko, Sato, Masako, Tobori, Fumio, Nakata, Ichiro, Teramoto, Norihiro, Hattori, Masakazu, Kaizaki, Yasuharu, Moki, Fumitaka, Sugiyama, Hiromi, Utada, Mai, Nishimura, Makiko, Yoshida, Keiichi, Kurosawa, Katsuki, Nemoto, Yuji, Narimatsu, Hiroto, Sakaguchi, Masahiko, Kanemura, Seiki, Naito, Michiko, Narisawa, Rintaro, Miyashiro, Isao, Nakata, Kayo, Mori, Daisuke, Yoshitake, Mayuko, Oki, Izumi, Fukushima, Norimasa, Shibata, Akiko, Iwasa, Keiichiro, Ono, Chie, Matsuda, Tomohiro, Nimri, Omar, Jung, Kyu-Won, Won, Young-Joo, Alawadhi, Eiman, Elbasmi, Amani, Ab Manan, Azizah, Adam, Farzaana, Nansalmaa, Erdenekhuu, Tudev, Undarmaa, Ochir, Chimedsuren, Al Khater, Al Hareth, El Mistiri, Mufid, Lim, Gek Hsiang, Teo, Yik Ying, Chiang, Chun-Ju, Lee, Wen-Chung, Buasom, Rangsiya, Sangrajrang, Suleeporn, Suwanrungruang, Krittika, Vatanasapt, Patravoot, Daoprasert, Karnchana, Pongnikorn, Donsuk, Leklob, Atit, Sangkitipaiboon, Somphob, Geater, Sarayut, Sriplung, Hutcha, Ceylan, Okan, Kög, Iskender, Dirican, Oya, Köse, Tülay, Gurbuz, Tulın, Karašahin, Füsun Emine, Turhan, Duygu, Aktaş, Umut, Halat, Yakup, Eser, Sultan, Yakut, Cankut, Altinisik, Merve, Cavusoglu, Yasar, Türkköylü, Ayşe, Üçüncü, Nuršen, Hackl, Monika, Zborovskaya, Anna, Aleinikova, Olga, Henau, Kris, Van Eycken, Liesbet, Atanasov, Trayan, Valerianova, Zdravka, Šekerija, Mario, Dušek, Ladislav, Zvolský, Miroslav, Steinrud Mørch, Lina, Storm, Hans, Wessel Skovlund, Charlotte, Innos, Kaire, Mägi, Margit, Malila, Nea, Seppä, Karri, Jégu, Jérémie, Velten, Michel, Cornet, Edouard, Troussard, Xavier, Bouvier, Anne-Marie, Guizard, Anne-Valérie, Bouvier, Véronique, Launoy, Guy, Dabakuyo Yonli, Sandrine, Poillot, Marie-Laure, Maynadié, Marc, Mounier, Morgane, Vaconnet, Lina, Woronoff, Anne-Sophie, Daoulas, Mélanie, Robaszkiewicz, Michel, Clavel, Jacqueline, Poulalhon, Claire, Desandes, Emmanuel, Lacour, Brigitte, Baldi, Isabelle, Pouchieu, Camille, Amadeo, Brice, Coureau, Gaëlle, Monnereau, Alain, Audoin, Magali, D'Almeida, Tania, Boyer, Séverine, Hammas, Karima, Trétarre, Brigitte, Colonna, Marc, Delafosse, Patricia, Plouvier, Sandrine, Cowppli-Bony, Anne, Molinié, Florence, Bara, Simona, Ganry, Olivier, Lapôtre-Ledoux, Bénédicte, Daubisse-Marliac, Laetitia, Bossard, Nadine, Estève, Jacques, Uhry, Zoé, Stabenow, Roland, Wilsdorf-Köhler, Heide, Eberle, Andrea, Luttmann, Sabine, Löhden, Imma, Nennecke, Alice, Kieschke, Joachim, Sirri, Eunice, Justenhoven, Christina, Zeissig, Sylke, Holleczek, Bernd, Eisemann, Nora, Katalinic, Alexander, Asquez, Rachelle, Kumar, Vijay, Petridou, Eleni, Ólafsdóttir, Elínborg, Tryggvadóttir, Laufey, Murray, Deirdre, Walsh, Paul, Sundseth, Hildrun, Mazzoleni, Guido, Vittadello, Fabio, Coviello, Enzo, Cuccaro, Francesco, Galasso, Rocco, Sampietro, Giuseppe, Magoni, Michele, Ardizzone, Antonino, D'Argenzio, Angelo, Di Prima, Alessia Anna, Ippolito, Antonella, Lavecchia, Anna Maria, Sutera Sardo, Antonella, Gola, Gemma, Ballotari, Paola, Giacomazzi, Erica, Ferretti, Stefano, Dal Maso, Luigino, Serraino, Diego, Celesia, Maria Vittoria, Filiberti, Rosa Angela, Pannozzo, Fabio, Melcarne, Anna, Quarta, Fabrizio, Andreano, Anita, Russo, Antonio Giampiero, Carrozzi, Giuliano, Cirilli, Claudia, Cavalieri d'Oro, Luca, Rognoni, Magda, Fusco, Mario, Vitale, Maria Francesca, Usala, Mario, Cusimano, Rosanna, Mazzucco, Walter, Michiara, Maria, Sgargi, Paolo, Boschetti, Lorenza, Chiaranda, Giorgio, Seghini, Pietro, Maule, Milena, Merletti, Franco, Spata, Eugenia, Tumino, Rosario, Mancuso, Pamela, Vicentini, Massimo, Cassetti, Tiziana, Sassatelli, Romano, Falcini, Fabio, Giorgetti, Stefania, Caiazzo, Anna Luisa, Cavallo, Rossella, Piras, Daniela, Bella, Francesca, Madeddu, Anselmo, Fanetti, Anna Clara, Maspero, Sergio, Carone, Simona, Mincuzzi, Antonia, Candela, Giuseppa, Scuderi, Tiziana, Gentilini, Maria Adalgisa, Rizzello, Roberto, Rosso, Stefano, Zanetti, Roberto, Caldarella, Adele, Intrieri, Teresa, Bianconi, Fortunato, Stracci, Fabrizio, Contiero, Paolo, Tagliabue, Giovanna, Rugge, Massimo, Zorzi, Manuel, Beggiato, Simonetta, Brustolin, Angelita, De Angelis, Roberta, Gatta, Gemma, Maurina, Anita, Oniščuka, Marija, Mousavi, Mohsen, Lipunova, Nadezda, Vincerzevskienė, Ieva, Agius, Dominic, Calleja, Neville, Siesling, Sabine, Visser, Otto, Johannesen, Tom, Larønningen, Siri, Trojanowski, Maciej, Mierzwa, Tomasz, Rachtan, Jadwiga, Kępska, Kamila, Kościańska, Beata, Wójcik-Tomaszewska, Joanna, Motnyk, Marcin, Gos, Anna, Bielska-Lasota, Magdalena, Didkowska, Joanna, Wojciechowska, Urszula, Forjaz de Lacerda, Gonçalo, Rego, Raul, Carrito, Branca, Pais, Ana, Bento, Maria José, Rodrigues, Jessica, Lourenço, Antonio, Mayer-da-Silva, Alexandra, Blaga, Luminita, Coza, Daniela, Valkov, Mikhail, Gusenkova, Lubov, Lazarevich, Olga, Prudnikova, Olga, Vjushkov, Dmitri Mikhailovich, Egorova, Alla, Orlov, Andrey, Pikalova, Lidiya, Zhuikova, Lilia, Adamcik, Juraj, Safaei Diba, Chakameh, Zadnik, Vesna, Zagar, Tina, De-La-Cruz, Marta, Lopez-de-Munain, Arantza, Aleman, Araceli, Rojas, Dolores, Chillarón, Rosario Jiménez, Navarro, Ana Isabel Marcos, Marcos-Gragera, Rafael, Puigdemont, Montse, Rodríguez-Barranco, Miguel, Sánchez Perez, María-José, Franch Sureda, Paula, Ramos Montserrat, Maria, Chirlaque López, Maria Dolores, Sánchez Gil, Antonia, Ardanaz, Eva, Guevara, Marcela, Cañete-Nieto, Adela, Peris-Bonet, Rafael, Carulla, Marià, Galceran, Jaume, Almela, Fernando, Sabater, Consol, Khan, Staffan, Pettersson, David, Dickman, Paul, Staehelin, Katharina, Struchen, Benjamin, Herrmann, Christian, Mousavi, Seyed Mohsen, Egger Hayoz, Céline, Bouchardy, Christine, Schaffar, Robin, Went, Philip, Bulliard, Jean-Luc, Maspoli-Conconi, Manuela, Kuehni, Claudia, Redmond, Shelagh, Bordoni, Andrea, Ortelli, Laura, Chiolero, Arnaud, Konzelmann, Isabelle, Rohrmann, Sabine, Wanner, Miriam, Broggio, John, Rashbass, Jem, Stiller, Charles, Fitzpatrick, Deirdre, Gavin, Anna, Morrison, David, Thomson, Catherine, Greene, Giles, Huws, Dyfed, Allemani, Claudia, Coleman, Michel, Di Carlo, Veronica, Girardi, Fabio, Matz, Melissa, Minicozzi, Pamela, Sanz, Natalia, Ssenyonga, Naomi, Stephens, Richard, Chalker, Elizabeth, Smith, Mirka, Gugusheff, Jessica, You, Hui, Qin Li, Shu, Dugdale, Sarah, Moore, Julie, Philpot, Shoni, Pfeiffer, Rhonda, Thomas, Helen, Silva Ragaini, Bruna, Venn, Alison, Evans, Sue, Te Marvelde, Luc, Savietto, Vedrana, Trevithick, Richard, Currow, David, Fowler, Christine, and Lewis, Chris

Abstract

Leukaemias comprise a heterogenous group of haematological malignancies. In CONCORD-3, we analysed data for children (aged 0–14 years) and adults (aged 15–99 years) diagnosed with a haematological malignancy during 2000–14 in 61 countries. Here, we aimed to examine worldwide trends in survival from leukaemia, by age and morphology, in young patients (aged 0–24 years).

Published
2022
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15. Pax6 controls progenitor cell identity and neuronal fate in response to graded Shh signaling

Author

Ericson, J., Rashbass, P., Schedl, A., Brenner-Morton, S., Kawakami, A., Heynigen, V. van, Jessell, T.M., and Briscoe, J.

Subjects
Cell differentiation -- Genetic aspects, Neurons -- Genetic aspects, Developmental neurology -- Genetic aspects, Biological sciences
Abstract

The role of the Sonic hedgehog or Shh proteins such as Pax6 and Nkx2.2 in the regulation of neuronal identity was analyzed in ventral progenitor cell populations. In vitro assays of of the progenitor cells indicated the prensence of Pax6 expression that was characterized by a ventral to dorsal gradient. Furthermore, Pax6 acted as an essential intermediary in graded Shh signaling in the caudal neural tube which modulated ventral progenitor cell identity.

Published
1997

18. Impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England: a population-based study

Author

Morris, Eva J A, Goldacre, Raphael, Spata, Enti, Mafham, Marion, Finan, Paul J, Shelton, Jon, Richards, Mike, Spencer, Katie, Emberson, Jonathan, Hollings, Sam, Curnow, Paula, Gair, Dominic, Sebag-Montefiore, David, Cunningham, Chris, Rutter, Matthew D, Nicholson, Brian D, Rashbass, Jem, Landray, Martin, Collins, Rory, Casadei, Barbara, and Baigent, Colin

Abstract

There are concerns that the COVID-19 pandemic has had a negative effect on cancer care but there is little direct evidence to quantify any effect. This study aims to investigate the impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England.

Published
2021
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19. Cancer prevention with aspirin in hereditary colorectal cancer (Lynch syndrome), 10-year follow-up and registry-based 20-year data in the CAPP2 study: a double-blind, randomised, placebo-controlled trial

Author

Burn, John, Sheth, Harsh, Elliott, Faye, Reed, Lynn, Macrae, Finlay, Mecklin, Jukka-Pekka, Möslein, Gabriela, McRonald, Fiona E, Bertario, Lucio, Evans, D Gareth, Gerdes, Anne-Marie, Ho, Judy W C, Lindblom, Annika, Morrison, Patrick J, Rashbass, Jem, Ramesar, Raj, Seppälä, Toni, Thomas, Huw J W, Pylvänäinen, Kirsi, Borthwick, Gillian M, Mathers, John C, Bishop, D Timothy, Boussioutas, Alex, Brewer, Carole, Cook, Jackie, Eccles, Diana, Ellis, Anthony, Hodgson, Shirley V, Lubinski, Jan, Maher, Eamonn R, Porteous, Mary EM, Sampson, Julian, Scott, Rodney J, and Side, Lucy

Abstract

Lynch syndrome is associated with an increased risk of colorectal cancer and with a broader spectrum of cancers, especially endometrial cancer. In 2011, our group reported long-term cancer outcomes (mean follow-up 55·7 months [SD 31·4]) for participants with Lynch syndrome enrolled into a randomised trial of daily aspirin versus placebo. This report completes the planned 10-year follow-up to allow a longer-term assessment of the effect of taking regular aspirin in this high-risk population.

Published
2020
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20. Nationwide Incidence of Metastatic Cutaneous Squamous Cell Carcinoma in England

Author

Venables, Zoë C., Autier, Philippe, Nijsten, Tamar, Wong, Kwok F., Langan, Sinéad M., Rous, Brian, Broggio, John, Harwood, Catherine, Henson, Katherine, Proby, Charlotte M., Rashbass, Jem, and Leigh, Irene M.

Abstract

IMPORTANCE: Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential, but epidemiologic data are poor. Changes to the National Cancer Registration and Analysis Service (NCRAS) in England have allowed more accurate data analysis of primary and metastatic cSCC since 2013. OBJECTIVE: To assess the national incidence of cSCC and metastatic cSCC (mcSCC) in England from 2013 through 2015. DESIGN, SETTING, AND PARTICIPANTS: This national population-based study identified a cohort of patients with cSCC and mcSCC in England from January 1, 2013, through December 31, 2015. Patients were identified using diagnostic codes derived from pathology reports in the NCRAS. Data were analyzed from March 1, 2017, through March 1, 2018. MAIN OUTCOMES AND MEASURES: Incidence rates across sex and risk factors for cSCC were derived from the NCRAS data. Risk of occurrence of mcSCC among the population with cSCC was assessed with Cox proportional hazards regression analysis to determine indicators of mcSCC. RESULTS: Among the 76 977 patients with first primary cSCC in 2013 through 2015 (62.7% male; median age, 80 years [interquartile range, 72-86 years]), the age-standardized rates for the first registered cSCC in England from 2013 through 2015 were 77.3 per 100 000 person-years (PY) (95% CI, 76.6-78.0) in male patients and 34.1 per 100 000 PY (95% CI, 33.7-34.5) in female patients. Increased primary cSCC tumor count was observed in older, white male patients in lower deprivation quintiles. After a maximum follow-up of 36 months, cumulative incidence of mcSCC developed in 1.1% of women and 2.4% of men with a primary cSCC. Significant increases in the risk of metastasis with adjusted hazard rates of approximately 2.00 were observed in patients who were aged 80 to 89 years (hazard ratio [HR], 1.23; 95% CI, 1.07-1.43), 90 years or older (HR, 1.35; 95% CI, 1.09-1.66), male (HR, 1.79; 95% CI, 1.52-2.10), immunosuppressed (HR, 1.99; 95% CI, 1.64-2.42), and in higher deprivation quintiles (HR for highest quintile, 1.64; 95% CI, 1.35-2.00). Primary cSCC located on the ear (HR, 1.70; 95% CI, 1.42-2.03) and lip (HR, 1.85; 95% CI, 1.29-2.63) were at highest risk of metastasis. CONCLUSIONS AND RELEVANCE: This study presents the first national study of the incidence of mcSCC. With limited health care resources and an aging population, accurate epidemiologic data are essential for informing future health care planning, identifying high-risk patients, and evaluating skin cancer prevention policies.

Published
2019
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22. Characterization of the Crumbs homolog 2 (CRB2) gene and analysis of its role in retinitis pigmentosa and Leber congenital amaurosis

Author

Hurk, J.A.J.M. van den, Rashbass, P., Roepman, R., Davis, J., Voesenek, K.E.J., Arends, M.L., Zonneveld-Vrieling, M.N., Roekel, M.H. van, Cameron, K., Rohrschneider, K., Heckenlively, J.R., Koenekoop, R.K., Hoyng, C.B., Cremers, F.P.M., and Hollander, A.I. den

Subjects
Genomic disorders and inherited multi-system disorders [IGMD 3], genetic structures, Genetics and epigenetic pathways of disease [NCMLS 6], Evaluation of complex medical interventions [NCEBP 2], Perception and Action [DCN 1], Neurosensory disorders [UMCN 3.3], sense organs, eye diseases, Renal disorder [IGMD 9]
Abstract

Contains fulltext : 48627.pdf (Publisher’s version ) (Open Access) PURPOSE: Mutations in the Crumbs homolog 1 (CRB1) gene cause autosomal recessive retinitis pigmentosa (RP) and Leber congenital amaurosis (LCA). Database searches reveal two other Crumbs homologs on chromosomes 9q33.3 and 19p13.3. The purpose of this study was to characterize the Crumbs homolog 2 (CRB2) gene on 9q33.3, to analyze its expression pattern, and to determine whether mutations in CRB2 are associated with RP and LCA. METHODS: The CRB2 mRNA and its expression pattern in human tissues were analyzed by reverse transcription-polymerase chain reaction (RT-PCR). The cellular expression of Crb2 in the mouse eye was determined by mRNA in situ hybridizations. The open reading frame and splice junctions of CRB2 were analyzed for mutations by single-strand conformation analysis and direct nucleotide sequencing in 85 RP patients and 79 LCA patients. RESULTS: The CRB2 gene consists of 13 exons and encodes a 1285 amino acid transmembrane protein. CRB2 is mainly expressed in retina, brain, and kidney. In mouse retina Crb2 expression was detected in all cell layers. Mutation analysis of the CRB2 gene revealed 11 sequence variants leading to an amino acid substitution. Three of them were not identified in control individuals and affect conserved amino acid residues. However, the patients that carry these sequence variants do not have a second sequence variant on the other allele, excluding autosomal recessive inheritance of CRB2 sequence variants as a cause of their disease. CONCLUSIONS: This study shows that CRB2 sequence variants are not a common cause of autosomal recessive RP and LCA. It is possible that a more complex clinical phenotype is associated with the loss or altered function of CRB2 in humans due to its expression in tissues other than the retina.

Published
2005

23. Crumbs homologue 1 is required for maintenance of photoreceptor cell polarization and adhesion during light exposure

Author

Van De Pavert, S.A., Kantardzhieva, A., Malysheva, A., Meuleman, J., Versteeg, I., Levelt, C., Klooster, J., Geiger, S., Seeliger, M.W., Rashbass, P., Le Bivic, A.L., Institut de Biologie du Développement de Marseille (IBDM), and Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.BC]Life Sciences [q-bio]/Cellular Biology
Published
2004

24. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries

Author

Allemani, Claudia, Matsuda, Tomohiro, Di Carlo, Veronica, Harewood, Rhea, Matz, Melissa, Nikšić, Maja, Bonaventure, Audrey, Valkov, Mikhail, Johnson, Christopher J, Estève, Jacques, Ogunbiyi, Olufemi J, Azevedo e Silva, Gulnar, Chen, Wan-Qing, Eser, Sultan, Engholm, Gerda, Stiller, Charles A, Monnereau, Alain, Woods, Ryan R, Visser, Otto, Lim, Gek Hsiang, Aitken, Joanne, Weir, Hannah K, Coleman, Michel P, Bouzbid, S, Hamdi-Chérif, M, Zaidi, Z, Meguenni, K, Regagba, D, Bayo, S, Cheick Bougadari, T, Manraj, S S, Bendahhou, K, Fabowale, A, Bradshaw, D, Somdyala, N I M, Kumcher, I, Moreno, F, Calabrano, G H, Espinola, S B, Carballo Quintero, B, Fita, R, Diumenjo, M C, Laspada, W D, Ibañez, S G, Lima, C A, De Souza, P C F, Del Pino, K, Laporte, C, Curado, M P, de Oliveira, J C, Veneziano, C L A, Veneziano, D B, Latorre, M R D O, Tanaka, L F, Rebelo, M S, Santos, M O, Galaz, J C, Aparicio Aravena, M, Sanhueza Monsalve, J, Herrmann, D A, Vargas, S, Herrera, V M, Uribe, C J, Bravo, L E, Garcia, L S, Arias-Ortiz, N E, Morantes, D, Jurado, D M, Yépez Chamorro, M C, Delgado, S, Ramirez, M, Galán Alvarez, Y H, Torres, P, Martínez-Reyes, F, Jaramillo, L, Quinto, R, Castillo, J, Mendoza, M, Cueva, P, Yépez, J G, Bhakkan, B, Deloumeaux, J, Joachim, C, Macni, J, Carrillo, R, Shalkow Klincovstein, J, Rivera Gomez, R, Poquioma, E, Tortolero-Luna, G, Zavala, D, Alonso, R, Barrios, E, Eckstrand, A, Nikiforuk, C, Noonan, G, Turner, D, Kumar, E, Zhang, B, McCrate, F R, Ryan, S, MacIntyre, M, Saint-Jacques, N, Nishri, D E, McClure, C A, Vriends, K A, Kozie, S, Stuart-Panko, H, Freeman, T, George, J T, Brockhouse, J T, O'Brien, D K, Holt, A, Almon, L, Kwong, S, Morris, C, Rycroft, R, Mueller, L, Phillips, C E, Brown, H, Cromartie, B, Schwartz, A G, Vigneau, F, Levin, G M, Wohler, B, Bayakly, R, Ward, K C, Gomez, S L, McKinley, M, Cress, R, Green, M D, Miyagi, K, Ruppert, L P, Lynch, C F, Huang, B, Tucker, T C, Deapen, D, Liu, L, Hsieh, M C, Wu, X C, Schwenn, M, Gershman, S T, Knowlton, R C, Alverson, G, Copeland, G E, Bushhouse, S, Rogers, D B, Jackson-Thompson, J, Lemons, D, Zimmerman, H J, Hood, M, Roberts-Johnson, J, Rees, J R, Riddle, B, Pawlish, K S, Stroup, A, Key, C, Wiggins, C, Kahn, A R, Schymura, M J, Radhakrishnan, S, Rao, C, Giljahn, L K, Slocumb, R M, Espinoza, R E, Khan, F, Aird, K G, Beran, T, Rubertone, J J, Slack, S J, Garcia, L, Rousseau, D L, Janes, T A, Schwartz, S M, Bolick, S W, Hurley, D M, Whiteside, M A, Miller-Gianturco, P, Williams, M A, Herget, K, Sweeney, C, Johnson, A T, Keitheri Cheteri, M B, Migliore Santiago, P, Blankenship, S E, Farley, S, Borchers, R, Malicki, R, Espinoza, J R, Grandpre, J, Wilson, R, Edwards, B K, Mariotto, A, Lei, Y, Wang, N, Chen, J S, Zhou, Y, He, Y T, Song, G H, Gu, X P, Mei, D, Mu, H J, Ge, H M, Wu, T H, Li, Y Y, Zhao, D L, Jin, F, Zhang, J H, Zhu, F D, Junhua, Q, Yang, Y L, Jiang, C X, Biao, W, Wang, J, Li, Q L, Yi, H, Zhou, X, Dong, J, Li, W, Fu, F X, Liu, S Z, Chen, J G, Zhu, J, Li, Y H, Lu, Y Q, Fan, M, Huang, S Q, Guo, G P, Zhaolai, H, Wei, K, Zeng, H, Demetriou, A V, Mang, W K, Ngan, K C, Kataki, A C, Krishnatreya, M, Jayalekshmi, P A, Sebastian, P, Nandakumar, A, Malekzadeh, R, Roshandel, G, Keinan-Boker, L, Silverman, B G, Ito, H, Nakagawa, H, Sato, M, Tobori, F, Nakata, I, Teramoto, N, Hattori, M, Kaizaki, Y, Moki, F, Sugiyama, H, Utada, M, Nishimura, M, Yoshida, K, Kurosawa, K, Nemoto, Y, Narimatsu, H, Sakaguchi, M, Kanemura, S, Naito, M, Narisawa, R, Miyashiro, I, Nakata, K, Sato, S, Yoshii, M, Oki, I, Fukushima, N, Shibata, A, Iwasa, K, Ono, C, Nimri, O, Jung, K W, Won, Y J, Alawadhi, E, Elbasmi, A, Ab Manan, A, Adam, F, Sanjaajmats, E, Tudev, U, Ochir, C, Al Khater, A M, El Mistiri, M M, Teo, Y Y, Chiang, C J, Lee, W C, Buasom, R, Sangrajrang, S, Kamsa-ard, S, Wiangnon, S, Daoprasert, K, Pongnikorn, D, Leklob, A, Sangkitipaiboon, S, Geater, S L, Sriplung, H, Ceylan, O, Kög, I, Dirican, O, Köse, T, Gurbuz, T, Karaşahin, F E, Turhan, D, Aktaş, U, Halat, Y, Yakut, C I, Altinisik, M, Cavusoglu, Y, Türkköylü, A, Üçüncü, N, Hackl, M, Zborovskaya, A A, Aleinikova, O V, Henau, K, Van Eycken, L, Valerianova, Z, Yordanova, M R, Šekerija, M, Dušek, L, Zvolský, M, Storm, H, Innos, K, Mägi, M, Malila, N, Seppä, K, Jégu, J, Velten, M, Cornet, E, Troussard, X, Bouvier, A M, Guizard, A V, Bouvier, V, Launoy, G, Arveux, P, Maynadié, M, Mounier, M, Woronoff, A S, Daoulas, M, Robaszkiewicz, M, Clavel, J, Goujon, S, Lacour, B, Baldi, I, Pouchieu, C, Amadeo, B, Coureau, G, Orazio, S, Preux, P M, Rharbaoui, F, Marrer, E, Trétarre, B, Colonna, M, Delafosse, P, Ligier, K, Plouvier, S, Cowppli-Bony, A, Molinié, F, Bara, S, Ganry, O, Lapôtre-Ledoux, B, Grosclaude, P, Bossard, N, Uhry, Z, Bray, F, Piñeros, M, Stabenow, R, Wilsdorf-Köhler, H, Eberle, A, Luttmann, S, Löhden, I, Nennecke, A L, Kieschke, J, Sirri, E, Emrich, K, Zeissig, S R, Holleczek, B, Eisemann, N, Katalinic, A, Asquez, R A, Kumar, V, Petridou, E, Ólafsdóttir, E J, Tryggvadóttir, L, Clough-Gorr, K, Walsh, P M, Sundseth, H, Mazzoleni, G, Vittadello, F, Coviello, E, Cuccaro, F, Galasso, R, Sampietro, G, Giacomin, A, Magoni, M, Ardizzone, A, D'Argenzio, A, Castaing, M, Grosso, G, Lavecchia, A M, Sutera Sardo, A, Gola, G, Gatti, L, Ricci, P, Ferretti, S, Serraino, D, Zucchetto, A, Celesia, M V, Filiberti, R A, Pannozzo, F, Melcarne, A, Quarta, F, Russo, A G, Carrozzi, G, Cirilli, C, Cavalieri d'Oro, L, Rognoni, M, Fusco, M, Vitale, M F, Usala, M, Cusimano, R, Mazzucco, W, Michiara, M, Sgargi, P, Boschetti, L, Borciani, E, Seghini, P, Maule, M M, Merletti, F, Tumino, R, Mancuso, P, Vicentini, M, Cassetti, T, Sassatelli, R, Falcini, F, Giorgetti, S, Caiazzo, A L, Cavallo, R, Cesaraccio, R, Pirino, D R, Contrino, M L, Tisano, F, Fanetti, A C, Maspero, S, Carone, S, Mincuzzi, A, Candela, G, Scuderi, T, Gentilini, M A, Piffer, S, Rosso, S, Barchielli, A, Caldarella, A, Bianconi, F, Stracci, F, Contiero, P, Tagliabue, G, Rugge, M, Zorzi, M, Beggiato, S, Brustolin, A, Berrino, F, Gatta, G, Sant, M, Buzzoni, C, Mangone, L, Capocaccia, R, De Angelis, R, Zanetti, R, Maurina, A, Pildava, S, Lipunova, N, Vincerževskiené, I, Agius, D, Calleja, N, Siesling, S, Larønningen, S, Møller, B, Dyzmann-Sroka, A, Trojanowski, M, Góźdź, S, Mężyk, R, Mierzwa, T, Molong, L, Rachtan, J, Szewczyk, S, Błaszczyk, J, Kępska, K, Kościańska, B, Tarocińska, K, Zwierko, M, Drosik, K, Maksimowicz, K M, Purwin-Porowska, E, Reca, E, Wójcik-Tomaszewska, J, Tukiendorf, A, Grądalska-Lampart, M, Radziszewska, A U, Gos, A, Talerczyk, M, Wyborska, M, Didkowska, J A, Wojciechowska, U, Bielska-Lasota, M, Forjaz de Lacerda, G, Rego, R A, Bastos, J, Silva, M A, Antunes, L, Laranja Pontes, J, Mayer-da-Silva, A, Miranda, A, Blaga, L M, Coza, D, Gusenkova, L, Lazarevich, O, Prudnikova, O, Vjushkov, D M, Egorova, A G, Orlov, A E, Kudyakov, L A, Pikalova, L V, Adamcik, J, Safaei Diba, C, Primic-Žakelj, M, Zadnik, V, Larrañaga, N, Lopez de Munain, A, Herrera, A A, Redondas, R, Marcos-Gragera, R, Vilardell Gil, M L, Molina, E, Sánchez Perez, M J, Franch Sureda, P, Ramos Montserrat, M, Chirlaque, M D, Navarro, C, Ardanaz, E E, Guevara, M M, Fernández-Delgado, R, Peris-Bonet, R, Carulla, M, Galceran, J, Alberich, C, Vicente-Raneda, M, Khan, S, Pettersson, D, Dickman, P, Avelina, I, Staehelin, K, Camey, B, Bouchardy, C, Schaffar, R, Frick, H, Herrmann, C, Bulliard, J L, Maspoli-Conconi, M, Kuehni, C E, Redmond, S M, Bordoni, A, Ortelli, L, Chiolero, A, Konzelmann, I, Matthes, K L, Rohrmann, S, Broggio, J, Rashbass, J, Fitzpatrick, D, Gavin, A, Clark, D I, Deas, A J, Huws, D W, White, C, Montel, L, Rachet, B, Turculet, A D, Stephens, R, Chalker, E, Phung, H, Walton, R, You, H, Guthridge, S, Johnson, F, Gordon, P, D'Onise, K, Priest, K, Stokes, B C, Venn, A, Farrugia, H, Thursfield, V, Dowling, J, Currow, D, Hendrix, J, and Lewis, C

Abstract

In 2015, the second cycle of the CONCORD programme established global surveillance of cancer survival as a metric of the effectiveness of health systems and to inform global policy on cancer control. CONCORD-3 updates the worldwide surveillance of cancer survival to 2014.

Published
2018
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25. Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Author

Luchtenborg, Margreet, Morris, Eva J A, Tataru, Daniela, Coupland, Victoria H, Smith, Andrew, Milne, Roger L, te Marvelde, Luc, Baker, Deborah, Young, Jane, Turner, Donna, Nishri, Diane, Earle, Craig, Shack, Lorraine, Gavin, Anna, Fitzpatrick, Deirdre, Donnelly, Conan, Lin, Yulan, Møller, Bjørn, Brewster, David H, Deas, Andrew, Huws, Dyfed W, White, Ceri, Warlow, Janet, Rashbass, Jem, and Peake, Michael D

Abstract

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

Published
2018
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26. Worldwide comparison of survival from childhood leukaemia for 1995–2009, by subtype, age, and sex (CONCORD-2): a population-based study of individual data for 89 828 children from 198 registries in 53 countries

Author

Bonaventure, Audrey, Harewood, Rhea, Stiller, Charles A, Gatta, Gemma, Clavel, Jacqueline, Stefan, Daniela C, Carreira, Helena, Spika, Devon, Marcos-Gragera, Rafael, Peris-Bonet, Rafael, Piñeros, Marion, Sant, Milena, Kuehni, Claudia E, Murphy, Michael F G, Coleman, Michel P, Allemani, Claudia, Bouzbid, S, Hamdi-Chérif, M, Zaidi, Z, Bah, E, Swaminathan, R, Nortje, SH, El Mistiri, MM, Bayo, S, Malle, B, Manraj, SS, Sewpaul-Sungkur, R, Fabowale, Ogunbiyi, OJ, Bradshaw, D, Somdyala, NIM, Stefan, DC, Abdel-Rahman, M, Jaidane, L, Mokni, M, Kumcher, I, Moreno, F, González, MS, Laura, EA, Espinola, SB, Calabrano, GH, Carballo Quintero, B, Fita, R, Garcilazo, DA, Giacciani, PL, Diumenjo, MC, Laspada, WD, Green, MA, Lanza, MF, Ibañez, SG, Lima, CA, de Oliveira, E Lobo, Daniel, C, Scandiuzzi, C, De Souza, PCF, Melo, CD, Del Pino, K, Laporte, C, Curado, MP, de Oliveira, JC, Veneziano, CLA, Veneziano, DB, Azevedo e Silva, G, Galaz, JC, Moya, JA, Herrmann, DA, Vargas, S, Herrera, VM, Uribe, CJ, Bravo, LE, Arias-Ortiz, NE, Jurado, DM, Yépez, MC, Galán, YH, Torres, P, Martínez-Reyes, F, Pérez-Meza, ML, Jaramillo, L, Quinto, R, Cueva, P, Yépez, JG, Torres-Cintrón, CR, Tortolero-Luna, G, Alonso, R, Barrios, E, Nikiforuk, C, Shack, L, Coldman, AJ, Woods, RR, Noonan, G, Turner, D, Kumar, E, Zhang, B, McCrate, FR, Ryan, S, Hannah, H, Dewar, RAD, MacIntyre, M, Lalany, A, Ruta, M, Marrett, L, Nishri, DE, McClure, C, Vriends, KA, Bertrand, C, Louchini, R, Robb, KI, Stuart-Panko, H, Demers, S, Wright, S, George, JT, Shen, X, Brockhouse, JT, O'Brien, DK, Ward, KC, Almon, L, Bates, J, Rycroft, R, Mueller, L, Phillips, C, Brown, H, Cromartie, B, Schwartz, AG, Vigneau, F, MacKinnon, JA, Wohler, B, Bayakly, AR, Clarke, CA, Glaser, SL, West, D, Green, MD, Hernandez, BY, Johnson, CJ, Jozwik, D, Charlton, ME, Lynch, CF, Huang, B, Tucker, TC, Deapen, D, Liu, L, Hsieh, MC, Wu, XC, Stern, K, Gershman, ST, Knowlton, RC, Alverson, J, Copeland, GE, Rogers, DB, Lemons, D, Williamson, LL, Hood, M, Hosain, GM, Rees, JR, Pawlish, KS, Stroup, A, Key, C, Wiggins, C, Kahn, AR, Schymura, MJ, Leung, G, Rao, C, Giljahn, L, Warther, B, Pate, A, Patil, M, Schubert, SS, Rubertone, JJ, Slack, SJ, Fulton, JP, Rousseau, DL, Janes, TA, Schwartz, SM, Bolick, SW, Hurley, DM, Richards, J, Whiteside, MA, Nogueira, LM, Herget, K, Sweeney, C, Martin, J, Wang, S, Harrelson, DG, Cheteri, MB Keitheri, Farley, S, Hudson, AG, Borchers, R, Stephenson, L, Espinoza, JR, Weir, HK, Edwards, BK, Wang, N, Yang, L, Chen, JS, Song, GH, Gu, XP, Zhang, P, Ge, HM, Zhao, DL, Zhang, JH, Zhu, FD, Tang, JG, Shen, Y, Wang, J, Li, QL, Yang, XP, Dong, J, Li, W, Cheng, LP, Chen, JG, Huang, QH, Huang, SQ, Guo, GP, Wei, K, Chen, WQ, Zeng, H, Demetriou, AV, Pavlou, P, Mang, WK, Ngan, KC, Swaminathan, R, Kataki, AC, Krishnatreya, M, Jayalekshmi, PA, Sebastian, P, Sapkota, SD, Verma, Y, Nandakumar, A, Suzanna, E, Keinan-Boker, L, Silverman, BG, Ito, H, Nakagawa, H, Hattori, M, Kaizaki, Y, Sugiyama, H, Utada, M, Katayama, K, Narimatsu, H, Kanemura, S, Koike, T, Miyashiro, I, Yoshii, M, Oki, I, Shibata, A, Matsuda, T, Nimri, O, Ab Manan, A, Pathy, N Bhoo, Chimedsuren, O, Tuvshingerel, S, Al Khater, AHM, El Mistiri, MM, Al-Eid, H, Jung, KW, Won, YJ, Chiang, CJ, Lai, MS, Suwanrungruang, K, Wiangnon, S, Daoprasert, K, Pongnikorn, D, Geater, SL, Sriplung, H, Eser, S, Yakut, CI, Hackl, M, Mühlböck, H, Oberaigner, W, Zborovskaya, AA, Aleinikova, OV, Henau, K, Van Eycken, L, Dimitrova, N, Valerianova, Z, Šekerija, M, Zvolský, M, Engholm, G, Storm, H, Innos, K, Mägi, M, Malila, N, Seppä, K, Jégu, J, Velten, M, Cornet, E, Troussard, X, Bouvier, AM, Faivre, J, Guizard, AV, Bouvier, V, Launoy, G, Arveux, P, Maynadié, M, Mounier, M, Fournier, E, Woronoff, AS, Daoulas, M, Clavel, J, Le Guyader-Peyrou, S, Monnereau, A, Trétarre, B, Colonna, M, Cowppli-Bony, A, Molinié, F, Bara, S, Degré, D, Ganry, O, Lapôtre-Ledoux, B, Grosclaude, P, Estève, J, Bray, F, Piñeros, M, Sassi, F, Stabenow, R, Eberle, A, Erb, C, Nennecke, A, Kieschke, J, Sirri, E, Kajueter, H, Emrich, K, Zeissig, SR, Holleczek, B, Eisemann, N, Katalinic, A, Brenner, H, Asquez, RA, Kumar, V, Ólafsdóttir, EJ, Tryggvadóttir, L, Comber, H, Walsh, PM, Sundseth, H, Devigili, E, Mazzoleni, G, Giacomin, A, Bella, F, Castaing, M, Sutera, A, Gola, G, Ferretti, S, Serraino, D, Zucchetto, A, Lillini, R, Vercelli, M, Busco, S, Pannozzo, F, Vitarelli, S, Ricci, P, Pascucci, C, Autelitano, M, Cirilli, C, Federico, M, Fusco, M, Vitale, MF, Usala, M, Cusimano, R, Mazzucco, W, Michiara, M, Sgargi, P, Maule, MM, Sacerdote, C, Tumino, R, Di Felice, E, Vicentini, M, Falcini, F, Cremone, L, Budroni, M, Cesaraccio, R, Contrino, ML, Tisano, F, Fanetti, AC, Maspero, S, Candela, G, Scuderi, T, Gentilini, MA, Piffer, S, Rosso, S, Sacchetto, L, Caldarella, A, La Rosa, F, Stracci, F, Contiero, P, Tagliabue, G, Dei Tos, AP, Zorzi, M, Zanetti, R, Baili, P, Berrino, F, Gatta, G, Sant, M, Capocaccia, R, De Angelis, R, Liepina, E, Maurina, A, Smailyte, G, Agius, D, Calleja, N, Siesling, S, Visser, O, Larønningen, S, Møller, B, Dyzmann-Sroka, A, Trojanowski, M, Gózdz, S, Mezyk, R, Gradalska-Lampart, M, Radziszewska, AU, Didkowska, JA, Wojciechowska, U, Blaszczyk, J, Kepska, K, Bielska-Lasota, M, Kwiatkowska, K, Forjaz, G, Rego, RA, Bastos, J, Silva, MA, Antunes, L, Bento, MJ, Mayer-da-Silva, A, Miranda, A, Coza, D, Todescu, AI, Valkov, MY, Adamcik, J, Safaei Diba, C, Primic-Žakelj, M, Žagar, T, Stare, J, Almar, E, Mateos, A, Quirós, JR, Bidaurrazaga, J, Larrañaga, N, Díaz García, JM, Marcos, AI, Marcos-Gragera, R, Vilardell Gil, ML, Molina, E, Sánchez, MJ, Sureda, P Franch, Montserrat, M Ramos, Chirlaque, MD, Navarro, C, Ardanaz, EE, Moreno-Iribas, CC, Fernández-Delgado, R, Peris-Bonet, R, Galceran, J, Khan, S, Lambe, M, Camey, B, Bouchardy, C, Usel, M, Ess, SM, Herrmann, C, Bulliard, JL, Maspoli-Conconi, M, Frick, H, Kuehni, CE, Schindler, M, Bordoni, A, Spitale, A, Chiolero, A, Konzelmann, I, Dehler, SI, Matthes, KL, Rashbass, J, Stiller, CA, Fitzpatrick, D, Gavin, A, Bannon, F, Black, RJ, Brewster, DH, Huws, DW, White, C, Finan, P, Allemani, C, Bonaventure, A, Carreira, H, Coleman, MP, Di Carlo, V, Harewood, R, Liu, K, Matz, M, Montel, L, Nikšić, M, Rachet, B, Sanz, N, Spika, D, Stephens, R, Peake, M, Murphy, MFG, Chalker, E, Newman, L, Baker, D, Soeberg, MJ, Aitken, J, Scott, C, Stokes, BC, Venn, A, Farrugia, H, Giles, GG, Threlfall, T, Currow, D, You, H, Hendrix, J, Lewis, C, Latorre, MRDO, and Tanaka, LF

Abstract

Global inequalities in access to health care are reflected in differences in cancer survival. The CONCORD programme was designed to assess worldwide differences and trends in population-based cancer survival. In this population-based study, we aimed to estimate survival inequalities globally for several subtypes of childhood leukaemia.

Published
2017
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34. A population-based validation of the prognostic model PREDICT for early breast cancer.

Author

Wishart, G.C., Bajdik, C.D., Azzato, E.M., Dicks, E., Greenberg, D.C., Rashbass, J., Caldas, C., and Pharoah, P.D.P.

Subjects
BREAST cancer prognosis, PREDICTION models, HEALTH outcome assessment, ADJUVANT treatment of cancer, CALIBRATION, CANCER invasiveness
Abstract

Abstract: Introduction: Predict (www.predict.nhs.uk) is a prognostication and treatment benefit tool developed using UK cancer registry data. The aim of this study was to compare the 10-year survival estimates from Predict with observed 10-year outcome from a British Columbia dataset and to compare the estimates with those generated by Adjuvant! (www.adjuvantonline.com). Method: The analysis was based on data from 3140 patients with early invasive breast cancer diagnosed in British Columbia, Canada, from 1989–1993. Demographic, pathologic, staging and treatment data were used to predict 10-year overall survival (OS) and breast cancer specific survival (BCSS) using Adjuvant! and Predict models. Predicted outcomes from both models were then compared with observed outcomes. Results: Calibration of both models was excellent. The difference in total number of deaths estimated by Predict was 4.1 percent of observed compared to 0.7 percent for Adjuvant!. The total number of breast cancer specific deaths estimated by Predict was 3.4 percent of observed compared to 6.7 percent for Adjuvant! Both models also discriminate well with similar AUC for Predict and Adjuvant! respectively for both OS (0.709 vs 0.712) and BCSS (0.723 vs 0.727). Neither model performed well in women aged 20–35. Conclusion: In summary Predict provided accurate overall and breast cancer specific survival estimates in the British Columbia dataset that are comparable with outcome estimates from Adjuvant! Both models appear well calibrated with similar model discrimination. This study provides further validation of Predict as an effective predictive tool following surgery for invasive breast cancer. [Copyright &y& Elsevier]

Published
2011
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35. The Apical Polarity Determinant Crumbs 2 Is a Novel Regulator of ESC‐Derived Neural Progenitors

Author

Boroviak, Thorsten and Rashbass, Penny

Abstract

ESCs undergoing neural differentiation in vitro display an intrinsic heterogeneity with a large subset of the cells forming polarized neural rosettes that maintain the neural progenitor microenvironment. This heterogeneity is not only necessary for normal development but also causes substantial technical challenges for practical applications. Here, we report a novel regulator of early neural progenitors, the apical polarity protein Crb2 (Crumbs homologue 2). Employing monolayer differentiation of mouse ESCs to model neurogenesis in vitro, we find that Crb2 is upregulated with Sox1 and Musashi at the onset of neuroepithelial specification and localizes to the apical side of neural rosettes. Stable Crb2‐knockdown (KD) lines die at the onset of neural specification and fail to stabilize several apical polarity proteins. However, these cells are able to proliferate under self‐renewing conditions and can be differentiated into mesodermal and endodermal lineages. Conversely, Crb2 overexpression during neural differentiation results in elevated levels of other apical polarity proteins and increases proliferation. Additionally, sustained overexpression of Crb2 reduces terminal differentiation into TuJ1‐positive neurons. Furthermore, we demonstrate that Crb2 overexpression under self‐renewing conditions increases glycogen synthase kinase (GSK)‐3β inhibition, correlating with an increase in clonogenicity. To confirm the importance of GSK‐3β inhibition downstream of Crb2, we show that Crb2‐KD cells can be forced into neural lineages by blocking GSK‐3β function and supplementing Epidermal Growth Factor (EGF) and basic Fibroblast Growth Factor (bFGF). Thus, this is the first demonstration that a member of the Crumbs family is essential for survival and differentiation of ESC‐derived neural progenitors. STEMCELLS2011;29:193–205

Published
2011
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36. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

Author

Wishart, Gordon, Azzato, Elizabeth, Greenberg, David, Rashbass, Jem, Kearins, Olive, Lawrence, Gill, Caldas, Carlos, and Pharoah, Paul

Abstract

The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

Published
2010
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37. Foxn1 is required for tissue assembly and desmosomal cadherin expression in the hair shaft

Author

Johns, Sarah A., Soullier, Stephan, Rashbass, Penny, and Cunliffe, Vincent T.

Abstract

The mouse nude mutation inactivates the gene encoding the Foxn1 transcription factor, causing defective hair morphogenesis. Here, we show for the first time that Foxn1 is required for proper assembly of the hair medulla, and we identify Foxn1‐regulated genes by transcript profiling. One such gene encodes the desmosomal cadherin, Dsc2. Significantly, Foxn1‐dependent Dsc2 expression is restricted to the hair medulla, and within these cells, Dsc2 protein is predominantly localized to specialized adhesion junctions between the cortex and the medulla. Our results reveal Foxn1 as an essential regulator of tissue assembly in the growing hair shaft and implicate Dsc2 as a downstream effector of this activity. Developmental Dynamics 232:1062–1068, 2005. © 2005 Wiley‐Liss, Inc.

Published
2005
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38. Pax6 is required to regulate the cell cycle and the rate of progression from symmetrical to asymmetrical division in mammalian cortical progenitors

Author

Estivill-Torrus, Guillermo, Pearson, Helen, van Heyningen, Veronica, Price, David J., and Rashbass, Penny

Abstract

In the proliferative zone of the developing cerebral cortex, multipotential progenitors predominate early in development and divide to increase the progenitor pool. As corticogenesis progresses, proportionately fewer progenitors are produced and, instead, cell divisions yield higher numbers of postmitotic neurones or glial cells. As the switch from the generation of progenitors to that of differentiated cells occurs, the orientation of cell division alters from predominantly symmetrical to predominantly asymmetrical. It has been hypothesised that symmetrical divisions expand the progenitor pool, whereas asymmetrical divisions generate postmitotic cells, although this remains to be proved. The molecular mechanisms regulating these processes are poorly understood. The transcription factor Pax6 is highly expressed in the cortical proliferative zone and there are morphological defects in the Pax6Sey/Sey (Pax6 null) cortex, but little is known about the principal cellular functions of Pax6 in this region. We have analysed the cell-cycle kinetics, the progenitor cleavage orientation and the onset of expression of differentiation markers in Pax6Sey/Sey cortical cells in vivo and in vitro. We showed that, early in corticogenesis at embryonic day (E) 12.5, the absence of Pax6 accelerated cortical development in vivo, shortening the cell cycle and the time taken for the onset of expression of neural-specific markers. This also occurred in dissociated culture of isolated cortical cells, indicating that the changes were intrinsic to the cortical cells. From E12.5 to E15.5, proportions of asymmetrical divisions increased more rapidly in mutant than in wild-type embryos. By E15.5, interkinetic nuclear migration during the cell cycle was disrupted and the length of the cell cycle was significantly longer than normal in the Pax6Sey/Sey cortex, with a lengthening of S phase. Together, these results show that Pax6 is required in developing cortical progenitors to control the cell-cycle duration, the rate of progression from symmetrical to asymmetrical division and the onset of expression of neural-specific markers.

Published
2002
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39. Pax6 is required to regulate the cell cycle and the rate of progression from symmetrical to asymmetrical division in mammalian cortical progenitors.

Author

Guillermo, Estivill-Torrus, Helen, Pearson, Veronica, van Heyningen, J, Price David, and Penny, Rashbass

Abstract

In the proliferative zone of the developing cerebral cortex, multipotential progenitors predominate early in development and divide to increase the progenitor pool. As corticogenesis progresses, proportionately fewer progenitors are produced and, instead, cell divisions yield higher numbers of postmitotic neurones or glial cells. As the switch from the generation of progenitors to that of differentiated cells occurs, the orientation of cell division alters from predominantly symmetrical to predominantly asymmetrical. It has been hypothesised that symmetrical divisions expand the progenitor pool, whereas asymmetrical divisions generate postmitotic cells, although this remains to be proved. The molecular mechanisms regulating these processes are poorly understood. The transcription factor Pax6 is highly expressed in the cortical proliferative zone and there are morphological defects in the Pax6(Sey/Sey) (Pax6 null) cortex, but little is known about the principal cellular functions of Pax6 in this region. We have analysed the cell-cycle kinetics, the progenitor cleavage orientation and the onset of expression of differentiation markers in Pax6(Sey/Sey) cortical cells in vivo and in vitro. We showed that, early in corticogenesis at embryonic day (E) 12.5, the absence of Pax6 accelerated cortical development in vivo, shortening the cell cycle and the time taken for the onset of expression of neural-specific markers. This also occurred in dissociated culture of isolated cortical cells, indicating that the changes were intrinsic to the cortical cells. From E12.5 to E15.5, proportions of asymmetrical divisions increased more rapidly in mutant than in wild-type embryos. By E15.5, interkinetic nuclear migration during the cell cycle was disrupted and the length of the cell cycle was significantly longer than normal in the Pax6(Sey/Sey) cortex, with a lengthening of S phase. Together, these results show that Pax6 is required in developing cortical progenitors to control the cell-cycle duration, the rate of progression from symmetrical to asymmetrical division and the onset of expression of neural-specific markers.

Published
2002

42. The DNA‐binding protein E12 co‐operates with XMyoD in the activation of muscle‐specific gene expression in Xenopus embryos.

Author

Rashbass, J., Taylor, M.V., and Gurdon, J.B.

Abstract

Two alternatively spliced products of the human E2A gene, E12 and E47, encode helix‐loop‐helix DNA‐binding proteins. Here we describe the isolation of two Xenopus cDNAs; one, XE12, is structurally similar to human E12 and the other contains a sequence similar to E47. Transcripts of both cDNAs are present at all the stages of Xenopus development tested and in all regions of the embryo. The DNA binding properties of in vitro translated XE12 are indistinguishable from those of human E12. We have shown previously that an embryonic muscle DNA‐binding activity, EMF1, that binds to a promoter sequence required for the expression of the cardiac actin gene, contains the Xenopus myogenic factor XMyoD. Here we show that it also contains protein that interacts with an anti‐E12 antiserum, suggesting that XE12 and XMyoD proteins, or very similar ones, are present in EMF1. We have addressed the functional role of XE12 in muscle gene transcription in Xenopus embryos by injecting in vitro synthesized RNA into the two cell embryo. Overexpression of XE12 and XMyoD augments by greater than 10‐fold the ectopic activation of the endogenous cardiac actin gene that can be produced by XMyoD alone. Our DNA binding results strongly suggest that this effect is mediated through a direct interaction of the XE12‐XMyoD complex with specific sites in the cardiac actin promoter. We suggest that XE12 is functionally important in muscle gene activation in embryonic development.

Published
1992
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