Welcome to The Christie Research Publications Repository

The repository contains the research outputs from staff and students at The Christie NHS Foundation Trust and Cancer Research UK Manchester Institute.

Current Repository Content:

Over 7000 peer reviewed articles, reviews and selected publications from 1933 onwards.

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Please contact Kostoris Library on 0161 446 3456/3455.

 

  • Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

    Robbins H A; Alcala K; Swerdlow A J; Schoemaker M J; Wareham N; Travis R C; Crosbie Philip A J; Callister M; Baldwin D R; Landy R; et al. (2021)
    Background: The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. Methods: We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). Results: Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). Conclusion: In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.
  • Technology clinical trials: Turning innovation into patient benefit

    Royle, J. K.; Hughes, Andrew M; Stephenson, L; Landers, D.; digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK. (2021)
    Health care needs to continuously evolve and innovate to maintain the health of populations. Technology has the potential to enable better patient engagement and ownership, as well as optimise therapeutic interventions and data-science approaches to facilitate improved health care decisions. Yet, to date, technological innovation has not resulted in the rate of change that could have been predicted from other sectors. This article discusses multiple reasons for this and proposes a newly tested and deployed solution: the technology clinical trial. The technology clinical trial methodology has been developed through working directly with patients, clinical and medical devicetrial experts. This approach enables researchers to use the complex environment of health care as an opportunity to transform the pace of innovation and create new care pathways. Instead of testing a single innovation, researchers can 'step back' and systematically review all areas of the patient's journey for potential optimization. Then integrate novel data science, technological advances, process updates, behavioural science, and patient engagement to co-create a streamlined multidisciplinary solution. As a result, this research has the potential for larger advances due to the emergent benefits that can arise when the individual elements work together as a whole. These potential benefits are then robustly tested, characterised and measured in the trial environment to ensure that future application of the innovative pathway is supported by the robust empirical data health care requires.
  • Fibroblast growth factor receptors (FGFRs) and noncanonical partners in cancer signaling

    Ferguson, H. R.; Smith, M. P; Francavilla, Chiara; Division of Molecular and Cellular Function, School of Biological Science, Faculty of Biology Medicine and Health (FBMH), The University of Manchester, Manchester M13 9PT, (2021)
    Increasing evidence indicates that success of targeted therapies in the treatment of cancer is context-dependent and is influenced by a complex crosstalk between signaling pathways and between cell types in the tumor. The Fibroblast Growth Factor (FGF)/FGF receptor (FGFR) signaling axis highlights the importance of such context-dependent signaling in cancer. Aberrant FGFR signaling has been characterized in almost all cancer types, most commonly non-small cell lung cancer (NSCLC), breast cancer, glioblastoma, prostate cancer and gastrointestinal cancer. This occurs primarily through amplification and over-expression of FGFR1 and FGFR2 resulting in ligand-independent activation. Mutations and translocations of FGFR1-4 are also identified in cancer. Canonical FGF-FGFR signaling is tightly regulated by ligand-receptor combinations as well as direct interactions with the FGFR coreceptors heparan sulfate proteoglycans (HSPGs) and Klotho. Noncanonical FGFR signaling partners have been implicated in differential regulation of FGFR signaling. FGFR directly interacts with cell adhesion molecules (CAMs) and extracellular matrix (ECM) proteins, contributing to invasive and migratory properties of cancer cells, whereas interactions with other receptor tyrosine kinases (RTKs) regulate angiogenic, resistance to therapy, and metastatic potential of cancer cells. The diversity in FGFR signaling partners supports a role for FGFR signaling in cancer, independent of genetic aberration.
  • Correction: Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

    Robbins, H. A.; Alcala, K.; Swerdlow, A. J.; Schoemaker, M. J.; Wareham, N.; Travis, R. C.; Crosbie, Philip A J; Callister, M.; Baldwin, D. R.; Landy, R; et al. (2021)
    None
  • Salvage stereotactic body radiotherapy (SBRT) for intraprostatic relapse after prostate cancer radiotherapy: An ESTRO ACROP Delphi consensus

    Jereczek-Fossa, B. A.; Marvaso, G.; Zaffaroni, M.; Gugliandolo, S. G.; Zerini, D.; Corso, F.; Gandini, S.; Alongi, F.; Bossi, A.; Cornford, P.; et al. (2021)
    Background and purpose: Between 30% and 47% of patients treated with definitive radiotherapy (RT) for prostate cancer are at risk of intraprostatic recurrence during follow-up. Re-irradiation with stereotactic body RT (SBRT) is emerging as a feasible and safe therapeutic option. However, no consensus or guidelines exist on this topic. The purpose of this ESTRO ACROP project is to investigate expert opinion on salvage SBRT for intraprostatic relapse after RT. Materials and methods: A 40-item questionnaire on salvage SBRT was prepared by an internal committee and reviewed by a panel of leading radiation oncologists plus a urologist expert in prostate cancer. Following the procedure of a Delphi consensus, 3 rounds of questionnaires were sent to selected experts on prostate re-irradiation. Results: Among the 33 contacted experts, 18 (54.5%) agreed to participate. At the end of the final round, participants were able to find consensus on 14 out of 40 questions (35% overall) and major agreement on 13 questions (32.5% overall). Specifically, the consensus was reached regarding some selection criteria (no age limit, ECOG 0-1, satisfactory urinary flow), diagnostic procedures (exclusion of metastatic disease, SBRT target defined on the MRI) and therapeutic approach (no need for concomitant ADT, consideration of the first RT dose, validity of Phoenix criteria for salvage SBRT failure). Conclusion: While awaiting the results of ongoing studies, our ESTRO ACROP Delphi consensus may serve as a practical guidance for salvage SBRT. Future research should address the existing disagreements on this promising approach.

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