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The repository contains the research outputs from staff and students at The Christie NHS Foundation Trust and Cancer Research UK Manchester Institute.
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Over 7000 peer reviewed articles, reviews and selected publications from 1933 onwards.
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Comparative performance of lung cancer risk models to define lung screening eligibility in the United KingdomIntroduction: The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the United Kingdom. Methods: We analysed current and former smokers aged 40-80 in the UK Biobank (N¼217,199), EPIC-UK (N¼30,982), and Generations Study (N¼25,849). 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, AUC¼0.82, 95%CI¼0.81-0.84), followed by the LCRAT (AUC¼0.81, 95%CI¼0.79-0.82) and the Bach model (AUC¼0.80, 95%CI¼0.79-0.81) (Figure). 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 1.30 for PLCOm2012 (95% CI¼1.23-1.36) to 2.16 for LLPv2 (95%CI¼2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF criteria classified 50.6% of future cases as screening-eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.1%), and LLPv2 (53.6%). Conclusion: Discrimination of lung cancer risk models in UK cohorts was highest for LCDRAT and LCRAT, and lowest for LLPv2. Our results highlight the importance of context-specific validation for prediction tools.
A systematic review of prostate cancer heterogeneity: understanding the clonal ancestry of multifocal diseaseContext: Studies characterising genomic changes in prostate cancer (PCa) during natural progression have greatly increased our understanding of the disease. A better understanding of the evolutionary history of PCa would allow advances in diagnostics, prognostication, and novel therapies that together will improve patient outcomes. Objective: To review the molecular heterogeneity of PCa and assess recent efforts to profile intratumoural heterogeneity and clonal evolution. Evidence acquisition: We screened a total of 1313 abstracts from PubMed published between 2009 and 2020, of which we reviewed 84 full-text articles. We excluded 49, resulting in 35 studies for qualitative analysis. Evidence synthesis: In studies of primary disease (16 studies, 4793 specimens), there is a lack of consensus regarding the monoclonal or polyclonal origin of primary PCa. There is no consistent mutation giving rise to primary PCa. Detailed clonal analysis of primary PCa has been limited by current techniques. By contrast, clonal relationships between PCa metastases and a potentiating clone have been consistently identified (19 studies, 732 specimens). Metastatic specimens demonstrate consistent truncal genomic aberrations that suggest monoclonal metastatic progenitors. Conclusions: The relationship between the clonal dynamics of PCa and clinical outcomes needs further investigation. It is likely that this will provide a biological rationale for whether radical treatment of the primary tumour benefits patients with oligometastatic PCa. Future studies on the mutational burden in primary disease at single-cell resolution should permit the identification of clonal patterns underpinning the origin of lethal PCa. Patient summary: Prostate cancers arise in different parts of the prostate because of DNA mutations that occur by chance at different times. These cancer cells and their origin can be tracked by DNA mapping. In this review we summarise the state of the art and outline what further science is needed to provide the missing answers.
Intrinsically connected: therapeutically targeting the cathepsin proteases and the Bcl-2 family of protein substrates as co-regulators of apoptosisTaken with the growing importance of cathepsin-mediated substrate proteolysis in tumor biology and progression, the focus and emphasis placed on therapeutic design and development is coming into fruition. Underpinning this approach is the invariable progression from the direction of fully characterizing cathepsin protease members and their substrate targets, towards targeting such an interaction with tangible therapeutics. The two groups of such substrates that have gained much attention over the years are the pro- and anti- apoptotic protein intermediates from the extrinsic and intrinsic signaling arms of the apoptosis pathway. As proteins that are central to determining cellular fate, some of them present themselves as very favorable candidates for therapeutic targeting. However, considering that both anti- and pro- apoptotic signaling intermediates have been reported to be downstream substrates for certain activated cathepsin proteases, therapeutic targeting approaches based on greater selectivity do need to be given greater consideration. Herein, we review the relationships shared by the cathepsin proteases and the Bcl-2 homology domain proteins, in the context of how the topical approach of adopting 'BH3-mimetics' can be explored further in modulating the relationship between the anti- and pro- apoptotic signaling intermediates from the intrinsic apoptosis pathway and their upstream cathepsin protease regulators. Based on this, we highlight important future considerations for improved therapeutic design.
Unsatisfactory quality of E. coli asparaginase biogenerics in India: Implications for clinical outcomes in acute lymphoblastic leukaemiaBackground: The biotherapeutic asparaginase is a cornerstone of therapy in acute lymphoblastic leukaemia (ALL). With limited access to the original native Escherichia coli-derived asparaginase (EcASNase), a variety of EcASNase biogenerics are used in low-middle-income countries (LMICs). The variable quality of these biogenerics potentially influences clinical outcomes. Procedure: Seven biogeneric EcASNases (P1-P7) marketed widely in India were evaluated, with P2 as an exemplar for in vivo monitoring. Therapeutic activity of P2 (10,000 IU/m2 /dose, intramuscular, every 72 hours) was monitored during induction therapy, and drug-related toxicities recorded. Molecular identity, purity and in vitro drug activity of seven biogenerics were characterised using multimodal analyses, and findings compared with reference EcASNase (R). Results: In patients (N = 62) receiving P2, subtherapeutic asparaginase activity (<100 U/L) was observed in 66% (46/70) of trough timepoints (72 hours postdose) during induction. Twelve patients (19%), 11 with high-risk ALL, developed hypersensitivity. Isoforms of EcASNase were identified in all seven biogenerics. All generic products contained impurities with batch-to-batch variability. These included high levels of protein aggregates and host cell protein contamination. In vitro assays of EcASNase activity and leukaemia cell line cytotoxicity were not discriminatory. Conclusions: Our findings confirm widespread concerns over the unsatisfactory quality and therapeutic activity of native EcASNase biogenerics marketed in LMICs. Appropriate use of these products requires monitored studies to identify clinical suitability and determine appropriate dosing and schedule. For large parts of the world, assured access to high-quality asparaginases remains an unmet therapeutic need.
Do traditional BMI categories capture future obesity? A comparison with trajectories of BMI and incidence of cancerIn 2016, 13 specific obesity related cancers were identified by IARC. Here, using baseline WHO BMI categories, latent profile analysis (LPA) and latent class trajectory modelling (LCTM) we evaluated the usefulness of one-off measures when predicting cancer risk vs life-course changes. Our results in LPA broadly concurred with the three basic WHO BMI categories, with similar stepwise increase in cancer risk observed. In LCTM, we identified 5 specific trajectories in men and women. Compared to the leanest class, a stepwise increase in risk for obesity related cancer was observed for all classes. When latent class membership was compared to baseline BMI, we found that the trajectories were composed of a range of BMI (baseline) categories. All methods reveal a link between obesity and the 13 cancers identified by IARC. However, the additional information included by LCTM indicates that lifetime BMI may highlight additional group of people that are at risk.