The Christie School of Oncology: Christie Research Publications Repository
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Overcoming data management challenges in oncology research: Lessons from an NHS, industry, technology start-up and academic collaboration
No full textBACKGROUND: The integration of large-scale genomic and multimodal data is critical to advancing oncology research. However, challenges related to data storage, sharing, and governance hinder its effective use. We share experience from conducting a multi-site, cross-industry UK project utilising large-scale genomic data obtained from tissue and liquid biopsies from patients with cancer, to produce recommendations for enabling and optimising the use of multimodal data in oncology research. METHODS: A collaborative approach involving NHS Trusts, industry, start-ups, and academic partners was adopted to develop a robust data management strategy. A data lake architecture was selected as the centralised repository to store and share diverse datasets securely. Key factors influencing the selection and implementation of this solution included data storage requirements, access control, ownership, and information governance. Processes for planning, deploying, and maintaining the data lake infrastructure were documented and evaluated. RESULTS: The data lake enabled secure, compliant, and federated storage of large-scale genomic and clinical data. Successful implementation required early engagement of stakeholders and the establishment of clear data governance frameworks. Lessons learned highlighted the importance of aligning technical solutions with governance, security, and accessibility requirements across diverse partners. CONCLUSIONS: Effective management of multimodal data in oncology requires early planning, multi-stakeholder engagement (among National Health Service [NHS] Trusts, industry, start-up collaborators, and academic institutions), and robust governance. The data lake model demonstrated a scalable and compliant approach to enabling secure, collaborative research using genomic data, providing a template for future initiatives in precision oncology
Quantifying evidence for phenotypic specificity (PP4) for syndromic phenotypes: large-scale integration of rare germline FH variants from diagnostic laboratory testing for HLRCC and renal cancer
No full textPURPOSE: Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare cancer susceptibility syndrome exclusively attributable to pathogenic variants in FH (HGNC:3700). This article quantitatively weights the phenotypic context (PP4/PS4) of such very rare variants in FH. METHODS: We collated clinical diagnostic testing data on germline FH variants from 387 individuals with HLRCC and 1780 individuals with renal cancer and compared the frequency of 'very-rare' variants in each phenotypic cohort with 562,295 population controls. We generated pan-gene very rare variant likelihood ratios (PG-VRV-LRs), domain-specific likelihood ratios for missense variants (DS-VRMV-LR) using spatial clustering analysis, and log(2.08) likelihood ratios (LLRs) as applicable within the updated American College of Medical Genetics and Genomics/Association for Molecular Pathology variant classification framework. RESULTS: For HLRCC, the PG-VRV-LR was estimated to be 2669.4 (95% CI 1843.4-3881.2, LLR 10.77) for truncating variants and 214.7 (95% CI 185.0-246.9, LLR 7.33) for missense variants. For renal cancer, the PG-VRV-LR was 95.5 (95% CI 48.9-183.0, LLR 6.23) for truncating variants and 5.8 (95% CI 3.5-9.3, LLR 2.39) for missense variants. Clustering analysis in HLRCC cases revealed 3 'hotspot' regions wherein the DS-VRMV-LR increased to 1226.9. CONCLUSION: These data provide quantitative measures for very rare missense and truncating variants in FH, which reflect the differing phenotypic specificity of HLRCC and renal cancer and may be applicable in clinical variant classification
Emotional strategies to enhance resilience in patients with cancer: a scoping review
No full textOBJECTIVE: To map and summarize existing evidence on emotional strategies recommended for enhancing resilience in cancer patients, identify research gaps and inform future research. METHODS: Following Joanna Briggs Institute (JBI) guidelines, a comprehensive search was conducted across 11 databases in English and Chinese, supplemented by citation tracking and manual searches for published and unpublished studies. Studies focusing on adult cancer patients and describing emotional strategies to enhance resilience were included and critically appraised using tools appropriate to its design. Data including qualitative descriptions of emotional strategies, quantitative resilience-related emotional variables, and emotional intervention details were extracted and analysed with NVivo 15. RESULTS: A total of 33 papers were included, primarily from China (n = 16) and published as journal articles (n = 30) with randomized controlled trial designs (n = 14). Three key themes were identified: (a) emotion identification; (b) effective emotion regulation; and (c) emotional support from others. Emotional strategies were primarily implemented by nurses (n = 11), delivered online (n = 6) or face-to-face (n = 13)."Positive" and"emotions" were the most frequently mentioned words. CONCLUSIONS: Emotion identification, effective emotion regulation, and emotional support from others are essential for enhancing resilience in cancer patients. Many promising strategies remain underutilized and require further validation. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (OSF) (DOI: 10.17605/OSF. IO/JBMZ9)
CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization
No full textSpatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics (ST) technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here, we present CellPie, a fast, unsupervised factor discovery method based on joint non-negative matrix factorization of spatial RNA transcripts and histological image features. CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional ST datasets. We assessed CellPie on three different human cancer types with different spatial resolutions, including a highly resolved Visium HD dataset, demonstrating both good performance and high computational efficiency compared to existing methods
Hemi-thoracic irradiation with proton therapy in malignant pleural mesothelioma (HIT-Meso)
No full textPlasma AR alterations and timing of intensified hormone treatment for prostate cancer: the STAMPEDE phase 3 randomized clinical trial
No full textHypoxia-associated gene signatures are not prognostic in high-risk localized prostate cancers undergoing androgen deprivation therapy with radiation therapy
No full textPurpose: Men with high-risk prostate cancer (PCa) are treated with androgen deprivation therapy (ADT) and radiation therapy, but the disease reoccurs in 30% of patients. Biochemical recurrence of PCa after treatment is influenced by tumor hypoxia. Tumors with high levels of hypoxia are aggressive, resistant to treatment, and have increased metastatic capacity. Gene expression signatures derived from diagnostic biopsies can predict tumor hypoxia and radiosensitivity, but none are in routine clinical use, due to concerns about the applicability of these biomarkers to new patient cohorts. There has been no or limited testing in cohorts of high-risk PCa. Methods and Materials: We generated transcriptomic data for cohorts of patients with high-risk PCa. Patients were treated with ADT followed by external beam radiation therapy with or without a brachytherapy boost. Biomarkers curated from the literature were calculated from pretreatment biopsy gene expression data. The primary endpoint for survival analyses was bio- chemical recurrence-free survival and the secondary endpoints were distant metastasis-free survival and overall survival. Results: The performance of the selected biomarkers was poor, with none achieving prognostic significance for biochemical recurrence-free survival or distant metastasis-free survival in any cohort. The brachytherapy boost cohort received shorter durations of ADT than the conventionally fractionated or hypofractionated cohorts (Wilcoxon rank sum test, P = 2.1 pound 10-18 and 2.3 pound 10-10, respectively) and had increased risk of distant metastasis (log-rank test, P = 8 pound 10-4). There were no consis- tent relationships between biomarker score and outcome for any of the endpoints. Conclusions: Hypoxia and radiosensitivity biomarkers were not prognostic in patients with high-risk PCa treated with ADT plus radiation therapy. We speculate that the lack of prognostic capability could be caused by the variable hypoxia-modifying effects of the ADT that these high-risk patients received before and during definitive treatment with radiation therapy. A deeper understanding of biomarker construction, performance, and inter-cohort transferability in relation to patient character- istics, sample handling, and treatment modalities is required before hypoxia biomarkers can be recommended for routine clini- cal use in the pretreatment setting. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/
The clinical value of psychological intervention in preparing young children with neurological tumors for radiotherapy
No full textAssociation of quality of life with mortality in patients with adenoid cystic carcinoma using an internationally-validated QoL questionnaire (EQ-5D-5L)
No full textBACKGROUND/OBJECTIVES: An evaluation of quality of life (QoL) is increasingly required for approval and reimbursement of new drug therapies. To support the evaluation of the impact of new drug therapies on QoL in single-arm studies in adenoid cystic carcinoma (ACC), we sought to determine the QoL baseline in a cohort of patients with ACC during routine follow up visits and to assess for associations with clinical or prognostic factors. METHODS: An internationally-validated QoL questionnaire (EQ-5D-5L) was completed by patients with ACC referred to an experimental medicine centre. EQ-5D value scores (EQV) were calculated from each questionnaire using the EuroQol England value set. A Cox proportional hazards model was built with EQV as a time-dependent variable. Non-linear mixed effects modelling (NLME) was used to test the relationship between EQV and predictors (time, NOTCH1 status, age at diagnosis, sex, local and/or metastatic recurrence, and primary site of disease). RESULTS: Between 2019 and 2023, 563 questionnaires were completed by 161 patients with ACC. Median EQV was 0.81 (range -0.22 to 1.0) and mean EQV was 0.79. A decrease in EQV from 1 to 0 was associated with an eightfold increase in risk of death in the total population (HR = 0.118, 95 % CI 0.057 to 0.244, p=<0.001). No predictor had a significant impact on EQV in NLME except time (p=<0.001). CONCLUSIONS: For patients with ACC, a worse QoL as measured by EQ-5D-5L was associated with a significantly increased risk of death. It remains unclear if poorer QoL has a causal relationship with mortality
Association for Clinical Genomic Science (ACGS) guidelines for the classification of oncogenicity of somatic variants in cancer: recommendations by the UK somatic variant interpretation group (SVIG-UK)
No full textComprehensive genomic testing in routine cancer care pathways has created the need to interpret the consequences of somatic (acquired) genomic variants beyond the currently well-characterised driver variants in cancer gene hotspots. While several guidelines have been published to determine the oncogenicity of somatic cancer gene variants, they lack a comprehensive and flexible approach that encompasses all available lines of evidence. Individual UK laboratories have developed local approaches to standardise somatic variant interpretation, often based on different sets of published guidelines, but a comprehensive national standardised framework is lacking. The absence of standardisation in approaches to somatic variant interpretation highlights a significant gap in the field of genomic medicine within the UK healthcare system. Key stakeholders from across the UK cancer genomics diagnostic community formed the UK Somatic Variant Interpretation Group (SVIG-UK) in September 2018 to develop a consensus approach for interpretation of somatic variants identified through genomic testing in patients with solid tumours and haematological malignancies. SVIG-UK scientists conducted a review of existing somatic variant interpretation classification systems and although they mostly agreed on evidence sources for variant interpretation, differences were identified in how the evidence should be used, weighted and combined. The SVIG-UK team subsequently developed a single, standardised UK-wide approach to somatic variant interpretation which encompassed both solid tumour and haematological cancer genomic testing. This framework was shared with stakeholders across the UK alongside variants for preliminary testing. Outcomes were then reviewed and following engagement sessions across the community, the variant interpretation recommendations were updated and ratified by the UK Association of Clinical Genomics Sciences. We present herein the SVIG-UK framework and recommendations, which provide a standardised, comprehensive and flexible approach for classifying the oncogenicity of somatic variants in cancer genes