NIHRDataInsights/HRCSData huggingface dataset:
Title: Exploratory & preclinical development & CRUKD/16/001 a first in human Phase I/IIa Study with BI-1206, an antibody to Fc gamma receptor IIB (FcgRIIB), and rituximab in patients with CD32+ B cell malignancy (Funded by Bloodwise)
Funder: Cancer Research UK

Abstract: The projects aims to develop function-blocking fully human antibodies to the inhibitory Fc gamma receptor IIB (FcgRIIB) for combination therapy with rituximab and, if shown to be safe, a range of other therapeutic antibodies. FcgRIIB is highly expressed on key immune cells and also certain B cell cancers, resulting in multiple modes of resistance to current antibody treatments (e.g. rituximab). Function-blocking anti-FcgRIIB antibodies are expected to prevent and overcome these resistance mechanisms which may translate into improved outcomes for patients. We propose the delivery of a first in man open label, two part, phase I/IIa study in patients with B-cell malignancies. In the first part (A), the biologically effective and safe dose of BI-1206 will be established through a conventional 3+3 schema of cohort dose escalation. In the second (B) the safety and the efficacy of BI-1206 will be investigated as a single agent or combination with rituximab, in patients with CD32b expressing lymphoid malignancies. Response will be evaluated by standardised disease specific criteria. Pharmacokinetic evaluation will be performed along with pharmacodynamic evaluation of CD32b+ tumour cells in blood and BI-1206 occupancy of cell expressed CD32b receptors.


Research activity codes: 6.2 CELLULAR AND GENE THERAPIES;5.2 CELLULAR AND GENE THERAPIES;6.1 PHARMACEUTICALS
Health categories: CANCER AND NEOPLASMS
Title: A03 - Optimising Deep Learning Models for Risk Classification of AMBLor in cutaneous melanoma
Funder: Cancer Research UK

Abstract: Background- The worldwide annual incidence of cutaneous melanoma is over 200,000 and increasing. Our laboratory has previously identified two novel prognostic biomarkers for early stage melanoma; Autophagy and beclin 1 regulator 1 (AMBRA1) and loricrin. However, despite advances in cutaneous melanoma biomarkers the process in scoring histological images remains labor intensive and influenced by subjectivity. Artificial intelligence (AI) has improved the accuracy of several pattern recognition tasks in digital images. AI has the potential to decrease dermatologist workloads, eliminate repetitive and routine tasks, and improve access to dermatological care. In this pump priming study we will investigate the use of artificial intelligence for melanoma patient stratification and disease subtyping to aid the clinical decision-making from digitised immunohistochemical images of tissue samples stained for epidermal AMBRA/Loricrin (AMBLor) expression. Aims- The principle aims of the present study are to use optimised algorithms within Visiopharm to determine AMBLor expression in 3 large independent cohorts of non-ulcerated AJCC stage I/II melanomas derived from the North East of England, Northern Ireland and Buffalo USA (totaling approximately 1000 cases) and to investigate the possibility of predicting 5 year disease free survival of patients using digitized images. Methods- Pilot data was analysed using Visiopharm image analysis in collaboration with colleagues at Oxford University and Cambridge University Hospitals NHS Trust. Software analysis enabled the creation of a dedicated training algorithm to quantify IHC expression of 36 AMBLor stains of primary cutaneous non-ulcerated AJCC stage I/II melanomas. Multiple auxiliary Analysis Protocol Packages were then run to identify the Regions of Interest, subsequently quantitatively analysed and risk stratified. Results revealed a strong correlation between mean tumour epidermal AMBRA1 density (as compared to the normal adjacent epidermis) and groups with Lost or Maintained AMBRA expression (Pearson r Correlation = 0.8312, p value= 0.0029), with similar results found for peritumoural epidermal Loricrin expression (Pearson r Correlation = 0.9437, p value<​0.0001). Furthermore, pilot data derived from this training cohort also revealed a trend for the association of loss of epidermal AMBLor expression with a high risk sub group, suggesting Visiopharm is a viable means to determine AMBLor expression. How the results of this result will be used- Data derived from this study will be used to confirm the diagnostic performance of AMBLor deep learning models, which ultimately could be incorporated into standard clinical workflow and aid the development of deep learning models for immunohistochemical prognostic biomarkers for other cutaneous disease.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Integrated characterisation of anti-tumour immunity following neoadjuvant immunotherapy of hepatocellular carcinoma
Funder: Cancer Research UK

Abstract: Background Surgery can be a potentially curative treatment for early-stage hepatocellular carcinoma (HCC). However, 70% patients relapse within 2 years after resection. To improve this outcome, a potential role of immunotherapy in the neoadjuvant setting is currently under investigation. PRIME-HCC (NCT03682276) is a phase Ib study testing the combination of ipilimumab (anti-cytotoxic T-lymphocyte antigen 4) and nivolumab (anti-programmed death 1) in 32 patients with early-phase HCC, undergoing surgical resection. Studies conducted with immune checkpoint inhibitors (ICI) in the neoadjuvant setting can provide unique data from the analysis of surgical specimens and biological samples of responders and non-responders, with the aim of identifying possible predictive factors of response to ICI. Aims The overarching aim of this proposal is to aid identification of mechanisms of action of immunotherapy taking advantage of a unique biorepository of clinical samples collected as part of the neoadjuvant clinical trial PRIME-HCC. We will test the hypothesis that ICI response can be related to CD8+ T cells exhaustion. Methods I will extensively analyze biological samples collected during the study, including tumour tissue (pre-ICI and post-surgery), peripheral blood mononuclear cells (PBMCs), serum, plasma and stool aliquots. I will focus on three main correlates of exhaustion: 1) I will evaluate tumour-infiltrating lymphocytes using multiparameter mass cytometry and using the Nanostring PanCancer Immune Panel for a complete immune-profiling of the malignancy. Relative expression of immune exhaustion markers will be analysed in relationship with response to treatment and recurrence-free survival. 2) I will perform a longitudinal assessment of T-cell receptor beta-chain repertoire in peripheral blood lymphocytes by sequencing of CDR3 region to assess for dynamic changes in clonality of the T-cell response. 3) I will perform gut microbiome characterization by sequencing of 16S rRNA gene from stool DNA to correlate bacterial phylogeny with the characteristics of the anti-tumour immune response. Impact of the research results The results of the research will help to optimize the use of ICI in HCC, by facilitating identification of patients who are more likely to respond to treatment and spare non-responders from unnecessary and potentially life-threatening toxicity. It will improve understanding of ICI-responsiveness focusing on an exclusive treatment setting, with broader reaching implications transferable to other oncological indications where ICIs are standard of care.


Research activity codes: 5.1 PHARMACEUTICALS
Health categories: CANCER AND NEOPLASMS
Title: T-cell Receptor Profiling in Early Stage Breast Cancer
Funder: Cancer Research UK

Abstract: Background Immunotherapies have transformed cancer treatment and how we understand and interpret the tumour microenvironment. Furthermore, it is becoming increasingly apparent that the immune system plays a key role in the effectiveness of chemotherapy. The Caldas laboratory has performed extensive immunogenomic analysis of patients (n=168) recruited to the TransNEO observational study. This study takes three serial biopsies from the same tumour site of patients with early breast cancer during standard of care neoadjuvant chemotherapy treatment. The Caldas group has noted that the adaptive immune infiltrate predicts pathological complete response. Aim To characterise key aspects of the T-cell infiltrate and assess their utility in predicting response and clinical outcomes. Primary End Points • Characterise the T-cell receptor (TCR) repertoire of breast tumours treated with neoadjuvant therapy (NAT) at diagnosis, midway through chemotherapy and on completion of chemotherapy. • Examine whether TCR repertoire diversity at baseline correlates with pathological complete response (pathCR) and the degree of residual disease as quantified using the Residual Cancer Burden (RCB) score. Secondary End Point • Incorporate TCR data into a novel integrated machine learning (ML) predictor of response developed by the Caldas Lab. This ML model is able to predict response to chemotherapy using DNA, RNA and digital pathology data. Methods We will sequence TCR-a and TCR-ß cDNA of each TransNEO patient's samples using digital sequencing. The reads will be filtered, forward and reverse reads will be paired and the sequence edited to leave TCR and complementarity-determining region 3 (CDR3) sequences. Network properties of the TCRs will be calculated to understand the specific sequences present and the proportionate number of identical reads. After enriching for patient-specific antigens, we will assign Gini scores of T cell clonality. Analysis of variance (ANOVA) will be performed whether different RCB scores at resection are associated with different distributions of TCR clonality scores. Then the data will be integrated into a multi-variable logistic regression model for response outcomes using R. How the Results of the Research will be Used I will accrue data and skills which will support my PhD applications at the end of this year. This data should be published either by myself as a complete manuscript or be part of a larger, higher impact publication. Either publication should expand the knowledge of how to interpret early breast cancer, help others to better plan treatment or trials in breast cancer, and could inform research strategy in further chemoresponsive cancers.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: A06 - Lung cancer and early diagnosis after Covid-19: identifying the impact of remote consulting on lung cancer referral
Funder: Cancer Research UK

Abstract: Background: The move to telephone consulting due to covid-19 has been associated with a significant reduction in the number of lung cancer referrals from primary care. As telephone consulting is likely to continue to be the default consultation model going forward, it is urgent that we understand how consulting in this way has reduced the number of referrals made by GPs, potentially contributing to delayed lung cancer diagnoses and poorer outcomes. Aims: Our aims are firstly to examine changes in patterns of urgent suspected lung cancer referrals since the beginning of the pandemic in the Greater Glasgow and Clyde, secondly to undertake an in-depth case study in a cluster of GP practices to understand how referral pathways have changed for lung cancer presentations during the pandemic, and thirdly to ascertain GP attitudes towards, and experiences of, telephone consulting with patients with possible lung cancer symptoms Methods: Data from ISD, Safe Haven and Greater Glasgow and Clyde Health Board from March 2020-December 2020 will be analysed and compared with equivalent pre-pandemic data. A case notes review of primary care records linked to urgent suspicion of cancer referrals in Greater Glasgow and Clyde Health Board will allow mapping of patient pathways from presentation to referral after the move to telephone consulting. A survey of 2000 RCGP members in the West of Scotland looking at attitudes and concerns about telephone consulting will be undertaken, with the results informing a framework for more in depth interviews with 25 GPs. How the results of this research will be used: Links with key stakeholders will be built on to produce guidance and recommendations based on our results, to be translated into everyday practice in order to improve the safety of telephone consulting for patients with possible lung cancer symptoms.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: CANCER AND NEOPLASMS
Title: Personalised, data-driven surgical management of pituitary tumours using artificial intelligence – a pilot study
Funder: Cancer Research UK

Abstract: Background: The gold standard treatment for most patients with symptomatic pituitary tumours is surgical excision via the endoscopic transsphenoidal approach (eTSA). These operations are technically challenging, with significant heterogeneity in surgical techniques used, which may result in differing surgical outcomes. Therefore, we generated an international consensus-driven analysis of the operative workflow (phases, steps, instruments, errors) in contemporary pituitary surgery. This workflow analysis can be used to systematically segment operations, allowing granular assessment of surgical technique to drive improvement in outcomes. Machine learning (ML), specifically artificial neural networks (ANNs), can be used to analyse workflow automatically and accurately. We have previously published a proof-of-concept for this technology using 50 anonymised eTSA videos. However, linking operative video workflow analysis to the wider clinical context (e.g. post-operative data) is, to our knowledge, entirely novel. This presents an opportunity to generate a personalised, data-driven approach to each individual’s care. Aims: We aim to build on our existing ML model for detecting surgical workflow in endoscopic pituitary adenoma resection by integrating post-operative outcome data – to enable outcome prediction based on operative videos, and outcome-driven improvement in surgical technique. Methods: Through our earlier work, we have established an early project infrastructure (including ethical approval) and built an interdisciplinary study team in the UK’s largest pituitary centre (performing ~ 150 operations/year). We will refine our existing operative workflow analysis ML model using a larger video dataset and more granular data labelling (e.g. annotating surgical instruments). This ANN consists of an intra-operative variable input layer (video data) and an output layer predicting the operative steps. A second ANN will be linked to the output of the first, and will integrate post-operative data from an internal prospective database, to predict post-operative outcomes. For all networks an iterative approach will be used to optimise network hyperparameters, and evaluation will include: accuracy, F1 score, averaged receiver-operating characteristic analysis, and comparison against logistic regression. We estimate at least 100 cases would be required for an initial exploratory analysis. How the results of this research will be used: The integration of post-operative data into intra-operative workflow analysis using AI has the potential for significant impact on surgically treated cancers. This includes data-driven post-operative outcome modelling tailored specifically to each case, and the outcome-driven optimisation of operative technique. If this pilot study using pituitary surgery as an exemplar is successful, this technology will be progressed through further study (ideally via doctoral fellowship).


Research activity codes: 5.4 SURGERY
Health categories: CANCER AND NEOPLASMS
Title: Investigating cutaneous T-cell trafficking during treatment with immune checkpoint blockade
Funder: Cancer Research UK

Abstract: Background Antibodies that block the immune checkpoint response proteins PD1 and CTLA4 have revolutionised the treatment of metastatic melanoma. There is substantial clinical heterogeneity in the treatment response however, with many patients not benefitting. Recent work has demonstrated that response to immune checkpoint blockade (ICB) is associated with increased clonal expansion of T cells in the peripheral blood. The degree to which these peripherally expanded T cell clones migrate into tumour and other tissues remains unclear. Aims The skin and peripheral blood provide easily accessible sites to evaluate the trafficking of immune cells following ICB therapy. This project aims to: (i) Examine the overlap between T cell clones in the skin and peripheral blood at baseline and post-ICB therapy in patients with metastatic melanoma using bulk T cell receptor (TCR) sequencing. (ii) Compare phenotypic changes within the T cell clones in the skin and peripheral blood following ICB therapy, at the single cell level. Methods Patients with metastatic melanoma treated with ICB therapy will be recruited via a specialised melanoma clinic in Oxford. Biopsies of non-cancerous skin and peripheral blood samples will be obtained from 12 individuals at baseline and post-cycle 1 of ICB therapy. Bulk TCR sequencing will be performed on the samples obtained from all 12 individuals. This will enable comparison of the baseline overlap in T cell clones within the skin and blood and assessment of the flux between these compartments with treatment. Single cell sequencing of T cells infiltrating the skin and blood from 4 individuals will be performed pre and post treatment using 10x Genomics Chromium kits. Skin and blood samples from the same individual will be multiplexed onto the same chip to save costs and minimise batch effects, with anti-CD45 antibodies conjugated to different nucleic acid ‘barcodes’ used to enable post-sequencing dissection of tissue origin. Using established bioinformatic pipelines, the gene expression across different T cell clones in the skin and blood at baseline and post-ICB therapy will be examined. Statistical methods will be applied to determine overlap between tissues, and the significance of changes in gene expression and clone size, with a view to integrating findings with clinical otucomes. How the results of this research will be used The results of this research will provide insights into the dynamics of T cell trafficking in the treatment of cancer with immunotherapy, shedding light onto clinical heterogeneity and aiding the application of stratified medicine.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Molecular analysis of samples in the FLAIR Trial
Funder: Leeds Hospitals Charity

Abstract: The project will apply the high throughput sequencing (HTS) lymphoid panel developed in HMDS as well as molecular analysis of the immunoglobulin gene in each patient to study the first 771 patients included in the first randomisation of the FLAIR Trial. The project aim is to provide a detailed molecular analysis in FLAIR to ensure a greater understanding and value of the results of this critical trial. The main hypothesis is that understanding the differences between different patients with CLL will allow much better understanding of which therapies are most appropriate for individual patients.

Lay abstract: This project is to support the detailed analysis of the mutated genes that cause or are part of the cause, of chronic lymphocytic leukaemia (CLL) within the UK National Cancer Research Institute (NCRI) FLAIR Trial. The NCRI FLAIR Trial is the largest trial for patients with CLL is run from Leeds with Professor Peter Hillmen as the Chief Investigator. Since starting the trial in 2014 our understanding of CLL has rapidly developed with new techniques that allow a greater understanding of the disease in individual patients. A key development has been the application of Next Generation Sequencing to understand the various genes that are mutated in CLL. These techniques have been pioneered by HMDS and the application of them to FLAIR will greatly increase the value of the trial.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS
Title: Molecular Control of Epithelial Polarity
Funder: Cancer Research UK

Abstract: Our lab focuses on a fundamental, yet largely unanswered question: How is the normal organisation of tissue disrupted to allow cells to form disarrayed tumours? Cells of many tissues are polarised - that is that cells collectively form configurations tailored to the needs of a tissue. During tumourigenesis, this exquisite organisation is lost. Despite loss of tissue polarity being an obligate event in cancer progression, we know little about this basic process. We use mini-avatars of tumours ex vivo to understand how cells collectively organise. Our efforts are focused on prostate, colorectal and ovarian cancers. We take two approaches to unravel the complexity of this process: 1) Building new tools to analyse tumour avatars ex vivo 2) Identifying the signalling processes that drive collective tumour invasion and metastasis. We collaborate extensively for a multi-disciplinary approach to understand tumour metastasis (Sansom, Zanivan, Blyth, Leung, Miller Labs). To develop better tools to understand how tumour cells loose polarity, we have developed cutting-edge, high-content microscopy and computational image analysis of tumour spheroids and organoids. Molecularly, we focus on two pathways: the ARF GTPases (and their regulators and effectors, which we call the 'ARF regulome'), and the role of the cell surface protein, Podocalyxin. Both molecules are highly overexpressed in metastatic prostate cancer tumours. Our ultimate aim is to investigate such changes in cell polarity as potential future biomarkers of cancer in patients, and possible targets for future therapeutic interventions.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS