RDC Biomarker Study
Rapid Diagnostic Centres Rapid Diagnostic Centres (RDC) were built to diagnose patients who have common symptoms that occur in cancer, but it is unclear if they have cancer or not. These symptoms include: – Weight loss – Fatigue – Cough – GP suspicion Only 1 in every 10 patients (10%) referred to an RDC will have cancer. Some of the patients with cancer may have been more likely to develop cancer due to inherited or environmental factors. Some of the patients who don't have cancer may also be at higher risk of developing cancer at another time due to inherited or environmental factors. Aims The goal of this observational study is to develop a new blood or non-blood test that could help doctors at RDC: – detect which patients have cancer through a simple and quick blood or non-blood test – detect patients who are at higher risk of having cancer. This is so they can be monitored or guided towards cancer-screening programmes Main End Points – The study will be considered a success if a test or mixture of tests is developed that can correctly sort patients into cancer or non-cancer groups. – Also, the study will be considered a success if a test or mixture of tests can show what type of cancer a patient has if they have cancer. Tests To create this new blood or non-blood test the study will take the following samples from 1000 patients in the RDC Biomarker Study: – Breath samples (around 300 patients) – People with cancer have different levels of chemicals in their breath than people without cancer. The study hopes to develop a breath test which could show if a patient has cancer or not. – Blood samples ( around 1000 patients) – The study hopes to develop a blood test that could show if a patient has cancer or not – Saliva samples (around 1000 patients) – For many cancers, while there is a genetic component there is no one single gene that causes cancer. Instead, it can be a combination of hundreds of genes that causes the risk of cancer in a person to go up. The study hopes to develop a test which could provide a risk score. This risk score is called a 'polygenic risk score' which would tell doctors how likely a patient is to get cancer. Method Patients who meet the criteria to be able to join the study will be asked either via telephone before their appointment, or face-to-face at an appointment at the RDC if they would like to join the study. If they agree to join the study they will read a patient information sheet and sign a consent form to say they understand what the study requires. The patient will then provide blood and saliva samples and in some cases breath samples at their first appointment. They will be then asked to provide further samples (up to three) at their follow-up appointments. Please see below for samples that will be asked for at each appointment: First appointment: Breath (not all sites), Blood, Saliva (not all sites), Survey Follow-up 1: Any samples that could not be taken at the first appointment Follow-up 2: Any samples that could not be taken at the first appointment The patient will be provided with a Study ID to identify their samples. This is a unique code to identify each person on the study. Only the site that recruited the participant will have access to the personal information that matches which patients is known by which Study ID. All organisations external to the site will only know the patient as the Study ID. An example of the study ID could be RDCRMH001. A trained clinical member of the research team at the RDC will take the sample and ship it to the relevant laboratory for testing. As well as blood and non-blood tests, information about the patients will be collected This includes routinely collected clinical data alongside investigation results. No patient identifiable information such as: – Name – Address – Date of birth – Contact details will be collected, and a Study ID will be used to identify the data. A patient questionnaire will be sent out to patients to complete for each appointment asking questions about the patient's health. The RDC doctors treating the patient will see survey answers before the appointment to allow them to act about anything worrying. For a small group of patients anonymised copies of thier scans from their medical records will also be taken. Study Duration Once the study has recruited 1000 patients, it will close. These patients will then be followed up for 12 months following the date they joined the study.
Full Title of Study: “Rapid Diagnostic Centre Biomarker Study”
- Study Type: Observational [Patient Registry]
- Study Design
- Time Perspective: Cross-Sectional
- Study Primary Completion Date: April 1, 2025
Background The proposed research will focus on Rapid Diagnostic Centres (RDCs) already established across multiple sites in England. Since 2019-2020, RDCs, have begun rolling out nationally across all cancer alliances. They are a single point of access for cancer diagnostics to allow personalised and rapid diagnosis of patients presenting with non-specific but concerning symptoms of cancer (NSCS). Cancer patients with NSCS typically have a longer interval in primary care to referral and have more advanced disease once a diagnosis is made, yet approximately 50% of cancer patients present with NSCS. These patients are poorly served by existing cancer referral pathways which can be circuitous when a clear pattern of symptoms is not present. The objectives of RDCs are to: – Support earlier and faster cancer diagnosis – Create increased capacity through more efficient diagnostic pathways – Deliver a better, personalised diagnostic experience for patients – Reduce unwarranted variation in referral for, access to and in the reliability of relevant diagnostic tests RDCs Limitations Although patients seen in RDCs are presumed to receive faster diagnosis and treatment, there is still very little known about how their vague symptoms developed over time and how these are linked to underlying health conditions, diagnosis, health outcomes and RDC effectiveness. Most RDC patients will not have cancer. Therefore, accurate and rapid triage will be essential and depend upon improved methods of risk stratification and cancer detection. There is both a significant need and opportunity to integrate quantitative research into RDCs to develop and validate novel diagnostic assays, and risk scores based on this unique population of patients. These patients both represent a primary care community, but the enriched cancer prevalence within RDCs could be utilised to facilitate more efficient biomarker development. Rationale Developing pathways to streamline care in this population group is important not only due to the importance of timely detection and treatment, but most patients in these pathways both with and without cancer are more likely to be from more deprived backgrounds or prone to being less engaged with healthcare provision. The implementation specification from NHS England highlights the need for RDCs to tackle health inequalities in their roll out. This study will allow us to generate a sample set and database to examine the potential to: i) discover high risk groups in RDC populations and ii) to detect cancer-specific signals in symptom profiling data; routine clinical and imaging data; and blood and non-blood biomarker tools. The primary aims focus on using descriptive analysis, to identify the following: 1. The number and clinical traits of Benign versus Malignant Diagnoses 2. The number and clinical traits of cancer subtypes (e.g. lung vs ovarian or other cancer) The primary aims also focus on describing how the variability between participants can delineate cancer versus benign pathology using molecular; germline risk, symptom-profiling; and clinical data from each of the four main work packages, independently or in combination using AI-based multi-parametric analysis of these traits: Descriptive analyses will provide feasibility data to develop and validate models using i) Peripheral blood and non-blood (e.g. breath and saliva) molecular signatures that can delineate between cancer and benign pathology. ii) Patient 'symptome' signatures delineating between cancer and benign pathology. iii) Digital health record signature from routinely collected clinical electronic health record (EHR) and/or imaging data and/or the 'standard' RDC dataset that delineate between cancer and benign pathology. iv) Risk stratification tools (e.g. epidemiological and genetic/PRS) that inform risk of cancer in RDC attendees and primary care. Patient Selection Overall, we expect an initial sample size of 1000 patients that will allow several exploratory analyses and simple model development/validation (in distinct datasets) within an observational platform study design. The target recruitment will be increased by the Trial Steering Group (TSG) if new relevant assays are identified, and extended funding is secured. This is supported by previous work which suggests that 1000 patients are more than sufficient to identify candidate biomarkers for further studies, it is a pragmatic approach and is not the result of power calculations. A target goal will be for 75% of cancer cases to have histological confirmation i.e. ground truth of final diagnosis to help power diagnostic accuracy and reliability for any future use of histopathological data Patients recruited will be removed from analysis where ground truth cannot be established by the time of completion of initial investigations or a period of clinical surveillance (not to exceed 12 months of surveillance after initial diagnostic pathway completed). The local Principal Investigator (PI), or members of the delegated clinical care team at participating trusts (e.g. Consultant physicians, nurse specialists, clinical fellows or admin staff), will identify suitable patients via RDC clinic appointments, RDC multidisciplinary team (MDT) lists or by RDC service evaluations, supported where necessary by clinical informatics approaches (e.g. structured query language searches) or local research team. We estimate that each participating hospital will see approximately 20-50 new RDC patients per week, thus we expect that we could recruit large numbers of patients from a small number of RDCs. This estimation may evolve considering the number of participating sites, as well as the evolving nature of how RDCs are utilised, particularly given the impact of the COVID-19 pandemic on cancer diagnosis. Recruitment will be consecutive in the order in which patients are seen within each respective RDC. Allocation of participants to a particular sample type/assay will be pragmatic based on site geography/capability of the RDC at which the patient is recruited. Study Duration The study will end when complete outcome or surveillance data is available for all participants, or after 12 months following recruitment ± completion of clinical assessment of the last patient, whichever occurs sooner. This duration is necessary to ensure confirmation of diagnosis, or stability after discharge and therefore ground truth. It is expected that recruitment will complete within two years. Patient data and samples will be stored for a total of ten years. The ten-year duration of storage is necessary to allow us to utilise more advanced laboratory and data analysis techniques, which may become available with evolving technology. Newer approaches may require very large datasets, and this timeframe would allow for a protocol amendment for expansion of our data and further research to be performed as the field evolves. Upon completion of the study, we will establish a research database for future work and will store patient data for a total of ten years. Assessment of imaging data quality and integrity and suitability of research specimens for laboratory analysis will be contemporaneous to patient recruitment/sample receipt and ongoing thereafter. Analysis will typically be performed on patients with confirmed ground truth by biopsy or sufficient clinical surveillance if benign e.g. 12 months (see above) but scans performed after this period may be accessed if future relevant data become available. Enrolment and Consent Patient enrolment into the study will occur once consent is taken and eligibility criteria is met. A trial number will be allocated to each patient for the identification research samples. In most cases, patients will donate a blood specimen on one visit (Baseline). Where recurrent visits to the hospital for surveillance CT scans are made over a longer period, then up to 3 blood specimens will be collected in total with a minimum of 21 days between each research specimen collection or a treatment intervention. This will allow the consideration of evolution of the biomarker signal in question as a determinant of cancer presence or absence (using the logic that a given signal is likely to increase alongside tumour growth and therefore be easier to detect in comparison with samples from patients without malignancy). All participants are free to withdraw at any time from the protocol treatment without giving reasons and without prejudicing further treatment. The patient can request that any remaining samples donated are destroyed. All members of the delegated research team will ensure that patient confidentiality is maintained in compliance with the UK Data Protection Act 2018 and General Data Protection Regulation Patient Samples, Acquisition and Processing The laboratory manual will provide further details of the collection, processing, and storage of specimens. Study Oversite A Trial Steering Group (TSG) will meet on a regular basis to provide overall study oversight and clinical or scientific steer – particularly for academic/research considerations. In addition, a Trial Management Group (TMG) will meet quarterly to discuss patient recruitment, data collection, data management, data analysis, reporting for adverse events, and change management. The minutes of these meetings will be made available, and a rolling agenda will highlight any ongoing actions or concerns. In addition, the trial management team will provide day-to-day support for trial administration, including all rules and regulations concerning research studies in the UK. This includes: UK Policy Framework for Health and Social Care Research, Data Protection Act 2018 and UK General Data Protection Regulations Data Analysis and Statistical Considerations Statistical guidance and study design oversight will be provided by RM-ICR guided by the CI and Lead Statistician/Data Scientist and will be undertaken in partnership with academic partner institutions named in the protocol ± support from other academic centres. Data sharing agreements will be in place in each case. A full Data Analysis Plan will be assembled and ratified by the Trial Steering Group at its creation and on any subsequent amendment. Clinical Data analysis The main analysis, including clinical data values, will be descriptive and will be focused to inform future prospective studies. For continuous variables, the mean and standard deviation will be presented, together with the mean between-group difference, and 95% confidence interval. For binary outcomes, the percentage and frequency of patients in the outcome category of interest will be presented. When necessary intracluster correlation coefficients will be reported, together with 95% confidence interval. Where appropriate p-values will be presented. Baseline characteristics, collected at the time of commencing the study will provide an overview of the study population, both at the RDC and site level. It is expected that patients may differ based on which RDC they are recruited at and described with summary statistics including the index of multiple deprivation score for the RDC postcode, percentage of patients with comorbidities, and the cancer type. At the individual patient level, variables can include gender, age at baseline data collection, individual Index of Multiple Deprivation (IMD) values, baseline measures of all physiological measurements, comorbidity. Baseline characteristics will be summarised as the mean, standard deviation and range for continuous, approximately symmetric variables; medians, interquartile range and range for continuous, skewed variables; frequencies/percentages of patients/RDC in each category for categorical variables. Laboratory Data Analysis Analysis of laboratory data will lie in the first instance with the clinical and scientific lead for each work package with further details provided in the laboratory manual and local laboratory protocols. The assays will give a read out for each patient that will be assessed using receiver operating characteristic curve analysis against each endpoint. Firstly, this will be performed using a single biological parameter for cancer versus non-cancer, or most likely cancer subtype, and then in combination with other laboratory and clinical endpoints as a multiparametric signature for the same endpoints as outlined in the protocol.
Clinical Trial Outcome Measures
- Clinical Data Analysis
- Time Frame: 5 years
- The main analysis, including clinical data values, will be descriptive and will be focused to inform future prospective studies. For continuous variables, the mean and standard deviation will be presented, together with the mean between-group difference, and 95% confidence interval. For binary outcomes, the percentage and frequency of patients in the outcome category of interest will be presented. When necessary intracluster correlation coefficients will be reported, together with 95% confidence interval. Where appropriate p-values will be presented.
- Polygenic Risk Scores
- Time Frame: 5 years
- For most laboratory data analysis, the known relevance of a positive detection through a clinical biomarker shall not be known prior to the completion of data analysis, however for PRS analysis this would theoretically be able to provide information on 10-year and lifetime cancer risk for breast, colon, endometrial, melanoma, ovarian, pancreas, and prostate cancers. The level of risk will be determined as a risk-ratio of 2 or ≥3 compared to the general population for moderate- and high-risk individuals respectively.
- Imaging Radiomics and Composite Analysis
- Time Frame: 5 years
- Inclusion of radiomics data will be assessed for subsets of patients where robust imaging data and appropriate techniques for analysis exist, noting the predominance evidence available for certain tumour types such as lung cancer.
- Multiparametric and ML Analyses
- Time Frame: 5 years
- The study will explore the role of multi-parametric predictors by optimising convolutional neural networks (CNN) or other deep learning tool to classify patients into outcome classes.
Participating in This Clinical Trial
- Patients referred to undergo investigation for suspected cancer within a non-tumour site specific RDC – Age > 18 years Exclusion Criteria:
- Previously treated (treatment completed within 5 years preceding recruitment) or currently confirmed diagnosis of active malignancy prior to entry to 'RDC pathway'. – Unable to or unwilling to give informed consent
Gender Eligibility: All
Minimum Age: 18 Years
Maximum Age: N/A
Are Healthy Volunteers Accepted: No
- Lead Sponsor
- Royal Marsden NHS Foundation Trust
- Provider of Information About this Clinical Study
- Overall Official(s)
- Richard Lee, Dr, Principal Investigator, Royal Marsden NHS Foundation Trust
- Overall Contact(s)
- Sejal Jain, 020 7808 2603, email@example.com
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