The purpose of the CCANED-CIPHER study is to develop and validate an AI-based blood test for early cancer detection and to monitor treatment effectiveness in cancer patients. This two-phase, multi-center observational study aims to identify specific transcriptomic biomarkers in platelets and immune cells that distinguish cancer patients from healthy individuals and correlate with treatment outcomes. By analysing blood samples using artificial intelligence, the study seeks to create a safe, non-invasive method to enhance cancer diagnosis and monitor treatment responses over time.
Age: Adults aged 40 years or older.
Confirmed diagnosis of one of the following common cancers: Non-Small Cell Lung Cancer (NSCLC), Glioblastoma Multiforme (GBM), Colorectal Cancer, Hepatocellular Carcinoma (HCC), Breast Cancer, Prostate Cancer, Ovarian Cancer, Pancreatic Cancer.
Currently pregnant.
Presence of any active infectious diseases.
Use of anticoagulant or antiplatelet drugs within the past 2 weeks.
Any medical or psychological conditions that may affect the participant's ability to comply with study procedures.
The CCANED-CIPHER study will enroll a diverse, geographically dispersed population to ensure the generalizability and robustness of its findings. The study is divided into two phases, utilizing up to 10 medical centers globally across the United Kingdom (UK), Europe, America, and Asia. Phase 1 (CCANED): Participants: 5,000 adults aged 40 years or older. * Cancer Patients: 3,500 individuals with confirmed diagnoses of common cancers. * Healthy Controls: 1,500 age-matched cancer-free individuals. Recruitment Strategy: Participants will be identified and enrolled through the participating medical centers, ensuring a representative sample across different geographical locations. Phase 2 (CIPHER): Participants: 1,000 adults aged 40 years or older diagnosed with HCC or NSCLC across stages I to IV. Recruitment Strategy: Cancer patients will be recruited from the participating cancer centers, ensuring a wide representation of disease stages and treatment backgrounds.
Other
Prospective
DiNanoQ: A multi-cancer early detection (MCED) blood test
Diagnostic testProcedure: Participants will undergo a single blood draw at baseline. Sample Analysis: Platelet Isolation: Platelets will be extracted from the collected blood samples. RNA Analysis: RNA from the isolated platelets will be extracted and analyzed using AI-based transcriptomic profiling to identify biomarkers associated with cancer.
DiNanoTrack: Therapeutic Response Monitoring Blood Test
Other interventionProcedures: Blood Sample Collection: Participants will have blood samples drawn at three time points: Baseline: Before therapy initiation. 6 Weeks Post-Therapy Initiation: To monitor early treatment response. 6 Months Post-Therapy Initiation: To assess longer-term therapeutic outcomes. Sample Analysis: Platelet and Immune Cell Isolation: Platelets: Extracted from each blood sample to continue monitoring RNA profiles. Immune Cells: Separated from the blood samples to analyse immune response to therapy. RNA Analysis: Platelet RNA: Analysed to observe changes in transcriptomic profiles over time using AI-based tools. Immune Cell RNA: Examined to assess transcriptomic changes associated with therapeutic responses. Data Correlation: Therapeutic Response Assessment: RNA profiles from platelets and immune cells will be correlated with clinical outcomes to identify biomarkers predictive of treatment efficacy, progression-free survival, relapse, and drug resistance.
Identification of Platelet RNA Biomarkers Distinguishing Cancer Patients from Controls
Utilise AI-based transcriptomic analysis of platelet RNA to identify biomarkers that differentiate between cancer patients and cancer-free controls.
Identification of RNA Biomarkers Correlating with Therapeutic Response (Phase 2)
Identify RNA biomarkers from immune cells and platelets that correlate with clinical treatment response, as measured by standard criteria (e.g., RECIST)
Association Between Immune Cell Transcriptomes and AI-Based Platelet Signals
Evaluate how changes in immune cell transcriptomes are associated with signals detected by the AI-based platelet profiling tool.
Sensitivity and Specificity of the AI-Based Diagnostic Tool (Phase 1)
Calculate the diagnostic accuracy of the AI-based tool in detecting cancer among participants.
Feasibility of Platelet Transcriptomic Profiling Implementation
Assess the practicality of sample collection, processing, and analysis in a clinical setting.
Development of Predictive Models for Treatment Outcomes (Phase 2)
Create and validate predictive models that integrate platelet and immune cell RNA profiles to predict treatment response and progression-free survival.
Identification of Biomarkers Predictive of Relapse and Drug Resistance (Phase 2)
Identify RNA biomarkers predictive of relapse and drug resistance at the 6-month follow-up.
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Javier Toledo
Lead sponsor
Dysplasia Diagnostics Limited
Sponsor institution
