Brest Cancer, Colorectal Cancer, Glioblastoma (GBM), Liver Carcinoma, Lung Cancer (NSCLC), Ovarian Cancer, Pancreatic Cancer, Adult, Prostate Cancers
Conditions
Keywords
cancer screening, Liquid Biopsy, AI-based Diagnostics, Early Cancer Detection, Circulating Tumor DNA (ctDNA), RNA Profiling, Biomarker, Precision Medicine, Oncology, Health Data Analysis, Platelets, treatment response, RNA, CircRNA, Splicing, Cancer
Brief summary
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.
Detailed description
The CCANED-CIPHER study aims to revolutionise cancer diagnostics and treatment monitoring by developing and evaluating an AI-based early cancer detection tool that profiles RNA biomarkers from platelets and immune cells in blood samples. This non-invasive approach leverages liquid biopsy methods to enhance early cancer detection and provide insights into therapeutic responses. Phase 1 (Common Cancer Early Detection \[CCANED\]): Early Cancer Detection Objective: To identify specific platelet-derived RNA biomarkers that can distinguish individuals with common cancers from healthy controls using AI-driven transcriptomic analysis. Methodology: * Enrol 3,500 patients with confirmed diagnoses of various common cancers and 1,500 cancer-free controls matched by age and sex. * Obtain a single blood sample from each participant at baseline. Laboratory Analysis: * Platelet Isolation from blood samples. * RNA Sequencing and transcriptomic profiling to identify RNA expression patterns. Data Analysis: * Use machine learning algorithms to analyse RNA data and identify biomarkers indicative of cancer presence. * Assess sensitivity and specificity of the diagnostic tool, and evaluate its ability to differentiate between cancer types. Expected Outcomes: * Identification of reliable RNA biomarkers for early cancer detection. * Validation of the AI-based diagnostic tool's accuracy and feasibility in a clinical setting. Phase 2 ( Cancer Immuno-Profiling of Hematologic and Extracellular RNA \[CIPHER\]): Therapeutic Response Monitoring Objective: To evaluate how RNA biomarkers from immune cells and platelets correlate with therapeutic responses, providing insights into treatment efficacy and potential relapse. Methodology: * Enrol 1,000 cancer patients diagnosed with HCC or NSCLC across stages I to IV. * Baseline: Collect blood samples before therapy initiation. * Follow-Up: Additional samples at 6 weeks and 6 months post-therapy initiation. Laboratory Analysis: * Isolation of Immune Cells and Platelets from blood samples. * Analysis of RNA expression changes over time. Data Analysis: * Evaluate associations between RNA biomarkers and clinical treatment responses. * Develop models integrating platelet and immune cell RNA profiles to predict outcomes. Expected Outcomes: * Identification of biomarkers that correlate with treatment responses and progression-free survival. * Development of predictive models for relapse and drug resistance. Significance of the Study The CCANED-CIPHER study addresses critical needs in oncology by providing: * A blood test that reduces the need for invasive tissue biopsies. * Potential for identifying cancers at an earlier, more treatable stage. * Tailored treatment strategies based on individual biomarker profiles. * Enhanced ability to monitor treatment effectiveness and adjust therapies accordingly. * Early detection of relapse or drug resistance, enabling prompt clinical interventions. Expected Impact and Future Applications: The identification of specific RNA biomarkers from platelets and immune cells has the potential to transform current practices in oncology, offering a more efficient, accurate and patient-friendly approach to cancer care.
Interventions
DIAGNOSTIC_TESTDiNanoQ: A multi-cancer early detection (MCED) blood test
Procedure: 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.
OTHERDiNanoTrack: Therapeutic Response Monitoring Blood Test
Procedures: 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.
Sponsors
Javier Toledo
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
Phase 1 (Common Cancer Early Detection - CCANED) Inclusion Criteria: * 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.
Exclusion criteria
* 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. Phase 2 ( Cancer Immuno-Profiling of Hematologic and Extracellular RNA - CIPHER) Inclusion Criteria: * Adults aged 40 years or older. * Confirmed diagnosis of: Hepatocellular Carcinoma (HCC), Non-Small Cell Lung Cancer (NSCLC) * Willingness to provide blood samples at the specified intervals (baseline, 6 weeks, and 6 months post-therapy initiation).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of Platelet RNA Biomarkers Distinguishing Cancer Patients from Controls | Baseline (single time point) | 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) | Baseline to 6 months post-therapy initiation | 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 | Baseline to 6 months post-therapy initiation | Evaluate how changes in immune cell transcriptomes are associated with signals detected by the AI-based platelet profiling tool. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and Specificity of the AI-Based Diagnostic Tool (Phase 1) | Baseline | Calculate the diagnostic accuracy of the AI-based tool in detecting cancer among participants. |
| Identification of Biomarkers Predictive of Relapse and Drug Resistance (Phase 2) | Baseline to 6 months post-therapy initiation | Identify RNA biomarkers predictive of relapse and drug resistance at the 6-month follow-up. |
| Feasibility of Platelet Transcriptomic Profiling Implementation | Phase 1 - 2 years | Assess the practicality of sample collection, processing, and analysis in a clinical setting. |
| Development of Predictive Models for Treatment Outcomes (Phase 2) | Phase 2 - Two years | Create and validate predictive models that integrate platelet and immune cell RNA profiles to predict treatment response and progression-free survival. |
Countries
Argentina, Nigeria, United Kingdom
Contacts
Primary ContactJavier Toledo, Medical Degree
Backup ContactOsagie Izuogu, PhD
Outcome results
None listed