An observational study in Brest Cancer, Lung Cancer (NSCLC) and Pancreatic Cancer, Adult, sponsored by Javier Toledo. Recruiting at 4 sites in 3 countries. Open to participants aged 40 Years to 75 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-01-02.
Sponsored by Javier Toledo · Observational
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.
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:
Laboratory Analysis:
Data Analysis:
Expected Outcomes:
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:
Laboratory Analysis:
Data Analysis:
Expected Outcomes:
Significance of the Study
The CCANED-CIPHER study addresses critical needs in oncology by providing:
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.
7,243 studies on the registry are indexed under Lung Neoplasms; 1,557 are open to participants now.
This study's planned enrollment of 6,000 is above the median of 189 across 1,514 observational studies indexed under Lung Neoplasms.
Browse Lung Neoplasms studies →This is the only study on the registry with Javier Toledo as lead sponsor.
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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.
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.
Phase 1 (Common Cancer Early Detection - CCANED)
Inclusion Criteria:
Exclusion Criteria:
Phase 2 ( Cancer Immuno-Profiling of Hematologic and Extracellular RNA - CIPHER)
Inclusion Criteria:
Exclusion Criteria:
This arm will include 3,500 individuals with confirmed diagnoses of common cancers such as Non-Small Cell Lung Cancer (NSCLC), Glioblastoma Multiforme (GBM), Colorectal Cancer, Hepatocellular Carcinoma (HCC), Breast Cancer, Prostate Cancer, Ovarian Cancer, and Pancreatic Cancer.
Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test
This arm will consist of 1,500 age- and sex-matched cancer-free individuals serving as controls.
Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test
This cohort will include 1,000 patients diagnosed with Hepatocellular Carcinoma (HCC) or Non-Small Cell Lung Cancer (NSCLC) across stages I to IV who are about to commence standard cancer therapy.
Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test · Other: DiNanoTrack: Therapeutic Response Monitoring 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.
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.
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.
Time frame: Baseline (single time point)
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)
Time frame: Baseline to 6 months post-therapy initiation
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.
Time frame: Baseline to 6 months post-therapy initiation
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.
Time frame: Baseline
Feasibility of Platelet Transcriptomic Profiling Implementation
Assess the practicality of sample collection, processing, and analysis in a clinical setting.
Time frame: Phase 1 - 2 years
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.
Time frame: Phase 2 - Two years
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.
Time frame: Baseline to 6 months post-therapy initiation
Plan to share: Undecided
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