The CCANED-CIPHER Study: Early Cancer Detection and Treatment Response Monitoring Using AI-Based Platelet and Immune Cell Transcriptomic Profiling

Brief summary public domain (ClinicalTrials.gov)

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 public domain (ClinicalTrials.gov)

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 …

Intervention public domain (ClinicalTrials.gov)

Diagnostic_Test: DiNanoQ: 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.

Intervention public domain (ClinicalTrials.gov)

Other: DiNanoTrack: 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.

Arm public domain (ClinicalTrials.gov)

Cancer Patients (Phase 1) (): 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.

Arm public domain (ClinicalTrials.gov)

Healthy Individuals (): This arm will consist of 1,500 age- and sex-matched cancer-free individuals serving as controls.

Arm public domain (ClinicalTrials.gov)

Cancer Patients Undergoing Treatment (): 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.

Primary outcome public domain (ClinicalTrials.gov)

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).

Primary outcome public domain (ClinicalTrials.gov)

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.

Primary outcome public domain (ClinicalTrials.gov)

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.

Secondary outcome public domain (ClinicalTrials.gov)

Sensitivity and Specificity of the AI-Based Diagnostic Tool (Phase 1). Time frame: Baseline.

Secondary outcome public domain (ClinicalTrials.gov)

Feasibility of Platelet Transcriptomic Profiling Implementation. Time frame: Phase 1 - 2 years.

Secondary outcome public domain (ClinicalTrials.gov)

Development of Predictive Models for Treatment Outcomes (Phase 2). Time frame: Phase 2 - Two years.

Secondary outcome public domain (ClinicalTrials.gov)

Identification of Biomarkers Predictive of Relapse and Drug Resistance (Phase 2). Time frame: Baseline to 6 months post-therapy initiation.

Eligibility public domain (ClinicalTrials.gov)

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). Exclusion Criteria: * Presence of another malignancy unless it has been in remission for at least 5 years. * Significant uncontrolled co-morbid conditions that may interfere with study participation or outcomes.

Enrollment public domain (ClinicalTrials.gov)

Enrollment: 6000 participants (estimated).