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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
| |
| Healthy Individuals | This arm will consist of 1,500 age- and sex-matched cancer-free individuals serving as controls. |
| |
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| DiNanoQ: A multi-cancer early detection (MCED) blood test | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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) | 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. | Baseline to 6 months post-therapy initiation |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Baseline |
| Feasibility of Platelet Transcriptomic Profiling Implementation |
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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:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Javier Toledo, Medical Degree | Contact | +44 (0)1223 496000 | research@dysplasiadx.com | |
| Osagie Izuogu, PhD | Contact | +44 (0)1223 496000 | info@dysplasiadx.com |
| Name | Affiliation | Role |
|---|---|---|
| Solomon Rotimi, PhD | Dysplasia Diagnostics Limited | Study Director |
| Javier Toledo, Medical Degree | Dysplasia Diagnostics Limited | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Various Cancer Centres | Active, not recruiting | Rosario | Argentina | |||
| NSIA- Lagos University Teaching Hospital Cancer Centre |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 12324479 | Background | Tsui NB, Ng EK, Lo YM. Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem. 2002 Oct;48(10):1647-53. | |
| 21714641 | Background | National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29. |
| Label | URL |
|---|---|
| WHO report | View source |
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The study will utilize various biological sources to profile RNA for cancer detection and treatment response. Blood samples will be collected from all participants, serving as the source for isolating platelets and immune cells for RNA analysis.
Platelet isolates will be extracted from these blood samples. RNA from platelets will be analyzed to identify transcriptomic profiles that may serve as biomarkers for cancer detection and monitoring. Platelets can absorb RNA from tumor cells, reflecting the cancer's molecular signature.
In Phase 2, immune cell isolates will be separated from the blood samples of cancer patients. RNA from these immune cells will be analysed to assess transcriptomic changes associated with therapeutic responses. Known driver mutations in immune cell DNA will be assessed for correlation with RNA alterations.
This non-invasive approach will leverage liquid biopsy methods to enhance early cancer detection and personalise treatment monitoring.
|
| DiNanoTrack: Therapeutic Response Monitoring Blood Test | Other | 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. |
|
Assess the practicality of sample collection, processing, and analysis in a clinical setting.
| 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. | 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. | Baseline to 6 months post-therapy initiation |
| Recruiting |
| Lagos |
| Nigeria |
|
| Babraham Research Institute | Enrolling by invitation | Cambridge | CB22 3AT | United Kingdom |
| Dysplasia Diagnostics Limited | Recruiting | London | W1W 7LT | United Kingdom |
|
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D002289 | Carcinoma, Non-Small-Cell Lung |
| C535836 | Pancreatic cancer, adult |
| D011471 | Prostatic Neoplasms |
| D010051 | Ovarian Neoplasms |
| D015179 | Colorectal Neoplasms |
| D005909 | Glioblastoma |
| D006528 | Carcinoma, Hepatocellular |
| D009369 | Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
| D005834 | Genital Neoplasms, Male |
| D014565 | Urogenital Neoplasms |
| D005832 | Genital Diseases, Male |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D011469 | Prostatic Diseases |
| D052801 | Male Urogenital Diseases |
| D004701 | Endocrine Gland Neoplasms |
| D010049 | Ovarian Diseases |
| D000291 | Adnexal Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D005833 | Genital Neoplasms, Female |
| D004700 | Endocrine System Diseases |
| D006058 | Gonadal Disorders |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
| D001254 | Astrocytoma |
| D005910 | Glioma |
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D008113 | Liver Neoplasms |
| D008107 | Liver Diseases |
Not provided
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| ID | Term |
|---|---|
| D006403 | Hematologic Tests |
| ID | Term |
|---|---|
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D008919 | Investigative Techniques |
Not provided
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