Brest CancerCCANED-CIPHERNCT06717295
The CCANED-CIPHER Study: Early Cancer Detection and Treatment Response Monitoring Using AI-Based Platelet and Immune Cell Transcriptomic Profiling
An observational study of Brest Cancer and Lung Cancer (NSCLC), run by Javier Toledo at 4 sites in 3 countries.
Trial 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… Read more about this study
- Who is it for?
- Ages 40 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Brest Cancer, Lung Cancer (NSCLC), Pancreatic Cancer, Adult
- What is being tested?
- DiNanoQ: A multi-cancer early detection (MCED) blood test and DiNanoTrack: Therapeutic Response Monitoring Blood Test
- Type of treatment
- Injection or infusion
- How many people
- 6,000 participants
- Where
- 4 sitesin 3 countries
- Study dates
- 20 Dec 2025to 1 Aug 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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.
- 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).
Who cannot join
- 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)
- 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.
Trial locations and contacts
4 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
4 sites in 3 countries
United Kingdom2 sites, 2 recruiting
- Enrolling by invitation
Babraham Research Institute
Cambridge, CB22 3AT
- Recruiting
Dysplasia Diagnostics Limited
London, W1W 7LT
Chief Medical Officer, Medical, +447494946013, research@dysplasiadx.com
Nigeria1 site, 1 recruiting
- Recruiting
NSIA- Lagos University Teaching Hospital Cancer Centre
Lagos
Adewumi Alabi, Oncology, +2348052843206, aalabi@nlcc.ng
Solomon Rotimi, Ph.D, solomon@dysplasiadx.com
About this study
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.
Read the 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.
- Conditions
- Brest Cancer, Lung Cancer (NSCLC), Pancreatic Cancer, Adult, Prostate Cancers, Ovarian Cancer, Colorectal Cancer
- Being tested
- DiNanoQ: A multi-cancer early detection (MCED) blood test, DiNanoTrack: Therapeutic Response Monitoring Blood Test
- 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
- Medical subject headings
- Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Pancreatic cancer, adult, Prostatic Neoplasms, Ovarian Neoplasms, Colorectal Neoplasms, Glioblastoma, Carcinoma, Hepatocellular
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
DiNanoQ: A multi-cancer early detection (MCED) blood testDiagnostic 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.
DiNanoTrack: Therapeutic Response Monitoring Blood TestOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test | |
| Healthy Individuals | This arm will consist of 1,500 age- and sex-matched cancer-free individuals serving as controls. Receives: Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test | |
| 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. Receives: Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test; Other: DiNanoTrack: Therapeutic Response Monitoring Blood Test |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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)
Measured: 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.
Measured: Baseline to 6 months post-therapy initiation
Secondary outcomes (4)
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.
Measured: Baseline
Feasibility of Platelet Transcriptomic Profiling Implementation
Assess the practicality of sample collection, processing, and analysis in a clinical setting.
Measured: 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.
Measured: 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.
Measured: Baseline to 6 months post-therapy initiation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Dec 2024
- Study start20 Dec 2025
- Primary completion1 Aug 2027 (estimated)
- Study completion1 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (29)Papers the sponsor has linked to this study.
- Other trials for Brest CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 6,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Javier Toledo
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor-investigator, Javier Toledo, Dysplasia Diagnostics Limited
- Investigators
- Solomon Rotimi, PhD, Study director, Dysplasia Diagnostics LimitedJavier Toledo, Medical Degree, Principal investigator, Dysplasia Diagnostics Limited
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06717295
- Sponsor's protocol ID
- CCANED-CIPHER
- Acronym
- CCANED-CIPHER
- Status last verified
- Dec 2025
- First submitted
- 28 Nov 2024
- Last updated
- 2 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 12324479National 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.
PubMed 21714641Smerage JB, Barlow WE, Hortobagyi GN, Winer EP, Leyland-Jones B, Srkalovic G, Tejwani S, Schott AF, O'Rourke MA, Lew DL, Doyle GV, Gralow JR, Livingston RB, Hayes DF. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J Clin Oncol. 2014 Nov 1;32(31):3483-9. doi: 10.1200/JCO.2014.56.2561. Epub 2014 Jun 2.
PubMed 24888818Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, Ponzetti A, Cremolini C, Amatu A, Lauricella C, Lamba S, Hobor S, Avallone A, Valtorta E, Rospo G, Medico E, Motta V, Antoniotti C, Tatangelo F, Bellosillo B, Veronese S, Budillon A, Montagut C, Racca P, Marsoni S, Falcone A, Corcoran RB, Di Nicolantonio F, Loupakis F, Siena S, Sartore-Bianchi A, Bardelli A. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015 Jul;21(7):795-801. doi: 10.1038/nm.3870. Epub 2015 Jun 1.
PubMed 26030179Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, McDermott U, Azizian N, Zou L, Fischbach MA, Wong KK, Brandstetter K, Wittner B, Ramaswamy S, Classon M, Settleman J. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010 Apr 2;141(1):69-80. doi: 10.1016/j.cell.2010.02.027.
PubMed 20371346Sequist LV, Waltman BA, Dias-Santagata D, Digumarthy S, Turke AB, Fidias P, Bergethon K, Shaw AT, Gettinger S, Cosper AK, Akhavanfard S, Heist RS, Temel J, Christensen JG, Wain JC, Lynch TJ, Vernovsky K, Mark EJ, Lanuti M, Iafrate AJ, Mino-Kenudson M, Engelman JA. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci Transl Med. 2011 Mar 23;3(75):75ra26. doi: 10.1126/scitranslmed.3002003.
PubMed 21430269Redwood N, Beggs D, Morgan WE. Dissemination of tumour cells from fine needle biopsy. Thorax. 1989 Oct;44(10):826-7. doi: 10.1136/thx.44.10.826.
PubMed 2595626Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013 Feb 18;200(4):373-83. doi: 10.1083/jcb.201211138.
PubMed 23420871Pantel K, Alix-Panabieres C. Liquid biopsy and minimal residual disease - latest advances and implications for cure. Nat Rev Clin Oncol. 2019 Jul;16(7):409-424. doi: 10.1038/s41571-019-0187-3.
PubMed 30796368Oxnard GR, Thress KS, Alden RS, Lawrance R, Paweletz CP, Cantarini M, Yang JC, Barrett JC, Janne PA. Association Between Plasma Genotyping and Outcomes of Treatment With Osimertinib (AZD9291) in Advanced Non-Small-Cell Lung Cancer. J Clin Oncol. 2016 Oct 1;34(28):3375-82. doi: 10.1200/JCO.2016.66.7162. Epub 2016 Jun 27.
PubMed 27354477Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velazquez Vega JE, Brat DJ, Cooper LAD. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979. doi: 10.1073/pnas.1717139115. Epub 2018 Mar 12.
PubMed 29531073Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009 Sep 3;361(10):947-57. doi: 10.1056/NEJMoa0810699. Epub 2009 Aug 19.
PubMed 19692680Lans H, Hoeijmakers JHJ, Vermeulen W, Marteijn JA. The DNA damage response to transcription stress. Nat Rev Mol Cell Biol. 2019 Dec;20(12):766-784. doi: 10.1038/s41580-019-0169-4. Epub 2019 Sep 26.
PubMed 31558824Kucab JE, Zou X, Morganella S, Joel M, Nanda AS, Nagy E, Gomez C, Degasperi A, Harris R, Jackson SP, Arlt VM, Phillips DH, Nik-Zainal S. A Compendium of Mutational Signatures of Environmental Agents. Cell. 2019 May 2;177(4):821-836.e16. doi: 10.1016/j.cell.2019.03.001. Epub 2019 Apr 11.
PubMed 30982602Joosse SA, Beyer B, Gasch C, Nastaly P, Kuske A, Isbarn H, Horst LJ, Hille C, Gorges TM, Cayrefourcq L, Alix-Panabieres C, Tennstedt P, Riethdorf S, Schlomm T, Pantel K. Tumor-Associated Release of Prostatic Cells into the Blood after Transrectal Ultrasound-Guided Biopsy in Patients with Histologically Confirmed Prostate Cancer. Clin Chem. 2020 Jan 1;66(1):161-168. doi: 10.1373/clinchem.2019.310912.
PubMed 31601564
Study at a glance
From the registry record, last updated by the sponsor on 2 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The CCANED-CIPHER Study: Early Cancer Detection and Treatment Response Monitoring Using AI-Based Platelet and Immune Cell Transcriptomic Profiling
- ClinicalTrials.gov ID
- NCT06717295
- Acronym / sponsor ID
- CCANED-CIPHER / CCANED-CIPHER
- Conditions
- Brest Cancer, Lung Cancer (NSCLC), Pancreatic Cancer, Adult, Prostate Cancers, Ovarian Cancer, Colorectal Cancer
- Keywords
- cancer screening, Liquid Biopsy, AI-based Diagnostics, Early Cancer Detection, Circulating Tumor DNA (ctDNA), RNA Profiling, Biomarker, Precision Medicine
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- Javier ToledoIndustry (pharma, biotech, devices)
- Study start
- 20 Dec 2025 (actual)
- Primary completion
- 1 Aug 2027 (estimated)
- Study completion
- 1 Aug 2028 (estimated)
- First posted
- 5 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Cancer Patients (Phase 1) | – | Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood testThis 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 | – | Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood testThis arm will consist of 1,500 age- and sex-matched cancer-free individuals serving as controls. |
| Cancer Patients Undergoing Treatment | – | Diagnostic Test: DiNanoQ: A multi-cancer early detection (MCED) blood test, Other: DiNanoTrack: Therapeutic Response Monitoring Blood TestThis 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. |
Who may qualify
- Ages
- 40 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (5 listed)
- 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.
- 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).
Key exclusion criteria (7 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| DiNanoQ: A multi-cancer early detection (MCED) blood 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. | Diagnostic test | Cancer Patients (Phase 1), Cancer Patients Undergoing Treatment, Healthy Individuals |
| DiNanoTrack: Therapeutic Response Monitoring Blood TestProcedures: 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. | Other | Cancer Patients Undergoing Treatment |
Primary outcome
Identification of Platelet RNA Biomarkers Distinguishing Cancer Patients from Controls
Time frame: 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)
Time frame: 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
Time frame: 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.
4 secondary outcomes
Sensitivity and Specificity of the AI-Based Diagnostic Tool (Phase 1)
Time frame: Baseline
Feasibility of Platelet Transcriptomic Profiling Implementation
Time frame: Phase 1 - 2 years
Development of Predictive Models for Treatment Outcomes (Phase 2)
Time frame: Phase 2 - Two years
Identification of Biomarkers Predictive of Relapse and Drug Resistance (Phase 2)
Time frame: Baseline to 6 months post-therapy initiation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jan 2026.
- Background: PubMed 12324479Tsui 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.
- Background: PubMed 21714641National 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.
- Background: PubMed 24888818Smerage JB, Barlow WE, Hortobagyi GN, Winer EP, Leyland-Jones B, Srkalovic G, Tejwani S, Schott AF, O'Rourke MA, Lew DL, Doyle GV, Gralow JR, Livingston RB, Hayes DF. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J Clin Oncol. 2014 Nov 1;32(31):3483-9. doi: 10.1200/JCO.2014.56.2561. Epub 2014 Jun 2.
- Background: PubMed 26030179Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, Ponzetti A, Cremolini C, Amatu A, Lauricella C, Lamba S, Hobor S, Avallone A, Valtorta E, Rospo G, Medico E, Motta V, Antoniotti C, Tatangelo F, Bellosillo B, Veronese S, Budillon A, Montagut C, Racca P, Marsoni S, Falcone A, Corcoran RB, Di Nicolantonio F, Loupakis F, Siena S, Sartore-Bianchi A, Bardelli A. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015 Jul;21(7):795-801. doi: 10.1038/nm.3870. Epub 2015 Jun 1.
- Background: PubMed 20371346Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, McDermott U, Azizian N, Zou L, Fischbach MA, Wong KK, Brandstetter K, Wittner B, Ramaswamy S, Classon M, Settleman J. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010 Apr 2;141(1):69-80. doi: 10.1016/j.cell.2010.02.027.
- Background: PubMed 21430269Sequist LV, Waltman BA, Dias-Santagata D, Digumarthy S, Turke AB, Fidias P, Bergethon K, Shaw AT, Gettinger S, Cosper AK, Akhavanfard S, Heist RS, Temel J, Christensen JG, Wain JC, Lynch TJ, Vernovsky K, Mark EJ, Lanuti M, Iafrate AJ, Mino-Kenudson M, Engelman JA. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci Transl Med. 2011 Mar 23;3(75):75ra26. doi: 10.1126/scitranslmed.3002003.
- Background: PubMed 2595626Redwood N, Beggs D, Morgan WE. Dissemination of tumour cells from fine needle biopsy. Thorax. 1989 Oct;44(10):826-7. doi: 10.1136/thx.44.10.826.
- Background: PubMed 23420871Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013 Feb 18;200(4):373-83. doi: 10.1083/jcb.201211138.
- Background: PubMed 30796368Pantel K, Alix-Panabieres C. Liquid biopsy and minimal residual disease - latest advances and implications for cure. Nat Rev Clin Oncol. 2019 Jul;16(7):409-424. doi: 10.1038/s41571-019-0187-3.
- Background: PubMed 27354477Oxnard GR, Thress KS, Alden RS, Lawrance R, Paweletz CP, Cantarini M, Yang JC, Barrett JC, Janne PA. Association Between Plasma Genotyping and Outcomes of Treatment With Osimertinib (AZD9291) in Advanced Non-Small-Cell Lung Cancer. J Clin Oncol. 2016 Oct 1;34(28):3375-82. doi: 10.1200/JCO.2016.66.7162. Epub 2016 Jun 27.
- Background: PubMed 29531073Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velazquez Vega JE, Brat DJ, Cooper LAD. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979. doi: 10.1073/pnas.1717139115. Epub 2018 Mar 12.
- Background: PubMed 19692680Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009 Sep 3;361(10):947-57. doi: 10.1056/NEJMoa0810699. Epub 2009 Aug 19.
- WHO reportLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Solomon Rotimi, PhD study director
Dysplasia Diagnostics Limited
Javier Toledo, Medical Degree principal investigator
Dysplasia Diagnostics Limited
4 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Brest Cancer and Lung Cancer (NSCLC), run by Javier Toledo at 4 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in United Kingdom, Argentina, Nigeria. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Javier Toledo, Medical Degree (+44 (0)1223 496000, research@dysplasiadx.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identification of Platelet RNA Biomarkers Distinguishing Cancer Patients from Controls (time frame: Baseline (single time point)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06717295, which also lists 29 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Jan 2026.