Breast CancerRENOVATENCT04781062
Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer
A study testing Blood and urine molecular analysis (Timing 0) and Blood and urine molecular analysis (Timing 1) in people with Breast Cancer, run by Ospedale Policlinico San Martino at 1 site in Italy.
Trial summary
This is a translational no-profit study. Our proposal aims at creating a noninvasive Horizontal Data Integration (HDI) classifier for early diagnosis of breast cancer, with the final goal of avoiding in most cases useless biopsies of suspect cases encountered during radiological screening. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Blood and urine molecular analysis (Timing 0) and Blood and urine molecular analysis (Timing 1)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 367 participants
- Where
- 1 siteItaly
- Study dates
- 19 Jan 2021to Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Written informed consent
- Breast lesions detected by digital bilateral mammography
- Eligible for diagnostic biopsy (tru-cut or VABB) as per normal clinical practice
- Ability and willfulness to comply with the protocol requirements
Who cannot join
- Previous history of cancer, any type
- Clinical or radiological suspicion of advanced or metastatic cancer at the time of screening
- Known history of active or treated autoimmune or manifest chronic or seasonal and active allergic disorders
- History of major trauma or surgery during the 24 weeks before screening
- History of active infectious disease, either chronic or acute but occurring during the 8 weeks before screening
- History of known acute or chronic cardiac, kidney, or liver disease disorders or acute cardiac events
Trial locations and contacts
1 site in 1 country.
About this study
This is a translational no-profit study. Our proposal aims at creating a noninvasive Horizontal Data Integration (HDI) classifier for early diagnosis of breast cancer, with the final goal of avoiding in most cases useless biopsies of suspect cases encountered during radiological screening.
Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples (35 ml) and urine samples (50 ml). Radiological images as well as demographic and anatomopathological data will be collected.
Objective of this project is to develop a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies. Such classifier will be developed based on the correlation between the molecular profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) collected at T0 (baseline, before diagnostic biopsy) and bioptic diagnosis. The assessment of the profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) at two time points for diagnosed pT1 breast cancers (T0: baseline, before biopsy; T1: after diagnosis of pT1 breast cancer) will allow us to distinguish between tumor- and host-specific molecular alterations in connection with the presence/absence of breast cancer.
Read the detailed description
Background: Currently, early diagnosis of invasive breast cancer relies on the combined use of mammogram and ultrasound. These approaches are still suboptimal in terms of accuracy, and confirmation biopsy or recall tests are needed in case of radiological suspect. Recently, the study of noninvasive biomarkers in cancer has received enormous interest, fostered by the advancement of technologies and the potential for early detection of malignancies. However, no study has so far tried to apply the simultaneous assessment of biologically different analytes and data-characterization algorithms (radiomics approaches) to increase the accuracy of early breast cancer diagnosis.
Hypothesis: Multiple biological analytes must be combined with the refinement of radiomics algorithms to overcome the current limitations of early breast cancer diagnosis. The overall goal of the project is to develop a horizontal data integration (HDI) classifier enabling early noninvasive diagnosis of invasive breast cancer with high accuracy.
Objectives: Aim 1: To test the performance for the diagnosis of small invasive breast cancers of a) ultrasensitive next-generation sequencing on circulating tumor DNA (ctDNA); b) aptamer-base proteomics arrays on plasmatic proteins; c) radiomics machine-learning algorithms. Aim 2: To develop an HDI classifier based on the aforementioned methods with the aim of reducing the needs for invasive procedures in early breast cancer diagnosis. Aim 3: To improve the performance of the HDI classifier by integrating other potentially transformative methods of noninvasive diagnosis.
Experimental Design: Peripheral blood samples and urine samples will be collected from a prospective cohort of 750 patients with radiologically suspect small breast lesions undergoing diagnostic biopsy at the Diagnostics Senology Unit of San Martino Hospital. Ultrasensitive Next Generation Sequencing (NGS) on plasma ctDNA will be performed using a custom tagged-amplicon panel designed by us on a cohort of 3,269 sequenced breast cancer cases from the GENIE initiative. We also will be applied a new protocol termed cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) in collaboration with Dana Farber Cancer Institute, Boston for methylome analysis of small quantities of ctDNA from plasma and urine. Potential cancer-related plasma proteins will be analyzed using SomaScan aptamer-base protein arrays in collaboration with the Sidra Medical Center, Doha, Qatar. A radiomics classifier developed by the Senology team on an exploratory subgroup of the ASTOUND trial, sponsored by the University of Genoa, will be trained and tested on the same cohort. Other noninvasive diagnostics methods will be assessed as well. An HDI classifier will be generated on ctDNA, proteomics, and radiomics results, using advanced machine learning methods. Our HDI classifier will finally be integrated as needed with other predictors and validated on our cohort.
Expected Results: 1. Assessment of the performance of cutting-edge noninvasive methodologies in the context of early breast cancer diagnosis. 2. Development of a noninvasive HDI classifier for early breast cancer. 3. Novel biological insights on small breast cancers.
Impact On Cancer: 1. Increase in early breast diagnosis accuracy over current methods. 2. Reduction in the need for recall and invasive tests in breast cancer diagnosis. 3. Long-term impact on breast cancer mortality.
- Conditions
- Breast Cancer
- Being tested
- Blood and urine molecular analysis (Timing 0), Blood and urine molecular analysis (Timing 1)
- Keywords
- Translational Study, Horizontal Data Integration Classifier
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Blood and urine molecular analysis (Timing 0)Diagnostic test
peripheral blood and urine sample collection
Blood and urine molecular analysis (Timing 1)Diagnostic test
peripheral blood and urine sample collection
| Study group | Role | What they receive |
|---|---|---|
| Breast Cancer Stage T1 Group | Experimental | Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples and urine samples at baseline. Radiological images as well as demographic and anatomopathological data will be collected.
If bioptically confirmed T1 breast cancer, patients will undergo a second peripheral blood and urine collection after primary breast cancer surgery. Receives: Diagnostic Test: Blood and urine molecular analysis (Timing 0); Diagnostic Test: Blood and urine molecular analysis (Timing 1) |
| Benign Breast Lesion Group | Active comparator | Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples and urine samples at baseline. Radiological images as well as demographic and anatomopathological data will be collected.
If bioptically confirmed benign lesion, no other samples will be collected. Receives: Diagnostic Test: Blood and urine molecular analysis (Timing 0) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies
Accuracy of a horizontal data integration (HDI) classifier in correctly classifying pT1 breast cancers from benign lesions (i.e., non-invasive breast adenocarcinoma) presenting with similar radiological features (i.e., maximum lesion diameter smaller or equal to 2 cm). The HDI classifier is defined as a variable mixture of features from different radiomics analyses on baseline mammograms and molecular analyses on peripheral blood (ctDNA methylation by cfMeDIPSeq, proteins using the SomaScan® Somalogic platform, miRNA sequencing from exosomes) and urine (ctDNA methylation by cfMeDIPSeq) collected at T0 (baseline, before diagnostic biopsy). This outcome will be compared with the accuracy of diagnostic biopsy on the same patients' cohort.
Measured: 5 years
Secondary outcomes (2)
Accuracy of the HDI classifier
Accuracy of the HDI classifier when taking into account and after removing host-specific variables by assessing the same variables after surgery.
Measured: 5 years
Analytical and clinical validity of the HDI classifier
Analytical and clinical validity of surrogate, less expensive methods to measure the same variables included in the HDI classifier (e.g., methylation-specific PCR assays, ELISA essays for selected proteins, quantitative real-time PCR for miRNAs).
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Mar 2021
- Study start19 Jan 2021
- Primary completionDec 2023 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Breast 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Model description
- Based on the results of breast lesion biopsy, patients are assigned to two different groups: * Breast Cancer Stage T1 Group * Benign Breast Lesion Group
- Masking
- None (open label)
- Enrolment
- 367 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ospedale Policlinico San Martino
- Sponsor type
- University, hospital or other
- Collaborators
- Associazione Italiana per la Ricerca sul Cancro, Universita degli Studi di Genova, Sidra Medicine, Dana-Farber Cancer Institute
- Responsible party
- Sponsor
- Investigators
- Gabriele Zoppoli, MD, PhD, Principal investigator, Ospedale Policlinico San Martino
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04781062
- Sponsor's protocol ID
- 4452
- Acronym
- RENOVATE
- Status last verified
- Oct 2022
- First submitted
- 23 Feb 2021
- Last updated
- 1 Nov 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ravera F, Cirmena G, Dameri M, Gallo M, Vellone VG, Fregatti P, Friedman D, Calabrese M, Ballestrero A, Tagliafico A, Ferrando L, Zoppoli G. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol. BMJ Open. 2021 Dec 31;11(12):e054256. doi: 10.1136/bmjopen-2021-054256.
PubMed 34972769
Study at a glance
From the registry record, last updated by the sponsor on 1 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer
- ClinicalTrials.gov ID
- NCT04781062
- Acronym / sponsor ID
- RENOVATE / 4452
- Conditions
- Breast Cancer
- Keywords
- Translational Study, Horizontal Data Integration Classifier
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 367 participants (actual)
- Lead sponsor
- Ospedale Policlinico San MartinoUniversity, hospital or other
- Collaborators
- Associazione Italiana per la Ricerca sul Cancro, Universita degli Studi di Genova, Sidra Medicine, Dana-Farber Cancer Institute
- Study start
- 19 Jan 2021 (actual)
- Primary completion
- Dec 2023 (estimated)
- Study completion
- Dec 2024 (estimated)
- First posted
- 4 Mar 2021 (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.
This is a translational no-profit study. Our proposal aims at creating a noninvasive Horizontal Data Integration (HDI) classifier for early diagnosis of breast cancer, with the final goal of avoiding in most cases useless biopsies of suspect cases encountered during radiological screening.
Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples (35 ml) and urine samples (50 ml). Radiological images as well as demographic and anatomopathological data will be collected.
Objective of this project is to develop a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies. Such classifier will be developed based on the correlation between the molecular profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) collected at T0 (baseline, before diagnostic biopsy) and bioptic diagnosis. The assessment of the profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) at two time points for diagnosed pT1 breast cancers (T0: baseline, before biopsy; T1: after diagnosis of pT1 breast cancer) will allow us to distinguish between tumor- and host-specific molecular alterations in connection with the presence/absence of breast cancer.
Detailed description
Background: Currently, early diagnosis of invasive breast cancer relies on the combined use of mammogram and ultrasound. These approaches are still suboptimal in terms of accuracy, and confirmation biopsy or recall tests are needed in case of radiological suspect. Recently, the study of noninvasive biomarkers in cancer has received enormous interest, fostered by the advancement of technologies and the potential for early detection of malignancies. However, no study has so far tried to apply the simultaneous assessment of biologically different analytes and data-characterization algorithms (radiomics approaches) to increase the accuracy of early breast cancer diagnosis.
Hypothesis: Multiple biological analytes must be combined with the refinement of radiomics algorithms to overcome the current limitations of early breast cancer diagnosis. The overall goal of the project is to develop a horizontal data integration (HDI) classifier enabling early noninvasive diagnosis of invasive breast cancer with high accuracy.
Objectives: Aim 1: To test the performance for the diagnosis of small invasive breast cancers of a) ultrasensitive next-generation sequencing on circulating tumor DNA (ctDNA); b) aptamer-base proteomics arrays on plasmatic proteins; c) radiomics machine-learning algorithms. Aim 2: To develop an HDI classifier based on the aforementioned methods with the aim of reducing the needs for invasive procedures in early breast cancer diagnosis. Aim 3: To improve the performance of the HDI classifier by integrating other potentially transformative methods of noninvasive diagnosis.
Experimental Design: Peripheral blood samples and urine samples will be collected from a prospective cohort of 750 patients with radiologically suspect small breast lesions undergoing diagnostic biopsy at the Diagnostics Senology Unit of San Martino Hospital. Ultrasensitive Next Generation Sequencing (NGS) on plasma ctDNA will be performed using a custom tagged-amplicon panel designed by us on a cohort of 3,269 sequenced breast cancer cases from the GENIE initiative. We also will be applied a new protocol termed cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) in collaboration with Dana Farber Cancer Institute, Boston for methylome analysis of small quantities of ctDNA from plasma and urine. Potential cancer-related plasma proteins will be analyzed using SomaScan aptamer-base protein arrays in collaboration with the Sidra Medical Center, Doha, Qatar. A radiomics classifier developed by the Senology team on an exploratory subgroup of the ASTOUND trial, sponsored by the University of Genoa, will be trained and tested on the same cohort. Other noninvasive diagnostics methods will be assessed as well. An HDI classifier will be generated on ctDNA, proteomics, and radiomics results, using advanced machine learning methods. Our HDI classifier will finally be integrated as needed with other predictors and validated on our cohort.
Expected Results: 1. Assessment of the performance of cutting-edge noninvasive methodologies in the context of early breast cancer diagnosis. 2. Development of a noninvasive HDI classifier for early breast cancer. 3. Novel biological insights on small breast cancers.
Impact On Cancer: 1. Increase in early breast diagnosis accuracy over current methods. 2. Reduction in the need for recall and invasive tests in breast cancer diagnosis. 3. Long-term impact on breast cancer mortality.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Based on the results of breast lesion biopsy, patients are assigned to two different groups: * Breast Cancer Stage T1 Group * Benign Breast Lesion Group
| Arm | Type | Receives |
|---|---|---|
| Breast Cancer Stage T1 Group | Experimental | Diagnostic Test: Blood and urine molecular analysis (Timing 0), Diagnostic Test: Blood and urine molecular analysis (Timing 1)Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples and urine samples at baseline. Radiological images as well as demographic and anatomopathological data will be collected. If bioptically confirmed T1 breast cancer, patients will undergo a second peripheral blood and urine collection after primary breast cancer surgery. |
| Benign Breast Lesion Group | Active comparator | Diagnostic Test: Blood and urine molecular analysis (Timing 0)Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples and urine samples at baseline. Radiological images as well as demographic and anatomopathological data will be collected. If bioptically confirmed benign lesion, no other samples will be collected. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Written informed consent
- Breast lesions detected by digital bilateral mammography
- Eligible for diagnostic biopsy (tru-cut or VABB) as per normal clinical practice
- Ability and willfulness to comply with the protocol requirements
Key exclusion criteria (6 listed)
- Previous history of cancer, any type
- Clinical or radiological suspicion of advanced or metastatic cancer at the time of screening
- Known history of active or treated autoimmune or manifest chronic or seasonal and active allergic disorders
- History of major trauma or surgery during the 24 weeks before screening
- History of active infectious disease, either chronic or acute but occurring during the 8 weeks before screening
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 |
|---|---|---|
| Blood and urine molecular analysis (Timing 0)peripheral blood and urine sample collection | Diagnostic test | Benign Breast Lesion Group, Breast Cancer Stage T1 Group |
| Blood and urine molecular analysis (Timing 1)peripheral blood and urine sample collection | Diagnostic test | Breast Cancer Stage T1 Group |
Primary outcome
Development of a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies
Time frame: 5 years
Accuracy of a horizontal data integration (HDI) classifier in correctly classifying pT1 breast cancers from benign lesions (i.e., non-invasive breast adenocarcinoma) presenting with similar radiological features (i.e., maximum lesion diameter smaller or equal to 2 cm). The HDI classifier is defined as a variable mixture of features from different radiomics analyses on baseline mammograms and molecular analyses on peripheral blood (ctDNA methylation by cfMeDIPSeq, proteins using the SomaScan® Somalogic platform, miRNA sequencing from exosomes) and urine (ctDNA methylation by cfMeDIPSeq) collected at T0 (baseline, before diagnostic biopsy). This outcome will be compared with the accuracy of diagnostic biopsy on the same patients' cohort.
2 secondary outcomes
Accuracy of the HDI classifier
Time frame: 5 years
Analytical and clinical validity of the HDI classifier
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Nov 2022.
- Derived from this study: PubMed 34972769Ravera F, Cirmena G, Dameri M, Gallo M, Vellone VG, Fregatti P, Friedman D, Calabrese M, Ballestrero A, Tagliafico A, Ferrando L, Zoppoli G. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol. BMJ Open. 2021 Dec 31;11(12):e054256. doi: 10.1136/bmjopen-2021-054256.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Gabriele Zoppoli, MD, PhD principal investigator
Ospedale Policlinico San Martino
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Blood and urine molecular analysis (Timing 0) and Blood and urine molecular analysis (Timing 1) in people with Breast Cancer, run by Ospedale Policlinico San Martino at 1 site in Italy.
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 4 inclusion and 6 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: status not confirmed, as of the sponsor's last update on 1 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies (time frame: 5 years). 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 NCT04781062, which also lists 1 linked publication.
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 1 Nov 2022.