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Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer (RENOVATE)

Primary Purpose

Breast Cancer

Status
Active
Phase
Not Applicable
Locations
Italy
Study Type
Interventional
Intervention
Blood and urine molecular analysis (Timing 0)
Blood and urine molecular analysis (Timing 1)
Sponsored by
Ospedale Policlinico San Martino
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Breast Cancer focused on measuring Translational Study, Horizontal Data Integration Classifier

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)FemaleAccepts Healthy Volunteers

Inclusion Criteria:

  • 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

Exclusion Criteria:

  • 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

Sites / Locations

  • Ospedale Policlinico San Martino

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

Breast Cancer Stage T1 Group

Benign Breast Lesion Group

Arm Description

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.

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.

Outcomes

Primary Outcome Measures

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.

Secondary Outcome Measures

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.
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).

Full Information

First Posted
February 23, 2021
Last Updated
October 31, 2022
Sponsor
Ospedale Policlinico San Martino
Collaborators
Associazione Italiana per la Ricerca sul Cancro, Universita degli Studi di Genova, Sidra Medical and Research Center, Dana-Farber Cancer Institute
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1. Study Identification

Unique Protocol Identification Number
NCT04781062
Brief Title
Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer
Acronym
RENOVATE
Official Title
Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer
Study Type
Interventional

2. Study Status

Record Verification Date
October 2022
Overall Recruitment Status
Active, not recruiting
Study Start Date
January 19, 2021 (Actual)
Primary Completion Date
December 2023 (Anticipated)
Study Completion Date
December 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Ospedale Policlinico San Martino
Collaborators
Associazione Italiana per la Ricerca sul Cancro, Universita degli Studi di Genova, Sidra Medical and Research Center, Dana-Farber Cancer Institute

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
Yes

5. Study Description

Brief 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. 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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Breast Cancer
Keywords
Translational Study, Horizontal Data Integration Classifier

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Sequential Assignment
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)
Allocation
Non-Randomized
Enrollment
367 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Breast Cancer Stage T1 Group
Arm Type
Experimental
Arm Description
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.
Arm Title
Benign Breast Lesion Group
Arm Type
Active Comparator
Arm Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
Blood and urine molecular analysis (Timing 0)
Intervention Description
peripheral blood and urine sample collection
Intervention Type
Diagnostic Test
Intervention Name(s)
Blood and urine molecular analysis (Timing 1)
Intervention Description
peripheral blood and urine sample collection
Primary Outcome Measure Information:
Title
Development of a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies
Description
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.
Time Frame
5 years
Secondary Outcome Measure Information:
Title
Accuracy of the HDI classifier
Description
Accuracy of the HDI classifier when taking into account and after removing host-specific variables by assessing the same variables after surgery.
Time Frame
5 years
Title
Analytical and clinical validity of the HDI classifier
Description
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).
Time Frame
5 years

10. Eligibility

Sex
Female
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria: 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 Exclusion Criteria: 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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Gabriele Zoppoli, MD, PhD
Organizational Affiliation
Ospedale Policlinico San Martino
Official's Role
Principal Investigator
Facility Information:
Facility Name
Ospedale Policlinico San Martino
City
Genova
ZIP/Postal Code
16132
Country
Italy

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
34972769
Citation
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.
Results Reference
derived

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Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer

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