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NCT Number: NCT06217900

a PROspective Case Control Study to Develop and Validate a Blood Test FOr mUlti-caNcers Early Detection(PROFOUND)

This study is a multi-center, case-control study aiming at developing and blinded testing machine learning-based multiple cancers early detection model by prospectively collecting blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis.

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Key information

Conditions

Age range

40 year–74 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University Cancer Hospital and Institute, Beijing, Beijing Municipality, China

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About this study

Blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis will be prospectively collected to identify cancer-specific circulating signals through integrative multi-omic analysis. Based on the comprehensive molecular profiling, a machine learning-driven model will be trained and blinded validated independent through a two-stage approach in clinically annotated individuals. Approximately 10327 cancer patients will be enrolled in this study and early-stage cancer patients will be enriched to improve the model sensitivity on distinguishing cancers with favorable prognosis. Approximately 6339 age and sex matched controls will be included in model development, which are volunteers without a cancer diagnosis after routine cancer screening tests.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

for Case Arm Participants:

  • 40-74 years old
  • Clinically and/or pathologically diagnosed cancer
  • No prior or undergoing any systemic or local antitumor therapy, including but not limited to surgical resection, radiochemotherapy, endocrinotherapy, targeted therapy, immunotherapy, interventional therapy, etc.
  • Able to provide a written informed consent and willing to comply with all part of the protocol procedures

Exclusion criteria

for Case Arm Participants:

  • Pregnancy or lactating women
  • Known prior or current diagnosis of other types of malignancies comorbidities
  • Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) or febrile illness (body temperature of ≥ 38.5 °C) within 14 days prior to screen
  • Recipients of organ transplant or prior bone marrow transplant or stem cell transplant
  • Recipients of blood transfusion within 30 days prior to screen
  • Recipients of therapy in past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide
  • Unsuitable for this trial determined by the researchers

Inclusion criteria

for Control Arm Participants:

  • 40-74 years old
  • Without confirmed cancer diagnosis
  • Able to provide a written informed consent and willing to comply with all part of the protocol procedures

Exclusion criteria

for Control Arm Participants:

  • Pregnancy or lactating women
  • Known prior or current diagnosis of other types of malignancies comorbidities
  • Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) or febrile illness (body temperature of ≥ 38.5 °C) within 14 days prior to screen
  • Recipients of organ transplant or prior bone marrow transplant or stem cell transplant
  • Recipients of blood transfusion within 30 days prior to screen
  • Recipients of therapy in the past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide
  • Unsuitable for this trial determined by the researchers

Treatment and study plan

Primary outcomes

  1. The performance of cfDNA methylation-based multiple cancers early detection model in case-control study

    Time frame: 12 months

    The sensitivity, specificity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.

Secondary outcomes

  1. The performance of cfDNA methylation-based multiple cancers early detection model in early stage cancer cases

    Time frame: 12 months

    The sensitivity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting stage I to II cancer at 95% confidence interval.

  2. The performance of multi-omic-based multiple cancers early detection model in case-control study

    Time frame: 12 months

    The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.

  3. The performance of different multi-cancer early detection models in different subgroups

    Time frame: 12 months

    The sensitivity and specificity of cfDNA methylation-based or multi-omic-based multiple cancers early detection model in different subgroups of the population (such as age, gender, cancer pathological classification, and clinical stage) at 95% confidence interval.

Other outcomes

  1. To develop a questionnaire to evaluate the risk factors in the multi-cancer early screening

    Time frame: 12 months

    To develop a questionnaire to evaluate the high-risk factors in the multi-cancer early screening, including lung cancer, gastrointestinal cancer, gynecological cancer, urogenital neoplasms, etc.

  2. To evaluate the performance of multi-omics early detection models in the population with suspected cancer

    Time frame: 12 months

    The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in in the population with suspected cancer at 95% confidence interval.

  3. To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models based on the cancer prevalence and staging data of individuals aged 40-75 years in China using multiple models

    Time frame: 12 months

    To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models(cfDNA methylation-based or multi-omic-based),based on the sensitivity, specificity and tissue origin accuracy,according to multi cancer prevalence and staging data of individuals aged 40-75 years in China.

  4. To simulate the benefits of clinical utility and health economics using different multi-cancer early detection models

    Time frame: 12 months

    To simulate the stage-shift and incremental cost-effective ratio (ICER) benefit when compared to usual care (SOC screening) using Markov model based on MCED test performance

  5. To explore biomarkers for cancer screening and construct a multimodal machine learning model based on multi-omics data

    Time frame: 12 months

    Exploring biomarkers in methylomics and fragmentomics,and constructing multimodal for multi-cancer early detection based on multiomics analysis

Study contacts

Contact information is provided by the study sponsor or research team.

Sponsors and collaborators

Lead sponsor

Shanghai Weihe Medical Laboratory Co., Ltd.

Industry

Collaborators

  • Peking University People's Hospital

Registry information

Official study title

PROFOUND Study: Development and Validation of a Multi-cancer Early Detection Model Based on Peripheral Blood Multi-omic Analysis and Machine Learning: a Multicenter, Prospective, Observational, Case-control Study

Acronym: PROFOUND

Important dates

Study start
2023
Primary completion
2026
Study completion
2027
First posted
Jan 23, 2024
Registry last updated
Jan 23, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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