Development and validation of risk prediction models for breast cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Principal Investigator
Name
Xin Hua
Degrees
M.D.
Institution
Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Position Title
Research Assistant
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-1209
Initial CDAS Request Approval
May 2, 2023
Title
Development and validation of risk prediction models for breast cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Summary
Breast cancer (BC) is the most common newly diagnosed malignancy annually and the main cause of cancer-related deaths in female patients worldwide. Despite the significant advances in the landscape of personalized diagnosis and therapy, breast cancer remains a huge challenge for women around the world, thus, screening and early diagnosis of BC is vital.
Mammography and ultrasonography are the most commonly used methods for breast cancer screening, but have been reported to be ineffective in the early diagnosis of early-stage BC. For population-based screening, an accurate BC risk prediction model for identifying high-risk subpopulation is urgently needed.
Available prediction models for BC are limited to individuals at elevated risk who are with family history of breast cancer or breast malignant occurrence. The majority of these models also need additional information from two aspects: 1) routine clinical workup markers, such as tumor biomarkers, immune markers, and hormones; 2) single nucleotide polymorphisms (SNPs) identified by genome-wide association study (GWAS).
Taken together, a comprehensive personalized risk prediction model for BC based on blood biomarkers, genetic susceptibility, routinely available predictors such as medical history, metabolic risk factors and lifestyle factors is urgently needed.
Mammography and ultrasonography are the most commonly used methods for breast cancer screening, but have been reported to be ineffective in the early diagnosis of early-stage BC. For population-based screening, an accurate BC risk prediction model for identifying high-risk subpopulation is urgently needed.
Available prediction models for BC are limited to individuals at elevated risk who are with family history of breast cancer or breast malignant occurrence. The majority of these models also need additional information from two aspects: 1) routine clinical workup markers, such as tumor biomarkers, immune markers, and hormones; 2) single nucleotide polymorphisms (SNPs) identified by genome-wide association study (GWAS).
Taken together, a comprehensive personalized risk prediction model for BC based on blood biomarkers, genetic susceptibility, routinely available predictors such as medical history, metabolic risk factors and lifestyle factors is urgently needed.
Aims
AIM 1: Our primary aim is to develop and validate a set of risk prediction models that including blood biomarkers, genetic susceptibility, routinely available predictors for breast cancer.
AIM 2: Our secondary aim is to explore the potential interaction and/or mediation effects between genetic susceptibility, blood biomarkers and traditional risk factors for breast cancer, which could enhance the understanding of the mechanisms of the development of breast cancer.
Collaborators
None.