Annual Meeting of the NCI Cohort Consortium (Abstract Submission): Submission #13

serial: '13'
sid: '151076'
uuid: ff8fc90e-5f3d-453d-83f7-a899287d4ccb
uri: '/egrp/cohortconsortium/abstracts?cid=eb_govdel'
created: '1757105604'
completed: '1757105604'
changed: '1757105604'
in_draft: '0'
current_page: ''
remote_addr: 10.208.24.102
uid: '0'
langcode: en
webform_id: cohort_2024_abstracts_submission
entity_type: node
entity_id: '1467'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  additional_authors:
    - add_author_degree: Ph.D.
      add_author_first_name: Tahania
      add_author_last_name: Ahmad
      add_author_middle: ''
      add_author_organization: 'Queen Mary University of London'
    - add_author_degree: M.D.
      add_author_first_name: Anne
      add_author_last_name: MacGregor
      add_author_middle: ''
      add_author_organization: 'Queen Mary University of London'
    - add_author_degree: Sc.D.
      add_author_first_name: Heather
      add_author_last_name: Eliassen
      add_author_middle: ''
      add_author_organization: ''
    - add_author_degree: Sc.D.
      add_author_first_name: Susan
      add_author_last_name: Hankinson
      add_author_middle: E
      add_author_organization: ''
    - add_author_degree: Ph.D.
      add_author_first_name: Roger
      add_author_last_name: Milne
      add_author_middle: L
      add_author_organization: 'Cancer Council Victoria'
    - add_author_degree: Ph.D.
      add_author_first_name: Celine
      add_author_last_name: Vachon
      add_author_middle: M
      add_author_organization: ''
    - add_author_degree: Ph.D.
      add_author_first_name: Amy
      add_author_last_name: Berrington
      add_author_middle: ''
      add_author_organization: 'The Institute of Cancer Research'
    - add_author_degree: Ph.D.
      add_author_first_name: Adam
      add_author_last_name: Brentnall
      add_author_middle: ''
      add_author_organization: 'Queen Mary University of London'
    - add_author_degree: Ph.D.
      add_author_first_name: Jack
      add_author_last_name: Cuzick
      add_author_middle: ''
      add_author_organization: 'Queen Mary University of London'
    - add_author_degree: Ph.D.
      add_author_first_name: Judith
      add_author_last_name: Offman
      add_author_middle: ''
      add_author_organization: 'Queen Mary University of London'
    - add_author_degree: M.D.
      add_author_first_name: Montserrat
      add_author_last_name: García-Closas
      add_author_middle: ''
      add_author_organization: 'The Institute of Cancer Research'
  degree_s_: MMath
  email: reuben.frost@icr.ac.uk
  first_name: Reuben
  last_name: Frost
  organization: 'The Institute of Cancer Research'
  poster_title: 'Adding serum hormone measurement to improve the Tyrer-Cuzick breast cancer risk model in postmenopausal women'
  short_biography_: |
    Introduction: The primary objective is to evaluate the potential of incorporating the ratio of serum estradiol (E2) and sex hormone binding globulin (SHBG) into breast cancer risk assessments using the Tyrer-Cuzick (T-C) breast cancer risk prediction model. Currently there is limited research on their integration in individual risk assessments.

    Methods: E2/SHBG levels will be added to the current T-C model, using a two-stage process applied to data from 2,666 cases (invasive breast cancers) and 4,512 non-cases from three nested case-control studies and one case-cohort study, all part of the B2RISK consortium to: 
    (1) develop a statistical model to explain variation in E2 and SHBG levels based on factors in the T-C model; and
     (2) estimate the odds ratio with the "hormone residual" (i.e., observed minus expected E2/SHBG from (1)), adjusted for T-C 10-year risk.  
    The model will be validated in the Breast Cancer Now Generations (BGS) prospective cohort in the UK including 42,075 eligible participants, 847 with incident breast cancer diagnosed within a 5-years follow-up.

    Results: Calibration of the current T-C model has been assessed in a BGS nested case-control sample with data on classical risk factors, 313-SNP polygenic risk score and mammographic density (1,777 controls and 610 breast cancer cases), using 5-year risk scores generated by T-C model v8 before adding hormones. The model was well calibrated (calibration slope 1.0 (95%CI 0.67-1.30) with an age-adjusted AUC=0.65 (95%CI 0.62-0.68).

    Conclusion:  T-C scores are well calibrated with modest risk discrimination. Further analyses will evaluate whether adding E2/SHBG improves the model.
  title: ''