Lymph node metastases in breast cancer: Investigating associations with tumor characteristics, molecular subtypes and polygenic risk score using a continuous growth model.
Gabriel IshedenFelix GrassmannKamila CzeneKeith HumphreysPublished in: International journal of cancer (2021)
We investigate the association between rate of breast cancer lymph node spread and grade, estrogen receptor (ER) status, progesteron receptor status, decision tree derived PAM50 molecular subtype and a polygenic risk score (PRS), using data on 10 950 women included from two different data sources. Lymph node spread was analyzed using a novel continuous tumor progression model that adjusts for tumor volume in a biologically motivated way and that incorporates covariates of interest. Grades 2 and 3 tumors, respectively, were associated with 1.63 and 2.17 times faster rates of lymph node spread than Grade 1 tumors (P < 10-16 ). ER/PR negative breast cancer was associated with a 1.25/1.19 times faster spread than ER/PR positive breast cancer, respectively (P = .0011 and .0012). Among the molecular subtypes luminal A, luminal B, Her2-enriched and basal-like, Her2-enriched breast cancer was associated with 1.53 times faster spread than luminal A cancer (P = .00072). PRS was not associated with the rate of lymph node spread. Continuous growth models are useful for quantifying associations between lymph node spread and tumor characteristics. These may be useful for building realistic progression models for microsimulation studies used to design individualized screening programs.
Keyphrases
- lymph node
- estrogen receptor
- sentinel lymph node
- neoadjuvant chemotherapy
- positive breast cancer
- type diabetes
- breast cancer risk
- big data
- poor prognosis
- polycystic ovary syndrome
- drinking water
- pregnant women
- deep learning
- metabolic syndrome
- early stage
- adipose tissue
- pregnancy outcomes
- young adults
- skeletal muscle
- lymph node metastasis
- locally advanced
- binding protein
- data analysis
- squamous cell