Abstract
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.
Highlights
IntroductionDifferent subtypes of breast cancer grow and spread at different rates, and they react differently to treatment
We model the effect of grade on rate of lymph node spread in two different ways: firstly by modeling the effect of grade as an ordinal variable, so that the rate of lymph node spread is amplified by the factor eβg, where β is the log effect and g is the grade; and secondly, as a discrete variable, so that the rate of lymph node spread is amplified by the factor eβ2g2þβ3g3, where Grade 1 is the reference, β2 and β3 correspond to the log effects of Grades 2 and 3, respectively, and g2, g3 are grade indicator variables
We model the effect of the PRS as a continuous variable, so that the rate of lymph node spread is amplified by a factor eβÁPRS, where β is the log effect and PRS is the polygenic risk score
Summary
Different subtypes of breast cancer grow and spread at different rates, and they react differently to treatment. There has been an interest in statistically modeling breast cancer heterogeneity in terms of disease progression.[1,2] Number of lymph node metastases present at diagnosis is associated with long-term breast cancer prognosis.[3,4] It is clinically relevant to understand breast cancer heterogeneity in terms of lymph node metastases at diagnosis. The purpose of this article is to investigate the association between breast cancer tumor characteristics, including molecular subtype, and rate of lymph node spread
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