Abstract 398P
Background
Breast Cancer (BC) is a complex disease with profound genomic aberrations. However, the underlying molecular disparity influenced by age and ethnicity remains elusive.
Methods
In this study, we aimed to investigate the molecular properties of 843 primary and metastatic BC patients enrolled in the K-MASTER program. By categorizing patients into two distinct age subgroups, we explored their unique molecular properties. Additionally, we leveraged large-scale genomic data from the TCGA and MSK-IMPACT studies to examine the ethnic-driven molecular and clinical disparities.
Results
We observed a high prevalence of PI3KCAmutations in K-MASTER(KM) HER2+ tumors, particularly in older patients. Moreover, we identified increased mutation rates in DNA damage response molecules, including ARID1A, MSH6,and MLH1. The KM patients were mainly comprised of TNBC and HER2-positive tumors, while the TCGA and MSK-IMPACT cohorts exhibited a predominance of hormone receptor-positive (HR+) subtype tumors. Importantly, GATA3mutations were less frequently observed in East Asian patients, which correlated with poor clinical outcomes. In addition to characterizing the molecular disparities, we developed a gradient-boosting multivariable model to identify a new molecular signature that could predict the therapeutic response to platinum-based chemotherapy.
Conclusions
Our study provides valuable insights into the understanding of age- and ethnic-driven molecular and clinical disparities in breast cancer patients. By unraveling the intricate relationship between genetic alterations and clinical outcomes, we underscore the potential for personalized treatment strategies in BC patients guided by molecular profiles. Nevertheless, further investigations are warranted to elucidate the underlying mechanisms that govern these dynamic processes. Continued research in this field will pave the way for advancements in tailored therapeutic interventions for various cancer types, including breast cancer.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
First author, corresponding author.
Funding
Korea Health Industry Development Institute, National Research Foundation of Korea (NRF).
Disclosure
The author has declared no conflicts of interest.
Resources from the same session
545P - HIBRID: Histology and ct-DNA based risk-stratification with deep learning
Presenter: Chiara Loeffler
Session: Poster session 15
546P - An artificial intelligence system integrating deep learning-proteomics, pathomics and clinicopathological features to determine risk of T1 colorectal cancer metastasis to lymph node
Presenter: Yijiao Chen
Session: Poster session 15