Soo‐Hwang Teo

The paper develops a population-specific gene signature, MyGESig, which improves survival prediction in Malaysian breast cancer patients compared to existing models.

This study analyzes the intra-tumoral microbiota in Asian breast cancer patients using bulk transcriptomic data and finds weak associations with immune scores and predictive potential using random forest models.

The paper uses case-control data from over 400,000 women to classify BRCA1 and BRCA2 variants, finding high consistency with ClinVar annotations for 785 previously unclassified variants.

This study evaluates the prognostic value of immune markers in Asian breast cancer using spatial immunophenotyping and multimodal machine learning, finding that integrated spatial and molecular profiling improves prognostication.

This study identifies immune phenotype-driven clustering in HR+/HER2- breast cancer from an Asian cohort, deviating from the luminal A-luminal B classification seen in European populations.

This paper evaluates polygenic risk scores for breast cancer prediction in Korean women, finding that combining Asian and European PRS improves discrimination over lifestyle factors.

The paper uses whole-exome sequencing data to identify novel breast cancer susceptibility genes and assess the contribution of coding variants to breast cancer risk.