Steffi Oesterreich

The paper develops a novel mouse cell line model of invasive lobular breast carcinoma that recapitulates human tumor phenotype and immune microenvironment, offering a preclinical platform for therapy evaluation.

The paper identifies increased GPCR pathway activity in breast cancer ovarian metastases and links it to migration and F-actin reorganization through CaSR.

This study shows that chronic inflammation and hormonal changes in older women create a tumor-permissive environment for ER+ breast cancer by increasing local estradiol and immune dysfunction.

This study shows HER2 mutations drive HER2/HER3 hyperactivation in ILC, making it sensitive to HER2/HER3-targeting ADCs, and highlights the synergy between T-DXd and P-DXd.

This study shows that ER-positive ILC xenograft models replicate metastatic patterns and endocrine response of human invasive lobular breast carcinoma.

This study shows that restoring E-cadherin in ILC models reduces metastasis to specific organs but has limited effects on primary tumor growth and 2D cell growth.

This study benchmarks 12 scRNA-seq CNV inference methods across 28 datasets, finding allele-aware tools perform better with high-quality allelic data, while expression-centric tools are reliable with raw data.

This study shows that elevated cfDNA and fragment shortening in cancer are due to saturated hepatic clearance, not tumor burden alone.

This study identifies and characterizes patient-derived xenograft models of invasive lobular breast carcinoma using multi-omics and histopathology, finding seven ILC PDX models with CDH1 mutations and low CDH1 expression.

The paper describes the Hope for Others rapid autopsy program for metastatic breast cancer, detailing its protocols, sample collection, and findings on metastasis research and model development.

This study identifies RET as a druggable target in estrogen receptor-positive breast cancer brain metastases and shows that Pralsetinib inhibits RET-driven metastatic properties.

This study investigates FGFR4's role in endocrine resistance in ER+ breast cancer and finds it does not generally mediate resistance, though HER2-like subtypes may respond to FGFR4 inhibition.

This paper develops a high-throughput screening method to identify drugs targeting polyploid giant cancer cells in breast cancer, combining empirical assays with a machine learning model for virtual screening.

EstroGene2.0 is a multi-omic database that compiles data on estrogen receptor responses and resistance in breast cancer, revealing diverse transcriptomic changes and identifying key targets.

The paper identifies S100A11 as a factor promoting macrophage recruitment in ER+ breast cancer through single-cell RNA-sequencing analysis and functional experiments.

The paper introduces MICA, a method to detect multi-class biomarkers across multiple transcriptomic studies, showing improved accuracy and biological insights in both bulk and single-cell data.