Britta Weigelt

This study shows that coexisting PTEN and PIK3CA mutations in endometrial cancers hyperactivate mTORC1, making them sensitive to mTORC1 inhibitors but not PI3K or AKT inhibitors.

This study tracks clonal evolution of drug resistance in ovarian cancer using structural variants in cfDNA, finding polyclonal resistance with dominant high-fitness clones and genomic features linked to drug resistance.

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

This study shows that ongoing whole-genome doubling in ovarian cancer promotes cell diversity and immune dysregulation through chromosomal instability and quiescent states.

This study identifies APOBEC3 mutagenesis as a common driver of therapy resistance in breast cancer, associated with poorer clinical outcomes.

This paper develops a multimodal transformer model using H&E images and text reports to predict recurrence scores in hormone receptor-positive breast cancer, achieving high accuracy and generalizability.

The paper identifies TP53 loss and MDM2 amplification as factors associated with poor long-term response to CDK4/6 inhibition in breast cancer and shows that CDK2 inhibition can overcome this resistance.

This study uses spatial molecular profiling to show that mixed invasive ductal-lobular breast cancers have distinct molecular subtypes, oncogenic signatures, and mutations in different tumor regions, leading to prognostic uncertainty and therapeutic challenges.

This study shows that SMARCA4 mutations in ovarian cancer increase immunogenicity through interferon response and epigenetic changes, enhancing T cell and NK cell activity in the tumor microenvironment.

This study evaluates the reliability of whole-exome sequencing in detecting intratumor genetic heterogeneity, finding that technical noise accounts for a significant proportion of apparent genetic differences.