Felipe Segato Dezem

This paper identifies the EZH2-TTP-mTORC1 axis as a driver of phenotypic plasticity and therapeutic vulnerability in prostate cancer, showing that combined inhibition of EZH2 and PI3K/mTORC1 improves anti-tumor activity.

STAMP enables low-cost, high-throughput single-cell analysis by integrating transcriptomics and proteomics imaging, supporting both single and multimodal profiling of diverse cell types.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

Head-to-head on serial prostate adenocarcinoma sections: Xenium gave higher sensitivity, wider dynamic range and closer agreement with snRNA-seq; CosMx segmented cells better out of the box.

This study introduces a method to generate high-quality single-nucleus transcriptomic data from FFPE samples using recent single-cell genomic technology.

The paper applies a one-class logistic regression model to predict stemness in single-cell and spatial omics datasets without requiring cell annotation.

This study performs cis- and trans-eQTL TWAS on breast and ovarian cancer data to identify 101 and 8 risk-associated genes, respectively, with several driven by trans-eQTLs.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper identifies RCCD1 as a susceptibility gene for breast and ovarian cancer by fine-mapping a risk locus and showing its expression and interaction patterns are associated with cancer risk.