Francesca Finotello

The paper identifies six distinct tumor microenvironment niches in glioblastoma that correlate with patient survival and immunotherapy response.

Rectangle provides a scalable deconvolution framework for bulk RNA-seq using single-cell data, achieving high accuracy and robustness across diverse benchmarks.

This study shows that TET2 loss increases lymphoma penetrance and promotes survival of premalignant B cells in MYC-driven lymphoma by reducing apoptosis.

This paper benchmarks second-generation single-cell-informed deconvolution tools for bulk RNA-seq, revealing performance differences based on data characteristics and providing an ecosystem called omnideconv.

NovumRNA predicts non-canonical tumor antigens from RNA sequencing data, showing robustness across cancer types and identifying therapy-induced antigens in glioblastoma.

The paper introduces a mathematical model, RaCInG, to infer cell-cell interaction networks in the tumor microenvironment using bulk RNA-seq data, revealing patient-specific features and immune response associations.

This paper develops a signature, iHet, to capture tumor microenvironment heterogeneity linked to immune activity and evasion using transcriptomic data from lung cancer.

This study identifies TLR3 as a conserved mediator of cardiac regeneration in zebrafish, showing its role in immune cell recruitment and DNA repair after myocardial injury.

SimBu simulates bulk RNA-seq data with variable cell type composition to create in silico gold standards for evaluating deconvolution methods.

The paper introduces easier, an R/Bioconductor package that predicts antitumor immune response from bulk RNA-seq data, outperforming existing methods in four cancers.

Scirpy provides a Python toolkit for analyzing single-cell T-cell receptor data and integrating it with transcriptomic data.

quanTIseq estimates absolute fractions of ten immune cell types from bulk RNA-seq; across TCGA, CXCL9/CXCR3 expression, not mutational load, tracks CD8+ T cell infiltration.

The paper evaluates computational methods for estimating immune-cell composition from bulk RNA-seq data using simulated and real datasets, finding high accuracy for well-defined cell-type signatures.

This study shows that neutral evolution dominates tumor shaping in a mouse CRC model, and PD-1/PD-L1 blockade enhances immunoediting and tumor homogeneity.

Immunogenomic profiling of 8243 TCGA samples across 20 solid cancers yields an immune atlas and a random-forest-derived immunophenoscore stratifying melanoma responders to checkpoint blockade.