Arnoldo Frigessi

The paper uses multi-omics analysis to show macrophages in breast tumors correlate with better treatment response but worse survival, driven by immune suppression mechanisms.

The paper externally validates five gene signatures in head and neck cancer patients, showing their prognostic and predictive value for treatment outcomes.

The paper uses scRNA-seq and explainable machine learning to analyze how tumor microenvironment cell types affect neoadjuvant chemotherapy outcomes in breast cancer.

The paper identifies host proteins modulating SARS-CoV-2 infection by expanding the virus-host protein interaction network and testing chemical inhibitors of these targets.

The paper identifies copy number motifs in breast cancer genomes that predict disease outcome and driver events, outperforming existing clinical and genomic markers.

The paper identifies haplotypes and LD patterns in 80 genes that differ between breast cancer patients and controls, and finds negative correlations in LD patterns for some genes.