Andrea Cavalli

A transcript-resolution prostate cancer atlas, built by uniformly reprocessing public RNA-seq cohorts, links isoform switches in cancer genes to oncogenic pathway activity and some to CRPC survival.

This study identifies and characterizes human monoclonal antibodies targeting conserved coronavirus spike S2 epitopes, revealing distinct antiviral functions and Fc-dependent protective mechanisms.

This study identifies hr2.016, a potent and broad SARS-CoV-2 neutralizing antibody, and shows its neutralization depends on kinetic stability and avidity.

This study uses AI to design a peptide inhibitor of WWP1, showing potent activity against breast cancer, especially TNBC, with promising cytotoxic effects and synergistic potential with PI3K inhibitors.

This paper develops a peptidomimetic inhibitor of eIF4E that suppresses cap-dependent translation in cancer and neurodevelopmental disorders, showing efficacy in cell and animal models.

The paper reports the first cryo-EM structure of ATTRv-F64S fibrils from a living patient's skin tissue, validating skin biopsy as a non-invasive diagnostic tool.

This study shows that disrupting FOXA1 and FOXA2 disrupts oncogenic programs in both AR-positive and AR-negative prostate cancers.

The paper designs AI-generated miniproteins that bind LCN2 with nanomolar affinity, demonstrating a computational workflow's effectiveness in generating functional binders.

The paper computationally optimizes a peptide inhibitor of the CXCL12/HMGB1 heterocomplex, resulting in a variant with significantly improved affinity and activity.

This paper identifies two resistance mechanisms to the CD37-targeting ADC naratuximab emtansine in lymphoma cell lines and suggests combination therapies to overcome resistance.

This study identifies HERV-K as a therapeutic target in DLBCL, develops a humanized nanobody with anti-lymphoma activity, and shows its potential through antibody-dependent cytotoxicity.

This paper introduces LICTOR, a machine learning model that predicts immunoglobulin light chain toxicity in AL using somatic mutations, achieving 83% accuracy in predicting clinical phenotypes.

CE-YAPP improves contact prediction by filtering false positives using structural self-consistency, enhancing accuracy in protein structure prediction from multiple sequence alignments.