Author

Paolo A. Ascierto

Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale"ORCID 0000-0002-8322-475XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Organism:human· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Alessandra CalabreseiD· 1 shared Alessandro MorabitoiD· 1 shared Andrea BelliiD· 1 shared Angela MaurielloiD· 1 shared Beatrice CavalluzzoiD· 1 shared Biagio PucciiD· 1 shared Biancamaria CembrolaiD· 1 shared Caterina CostaiD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperImmunology

Alessandra CalabreseORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Differential serum binding patterns predicting healthy subjects and cancer patients

Beatrice Cavalluzzo, Biancamaria Cembrola, Simona Mangano, Andrea Belli, Francesco Izzo, Rosa D'Angelo, Maria Grazia Chiofalo, Cira Antonietta Forte, Alessandro Morabito, Alessandra Calabrese, Michelino De Laurentiis, Vito Vanella, Paolo A. Ascierto, Fernanda Picozzi, Ottavia Clemente, , Luigi Buonaguro

The paper identifies differential serum binding patterns that classify healthy individuals from cancer patients using serological data from the VirScan platform.

bioRxiv · May 29, 2026 · PreprintImmunology

Inhibition of the Adenosine pathway activates the immune response against Mesothelioma.

Caterina Costa, Steve Gray, Giulia Pinton, Laura Moro, Erika Del Grosso, Cristiana Bellan, Laura Addi, Rita Lombardi, Francesca Bruzzese, Davide De Biase, Biagio Pucci, Elena Di Gennaro, Paolo A. Ascierto, Giovanni Luca Gravina, Luciano Mutti

This study shows that inhibiting the adenosine pathway with AB680 or TT-4, alone or with anti-PD-1, reduces mesothelioma growth in vitro and in vivo.

bioRxiv · May 13, 2026 · PreprintCancer Biology

SRF-CLICAL: an approach for patient risk stratification using random forest models

Felipe A. Simão, Reija Hieta, Jane A. Pulman, Gabriele Madonna, Paolo A. Ascierto, Rolf Lewensohn, Giuseppe Valentino Masucci

SRF-CLICAL uses random forest models to stratify cancer patients into risk groups based on clinical data, demonstrating its effectiveness on two patient cohorts.

bioRxiv · Jun 22, 2021 · PreprintBioinformatics