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Start Over Author "Sarah Perrone" Topic genomics
2 results on '"Sarah Perrone"'

1. Impaired neutrophil-mediated cell death drives Ewing’s Sarcoma in the background of Down syndrome

Author

Serena Peirone, Elisa Tirtei, Anna Campello, Caterina Parlato, Simonetta Guarrera, Katia Mareschi, Elena Marini, Sebastian Dorin Asaftei, Luca Bertero, Mauro Papotti, Francesca Priante, Sarah Perrone, Matteo Cereda, and Franca Fagioli

Subjects
Ewing Sarcoma, Down syndrome, paediatric bone sarcoma, genomics, transcriptomics, neutrophils, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

IntroductionEwing Sarcoma (EWS) has been reported in seven children with Down syndrome (DS). To date, a detailed assessment of this solid tumour in DS patients is yet to be made.MethodsHere, we characterise a chemo-resistant mediastinal EWS in a 2-year-old DS child, the youngest ever reported case, by exploiting sequencing approaches.ResultsThe tumour showed a neuroectodermal development driven by the EWSR1-FLI1 fusion. The inherited myeloperoxidase deficiency of the patient caused failure of neutrophil-mediated cell death and promoted genomic instability.DiscussionIn this context, the tumour underwent genome-wide near haploidisation resulting in a massive overexpression of pro-inflammatory cytokines. Recruitment of defective neutrophils fostered rapid evolution of this EWS.

Published
2024
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2. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Author

Sarah Perrone, Francesca Priante, Fabiola Varese, Elisa Tirtei, Serena Peirone, Franca Fagioli, Matteo Cereda, and Marco Del Giudice

Subjects
0301 basic medicine, Computer science, QH301-705.5, Sequencing data, Genomics, Review, cancer heterogeneity, Catalysis, Inorganic Chemistry, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, Biomarkers, Tumor, Tumor Microenvironment, Transcriptome Profiles, Humans, Physical and Theoretical Chemistry, Biology (General), Precision Medicine, Molecular Biology, QD1-999, Spectroscopy, business.industry, Human intelligence, Sequence Analysis, RNA, Organic Chemistry, RNA sequencing, General Medicine, Precision medicine, artificial intelligence, Prognosis, Rare cancer, Computer Science Applications, Patient management, Chemistry, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, Precision oncology, 030220 oncology & carcinogenesis, Artificial intelligence, Single-Cell Analysis, business, Algorithms
Abstract

Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

Published
2021

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