{"id":10295,"date":"2025-01-10T11:01:11","date_gmt":"2025-01-10T10:01:11","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=events&p=10295"},"modified":"2025-01-28T12:32:56","modified_gmt":"2025-01-28T11:32:56","slug":"biomedical-data-series-lperfetto","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/lcsb-en\/events\/biomedical-data-series-lperfetto\/","title":{"rendered":"Causal Analysis of Biomedical Data – Prof. Livia Perfetto"},"content":{"rendered":"\n
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This is a hybrid event. <\/strong>Join in person at the LCSB, or tune in remotely via Webex<\/a>.<\/p>\n\n\n\n

\nPatientProfiler<\/em>, a network-based approach to support personalised medicine<\/strong><\/h2>\n\n\n\n

Deciphering the intricate mechanisms underlying reprogramming in cancer cells is a crucial challenge in oncology, as it holds the key to advancing our ability to diagnose and treat cancer patients. For this reason, comprehensive and patient-specific multi-omic characterisation of tumour specimens has become increasingly common in clinical practice. Despite these efforts, no effective approach for identifying the molecular mechanisms dysregulated at a patient-resolution level has been developed, nor have these advances been effectively employed to define personalised therapeutic regimens. The main shortcoming is the absence of a robust computational framework to exploit such information by integrating and interpreting the available multi-dimensional data to drive translational solutions.<\/p>\n\n\n\n

To fill this gap, we developed PatientProfiler, a computational pipeline that leverages causal interaction data, annotated in our in-house manually curated resource, SIGNOR, to address how the genetic and molecular background of individual patients contributes to the establishment of a malignant phenotype. PatientProfiler is an open-source, R-based package composed of several functions that allow for multi-omic data analysis and standardisation, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers.<\/p>\n\n\n\n

To benchmark the tool, we retrieved genomic, transcriptomic, (phospho)proteomic and clinical data derived from 122 treatment-na\u00efve breast cancer biopsies, available at the CPTAC portal. Thanks to this approach, we have identified patient-specific mechanistic models (one patient, one network) that recapitulate dysregulated signalling pathways in breast cancer. This collection of models provides valuable insights into the underlying mechanisms of tumourigenesis and disease progression. Moreover, in-depth topological exploration of these networks has allowed us to define seven communities (subnetworks), each associated with a unique transcriptomic signature and a distinct prognostic value.<\/p>\n\n\n\n

In summary, our work demonstrates that PatientProfiler is a tool for patient-specific network analysis, advancing personalised medicine by identifying actionable biomarkers and supporting tailored therapeutic strategies.<\/p>\n\n\n

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\nAbout the speaker<\/h3>\n\n\n\n

Livia Perfetto is Group Leader and Assistant Professor at La Sapienza University<\/a> in Rome, Italy. She received her PhD from the University of Rome Tor Vergata <\/a>and held positions at the European Bioinformatics Institute<\/a> and Human Technopole<\/a>. Prof. Perfetto\u2019s research interests include scientific data curation, signalling network analysis, and the development of computational methods to interpret -omic data through her work with the SIGNOR<\/a> database.<\/p>\n\n\n

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