Research

Enhancing copy number variants (CNVs) detection from sequencing

by Romina D'Aurizio, Elia Ceroni CNV, WES, Gene Panel, Targeted Sequencing, Structural Variant

We develop computational methods for the identification of copy number variants (CNVs), which refer to the presence of an abnormal number of copies of a particular DNA segment. The fraction of the human genome affected by CNVs is substantial (5-10%) and contributes to phenotypic variation, but it also impacts several diseases, including cancer, cardiovascular disease, HIV acquisition and progression, autoimmune diseases, and Alzheimer’s and Parkinson’s diseases in complex and as yet unexplored ways.

Identification of actionable cancer neoepitopes using just tumor RNAseq

by Danilo Tatoni Transcriptomics, Cancer, Immunotherapy

Neoepitopes arising from somatic mutations are capable of triggering host immune response which may result in tumor eradication. Their private nature makes it necessary to develop individualized detection and manufacturing processes, resulting in a long and expensive process. In this setting, avoiding false positives while keeping high sensitivity is key. To date multiple ongoing trials have focused on the screening of genomic data (WES/WGS) for the detection of somatic alterations, requiring at the meantime transcriptomic data (RNA-seq) to confirm the expression of the mutated transcript.

Leveraging Quantum Machine Learning for Tumor Subtyping

by Valeria Repetto Quantum Machine Learning, multi-omics data, tumoral data

The emergence of high-throughput technologies such as next-generation sequencing (NGS) in the last two decades has not only revolutionized our understanding in the fields of biology and medicine, but also transformed these domains into highly quantitative ones. More recently, the advances in statistical models and ML/DL methods are pushing the transition toward Precision Oncology. However, current computational models still struggle to capture and reconcile the real and complex dynamics of biological systems as classical computing lacks the power to detect non-trivial relationships from large and complex datasets.

Modeling 3-Dimensional chromatin interactions rearrangments in Neurodevelopmental Disorders

by Orazio Catona

The project’s aim is to get a better understanding on how Promoter-Enhancer Interactions regulate gene expression and how the disruption of such interactions affects the balance of the regulation, allowing us to discover new genomic hotspots related to diseases. What we want is to define the set of interactions possible for a specific tissue or cell type taking into account their chromatin state and their Topologically Associated Domains (TAD), we want to change this network based on the alterations in a patient to see what can possibly change with Copy Number Variants (CNVs) and rank their pathogenicity and identify how non-coding regions can affect pathogenicity.

Rare and common variants integration for Breast Cancer prediction

by Giulia Brunelli

It has been shown that combining rare and common genetics variants improves population risk stratification for breast cancer. Several models have been defined to combine PRS with rare truncating variants on a short list of genes for which robust breast cancer risk estimates are available. However, there is evidence that missense variants confer elevated breast cancer risks and that there are other genes for which the association with breast cancer was established (eg.

The landscape of BRAF transcript variants in human cancer

by Maurizio Podda Transcriptomics", Cancer, Splicing Isoforms

The BRAF protein kinase is extensively researched due to its role as a cancer driver and potential therapeutic target. Our study focuses on quantifying and analyzing clinical outcomes related to BRAF isoforms, specifically examining the ratio between the primary isoform (BRAF-220) and an alternative one (BRAF-204). We developed IsoWorm, a bioinformatics pipeline tailored to accurately distinguish and quantify BRAF isoforms, which we applied to over 500 cancer cell lines and 700 cancer tissue samples.

SI-MARKERS: A NEW “SINLAB” AI-BASED MODULE TO SUPPORT THE CLINICIAN IN THE OUTCOME PREDICTION OF PATIENTS WITH BRAIN TUMORS

by Barbara Iadarola Multi-omics Integration, Imaging, Artificial Intelligence, Personalized Medicine

Gliomas, characterized by diverse clinical outcomes and limited effective therapeutic options, represent highly heterogeneous brain tumors. This project seeks to demonstrate the benefits of integrating multi-omics data within an AI-based framework, providing insights into tumor complexity across various biological dimensions. The ultimate goal is to develop a tool that predicts glioma patient outcomes by integrating imaging, clinical, and fluid biomarkers, thus facilitating personalized therapeutic decision-making. Supported by funding from Regione Toscana and the Institute of Informatics and Telematics of CNR in 2022-2024, this collaborative initiative involves Siena Imaging Srl, with expertise in MRI biomarker development.