A5011678234- Bladder and Urothelial Cancer Treatments
- CRISPR and Genetic Engineering
- Cancer Cells and Metastasis
- Cancer Genomics and Diagnostics
- Molecular Biology Techniques and Applications
- Single-cell and spatial transcriptomics
- Epigenetics and DNA Methylation
- DNA Repair Mechanisms
- Liver physiology and pathology
- AI in cancer detection
- Gene expression and cancer classification
- Prostate Cancer Treatment and Research
- Metabolomics and Mass Spectrometry Studies
- Plant Virus Research Studies
- PARP inhibition in cancer therapy
- Cancer Research and Treatments
- Plant Gene Expression Analysis
University of Trento
2021-2025
National Cancer Institute
2024-2025
Center for Cancer Research
2024
Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized approach. Patient-derived organoids (PDOs) have been successfully used as functional model for predicting drug response in different cancers. In our study, we establish PDO cultures BLCa stages and grades. PDOs preserve histological molecular parental tumors, including their multiclonal genetic landscapes, consistently share key...
Abstract Background: Spatial transcriptomics (ST) is revolutionizing our understanding of tumor heterogeneity by providing high-resolution, location-specific gene expression mapping across the microenvironment (TME). Despite its transformative potential, ST’s high cost limits widespread clinical application, particularly for large-scale biomarker discovery. Methods: To address these challenges, we developed Path2Space, a deep learning model trained on ST data, to predict spatial directly...
Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates with protein–protein interactions to generate neighborhood-informed essential (NIE) context (NICE) scores. It outperforms the canonical effect approach in recovering missed genes shRNA screens prioritizing...
Abstract The rise of spatial transcriptomics (ST) is transforming our understanding tumor heterogeneity by enabling high-resolution, location-specific mapping gene expression across the microenvironment. However, translational potential still limited its high cost, hindering assembly large patient cohorts needed for robust biomarker discovery. Here we present Path2Space , a deep learning model trained on data to predict directly from histopathology slides. Studying breast cancer, was first...
Mutual Exclusivity analysis of genomic aberrations contributes to the exploration potential synthetic lethal (SL) relationships thus guiding nomination specific cancer cells vulnerabilities. When multiple classes and large cohorts patients are interrogated, exhaustive genome-wide analyses not computationally feasible with commonly used approaches. Here we present Fast (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation Fisher's exact test achieve...
PARP inhibitors (PARPi) have received regulatory approval for the treatment of several tumors, including prostate cancer (PCa), and demonstrate remarkable results in castration-resistant (CRPC) patients characterized by defects homologous recombination repair (HRR) genes. Preclinical studies showed that DNA genes (DRG) other than HRR may therapeutic value context PARPi. To this end, we performed multiple CRISPR/Cas9 screens PCa cell lines using a custom sgRNA library targeting DRG combined...
Abstract Bladder Cancer (BLCa) inter-patient heterogeneity is considered the primary cause of tumor reoccurrence and treatment failure, suggesting that BLCa patients could benefit from a more personalized approach. Patient-derived organoids (PDOs) have been successfully used as functional model for predicting drug response in different cancer types. In our study, we established PDO cultures stages. PDOs preserve histological molecular parental tumors, including their multiclonal genetic...
Abstract Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized approach. Patient-derived organoids (PDOs) have been successfully used as functional model for predicting drug response in different cancers. In our study, we established PDO cultures BLCa stages. PDOs preserve histological and molecular parental tumors (PT), including their multiclonal genetic landscapes, consistently share key...
Abstract Introduction: Interpatient heterogeneity is one of the causes treatment failure in bladder cancer (BLCa) patients, who would therefore profit from tailor-made therapies. Toward direction precision medicine, patient-derived organoids (PDOs), which retain parental tumor (PT) features, have been successfully tested different types and shown to predict patients’ responses. Therefore, our study aimed explore ability BLCa PDOs PT features determine patient drug sensitivity. We correlated...
Abstract Summary The classification of biological samples by means their respective molecular profiles is a topic great interest for its potential diagnostic, prognostic and investigational applications. rScudo an R package the based on radically new approach consisting in analysis similarity rank-based sample-specific signatures. validity unconventional has been validated through direct comparison with current methods international SBV IMPROVER Diagnostic Signature Challenge. Due to...
You have accessJournal of UrologyCME1 May 2022MP06-07 APPLICATION OF BLADDER CANCER PATIENT-DERIVED ORGANOIDS AS PRE-CLINICAL MODEL FOR PERSONALIZED MEDICINE Martina Minoli, Mirjam Kiener, Thomas Cantore, Tarcisio Fedrizzi, Paola Gasparini, Francesca Demichelis, George N. Thalmann, Roland Seiler-Blarer, and Marianna Kruithof-de Julio MinoliMartina Minoli More articles by this author , KienerMirjam Kiener CantoreThomas Cantore FedrizziTarcisio Fedrizzi GaspariniPaola Gasparini...