Ricerca Gruppi di ricerca e contributi alla ricercaPrecision Oncology Program

Precision Oncology Program

Research Team’s name

Precision Oncology Program

 

Coordinator

Name: Matteo Benelli

Position: Associate Professor of Biochemistry

e-mail: matteo.benelli@unifi.it

phone: +39 055 2751238

 

Brief Biographical sketch of the Coordinator

Matteo Benelli earned a Master's degree in Physics from the University of Florence and completed his Ph.D. in Non-linear Dynamics and Complex Systems at the same institution, focusing on developing computational methods in genomics. He then joined the Laboratory of Computational and Functional Oncology at the University of Trento as a postdoc fellow, investigating the genomics and epigenetics of advanced prostate cancer under the supervision of Prof. Francesca Demichelis. In 2017, Dr. Benelli became a group leader at the Oncology Department of the Hospital of Prato, led by Dr. Angelo Di Leo. There, he established a new research group dedicated to breast cancer omics and their translational applications. In 2021, Dr. Benelli was appointed as co-Principal Investigator and coordinator of the Data Analysis Committee of the multi-national AURORA-EU program, dedicated to molecular characterization of metastatic breast cancer. In 2024, Matteo Benelli assumed the role of Associate Professor of Biochemistry at the University of Florence.

 

Member of the following Boards

• Steering Committee, Coordinator of the Data Analysis Committee and co-PI of the AURORA-EU program (NCT02102165)
• Technical Scientific Committee (CTS) of the Fondazione AIRC per la ricerca sul cancro
• Junior Faculty of the NIBIT association (Network Italiano per la Bioterapia e l'Immunoterapia dei Tumori)
• PhD program in Biomedical Sciences, University of Florence

 

Research Team

The team consists of members from the department faculty and their research groups with diverse, complementary expertise. This multidisciplinary team is committed to launching and consolidating a program in precision oncology.

Clinics:

• Gabriele Dragoni
• Massimiliano Fambrini
• Icro Meattini
• Laura Papi
• Linda Vignozzi

Clinical imaging:

• Vittorio Miele
• Linda Calistri

Translational research:

• Elena Lazzeri
• Michaela Luconi
• Tommaso Mello
• Serena Pillozzi
• Pamela Pinzani

Artificial Intelligence:

• Livia Marrazzo
• Stefania Pallotta
• Cinzia Talamonti

Preclinics:

• Francesca Bianchini
• Paola Chiarugi
• Anna Laurenzana
• Andrea Morandi
• Laura Papucci
• Elisabetta Rovida

Context

In recent years, oncology has advanced significantly in treating cancer across all stages. The use of new high throughput biomedical technologies, in combination with bioinformatics and artificial intelligence are leading to the identification of new diagnostic and therapeutic biomarkers. This progress has driven the development of precision oncology, focusing on early diagnosis and tailoring the most appropriate treatment for each patient at the optimal time.

Technologies like next-generation sequencing (NGS), advanced biomedical imaging, bioinformatics, and Artificial Intelligence (AI) applied to biomedical big data are revolutionizing cancer diagnosis and treatment. Omics disciplines (genomics, transcriptomics, epigenomics) have identified new biomarkers, enhancing diagnostic precision and serving as molecular targets for more effective oncological drugs.

 

Bioinformatics and Omics

Bioinformatics is an interdisciplinary field that analyzes and interprets biomedical data by integrating advanced tools from computer science, mathematics, statistics, and physics. As personalized medicine progresses, bioinformatics is becoming increasingly crucial in managing complex diseases, particularly in oncology. It plays a key role in handling and analyzing data derived from omics approaches applied to biological samples. Omics data are becoming increasingly relevant in oncology, as they help address the complexity of this disease. In particular, genomics is used to identify DNA alterations for diagnosis and targeted treatment; transcriptomics provides insights into gene expression, aiding in tumor subtype classification and the identification of biological pathways associated with drug resistance; and epigenetics is emerging as a promising tool for liquid biopsy analysis and as an indicator of biological aging, a factor closely linked to cancer risk.

 

Pharmacogenomics

Individuals differ from each other in a small percentage of the human genome, primarily due to single nucleotide polymorphisms (SNPs). This genetic variation is typically associated with phenotypic, non-pathological factors, such as differences in physical traits, metabolism, or response to environmental stimuli. However, in some cases, genetic variation influences drug response in patients, affecting a drug’s efficacy or potential toxicity. Pharmacogenomics, the study of how genes influence an individual’s response to drugs, holds the promise that medications could one day be tailor-made for individuals, allowing treatments to be personalized based on each person’s unique genetic makeup. This approach aims to improve drug effectiveness, minimize adverse effects, and optimize therapeutic outcomes.

 

Liquid Biopsy

In recent years, one of the most significant advancements in precision medicine is liquid biopsy, enabled by new technologies and a deeper understanding of tumor molecular characteristics. This non-invasive method analyzes circulating tumor DNA (ctDNA), Circulating Tumor Cells (CTC) or other tumor-derived analytes from blood or other body fluids, detecting molecular alterations like DNA mutations, DNA copy number alterations, DNA methylation changes or gene expression. Liquid biopsy is increasingly relevant across all cancer stages. It enables early detection, particularly when surgery could be curative.  Several studies indicated that, applied post-surgery, the absence of ctDNA indicates a favorable prognosis; conversely, its presence may indicate early recurrence, suggesting specific therapies or treatment intensification. During adjuvant therapy, it allows for frequent, non-invasive monitoring to detect relapse or resistance. It also offers an alternative to tissue biopsy for characterizing metastatic tumors. It reduces patient risk being non-invasive and may enable the identification of emerging mechanisms of resistance, therefore suggesting targets for personalized treatments. In metastatic disease, liquid biopsy also allows for frequent therapy response monitoring, detecting resistance earlier than traditional imaging methods, optimizing therapeutic strategies.

 

Artificial Intelligence in Biomedical Imaging

Artificial intelligence (AI) is becoming an essential tool in analyzing biomedical images, including CT, PET, MRI, and mammography. Studies show its potential in supporting specialists, from segmenting regions of interest in radiology to enhancing lesion classification, with promising applications in mammography screening. In particular, radiomics is an innovative AI application that enables advanced quantitative imaging analysis. Machine learning models applied to hundreds of imaging features have been successfully applied in different clinical applications, such as differential diagnosis, predicting clinical outcomes, and identifying molecular characteristics of tumors. This approach can guide targeted treatments while reducing the need for invasive molecular profiling, especially in challenging metastatic contexts.

 

Precision Oncology Research Program

 

The unit focuses on developing, testing, and implementing experimental strategies, bioinformatics methods and AI in the context of precision oncology. The unit seeks to establish new precision oncology research program, leveraging the department’s cross-disciplinary expertise and innovative technologies.

 

Main activities

• Development, implementation, and testing of laboratory protocols for molecular analysis of tumor samples, utilizing automated approaches and high-throughput technologies.
• Implementation of protocol standardization procedures in compliance with international regulations.
• Development, implementation, and testing of scalable pipelines for the bioinformatic analysis of laboratory data.
• Analysis and development of tissue and circulating biomarkers (liquid biopsy).
• Development and application of bioinformatic algorithms for the analysis and integration of omics data.
• Development and application of artificial intelligence methods for biomedical imaging analysis and integration with molecular characteristics (radiomics).
• Mining multimodal information and handling incomplete/missing limited imaging datasets.
• Development and maintenance of databases for managing laboratory and omics data, with user-friendly interfaces for consultation, ensuring GDPR compliance for sensitive data.
• Participation in research calls to attract funding, strengthening involvement in international consortium projects.
• Dissemination of results through specific courses, workshops, and public outreach.
• Training and mentoring activities in bioinformatics / AI.
• Technology transfer activities, including the industrialization of internally developed innovative technologies and initiating collaborations with startups and biotech companies to accelerate market transition.
• Development of protocols and IT procedures to minimize the environmental impact of activities, such as reducing reagent and plastic consumption and optimizing computational resources.

 

Future objectives

A center of excellence for advanced clinical-diagnostic testing. The lab will offer cutting-edge diagnostic services to local hospitals and healthcare providers, granting access to innovative precision oncology methodologies. Managed by a multidisciplinary team, the center will integrate diagnostic, clinical, and bioinformatics expertise. This will meet regional healthcare needs and set the center as a national leader in precision medicine. By providing innovative diagnostic tools, the project aims to enhance patient care with more personalized and effective treatments, improving well-being and quality of life.

 

Commitment in scientific, technological, and cultural transfer. The lab’s integrated approach will promote high-impact scientific results and facilitate the technological transfer of research outcomes, such as developing patents for new laboratory protocols or bioinformatics methods. It also aims to attract private funding for advancing laboratory and computational methodologies. Internship and training opportunities will equip students with in-demand skills for the healthcare, pharmaceutical, and academic sectors. Current active participation of lab members in national and international collaborations will promote knowledge exchange, access to advanced resources, and the proposal and development of innovative, EU/international research projects.

 

Instrumentation

The Department of Experimental and Clinical Biomedical Sciences “Mario Serio” at the University of Florence, awarded “Department of Excellence” from the Italian Ministry of University and Research for 2018–2022 and 2023–2027, is equipped with a comprehensive infrastructure to support precision oncology research, encompassing preclinical, computational, and clinical applications.

 

Microscopy: confocal microscope with time-lapse cabinet (SP8 Leica) and a super-resolution microscope (SP8 STED 3X Leica).

Flow cytometry:  flow cytometer (BD FACSCanto II Analyzer) and a cell sorter (BD FACS Melody Cell Sorter).

Metabolomic: Agilent Intuvo 9000/5977B GC/MSD and LC-MS Agilent 6546 instruments for metabolomic analysis and a Seahorse XFe96 Analyzer coupled with an Oroboros O2K Fluorespirometer for functional metabolic analysis.

In vivo metabolomic: metabolic cage (Oxymax Open Circuit Calorimeter) and Tapis Roulant for small animals metabolic data collection.

Bioprinting: BIO  X 3D Bioprinter (Twin Helix configuration).

Histopathology: Leica ASP300S tissue processor, HistoCore Arcadia Pathology Embedder with heated embedding module and cold plate, HistoCore AUTOCUT automated rotary microtome, LEICA CM1860 cryostat, Leica ST5010- CV5030 integrated workstation, Leica BOND RXm, Leica Aperio GT 450 DX Automated High Capacity Digital Pathology Slide Scanner and New Laser Microdissection Microscope Series with LED for Transmitted Light (Leica LMDS)

In vivo imaging:  IVIS Lumina S5 (PerkinElmer).

Extracellular vesicles (EXACT): cytoFLEX flow cytometer to detect particles with a lower refractive index such as EVs and the Nanoimager ONI, a benchtop super-resolution microscope for single-molecule imaging for the multiparametric characterization of extracellular vesicles (EVs) in biological fluids.

Spatial Molecular Imaging: Nanostring CosMx for single-cell spatial transcriptomics and proteomics analyses, from sample preparation to bioinformatic analysis.

Single-cell RNA sequencing: 10X Genomics Chromium system (10x Genomics) for single-cell transcriptomics analyses.

IT infrastructure (Florence Omics Warehouse, FLOW): three HPC nodes (192 cores and 3.5 TB RAM in total) and a Qumulo storage node (110 TB), with secure access dedicated to store, manage and analyse large omics datasets.

Circulating Tumor Cell platforms for enrichment and single cell analysis in suspension: ParsortixTM Technology (Angle PLC, Surrey, UK), a microfluidic device for the separation of cells in suspension based on size and deformability properties; the recovered cells can be subsequent sorted and analyzed at single cell on antigen expression level by  DEPArray Platform (Menarini Silicon Biosystems).

Malvern – Nanosight NS300 instrument: for quantification and characterization of nanostructures in the range 30-1000 nm (Nanoparticles Tracking Assay).

 

Key words

CANCER, ONCOLOGY, MEDICINE, GENOMICS, EPIGENETICS, OMICS, COMPUTATIONAL BIOLOGY, BIOINFORMATICS, DATA ANALYSIS, DATA SCIENCE, MACHINE LEARNING, RADIOMICS, BREAST CANCER, TREATMENT RESISTANCE, BIOMARKERS, LIQUID BIOPSY, TUMOR MICROENVIRONMENT, METABOLOMICS, TRANSLATIONAL RESEARCH, ARTIFICIAL INTELLIGENCE, IMAGING, PGx, PHARMACOGENOMICS, TARGETED THERAPY, PERSONALIZED MEDICINE

 

Current/recent sources of funding (selected)

 

Title	Funder/ Program	PI/coPI/collaborator	Year
Nanoparticle-Assisted Liquid Biopsy for Early Diagnosis of Glioblastoma	HORIZON-MSCA-SE-2024 (2025–2028)	Anna Laurenzana	2025
EXACT_Core – Extracellular Vesicles Characterization Core	Fondazione CR Firenze	Andrea Galli	2024
EPIC-Exploring Peroxisome Insights to Combat resistance and extend the efficacy of targeted therapy in ER+ breast cancer	AIRC - Investigator Grant	Andrea Morandi	2024
Multiomic Integration to Identify Schwannomatosis Predisposition Genes and Tumorigenesis	Children Tumor Foundation (USA)	Laura Papi (co-PI)	2024
Support of Personalized Medicine Approaches in Cancer (SPARC)	EU4H-2024-PJ-03	Andrea Galli (unit PI)	2024
Gender Medicine: from Single Cell to the Clinics	Italian Department of Excellence Program (2023–2027)	SBSC	2023
Innovative Therapeutic Interventions Against Idiopathic Pulmonary Fibrosis	PRIN 2022	Francesca Bianchini	2023
Tuscany Health Ecosystem (THE)	PNRR 2023		2023
LB-MOP – Liquid Biopsy Multi-Omics Platform	Ministry of University and Research (MUR)	Serena Pillozzi (collaborator)	2022
A Multifaceted Quantitative Approach by Magnetic Resonance Imaging to Stratify Liver Derangement and Dysfunction in Chronic Liver Disease	PRIN 2022	Linda Calistri	2022
SALUTE – Spatial Multi-Omics Platform for Inflammatory and Oncological Diseases	UNIFI / Fondazione CR Firenze	SBSC	2022
INIT: Integrative Analysis of Multi-Omics Molecular and Imaging Data to Improve Diagnosis and Treatment of Cancer	AIRC - Investigator Grant	Matteo Benelli	2022
InTrEPID: In vivo 3D dosimetry in radiotherapy Treatments with EPID using neural network	Ministry of University and Research (MUR) - PRIN	Cinzia Talamonti	2022
COOL-REACT – Targeted Approach to Adrenocortical Tumors	Ministry of University and Research (MUR) - PRIN	Michaela Luconi	2022
Advanced Lung Phantom for Multimodal Theranostics	Ministry of University and Research (MUR) - PRIN	Stefania Pallotta	2022
Deciphering the lactate-driven metabolic and epigenetic reprogramming in prostate cancer	AIRC / Investigator Grant	Paola Chiarugi	2020
INSTAND-NGS4P – Standardized NGS Workflows for Personalized Therapy	H2020, Grant Agreement No. 874719	Pamela Pinzani	2020
Artificial Intelligence in Medicine: (AIM) [2019-2021] Multicenter data harmonization, quantification, predictive models next_AIM [2022-2024] No so big data and explainable AI AIM-MIA 2025-2027 Multi input analisys	National Institute for Nuclear Physics (INFN)	Livia Marrazzo / Stefania Pallotta / Cinzia Talamonti (collaborators)	2019
Thermoablation of Melanoma and Mammary Carcinoma with Injected Nanoparticles Coupled Radiotherapy	Regione Toscana – Bando Salute 2018	Anna Laurenzana	2019
Role of ERK5 in Senescence and Drug Resistance in Melanoma	AIRC / Investigator Grant	Elisabetta Rovida	2019
Integrated Soft Tissue Sarcoma Molecular Approach	Regione Toscana – Bando Salute 2018	Serena Pillozzi (coPI)	2019

Best publications (years 2020-2025)

• Meattini I et al. Biomarker-Directed Radiotherapy in Breast Cancer: A Narrative Review. JAMA Oncol. 2025 Mar 1;11(3):329-339. doi: 10.1001/jamaoncol.2024.5780. PMID: 39820307.
• Gelmini S et al. Circulating Tumor Cell-Free DNA as Prognostic Biomarker in NSCLC Patients Undergoing Immunotherapy: The CORELAB Experience. Int J Mol Sci. 2025 Jan 13;26(2):611. doi: 10.3390/ijms26020611. PMID: 39859325.
• Dragoni G et al. Peptidyl Arginine Deiminase 4-Dependent Neutrophil Extracellular Trap Formation in Early Development of Intestinal Fibrosis in Crohn’s Disease. J Crohns Colitis. 2025 Jan 11;19(1):jjae121. doi: 10.1093/ecco-jcc/jjae121. PMID: 39126198.
• Meattini I et al.; EUROPA Trial Investigators. Single-modality endocrine therapy vs radiotherapy after breast-conserving surgery in women ≥70 years with luminal A-like early breast cancer: EUROPA trial interim analysis. Lancet Oncol. 2025 Jan;26(1):37-50. doi: 10.1016/S1470-2045(24)00661-2. Epub 2024 Dec 12. PMID: 39675376.
• Anceschi C et al. Antitumoral efficacy of AuNRs-laden ECFCs in vitro and in vivo: Heat and Rays combo in Breast Cancer and Melanoma. Adv Healthc Mater In press, 2025.
• Ippolito L et al. Lactate supports cell-autonomous ECM production to sustain metastatic behavior in prostate cancer. EMBO Rep. 2024;25(8):3506-3531. doi: 10.1038/s44319-024-00180-z.
• Petroni G et al. Exploiting Tertiary Lymphoid Structures to Stimulate Antitumor Immunity. Cancer Res. 2024 Apr 15;84(8):1199-1209. doi: 10.1158/0008-5472.CAN-23-3325. PMID: 38381540.
• Bacci M et al. Acetyl-CoA carboxylase 1 controls lipid droplet-peroxisome axis and vulnerability of endocrine-resistant ER+ breast cancer. Sci Transl Med. 2024 Feb 28;16(736):eadf9874. doi: 10.1126/scitranslmed.adf9874. PMID: 38416843.
• Meattini I et al. ESTRO-endorsed recommendations on radiotherapy integration with new systemic treatments in breast cancer. Lancet Oncol. 2024 Feb;25(2):e73-e83. doi: 10.1016/S1470-2045(23)00534-X. PMID: 38301705.
• Cioppi F et al. Targeted NGS and its clinical application in adrenocortical cancer. Eur J Endocrinol. 2024 Jul 2;191(1):17-30. doi: 10.1093/ejendo/lvae077.
• Andreucci E et al. CAIX inhibitor SLC-0111 in gastric cancer: anti-cancer effects and chemosensitization. Cancer Lett. 2023 Sep 1;571:216338. doi: 10.1016/j.canlet.2023.216338. PMID: 37549770.
• Marchiani S et al. Semen cryopreservation: 24 years’ experience of an Italian bank. J Clin Med. 2023 Jul 13;12(14):4657. doi: 10.3390/jcm12144657. PMID: 37510772.
• Andreucci E et al. Nintedanib-αVβ6 integrin ligand conjugates reduce TGFβ-induced EMT in NSCLC. Int J Mol Sci. 2023;24(2):1475. doi: 10.3390/ijms24021475.
• Devonshire A et al, Interlaboratory evaluation of quality control methods for circulating cell-free DNA extraction. New Biotechnol. 2023 Dec 25;78:13-21. doi: 10.1016/j.nbt.2023.09.005. PMID: 37730172.
• Luconi M et al. Prognostic value of microscopic tumor necrosis in adrenal cortical carcinoma. Endocr Pathol. 2023. doi: 10.1007/s12022-023-09760-6.
• Pardella E et al. Therapy-induced stromal senescence in prostate and ovarian cancer. Cells. 2022 Dec 13;11(24):4026. doi: 10.3390/cells11244026. PMID: 36552790.
• Chiti G et al. Radiomic model for tumor grade prediction in GEP-NENs. Radiol Med. 2022 Sep;127(9):928-938. doi: 10.1007/s11547-022-01529-x. PMID: 35917099.
• Tubita A et al. ERK5 inhibition triggers p21-mediated senescence in melanoma. Cancer Res. 2022;82(3):447-457. doi: 10.1158/0008-5472.CAN-21-0993.
• Salvianti F et al. Circulating tumor cells and cell-free DNA as prognostic factor in metastatic colorectal cancer: OMITERC study. Br J Cancer. 2021 Jul;125(1):94-100. doi: 10.1038/s41416-021-01399-6. PMID: 33953347.
• Retico A et al. Enhancing the impact of artificial intelligence in medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Med. 2021;91:140-150. doi: 10.1016/j.ejmp.2021.10.005.
• Peired AJ et al. From kidney injury to kidney cancer. Kidney Int. 2021;100(1):55-66.
• Peired AJ et al. Sex and gender differences in kidney cancer: Clinical and experimental evidence. Cancers (Basel). 2021;13(18):4588.
• Calistri L et al. Imaging chemotherapy-induced hepatic damage. World J Gastroenterol. 2021 Dec 14;27(46):7866-7893. doi: 10.3748/wjg.v27.i46.7866. PMID: 35046618.
• DNA Damage Response Protein CHK2 regulates metabolism in liver cancer. Cancer Res. 2021 Jun 1;81(11):2861-2873. doi: 10.1158/0008-5472.CAN-20-3134. PMID: 33762357.
• Aftimos P et al. Genomic and transcriptomic analyses of breast cancer primaries and metastases in AURORA. Cancer Discov. 2021 Jun 28. doi: 10.1158/2159-8290.CD-20-1647.
• Peired AJ et al. Acute kidney injury promotes development of papillary renal cell adenoma and carcinoma from renal progenitor cells. Sci Transl Med. 2020;12(536):eaaw6003.
• Mansouri S et al. Epigenomic, genomic, and transcriptomic landscape of schwannomatosis. Acta Neuropathol. 2021 Jan;141(1):101-116. doi: 10.1007/s00401-020-02230-x. PMID: 33025139, 33112994.
• Armanetti P et al. AuNP-enriched endothelial colony forming cells in melanoma therapy and imaging. Adv Sci (Weinh). 2020 Dec 21;8(4):2001175. doi: 10.1002/advs.202001175.
• Benelli M et al. Comprehensive analysis of radiomic datasets by RadAR. Cancer Res. 2020;80(15):3170-3174. doi: 10.1158/0008-5472.CAN-20-0332.

 

Collaborations

Our research team is actively involved in a large network of research collaborations, working with leading universities and research institutions, including national and international partners.

 

National (academic): UNISI, UNIPI, UNITN, UNITO, UNIPV, UNIBO, UNIPD, UNIGE

National (non-academic): AOU Careggi, IRCCS Meyer, ISPRO, AUSLTC, IRCCS INT, IRCCS Candiolo, IRCCS IOV, IRCCS CRO, IEO, CNR

International (EU): IBMCC (ES), Jules Bordet Institute (BE), U Athens (GR), , Imperial College (UK), U Karlova (CZ), ICR (UK), Queen Mary University of London (UK), AURORA EU (60+ institutes across 14 European countries), The European Network for the Study of Adrenal Tumors (ENS@T)

International (non-EU): Dana-Farber Cancer Institute (US), UT Southwestern (US), U Hoshi (JP), U Panjab (IN), PUC-Rio (BR), Vanderbilt U (US)