POSITive trial (Principal Investigators: Luca Mazzarella and Enrico Derenzini)
The study named POSITive (Prospective Observational Study for Multidimensional Analyses of Resistance and Toxicity to Immune- and Targeted-Therapies) has the overarching goal of identifying and validating biomarkers for predicting responses to targeted and immune therapies in both solid tumors and hematologic malignancies. This project plans the execution of the full “Core” omic analysis (WES, RNA-sequencing, WMS and High-throughput cytokine bead array) on all eligible patients candidate to innovative therapies in IEO, such as Targeted Therapy (TT) and Immune Therapy (IT).
GerSom Project (within Alleanza Contro il Cancro network)
GerSom is a multicentric study aiming to demonstrate, at the time of diagnosis of cancer, the feasibility of a joint diagnostic path for identification of altered genes in tumors (to better treat patients by identifying the potentially most effective drugs) and cancer predisposing genes (CPGs) in the germline (for mapping cancer genetic risk and preventing cancers in their high-risk family members by directing them towards dedicated prevention pathways).
For this project, we developed and validated a cost-effective custom gene panel that can be run quickly (<1 week) to analyze 467 altered tumor genes, including 172 CPGs, that will be used on 4,000 cancer patients with a genetic predisposition to develop tumors.
Single cell and molecule sequencing (SCM-seq) (Principal Investigator: Pier Giuseppe Pelicci)
In order to comprehensively assess acute myeloid leukemia (AML) intra-tumor heterogeneity, we have established SCM-seq (Single-Cell-Molecule-sequencing), a novel multi-omics platform that enables high-throughput single-cell analysis of gene expression and full-length isoforms, coupled to the detection of expressed mutations and cell-level genotype. SCM-seq combines standard short-read high-throughput scRNA-seq with long-read sequencing by ONT on thousands of single cells tagged with cell barcodes (CB). Shared CB across the different sequencing datasets enable data integration and multi-omics characterization at single cell level. We are currently applying SCM-seq to pediatric as well as adult AML patients.
Evaluation of the risk of secondary leukemias in cancer survivors (Principal Investigators: Myriam Alcalay and Chiara Ronchini)
Therapy-related myeloid neoplasms (t-MNs) are frequent haematological complications of chemotherapy and/or radiotherapy in cancer survivors. Our hypothesis is that chemotherapy can induce or promote clonal selection of haematopoietic stem cell harbouring somatic mutations (clonal haematopoiesis, CH). Moreover, the presence of germline mutations in CPGs can increase the risk of t-MNs. The purpose of this study is to define the predictive values of CH and germline mutations for the development of t-MN in two clinical settings: lymphomas patients and ovarian cancer patients eligible for treatment with PARP inhibitors. We designed a prospective study in which CH is assessed on peripheral blood cells collected at different time points: at diagnosis, during chemotherapeutic treatment and at follow-up up to 5 years. Given the low frequency of CH mutations, we use error corrected sequencing methods, exploiting the use of UMI for identification of variants at high sensitivity in up to the 80 most frequently mutated genes in CH. For assessment of germline variants, we designed the Myelo Panel, which covers 255 genes: drivers, CPGs and actionable genes in haematological tumours.
Preserve trial (Principal Investigator: Susanna Chiocca)
The Preserve study aims at increasing the rate of larynx preservation (LP) as an alternative to total laryngectomy in locally advanced laryngeal (LAR) and hypopharyngeal (HYPO) cancer.
The main objectives are to assess a multimodal signature predictive of response to induction chemotherapy (IC) and to define alternative pathways to be tackled in patients non-responding to IC in LAR/HYPO cancer. Transcriptomic analysis, molecular data on cell lines and radiomic evaluation are main components of these signatures. In particular, the Data Science Unit of DIMA is actively supporting the Preserve study by leveraging machine learning models to extract transcriptomic signatures, which should allow for highly accurate differentiation between patients who respond positively to IC and those who do not.