Università degli studi di Milano-Bicocca
Integration of clonal evolution and single-cell mutational and expression profiles in ALK+ ALCL
Università degli studi di Milano - BICCOCA
Supervisor: Carlo GAMBACORTI-PASSERINI
To generate single-cell gene expression and mutational profiles of ALCL.
We plan to run single-cell RNA (scRNA) sequencing to prospectively analyse the gene expression profile of ALK+ ALCL single cells from 20 newly diagnosed patients at presentation. Mononuclear cells will be isolated from bone marrow using a Ficoll gradient, according to a standard protocol. To enrich the samples for lymphoma cells, CD30+ tumour cells will be isolated by fluorescence activated cell sorting (FACS) using an anti-CD30 antibody. CD30+ cells will be loaded onto a Chromium 10x microfluidic instrument to generate CD30+ enriched scRNA libraries. On average 8000 cells per sample will be sequenced at a read-depth of 30,000 reads/cell. The libraries will be sequenced on a Novaseq 6000 instrument (Illumina). Gene expression signatures will be obtained from scRNAseq data and used to identify subgroups of patients with different prognoses. Gene-set enrichment and pathway analyses will be performed, starting from gene expression data, using off-the-shelf bioinformatic tools. Signatures will be correlated to the outcome of treatment.
At the same time, single-cell cDNA genotyping will be used to match mutational data to the gene expression profile from the same cell. This information will also allow a study of the clonal evolution of the disease; 2. Use these data to build molecular signatures of ALCL as a tool for stratification and treatment of relapsed patients: scRNAseq data will be employed to identify subpopulations associated with relapse or with long-term remission in single samples and across patients, through similarity analyses. Single-cell clustering methods, such as SIMLR or the Louvain algorithm, will be used to identify distinct cell clusters and visualize them in a lower-dimension space (e.g., t-SNE, UMAP). The genetic and transcriptomics data generated by scRNAseq analyses will be integrated into bioinformatics pipelines to develop a score to predict therapy outcome for individual patients. Single-cell molecular cell identity will be built using bioinformatics tools developed at the University of Milano-Bicocca. Somatic mutations and gene expression profiles, as well as clonal hierarchy and pathway analyses, will feed into an algorithm able to identify patients at risk of short term or no response to TKI therapy.
The data generated at single-cell level are expected to provide an overall final score that describes the probability of a patient’s response to a certain therapy; We expect to develop a tool based on genetic data that will allow the identification of differences in the molecular profiles between ALK+ ALCL patients who achieve long-term remission versus those that either show early relapse or do not respond to TKI treatment; Furthermore, we expect to identify clinically valid molecular prognostic markers to be used in the routine management of ALK+ ALCL patients; The molecular classifier could help stratify patients for risk and therapy outcome
Year 1: Galseq: To learn and conduct sequencing of samples using equipment available at the host lab who will provide important training in data analysis (4 months)
Year 3: Pangaea Data: Development of AI/ML algorithms to integrate clinical data with patient outcome learning from the unique expertise of the company who have developed these technologies (3 months)