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The patterns by which primary tumors spread to metastatic sites remain poorly understood. Here, we define patterns of metastatic seeding in prostate cancer (PCa) using a novel injection-based mouse model — EvoCaP (Evolution in Cancer of the Prostate), featuring aggressive metastatic cancer to bone, liver, lungs, and lymph nodes. To define migration histories between primary and metastatic sites, we used our EvoTraceR pipeline to track distinct tumor clones containing recordable barcodes. We detected widespread intratumoral heterogeneity from the primary tumor in metastatic seeding, with few clonal populations (CPs) instigating most migration. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within the tissue. Migration patterns in our model were congruent with human PCa seeding topologies. Our findings support the view of metastatic PCa as a systemic disease driven by waves of aggressive clones expanding their niche, infrequently overcoming constraints that otherwise keep them confined in the primary or metastatic site.

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multiomics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

La crescente disponibilità di dati generati da esperimenti di sequenziamento di campioni biologici richiede strategie algoritmiche efficienti, scalabili e riproducibili che ne permettano analisi e integrazione. Questo fenomeno si osserva anche nell'ambito della ricerca sul cancro, dove grandi quantità di dati ad alta dimensionalità a risoluzione delle singole cellule possono essere oggi generati a partire da biopsie e organoidi derivati da pazienti. In questo lavoro, presenterò i risultati ottenuti in tre task principali, ovvero: (A) lo sviluppo di metodi per l'analisi di diversi tipi di dati omici (DNA, RNA e ATAC), (B) la progettazione di strategie per la loro integrazione e (C) l'implementazione di una pipeline riproducibile, scalabile e flessibile per l'analisi dei dati a risoluzione di singola cellula, che include sia i nuovi metodi sviluppati all'interno dei task (A) e (B), sia ulteriori tecniche presenti nello stato dell'arte. Tutte queste attività sono state svolte all'interno del programma di Bioinformatica del progetto Accelerator CRUK/AIRC dal titolo "Single-cell Cancer Evolution in the Clinic" (SCCEiC), che mira a integrare gli sforzi di biologi ed esperti computazionali per fornire una caratterizzazione precisa dell'evoluzione del cancro, con ripercussioni anche in contesti clinici. Tuttavia, i risultati di questo lavoro avranno un impatto più generale, soprattutto nel campo delle scienze computazionali e, in particolare, in quello della data science applicata al cancro (cancer data science) e dell'Intelligenza Artificiale. Per quanto riguarda il task (A), mostrerò il più esteso benchmarking ad oggi disponibile riguardante metodi di denosing e di imputazione per i dati di sequenziamento a singola cellula di RNA, che potrebbe guidare i ricercatori sia nell'applicazione di metodi esistenti a problemi reali, sia nella progettazione di nuove strategie algoritmiche. Presenterò quindi un nuovo algoritmo applicato all'inferenza di alberi clonali a par, The ever-increasing availability of data generated from sequencing experiments of biological samples brings the need for efficient, scalable and reproducible algorithmic strategies for their analysis and integration. This is particularly true in cancer research, in which large amounts of high-dimensional data at the single-cell resolution can be now generated from patient biopsies and patient-derived models. In this work, I will present the achievements obtained in three main areas, namely: (A) the development of methods for the analyses of single omics data types (DNA, RNA and ATAC), (B) the design of strategies for their integration, (C) the implementation of a reproducible, scalable and flexible pipeline for the comprehensive analysis of single-cell data, which includes both the new methods developed in tasks (A) and (B), and additional state of the art techniques. All these tasks were carried out within the Bioinformatics programme of the "Single-cell Cancer Evolution in the Clinic"(SCCEiC) CRUK/AIRC Accelerator project, which aims at integrating the efforts of wet- and dry-lab scientists to deliver a fine characterization of cancer evolution, with expected repercussions in clinical settings. However, the achievements of this work will have a more general impact, especially on the broad field of computational science and, in particular, in that of cancer data science and biomedical Artificial Intelligence. In regard to task (A), I will show the most extensive to-date benchmarking of denosing and imputation methods for single-cell RNA-sequencing data, which might guide researchers both in the application of existing methods to real-world problems, and in the design of new algorithmic strategies. I will then present a new algorithmic framework applied to the inference of clonal trees from single-cell mutational profiles, which provides both the first method to characterize and visualise the solution space explored during \acrshort{mcmc} search, and a new method to

In recent years, many algorithmic strategies have been developed to exploit single-cell mutational profiles generated via sequencing experiments of cancer samples and return reliable models of cancer evolution. Here, we introduce the COB-tree algorithm, which summarizes the solutions explored by state-of-the-art methods for clonal tree inference, to return a unique consensus optimum branching tree. The method proves to be highly effective in detecting pairwise temporal relations between genomic events, as demonstrated by extensive tests on simulated datasets. We also provide a new method to visualize and quantitatively inspect the solution space of the inference methods, via Principal Coordinate Analysis. Finally, the application of our method to a single-cell dataset of patient-derived melanoma xenografts shows significant differences between the COB-tree solution and the maximum likelihood ones.

The strong nonlinearity of large and highly connected reaction systems, such as metabolic networks, hampers the determination of variations in reaction fluxes from variations in species abundances, when comparing different steady states of a given system. We hypothesize that patterns in species abundance variations exist that mainly depend on the kernel of the stoichiometric matrix and allow for predictions of flux variations. To investigate this hypothesis, we applied a multi-target regression Deep Neural Network (DNN) to data generated via numerical simulations of an Ordinary Differential Equation (ODE) model of yeast metabolism, upon Monte Carlo sampling of the kinetic parameters. For each parameter configuration, we compared two steady states corresponding to different environmental conditions. We show that DNNs can predict relative fluxes impressively well even when a random subspace of input features is supplied, supporting the existence of recurrent variation patterns in abundances of chemical species, which can be recognized automatically.

Motivation: The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing experiments are hindered by several technical issues, which cause output data to be noisy, impacting the reliability of downstream analyses. Therefore, a growing number of data science methods has been proposed to recover lost or corrupted information from single-cell sequencing data. To date, however, no quantitative benchmarks have been proposed to evaluate such methods. Results: We present a comprehensive analysis of the state-of-the-art computational approaches for denoising and imputation of single-cell transcriptomic data, comparing their performance in different experimental scenarios. In detail, we compared 19 denoising and imputation methods, on both simulated and real-world datasets, with respect to several performance metrics related to imputation of dropout events, recovery of true expression profiles, characterization of cell similarity, identification of differentially expressed genes and computation time. The effectiveness and scalability of all methods were assessed with regard to distinct sequencing protocols, sample size and different levels of biological variability and technical noise. As a result, we identify a subset of versatile approaches exhibiting solid performances on most tests and show that certain algorithmic families prove effective on specific tasks but inefficient on others. Finally, most methods appear to benefit from the introduction of appropriate assumptions on noise distribution of biological processes.

A current challenge in cancer research is the development of therapeutic strategies aimed at reducing the toxicity of treatments, since Adverse Events (AEs) typically cause substantial problems and long-term damages to the patients. A possible solution to this issue lies in the personalization of therapy dosages according to demographic factors and in the employment of optimized data-driven drug administration protocols. Control theory can be exploited to this end, as its application in pharmacology allows to define optimized dosages and schedules, aimed at minimizing AEs and maximizing the therapy efficacy. However, an effective application of control theory approaches to this issue is constrained by our ability in inferring the parameters of the mathematical models from currently available data.We here present a closed-loop optimization framework of patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, combined with a mathematical model of a liquid tumor, which aims at overcoming such limitations. The most relevant feature of our framework is the ability to learn the value of patient-specific parameters via a Bayesian update, by exploiting a feedback signal obtained monitoring the tumor burden dynamics of the patient. Our framework employs CasADi, an open-source tool for nonlinear optimization, and guarantees a good and robust numerical estimation of the optimized schedule and a parsimonious use of computational time.As a case study, we present the application of our framework to Tyrosine Kinase Inhibitor administration in Chronic Myeloid Leukemia (CML), in which we show that our optimized protocols result in a faster decay of CSCs and in a reduction of the overall toxicity.