Simulating Brain Tumor Heterogeneity with a Multiscale Agent-Based Model: Linking Molecular Signatures, Phenotypes and Expansion Rate

We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an explicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.Comment: 37 pages, 10 figure

Clonal deconvolution of transcriptomic signatures and their spatial organisation in a mouse model of breast cancer

Intratumour heterogeneity is a phenomenon during cancer progression in which cancer cells diverge and form clonal populations with distinct phenotypic, genetic, or epigenetic states within the same tumour. This intrinsic heterogeneity provides a fuel for cancer evolution enabling tumour cell populations to adapt to selective pressures imposed by the tumour microenvironment or therapeutic interventions. Lineage-tracing approaches shed light into the clonal dynamics of complex populations, but generally lack the ability to directly associate clonal lineage with measurements that infer phenotype such as epigenetics and transcriptomics. In contrast, single-cell sequencing techniques can provide insight into the makeup of complex biological ecosystems, revealing the presence of rare cell populations that are typically masked in bulk analyses, but lack the ability to link these cell populations to clonal lineages. To address this challenge, we developed the WILDseq platform, a novel approach that allows clonal characterisation at the single-cell transcriptomic level while facilitating the prospective analysis of dynamic regulation of phenotypic heterogeneity under the selective pressure of therapeutic intervention. WILDseq relies on uniquely labelling individual cells with a heritable, expressed DNA barcode coupled with high-throughput single-cell RNA-sequencing. Importantly, this lentiviral-labelling approach can be deployed in any model system that is susceptible to viral transfection. Thus, this platform allows the comprehensive and systematic characterisation of clonal phenotypic di fferences within complex populations. Here we demonstrate how this technology can be used to determine clonal populations which are sensitive or resistant to a particular therapeutic intervention, identify transcriptomic signatures that correlate with these phenotypes and analyse how these cells adapt their transcriptomes to escape therapy. We have applied WILDseq to the study of di fferential clonal responses to chemotherapy in the heterogeneous 4T1 model of breast cancer and validated transcriptomic signatures of therapeutic resistance and sensitivity in primary patient data. We additionally used WILDseq to study the clonal response to the epigenetic regulator JQ1 which revealed intrinsic signatures that primed clones to JQ1 sensitivity. We observed JQ1-dependent depletion of CD8+ cytotoxic T-cells and suggest that this drives changes in clonal distribution. Finally, we are working on developing a high throughput FISH assay to leverage the WILDseq technology for mapping clonal and transcriptional identities spatially. Collectively, this thesis contributes to the characterisation and understanding of breast cancer heterogeneity and the impact of clonal architecture on tumour progression and response to therapy

Genotypic and phenotypic heterogeneity in Streptococcus mutans isolated from diabetic patients in Rome, Italy

Our study focuses on the antimicrobial susceptibility, genotypic and phenotypic heterogeneity, and serotype classification of the Streptococcus mutans isolated from type II diabetic patients (n = 25; age 42-68). Eighty-two percent of isolates were classified as serotype c. No serotype k was present. Macrorestriction analysis of genomic DNA of the isolates exhibited a clonal diversity that paralleled the phenotypic heterogeneity, which was also assessed in terms of biofilm forming ability. Isolates were susceptible to all the classes of antibiotics. In conclusion a great heterogeneity and no antimicrobial resistance were apparent in the considered S. mutans strains from diabetic patients

Cancer cell lines show high heritability for motility but not generation time

Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well explored. Here, we estimate the broad-sense heritability (H2) of two cell traits related to cancer hallmarks––cell motility and generation time––within populations of four cancer cell lines in vitro and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation

Simultaneous evolutionary expansion and constraint of genomic heterogeneity in multifocal lung cancer.

Recent genomic analyses have revealed substantial tumor heterogeneity across various cancers. However, it remains unclear whether and how genomic heterogeneity is constrained during tumor evolution. Here, we sequence a unique cohort of multiple synchronous lung cancers (MSLCs) to determine the relative diversity and uniformity of genetic drivers upon identical germline and environmental background. We find that each multicentric primary tumor harbors distinct oncogenic alterations, including novel mutations that are experimentally demonstrated to be functional and therapeutically targetable. However, functional studies show a strikingly constrained tumorigenic pathway underlying heterogeneous genetic variants. These results suggest that although the mutation-specific routes that cells take during oncogenesis are stochastic, genetic trajectories may be constrained by selection for functional convergence on key signaling pathways. Our findings highlight the robust evolutionary pressures that simultaneously shape the expansion and constraint of genomic diversity, a principle that holds important implications for understanding tumor evolution and optimizing therapeutic strategies.Across cancer types tumor heterogeneity has been observed, but how this relates to tumor evolution is unclear. Here, the authors sequence multiple synchronous lung cancers, highlighting the evolutionary pressures that simultaneously shape the expansion and constraint of genomic heterogeneity

Blood Vessel Tortuosity Selects against Evolution of Agressive Tumor Cells in Confined Tissue Environments: a Modeling Approach

Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature renormalization approaches

Phenotypic identification of subclones in multiple myeloma with different chemoresistant, cytogenetic and clonogenic potential

Knowledge about clonal diversity and selection is critical to understand multiple myeloma (MM) pathogenesis, chemoresistance and progression. If targeted therapy becomes reality, identification and monitoring of intraclonal plasma cell (PC) heterogeneity would become increasingly demanded. Here we investigated the kinetics of intraclonal heterogeneity among 116 MM patients using 23-marker multidimensional flow cytometry (MFC) and principal component analysis, at diagnosis and during minimal residual disease (MRD) monitoring. Distinct phenotypic subclones were observed in 35116 (30%) newly diagnosed MM patients. In 1035 patients, persistent MRD was detected after 9 induction cycles, and longitudinal comparison of patient-paired diagnostic vs MRD samples unraveled phenotypic clonal tiding after therapy in half (510) of the patients. After demonstrating selection of distinct phenotypic subsets by therapeutic pressure, we investigated whether distinct fluorescence-activated cell-sorted PC subclones had different clonogenic and cytogenetic profiles. In half (510) of the patients analyzed, distinct phenotypic subclones showed different clonogenic potential when co-cultured with stromal cells, and in 611 cases distinct phenotypic subclones displayed unique cytogenetic profiles by interphase fluorescence in situ hybridization, including selective del(17p13). Collectively, we unravel potential therapeutic selection of preexisting diagnostic phenotypic subclones during MRD monitoring; because phenotypically distinct PCs may show different clonogenic and cytogenetic profiles, identification and follow-up of unique phenotypic-genetic myeloma PC subclones may become relevant for tailored therapy.Peer Reviewe

Tumour heterogeneity: principles and practical consequences

Two major reasons compel us to study tumour heterogeneity: firstly, it represents the basis of acquired therapy resistance, and secondly it may be one of the major sources of the low level of reproducibility in clinical cancer research. The present review focuses on the heterogeneity of neoplastic disease, both within the primary tumour, and between primary tumour and metastases. We discuss different levels of heterogeneity and the current understanding of the phenomenon, as well as imminent developments relevant for clinical research and diagnostic pathology. It is necessary to develop new tools to study heterogeneity and new biomarkers for heterogeneity. Established and new in situ methods will be very useful. In future studies, not only clonal heterogeneity needs to be addressed, but also non-clonal phenotypic heterogeneity which might be important for therapy resistance. We also review heterogeneity established in major tumour types, in order to explore potential similarities that might help to define new strategies for targeted therapy