Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers

Network Theoretic Tools in the Analysis of Complex Diseases

In this thesis we consider the application of network theoretic tools in the analysis of genome wide gene-expression data describing complex diseases, displaying defects in differentiation. After considering the literature, we motivate the construction of entropy based network rewiring methodologies, postulating that such an approach may provide a systems level correlate of the differentiation potential of a cellular sample, and may prove informative in the analysis of pathology. We construct, analytically investigate and validate three such network theoretic tools: Network Transfer Entropy, Signalling Entropy and Interactome Sparsification and Rewiring (InSpiRe). By considering over 1000 genome wide gene expression samples corresponding to healthy cells at different levels of differentiation, we demonstrate that signalling entropy is a strong correlate of cell potency confirming our initial postulate. The remainder of the thesis applies our network theoretic tools to two ends of the developmental pathology spectrum. Firstly we consider cancer, in which the power of cell differentiation is hijacked, to develop a malicious new tissue. Secondly, we consider muscular dystrophy, in which cell differentiation is inhibited, resulting in the poor development of muscle tissue. In the case of cancer we demonstrate that signalling entropy is a measure of tumour anaplasia and intra-tumour heterogeneity, which displays distinct values in different cancer subtypes. Moreover, we find signalling entropy to be a powerful prognostic indicator in epithelial cancer, outperforming conventional gene expression based assays. In the case of muscular dystrophy we focus on the most prevalent: facioscapulohumeral muscular dystrophy (FSHD). We demonstrate that muscle differentiation is perturbed in FSHD and that signalling entropy is elevated in myoblasts over-expressing the primary FSHD candidate gene DUX4. We subsequently utilise InSpiRe, performing a meta-analysis of FSHD muscle biopsy gene-expression data, uncovering a network of DUX4 driven rewired interactions in the pathology, and a novel therapeutic target which we validate experimentally

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs). In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis. For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06-2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04-4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15-2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways. CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy. National Institue of Health and US Department of Defense

Beyond molecular tumor heterogeneity : protein synthesis takes control

Altres ajuts: 245 SRYC acknowledges support from Fondo de Investigaciones Sanitarias (P1170185 and PI 14/01320), Redes Temáticas de Investigación Cooperativa en Salud (RTICC, RD 12/0036/0057), and CIBERONC 2017.One of the daunting challenges facing modern medicine lies in the understanding and treatment of tumor heterogeneity. Most tumors show intra-tumor heterogeneity at both genomic and proteomic levels, with marked impacts on the responses of therapeutic targets. Therapeutic target-related gene expression pathways are affected by hypoxia and cellular stress. However, the finding that targets such as eukaryotic initiation factor (eIF) 4E (and its phosphorylated form, p-eIF4E) are generally homogenously expressed throughout tumors, regardless of the presence of hypoxia or other cellular stress conditions, opens the exciting possibility that malignancies could be treated with therapies that combine targeting of eIF4E phosphorylation with immune checkpoint inhibitors or chemotherapy

Tumor infiltrating effector memory Antigen-Specific CD8+ T Cells predict response to immune checkpoint therapy

Immune checkpoint therapy (ICT) results in durable responses in individuals with some cancers, but not all patients respond to treatment. ICT improves CD8+ cytotoxic T lymphocyte (CTL) function, but changes in tumor antigen-specific CTLs post-ICT that correlate with successful responses have not been well characterized. Here, we studied murine tumor models with dichotomous responses to ICT. We tracked tumor antigen-specific CTL frequencies and phenotype before and after ICT in responding and non-responding animals. Tumor antigen-specific CTLs increased within tumor and draining lymph nodes after ICT, and exhibited an effector memory-like phenotype, expressing IL-7R (CD127), KLRG1, T-bet, and granzyme B. Responding tumors exhibited higher infiltration of effector memory tumor antigen-specific CTLs, but lower frequencies of regulatory T cells compared to non-responders. Tumor antigen-specific CTLs persisted in responding animals and formed memory responses against tumor antigens. Our results suggest that increased effector memory tumor antigen-specific CTLs, in the presence of reduced immunosuppression within tumors is part of a successful ICT response. Temporal and nuanced analysis of T cell subsets provides a potential new source of immune based biomarkers for response to ICT

Understanding Metastasis Organotropism Patterns Through Within-cell and Between-cells Molecular Interaction Networks

Tese de mestrado, Bioquímica e Biomedicina, 2023, Universidade de Lisboa, Faculdade de CiênciasMetastasis is responsible for the majority of cancer-related deaths. It occurs when cells from a primary tumour disseminate and initiate new tumours at distant organ sites. Metastasizing cells have to exhibit especial characteristics that allow them to surpass all barriers and bottlenecks in their way to effective colonization. Ensuring survival throughout this process depends on how those cells communicate with the surrounding environments. Patterns of metastasis are remarkably variable between cancer types. In fact, distinct cancers seem to be predisposed to metastasize to specific organs, a feature known as metastasis organotropism. Our work is based on the hypothesis that organotropism can be partially explained by the extent of intercellular communication between metastasizing cells and cells in the secondary organ. Some proteins that establish intercellular interactions are tissue-specific and can be expressed in pre-cancerous tissue. Using RNA-seq data from non-diseased tissue, we built networks of intercellular proteinprotein interactions between cells from the primary cancer tissue and cells from a potential metastasis tissue. Controlling for other factors that affect organotropism, we found that sites where cancers metastasize more often tend to establish a larger number of intercellular interactions than sites with low incidence of metastasis. We detected 528 literature curated interactions that might play a role in metastasis formation and contribute to the observed differences in cellcell communication, some previously known to be related to cancer and/or metastasis. Finally, using a network of signalling pathways, we observed that proteins involved in metastasisassociated interactions and their closest neighbours in the network are enriched in cancer driver genes and biological processes linked to invasion and metastasis. In conclusion, we identified intercellular interactions and proteins that drive metastasis development and help explain organotropism. These insights might constitute new research and therapeutic opportunities to treat and prevent metastasis

The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Tumour mutational burden (TMB) predicts immunotherapy outcome in non-small cell lung cancer (NSCLC), consistent with immune recognition of tumour neoantigens. However, persistent antigen exposure is detrimental for T cell function. How TMB affects CD4 and CD8 T cell differentiation in untreated tumours and whether this affects patient outcomes is unknown. Here, we paired high-dimensional flow cytometry, exome, single-cell and bulk RNA sequencing from patients with resected, untreated NSCLC to examine these relationships. TMB was associated with compartment-wide T cell differentiation skewing, characterized by loss of TCF7-expressing progenitor-like CD4 T cells, and an increased abundance of dysfunctional CD8 and CD4 T cell subsets with strong phenotypic and transcriptional similarity to neoantigen-reactive CD8 T cells. A gene signature of redistribution from progenitor-like to dysfunctional states was associated with poor survival in lung and other cancer cohorts. Single-cell characterization of these populations informs potential strategies for therapeutic manipulation in NSCLC

Features of the intratumoral T cell receptor repertoire associated with antigen exposure in cancer patients

The clinical success of immunotherapies demonstrates the importance of the immune system in tumour control, but the response rates remain low and many biological mechanisms underlying how these therapies work are still uncharacterised. In particular, the specificity of the anti-tumour immune response pre-existing in treatment-naive patients or induced by treatment remains poorly described. In this thesis, I explore how T cell receptor (TCR) sequencing data in multi-omics contexts can be utilised to identify features associated with antigen exposure in cancer patients. In treatment-naive non-small cell lung cancer (NSCLC) patients, multi-region TCR sequencing revealed a pattern of heterogeneity in the TCR repertoire resembling the heterogeneity observed in the mutational profile of these tumours and a range of clonotype frequency values associated with tumour specificity. A novel method was built in order to identify distinct TCR populations that spatially follow the pattern of the well-established clonal/subclonal mutational dichotomy. The impact of immune checkpoint blockade therapy on the TCR repertoire distribution was assessed in advanced renal cell carcinoma in the context of anti- PD1 treatment. TCRs with frequency distribution characteristics similar to what was observed in NSCLC were maintained upon treatment and associated with clinical response. In addition, RNA-sequencing analysis identified a gene expression profile consistent with specific activation of T cells through TCR signalling. Finally, the same methodology was applied to bone marrow samples harvested from B cell acute lymphoblastic leukaemia (B-ALL) patients. A statistical framework was developed in order to efficiently distinguish leukaemic re-arrangements from the non- leukaemic TCR repertoire of B-ALL patients. Subsequently, longitudinal analysis revealed TCR distributions that suggested the presence of cytotoxic T cells which was further characterised in matched single-cell RNA sequencing data