Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement

Cytokines such as TNF and FASL can trigger death or survival depending on cell lines and cellular conditions. The mechanistic details of how a cell chooses among these cell fates are still unclear. The understanding of these processes is important since they are altered in many diseases, including cancer and AIDS. Using a discrete modelling formalism, we present a mathematical model of cell fate decision recapitulating and integrating the most consistent facts extracted from the literature. This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals. Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types. We also propose a reduced conceptual model implementing the logic of the decision process. This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made. Moreover, our current model can be used to assess contradictory or controversial data from the literature. Ultimately, it constitutes a valuable reasoning tool to delineate novel experiments

Rho-associated kinase signalling and the cancer microenvironment: novel biological implications and therapeutic opportunities

The Rho/ROCK pathway is involved in numerous pivotal cellular processes that have made it an area of intense study in cancer medicine, however, Rho-associated coiled-coil containing protein kinase (ROCK) inhibitors are yet to make an appearance in the clinical cancer setting. Their performance as an anti-cancer therapy has been varied in pre-clinical studies, however, they have been shown to be effective vasodilators in the treatment of hypertension and post-ischaemic stroke vasospasm. This review addresses the various roles the Rho/ROCK pathway plays in angiogenesis, tumour vascular tone and reciprocal feedback from the tumour microenvironment and explores the potential utility of ROCK inhibitors as effective vascular normalising agents. ROCK inhibitors may potentially enhance the delivery and efficacy of chemotherapy agents and improve the effectiveness of radiotherapy. As such, repurposing of these agents as adjuncts to standard treatments may significantly improve outcomes for patients with cancer. A deeper understanding of the controlled and dynamic regulation of the key components of the Rho pathway may lead to effective use of the Rho/ROCK inhibitors in the clinical management of cancer

EVALUATING THE THERAPEUTIC EFFICACY OF RESTORING WILD-TYPE P53 ACTIVITY IN P53-MUTANT TUMORS

The p53 transcription factor is the most frequently altered in human cancers usually via missense mutations that undermine its transcriptional activity. Clinically, TP53 mutations have been shown to be remarkably predictive of refractoriness to treatment, resulting in poor outcome. Consequently, the development of p53 pathway activating agents is rapidly evolving and gaining more attention in cancer therapeutics research, with several small molecule compounds currently in preclinical and clinical trials. However, it remains largely unknown what types or proportions of p53-mutant tumors will respond to p53 restoration-based therapies. Using a mouse model of Li Fraumeni syndrome, we genetically restored wild-type p53 in mice carrying a germline p53R172H(corresponding to the TP53R175H hotspot in humans) missense mutation and observed heterogeneous responses. We found that approximately 50% of tumors responded by regressing in volume whereas 50% of tumors failed to regress after p53 reinstatement. To gain insight into the molecular events underlying therapeutic response to p53 restoration, we sequenced the transcriptome of twelve p53-mutant thymic lymphomas that were sensitive (n=8) or resistant (n=4) to p53 restoration. Differential gene expression analyses suggested a critical role for the TNF pathway and RARγ, an effector in the TNF pathway, in promoting response as they were up-regulated in tumors sensitive to p53 restoration. Furthermore, we demonstrate that pharmacological activation of RARγ with the synthetic retinoid, CD437, sensitizes resistant tumors to p53 restoration while additively improving outcome and survival in tumors inherently sensitive to p53 restoration

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field

Targeting the phosphatidylinositol 3-kinase/Akt/mechanistic target of rapamycin signaling pathway in B-lineage acute lymphoblastic leukemia: An update

Despite considerable progress in treatment protocols, B-lineage acute lymphoblastic leukemia (B-ALL) displays a poor prognosis in about 15–20% of pediatric cases and about 60% of adult patients. In addition, life-long irreversible late effects from chemo- and radiation therapy, including secondary malignancies, are a growing problem for leukemia survivors. Targeted therapy holds promising perspectives for cancer treatment as it may be more effective and have fewer side effects than conventional therapies. The phosphatidylinositol 3-phosphate kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) signaling pathway is a key regulatory cascade which controls proliferation, survival and drug-resistance of cancer cells, and it is frequently upregulated in the different subtypes of B-ALL, where it plays important roles in the pathophysiology, maintenance and progression of the disease. Moreover, activation of this signaling cascade portends a poorer prognosis in both pediatric and adult B-ALL patients. Promising preclinical data on PI3K/Akt/mTOR inhibitors have documented their anticancer activity in B-ALL and some of these novel drugs have entered clinical trials as they could lead to a longer event-free survival and reduce therapy-associated toxicity for patients with B-ALL. This review highlights the current status of PI3K/Akt/mTOR inhibitors in B-ALL, with an emphasis on emerging evidence of the superior efficacy of synergistic combinations involving the use of traditional chemotherapeutics or other novel, targeted agents