Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions

Unique metabolic features of pancreatic cancer stroma: relevance to the tumor compartment, prognosis, and invasive potential.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis. The aggressiveness and therapeutic recalcitrance of this malignancy has been attributed to multiple factors including the influence of an active desmoplastic stroma. How the stromal microenvironment of PDAC contributes to the fatal nature of this disease is not well defined. In the analysis of clinical specimens, we observed diverse expression of the hypoxic marker carbonic anhydrase IX and the lactate transporter MCT4 in the stromal compartment. These stromal features were associated with the epithelial to mesenchymal phenotype in PDAC tumor cells, and with shorter patient survival. Cultured cancer-associated fibroblasts (CAFs) derived from primary PDAC exhibited a high basal level of hypoxia inducible factor 1a (HIF1α) that was both required and sufficient to modulate the expression of MCT4. This event was associated with increased transcription and protein synthesis of HIF1α in CAFs relative to PDAC cell lines, while surprisingly the protein turnover rate was equivalent. CAFs utilized glucose predominantly for glycolytic intermediates, whereas glutamine was the preferred metabolite for the TCA cycle. Unlike PDAC cell lines, CAFs were resistant to glucose withdrawal but sensitive to glutamine depletion. Consistent with the lack of reliance on glucose, CAFs could survive the acute depletion of MCT4. In co-culture and xenograft studies CAFs stimulated the invasive potential and metastatic spread of PDAC cell lines through a mechanism dependent on HIF1α and MCT4. Together, these data indicate that stromal metabolic features influence PDAC tumor cells to promote invasiveness and metastatic potential and associate with poor outcome in patients with PDAC

Cancer Niche as a Garbage Disposal Machine: Implications of TCM-Mediated Balance of Body-Disease for Treatment of Cancer.

Cancer epidemic led to worldwide to search for a new "game changer" concept to govern cancer research and cancer treatment. Western medicine-based cancer research has been extending the impasse without resolution in sigh for improving survival of patients with solid malignant tumors in the last four decades due to heterogeneity in cancer tissues. Such a deadlock charts a course to learn lessons from the developing countries, directly or indirectly to complement the exhausted Western medicine. We propose a new concept of "Cancer niche as a garbage disposal machine" with implications of traditional Chinese medicine-mediated restoration of normal balance between body and disease to bring the fight against cancer under control

Early rheumatoid arthritis is characterized by a distinct and transient synovial fluid cytokine profile of T cell and stromal cell origin

Pathological processes involved in the initiation of rheumatoid synovitis remain unclear. We undertook the present study to identify immune and stromal processes that are present soon after the clinical onset of rheumatoid arthritis ( RA) by assessing a panel of T cell, macrophage, and stromal cell related cytokines and chemokines in the synovial fluid of patients with early synovitis. Synovial fluid was aspirated from inflamed joints of patients with inflammatory arthritis of duration 3 months or less, whose outcomes were subsequently determined by follow up. For comparison, synovial fluid was aspirated from patients with acute crystal arthritis, established RA and osteoarthritis. Rheumatoid factor activity was blocked in the synovial fluid samples, and a panel of 23 cytokines and chemokines measured using a multiplex based system. Patients with early inflammatory arthritis who subsequently developed RA had a distinct but transient synovial fluid cytokine profile. The levels of a range of T cell, macrophage and stromal cell related cytokines ( e. g. IL-2, IL-4, IL-13, IL-17, IL-15, basic fibroblast growth factor and epidermal growth factor) were significantly elevated in these patients within 3 months after symptom onset, as compared with early arthritis patients who did not develop RA. In addition, this profile was no longer present in established RA. In contrast, patients with non-rheumatoid persistent synovitis exhibited elevated levels of interferon-gamma at initiation. Early synovitis destined to develop into RA is thus characterized by a distinct and transient synovial fluid cytokine profile. The cytokines present in the early rheumatoid lesion suggest that this response is likely to influence the microenvironment required for persistent RA

Protein microenvironments for topology analysis

Previously held under moratorium from 1st December 2016 until 1st December 2021Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined by the properties of its surrounding residues. The term microenvironment is used herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density, boundaries between domains and junction complexity. These quantifications are used to project a protein’s backbone structure into a series of scores. The hypothesis was that these sequences of scores can be used to discover protein domains and motifs and that they can be used to align and compare groups of 3D protein structures. This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The computation of the microenvironments was the most challenging aspect in terms of performance, and the optimisations required are described. Methods of scoring microenvironments were developed to enable the extraction of domain and motif data without 3D alignment. The problem of allosteric site detection was addressed with a classifier that gave high rates of allosteric site detection. Overall, this work describes the development of a system that scales well with increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process large datasets by application to allosteric site detection, a problem that has not previously been adequately solved.Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined by the properties of its surrounding residues. The term microenvironment is used herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density, boundaries between domains and junction complexity. These quantifications are used to project a protein’s backbone structure into a series of scores. The hypothesis was that these sequences of scores can be used to discover protein domains and motifs and that they can be used to align and compare groups of 3D protein structures. This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The computation of the microenvironments was the most challenging aspect in terms of performance, and the optimisations required are described. Methods of scoring microenvironments were developed to enable the extraction of domain and motif data without 3D alignment. The problem of allosteric site detection was addressed with a classifier that gave high rates of allosteric site detection. Overall, this work describes the development of a system that scales well with increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process large datasets by application to allosteric site detection, a problem that has not previously been adequately solved

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

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

Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.

Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity

Deciphering the Immune Evolution Landscape of Multiple Myeloma Long-Term Survivors Using Single Cell Genomics

Multiple myeloma (MM) is a malignant bone marrow (BM) disease characterized by somatic hypermutation and DNA damage in plasma cells; leading to the overproduction of dysfunctional malignant myeloma cells. Accumulation of myeloma cells has direct and indirect effects on the BM and other organs. Despite the development of new therapeutic options; MM remains incurable and only a small fraction of patients experiences long-term survival (LTS). The past has shown that ultimately all patients still relapse; leading to the hypothesis that a state of active immune-surveillance is required to control the residual disease. To understand the long-term survival phenomenon and its link to the immune-phenotypes in MM disease; we collected paired bone marrow samples from 24 patients who survived for about 7 to 17 years after Autologous Stem Cell Transplant (ASCT), with a high plasma cell infiltration in the BM (median 49.5%) at diagnosis time. Response assessment according to the International Myeloma Working Group (IMWG) revealed that 15 patients were in complete remission (CR), whereas 9 patients were in non-complete remission (non-CR) that had tumor cells which remained stable over recent years. We performed single-cell RNA-seq sequencing on more than 290,000 bone marrow cells from 11 patients before treatment (BT) and in LTS, as well as three healthy controls using 10x Genomics technology. I developed a computational approach using the state-of-the-art single cell methods, statistical inference and machine learning models to decipher the bone marrow immune cell types and states across all clinical groups. I performed in-depth analyses of the bone marrow immune microenvironment across all captured cell types, and provided the global landscape of cellular states across all clinical groups. In this work, I defined new cellular states, marker genes, and gene signatures associated with the patients’ clinical and survival states. Additionally, I defined a new myeloid population termed Myeloma-associated Neutrophils (MAN) cells and a T cell exhaustion population termed Aberrant Memory Cytotoxic (AMC) CD8+ T cells in newly diagnosed Multiple Myeloma patients. Moreover, I propose new therapeutic targets CXCR3 and NR4A2 in AMC CD8+ T cells, which could be further investigated to reverse the T cell exhaustion state in newly diagnosed MM patients. Furthermore, I defined new prognostic markers in the CD8+ T cell compartment which could be predictive for the global disease state. Finally, I propose that MM long-term survivors go through a complex and evolving immune landscape and acquire cellular states in a stepwise manner. Furthermore, I propose the Continuum Immune Landscape (CIL) Model which explains the immune landscape of MM patients before and after long-term survival. Additionally, I introduced the Disease-State Trajectories (DST) hypothesis regarding the disease-associated dysregulated cellular states in MM context, which could be generalized into other tumor entities and diseases