Immune-Inhibitory Gene Expression is Positively Correlated with Overall Immune Activity and Predicts Increased Survival Probability of Cervical and Head and Neck Cancer Patients

Background: Limited immunotherapy options are approved for the treatment of cervical cancer and only 10–25% of patients respond effectively to checkpoint inhibition monotherapy. To aid the development of novel therapeutic immune targets, we aimed to explore survival-associated immune biomarkers and co-expressed immune networks in cervical cancer. Methods: Using The Cancer Genome Atlas (TCGA) Cervical Squamous Cell Carcinoma (CESC) data (n = 304), we performed weighted gene co-expression network analysis (WGCNA), and determined which co-expressed immune-related genes and networks are associated with survival probability in CESC patients under conventional therapy. A “Pan-Immune Score” and “Immune Suppression Score” was generated based on expression of survival-associated co-expressed immune networks and immune suppressive genes, which were subsequently tested for association with survival probablity using the TCGA Head Neck Squamous Cell Carcinoma (HNSCC) data (n = 528), representing a second SCC cancer type. Results: In CESC, WGCNA identified a co-expression module enriched in immune response related genes, including 462 genes where high expression was associated with increased survival probability, and enriched for genes associated with T cell receptor, cytokine and chemokine signaling. However, a high level of expression of 43 of the genes in this module was associated with decreased survival probability but were not enriched in particular pathways. Separately, we identified 20 genes associated with immune suppression including inhibitory immune checkpoint and regulatory T cell-related genes, where high expression was associated with increased survival probability. Expression of these 20 immune suppressive genes (represented as “Immune Suppression Score”) was highly correlated with expression of overall survival-associated immune genes (represented as “Pan-Immune Score”). However, high expression of seven immune suppression genes, including TWEAK-R, CD73, IL1 family and TGFb family genes, was significantly associated with decreased survival probability. Both scores also significantly associated with survival probability in HNSCC, and correlated with the previously established “Immunophenoscore.” Conclusion: CESC and HNSCC tumors expressing genes predictive of T cell infiltrates (hot tumors) have a better prognosis, despite simultaneous expression of many immune inhibitory genes, than tumors lacking expression of genes associated with T cell infiltrates (cold tumors) whether or not these tumor express immune inhibitory genes.</p

Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

Heterogeneity analysis of low-risk HPV infection and high-risk HPV infection, HPV-positive and HPV-negative cancers

Zahlreiche Studien belegen, dass humane Papillomaviren (HPV) Krebs verursachen können. Allerdings haben nur wenige Studien die Heterogenität von HPV-infizierten oder nicht-infizierten (HPV-pos. und HPV-neg.) Krebserkrankungen untersucht. Zu-dem werden HPV-Infektionen mit niedrigem Risiko in der Regel mit gutartigen Läsio-nen in Verbindung gebracht, wobei sich nur wenige Studien mit der Heterogenität von HPV-Infektionen mit niedrigem und hohem Risiko befasst haben. Mit der Ent-wicklung von Next-Generation-Sequenzierung ist es möglich, Genome auf Einzelzel-lebene zu amplifizieren und zu sequenzieren, so dass die Heterogenität der Zellen mithilfe der Einzelzellsequenzierungstechnologie beobachtet werden kann. Auf die-ser Grundlage werden zukünftig eine genauere Diagnose und Behandlung von HPV-Patienten möglich sein. In dieser Studie wurde versucht, dieses Problem zu lösen, indem die Einzelzellse-quenzierung mit der bulk-RNA-Sequenzierung kombiniert wurde, wobei die Einzel-zellsequenzierungsdaten Veränderungen auf zellulärer Ebene lieferten und die bulk-RNA-Sequenzierungsdaten große Stichprobengrößen mit passenden klinischen In-formationen enthielten. Die beiden wurden kombiniert, um mit Hilfe mehrerer Me-thoden wie Seurat für Zellcluster, CIBERSORT für Variationen der Immuninfiltration, WGCNA für charakteristische assoziierte Gencluster usw. zu analysieren. Zunächst wurde die Hypothese aufgestellt, dass es eine Heterogenität zwischen HPV-pos. und HPV-neg.-Karzinomen gibt, und zwar von der Transkriptionsebene bis hin zur Immuninfiltration. Durch die Untersuchung von Gebärmutterhalskrebs, die mit Hochrisiko-HPV-Infektionen assoziiert ist, wurde bestätigt, dass CD8+ T-Zellen und B-Zellen herunterreguliert wurden, während T-Reg-Zellen, CD4+ T-Zellen und Epithelzellen in der HPV-pos.-Zervixkrebsgruppe hochreguliert waren. Die Analyse gutartiger Läsionen, die mit einer Niedrigrisiko-HPV-Infektion assoziiert sind, ergab Folgendes: Eine Niedrigrisiko-HPV-Infektion weist ähnliche genetische Veränderun-gen auf wie eine Hochrisiko-HPV-Infektion. Genetische Veränderungen, die durch eine Niedrigrisiko-HPV-Infektion verursacht werden, können auch die Prognose von Krebspatienten beeinflussen. Die Analyse von AIN3 und ASCC, CIN3 und CESC, präkanzerösen Läsionen und Tumoren, die in engem Zusammenhang mit Hochrisiko-HPV-Infektionen stehen, bestätigte auch, dass die Auswirkungen von Niedrigrisiko-HPV-Infektionen und Hochrisiko-HPV-Infektionen Ähnlichkeiten und Unterschiede aufweisen. Die Veränderungen in den Immunzellen waren bei den verschiedenen HPV-Infektionen teilweise gleich, während der Rest der Veränderungen in den Im-munzellen durch die Krankheit selbst verursacht werden kann. Die Induktion von oxidativem Stress ist bei den verschiedenen HPV-Infektionen gleich, was zu oxidati-vem Stress führt, der DNA-Schäden verursacht und das optimale Umfeld für eine maligne Transformation schafft. Schließlich wurde diese Erkenntnis durch in-vitro-Experimente bestätigt, bei denen sowohl normale als auch Krebszelllinien, die mit HPV transfiziert wurden, eine erhöhte Proliferation und eine hohe Expression von mit oxidativem Stress verbundenen Genen aufwiesen, und die hoch exprimierten Gene waren in der Lage, die Empfindlichkeit von mit oxidativem Stress assoziierten Inhibitoren zu erhöhen. Die Bedeutung dieser Studie liegt in der Erklärung der Heterogenität zwischen HPV-pos. und HPV-neg.-Karzinomen und in der Entdeckung, dass die Gemeinsamkeit zwi-schen Niedrigrisiko-HPV-Infektionen und Hochrisiko-HPV-Infektionen im oxidativen Stress liegt. Dies könnte eine wichtige Entdeckung für eine künftige Präzisionsmedi-zin sein, damit Patienten mit HPV-assoziierten Krebserkrankungen präzise gezielte Behandlungen erhalten können.Numerous studies have established the causal relationship between Human Papil-lomavirus (HPV) and cancer development. Nevertheless, there exists a paucity of research efforts dedicated to investigating the heterogeneity observed in HPV-associated cancers, differentiating between HPV-positive (HPV-pos.) and HPV-negative (HPV-neg.) cases. Furthermore, while low-risk HPV infections have tradi-tionally been linked to benign lesions, investigations exploring the intricacies within both low-risk and high-risk HPV infections have been limited in scope. The advent of next-generation sequencing technologies, particularly single-cell sequencing, has revolutionized the field by facilitating genome amplification and sequencing at the single-cell level. This cutting-edge technology offers the unique capability to eluci-date cellular heterogeneity, presenting a promising avenue for enhancing the preci-sion of diagnostic and therapeutic approaches for HPV-infected patients, capitalizing on this newfound understanding of variability within the disease. This study aimed to address this issue by integrating single-cell sequencing with bulk-RNA sequencing and DNA methylation sequencing. Single-cell sequencing data illuminated cellular-level alterations, while bulk-RNA sequencing and DNA methyla-tion sequencing data encompassed larger sample sizes with accompanying clinical information. The analytical toolbox included methods such as Seurat for cell cluster-ing, CIBERSORT for assessing immune infiltration variations, and WGCNA for identi-fying clusters of genes with characteristic associations. Initially, we hypothesized that heterogeneity exists between HPV-pos. and HPV-neg. cancers, spanning from the transcriptional level to immune infiltration. In the con-text of the most well-established high-risk HPV infection-associated cancer, cervical cancer, CD8+ T cells and B cells were observed to be down-regulated, whereas T-reg cells, CD4+ T cells, and epithelial cells were up-regulated in the HPV-pos. cervical can-cer group. Subsequent analysis of benign lesions associated with low-risk HPV infec-tion revealed shared genetic alterations with high-risk HPV infection, and these ge-netic alterations impacted the prognosis of cancer patients. Furthermore, the exam-ination of AIN3 and ASCC, CIN3 and CESC, precancerous lesions, and tumors firmly linked to high-risk HPV infection reaffirmed that while low-risk HPV infection and high-risk HPV infection share similarities, differences also exist. Changes in immune cells were partially consistent across different HPV infections, while other immune cell alterations may be attributed to the disease itself. It was observed that oxida-tive stress, common to various HPV infections, induced DNA damage, creating an environment conducive to malignant transformation. This finding was further cor-roborated by in vitro experiments, where both normal and cancer cell lines trans-fected with HPV exhibited increased proliferation and upregulated expression of oxidative stress-related genes. These highly expressed genes increased sensitivity to oxidative stress-associated inhibitors. The significance of this study lies in elucidating the heterogeneity between HPV-pos. and HPV-neg. cancers and identifying oxidative stress as a common factor in low-risk and high-risk HPV infections. Additionally, the altered sensitivity to genetic inhibi-tors resulting from HPV-induced genetic changes offers novel prospects for the treatment of HPV-pos. cancers, potentially paving the way for precision medicine approaches tailored to patients with HPV-associated cancers

Genomic expression differences between cutaneous cells from red hair colour individuals and black hair colour individuals based on bioinformatic analysis

The MC1R gene plays a crucial role in pigmentation synthesis. Loss-of-function MC1R variants, which impair protein function, are associated with red hair color (RHC) phenotype and increased skin cancer risk. Cultured cutaneous cells bearing loss-of-function MC1R variants show a distinct gene expression profile compared to wild-type MC1R cultured cutaneous cells. We analysed the gene signature associated with RHC co-cultured melanocytes and keratinocytes by Protein-Protein interaction (PPI) network analysis to identify genes related with non-functional MC1R variants. From two detected networks, we selected 23 nodes as hub genes based on topological parameters. Differential expression of hub genes was then evaluated in healthy skin biopsies from RHC and black hair color (BHC) individuals. We also compared gene expression in melanoma tumors from individuals with RHC versus BHC. Gene expression in normal skin from RHC cutaneous cells showed dysregulation in 8 out of 23 hub genes (CLN3, ATG10, WIPI2, SNX2, GABARAPL2, YWHA, PCNA and GBAS). Hub genes did not differ between melanoma tumors in RHC versus BHC individuals. The study suggests that healthy skin cells from RHC individuals present a constitutive genomic deregulation associated with the red hair phenotype and identify novel genes involved in melanocyte biology

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

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis

Investigating the molecular etiologies of sporadic ALS (sALS) using RNA-Sequencing

ALS is an often lethal disease involving degeneration of motor neurons in the brain and spinal cord. Current treatments only extend life by several months, and novel therapies are needed. We combined RNA-Sequencing, systems biology analyses, and molecular biology assays to elucidate sporadic ALS group-specific differences in postmortem cervical spinal sections (7 sALS and 8 control samples) that may be relevant to disease pathology. \u3e55 million 2X150 RNA-sequencing reads per sample were generated and processed. In Chapter 2, we used bioinformatics tools to identify nuclear differentially expressed genes (DEGs) between our two groups. Further, we used Weighted Gene Co-Expression Network Analysis to identify gene co-expression networks associated with disease status. Qiagen’s Ingenuity Pathway Analysis revealed our sALS group-specific DEGs and a sALS group-specific gene co-expression network were associated with inflammatory processes and TNF-α signaling. Further, TNFAIP2 was identified as a sALS group-specific upregulated DEG and a network hub gene within that network. We hypothesized TNFAIP2’s upregulation in our ALS samples reflected increased TNF-α signaling and that TNFAIP2 promoted motor neuron death via TNF superfamily apoptotic pathways. Transient overexpression of TNFAIP2 decreased cell viability in both neural stem cells and induced pluripotent stem cell-derived motor neurons. Further, inhibition of activated caspase 9 (a protein necessary for TNF superfamily mitochondrial-mediated apoptosis) reversed this effect in neural stem cells. In Chapters 3 and 4, we used bioinformatics tools to identify sALS group-specifc mitochondrial DEGs and differentially used exons (DUEs). Qiagen’s Ingenuity Pathway Analysis revealed our sALS group-specific DUEs were associated with cholesterol biosynthesis

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

Functional Analysis of Human Long Non-coding RNAs and Their Associations with Diseases

Within this study, we sought to leverage knowledge from well-characterized protein coding genes to characterize the lesser known long non-coding RNA (lncRNA) genes using computational methods to find functional annotations and disease associations. Functional genome annotation is an essential step to a systems-level view of the human genome. With this knowledge, we can gain a deeper understanding of how humans develop and function, and a better understanding of human disease. LncRNAs are transcripts greater than 200 nucleotides, which do not code for proteins. LncRNAs have been found to regulate development, tissue and cell differentiation, and organ formation. Their dysregulation has been linked to several diseases including autism spectrum disorder (ASD) and cancer. While a great deal of research has been dedicated to protein-coding genes, the relatively recently discovered lncRNA genes have yet to be characterized. LncRNA function is tied closely to when and where they are expressed. Co-expression network analysis offer a means of functional annotation of uncharacterized genes through a guilt by association approach. We have constructed two co-expression networks using known disease-associated protein-coding genes and lncRNA genes. Through clustering of the networks, gene set enrichment analysis, and centrality measures, we found enrichment for disease association and functions as well as identified high-confidence lncRNA disease gene targets. We present a novel approach to the identification of disease state associations by demonstrating genes that are associated with the same disease states share patterns that can be discerned from transcriptomes of healthy tissues. Using a machine learning algorithm, we built a model to classify ASD versus non-ASD genes using their expression profiles from healthy developing human brain tissues. Feature selection during the model-building process also identified critical temporospatial points for the determination of ASD genes. We constructed a webserver tool for the prioritization of genes for ASD association. The webserver tool has a database containing prioritization and co-expression information for nearly every gene in the human genome

Insight of novel biomarkers for papillary thyroid carcinoma through multiomics

IntroductionThe overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness.Methods6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets.ResultsThe basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets).DiscussionMultiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy