83 research outputs found
Artificial intelligence (AI) in rare diseases: is the future brighter?
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.info:eu-repo/semantics/publishedVersio
the contribution of mass spectrometry based proteomics to understanding epigenetics
Chromatin is a macromolecular complex composed of DNA and histones that regulate gene expression and nuclear architecture. The concerted action of DNA methylation, histone post-translational modifications and chromatin-associated proteins control the epigenetic regulation of the genome, ultimately determining cell fate and the transcriptional outputs of differentiated cells. Deregulation of this complex machinery leads to disease states, and exploiting epigenetic drugs is becoming increasingly attractive for therapeutic intervention. Mass spectrometry (MS)-based proteomics emerged as a powerful tool complementary to genomic approaches for epigenetic research, allowing the unbiased and comprehensive analysis of histone post-translational modifications and the characterization of chromatin constituents and chromatin-associated proteins. Furthermore, MS holds great promise for epigenetic biomarker discovery and represents a useful tool for deconvolution of epigenetic drug targets. Here, we will provide an ov..
Urological Cancer 2020
This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research
Liver Tumors
This book is oriented towards clinicians and scientists in the field of the management of patients with liver tumors. As many unresolved problems regarding primary and metastatic liver cancer still await investigation, I hope this book can serve as a tiny step on a long way that we need to run on the battlefield of liver tumors
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Intratumoral B and T cell receptors: reconstruction and analysis
When cells divide, mistakes happen. However, an intricate surveillance system has evolved to detect and eliminate anomalous cells before they become detrimental to the host organism. In cancer, abnormal cells manage to escape the immune system and grow uncontrollably. In this sense, cancer can be considered as an oversight of the immune system, as immune escape is a defining feature of clinically detectable cancers. The role of the immune system in fighting cancer is becoming increasingly indisputable as our understanding of its underlying mechanisms expand owing to technological advances in genomics, cancer biology, and computational sciences. In particular, significant research effort is undertaken in the field of cancer immunotherapy, where the immune system is stimulated to recognize and attack cancerous cells. In this thesis, I investigate certain aspects of the immune system in the context of cancer by computationally reconstructing and analyzing intratumoral B and T cell receptors.
Applying a novel immune cell receptor profiling protocol to original single-cell RNA sequencing (scRNA-seq) data obtained from melanoma patients, I present a complete computational reconstruction of intratumoral immune receptors in this cancer type. The scRNA-seq results are consistent with the presence of an ongoing intratumoral immune response, likely involving tertiary lymphoid structures and the cooperation between B and T cells.
Additionally, using a dataset of paired tumor biopsies collected pre- and post-treatment, I show that B cell infiltration increases after immunotherapy in pancreatic and colorectal cancer. This thesis includes the sequences of the most clonally expanded intratumoral antibodies expressed in these biopsies which I computationally reconstructed from bulk RNA sequencing reads.
Furthermore, by combining scRNA-seq and immune cell receptor profiling of samples collected from a novel mouse model, I present a comprehensive statistical analysis of gene expression in clonal tumor-reactive T cells. I also show the distribution of tumor-reactive clones across the tumor and spleen. This study forms a first proof-of-principle effort for the in-depth assessment of tumor-reactive T and B cell clones, in-vivo, and paves the way for further, more extensive experiments
Genomic landscape of local prostate cancer in Sardinia population
Race and ethnicity are risk factors for prostate cancer. In the United States, African American men have the highest rate of mortality followed by Caucasians, and Asian Americans. The effects of race and ethnicity on prostate cancer are also reflected in different frequencies of ETS family fusion in different groups. ETS family fusions is the most common alteration in prostate cancer of Caucasian men at a frequency of ~50%, however, they are lower in African Americans and Chinese at 20-30%. Most of the genomic prostate cancer studies are focused on cohorts of European ancestry, leaving minority groups underrepresentation. Furthermore, in racial mixing, the ethnic contribution to risk is unclear. Sardinia population is an isolated Mediterranean population, and a purported refuge population of Neolithic ancestry with much lower incidence of prostate cancer than that in mainland Europe. Here, we conducted a genomic prostate cancer genomic study on a Sardinia cohort diagnosed with local prostate cancer. We identified a novel germline risk mutation ARSD-G320D occurring in 53 percent of the patients, somatic UGT family amplifications which occurred in 20% the patients, a novel in-frame fusion BTBD7-SLC2A5 occurred in 12 % of the patients. In addition, we pointed out that IRF8 deletion at 16q24.2 is a candidate driver in prostate cancer and patients with IRF8 deletion have worse prognosis. Our data revealed similarities and disparities in genomic alterations of prostate cancer between Sardinians and other ethnic groups. As well we have conducted a study based on Chinese prostate cancer cohort and have seen greater molecular disparities from TCGA cohort than in the Sardinian prostate cancer cohort. In Chinese cohort we have identified 37 genes significantly mutated and 20 of them have not implicated in prostate cancer in Caucasian and reveals a set of genomic markers that may inform the ethnic disparities
Investigating the Transcriptome Signature of Depression: Employing Co-expression Network, Candidate Pathways and Machine Learning Approaches
Depression is the leading cause of disability worldwide and is one of the major contributors to the overall global burden of disease. Despite significant advances in elucidating the neurobiology of depression in recent years, the molecular factors involved in the pathophysiology of depression remain poorly understood. Chapter 1: An overview of Major Depressive Disorder (MDD) from epidemiological and clinical perspectives with a summary of the current knowledge of the underlying biology is provided. A review of the major pathophysiological hypotheses of MDD highlights a need for a more comprehensive approach that allows studying complex molecular interactions involved in depression. Chapter 2: Transcriptome signature of depression was examined using the measure of replication at individual gene level across different tissues and cell types in both brain and periphery. Fifty-seven replicated genes were reported as differentially expressed in the brain and 21 in peripheral tissues. In-silico functional characterisation of these genes was provided, implicating shared pathways in a comorbid phenotype of depression and cardiovascular disease. Chapter 3: The molecular basis of MDD using co-expression network analysis was investigated. The Weighed Gene Co-expression Network Analysis (WGCNA) allowed for studying complex interactions between individual genes influencing biological pathways in MDD. Utilising the Sydney Memory and Aging Study (sMAS) and the Older Australian Twin Study (OATS) as discovery and replication cohorts respectively, it was found that the eigengenes of four clusters containing over 3,000 highly co-regulated genes are involved in 13 immune- and pathogen-related pathways and associated with recurrent MDD. However, the findings were not replicated on an independent cohort at the network level. Chapter 4: Using a machine learning (ML) approach, a predictive model was built to identify the genome-wide gene expression markers of recurrent MDD. Fuzzy Forests (FF) is a novel ML algorithm, which works in conjunction with WGCNA and was designed to reduce the bias seen in feature selection caused by the presence of correlated transcripts in transcriptome data. FF correctly classified 63% of recurrently depressed individuals in test data using the single top predictive feature (TFRC, encodes for transferrin receptor). This suggests that TFRC can represent a putative marker for recurrent MDD. Chapter 5: Following the findings on immune-related pathways being associated with recurrent MDD in the elderly (Chapter 3), the role of these pathways in recurrent MDD was examined at individual gene levels in an independent cohort (OATS). To target the immune pathways, all known genes (KEGG) involved in these 13 pathways were selected and a differential expression analysis was conducted on 1,302 candidates between individuals with recurrent MDD and those without. We found that CD14 was significantly downregulated in recurrent MDD (FDR < 5%). Considering the key role of CD14 for facilitating the innate immune response, we suggest that CD14 can potentially serve as a peripheral marker of immune dysregulation in recurrent MDD. Chapter 6: A discussion on obtained findings is provided and future directions are outlined with a particular focus on how co-expression network and machine learning approaches that can enhance translation of molecular findings into clinical translation.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 201
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