A Novel Approach to the Comparative Genomic Analysis of Canine and Human Cancers

abstract: Study of canine cancer’s molecular underpinnings holds great potential for informing veterinary and human oncology. Sporadic canine cancers are highly abundant (~4 million diagnoses/year in the United States) and the dog’s unique genomic architecture due to selective inbreeding, alongside the high similarity between dog and human genomes both confer power for improving understanding of cancer genes. However, characterization of canine cancer genome landscapes has been limited. It is hindered by lack of canine-specific tools and resources. To enable robust and reproducible comparative genomic analysis of canine cancers, I have developed a workflow for somatic and germline variant calling in canine cancer genomic data. I have first adapted a human cancer genomics pipeline to create a semi-automated canine pipeline used to map genomic landscapes of canine melanoma, lung adenocarcinoma, osteosarcoma and lymphoma. This pipeline also forms the backbone of my novel comparative genomics workflow. Practical impediments to comparative genomic analysis of dog and human include challenges identifying similarities in mutation type and function across species. For example, canine genes could have evolved different functions and their human orthologs may perform different functions. Hence, I undertook a systematic statistical evaluation of dog and human cancer genes and assessed functional similarities and differences between orthologs to improve understanding of the roles of these genes in cancer across species. I tested this pipeline canine and human Diffuse Large B-Cell Lymphoma (DLBCL), given that canine DLBCL is the most comprehensively genomically characterized canine cancer. Logistic regression with genes bearing somatic coding mutations in each cancer was used to determine if conservation metrics (sequence identity, network placement, etc.) could explain co-mutation of genes in both species. Using this model, I identified 25 co-mutated and evolutionarily similar genes that may be compelling cross-species cancer genes. For example, PCLO was identified as a co-mutated conserved gene with PCLO having been previously identified as recurrently mutated in human DLBCL, but with an unclear role in oncogenesis. Further investigation of these genes might shed new light on the biology of lymphoma in dogs and human and this approach may more broadly serve to prioritize new genes for comparative cancer biology studies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

The search for cis-regulatory driver mutations in cancer genomes

With the advent of high-throughput and relatively inexpensive whole-genome sequencing technology, the focus of cancer research has begun to shift toward analyses of somatic mutations in non-coding cis-regulatory elements of the cancer genome. Cis-regulatory elements play an important role in gene regulation, with mutations in these elements potentially resulting in changes to the expression of linked genes. The recent discoveries of recurrent TERT promoter mutations in melanoma, and recurrent mutations that create a super-enhancer regulating TAL1 expression in T-cell acute lymphoblastic leukaemia (T-ALL), have sparked significant interest in the search for other somatic cis-regulatory mutations driving cancer development. In this review, we look more closely at the TERT promoter and TAL1 enhancer alterations and use these examples to ask whether other cis-regulatory mutations may play a role in cancer susceptibility. In doing so, we make observations from the data emerging from recent research in this field, and describe the experimental and analytical approaches which could be adopted in the hope of better uncovering the true functional significance of somatic cis-regulatory mutations in cancer.Link_to_subscribed_fulltex

The metabolomics of psoriatic disease.

Metabolomics is an emerging new "omics" field involving the systematic analysis of the metabolites in a biologic system. These metabolites provide a molecular snapshot of cellular activity and are thus important for understanding the functional changes in metabolic pathways that drive disease. Recently, metabolomics has been used to study the local and systemic metabolic changes in psoriasis and its cardiometabolic comorbidities. Such studies have revealed novel insights into disease pathogenesis and suggest new biochemical signatures that may be used as a marker of psoriatic disease. This review will discuss common strategies in metabolomics analysis, current findings in the metabolomics of psoriasis, and emerging trends in psoriatic metabolomics

Analysis of SIGLEC12 expression, IMMUNOMODULATION and prognostic value in RENAL cancer using multiomic databases

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Siglecs belong to a family of immune regulatory receptors predominantly found on hematopoietic cells. They interact with Sia, resulting in the activation or inhibition of the immune response. Previous reports have suggested that the SIGLEC12 gene, which encodes the Siglec-XII protein, is expressed in the epithelial tissues and upregulated in carcinomas. However, studies deciphering the role of Siglec-XII in renal cancer (RC) are still unavailable, and here we provide insights on this question. We conducted expression analysis using the Human Protein Atlas and UALCAN databases. The impact of SIGLEC12 on RC prognosis was determined using the KM plotter, and an assessment of immune infiltration with SIGLEC12 was performed using the TIMER database. GSEA was conducted to identify the pathways affected by SIGLEC12. Finally, using GeneMania, we identified Siglec-XII interacting proteins. Our findings indicated that macrophages express SIGLEC12 in the kidney. Furthermore, we hypothesize that Siglec-XII expression might be involved in the increase of primary RC, but this effect may not be dependent on the age of the patient. In the tumour microenvironment, oncogenic pathways appeared to be upregulated by SIGLEC12. Similarly, our analysis suggested that SIGLEC12-related kidney renal papillary cell carcinomas may be more suitable for targeted immunotherapy, such as CTLA-4 and PD-1/PD-L1 inhibitors. These preliminary results suggested that high expression of SIGLEC12 is associated with poor prognosis for RC. Future studies to assess its clinical utility are necessitated.Peer reviewe

Conducting bioinformatics analysis to predict sulforaphane-triggered adverse outcome pathways in healthy human cells

Sulforaphane (SFN) is a naturally occurring molecule present in plants from Brassica family. It becomes bioactive after hydrolytic reaction mediated by myrosinase or human gastrointestinal microbiota. Sulforaphane gained scientific popularity due to its antioxidant and anti-cancer properties. However, its toxicity profile and potential to cause adverse effects remain largely unidentified. Thus, this study aimed to generate SFN-triggered adverse outcome pathway (AOP) by looking at the relationship between SFN-chemical structure and its toxicity, as well as SFN-gene interactions. Quantitative structure-activity relationship (QSAR) analysis identified 2 toxophores (Derek Nexus software) that have the potential to cause chromosomal damage and skin sensitization in mammals or mutagenicity in bacteria. Data extracted from Comparative Toxicogenomics Database (CTD) linked SFN with previously proposed outcomes via gene interactions. The total of 11 and 146 genes connected SFN with chromosomal damage and skin diseases, respectively. However, network analysis (NetworkAnalyst tool) revealed that these genes function in wider networks containing 490 and 1986 nodes, respectively. The over-representation analysis (ExpressAnalyst tool) pointed out crucial biological pathways regulated by SFN-interfering genes. These pathways are uploaded to AOP-helpFinder tool which found the 2321 connections between 19 enriched pathways and SFN which were further considered as key events. Two major, interconnected AOPs were generated: first starting from disruption of biological pathways involved in cell cycle and cell proliferation leading to increased apoptosis, and the second one connecting activated immune system signaling pathways to inflammation and apoptosis. In both cases, chromosomal damage and/or skin diseases such as dermatitis or psoriasis appear as adverse outcomes

MedCo: Enabling Privacy-Conscious Exploration of Distributed Clinical and Genomic Data

Being able to share large amounts of sensitive clinical and genomic data across several institutions is crucial for precision medicine to scale up. Unfor- tunately, existing solutions only partially address this challenge and are still unable to provide the strong privacy and security guarantees required by regulations (e.g., HIPAA, GDPR). As a result, currently only very limited datasets of non-sensitive and moderately useful information can be shared. In this paper, we introduce MedCo, the first operational system that enables an investigator to explore sensi- tive medical information distributed at several sites and protected with collective homomorphic encryption. MedCo builds on top of established and widespread technology from the biomedical informatics community, such as i2b2 and SHRINE, and relies on state-of-the-art secure protocols for processing encrypted distributed data and complying with regulations. As such, MedCo can be easily adopted by clinical sites thus paving the way to new unexplored data-sharing use cases. We tested MedCo in a real network of three institutions (EPFL, UNIL and CHUV) by focusing on an oncology use-case with real somatic mutations and clinical tumor data. The relatively low overhead introduced by MedCo shows that it represents a concrete and scalable solution for sharing privacy-conscious medical data

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent highthroughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine

A Systems Biology Approach to Epigenetic Gene Regulation

The ability to control when, and how much of the genetic code is being expressed is the underlying principle behind gene regulation. Control of gene production is able to influence a cell's phenotype by determining which structural components of the cell's observable traits (shape, growth, and behavior) are made. In multicellular organism’s different cell types are able to arise from the same genetic code due to a difference in the patterns of genes being expressed. Essentially anywhere in the process of gene expression from transcription, RNA processing, translation, and post-translational modifications of the protein is subject to regulation. As transcription is the first step in the process of gene expression, it is the first level of regulation for influencing the cell phenotype. The actions of transcription factors, histone modifiers, and other proteins work together to influence RNA polymerase's ability to complete the process of transcription. The actions of transcription factors are able to influence transcription by controlling the ability of RNA polymerase to be recruited to the start of a protein coding region and histone modifiers can rearrange the histones of the chromatin causing entire regions of a chromosome to become exposed or sequestered. These transcriptional regulators are able to work in a combinatorial fashion with one another to either activate and/or repress wide repertoires of transcriptional targets. Mapping out a network of interactions between these transcriptional regulators in gene expression programs allows researchers to understand how each protein is able to influence the phenotype of the cell, and how mutations to any of these transcriptional regulators are able to drive the cell into a diseased state. In the case of cancer, changes in the mechanisms of gene regulation brought on by mutations to these transcriptional regulators may drive the cell's hyper proliferative state. With the creation of next generation sequencing researchers are now better able to define where regulation is taking place in the genome, and how much it is able to influence gene expression. This gives researchers the ability to build these gene regulatory networks and evaluate their impact on gene expression. The subsequent chapters of this dissertation are a reflection of my published work investigating the contribution of oncogenic processes to gene regulatory networks in cancer through the study of hyperactivating somatic mutation of a histone modifier, changes in transcription factor response element specificity, epigenetic regulation of transcription factor signaling, and a transcription factor coactivation network