Using bioinformatic analyses to understand prostate cancer cell biology

Le cancer de la prostate (CaP) affecte 1 homme sur 7 au cours de sa vie. C’est le cancer numéro un diagnostiqué chez l'homme. Il s'agit du quatrième cancer le plus fréquent au Canada. Le CaP est une maladie hormonodépendante diagnostiquée chez l'homme. Les androgènes jouent un rôle vital dans la progression de la maladie. La première ligne de traitement, suivant une intervention chirurgicale ou un traitement de radiothérapie, est la thérapie de déprivation aux androgènes. Malgré une réponse initiale positive à l'inhibition des androgènes, la progression de la maladie vers un cancer de la prostate résistant à la castration (CRPC) est presque inévitable. Aux différentes étapes du CaP, le récepteur des androgènes joue un rôle majeur. Ainsi, cette thèse décrit les méthodes développées et utilisées pour mieux comprendre la biologie du CaP et le rôle joué par les androgènes dans cette maladie. Le travail démontré dans cette thèse se compose principalement d'analyses bioinformatiques effectuées sur des ensembles de données accessibles au public et d'un « pipeline » construit pour analyser des données RNA-Seq. Un pipeline RNA-Seq a été développé pour comprendre l'impact des androgènes et des gènes régulés lors du traitement aux androgènes dans les modèles de cellules de CaP. Ce pipeline bioinformatique se compose de divers outils qui ont été décrits ci-dessous dans le chapitre 1. L'objectif principal de ce projet était de développer un pipeline pour analyser les données RNA-Seq qui aide à comprendre et à définir les voies et les gènes métaboliques qui sont régulés par les androgènes, et qui jouent un rôle important dans la progression du CaP. Le flux de travail expérimental consistait en deux lignées cellulaires positives aux récepteurs aux androgènes LNCaP et LAPC4. Toutes les données utilisées dans ce projet ont été rendues publiques pour que la communauté de recherche puisse effectuer diverses autres études et analyses comparatives pour comprendre les fonctions des androgènes dans un sens beaucoup plus profond afin de développer de nouvelles thérapies pour traiter le CaP. Dans un autre projet décrit au chapitre 2, des analyses bioinformatiques ont été réalisées sur des données accessibles au public pour comprendre la fréquence de la perte et de l'altération génomique du gène PTEN localisé à 10q23. Ces analyses ont mis en évidence la fréquence d'altération génomique de PTEN qui est beaucoup plus élevée dans le CRPC que dans le CaP localisé. Ces analyses ont également aidé à identifier d'autres gènes altérés dans le CaP. Ces gènes n’ont pas été beaucoup étudiés dans la littérature, mais il semble que certains d’entre eux possèdent des caractéristiques de suppresseurs de tumeurs. Ces résultats pourraient être un bon début pour des analyses plus approfondies concernant la perte de gènes.La compréhension des fonctions de AR et de la suppression de PTEN aidera à développer de nouvelles stratégies et approches pour diagnostiquer et traiter le CaP. L'intégration des analyses bioinformatiques à la recherche clinique ouvre une nouvelle perspective dans le domaine de la recherche du CaP.Prostate Cancer (PCa) affects 1 in 7 men in their lifetime and is the number one diagnosed cancer in men. It is the 4th most common cancer in Canada. PCa is a hormone-dependent disease diagnosed in men. Androgens play a vital role in the disease progression. The standard of care to treat PCa, following surgery or radiation therapy, is the androgen deprivation therapy (ADT). In spite of initial positive response to androgen inhibition, the progression of the disease to castration-resistant prostate cancer (CRPC) is almost inevitable. Across the various stages of PCa, the androgen receptor (AR) plays a major role. This thesis portrays the methods developed and used to understand PCa biology. The work demonstrated in this thesis majorly consists of bioinformatic analyses performed on publicly available data sets and a pipeline built to analyse RNA-Seq data. An RNA-Seq pipeline has been developed to understand the impact of androgens and the genes regulated upon androgen treatment in PCa cell models. This bioinformatic pipeline consists of various tools which have been described below in chapter 1. The major goal of this project was to develop a pipeline to analyse the RNA-Seq data which helps to understand and define the metabolic pathways and genes regulated by androgens which play an important role in PCa disease progression. The experimental workflow consisted of two androgen receptor positive cell lines LNCaP and LAPC4. All the data used in this project has been made publicly available for the research community to perform various other comparative studies and analyses to understand the functions of androgens in a much deeper sense to develop novel therapies to treat PCa. In another project described in chapter 2, bioinformatic analyses have been performed on publicly available data to understand the loss and genomic alteration frequency of the gene PTEN occurring at 10q23. These analyses highlighted that the genomic alteration frequency of PTEN is much higher in CRPC than in localised PCa, and also helped in identifying other genes which are lost along with PTEN. The lost genes have not been studied much in literature, but few studies demonstrated that they might possess tumor suppressor characteristics. These results might be a good start for further deeper analyses regarding the lost of genes. Understanding the functions of AR and the deletion of PTEN will help for the development of novel strategies and approaches to diagnose and treat PCa. Integration of bioinformatic analyses with clinical research open up a new perspective in the PCa research domain

Identifying and characterizing functionally relevant microRNAs and 5’isomiRs in triple-negative breast cancer

Triple-negative breast cancer is a highly aggressive breast cancer subtype and the treatment options are mainly limited to chemotherapy, however, the patients frequently develop resistance. As endogenous regulators of gene expression, microRNAs are involved in tumor development, progression and treatment resistance. microRNA sequence variants with a shifted seed sequence are termed 5’isomiRs and extend the complexity and impact of the miRNome in cancer. A shift in the seed sequence by only one nucleotide can drastically alter the target spectrum of a 5’isomiR compared to its canonical microRNA. Hence, this study aims at identifying microRNAs and 5’isomiRs with a potential role in tumorigenesis and chemoresistance and focuses on characterizing their functional differences in triple-negative breast cancer. I selected microRNAs and 5’isomiRs that were differentially expressed between tumor and normal tissue of patients from the TCGA cohort and, thus, potentially involved in tumorigenesis and chemoresistance. Growing mammospheres from MDA-MB-231, HCC1806 and SUM-159 cells that overexpressed the selected microRNAs as pooled library enriched for cells with increased stemness and chemoresistance. Read-out of the library composition by NanoString after several sphere generations revealed strong enrichment of pre-miR-103a-1. In validation experiments, pre-miR-103a-1 overexpression did not influence stemness or chemoresistance. In the second part of the project, I focused on the functional characterization of miR-1307-3p I0 and its 5’isomiR miR-1307-3p I1. Both were selected from the list of differentially expressed microRNAs based on their similar expression levels. Phenotypic assays in triple-negative breast cancer cell lines showed that both microRNAs reduce migration, miR-1307-3p I0 in a cell line-specific manner and less pronounced than miR-1307-3p I1. miR-1307-3p I1 repressed proliferation in a cell line-dependent context. Target predictions identified genes that might contribute to these phenotypes and explain differences between cell lines. The putative targets suggested that miR-1307-3p I0 plays a role in autophagy. In summary, I showed that miR-1307-3p I0 and I1 influence different and similar phenotypes in a partially cell line-dependent manner by targeting specific as well as shared putative target subsets. This study underlines how complex and context-dependent microRNAs and their 5’isomiRs modulate gene expression and that they are of biological relevance. Consequently, diagnostic, prognostic and therapeutic approaches should discriminate between 5’isomiRs

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology. Keywords: Precision oncology, Transcriptomics, RNA interference, microRNA, Breast cance