Animal models have been widely used for experimentation and the mouse is a signature organism that is present in many labs. In this project, we will learn about the technological advances that allow us to improve the animal models in ways that enable discovery of biomarkers that could be more relevant to a disease detected in humans.
In a PDX model, a human tumor sample is implanted into immune-deficient mice, and human stroma is generally replaced by mouse stroma. Recently it was recognized that this approach also provides a simple and reliable method for separation of tumor and stroma expression profiles. The completeness of replacement can be evaluated by measuring the expression levels of human stromal markers, such as CAF markers fibroblast activation protein alpha (FAP) and chondroitin sulfate proteoglycan 4 (CSPG4). Thus, sequencing PDX-samples and measuring expression level of genes we can consider positively expressed human genes as genes expressed in tumor cells, and positively expressed mouse genes as genes expressed in stroma cells. These types of studies have their own limitations, but allow extensive characterization of human or even a specific patient tissue behavior in a lab environment.
This is an exciting project for those interested in translational research and biomarker use in cancer subtype characterization and associated technical skills needed to properly process data, eliminate technical issues and use machine learning tools to extract meaningful insights from complex molecular datasets.
Tumor Microenvironment – extracellular matrix, immune system and tumor cells: In the tumour microenvironment, cancer cells directly interact with both the immune system and the stroma. It is firmly established that the immune system, historically believed to be a major part of the body’s defence against tumour progression, can be reprogrammed by tumour cells to be ineffective, inactivated, or even acquire tumour promoting phenotypes. Likewise, stromal cells and extracellular matrix can also have pro- and anti-tumour properties.
Breast cancer subtypes: This project will cover several types of cancer subtypes that are often times determined by the origin of the tumor cell type. The subtypes can be hard to determine and categorize, but recent studies in gene expression have led to a number of methods that leverage gene expression for this purpose.
Patient-derived Xenograft Models: PDX models are animals that were modified to be used as an environment for human tumors that can be studied.
Drug Efficacy Studies: In drug discovery, the challenge is to identify an active molecule or intervention that is effective and at the same time not too toxic to normal cells that it ultimately treats disease. Efficacy can be defined as the performance of an intervention under ideal and controlled circumstances, whereas effectiveness refers to its performance under ‘real-world’ conditions.
RNA-seq: Next Generation Sequencing data has specific characteristics requiring an appropriate method to process the raw data and turn it into a useful resource we can study. The short reads need to be aligned to a reference genome, accurately measured and put into readable form.
Generating a table of gene expression using the RNA-Seq by Expectation Maximization (RSEM) method takes the prepared data that is annotated with position information extracted from a reference and quantified using a statistical method that converts the “count” of reads with various lengths into a “level of expression” number for the whole gene or isoform.
Classification and Feature Selection: using the gene expression table we will learn how to train a statistical model to classify a dataset using an annotated “training dataset” and use feature selection to identify the minimum number of features that are most informative in this process.