Analysis of the Changing Immune Response in Cancer
Macrophages (white blood cells) represent an important component of the tumor microenvironment that includes the entire cellular environment surrounding the tumor and plays a complex role in cancer progression. These cells are characterized by their ability to adapt and alter their phenotype in response to local environmental cues. This study used an animal model, so the tissue transplant (lung tumor) was grafted into the same place as the tumor originated (into the lung – orthotopic model), thus lung cancer cells were placed directly into the left lobe of immunocompetent (where immune system is not artificially weakened) mice. Then, the authors defined and removed several distinct populations of macrophages/monocytes at different time stages of progression using a multimarker cell sorter also known as flow cytometry. Next RNA-seq was used to define the distinct expression of each population and how this changed over time (tumor growth). Populations of cells that did not change in number or expression were the alveolar macrophage population. A second tumor associated macrophage population was found to increase dramatically with tumor growth and to express genes such as chemokines. The third population was identified as tumor associated monocytes, and expressed large number of genes in matrix remodeling, or the movement or restructuring of the extracellular matrix. The data underscores the complexity of monocytes/macrophages in the tumor microenvironment, and suggests that distinct populations play specific roles in tumor progression.
Pathways associated with Cancer Development and Progression: We can use molecular data such as gene expression to identify processes specific to tumor cells and surrounding tissue. This can be done by identifying regions of interest, seeing which genes are encoded in these regions and using existing databases to understand what is known about these gene functions and pathways in which they are involved.
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.
Immune cells types and their role in tumor progression: Macrophages – when macrophages (types of white blood cells) are activated upon recognition of a foreign body, they can become differentiated into 2 types: pro-inflammatory (inhibit cancer progression – A) and pro-antigenic (enhances cancer progression – B). In this project, we will look at these types of Monocytes/Macrophages in the context of an animal model (mouse) used to grow and study the tissue.
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.
Differential Expression: Estimating variance-mean dependence in gene expression data and testing for differential expression using the negative binomial distribution (DeSeq). Detecting statistically significant difference between “levels of expression” is not as straightforward as it might seem, so we will learn to use a method that accounts for within-group variability to select genes that are differently expressed between assigned groups in a statistically significant way.
Principal Component Analysis: We will learn to use normalized data and run a PCA to visualize data structure and select the best components to determine sample separation based on gene and isoform expression.
Factorial Regression Analysis: using regression to determine relationships between variables in the data, where known time points and type of cells are associated with groups of genes behaving in a characteristic way.
- Lectures 10
- Quizzes 0
- Students 190
- Certificate Yes
- Assessments Yes