Malignant tumours do not only contain cancer cells but are also closely knit with normal cells from the body. These often include a variety of immune cells that can help detect and kill cancer cells. Modulating these immune cells hold huge potential for cancer treatment opportunities, yet studying the immune cells in a tumour remains challenging.
Conventionally people used methods (flow cytometry) that required researchers to first separate tissues into individual cells and then analyse them. This is a rough process that destroys certain cell types and renders some tissues useless for study. In addition, the traditional method of preserving tumour samples makes it impossible to process them in this way. Also, once the tissues have been dissociated into single cells, they need to be labelled using fluorescent antibodies. Antibodies may not be available for all proteins of interest.
The solution the researchers came up with is to, blend the tissue and look at its contents (mRNA) to computationally tell what kind of cells they came from. The method is analogous to analysing a fruit smoothie to find what fruits went into making it. This methodology not only overcomes the dissociation and tissue preservation problem but also serves to be robust when researchers want to analyse hundreds or thousands of tumours at the same time.
We developed a computational tool called “imsig” that can accurately detect all the main immune cell types from tumour samples. It utilises the level of expression of a compendium of carefully identified sets of genes to predict how many of each type of cells are present in a tumour. It is freely available online. We have also described a network-based analysis framework for studying tumours along with imsig. This kind of analysis can help us and other researchers learn key information of the cellular states that immune cells can exist close to the cancer cells and how they talk to each other.
Since the raw materials (transcriptomics data) required to apply this methodology have already been generated for several thousands of patients together with clinical data, researchers could use imsig to investigate what cell types are present in these tumours and figure out how it affects cancer growth or how well a particular treatment works on it.
Using this methodology we also showed how immune cells can be both good and bad for the survival an individual depending upon the cancer type.
In the future, this information could help to identify the best treatment for a particular patient and may reveal new genes that could be targeted with drugs for modulating the immune system such as to control the growth or spread of cancer.
Research Publication: Cancer Immunology Research