by Chiara Facciotto and Tiia Pelkonen

When people think of cancer, they might be imagining one disease. Though cancers have common properties, such as out-of-control cell division, every cancer is unique. This is why novel technologies that allow us to study tumors one cell at a time are bringing us closer to understanding their complex nature. To discuss the heterogeneous nature of cancer, we interviewed Anna Vähärautio, a research group leader at the University of Helsinki and in the HERCULES consortium, and an expert in the new single-cell sequencing technology.

First of all, what is cancer heterogeneity?

Heterogeneity basically means diversity. Cancer is a dynamic disease, which evolves in time and displays heterogeneity on many levels.

Cancers originating from different organs are of course different from each other and require different treatment strategies. However, even cancers found in the same organ can be totally different diseases. For instance, there are different types of ovarian cancers, including endometrioid, clear cell, mucinous, low-grade serous, and high-grade serous carcinomas. All of these cancers originate in the ovaries or fallopian tubes, but they have very different characteristics and behaviors, which need to be investigated and understood to identify new ways to treat each disease. In HERCULES, we focus on high-grade serous ovarian cancer.

One more level of heterogeneity is observed when comparing the same cancer type in different patients. Each patient has a unique set of aberrations in the DNA of the cancer cells, which creates a slightly different kind of disease as well as a different kind of response to treatments. In addition, a cancer can spread from its tissue of origin to other locations in the body, forming new tumor masses called metastases, which can be quite different from one another. Finally, even cancer cells at the same location can be different from each other because different random changes happen in the DNA of the cells when they multiply. Cells can also be in different states, for example some are proliferating (one cell divides into two cells) and some might be quiescent (not proliferating). This can affect how effective a treatment is because many cancer therapies, like chemotherapy, only target proliferating cells.

When analyzing clinical tumor samples, the different cell types that form the tumor mass also form a very important level of heterogeneity. In fact, each sample typically contains not only cancer cells, but also for example immune cells and cells forming the connective tissue (called stromal cells).

Image depicting cells.
Cancer cells can now be separated and studied one at a time, thanks to technologies such as single-cell sequencing. Image from Pixabay.

What is single-cell sequencing and how does it help us study cancer?

Sequencing technologies allow us to “read” the content of our DNA, as well as the parts of it, genes, that are transcribed into RNA to produce proteins, the building blocks of our bodies. Until recently, we could only sequence multiple cells at the same time (bulk sequencing). However, recent advances in this type of technologies allow us to now sequence DNA or RNA from a single cell.

Single-cell sequencing of DNA is still quite expensive and not very informative because of the little amount of DNA contained in each cell. Sequencing DNA also doesn’t tell you anything about the cell’s state, it only provides information on the cell’s genetic material (meaning the presence of certain mutations in the DNA). On the other hand, single-cell sequencing of RNA allows us to get a snapshot of which genes are active in each cell, which determines the structure and properties of the cell itself.

Single-cell sequencing can be especially useful when analyzing highly heterogeneous tumor samples containing multiple different cell types. This technology helps us separate the signal that comes from tumor cells from the signal that comes from the surrounding cells. The signal from non-cancer cells is also very important, but it’s fundamental to understand from which cell type the signal originates. In addition, using single-cell sequencing we can look at co-expression patterns, meaning genes that are turned on at the same time in different cells. From bulk data we don’t get very exact information of which genes are expressed at the same time.

Here is a metaphor to better understand the difference between bulk and single-cell sequencing. Imagine that you are studying fruits and you have different kinds of strawberries (representing the different cancer cells) and other fruits such as bananas, oranges, and pineapples (representing the other cells in your tumor sample). Now, let’s say you want to know which kind of strawberries taste the sweetest  (which cancer cells have a certain property). If you were to answer this question using bulk sequencing, it would be like putting all the different fruits in a bowl and blending them into a smoothie. Performing single-cell sequencing is more like making a fruit salad. In the smoothie it’s much harder to know which strawberry is the sweetest, or if the sweetness actually comes from the other fruits. Instead, a fruit salad  allows you to individually taste each fruit and determine that, for instance, the red strawberries are sweeter than the green or white ones. 

Photo of strawberries, bananas, oranges, pineapple, and a smoothie.
Bulk sequencing of tumor RNA is like guessing the flavor of the strawberries from a fruit smoothie, while single-cell sequencing allows you taste each piece of fruit one at a time. Photo by Element5 Digital on Unsplash.

Are there benefits for patients from single-cell studies? 

At this point single-cell sequencing is still used for basic science. Of course if something important is found, for example specific mutations that might justify a change in the treatment plan of a patient, this information is shared with their doctors. However, at the moment the main goal is to accumulate more samples and data, to understand how gene expression patterns from tumors can affect clinical response and, eventually, predict treatment response or even find better treatments for patients who do not respond well to the current standard therapies. At this stage single-cell sequencing is not (commonly) used for personalized medicine, but hopefully it will be in the future.

Photo of Anna Vähärautio

The expert interviewed for this post is Anna Vähärautio, PhD, leader of the Single-cell Transcriptomics of Cancer research group at the University of Helsinki, Finland.