Science

Inferring tumour evolution 5 – single cell data


Series on Tumor Evolution

ResearchBlogging.org

Welcome back to the intra-tumour phylogeny problem. Let’s take a quick breather and see what we have got to so far:

  1. Introducing the intra-tumour phylogeny problem;
  2. Comparison to classical phylogeny;
  3. Methods for single samples;
  4. Methods for multiple samples.

And today’s topic finally is:

Single cell analysis

Single cell sequencing wherever you look!

In breast cancer (e.g. here and here). In leukemia (e.g. here and here). And some very visible studies from the BGI in renal carcinoma, a myeloproliferative neoplasm, bladder cancer and colon cancer. That’s certainly enough material to start reviewing it.

Cancer genomics: one cell at a time” by Nicholas Navin gives a very good overview of methods to isolate single cancer cells, amplify their genomes, profile mutations and reconstruct evolutionary trajectories. And -even better- the review goes beyond a simple laundry list of methods to comment on their strengths and limitations. If you are interested in single cell genomics in cancer, this is a must-read.

I had originally planned to write a more methods-focused post (on what you actually do with all those genomes), but this will have to wait and here I will use Navin’s review as a starting point for my own discussion of some conceptual points that went through my mind while I read it:

  1. Cells are part of a tissue (Not really a big surprise, I hope, but something often forgotten in genomics studies);
  2. Phylogenetic methods will have to be adapted for single cell data (yes, you have seen that idea before);
  3. Single cell genomics will not completely replace bulk-sequencing for clinical applications (which means that all the stuff we have discussed so far will stay important).

Cells in context: In situ analysis

“Biologists have been studying single cancer cells since the invention of the microscope by Antonie van Leeuwenhoek in 1665.

Many initial observations were based on the morphological differences between tumor cells, as recorded in the late 1800s by early pathologists, such as Rudolf Virchow.” (Navin, 2014)

This is an important point. In a technology-driven field like genomics, it’s particular important to highlight the long history most research questions have.

“These observations were greatly improved by the development of cellular staining techniques, such as hematoxylin and eosin (H&E).

In the 1980s, the development of cytogenetic techniques, including spectral karyotyping (SKY) and fluorescence in situ hybridization (FISH), galvanized the field by allowing researchers to visualize the genomic diversity of chromosome aberrations directly in single tumor cells.” (Navin, 2014, references removed, Wikipedia links added)

Staining techniques like FISH give you a glimpse of single cell genomes. And even more: you can look at the spatial context of each cell because the staining is in situ. Are the same aberrations shared by neighboring cells or do they differ? These are important questions about the location and spread of cancer clones that can only be answered by looking at cells in their tissue context.

And, what’s even better, you can deduce tumour phylogenies from FISH data; see for example Russel Schwartz’s work on modeling copy number changes (PMID 25078894, 23812984)

But Navin continues:

“However, only in the past four years has the field moved from qualitative imaging data to quantitative datasets that are amenable to statistical and computational analysis.” (Navin, 2014)

No, I’m afraid, I don’t completely agree with this.

This sentence is from the beginning of the review and not at its heart — but calling genomics quantitative and imaging qualitative bothers me enough to comment on it.

Images can be as quantitative as genomes – it really all depends on how you analyse them. You can use computation and statistics on image data as easily as on genomes. Here is an example from my own lab: by analyzing standard pathology H&E slides we showed how quantitative image analysis of cellular heterogeneity in breast tumours complements genomic profiling.

And H&E is not the only example. Kornelia Polyak’s lab used a combination of immuno-fluorescence with FISH (called IFISH) for the quantitative analysis of genetic and phenotypic features and their spatial distribution in breast tumours.

To make the analysis even more quantitative, Anne Trinh in my group has developed GoIFISH for the quantification of genomic alterations and protein expression obtained from IFISH data (the paper appeared in the same collection as Navin’s review). We are currently using GoIFISH on larger patient cohorts – more on this once we have results.

So, in summary, the situation is this: either you get spatial information about the tissue (but only for a few markers and not the complete genome) or you get the genomes of individual cells but lose the spatial information of where the cells sit in the tissue. Tissue and genome are important, but with current technologies you can’t have both at the same time.

Single cell genomics

Ok, let’s move on to the next part of Navin’s review, which highlights the importance of single cancer cells in tumour initiation and progression:

“While there is substantial evidence that tumor cells can communicate with their neighbors and the stroma, there are also many complex biological processes that occur through the actions of individual cancer cells.

These processes include the initial transformation event in a normal cell, clonal expansion within the primary tumor, metastatic dissemination and the evolution of chemoresistance” (Navin, 2014; emphasis added)

Now this is the second thing I am not completely happy with. Navin refers to the “substantial evidence” only to dismiss it again immediately.

And there is some ambiguity: what does “occur through the actions of individual cancer cells” really mean? Does it mean (1) “involving individual cancer cells (plus other factors)” or does it mean (2) “involving only an individual cancer cell without any other important factors”.

I think (1) is the correct answer:

  • Initial transformation: True, it starts with one renegade cell, but the new tumour has to compete with the normal microenvironment to overcome antitumourigenic pressures, which is one of the reasons why we don’t get more cancer. Thus, already the first steps of tumour formation are multi-cellular processes and do not occur just through the action of an individual cancer cell.
  • Metastatic dissemination: Individual cancer cells that are shed from the primary tumour in search of a new niche to colonize are obviously an important part of metastatic spread, but they are certainly not the only important factor! For example, not every cancer can metastasize to every organ, because it needs favourable tumour-stroma interactions (the so called seed and soil hypothesis). Thus, metastatic dissemination is a multi-cell process involving both cancer and normal cells.
  • Clonal expansion within the primary tumour: I hand it over to the Polyak lab again. In a recent paper they show that non-cell-autonomous mechanisms can drive tumour growth. A less fit, small clone can support the growth of the whole tumour and when it gets outcompeted by faster proliferating competitors the tumour collapses. Thus, clonal expansion is a multi-cellular process.

None of these points argues against looking at the genomes of single cells – but, please, let’s not forget about the context in which these genomes matter.

Single cells in the clinic

“In the near future, [single-cell sequencing] will begin to be applied to the clinic in early detection, prognostics, diagnostics and therapeutic targeting and thereby will have a direct impact on reducing morbidity in many human cancer patients.” (Navin, 2014)

I hope this is true. Everything that helps patients is good.

But what does “in the near future” mean? All histories of cancer research I have ever seen only allow one conclusion: If they have started to work on it now, there might be individual clinical research projects with results in 5 years, and whole programs in 10, but routine use outside of cutting-edge cancer centers will be more than 20 years from now. And that all depends on single cell genomic techniques to prove themselves superior to existing methods.

From a basic biology perspective I am all for single cell studies. In theory they should allow the most highly resolved view of genetic heterogeneity in a tumour. And for me this is already enough motivation to look at these data – not everything has to be translational.

But for clinical use, I am not sure we have even started to exploit the information we can get from sequencing mixed tissue biopsies. As you know from the last posts in this series, there is a lot of information in mixed samples.

And there will always be tissue biopsies until we have understood much better how the individual tumour cells and DNA fragments that circulate in the blood (and that are marketed as liquid, noninvasive biopsies) get there, what biases they have, and how much they can actually tell us about the tumour.

There is still a lot of hard work to be done until we can test whether single-cell methods are more accurate in finding actionable mutations in minor sub-clones and lead to better treatment at the same cost as sequencing bulk tissue.

A cancer is more than the sum of its cells

Just to be clear: I am not arguing that single cell sequencing is not informative or important. It certainly is!

I just don’t want their importance being oversold. Single cell techniques are the newest and hippest kid on the block, but that doesn’t mean everything else is outdated. And we still have a long way to go until single-cell studies reach peak performance (which Navin (2014) describes very well):

  1. Technological improvements need to reduce measurement biases and improve overall data quality.
  2. Better algorithms need to bring down the error rates in calling mutations in single genomes.
  3. Additionally: All the single-cell papers listed in the beginning use classical phylogenetic methods for inference of clonal evolution, even though (i) they work on cells, not clones, (ii) they are undirectional, and (iii) inner nodes are by definition unobservable – all of which don’t hold in clonal evolution.

Tumors are more than collections of individual cells. But cellular interactions and the influence of tissue architecture are generally ignored in genomics studies.

This is really a limiting factor for evolutionary analysis. After all you cannot talk about the fitness of clones and ignore the environment for which they have to be fit for.

What we really want in the future is a genetic and phenotypic 3D model of a tumour: Which cell with what mutations sits next to which other type of cell? For example: Does mutation X only appear in lymphocyte-rich parts of the tumour? Also: What morphologies do cells with that particular mutation have? Or other similar questions.

Single-cell sequencing is great, but only the first step to a comprehensive characterization of tumours which needs to combine genomes with spatial tissue organization. No single technology will allow this comprehensive view and the future goal will be to integrate the limited snapshot each individual technology can provide.

Long way to go …

Florian

Acknowledgements: Thanks to Edith Ross for a rigorous review of early drafts of this post.

Some references

Navin, N. (2014). Cancer genomics: one cell at a time Genome Biology, 15 (8) DOI: 10.1186/s13059-014-0452-9

Yuan, Y., Failmezger, H., Rueda, O., Ali, H., Graf, S., Chin, S., Schwarz, R., Curtis, C., Dunning, M., Bardwell, H., Johnson, N., Doyle, S., Turashvili, G., Provenzano, E., Aparicio, S., Caldas, C., & Markowetz, F. (2012). Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling Science Translational Medicine, 4 (157), 157-157 DOI: 10.1126/scitranslmed.3004330

Trinh, A., Rye, I., Almendro, V., Helland, A., Russnes, H., & Markowetz, F. (2014). GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images Genome Biology, 15 (8) DOI: 10.1186/s13059-014-0442-y

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