Science

Inferring tumour evolution 7 – the roots of metastasis


Series on Tumour Evolution

What makes a cancer deadly is not necessarily the growth at the location where it started (the primary tumour) but its spread through the body to other organs and tissues (called metastasis). Better understanding the metastatic process is one of main reasons we are interested in inferring cancer evolution.

Today I would like to summarize and discuss two recent papers on cancer phylogenetics and metastasis. The first paper is the comprehensive review by Naxerova and Jain in Nature Reviews Clinical Oncology titled “Using tumour phylogenetics to identify the roots of metastasis in humans.” The second paper is an Opinion paper by Hong, Shpak and Townsend in Cancer Research  titled “Inferring the origin of metastases from cancer phylogenies.”

Using tumour phylogenetics to identify the roots of metastasis in humans

Naxerova and Jain’s review has two parts. In the first part they describe the features of different models of metastasis, the second part describes different measurement techniques (histopathology, somatic copy-number alterations, single nucleotide variants, X-chromosome inactivation, CpG methylation, microsatellite analysis, whole genome studies — nice summary table here). I will focus on the models.

Models of metastatic spread

The systemic disease model assumes that there is no connection at all between different tumours in the same patient, which all arose independently of each other.

The linear progression model assumes that metastasis happens by a genetically advanced cancer cell late in the life of the primary tumour, which leads to a small genetic distance between primary and secondary neoplasm. If this happens multiple times you will get a star topology of metastatic spread (with short edges).

The metastatic cascade model additionally assumes that one metastasis can shed cells to start the next metastasis, which can lead to a more complex tree model.

The parallel progression model, on the other hand, posits that metastasis occurs early and that primary and secondary tumours develop independently, leading to high genetic distance and again a star topology (but this time with long edges).

And just to make things even more complex the self seeding model allows for cancer cells to come back to the primary, that is “bidirectional, dynamic cell exchange between synchronous lesions”. Uh oh, that means the graphs might get messy. In a standard phylogeny the clones you find in a sample form one subtree of the lineage tree, but if you allow self seeding then the samples can spread more than one subtree.

These different models are not mutually exclusive and a priori there is no reason why not early on a secondary tumour could start to develop independently while the primary is at the same time starting a metastatic cascade.

I have collected some features in this table:

Model genetic difference
primary-metastasis
time of metastasis phylogeny
Systemic disease high (all independent) NA no edges
Linear progression low late linear/star
Metastatic cascade low (in mets); high (to primary) late tree
Parallel progression high early star
Self seeding lowering it ? cyclic graph of samples

And as always with the Nature Reviews journals they have very well done overview figures like the one on the right to illustrate the different models. Click on the figure to go to the Nature Reviews Clin Onc page.

Challenges to inferring roots of metastasis

Importantly the review also identifies some challenges and limitations to inferring the roots of metastasis from patient samples. One of them is dormancy which can distort estimates of the age of tumours:

“[W]hether a metastatic lesion arose after a prolonged latency period because it disseminated late in cancer progression or because it underwent a period of dormancy at the distant site might be difficult to judge.” *

Another challenge is underestimating the clonal diversity of the primary tumour:

Because we cannot sample every single part of a primary tumour —in the clinical setting, analysing all of the tumour is de facto impossible because some parts are required for diagnostic purposes— one cannot exclude the possibility that the area harbouring the pre-metastatic clone was missed. *

Evolutionary theory to the rescue?

This directly brings me to the second paper by Hong, Shpak and Townsend (HST for short), who were trained as evolutionary biologists and comment on research in cancer evolution from their perspective.

They reiterate the issue of underestimating the clonal diversity of the primary tumour:

We argue that the chronology of metastatic events cannot be established without complete information on the phylogeny of subclonal lineages within the primary tumor. *

Their figure 1 shows how very different evolutionary histories can result in the same inferred phylogeny if not all clones in the tumour are sampled.

Figure 1 of Hong, Shpak and Townsend “Inferring the origin of metastases from cancer phylogenies.”

It is essential to have people trained in evolutionary biology start contributing to cancer research — because, as you know, the biggest problem in cancer evolution is that mostly people like me are doing it. (As an aside, I found it remarkable that the Nature gathering on cancer heterogeneity did not include a single evolutionary biologist — it shows the intellectual biases in the community.)

Now, what do HST have to offer? Experimentally their advise is to use one of two data gathering strategies:

  1. Sample a large number of small, spatially separated sections of the tumor (assuming that genetic diversity is spatially partitioned and each small portion is homogeneous)
  2. Perform single-cell sequencing of enough cells (HST acknowledge that it is not clear how many that might be).

HST explicitly criticize our ovarian cancer paper, “because of the absence of necessary information on spatially distinct subclonal heterogeneity within the primary tumor,” but failed to spot that we were actually following their approach number 1.

The problem is that the biology of ovarian cancer does not easily fit into the `localized primary and mets’ scenario HST discuss. At presentation most ovarian cancers are already spread throughout the lower body and there is little evidence where the tumour started. Clinically this spread-out tumour mass is often considered the primary tumour. So the different samples we used in our paper are not metastases (in the way HST think about it) but bits and pieces of the same “primary” tumour.

I understand this is not obvious to people not working in ovarian cancer and we could maybe have been more careful qualifying the word ‘metastasis’ in our paper.

What do HST have to offer on the methods side? Not very much. They don’t like the methods we use in the cancer field and would like to see them replaced by “well-established character-based phylogeny reconstruction methods based on molecular evolutionary models” without giving any detail on how that might look like.

In particular they don’t like our clonal expansion index:

It is not the null expectation for any population evolving along a phylogenetic tree, nor is it the null expectation for the distributions of genotypes of organisms in a population, all for the same reasons: genealogical relatedness, finite time, and spatial structure. *

I agree with them.

We thought of our work as the beginning of the story, not the end of it.

Please, show me how it’s done better!

My prediction is that HST will struggle to directly apply the “well-established evolutionary methods” they have been trained in to the complexity and noise of cancer genomics data. The evolutionary methods they will eventually discover to work well on these data will look very different from the textbook examples they had in mind while writing their paper.

HST have talked the talk, now let’s see how successfully they walk the walk.

Florian

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3 thoughts on “Inferring tumour evolution 7 – the roots of metastasis

  1. Well, I have no read the HTS paper yet, but is on my list. I did read the Naxerova one which I believe does a good job summarizing some fundamental ideas. BTW, note that a self-seeding model will not result in a network, the only difference among metastases models in the resulting treesl will be on the distribution of the geographical labels (forming monophyletic, paraphyletic or Polyphyletic groups), and on branch lenghts (seeds should show long branches)

    In my opinion, cancer evolution itself is not more complex than germline evolution. In many senses is simpler (in principle no recombination, introgression, hybridization) and in others it can be more complex (strong selection). What is very complex in cancer is obtaining good individual (cell) samples from multiple regions, time points and patients, but this eventually will happen.

    Once we have individuals, the sophisticated machinery of phylogenetics and popgen will be applicable to cancer (I’ve written a short column about this for the Journal of Molecular Evolution that will appear shortly; I will post the link here)

    And I agree, it might be a good idea for the cancer genomic community to get some input from the evolutionary community before trying to reinvent the wheel …;-)

    D

  2. I finally read the HTS paper. Nothing new under the sky. What they said about sampling biases is very obvious and has been said before several times in the cancer community. I found the term “complete” somehow funny. Indeed, we will never sample every allele (sorry, I mean clone 😉 in the primary tumor. Furthermore, they do not mention other confounding issues, like lineage sorting along regions (a classic in biogeography).

    Regarding applying standard phylogenetics methods clearly the key is single cell data. They seem to forget that bulk data in cancer is pool-seq, lacking individual information.

    As Florian says, we (‘smart’ evolutionary biologists) need to demonstrate the potential of pylogenetics and pogen for cancer research *in real cases*. Well, I am working on that 😉

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