My views on a saltationist theory of cancer evolution

One easy way to spot who reviewed a paper is to observe who is writing a News and Views afterwards.

So for example, Nick Navin just published a paper in Nature Genetics describing “Punctuated copy number evolution and clonal stasis in triple-negative breast cancer” and, looky-look, someone wrote a News and Views about it.

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Systems Genetics of Cancer 2016

I spent the last days of the British summer this week at Lucy-Cavendish College in Cambridge, where Peter van Loo and I had invited 20 equally opinionated researchers from all over the world to discuss what is new and hot in cancer research.

The workshop was called Systems Genetics of Cancer 2016 (and if you click this link to the workshop webpage you will find an impressive list of participants). And because we like to be special, we did not allow any Powerpoint slides. All talks were chalk talks – or rather pen on flip-chart. Among many advantages, this allowed us to take full advantage of the college garden.

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Research Highlight: Computing tumor trees from single cells

Edith‘s OncoNEM paper made it into the Genome Biology Special Issue on Single-Cell Omics, together with a paper on a tree inference method called SCITE by Niko Beerenwinkel’s group.

If you need any more evidence that our two papers were -at least in my totally unbiased opinion- the obvious highlights of the whole Special Issue, just observe that Alexander Davis and Nick Navin chose us to write a Research Highlight about. They conclude:

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Creativity, Science

A 4D Atlas of Cancer

Welcome to the future of cancer research!

I collaborate in a CRUK Grand Challenge application:

Professor Ehud Shapiro from the Weizmann Institute, Israel with collaborators from Israel, the UK and USA will find a way of mapping tumour at the molecular and cellular level. [ Read more ]
And here is how the result will look like:


Now we just hope that the nice people of CRUK are kind enough to give us the 20 million quid we need …




Sustaining reproducibility

Our paper on tumor evolution in ovarian cancer (see here) came with a nice knitR file to reproduce the survival results, which I used as an example in my recent talk about reproducibility (see here).

I thought that was a nice test scenario to see if I could reproduce the results I got more than a year ago.

How reproducible am I?

Downloading the Rnw from the journal webpage (link) was easy, but -of course- it didn’t run through smoothly.

LaTeX failed and there were several R error messages.

The joys and frustrations of reproducibility

First of all, I had linked to a BibTeX file instead of just copying the bibliography in to the Rnw as I should have done.

Second, I ran into problems with the survival analysis, because one of the packages had changed.

rms::survplot() used to allow plotting a survfit object through survplot.survfit() function. However, this function has been deprecated as of version 4.2.

Luckily I found an easy workaround, just use npsurv() instead of survfit().

The updated Rnw is here on my webpage:

Together with a PDF so you can see what the output should look like.

Click to access journal.pmed.1001789.s020-v2.pdf

Take-home message for me: Even with a knitR file I did myself, reproducibility is not a one-click thing.

To make reproducibility sustainable I would have to check all published analysis scripts in regular intervals (e.g. once every year or every 6 months). Am I prepared to do this? And for how long?



Evolution in cancer: Yes! Darwin: No?

Series on Tumor Evolution
Evolution is a fancy word for gradual change. In this general sense, all kinds of things evolve. The universe evolves, societies evolve, finches evolve.

The mechanisms and principles of these three evolutions are all different. For example, the finches change by Darwinian evolution, which is one particular type of evolution based on natural selection: There is diversity in traits between individuals in a population; because of their traits some individuals have more offspring than others; the traits are heritable and can be passed on to offspring. Over time the favorable traits will become dominant in the population – and with them the genotypes underlying them.

This is how it works for finches. How about cancer? Is that developing by Darwinian evolution too? Not so fast, say Sidow and Spies:

The forced application of terms and concepts from organismal population genetics can distract from the fundamental simplicity of cancer evolution,

write Sidow and Spies in their review ‘Concepts in solid tumor evolution‘ (TiG 2015) and they plan to set the record straight.

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Ripples in the pond …

Our PLoS Med paper (see yesterday’s post) on tumor heterogeneity and survival in ovarian cancer is getting some media attention – not the front page of the New York Times, but hey, beggars can’t be choosers.

For those of you in UK, here you can see me stutter and sweat on regional television (for all of ~3 seconds): (starts at 11.47 mins)

Cambridge News:

GenEng News:

Medical Xpress:

News Medical:

CRUK press release:


Tumor heterogeneity is bad for you

Series on Tumor Evolution

Heterogeneity everywhere! The lists of clonal and sub-clonal aberrations found here and there in many tumors get longer and longer. But is this whole heterogeneity business actually useful for anything?

Actually it is, as we show in a paper that just came out in PLoS Medicine: Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction. And as a free bonus it comes with an editorial by Andy Beck from Harvard, who says “open access to large scale datasets is needed to translate knowledge of cancer heterogeneity into better patient outcomes” — right he is!

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BitPhylogeny – Bayesian inference for intra-tumor phylogeny reconstruction

Series on Tumor Evolution

Do you remember the first post in this series, where we stated the intra-tumor phylogeny problem? No worries, if not – here it is again: Given a sample of the genomes of a heterogeneous tumor, identify the genetic clones and infer their evolutionary relationships.

Finally it’s time to announce our own approach to this problem, which has just come out in Genome Biology:

Yuan*, Sakoparnig*, Markowetz^, Beerenwinkel^. (2015). BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biology 2015, 16:36

BitPhylogeny stands for `Bayesian inference for intra-tumor phylogenies’ – I am sure we mention this in the paper somewhere but am hard pressed to put my finger on where exactly this is. Well, now you know. Links to the code are for example here on my webpage.

How does BitPhylogeny work?

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The biggest problem in cancer evolution? That mostly people like me are doing it.

Series on Tumor Evolution

Do you know how I ended up working on cancer evolution? I would like to say it was deep thinking, profound insights and scientific vision that led me there, but -alas- the truth is that I was just really bad at proposing interesting projects to one of my first postdocs.

“Evolution? What’s that? Darwin and his finches? Forget it!”

Every time I said ‘How about integrating copy number and gene expression’ or ‘How about reconstructing networks from expression profiles’ he answered politely: “Yes, that sounds very interesting, but I would rather do evolution.”

“Evolution?” I said. “Like Darwin and his finches? Forget it! I don’t think we have that in cancer!” I said. “Be sensible and don’t ruin your career. Do genomics, like I tell you. That’s the future!” I said.

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Inferring tumour evolution 5 – single cell data

Series on Tumor Evolution

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:

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Inferring tumour evolution 3 – Methods for single samples

Series on Tumor Evolution

Figure 1: clonal evolution tree (details in previous post)

In the first post in the series I described a simple toy example to illustrate key concepts of tumour heterogeneity and evolution. A quick summary of the population composition and evolutionary relationsships is displayed in Figure 1 on the right. There are three clones present in the sample A, ABC, ABD characterized by four sets of somatic mutations A, B, C, D.

Our first discovery, when discussing this simple example in the last post, was that classical phylogenetic approaches might not capture important features of cancer evolution. So, which other methods are there to understand the evolution of clones in a tumour?

Principles of inferring tumour evolution

In this post and the next I want to discuss analysis approaches proposed in the last couple of years. Figure 2 organizes research strategies along basic principles, and this post (together with the next one) will discuss examples of each strategy in more detail.

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