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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How to compare trees of clonal evolution?

Series on Tumor Evolution

While working on the BitPhylogeny paper, we stumbled on the problem of how to compare trees of clonal evolution.

Clonal evolution trees combine a clustering of molecular markers with tree inference. There are methods to compare clusterings and methods to compare trees, but how do you compare both at the same time?

Here is how we did it:

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Europe – land of the talented, home of the brave

I have just read the second opinion piece by Alberts, Kirschner, Tilghman, Varmus in PNAS: Addressing systemic problems in the biomedical research enterprise.

They (again) describe a huge demographic shift in the US biomedical sciences due to the current hyper-competitive environment (too many people chasing too little money).

This has led to a longer and longer path to independence. Young scientists in the US are no longer young when they start their independent careers.

The potential consequences of this huge demographic shift on the productivity and preeminence of American science were judged to be serious.

[T]he United States has traditionally been viewed as the land of opportunity for young scientists, offering the most talented of them the chance to test their own ideas, raise radically new questions, and forge original paths to the answers.

Land of opportunity? No longer so, it seems.

I know why I went back to Europe.

Speaking of opportunities

At my institute in Cambridge (UK, not MA!) we are still hiring group leaders at all levels. From the famous and senior to the newly graduated.

For example, we just hired Greg Hannon from CSHL and Martin Miller from MSKCC. We also maintain a fellows program for rising stars fresh out of their PhDs or first postdocs.

What you will get is

    • Secure core funding. (No soft money bullshit!)
    • A research environment like no other on this planet!
    • Complete independence!

What are you waiting for?

Come to Europe, the land of opportunity for young scientists, offering the most talented of them the chance to test their own ideas, raise radically new questions, and forge original paths to the answers.

We have ten positions to fill and the job search has been going on for a while. That’s why you might not be able to find the original job ad, which was very general (“Everbody apply!”). But specialized adverts (eg for clinical group leaders) are coming out.

If you are from a computational background and looking for a job, send me an email with your CV and we will discuss your options.


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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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Your ChIP in “50 shades of grey”!

Florian Markowetz:

Interested in quality control of sequencing data? You should be! And a new blog will answer all the questions you were too afraid to ask. Here is an example:

Originally posted on Seq QC:

Since the movie ’50 shades of Grey’ is about to be released I thought this is the perfect opportunity to introduce everybody to the concept of “grey lists” and the recent R package developed by Gordon Brown at my institute: The GreyListChIP R package!


ChIP-seq and many other NextGen sequencing experiments (e.g. MNase-seq, DNase-seq, FAIRE-seq) often produce artifact signal in certain regions of the genome. These so called blacklisted regions are often found at repeat elements (such as satellite, centromeric and telomeric repeats), and show unstructured and high signal (excessive pile up of reads) independently of cell line and experiment type. The ENCODE project generated two sets of human blacklists (the DAC and DUKE regions, see here: http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeMapability).

Blacklisted regions are known to present problems for fragment length estimation and signal normalisation between samples, and although often found at repeat elements, reads typically map uniquely to these regions…

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The Return of the Duke Saga — brave Med student blew the whistle!

Hey, I had almost forgotten about the Duke breast cancer train wreck. But yesterday Keith Baggerly announced new developments via email:

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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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Fancy a challenge? A DREAM of intra-tumour phylogenies

You are into tumor evolution? And got a fancy model? Want to battle with the best?

Then check out the ICGC-TCGA DREAM Somatic Mutation Calling – Tumour Heterogeneity Challenge (SMC-Het).

These are the days of Big Science, my friend. You can’t just have a short name …

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Technocrats versus scientists – the managerial mindset in UK elite universities

You must have heard about the death of Prof Stefan Grimm, who apparently had been bullied by his departmental line managers at Imperial College London.

In case you have missed it, you can read the whole sad story at DC’ science: Publish and perish at Imperial College London: the death of Stefan Grimm.

ICL will of course claim that bullying is not endemic and this was a very sad but isolated case.

Evidence-based decision making in academic research

However, rather helpfully, a Mr John T Green has written a paper about the managerial mindset at ICL: Evidence-based decision making in academic research: The “Snowball” effect.

The paper appeared in 2013 in a journal called The Academic Executive Brief – welcome to the Dark Side!

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Cancer heterogeneity and evolution – the review to end all reviews

Series on Tumor Evolution

“A particular successful guide to understanding and modeling cancer progression has been evolutionary theory, which has a long tradition in cancer research. Already 40 years ago, seminal work established an evolutionary view of cancer (Nowell 1976; Dexter et al. 1978; Fidler 1978), in which carcinogenesis is regarded as an evolutionary process driven by stepwise somatic mutations and clonal expansions,”

write the authors of an awesome new review paper titled Cancer evolution: mathematical models and computational inference. (Obviously, I am not biased at all. I would call it ‘awesome’ even if I wasn’t one of the authors – I swear!)

Writing the review article made me wonder how the long tradition of an evolutionary understanding of cancer plays out on PubMed. Using code by R-psychologist I plotted the following figure, which shows the number of PubMed hits for queries on ‘cancer evolution’ (red), ‘cancer heterogeneity’ (yellow), as well as reviews on these topics (green). You can find my complete analysis as an R markdown document on my webpage.

bla bla bla

Figure 1: The number of papers on pubmed on ‘cancer evolution’ (red), ‘cancer heterogeneity’ (yellow), as well as reviews on these topics (green). Code and exact PubMed queries in R markdown document on my webpage.

What is the smallest reviewable result?

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The benefits of being a big name. Or: In science your name is your brand

Being a big name in science brings benefits, writes Chris Woolston in Nature, but a “study that links scientists’ reputations with their citations triggers online talk.”

And knowing ‘online talk’ it’s save to assume most of it was negative.

So let’s see what it is all about. Woolston summarizes the situation nicely:

“Scientists develop reputations that often work to their advantage.” *

I am happy to hear this: If you have a reputation for doing good work it bloody well should make your life easier.

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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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And the 2014 Ponder Prize for Teambuilding goes to …

The highlight of every annual Institute Retreat is the team building challenge.

There even is a trophy for it, called the ‘Ponder Prize for Teambuilding’.

This year’s challenge was to build a marble run within 1.5h using only paper and other cheap stuff.

And guess who won the competition!?

No, not them.

It was us!

You can see the prize-winning result in the video below.

The music is straight from the ‘Bavarian feast’ that followed the retreat.

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Inferring tumour evolution – time for a commercial break


If science blogs are good, citable papers might be even better.

So go on then, cite this:

Cancer evolution: mathematical models and computational inference
by Niko Beerenwinkel, Roland F Schwarz, Moritz Gerstung and yours truly,
Advance Access at Systematic Biology:

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development.

Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data.

We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations.

Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.



Beerenwinkel, N., Schwarz, R., Gerstung, M., & Markowetz, F. (2014). Cancer evolution: mathematical models and computational inference Systematic Biology DOI: 10.1093/sysbio/syu081

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