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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How to bounce back from grant rejection – now with real tips

Nature Jobs had a recent post called “How to bounce back from grant rejection“. The article is nice but the title is misleading: instead of a how-to guide it rather is a list of the “six stages of scientific grief” directly adapted from Kübler-Ross.

Have a look at it, it’s a nice read, but afterwards you still don’t know how to actually make sure that you bounce back on track after getting your wonderful proposal slapped in your face.

We regret to inform you …

The situations I have in mind are like this: When you applied for a fancy-pants grant (like the orange circle ones) and you made it to the last round of interviews – and then you get rejected!

Or when you applied for a super-duper funded PI position, got short-listed and interviewed – and then you get rejected!

Your hopes were high, you thought you could make it, the champagne was already on ice – and then you get rejected!

What to do then? Here is the guide from the front lines.

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Inferring tumour evolution 4 – methods for multiple samples of the same tumour

Here is the table again that I introduced last time to organise tumor phylogeny approaches along some basic principles:

Figure 1

Figure 1 as introduced in the last post

In the last post we discussed 1a and 1b, now we are off to 2a and 2b.
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You’re not allowed bioinformatics anymore

Florian Markowetz:

Our institute director is a bioinformatician – so this could actually happen. But then again, none of my experimental colleagues would be as stupid as Prof Smith.

Originally posted on opiniomics:

Ah welcome! Come in, come in!” said the institute director as Professor Smith appeared for their scheduled 2pm meeting. “I want to talk to you about your latest proposal”, the director continued.

“Oh?” replied Smith.

“Yes. Now, let’s see. It’s an amazing, visionary proposal, a great collaboration, and congratulations on pulling it together. I just have one question” said the director “This proposal will generate a huge amount of data – how do you plan to deal with it all?”

“Oh that’s easy!” answered Smith. “It’s all on page 6. We’ve requested funds to employ a bioinformatician for the lifetime of the project. They’ll deal with all of the data” he stated, triumphantly.

The director frowned.

“I see. Do you yourself have any experience of bioinformatics?”

Smith seemed uncertain.

“Well, no…..”

“Then how will you be able to guide the bioinformatician, to ensure they are using appropriate tools? How…

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Managing upwards works! Until it doesn’t

“You are not working for me, I am working with you on your project”

This is one of the first sentences in a document I have written for new starters in my lab. I want to be explicit about the expectations I have of them. And being proactive and independent is very high on my list.

It’s OK to be pushy!

Screen Shot 2014-07-02 at 21.35.15

I also give all new starters a copy of Kearns and Gardiner’s Nature article The care and maintenance of your adviser:

“Maintaining your adviser means asking for what you need rather than hoping that he or she will know what to provide. …

[A]lthough it is natural to complain about your adviser — and can even be cathartic — it is not enough.

If your adviser is not giving you what you need, you need to go out and get it. “

I think this is very helpful advise for students and postdocs: It is Ok to be pushy!

The way I understand this part of a supervisor-student relationship is: If you need me I’ll help you in all ways I can. But you need to tell me what you need.

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If not my science, at least my blog made it into Nature

Chris Woolston writes Nature’s roundup of the papers and issues gaining traction on social media. And in his newest article `Sanger’s legacy stirs up digital chatter‘ he cites my recent blog post `Don’t worry, Fred Sanger, you’d be fine! Today’s science ain’t that bad after all‘.

My own post provides the upbeat counter-point to what François Gould, a palaeontology postdoc at Northeast Ohio Medical University, had said.

Gould explained that his tweet reflects his frustration with the grant process.

“Most of us trying to make it as junior scientists aren’t playing in Fred Sanger’s league,” he said.

From his perspective, it is still hugely challenging to convince a grant committee to take a chance on a young principal investigator who lacks a long history of publications.

I certainly sympathize with Gould. Not very many of us play in Sanger’s league – and we certainly shouldn’t have to to make a living in science.

However, my own former postdocs, who have now started independent labs, all struggle to secure funding, so I think Gould is completely right.

Maybe my more relaxed view is just very easily explained by the security that tenure in a core funded institute brings …



Grep beats FusionMap, FusionFinder and ChimeraScan

This is amazing!

I have never used FusionMap, FusionFinder or ChimeraScan myself, so I don’t know if they belong into the class of fancy named methods with only marginal improvements that I have been known to rant about (on and on) – but kudos to Panagopoulos et al for showing the amazing power of grep:

The “Grep” Command But Not FusionMap, FusionFinder or ChimeraScan Captures the CIC-DUX4 Fusion Gene from Whole Transcriptome Sequencing Data on a Small Round Cell Tumor with t(4;19)(q35;q13).
Panagopoulos I, Gorunova L, Bjerkehagen B, Heim S.
PLoS One. 2014 Jun 20;9(6):e99439. doi: 10.1371/journal.pone.0099439. eCollection 2014.

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Submitting to PNAS as a commoner

As PNAS marches into its second century, debate about its idiosyncratic publishing mechanisms is sure to continue,

writes Peter Aldhous in a News Feature in the current issue of Nature. Most of the discussion seems to be about the ‘contributed’ publication track, which makes PNAS look like an old boys’ club:

[S]uccessive editors-in-chief have been dogged by the view that PNAS is a club for academy members. “We want to remove this perception,” says current editor-in-chief Inder Verma [...]. The steady growth of direct submissions bears witness to efforts by Verma and his predecessors to make the journal attractive to scientists who are not academy members. (Aldhous 2014)

Hey, they are talking about me! I am not an academy member (not this one, not any other) and, yes, last year it somehow seemed like a good idea to submit a paper to PNAS.

So here is my experience with this journal (N=1 and won’t become larger any time soon):

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Don’t worry, Fred Sanger, you’d be fine! Today’s science ain’t that bad after all

Fred Sanger (1918 – 2013) [Photo: Huffington Post]

Science is never at rest. Questions develop. Technologies improve. Research practice changes. If you have spent your whole life in science you might look up from your desk one day and find the scientific world around you changed beyond all recognition. The days of your youth are gone and so are some of the ways to do science that you were used to.

This feeling of disconnect must be the reason for Sydney Brenner’s bleak outlook on Fred Sanger’s chances to have a scientific career today. In a retrospective on Sanger, who died last year, Brenner wrote in Science a few months ago:

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Inferring tumour evolution 3 – Methods for single samples


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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Inferring tumour evolution 2 – Comparison to classical phylogenetics

Quick recap: Last time we talked about tumor evolution and I presented a toy example to introduce key concepts. I also introduced the intra-tumor phylogeny problem: Given a sample of the genomes of clones in a tumour, reconstruct its `life history’. This problem consists of two sub-problems: (1) identification of clones, and (2) inferring evolutionary relationships between clones.

This problem falls into the general area of reconstructing phylogenetic trees — so how does inferring clonal trees compare to classical phylogenetic methods?

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Inferring tumour evolution 1 – The intra-tumour phylogeny problem

“Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer’s life history is encrypted in the somatic mutations present,”

write Nik-Zainal et al in the abstract of their 2012 Cell paper `The life history of 21 breast cancers‘. The key figure of their paper shows a phylogenetic tree of tumor development in a patient. The paper contains lots of computational work on analyzing and interpreting mutations based on deep-sequencing data, but –a big surprised but– the very last step of putting together the tree was done manually. Half the paper is describing the reasoning that Peter Campbell and his group used to condense all the evidence they had gathered from genomic data into the tree – but there is no algorithm.

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Methods vs Insights #4: The four stages of a project (and the fifth you should avoid)

Methods vs Insights is back. Today with a discussion of general research practice.

Most projects in my lab take years from start to finish. So it is important for me to manage the expectations my students and postdocs may have. Here is a plot I have developed to discuss the different stages of a scientific project with them and to prepare them for what’s ahead.


The four stages of a scientific project: Explore! Dig! Refine! Sell! And the stage you want to avoid: Waste! Plus the prevalent emotion in each stage and the key skill you will need to successfully navigate it. (x-axis it time, y-axis is work you’ve put in.)

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“The well-trained but uneducated scientist now dominates the scene,” says Gottfried Schatz

I have just read Gottfried Schatz’s editorial The faces of Big Science in the current issue of Nature Reviews Molecular Cell Biology.

Schatz has served as Secretary General of EMBO and as President of the Swiss Science and Technology Council – and thus, as expected for such a senior scientist, the editorial is a bird’s eye view of the current life science scene tracing the development from Small Science to Big Science over the last 50 years.

The paragraph that really caught my eye was this one:

Big Science is no longer a calling of the few but is a huge professional enterprise [...]

[O]ur science curricula teach scientific facts, technical tricks, ‘professional ethics’ and ‘research responsibility’ but not what science is, what it demands from us or how it changes our view of us and the world.

The well-trained but uneducated scientist now dominates the scene.

Well-trained but uneducated, hmmm.

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Methods vs Insights #3: the data don’t fall from the sky

Methods vs Insights is back! In the first post on this topic, I distinguished between computational biologists and computational biologists. The boundaries between the two groups are blurred and my own group has people with computational and biological backgrounds working on very similar problems.

Having The Talk

But when a new team member joins us fresh out of a computer science or statistics degree, I need to have The Talk with them. The Talk about how our work at a biomedical research institute differs from the work in a computer science department. The Talk about how to get into journals with an impact factor bigger than 5.

I generally start by sketching a plot on my white board, which looks like this (yes, that’s true, my hand-drawn plots look just like fresh out of Illustrator):

The difference between biomedical research and methodological research.

The difference between biomedical research and methodological research.

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