Duty Calls, Science

Shit My Reviewers Say: “This is meaninglessness squared.”

What happens if my pragmatic approach to data analysis gets reviewed by a card-carrying statistician? “The absence of competent statistical guidance in this MS is becoming painfully obvious.” Oh my!

It’s been almost a year since Trinh et al, “Practical and Robust Identification of Molecular Subtypes in Colorectal Cancer by Immunohistochemistry” came out in Clinical Cancer Research. Let me tell you a bit about the reviews we got back in summer 2016.

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Science

No witchhunt anywhere – Tim Errington’s talk on the Reproducibility Project: Cancer Biology

How reproducible is cancer biology?” stood in bold letters on a poster I had designed to advertise a talk by Tim Errington, one of the leaders of the Reproducibility Project: Cancer Biology (RP:CB), in Cambridge a few weeks ago. And I had told everyone “Come and learn who’s good, who’s bad and who’s ugly in cancer research!”

The RP:CB results are collected at eLife and the splash they made was big enough to be covered by Nature and Science. So it was great to finally meet somebody leading this project to learn first-hand which ideas are guiding their work.

Tim’s talk had the rather technical title “Improving Openness and Reproducibility of Scientific Research.” You can easily see why I felt the need to spice things up. To get a lecture theatre full of people you need to promise them blood, not balanced and nuanced views (limitations I luckily have never been accused of myself).

With all the effort I had put into advertising the talk, the people at my institute knew what to expect: A witchhunt by a posse of replication vigilantes, who abuse money diverted from real science to name and shame the actually successfull researchers! Hang them higher! Yihaah!

When Tim arrived, he took one look at the way I had advertised the talk, gently shook his head, and said “Well, you can of course do this, but I wouldn’t. It’s not really important which study reproduces and which doesn’t.”

Wait, what?

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Duty Calls, Science

The wrong type of consensus — Response to Maley et al, Nature Reviews Cancer 2017

In a recent paper in Nature Reviews Cancer, Maley et al set out to define a consensus framework for classifying neoplasms. The paper’s premise is that such a theoretical framework is a necessary first step for developing new quantitative approaches. I disagree. I argue that the paper highlights the limited practical relevance of a purely intellectual exercise. Solid classification frameworks of clinical relevance need more detail and need to be grounded on applicability to real data in clinical practice.

TL;DR

For those of you in hurry, let me sum up what my claims are:

  • This is a very good review of the field. Its particular strength is combining cancer evolution with the tissue microenvironment. You should definitely read it.
  • However, the review poses as something it is not: a classification scheme of clinical relevance.
  • The proposed classification scheme fails because (a) there is no practical way how to classify patients with it, and (b) evidence of clinical impact is circumstantial and anecdotal.
  • The authors recognise all these problems, but dismiss them as areas of future research, rather than testing prototypes of their scheme on real data.
  • Methodological and measurement innovations happen as we speak – no one needed this framework to kick start innovation.
  • Consensus on specific approaches will be much harder, much more interesting and much more useful, than consensus on lofty ideas.

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Science

MEDICC — highly recommended!

It’s good to get feedback now and then. Better still if it is positive.

Ian Holmes highly recommends our paper introducing MEDICC (Minimum Event Distance for Intra-tumor Copy-number Comparisons; more here) at F1000:

High-throughput sequencing of tumors should be informative about the stages of cancer progression. This paper is one of several that exploit the interesting observation that cancer progression is essentially a phylogenetic reconstruction problem. Of course, that should not be surprising since cancer is an evolutionary disease.

This paper looks at copy number variation (CNV) in particular, reducing CNV to an integer vector by considering SNPs in a series of windows along the genome. It addresses both allele phasing and phylogeny.

Most interestingly, from a methodological viewpoint, it does so using techniques from language and automata theory (specifically, context-free grammars and finite-state transducers). These are both tools that have found application in phylogenetics, in fact, as rather advanced tools for modelling the evolution of things like indels and RNA structure.

So, this paper represents an example of the state-of-the-art in one field (phylogenetics) being applied to advance another (computational cancer biology).

Highly recommended!

Thank you. Very appreciated.

Florian

Reference:

  • Schwarz RF, Trinh A, Sipos B, Brenton JD, Goldman N, Markowetz F.
    Phylogenetic quantification of intra-tumour heterogeneity.
    PLoS Comput Biol. 2014 Apr 17;10(4):e1003535.
    doi: 10.1371/journal.pcbi.1003535.  PMID: 24743184;
Career, Science

Come and work with me: Postdoc in Evolutionary biology of cancer

Tired of viruses and fruit flies? Want to work on something really important for a change? Come and help us to figure out cancer evolution!

We are looking for outstanding candidates to work on inferring patterns of tumor evolution from genomics data. We work with a close group of clinical collaborators, both locally and internationally, who will provide multi-sample bulk sequencing and single-cell data sets. We plan to adapt methods from population genetics and phylogenetics to the cancer setting. Key questions will be to compare mutation rates and selection hotspots between the genomes of cancer clones.

This position is ideal for somebody trained in evolutionary biology in model systems to make the transition to biomedical applications in cancer.

The successful applicant will have a PhD in a quantitative field like mathematics, statistics, physics, engineering, bioinformatics, or computer science. A background in evolutionary biology, molecular evolution or population genetics is highly desired. The applicant should have a good biological background and excellent computing skills. The atmosphere at CI is very collaborative and interactive; good communication skills are key.

To apply, please visit http://www.jobs.cam.ac.uk/job/12614/

References

  1. Beerenwinkel et al (2014) Cancer evolution: mathematical models and computational inference, Systematic Biology.
  2. Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
  3. Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
  4. Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biology, 16:36
Science

Measuring cancer evolution from the genome

Trevor and Andrea just published a really nice review in the Journal of Pathology:

Measuring cancer evolution from the genome
http://dx.doi.org/10.1002/path.4821

In this review, we describe how a cancer’s genome can be analysed to reveal the temporal history of mutation and selection, and discuss why both selective and neutral evolution feature prominently in carcinogenesis. We argue that selection in cancer can only be properly studied once we have a handle on what the absence of selection looks like. We review the data describing punctuated evolution in cancer, and reason that punctuated phenotype evolution is consistent with both gradual and punctuated genome evolution.

Even Hopeful Monsters make an appearance – I predict they are the next big thing in cancer research!

Florian

Science

Why are those ugly devils not dead yet?

Devil facial tumour disease (DFTD) is a transmissable cancer that affects Tasmanian devils and has substantially depleted their population, rasing concern that the species faces extinction. However, a new study offers some hope. Epstein et al. report that three populations of Tasmanian devil are exhibiting immune-modulated resistance to DFTD owing to modifications in certain genomic regions that may overcome immune suppression (which is how DFTD spreads between individuals). The selective pressure imposed by DFTD may therefore be encouraging its own undoing.

writes Gemma Alderton in Nature Reviews Cancer to highlight a study in Nat Comm by Epstein et al. The evolution of cancer in Tasmanian devils is really interesting, because it is not intra-tumour evolution, like the rest of the stuff I write about, but the evolution of a transmissible cancer from one devil to the next. It seems they like to bite each others faces. And that spreads the cancer.

Now … if transmissible face cancer is what floats your boat, make sure you also read Dan Graur’s take on it: “All #Hype, No Evidence: Have #TasmanianDevils Evolved Resistance to Facial Tumor Disease? Who knows?

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Science

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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Science

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 …

Florian

 

Career, Science

3 open positions in Roland Schwarz’ new lab in Berlin

The Max Delbrück Center for Molecular Medicine (Berlin) and the Berlin Institute for Medical Systems Biology (BIMSB) invite applications for

  1. PhD student (10681/2016)
  2. Postdoc (10680/2016)
  3. Scientific Programmer (10682/2016)

in the research group “Evolutionary and cancer genomics” of Dr Roland Schwarz.

The Schwarz lab investigates the relationship between genetic variation and complex phenotypes from an evolutionary perspective. A focus is thereby on the aetiology and functional implications of intra-tumour heterogeneity in human cancers. We are particularly interested in understanding the effect of structural variants and copy-number changes on cancer evolution in-vivo and closely collaborate with clinical partners to achieve this goal.

Send your application to roland.schwarz@mdc-berlin.de and mention the reference number 1068x/2016 where x is in {0,1,2}.

Florian

Duty Calls, Science

If I don’t get it, you should be concerned.

The latest post at Shit My Reviewers Say is “My first concern is that I don’t get it.

And the obvious response is illustrated by a picture saying “Your problems with me are not my problems, those are your problems.

What can you do as an author if the reviewer is just too stupid to understand your ingenuity?

But … and this is a big but … there are areas of research where I would use that reviewer’s comment myself. If, say, you are writing about probabilistic models in cancer genomics and I can’t make any sense of what you are saying, it is your problem, not mine.

Here is an example. The ABSOLUTE paper on “Absolute quantification of somatic DNA alterations in human cancer”.

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