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

Tracking the Evolutionary History of a Tumor @ The Scientist

Check out Amber Dance’s comparison of single cell tumor phylogeny methods at The Scientist.

http://www.the-scientist.com/?articles.view/articleNo/48972/title/Tracking-the-Evolutionary-History-of-a-Tumor/

Almost makes it look like Niko and I know what we are doing in this field.

Florian

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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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

Science

Ask me anything at PLOS Science Wednesday on May 18

PLOS Science Wednesday is a weekly science communication series featuring live, direct chats with PLOS authors on redditscience (/r/science), the popular online gathering place for researchers, students and others interested in science which has over 8 million registered members. The series provides a forum for PLOS authors to communicate their work and interact directly with fellow researchers and the public.

You can find the complete schedule here.

And on May 18th it’s my turn to answer anything together with my colleague James Brenton.

And when I say ‘anything’ I mean ‘anything about cancer evolution’.

Florian

Career, Science

Open positions – cancer evolution and networks

I have three open positions in my lab:

  1. A PhD student position for “Single-cell analysis of cancer evolution” http://www.jobs.cam.ac.uk/job/10282/
  2. A postdoc position for “Evolutionary biology in cancer”. This position is ideal for somebody trained in evolutionary biology in model systems to make the transition to biomedical applications in cancer.
  3. And finally a postdoc position broadly advertised as “Computational cancer genomics” but actually having a strong network focus. http://www.jobs.cam.ac.uk/job/10265/

More info here http://www.markowetzlab.org/positions.php

Any questions, just contact me directly.

Florian

Science

Inferring tumor evolution from single-cell genomes

Series on Tumor Evolution

Everything is better if you do it with a Nested Effects Model – even inferring tumor evolution.

Let me introduce to you Oncogenetic Nested Effects Models, or for short OncoNEMs, which we just published in the new Single Cell collection of Genome Biology (see here). They exploit the fact that tumors accumulate mutations while they evolve, which leads to (noisy) subset relations between clones – exactly the type of pattern NEMs were made for.

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