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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Tracking the Evolutionary History of a Tumor @ The Scientist

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


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



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

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!


Career, Science

Making peer review more transparent … and earning bragging rights!

How do you procrastinate? In my case, when deadlines loom, I suddenly feel the urge to upload all my personal information to some randomly selected web-service that promises to make me rich and famous … or at least a better human being or scientist.

The latest thing I went for is called Publons.

Publons works with the world’s top publishers so you can effortlessly track, verify and showcase your peer review contributions across the world’s journals.

And who wouldn’t want to work with the world’s top publishers?

So I signed up for it. Check out my profile here.

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Inferring tumour evolution 7 – the roots of metastasis

Series on Tumour Evolution

What makes a cancer deadly is not necessarily the growth at the location where it started (the primary tumour) but its spread through the body to other organs and tissues (called metastasis). Better understanding the metastatic process is one of main reasons we are interested in inferring cancer evolution.

Today I would like to summarize and discuss two recent papers on cancer phylogenetics and metastasis. The first paper is the comprehensive review by Naxerova and Jain in Nature Reviews Clinical Oncology titled “Using tumour phylogenetics to identify the roots of metastasis in humans.” The second paper is an Opinion paper by Hong, Shpak and Townsend in Cancer Research  titled “Inferring the origin of metastases from cancer phylogenies.”

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A unifying theory of cancer evolution: genomes in context

Finally a review article on cancer evolution that I really enjoyed. Maybe because it’s not a Review but an Opinion piece: “Evolutionary dynamics of carcinogenesis and why targeted therapy does not work” by Gillies, Verduzco and Gatenby (GVG for short).

Extra brownie points for a provocative title.

The first publication on tumor heterogeneity

First of all, GVG extended my knowledge of the history of tumor heterogeneity by citing a paper from 1930:

Ö. Winge, Zytologische Untersuchungen über die Natur maligner Tumoren, Zeitschrift für Zellforschung und Mikroskopische Anatomie, 6. JUNI 1930, Volume 10, Issue 4, pp 683-735,

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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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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