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When cancer goes BOOM – what is the difference between the Big Bang and clonal expansion models of tumor growth?

Series on Tumor Evolution

ResearchBlogging.org

And the prize for best paper title 2015 (so far) goes toooooooo……

Andrea Sottoriva, Christina Curtis and their coworkers for
A Big Bang model of human colorectal tumor growth.

Big Bang, Big Bang, … reminds me of (a) the prevailing cosmological model of how everything we know came about and (b) Sheldon Cooper. So maybe this is a genius paper that revolutionizes our basic understanding of cancer. It certainly is an eye-catching title.

What is the Big Bang model?

The Big Bang model is an alternative to the clonal expansion model, which is (has been?) the prevailing model of how cancer comes about.

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Ripples in the pond …

Our PLoS Med paper (see yesterday’s post) on tumor heterogeneity and survival in ovarian cancer is getting some media attention – not the front page of the New York Times, but hey, beggars can’t be choosers.

For those of you in UK, here you can see me stutter and sweat on regional television (for all of ~3 seconds): http://www.bbc.co.uk/iplayer/episode/b052y909/look-east-east-25022015 (starts at 11.47 mins)

Cambridge News: http://www.cambridge-news.co.uk/Cambridge-scientists-discover-8216-patchwork-8217/story-26077501-detail/story.html

GenEng News: http://www.genengnews.com/gen-news-highlights/ovarian-cancer-more-deadly-if-genetically-motley/81250964/

Medical Xpress: http://medicalxpress.com/news/2015-02-patchwork-ovarian-cancer-deadly.html

News Medical: http://www.news-medical.net/news/20150225/Serous-ovarian-cancer-is-more-deadly-shows-Cancer-Research-UK-study.aspx

CRUK press release: https://www.cancerresearchuk.org/about-us/cancer-news/press-release/2015-02-24-patchwork-ovarian-cancer-more-deadly

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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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BitPhylogeny – Bayesian inference for intra-tumor phylogeny reconstruction

Series on Tumor Evolution

ResearchBlogging.org

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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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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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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Inferring tumour evolution 5 – single cell data

Series on Tumor Evolution

ResearchBlogging.org

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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Inferring tumour evolution – time for a commercial break

ResearchBlogging.org

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.

Enjoy!

Florian

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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Inferring tumour evolution 3 – Methods for single samples

Series on Tumor Evolution

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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

Series on Tumor Evolution

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

Series on Tumor Evolution

“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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