The Six Terrible Lab Interviewees published on The Upturned Microscope.
I just had a quick look at last week’s Nature Genetics Editorial Cause, correlation, conjecture.
[W]e have been struck again by the amount of repetition of claims and arguments in most research articles.
The main claims of the paper are detailed in the title, abstract, introduction, results, figures and discussion as well as in the methods as if to hypnotize the reader into accepting the authors’ conclusions. [My emphasis]
Repetitiveness shows lack of confidence. One more reason to remove it.
And the Nat Gen editors even mention a tool to better structure a paper:
Our recommendation in planning a research paper is to lay out the claims together with the supporting evidence and methods in a three-column table. The rows follow one another logically as one experiment or analysis follows necessarily from its predecessor.
It is unfortunate that the short article doesn’t contain an example of such a table. But I like the idea and might just try it next time we write a paper.
Cause, correlation, conjecture. Nature Genetics 47, 305(2015) doi:10.1038/ng.3271 http://www.nature.com/ng/journal/v47/n4/full/ng.3271.html
Over at Connecting the dots … Jakob Scott describes an art (and book) project involving histology slides:
A project he began, called My Sarcoma, during which Ray painted over the top of his OWN histology images, transformed Ray from a sick and dying patient back into a living and vibrant artist.
An example of collaborative art:
Each of the paintings that Ray has made during this journey has had more than just Ray’s hands involved. Indeed, to make the paintings as you see them, a surgeon had to cut out his tumor, a pathologist had to stain and mount the tissue and a screen printer had to prepare the canvas.
What do you picture when you hear the word ‘clone’? A white-clad imperial stormtrooper from Star Wars: Attack of the clones? Or a fluffy sheep called Dolly? Both are good choices. Both are good, solid, well understood clones. But how is the situation in cancer? This is where it gets difficult. In most talks (at least the ones I sit in) the word ‘clone’ is used very loosely like it was a trivial concept. My goal for today is to show that reality is more complex than the ‘plain vanilla’ version that is often described on some introductory slide.
One of the more helpful pieces of advice I recommend to new starters in my team are Steven Weinberg’s “Four golden lessons“. Weinberg is a physics Nobel laureate and “considered by many to be the preeminent theoretical physicist alive in the world today” (says Wikipedia). I guess this means he knows his physics … and his four golden lessons certainly are helpful:
“Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue,” is the title of another recent paper that caught my attention.
I like long titles, because they often already contain the full story without the bothersome detail of the rest of the paper. Let’s look at the pieces of this one; two things stand out:
First of all, prostate cancer is generally multifocal, which means that cancer develops in different regions of the prostate, and the authors have found independent clonal expansions for these different foci. So it is not that the cancer started in one spot and then spread, these different tumors in the prostate developed independently from each other.
Morphologically normal is not always genetically normal
Based on the 2010 book by Siddhartha Mukherjee (Science, 22 April 2011, p. 423), this three-part documentary weaves together a sweeping history of cancer with intimate stories of contemporary patients. Told largely through interviews with researchers and oncologists, the series highlights Sidney Farber’s efforts to galvanize a national “war on cancer” in the 1940s, delves into the development of targeted drug compounds in the late 20th century, and explores the promise of personalized immunotherapies.
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.
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)
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!
While working on the BitPhylogeny paper, we stumbled on the problem of how to compare trees of clonal evolution.
Clonal evolution trees combine a clustering of molecular markers with tree inference. There are methods to compare clusterings and methods to compare trees, but how do you compare both at the same time?
Here is how we did it:
I have just read the second opinion piece by Alberts, Kirschner, Tilghman, Varmus in PNAS: Addressing systemic problems in the biomedical research enterprise.
They (again) describe a huge demographic shift in the US biomedical sciences due to the current hyper-competitive environment (too many people chasing too little money).
This has led to a longer and longer path to independence. Young scientists in the US are no longer young when they start their independent careers.
The potential consequences of this huge demographic shift on the productivity and preeminence of American science were judged to be serious.
[T]he United States has traditionally been viewed as the land of opportunity for young scientists, offering the most talented of them the chance to test their own ideas, raise radically new questions, and forge original paths to the answers.
Land of opportunity? No longer so, it seems.
I know why I went back to Europe.
Speaking of opportunities
At my institute in Cambridge (UK, not MA!) we are still hiring group leaders at all levels. From the famous and senior to the newly graduated.
What you will get is
- Secure core funding. (No soft money bullshit!)
- A research environment like no other on this planet!
- Complete independence!
What are you waiting for?
Come to Europe, the land of opportunity for young scientists, offering the most talented of them the chance to test their own ideas, raise radically new questions, and forge original paths to the answers.
We have ten positions to fill and the job search has been going on for a while. That’s why you might not be able to find the original job ad, which was very general (“Everbody apply!”). But specialized adverts (eg for clinical group leaders) are coming out.
If you are from a computational background and looking for a job, send me an email with your CV and we will discuss your options.
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?
Interested in quality control of sequencing data? You should be! And a new blog will answer all the questions you were too afraid to ask. Here is an example:
Originally posted on Sequencing QC and data analysis blog:
Since the movie ’50 shades of Grey’ is about to be released I thought this is the perfect opportunity to introduce everybody to the concept of “grey lists” and the recent R package developed by Gordon Brown at my institute: The GreyListChIP R package!
ChIP-seq and many other NextGen sequencing experiments (e.g. MNase-seq, DNase-seq, FAIRE-seq) often produce artifact signal in certain regions of the genome. These so called blacklisted regions are often found at repeat elements (such as satellite, centromeric and telomeric repeats), and show unstructured and high signal (excessive pile up of reads) independently of cell line and experiment type. The ENCODE project generated two sets of human blacklists (the DAC and DUKE regions, see here: http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeMapability).
Blacklisted regions are known to present problems for fragment length estimation and signal normalisation between samples, and although often found at repeat elements, reads typically map uniquely to these regions…
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Hey, I had almost forgotten about the Duke breast cancer train wreck. But yesterday Keith Baggerly announced new developments via email: