Born On Wings

 

Alexander Porfiryevich Borodin is a name that will perhaps be familiar only to musical folk of a fairly dedicated kind. Which is a shame because he wrote some wonderful music particularly in his symphonies, in the magical portrait of the steppes of Central Asia and in his opera Prince Igor, albeit not finishing the latter. But Borodin was more than just a gifted composer for he started life as an illegitimate child, qualified as a doctor in Saint Petersburg and became a Professor of Chemistry at the Imperial Medical-Surgical Academy in that city. He carried out some very significant chemical research – he’s even got a reaction named after him – whilst, as a hobby, becoming a sufficiently outstanding composer to be one of The Mighty Handful. Along the way he founded the School of Medicine for Women in Saint Petersburg, the first Russian medical institute for women.

Advanced chemistry

With that background we can be sure that Borodin would have been thrilled to note the recent headlines about the trial of a breathalyser test for cancers. It’s being run by my colleague Rebecca Fitzgerald of the Cancer Research UK Cambridge Centre (and of tea bag fame: see Open Wide for Pasty’s Throat), initially for people suspected to have oesophageal or stomach cancers but in time to be extended to other cancers. What would have excited the chemist in Borodin is that the test collects airborne molecules from the breath and uses the most advanced modern chemistry to analyse them (the details don’t matter but, for the record, the critical method is called Field Asymmetric Ion Mobility Spectroscopy (FAIMS) which distinguishes molecules by how fast they move when driven through a gas by an electric field).

What’s new?

At first pass it may sound fanciful to think of detecting cancer on the breath but perhaps it shouldn’t. After all, we’re familiar with breathalysers that detect alcohol levels and, more generally, we all know that ‘bad breath’ isn’t a good sign. For example, the smell of acetone on the breath can arise from type I diabetes, when the body increases its use of fat due to low insulin levels. My old chemistry teacher was known throughout the school as ‘Fruity’ — the word he used with relish to describe the scent of ketones (acetone is the smallest member of the ketones).

The general point is that molecules released from cells can find their way into the lungs and emerge in the breath and now they can be identifed to find signatures indicative of disease.

The detection of breath-born chemicals can inform diagnosis and treatment of disease. From ebook “Breath Biopsy: The Complete Guide” by Owlstone Medical Ltd.

Pioneering this approach is a company called Owlstone Medical whose Breath Biopsy analytical platform carries out the spectroscopy of airborne molecules (volatile organic compounds). The idea is that someone exhales into a mask and chemicals born on the breath are collected by a cartridge for subsequent analysis. More than 1000 different compounds can be identified by this state-of-the-art technology and for cancer detection these may include substances released by tumour cells and also those emanating from host cells that have been drawn into the tumour microenvironment.

Followers of this blog will know of my enthusiasm for cancer early detection, in particular the liquid biopsy method that permits the isolation of tumour DNA from small blood samples. The breathalyzer system is a different approach to the same problem — and it may be that, in the end, both will have useful roles to play. I should add that my publicizing Owlstone Medical is entirely on account of the apparent potential of their system for cancer screening. Although the company is based in Cambridge, I have no connection with them. I rather wish I had.

If Borodin was here to comment he might wryly observe that in his opera the breaths launched by the enslaved captives when they start to sing ‘Born on wings …’ carried only grief and sorrow but with Breath Biopsy it may be that bad news enwraps good — if it carries a warning of cancers or other diseases sufficiently early that they may be stopped in their tracks.

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I Know What I Like

 

I guess most of us at some time or other will have stood gazing at a painting for a while before muttering ‘Wow, that’s awesome’ or words to that effect if we’re not into the modern argot. Some combination of subject, style and colour has turned our crank and left us thinking we wouldn’t mind having that on our kitchen wall.

Given the thousands of years of man’s daubing and the zillions of forms that have appeared from pre-historic cave paintings through Eastern painting, the Italian Renaissance, Impressionism, Dadaism and the rest to Pop Art, it’s amazing that everyone isn’t a fanatic for one sort or another. The sane might say the field’s given itself a bad name by passing off tins of baked beans, stuff thrown at a canvas and unmade beds as ‘art’ but, even so, it seems odd that it remains a minority obsession.

Can science help?

Science is wonderful, as we all know, but the notion that it might arouse the collective artistic lust seems fanciful. Nevertheless, unnoticed by practically everyone, our vast smorgasbord of smears has been surreptitiously joined over the last 30 years by a new form: an ever-expanding avalanche of pics created by biologists trying to pin down how animals work at the molecular level. The crucial technical development has been the application of fluorescence in the life sciences: flags that glow when you shine light on them and can be stuck on to molecules to track what goes on in cells and tissues. The pioneer of this field was Roger Tsien who died, aged 64, in 2016.

Because this has totally transformed cell biology we’ve run into lots of brilliant examples in these pages — recently in Shifting the Genetic Furniture, in Caveat Emptor and John Sulston: Biologist, Geneticist and Guardian of our Heritage and in the use of red and green tags for picking out individual types of proteins that mark mini-cells within cells in Lorenzo’s Oil for Nervous Breakdowns.

To mark the New Year this piece looks at science from a different angle by focussing not on the scientific story but on the beauty that has become a by-product of this pursuit of knowledge.

Step this way: entrance free

So let’s take a stroll through our science gallery and gaze at just a few, randomly selected works of art.

  1. Cells grown in culture:

This was one of the first experiments in my laboratory using fluorescently labelled antibodies, carried out by a student, Emily Hayes, so long ago that she now has a Ph.D., a husband and two children. The cells are endothelial cells (that line blood vessels). Blue: nuclei; green: F-actin; red: Von Willebrand factor, a protein marker for endothelium.

 

  1. Two very recent images taken by my colleague Roderik Kortlever of a senescent mouse fibroblast and of mouse breast tissue:

 

 

 

 

 

 

3. Waves of calcium in firing neurons:

One of my fondest memories is helping to do the first experiment that measured the level of calcium within a cell, carried out with my colleague the late Roger Tsien and two other friends. I only grew the cells: Roger had designed and made the molecule, quin2. We didn’t know it at the time but Roger’s wonder molecule was the first of many intracellular ‘reporters.’ Roger shared the 2008 Nobel Prize in Chemistry for his discovery and development of the green fluorescent protein with organic chemist Osamu Shimomura and neurobiologist Martin Chalfie.

This wonderful video of a descendant of quin2 in nerve cells was made in Dr. Sakaguchi’s lab at Iowa State University.

 

4. Calcium wave flooding a fertilized egg: Taro Kaneuchi and colleagues at the Tokyo Metropolitan University:

Click for a time-lapse movie of an egg cell that has been artificially stimulated to show the kind of calcium change that happens at fertilization. In this time-lapse movie the calcium level reaches a maximum signal intensity after about 30 min before gradually decreasing to the basal level.

 

5. The restless cell (1):

This movie shows how protein filaments in cells can continuously break down and reform – called treadmilling. Visualised in HeLa cells using a green fluorescent protein that sticks to microtubules (tubular polymers made up of the protein tubulin) by HAMAMATSU PHOTONICS.

 

6. The restless cell (2):

This movie shows how mitochondria (organelles within the cell) are continuously changing shape and moving within the cell’s interior (cytosol). Red marks the mitochondria; green DNA within the nucleus. HAMAMATSU PHOTONICS.

 

7. Cell division:

Pig kidney cells undergoing mitosis. Red marks DNA (nucleus); green is tubulin: HAMAMATSU PHOTONICS.

 

8. DNA portrait of Sir John Sulston by Marc Quinn commissioned by the National Portrait Gallery:This image looks a bit drab in the present context but in some ways it’s the most dramatic of all. John Sulston shared the 2002 Nobel Prize in Physiology or Medicine with Sydney Brenner and Robert Horvitz for working out the cell lineage of the roundworm Caenorhabditis elegans (i.e. how it develops from a single, fertilized egg to an adult). He went on to sequence the entire DNA of C. elegans. Published in 1998, it was the first complete genome sequence of an animal — an important proof-of-principle for the Human Genome Project that followed and for which Sulston directed the British contribution at the Sanger Centre in Cambridgeshire, England. The project was completed in 2003.

The portrait shows colonies of bacteria in a jelly that, together, carry all Sulston’s DNA. This represents DNA cloning in which DNA fragments, taken up by bacteria after insertion into a circular piece of DNA (a plasmid), are multiplied to give many identical copies for sequencing.

 

9. “Brainbow” mice by Tamily Weissman at Harvard University:

The science behind this astonishing image builds on the work of Roger Tsien. Mice are genetically engineered to carry three different fluorescent proteins corresponding to the primary colours red, yellow and blue. Within each cell recombination occurs randomly, giving rise to different colours. The principle of mixing primary colours is the same as used in colour televisions.  In this view individual neurons in the brain (specifically a layer of the hippocampus) project their dendrites into the outer layer. Other magnificent pictures can be seen in the Cell Picture Show.

It’s certainly science – but is it art?

A few years ago the Fitzwilliam Museum in Cambridge staged Vermeer’s Women, an exhibition of key works by Johannes Vermeer and over thirty other masterpieces from the Dutch ‘Golden Age’. I tried the experiment of standing in the middle of each room and picking out the one painting that, from a distance, most caught my amateur eye. Funny thing was: not one turned out to be by the eponymous star of the show! Wondrous though Vermeer’s paintings were, the ones that really took my fancy were by Pieter de Hooch, Samuel van Hoogstraten and Nicolaes Maes, guys I’d never heard of.

Which made the point that you don’t need to be a big cheese to make a splash and that in the new Dutch Republic of the 17th century, the most prosperous nation in Europe, there was enough money to keep a small army of splodgers in palettes and paint. Skillful and incredibly patient though these chaps were, they simply used the tools available to paint what they saw in the world before them — as for the most part have artists down the ages.

But hang on! Isn’t that what we’ve just been on about? Scientists applying enormous skill and patience in using the tools they’ve developed to visualize life — to image what Nature lays before them. So the only difference between the considerable army of biological scientists around the world making a new art form and the Old Masters is that the newcomers are unveiling life — as opposed to the immortalizing a rather dopy-looking aristocrat learning to play the virginal or some-such.

Controversial?

Not really. Let’s leave the last word to Roger Tsien. In our final picture there are eight bacterial colonies each expressing a different colour of fluorescent protein arranged to grow as a San Diego beach scene in a Petri dish. It became the logo of Roger’s laboratory.

Shifting the Genetic Furniture

 

Readers of these pages will know very well that cells are packets of magic. Of course, we often describe them in the simplest terms: ‘Sacs of gooey stuff with lots of molecules floating around.’ And it’s true that we know a lot about the protein pathways that capture energy from the food we eat and about the machinery that duplicates genetic material, makes new proteins and sustains life. Even so, although we’ve worked out much molecular detail, we have scarcely a clue about how ‘stuff’ in cells is organised. How do the tens of thousands of different types of proteins find their places in the seemingly chaotic jumble of a cell so that they can do their job? If that remains a mystery there’s an even more perplexing one in the form of the nucleus. That’s a smaller sac (i.e. a compartment surrounded by a membrane) that is home to most of our genetic material — i.e. DNA.

Sizing up the problem

It’s easy to see why evolution came up with the idea of a separate enclosure for DNA which only has to do two things: reproduce itself and enable regions of its four base code to be transcribed into molecules that can cross the nuclear membrane to be translated into proteins in the body of the cell. But here’s the puzzle. The nucleus is very small and there’s an awful lot of DNA — over 3,000 million bases in each of the two strands of human DNA (and, of course, two complete sets of chromosomes go to make up the human genome) — so 2 metres of it in every cell. A rather pointless exercise, unless you go in for pub quizzes, is to work out the length of all your DNA if you put it together in a single string. 1013 cells (i.e. 1 followed by 13 zeros) in your body: 2 metres per cell. Answer: your DNA would stretch to the sun and back 67 times.

Mmm. More relevantly, the nucleus of a cell is typically about 6 micrometres (µm) in diameter — that’s six millionths of a metre (6/1,000,000 metre), into which our 2 metres must squeeze.

Time for some serious packing to be done but it’s not just a matter of stuffing it in any old how and sitting on the lid. As we’ve just noted, every time cells divide all the DNA has to be replicated and regions (i.e. genes) are continually being “read” to make proteins. So the machinery in the nucleus has to be able to get at specific regions of DNA and disentangle them sufficiently for code reading. Part of evolution’s solution to these problems has been to add proteins called histones to DNA (the term chromosome refers to DNA together with histone packaging proteins and other proteins). To understand how this leads to “more being less”, consider DNA as a length of cotton. If you just scrunch the cotton up into a ball you get a tangled mess. But if you use cotton reels (aka histones — two or three hundred million per cell), you can reduce the great length to smaller, more organized blocks — which is just as well because they’re all that stands between life and a tangled mess.

Thinking of histones as cotton reels helps a bit in thinking about how the nucleus achieves the seemingly impossible but the fact of the matter is that we have no real idea about how DNA is unravelling is controlled so that the two strands can be unzipped and replicated, yet alone the way in which starting points for reading genes are found by proteins.

Undeterred by our profound ignorance Haifeng Wang and colleagues at Stanford University have just done something really amazing. They came up with a way of moving regions of DNA from the jumble of the nuclear interior to the membrane and they showed that this can change the activity of genes. They used CRISPR (that we described in Re-writing the Manual of Life) to insert a short piece of DNA next to a chosen gene. The insert was tagged with a protein designed to attach to a hormone that also binds to a protein (called emerin) that sits in the nuclear membrane. So the idea was that when the hormone is added to cells it can hook on to the DNA tag and, by attaching to emerin, can drag the chosen gene to the membrane. The coupling agent is a plant hormone (abscisic acid) although it also occurs in other species, including humans. Wang & Co christened their method CRISPR-GO for CRISPR-Genome Organizer.

Tagging a DNA insert with a protein so that a coupling molecule can pull a region of DNA to a protein in the membrane of the nucleus. From Wang et al., 2018.

Repositioning regions of DNA in the nucleus. DNA is labeled blue which defines the shape of the nucleus. Red dots are specific genes before (left) and after (right) adding the coupling agent. From Pennisi 2018.

How did CRISPR-GO go?

Astonishingly well. Not only could it shift tagged DNA from the interior to the membrane of the nucleus but the rearrangements could change the way cells behaved. Depending on which regions were moved and where to, cells grew more slowly or more rapidly.

So this is a really remarkable technical feat — but it’s not just molecular pyrotechnics for fun. It looks as though this approach may offer at long last a way of dissecting how cells go about getting a controlled response out of the mind-boggling complexity that is their genetic material.

References

Wang, H. et al. (2018). CRISPR-Mediated Programmable 3D Genome Positioning and Nuclear Organization. Cell 175, 1405-1417.

Pennisi, E. (2018). Moving DNA to a different part of the nucleus can change how it works. Science Oct. 11th.

Food Fix For Pharma Failure

 

If you held a global quiz, Question: “Which biological molecules can you name?” I guess, setting aside ‘DNA‘, the top two would be insulin and glucose. Why might that be? Well, the World Health Organization reckons diabetes is the seventh leading cause of death in the world. The number of people with diabetes has quadrupled in the last 30 years to over 420 million and, together with high levels of blood glucose (sugar), it kills nearly four million a year.

There are two forms of diabetes: in both the level of glucose in the blood is too high. That’s normally regulated by the hormone insulin, made in the pancreas. In Type 1 diabetes insulin isn’t made at all. In Type 2 insulin is made but doesn’t work properly.

When insulin is released into the bloodstream it can ‘talk’ to cells by binding to protein receptors that span cell membranes. Insulin sticks to the outside, the receptor changes shape and that switches on signalling pathways inside the cell. One of these causes transporter molecules to move into the cell membrane so that they can carry glucose from the blood into the cell. When insulin doesn’t work it is this circuit that’s disrupted.

Insulin signalling. Insulin binds to its receptor which transmits a signal across the cell membrane, leading to the activation of the enzyme PIK3. This leads indirectly to the movement of glucose transporter proteins to the cell membrane and influx of glucose.

So the key thing is that, under normal conditions, when the level of blood glucose rises (after eating) insulin is released from the pancreas. Its action (via insulin receptors on target tissues e.g., liver, muscle and fat) promotes glucose uptake and restores normal blood glucose levels. In diabetes, one way or another, this control is compromised.

Global expansion

Across most of the world the incidence of diabetes, obesity and cancer are rising in parallel. In the developed world most people are aware of the link between diabetes and weight: about 90% of adults with diabetes are overweight or obese. Over 2 billion adults (about one third of the world population) are overweight and nearly one third of these (31%) are obese — more than the number who are underweight. The cause and effect here is that obesity promotes long-term inflammation and insulin resistance — leading to Type 2 diabetes.

Including cancer

The first person who seems to have spotted a possible connection between diabetes and cancer was the 19th-century French surgeon Theodore Tuffier. He was a pioneer of lung and heart surgery and of spinal anaesthesia and he’s also a footnote in the history of art by virtue of having once owned A Young Girl Reading, one of the more famous oil paintings produced by the prolific 18th-century artist Jean-Honoré Fragonard (if you want to see it head for the National Gallery of Art in Washington DC). Tuffier noticed that having type 2 diabetes increased the chances of patients getting some forms of cancer and pondered whether there was a relationship between diabetes and cancer.

It was a good question then but it’s an even better one now when this duo have become dominant causes of morbidity and mortality worldwide.

We now know that being overweight increases the risk of a wide range of cancers including two of the most common types — breast and bowel cancers. Unsurprisingly, the evidence is also clear that diabetes (primarily type 2) is associated with increased risk for some cancers (liver, pancreas, endometrium, colon and rectum, breast, bladder).

With all this inter-connecting it’s perhaps not surprising that the pathway by which insulin regulates glucose also talks to signalling cascades involved in cell survival, growth and proliferation — in other words, potential cancer initiators. The central player in all this is a protein called PIK3 (it’s an enzyme that adds phosphate groups (so it’s a ‘kinase’) to a lipid called phosphatidylinositol bisphosphate, an oily, water-soluble component of the plasma membrane). It’s turned out that PIK3 is one of the most commonly mutated genes in human cancers — e.g., PIK3 mutations occur in 25–40% of all human breast cancers.

Signalling pathways switched on by mutant PIK3. A critical upshot is the activation of cell survival and growth that leads to cancer.

Accordingly, much effort has gone into producing drugs to block the action of PIK3 (or other steps in this signal pathway). The problem is that these have worked as cancer treatments either very variably or not at all.

The difficulty arises from the inter-connectivity of signalling that we’ve just described: a drug blocking insulin signalling causes the liver to release glucose and prevents muscle and fats cells from taking up glucose. Result: blood sugar levels rise (hyperglycaemia). This effect is usually transient as the pancreas makes more insulin that restores normal glucose levels.

Blockade of mutant PIK3 by an inhibitor. This blocks the route to cancer but glucose levels rise in the circulation (hyperglycaemia) promoting the release of insulin (top). Insulin can now signal through the normal pathway (bottom), overcoming the effect of the anti-cancer drug. Note that the cell has two copies of the PIK3 gene/protein, one of which is mutated, the other remaining normal.

Is our journey really necessary?

By now you might be wondering whether there is anything that makes grappling with insulin signaling worth the bother. Well, there is — and here it is. It’s a recent piece of work by Benjamin Hopkins, Lewis Cantley and colleagues at Weill Cornell Medicine, New York who looked at ways of getting round the insulin feedback response so that the effect of PIK3 inhibitors could be boosted.

Sketch showing the effect of diet on the potency of an anti-cancer drug in mice. The red line represents normal tumour growth. The black line shows the effect of PIK3 blockade when the mice are on a ketogenic diet: tumour growth is suppressed. On a normal diet the drug alone has only a slight effect on tumour growth. Similar results were obtained in a variety of model tumours (Hopkins et al., 2018).

They first showed that, in a range of model tumours in mice, insulin feedback caused by blockade of PIK3 was sufficient to switch on signalling even in the continued presence of anti-PIK3 drugs. The really brilliant result was that changing the diet of the mice could offset this effect. Switching the mice to a high-fat, adequate-protein, low-carbohydrate (sugar) diet essentially stopped the growth of tumours driven by mutant PIK3 treated with PIK3 blockers. This is a ketogenic (or keto) diet, the idea being to deplete the store of glucose in the liver and hence limit the rise in blood glucose following PIK3 blockade.

Giving the mice insulin after the drug drastically reduces the effect of the PIK3 inhibitor, supporting the idea that that a keto diet improves responses to PIK3 inhibitors by reducing blood insulin and hence its capacity to switch on signalling in tumour cells.

A few weeks prior to the publication of the PIK3 results another piece of work showed that adding the amino acid histidine to the diet of mice can increase the effectiveness of the drug methotrexate against leukemia. Methotrexate was one of the first anti-cancer agents to be made and has been in use for 70 years.

These are really remarkable results — as far as I know the first time diet has been shown to influence the efficacy of anti-cancer drugs. It doesn’t mean that all tumours with mutations in PIK3 have suddenly become curable or that the long-serving methotrexate is going to turn out to be a panacea after all — but it does suggest a way of improving the treatment of many types of tumour.

References

Hopkins, B.D. et al. (2018). Suppression of insulin feedback enhances the efficacy of PI3K inhibitors. Nature 560, 499-503.

Kanarek, N. et al. (2018). Histidine catabolism is a major determinant of methotrexate sensitivity. Nature 559, 632–636.

Almighty Power

 

The world changed in 1953 when Watson and Crick and their chums revealed the shape of the stuff of life. Our genetic material, DNA, was made up of pairs of long, intertwined chains of nucleic acids. Whether at the time we realised it, the publication of their little paper in Nature launched the age of molecular biology.

Sketch of DNA made by Odile Crick, Francis Crick’s wife, for Watson and Crick’s 1953 paper. The two ribbons represent the phosphate sugar backbones and the horizontal rods the pairs of bases holding the chains together. The arrows show that the two chains run in opposite directions. From Watson and Crick, 1953.

A couple of years later Francis Crick gave a remarkable talk to the RNA Tie Club in Cambridge in which he outlined how the structure of DNA offered mechanisms by which essentially all the fundamental processes of life might work. If the double helix was ‘unzipped’ each strand could provide a template for a new strand to be made with a complementary sequence of base units. Thus when cells divided two identical sets of double-stranded DNA were available, one for each new daughter cell.

Deciphering the code

Pulling the two strands apart would also make them available to be copied into an intermediate (RNA) that could carry genetic information to the cellular machine that makes proteins (that we now know as a ribosome). Crick had the idea that a set of adapter molecules must exist that could pick up amino acids (the building blocks of proteins) and present them to this machine in a way that enabled the correct one out of the 20 or so in the cell to be joined to the growing protein, as specified by the DNA/RNA sequence of a gene.

All this duly came to pass in a spectacular series of discoveries.

The first, in 1958, was the experiment in which Matthew Meselson and Franklin Stahl showed that each of the two double-stranded DNA helices arising from DNA replication comprised one strand from the original helix and one newly synthesized — ‘semiconservative replication’.  John Cairns, the British physician and molecular biologist, described this as “the most beautiful experiment in biology”, as vividly recorded by Horace Judson in his wonderful account of those days, The Eighth Day of Creation.

In 1961 Sydney Brenner, François Jacob and Matthew Meselson discovered messenger RNA, the intermediate nucleic acid that carries the code of genes to the ribosome. In the same year Crick, Brenner, Leslie Barnett and R.J.Watts-Tobin showed that the genetic code uses a ‘codon’ of three nucleotide bases that corresponds to an amino acid. Marshall Nirenberg and Heinrich Matthaei deciphered the first of the 64 triplet codons and Har Gobind KhoranaRobert Holley and Marshall Nirenberg shared the 1968 Nobel Prize in Physiology or Medicine for determining the complete genetic code by which every amino acid is encoded by three base sequences of RNA.

Protein synthesis by ribosomes. Ribosomes are made up of two complexes (a large and a small subunit) of over 50 proteins and RNAs. They are present in all living cells. In the process of protein synthesis (translation) ribosomes link amino acids together in the order specified by messenger RNA (mRNA). The link is a peptide bond, hence the process is called peptide synthesis.

Ribosomes move along mRNA molecules sequentially, capturing transfer RNAs carrying the appropriate amino acids to be matched by base-pairing through the anti-codons of the tRNA with successive triplet codons in mRNA. As amino acids are linked together the protein chain grows and the tRNAs, no longer carrying amino acids, are released.

The largest protein (titin — it folds up like a molecular spring to give elasticity to muscle) has over 30,000 amino acids. Very short proteins are usually called peptides (e.g., insulin with 51 amino acids, oxytocin with 9 amino acids).

Thus were revealed the pieces of molecular lego upon which all life depends. It had all occurred in an extraordinarily short space of time after the resolution of the structure of DNA. The story goes that when Watson and Crick finally convinced themselves that they had worked out DNA they retired to The Eagle pub for lunch where Crick announced to the startled clientele that they had just discovered the Secret of Life! Francis Crick, notoriously, is not remembered for either his reticence or modesty and his outburst must have been met with shaking heads and shrugged shoulders from the assembled lunchers. However, with the benefit of hindsight, it’s hard to argue with his assessment.

Fast forward to the twenty-first century

The discoveries of the 1950s and 1960s, although revealing the heart of life, were only the beginning and the ensuing years have witnessed truly breath-taking advances in our knowledge of the living world. One can’t help wondering whether, back in those days, Crick or Brenner or some other visionary might have peered into their morning coffee and muttered “Now we know how it works, wouldn’t it be fun to be able to tinker with the genetic code — to be able to make proteins to order, so to speak.”

It’s taken a while but in the 1970s genetic engineering came into existence as a way of changing the genetic make-up of cells — knocking out genes or introducing new genes. Over the last ten years or so this has progressed to using modified viruses to boost the immune response in patients with blood cancers and to treat other diseases.

This period has also seen the emergence of genome (or gene) editing that, in contrast to genetic engineering, makes specific changes to the DNA of a cell. This uses ‘molecular scissors’ to cut DNA at a known sequence. When the cut is repaired by the cell a change or ‘edit’ is made to the sequence. The best-known method is CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) or CRISPR/Cas9, Cas9 referring to the enzymes that do the cutting. We summarized how CRISPR works in Re-writing the Manual of Life but it’s worth reminding ourselves that this method is yet another example of using what nature provides. It’s based on the way bacteria build their antiviral defense system by incorporating sequences of DNA fragments from previous viral infections that are used to detect and destroy DNA from similar viruses during subsequent infections.

Upping the ante

Until now gene editing has mainly been used to make single changes at one or a few points in genomic DNA. Gregory Findlay, Lea Starita, Jay Shendure and colleagues at the University of Washington in Seattle have just changed all that by, in effect, making all possible variants in the BRCA1 gene and seeing what each did to the activity of the BRCA1 protein.

The object of their attentions is a very big gene that encodes a very big protein: it has 1884 amino acids (so that’s 3 x 1884 = 5652 coding bases). Why did they pick such a tough nut for their tour de force?

The BRCA1 (breast cancer 1) protein is essential for one of the processes by which cells can repair damaged DNA. If this protein doesn’t work properly mutations may accumulate and lead to cancer, making BRCA1 a ‘tumour suppressor’. As we explained in A Taxing Inheritance, women with a faulty BRCA1 gene have a 60 – 90% lifetime risk of breast cancer and a 40 – 60% risk of ovarian cancer (compared to 12% and 1.5% in the general population, respectively), facts that have been publicized by the actress Angelina Jolie.

BRCA1 has been sequenced in millions of women and a huge number of variants in the DNA sequence have been found. As more is learned about BRCA1, ways of targeting cells with mutant forms are being developed (e.g. by an effect called synthetic lethality). But these advances highlight the problem that we simply don’t know what, if anything, most of the sequence variants do. They’re delicately called ‘variants of uncertain significance’ — science-speak for ‘we haven’t a clue.’ And until we do have a clue there are no grounds on which to decide whether a particular mutant might turn out to be harmful.

Covering all bases

To be accurate Findlay et al. didn’t quite ‘do’ all the possible variants — they just covered the regions known to be important in the actions of the protein. Even so, in applying ‘saturation genome editing’ to these key regions of BRCA1, they were able to test the effects on BRCA1 activity of 3,893 single nucleotide variants (SNVs).

 

Mind blowing. Don’t try to read the detail but this extraordinary picture represents the critical regions of BRCA1. The letters are the sequence of bases (A, C, G & T) of the normal BRCA1 gene, the vertical columns of four boxes representing the possible variants at each position. In the work by Findlay et al. every possible variant was made — almost 4,000 — and tested for activity. From Findlay et al. 2018.

 The test vehicle for this ‘shot-gun’ approach was a cell line grown in culture that can only survive if BRCA1 is working normally. The cells were infected with small circular DNA carrying CRISPR and Cas9 sequences together with a library of edited BRCA1 exon sequences (exons are the bits of gene sequence that appear in mature RNA after non-coding bits, introns, have been removed). The result was that mutant exons were inserted in many of the cells. The cells were grown for 11 days during which time some of the cells died and some survived. Deep sequencing of DNA extracted from the cells confirmed which BRCA1 mutations caused cell death.

The idea behind ‘deep sequencing.’ The term simply means sequencing the same region many times so that DNA present in small amounts (e.g., in just a few cells in the sample) is detected. 

The bottom line

From the nearly 4,000 SNVs tested by Findlay et al. just over 400 were missense mutations i.e. the change of a single base (nucleotide) changed an amino acid in the protein so that BRCA1 was disabled. A further 300 SNVs reduced protein expression. Critically, Findlay et al. identified variants already known to block BRCA1 activity, thereby confirming the accuracy of their method.

It is probable that more sophisticated models will be used in the future but it seems that these results will be immediately helpful in screening BRCA1 variants detected in breast tissue so that a rational decision can be made as to whether a given SNV represents a cancer risk or whether it is harmless.

Deluded?

One might wonder whether, now that molecular biology has reached a stage where artificial proteins can be made in the lab, its practitioners may feel they have acquired almighty power — become human manifestations of a supernatural creator of the kind Richard Dawkins demolished in the The God Delusion. “No chance” I would say, in my cheerfully self-appointed role as spokesman for the community, happily aligning myself with Dawkins’ view that belief in a personal god is merely self-delusion.

The experiments tinkering with BRCA1 are quite astonishing but, I’m sure Gregory Findlay and his friends would readily admit, they’re merely using the methods now available to ask important questions. In thinking about how natural selection works Dawkins came up with the analogy of a blind watchmaker. You might argue that twenty-first century biologists are molecular watchmakers with 20-20 vision, able to produce bespoke proteins. No supernatural forces are needed, merely the knowledge and skill to be able to tweak nature’s own machinery.

References

Watson, J.D. and Crick, F.H.C. (1953). Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid. Nature 171, 737-738.

Findlay, G.M. et al. (2018). Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222.

H.F. Judson (1996). The Eighth Day of Creation: Makers of the Revolution in Biology. Cold Spring Harbor Laboratory Press, Plainview.

Crick, F. (1955). A Note for the RNA Tie Club (Speech). Cambridge, England. Archived from the original (PDF) on 1 October 2008.

Meselson, M and Stahl, F.W. (1958). The replication of DNA in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America. 44, 671–82.

Brenner, S., Jacob, F. and Meselson, M. (1961). An unstable intermediate carrying information from genes to ribosomes for protein synthesis. Nature 190, 576-581.

Taking Aim at Cancer’s Heart

 

Cancer is a unique paradox. At one level it’s as easy as can be to describe: damage to DNA (aka mutations) drives cells to behave abnormally — to make more of themselves when they shouldn’t.

But we all know that cancer’s fiendishly complicated — at least at the level of fine detail. Over the last decade or so the avalanche of sequenced DNA has revealed that every cell in a tumour is different: compare one cell to its neighbour and you’ll find variations in the individual units (the bases A, C, G & T) that make up the chains of DNA.

It’s a nightmare: every cancer is different so we need an infinite number of treatments to control or cure each one. Time to give up and retire to the pub.

Drivers and passengers

Not quite. DNA sequencing has also revealed that, amongst all the genetic mayhem, some mutations are more important than others. The movers and shakers have been dubbed ‘drivers’: those that come along for the ride are ‘passengers’. The hangers-on are heavily in the majority but, even so, several hundred drivers (i.e. mutated genes that give rise to abnormal proteins) have been identified. As it needs a group of drivers to work together to make a cancer we still have the problem that the number of critical combinations that can arise is essentially infinite.

One way of reducing the scale of the problem has been to look at what ‘driver’ proteins do in cells and to target those acting at key points to push cell proliferation beyond the normal.

Playing games

Just recently Giulio CaravagnaAndrea Sottoriva and colleagues at the Institute of Cancer Research, London and the University of Edinburgh have come up with a different approach. The idea goes back to the 1950s when a clever chap from Kansas by the name of Arthur Samuel came up with a program for IBM’s first commercial computer so that it could play draughts (checkers as our American friends call it) in its spare time. The program defined the patterns that could be formed by the pieces on the chequerboard so that, given enough of these, IBM 701 could indicate the optimal moves. Samuel called this machine learning, a precursor of the idea of artificial intelligence.

Perhaps the most famous moment in this saga came in 1997 when a later IBM computer, Deep Blue, beat the then world chess champion Garry Kasparov. Unsurprisingly, Kasparov was a bit miffed and accused IBM of cheating — to wit, getting a human to tell the machine what to do. Let’s hope that in the end he came to terms with the fact that Deep Blue could crank through 200 million positions per second and, however many games Grandmasters have in their heads, they can’t compete with that.

The cancer team realized that the mutations driving the evolution of cancer cells emerge as patterns in the sequence of DNA as a cell moves towards becoming independent of normal controls. Think of each cancer as a family tree of mutations, the key question being which branch leads to the most potent combination.

To pick out these patterns they applied a machine-learning approach known as transfer learning to the DNA sequences from a large number of cancers. They called this ‘repeated evolution in cancer’ — REVOLVER — aimed at picking out mutation patterns at the heart of cancer that foreshadow future genetic changes and can be used to predict how they will evolve.

Identifying patterns of mutation common to different tumours.

Samples are taken from different regions of a patient’s tumour (represented by the coloured dots). Their DNA sequences will have multiple variations that can mask underlying patterns of driver mutations present in some subgroups. The five trees show mutations picked up in those patients. REVOLVER uses transfer learning to screen the sequence data from many patients and pull out evolutionary trajectories shared by subgroups. The dotted red lines highlight common patterns that are represented in the lower strip. From Caravagna et al. 2018.

REVOLVER was applied to sequences from lung, breast, kidney and bowel cancers but there’s no reason it shouldn’t work with other tumours. The big attraction is that if these mini-sequence mutation patterns can be associated generally with how a given tumour develops they should help to inform treatment options and predict survival.

We have in the past referred to the ways cancers evolve as ‘genetic roulette’ — so perhaps it’s appropriate if game-playing computer programs turn out to be useful in teasing out behavioural clues.

Reference

Caravagna, G. et al. (2018). Detecting repeated cancer evolution from multi-region tumor sequencing data. Nature Methods 15, 707–714.

Turning Ourselves On

 

It may seem a bit tasteless but we have to admit that cancer’s a very ‘trendy’ field. That is, there’s always a current fad — something for which either the media or cancer scientists themselves have the hots. Inevitable I suppose, given the importance of cancer to pretty well everyone and the fact that something’s always happening.

If you had to pick the front-running trends of late I guess most of us might go for ‘personalized medicine’ and ‘immunotherapy.’ The first means tailoring treatment to the individual patient, the second is boosting the innate power of the immune system to fight cancer.

Few things are trendier than this blog so it goes without saying that we’ve done endless pieces on these topics (e.g. Fantastic Stuff, Outsourcing the Immune Response, Self-Help – Part 2, bla, bla, bla).

How considerate then of Krijn Dijkstra, Hans Clevers, Emile Voest and colleagues from the Netherlands Cancer Institute to have neatly combined the two in their recent paper.

Simple really

What they did was did was easy — in principle. They grew fresh tumour tissue from patients in dishes in the laboratory. Although it doesn’t work every time, most of the main types of cancer have been grown in this way to give 3D cultures called tumour organoids — tumours-in-a-dish. That’s the ‘personalized’ bit.

Then they took blood from the patient and grew the lymphocytes therein in a dish to expand the T cells that were specific for the patient’s tumour. That’s the ‘‘immuno’ bit.

Growing tumour tissue (from non-small-cell lung cancer (NSCLC) and colorectal cancers [CRC] in culture as tumour organoids. This permits the expansion of T cells from peripheral blood to give an enlarged population of cells that will kill those tumours. From Dijkstra et al. 2018.

And the results?

They were able to show that enriched populations of tumour-reactive T cells could kill tumour organoids and, importantly, that organoids formed from healthy tissue were not attacked by these T cells.

Stained organoids (left) and original tissue (right) from two colorectal cancers (CRC-2 & CRC-5) showing how the organoids grow to have an architecture similar to the original tumour. From Dijkstra et al. 2018.

Their method worked for both bowel tumours and non-small-cell lung cancer but there’s no reason to suppose it can’t be extended to other types of cancer.

Some of their videos showing tumour organoids being chomped up by enriched killer T cells are quite dramatic. Cells labelled green that can be seen in this video are dying.

So there you have it: DIY tumour therapy!

Reference

Dijkstra, K.K. et al. (2018). Generation of Tumor-Reactive T Cells
by Co-culture of Peripheral Blood Lymphocytes and Tumor Organoids. Cell 174, 1–13.

Now wash your hands!

 

You must have spent the last 20 years on a distant planet if you’re unaware that we’re heading for Antibiotic Armaggedon — the rise of “Superbugs”, i.e., bacteria resistant to once-successful medication. Microbes resistant to multiple antimicrobials are called multidrug resistant. It’s a desperate matter because it means trivial infections may become fatal and currently safe surgical procedures may become dangerous.

Time-line of the discovery of different antibiotic classes in clinical use. The key point is that the last antibiotic class to become a successful treatment was discovered in 1987.

What’s the problem?
It’s 30 years since we came up a new class of antibiotics. The golden age launched by Fleming’s celebrated discovery of penicillin is long gone and while the discovery curve has drifted ever downwards since 1960 the bugs have been busy.

Just how busy a bug can be was shown by a large-scale experiment carried out by Roy Kishony and friends. They built a “Mega-Plate” — a Petri Dish 2 ft by 4 ft filled with a jelly for the bacteria to grow in. The bugs were seeded into channels at either end so they would grow towards the middle. The only thing stopping them was four channels dosed with antibiotic at increasing concentrations — 10 times more in each successive channel.

The bugs grow until they hit a wall of antibiotic. There they pause for a think — and, after a bit, an intrepid little group start to make their way into the higher dose of drug. Gradually the number of groups expand until a tidal wave sweeps over that barrier. This is repeated at each new ‘wall’ — four times until the whole tray is a bug fest.

When they pause at each new ‘wall’ they’re not ‘thinking’ of course. They’re just picking up random mutations in their DNA until they are able to advance into the high drug environment. So this experiment is a fantastic visual display of bugs becoming drug-resistant. And it’s terrifying because it takes about 11 days for them to overcome four levels of drug. It’s even more scary in the speeded-up movie as that lasts less than two minutes.

Sound familiar?
It should do as this is a cancer column and readers will know that cancers arise by picking up mutations. To highlight the similarities the picture below is the left-hand half of the bug tray with new colonies shown as linked dots. You could perfectly well think of these as early stage cancer cells acquiring mutations in ‘driver’ genes that push them towards tumour formation.

So that’s pretty scary too and the only good news is that animal cells reproduce much more slowly than bacteria. The fastest they can manage is about 48 hours to grow and divide into two new cells and for many it’s much slower than that. Bugs, on the other hand, can do it in 20 minutes if you feed them enough of the right stuff.

Which is why we don’t all get zonked by cancer at an early age.

The evolution of bacteria on a “Mega-Plate” Petri Dish. The vertical red lines mark the boundaries of increasing antibiotic concentrations. You could equally think of each dot that represents a new bacterial colony being early stage cancer cells acquiring mutations in ‘driver’ genes (white arrows) that push them towards tumour formation. From Roy Kishony’s Laboratory at Harvard Medical School.

Enough of that!
But for once I don’t want to talk about cancer but about a really fascinating piece of work that caught my eye in the journal Cell Reports. It’s by Gianni Panagiotou, Kang Kang and colleagues from The University of Hong Kong and The Hans Knöll Institute, Jena, Germany and it’s all about their travels on the Hong Kong MTR (Mass Transit Railway). This is the network of over 200 km of railway lines with 159 stations that serves the urbanised areas of Hong Kong IslandKowloon, and the New Territories and has a cross- border connection to the neighboring city of Shenzhen in mainland China.

An MTR train on the Tung Chung line that links Lantau Island with Hong Kong Island.

Being scientists of course they weren’t just having a day out. They wanted to know the contents of the microbiome that they and their fellow travellers picked up on the palms of their hands when riding the rails. ‘Microbiome’ means all of the collection of microorganisms — though in practice this is almost entirely bacteria. So they swabbed the palms of volunteers and then threw the full power of modern DNA sequencing and genetic analysis at what they’d scraped off. Or, as they put it: “We conducted a metagenomic study of the Hong Kong MTR system.”

And if you’re thinking it might be possible to take a trip on the Hong Kong Metro without grabbing a handrail or otherwise engaging in what on the London Underground used to be called ‘strap-hanging’ you clearly haven’t tried it!

Hong Kong MTR.

 

The MTR System and Sampling Procedure. Left: The eight urban lines studied: the Airport Express line and Disneyland Resort branch were excluded. The Central-Hong Kong station and the cross-border rail stations connecting with the MTR and the Shenzhen metro system are labeled. Right: The sampling procedure included handwashing, handrail touching for 30 min and swabbing. From Kang et al. 2018.

Hold very tight please! 

It’s going to become a seriously bumpy ride. The major findings were:

  1. Four groups (phyla) of bacteria dominated: Actinobacteria [51%], Proteobacteria [27%], Firmicutes [11%] and Bacteroidetes [2%]. Followers of this blog will be delighted to spot the last two (B & F) as we’ve met them several times before (in Hitchhiker Or Driver?, Fast Food Fix Focuses on Fibre, Our Inner Self, The Best Laid Plans In Mice and Men, and, of course, in it’s a small world) — that’s how important they are in the context of cancer.
  2. The dominant organism (29% of the community) was P. acnes (one of the Actinobacteria — it’s the bug linked to the skin condition of acne).
  3. Some non-human-associated species (e.g., soil organisms) also popped up that varied enormously in amount from day to day — perhaps because of weather conditions (e.g., humidity).
  4. Variation in the make-up of the microbial communities picked up depended, more than anything else, on the time of day. There was a marked decrease in diversity in afternoon samples compared with those taken in the morning.
  5. Specific species of bacteria associated with individual metro lines. That is, sets of bug types are relatively abundant on a given line compared with all other lines, giving a kind of line-specific signature — though the distinction declines from morning to afternoon. The most physically isolated line, MOS (Ma On Shan), had a greater number of signature species. The MOS runs entirely above ground alongside the Shing Mun Channel, a polluted brackish river, and its ‘signature’ includes bacteria found in sewage.
  6. All of which brings us to bugs with antibiotic resistance genes (ARGs). Across the network 136 ARG families were detected including 24 that are clinically important. Strikingly, lines closer to Shenzhen (ER (East Rail) and MOS) tend to have higher ARG input during the day. Critically, the ER line a.m. signatures become p.m.-enriched in all MTR lines far from Shenzhen — that is, these ARG families spread over the network during the day.

Simplified map of the Hong Kong MTR indicating how antibiotic resistance genes spread during the day from the ER and MOS lines to the entire network. Tetracycline resistance genes: tetA, tetO, tetRRPP and tetMWOS; vancomycin resistance genes: vanC, vanX. From Kang et al. 2018.

These results clearly suggest that the ER line, the only cross-border line linked to mainland China, may be a source of clinically important ARGs, especially against tetracycline, a commonly used antibiotic in China’s swine feedlots. Antibiotics, including tetracycline, can be detected in the soil in the Pearl River Delta area where the cities of Hong Kong and Shenzhen are located.

It should be said that this is by no means the first survey of bugs on rails. Notable ones have looked at the New York and Boston metro systems and they too revealed the potential health risks of the bug communities found on trains and in the stations, including the presence of pathogens and antibiotic resistance. The Boston survey highlighted that different types of materials have surfaces that are preferred by different microbes with high variation in functional capacity and pathogenic potential.

One obvious suggestion from these studies is that world-wide we could do a lot to improve sanitation, e.g., by having hand sanitizer dispensers in all sensible places (at the exits of metro, railway and bike-sharing stations and airports and of course in hospitals). The Hong Kong data are seriously frightening and most people seem blissfully unaware that the invisible world they reveal carries the potential for the destruction of us all.

But, as ever, there’s two sides to the matter. We’ve evolved over millions of years to live with bugs and they with us. However you wash your hands you won’t get rid of every bug and anyway, as what’s-his-name almost says, “They’ll be back!” We all carry around micro-organisms that can be fatal if they get to the wrong place. But, if you’re reasonably fit, there’s a lot to be said for simply following sensible, basic hygiene rules with a philosophy of ‘live and let live.’

Have a nice day commuters, wherever you are!

References

Kang K., et al. (2018). The Environmental Exposures and Inner- and Intercity Traffic Flows of the Metro System May Contribute to the Skin Microbiome and Resistome. Cell Reports 24, 1190–1202.

Wu, N., Qiao, M., Zhang, B., Cheng, W.D., and Zhu, Y.G. (2010). Abundance and diversity of tetracycline resistance genes in soils adjacent to representative swine feedlots in China. Environ. Sci. Technol. 44, 6933–6939.

Li, Y.W., Wu, X.L., Mo, C.H., Tai, Y.P., Huang, X.P., and Xiang, L. (2011). Investigation of sulfonamide, tetracycline, and quinolone antibiotics in vegetable farmland soil in the Pearl River Delta area, southern China. J. Agric. Food Chem. 59, 7268–7276.

Leung, M.H., Wilkins, D., Li, E.K., Kong, F.K., and Lee, P.K. (2014). Indoor-air microbiome in an urban subway network: diversity and dynamics. Appl. Environ. Microbiol. 80, 6760–6770.

Robertson, C.E., Baumgartner, L.K., Harris, J.K., Peterson, K.L., Stevens, M.J., Frank, D.N., and Pace, N.R. (2013). Culture-independent analysis of aerosol microbiology in a metropolitan subway system. Appl. Environ. Microbiol. 79, 3485–3493.

Afshinnekoo, E., Meydan, C., Chowdhury, S., Jaroudi, D., Boyer, C., Bernstein, N., Maritz, J.M., Reeves, D., Gandara, J., Chhangawala, S., et al. (2015). Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics. Cell Syst 1, 72–87.

Hsu, T., Joice, R., Vallarino, J., Abu-Ali, G., Hartmann, E.M., Shafquat, A., Du- Long, C., Baranowski, C., Gevers, D., Green, J.L., et al. (2016). Urban Transit System Microbial Communities Differ by Surface Type and Interaction with Humans and the Environment. mSystems 1, e00018–e00016.

Keeping Up With Cancer

 

Cancer enthusiasts will know that there are zillions of web sites giving info on cancer stats — incidence, mortality, etc. — around the world. Notable is the World Health Organization’s Globocan, an amazing compilation of data on all cancers from every country. The Global Burden of Disease Cancer Collaboration audits diagnosis rates and deaths for 29 types of cancer around the world each year. Needless to say, this too is a vast undertaking involving hundreds of scientists around the world. The organizing genius is Dr. Christina Fitzmaurice of the Institute for Health Metrics and Evaluation in the University of Washington, Seattle and, under her guidance, their update for 2016 has just come out.

What’s new?

In 2016 there were 17.2 million people diagnosed with cancer. 8.9 million died from cancers. By 2030 the number of new cancer cases per year is expected to reach 24 million. Well, you knew the numbers were going to be big — almost incomprehensibly so. But here’s the real shaker: the 17.2 M is 28% up on the 2006 figure — yes, that’s a rise of more than one quarter.

Yearly global cancer deaths from 1990 to 2016.

The green line is total deaths per 100,000 people.

Red line: Cancer death rates taking account of the increase in world population.

Blue line: Age-standardized death rates: these are corrected for population size and age structure. Age-standardization therefore gives a better indication of the prevalence and incidence of underlying cancer risk factors between countries and with time without the influence of demographic and population structure changes.

The numbers on the vertical axis are deaths per 100,000 people. From Our World in Data.

Any real surprises?

No. An increase of more than one quarter in cancer cases does indeed make you think but the grim numbers are only what you would predict from looking at the trends over the last 40 years. The graph shows death rates that, of course, reflect incidence. The total figure (top) shows starkly how the rise in the population of the world and our increasing life-span is steadily pushing up the overall cancer burden.

So it’s a mega-problem but the trends are smooth and gradual. There’s been no drastic upheaval.

Global trends

Yearly global cancer deaths from 1990 to 2016 for the four major cancer types.

The numbers on the vertical axis are age-standardized death rates per 100,000 people.

Because age-standardization assumes a constant population age & structure it permits comparisons between countries and over time without the effects of a changing age distribution within a population. From Our World in Data.

The global trends in deaths from the four major cancers look mildly encouraging (above). However, these should not cheer us up too much. In the developed world there are some positives. In the USA, for example, over the last 17 years deaths from prostate are down from 31.6 to 18.9, for breast from 26.6 to 20.3 and for lung from 55.4 to 40.6 per 100,000 people. For bowel cancer there’s been a slight increase (4.1 to 4.8).

In the wider world, however, the really dispiriting thing shown by the latest figures is that the increases in incidence and deaths are greatest in low- and middle-income countries.

What can we do?

Lung cancer (includes cancers of the trachea and bronchi) remains the world’s biggest cancer killer, accounting for 20% of all deaths in 2016. Over 90% of these were caused by tobacco. In the UK and the USA lung cancer deaths in men have markedly declined as a result of widespread smoking bans, as the graph below shows, and the female figures have started to show a sight decline.

Lung cancer deaths per 100,000 by sex from 1950 to 2002 for the UK and the USA. From Our World in Data.

Asia contributes over half the global burden of cancer but the incidence in Asia is about half that in North America. However, the ratio of cancer deaths to the number of new cancer cases in Asia is double that in North America. Although the leading cause of death world-wide is heart disease, in China it is cancer. Every year more than four million Chinese are diagnosed with the disease and nearly three million die from it. Overall, tobacco smoking is responsible for about one-quarter of all cancer deaths in China. Nevertheless, Chinese smoking rates continue to rise and air pollution in the major cities is fuelling the problem.

The under-developed world, however, continues to be targeted by the tobacco industry and the successful promotion of their products means that there is no end in sight to one of mankind’s more bizarre and revolting forms of self-destruction.

There are, of course, other things within our control that contribute significantly to the global cancer burden. If only we could give everyone clean water to drink, restrict our red meat and processed food consumption and control our exposure to uv in sunlight we would cut cancer by at least one half.

If only …

Reference

The Global Burden of Disease Cancer Collaboration (2018). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. Published online June 2, 2018. doi:10.1001/jamaoncol.2018.2706.

Same Again Please

 

It’s often said that every family has its secret — Uncle Fred’s fondness for the horses, Cousin Bertha’s promiscuity, etc. — whatever it is that ‘we don’t talk about.’ If that’s true the scientific community is no exception. For us the unutterable is reproducibility — meaning you’ve done an experiment, new in some way, but the key questions are: ‘Can you do it again with the same result?’ and, even more important: ‘Can someone else repeat it?’

Once upon a time in my lab we had a standing joke: whoever came bounding along shouting about a new result would be asked ‘How reproducible is it?’ Reply: ‘100%!’ Next question: ‘How often have you done the experiment?’ Reply: ‘Once!!’ Boom, boom!!!

Not a rib-tickler but it did point to the knottiest problem in biological science namely that, when you start tinkering with living systems, you’re never fully in control.

How big is the problem?

But, as dear old Bob once put it, The Times They Are a-Changin’. Our problem was highlighted in the cancer field by the Californian biotechnology company Amgen who announced in 2012 that, over a 10 year period, they’d selected 53 ‘landmark’ cancer papers — and failed to replicate 47 of them! Around the same time a study by Bayer HealthCare found that only about one in four of published studies on potential drug targets was sufficiently strong to be worth following up.

More recently the leading science journal Nature found that almost three quarters of over 1,500 research scientists surveyed had tried to replicate someone else’s experiment and failed. It gets worse! More than half of them owned up to having failed to repeat one of their own experiments! Hooray! We have a result!! If you can’t repeat your own experiment either you’re sloppy (i.e., you haven’t done exactly what you did the first time) or you’ve highlighted the biological variability in the system you’re studying.

If you want an example of biological variation you need look no further than human conception and live births. Somewhere in excess of 50% of fertilized human eggs don’t make it to birth. In other words, if you do a ‘thought experiment’ in which a group of women carry some sort of gadget that flags when one of their eggs is fertilized, only between one in two and one in five of those ‘flagged’ will actually produce an offspring.

However you look at it, whether it’s biological variation, incompetence or plain fraud, we have a problem and Nature’s survey revealed that, to their credit, the majority of scientists agreed that there was a ‘significant crisis.’

The results of the survey by Nature from Baker 2016.

Predictably, but disturbingly for us in the biomedical fields, the greatest confidence in published results was shown by the chemists and physicists whereas only 50% of data in medicine were thought to be reproducible. Oh dear!

Tackling the problem in cancer

The Reproducibility Project: Cancer Biology, launched in 2013, is a collaboration between the Center for Open Science and Science Exchange.

The idea was to take 50 cancer papers published in leading journals and to attempt to replicate their key findings in the most rigorous manner. The number was reduced from 50 to 29 papers due to financial constraints and other factors but the aim remains to find out what affects the robustness of experimental results in preclinical cancer research.

It is a formidable project. Before even starting an experiment, the replication teams devised detailed plans, based on the original reports and, as the result of many hours effort, came up with a strategy that both they and the original experimenters considered was the best they could carry out. The protocols were then peer reviewed and the replication plans were published before the studies began.

Just to give an idea of the effort involved, a typical replication plan comprises many pages of detailed protocols describing reagents, cells and (where appropriate) animals to be used, statistical analysis and any other relevant items, as well as incorporating the input from referees.

The whole endeavor is, in short, a demonstration of scientific practice at its best.

To date ten of these replication studies have been published.

How are we doing?

The critical numbers are that 6 of the 10 replications ‘substantially reproduced’ the original findings, although in 4 of these some results could not be replicated. In 4 of the 10 replications the original findings were not reproduced.

The first thing to say is that a 60% rate of ‘substantial’ successful replication is a major improvement on the 11% to 25% obtained by the biotech companies. The most obvious explanation is that the massive, collaborative effort to tighten up the experimental procedures paid dividends.

The second point to note is that even when a replication attempt fails it cannot be concluded that the original data were wrong. The discrepancy may merely have highlighted how fiendishly tricky biological experimentation can be. The problem is that with living systems, be they cells or animals, you never have complete control. Ask anyone who has a cat.

More likely, however, than biological variation as a cause of discrepancies between experiments is human variation, aka personal bias.

This may come as a surprise to some but, rather than being ‘black and white’ much of scientific interpretation is subjective. Try as I might, can I be sure that in, say, counting stained cells I don’t include some marginal ones because that fits my model? OK: the solution to that is get someone else to do the count ‘blind’ — but I suspect that quite often that’s not done. However, there are even trickier matters. I do half a dozen repeats of an experiment and one gives an odd result (i.e., differs from the other five). Only I can really go through everything involved (from length of coffee breaks to changes in reagent stocks) and decide if there are strong enough grounds to ignore it. I do my best to avoid personal bias but … scientists are only human (fact!).

A closer look at failure

One of the failed replications is a particularly useful illustration for this blog. The replication study tackled a 2012 report that bacterial infection (specifically a bacterium, Fusobacterium nucleatum, that occurs naturally in the human oral cavity) is present in human colon cancers but not in non-cancerous colon tissues. It hit the rocks. They couldn’t detect F. nucleatum in most tumour samples and, when they did, the number of bugs was not significantly different to that in adjacent normal tissue.

Quite by chance, a few months ago, I described some more recent research into this topic in Hitchhiker or Driver?

I thought this was interesting because it showed that not only was F. nucleatum part of the microbiome of bowel cancer but that when tumour cells spread to distant sites (i.e., underwent metastasis) the bugs went along for the ride — raising the key question of whether they actually helped the critical event of metastasis.

So this latest study was consistent with the earlier result and extended it — indeed they actually showed that antibiotic treatment to kill the bugs slowed the growth of human tumour cells in mice.

Where does that leave us?

Well, helpfully, the Reproducibility Project also solicits comments from independent experts to help us make sense of what’s going on. Step forward Cynthia Sears of The Johns Hopkins Hospital. She takes the view that, although the Replication Study didn’t reproduce the original results, the fact that numerous studies have already found an association between F. nucleatum and human colon cancer means there probably is one — consistent with the work described in Hitchhiker or Driver?

One possible explanation for the discrepancy is that the original report studied colon tissue pairs (i.e., tumour and tumour-adjacent tissues) from colon cancer patients but did not report possibly relevant factors like age, sex and ethnicity of patients. In contrast, the replication effort included samples from patients with cancer (tumour and adjacent tissue) and non-diseased control tissue samples from age, sex and ethnicity matched individuals.

So we now know, as Dr. Sears helpfully remarks, that the association between F. nucleatum bugs and human colon cancer is more complicated first appeared! Mmm. And, just in case you were in any doubt, she points out that we need to know more about the who (which Fusobacterium species: there are 12 of them known), the where (where in the colon, where in the world) and the how (the disease mechanisms).

Can we do better?

In the light of all that the obvious question is: what can we do about the number of pre-clinical studies that are difficult if not impossible to reproduce? Answer, I think: not much. Rather than defeatist this seems to me a realistic response. There’s no way we could put in place the rigorous scrutiny of the Reproducibility Project across even a fraction of cancer research projects. The best we can do is make researchers as aware as possible of the problems and encourage them towards the very best practices — and assume that, in the end, the solid results will emerge and the rest will fall by the wayside.

Looking at the sharp end, it’s worth noting that, if you accept that some of the variability in pre-clinical experiments is down to the biological variation we mentioned above, it would at least be consistent with the wide range of patient responses to some cancer treatments. The reason for that, as Cynthia Sears didn’t quite put it, is that we just don’t know enough about how the humans we’re tinkering with actually work.

References

Baker, M. (2016). Is There a Reproducibility Crisis? Nature 533, 452-454.

Jarvis, G.E. (2017). Early embryo mortality in natural human reproduction: What the data say [version 2; referees: 1 approved, 2 approved with reservations] F1000Research 2017, 5:2765 (doi: 10.12688/f1000research.8937.2).

Monya Baker & Elie Dolgin (2017). Cancer reproducibility project releases first results. Nature 541, 269–270. doi:10.1038/541269a.

Begley, C.G. and Ellis, L.M. (2012). Drug development: Raise standards for preclinical cancer research. Nature 483, 531–533.

Prinz,F., Schlange,T. and Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? NatureRev. Drug Discov. 10, 712.