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.

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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.

Fantastic Stuff

 

It certainly is for Judy Perkins, a lady from Florida, who is the subject of a research paper published last week in the journal Nature Medicine by Nikolaos Zacharakis, Steven Rosenberg and their colleagues at the National Cancer Institute in Bethesda, Maryland. Having reached a point where she was enduring pain and facing death from metastatic breast cancer, the paper notes that she has undergone “complete durable regression … now ongoing for over 22 months.”  Wow! Hard to even begin to imagine how she must feel — or, for that matter, the team that engineered this outcome.

How was it done?

Well, it’s a very good example of what I do tend to go on about in these pages — namely that science is almost never about ‘ground-breaking breakthroughs’ or ‘Eureka’ moments. It creeps along in tiny steps, sideways, backwards and sometimes even forwards.

You may recall that in Self Help – Part 2, talking about ‘personalized medicine’, we described how in one version of cancer immunotherapy a sample of a patient’s white blood cells (T lymphocytes) is grown in the lab. This is a way of either getting more immune cells that can target the patient’s tumour or of being able to modify the cells by genetic engineering. One approach is to engineer cells to make receptors on their surface that target them to the tumour cell surface. Put these cells back into the patient and, with luck, you get better tumour cell killing.

An extra step (Gosh! Wonderful GOSH) enabled novel genes to be engineered into the white cells.

The Shape of Things to Come? took a further small step when DNA sequencing was used to identify mutations that gave rise to new proteins in tumour cells (called tumour-associated antigens or ‘neoantigens’ — molecular flags on the cell surface that can provoke an immune response – i.e., the host makes antibody proteins that react with (stick to) the antigens). Charlie Swanton and his colleagues from University College London and Cancer Research UK used this method for two samples of lung cancer, growing them in the lab to expand the population and testing how good these tumour-infiltrating cells were at recognizing the abnormal proteins (neo-antigens) on cancer cells.

Now Zacharakis & Friends followed this lead: they sequenced DNA from the tumour tissue to pinpoint the main mutations and screened the immune cells they’d grown in the lab to find which sub-populations were best at attacking the tumour cells. Expand those cells, infuse into the patient and keep your fingers crossed.

Adoptive cell transfer. This is the scheme from Self Help – Part 2 with the extra step (A) of sequencing the breast tumour. Four mutant proteins were found and tumour-infiltrating lymphocytes reactive against these mutant versions were identified, expanded in culture and infused into the patient.

 

What’s next?

The last step with the fingers was important because there’s almost always an element of luck in these things. For example, a patient may not make enough T lymphocytes to obtain an effective inoculum. But, regardless of the limitations, it’s what scientists call ‘proof-of-principle’. If it works once it’ll work again. It’s just a matter of slogging away at the fine details.

For Judy Perkins, of course, it’s about getting on with a life she’d prepared to leave — and perhaps, once in while, glancing in awe at a Nature Medicine paper that does not mention her by name but secures her own little niche in the history of cancer therapy.

References

McGranahan et al. (2016). Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 10.1126/science.aaf490 (2016).

Zacharakis, N. et al. (2018). Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nature Medicine 04 June 2018.

Now You See It

 

In the pages of this blog we’ve often highlighted the power of fluorescent tags to track molecules and see what they’re up to. It’s a method largely pioneered by the late Roger Tsien and it has revolutionized cell biology over the last 20 years.

In parallel with molecular tagging has come genetic engineering that permits novel genes, usually carried by viruses, to be introduced to cells and animals. As we saw in Gosh! Wonderful GOSH and Blowing Up Cancer, various ‘virotherapy’ approaches have been used with some success to treat leukemias and skin cancers and a trial is underway in China treating metastatic non-small cell lung cancer.

A major aim of genetic engineering is to be able to control the expression of novel genes (i.e. protein production from the encoding DNA sequence) that have been introduced into an animal — in the jargon, to ‘switch’ on or off at will. That can be done but only by administering a drug or some other regulator, either in drinking water, by injection or squirting directly into the lungs. An ideal would be something that’s more controlled and less invasive. How about shining a light on the relevant spot?!

Wacky or what?

That may sound as though we’re veering towards science fiction but reflect for a moment that every animal with vision, however rudimentary, sees by transforming light entering the eyes into electrical signals that the brain turns into a picture of the world around them. This relies on photoreceptor proteins that span the membranes of retinal cells.

How vision works. Light passes through the lens and falls on the retina at the back of the eye. The photoreceptor cells it activates are rod cells (that respond to low light levels — there’s about 100 million of them) and cone cells (stimulated by bright light). Sitting across the membranes of these cells are photoreceptor proteins — rhodopsin in rods and photopsin in cones. Photoreceptor proteins change shape when light falls on them — the driver for this being a small chemical attached to the proteins called retinal, one of the many forms of vitamin A. This shape change allows the proteins to ‘talk’ to the inside of the cell, i.e. to interact with other proteins to switch on enzymes and change the level of ions (sodium and calcium). The upshot is that the signal is passed through neural cells in the optic nerve to the brain where the incoming light signals are processed into the images that we perceive. © Arizona Board of Regents / ASU Ask A Biologist.

The seemingly far-fetched notion of controlling genes by light was floated by Francis Crick in 1999. The field was launched in 2002 by Boris Zemelman and Gero Miesenböck who engineered neurons to express one form of rhodopsin. This gave birth to the subject of optogenetics — using light to control cells in living tissues that have been genetically modified to express light-sensitive ion channels such as rhodopsin. By 2010 optogenetics had advanced to being the ‘Method of the Year’ according to the research journal Nature Methods.

Dropping like flies

One of the most dramatic demonstrations of the power of optogenetics has come from Robert Kittel and colleagues in Würzburg and Göttingen who made a mutant form of a protein called channelrhodopsin-1 (found in green algae) and expressed it in fruit flies (Drosophila melanogaster). The mutant protein (ChR2-XXL) carries very large photocurrents of ions (critically sodium and calcium) with the result that photostimulation can drastically change the behaviour of freely moving flies.

Light-induced stimulation of motor neurons in adult flies expressing a mutant form of rhodopsin ChR2-XXL. Click to run movie.

Left hand tube: Activation of ChR2-XXL in motor neurons with white light LEDs caused reversible immobilization of adult flies. In contrast (right hand tube) flies expressing normal (wild-type) channelrhodopsin-2 showed no response. From Dawydow et al., 2014.

Other optogenetic experiments on flies can be viewed on You Tube, e.g., the TED talk of Gero Miesenböck and the Manchester Fly Facility video of fly maggots, engineered to have a channel protein (channelrhodopsin) in their neurons, responding to blue light.

Of flies … and mice … and men

This is stunning science and it’s opened a new vista in neurobiology. But what about the things we’re concerned with in these pages — treating diseases like diabetes and cancer?

Scheme showing how genetic engineering can make the release of insulin from cells controllable by light. Normally cells of the pancreas (beta cells) take up glucose when its level in the circulation rises (via a glucose transporter protein). The rise in glucose triggers ATP production in the cell. This in turn causes potassium channels in the membrane to close (called depolarization) and this opens calcium channels. The increase in calcium in the cell drives insulin secretion. From Kushibiki et al., 2015.

The left-hand scheme above shows how glucose triggers the pancreas to produce the hormone insulin. Diabetes occurs when either the pancreas doesn’t make enough insulin or when cells of the body don’t respond properly to insulin by taking up glucose.

As a first step to see whether optogenetic regulation of calcium levels in pancreatic cells could trigger insulin release, Toshihiro Kushibiki and colleagues at the National Defense Medical College in Saitama, Japan engineered the channelrhodopsin-1 protein into mouse cells and hit them with laser light of the appropriate frequency. An hour after a short burst of light (a few seconds) the insulin levels had doubled.

The photo below shows a clump of these cells: the nuclei are blue and the channel protein (yellow) can be seen sitting across the cell membranes.

 

Cells expressing a fluorescently tagged channelrhodopsin protein (yellow). Nuclei are blue. From Kushibiki et al., 2015.

 

 

To show that this could work in animals they suspended the engineered cells in a gel and inoculated blobs of the goo under the skin of diabetic mice. Laser burst again: blood glucose levels fell and they showed this was due to the irradiated, implanted cells producing insulin.

Fast forward three years

Those brilliant results highlighted the potential of optogenetic technology as a completely novel approach to a disease that afflicts over 300 million people worldwide.

Scheme showing a Smartphone can be used to regulate the release of insulin from engineered cells implanted in a mouse with diabetes. The key events in the cell are that the light-activated receptor turns on an enzyme (BphS) that in turn controls a transcription regulator (FRTA) that binds to a DNA construct to switch on the Gene Of Interest (GOI) — in this case encoding insulin. (shGLP1, short human glucagon-like peptide 1, is a hormone that has the opposite effect to insulin). From Shao et al., 2017.

In a remarkable confluence of technologies Jiawei Shao and colleagues from a number of institutes in Shanghai, including the Shanghai Academy of Spaceflight Technology, and from ETH Zürich have recently published work that takes the application of optogenetics well and truly into the twenty-first century.

They figured that, as these days nearly everyone lives with their smartphone, the world could use a diabetes app. Essentially they designed a home server SmartController to process wireless signals so that a smartphone could control insulin production by cells in gel capsules implanted in mice. There are differences in the genetic engineering of these cells from those used by Kushibiki’s group but the critical point is unchanged: laser light stimulates insulin release. The capsules carry wirelessly powered LEDs.

The only other thing needed is to know glucose levels. Because mice are only little and they’ve already got their gel capsule, rather than implanting a monitor they took a drop of blood from the tail and used a glucometer. However, looking ahead to human applications, continuous glucose monitors are now available that, placed under the skin, can transmit a radio signal to the controller and, ultimately, it will be possible for the gel capsules to have a built-in battery plus glucose sensor and the whole thing could work automatically.

Any chance of illuminating cancer?

This science is so breathtaking it seems cheeky to ask but, well, I’d say ‘yes but not just yet.’ So long as the ‘drug’ you wish to use can be made biologically (i.e. from DNA by the machinery of the cell), rather than by chemical synthesis, Shao’s Smartphone set-up can readily be adapted to deliver anti-cancer drugs. This might be hugely preferable to the procedures currently in use and would offer an additional advantage by administering drugs in short bursts of lower concentration — a regimen that in some mouse cancer models at least is more effective.

References

Dawydow, A., Kittel, R.J. et al., 2014. Channelrhodopsin-2–XXL, a powerful optogenetic tool for low-light applications. PNAS 111, 13972-13977.

Kushibiki et al., (2015). Optogenetic control of insulin secretion by pancreatic beta-cells in vitro and in vivo. Gene Therapy 22, 553-559.

Shao, J. et al., 2017. Smartphone-controlled optogenetically engineered cells enable semiautomatic glucose homeostasis in diabetic mice. Science Translational Medicine 9, Issue 387, eaal2298.

No It Isn’t!

 

It’s great that newspapers carry the number of science items they do but, as regular readers will know, there’s nothing like the typical cancer headline to get me squawking ‘No it isn’t!” Step forward The Independent with the latest: “Major breakthrough in cancer care … groundbreaking international collaboration …”

Let’s be clear: the subject usually is interesting. In this case it certainly is and it deserves better headlines.

So what has happened?

A big flurry of research papers has just emerged from a joint project of the National Cancer Institute and the National Human Genome Research Institute to make something called The Cancer Genome Atlas (TCGA). This massive initiative is, of course, an offspring of the Human Genome Project, the first full sequencing of the 3,000 million base-pairs of human DNA, completed in 2003. The intervening 15 years have seen a technical revolution, perhaps unparalled in the history of science, such that now genomes can be sequenced in an hour or two for a few hundred dollars. TCGA began in 2006 with the aim of providing a genetic data-base for three cancer types: lung, ovarian, and glioblastoma. Such was its success that it soon expanded to a vast, comprehensive dataset of more than 11,000 cases across 33 tumor types, describing the variety of molecular changes that drive the cancers. The upshot is now being called the Pan-Cancer Atlas — PanCan Atlas, for short.

What do we need to know?

Fortunately not much of the humungous amounts of detail but the scheme below gives an inkling of the scale of this wonderful endeavour — it’s from a short, very readable summary by Carolyn Hutter and Jean Claude Zenklusen.

TCGA by numbers. The scale of the effort and output from The Cancer Genome Atlas. From Hutter and Zenklusen, 2018.

The first point is obvious: sequencing 11,000 paired tumour and normal tissue samples produced mind-boggling masses of data. 2.5 petabytes, in fact. If you have to think twice about your gigas and teras, 1 PB = 1,000,000,000,000,000 B, i.e. 1015 B or 1000 terabytes. A PB is sometimes called, apparently, a quadrillion — and, as the scheme helpfully notes, you’d need over 200,000 DVDs to store it.

The 33 different tumour types included all the common cancers (breast, bowel, lung, prostate, etc.) and 10 rare types.

The figure of seven data types refers to the variety of information accumulated in these studies (e.g., mutations that affect genes, epigenetic changes (DNA methylation), RNA and protein expression, duplication or deletion of stretches of DNA (copy number variation), etc.

After which it’s worth pausing for a moment to contemplate the effort and organization involved in collecting 11,000 paired samples, sequencing them and analyzing the output. It’s true that sequencing itself is now fairly routine, but that’s still an awful lot of experiments. But think for even longer about what’s gone into making some kind of sense of the monstrous amount of data generated.

And it’s important because?

The findings confirm a trend that has begun to emerge over the last few years, namely that the classification of cancers is being redefined. Traditionally they have been grouped on the basis of the tissue of origin (breast, bowel, etc.) but this will gradually be replaced by genetic grouping, reflecting the fact that seemingly unrelated cancers can be driven by common pathways.

The most encouraging thing to come out of the genetic changes driving these tumours is that for about half of them potential treatments are already available. That’s quite a surprise but it doesn’t mean that hitting those targets will actually work as anti-cancer strategies. Nevertheless, it’s a cheering point that the output of this phenomenal project may, as one of the papers noted, serve as a launching pad for real benefit in the not too distant future.

What should science journalists do to stop upsetting me?

Read the papers they comment on rather than simply relying on press releases, never use the words ‘breakthrough’ or ‘groundbreaking’ and grasp the point that science proceeds in very small steps, not always forward, governed by available methods. This work is quite staggering for it is on a scale that is close to unimaginable and, in the end, it will lead to treatments that will affect the lives of almost everyone — but it is just another example of science doing what science does.

References

Hutter, C. and Zenklusen, J.C. (2018). The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell 173, 283–285.

Hoadley, K.A. et al. (2018). Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell 173, 291–304.

Hoadley, K.A. et al. (2014). Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin. Cell 158, 929–944.

John Sulston: Biologist, Geneticist and Guardian of our Heritage

 

Sir John Sulston died on 6 March 2018, an event reported world-wide by the press, radio and television. Having studied in Cambridge and then worked at the Salk Institute in La Jolla, California, he joined the Laboratory of Molecular Biology in Cambridge to investigate how genes control development and behaviour, using as a ‘model organism’ the roundworm Caenorhabditis elegans. This tiny creature, 1 mm long, was appealing because it is transparent and most adult worms are made up of precisely 959 cells. Simple it may be but this worm has all the bits required for to live, feed and reproduce (i.e. a gut, a nervous system, gonads, intestine, etc.). For his incredibly painstaking efforts in mapping from fertilized egg to mature animal how one cell becomes two, two becomes four and so on to complete the first ‘cell-lineage tree’ of a multicellular organism, Sulston shared the 2002 Nobel Prize in Physiology or Medicine with Bob Horvitz and Sydney Brenner.

Sir John Sulston

It became clear to Sulston that the picture of how genes control development could not be complete without the corresponding sequence of DNA, the genetic material. The worm genome is made up of 100 million base-pairs and in 1983 Sulston set out to sequence the whole thing, in collaboration with Robert Waterston, then at the University of Washington in St. Louis. This was a huge task with the technology available but their success indicated that the much greater prize of sequencing of the human genome — ten times as much DNA as in the worm — might be attainable.

In 1992 Sulston became head of a new sequencing facility, the Sanger Centre (now the Sanger Institute), in Hinxton, Cambridgeshire that was the British component of the Human Genome Project, one of the largest international scientific operations ever undertaken. Astonishingly, the complete human genome sequence, finished to a standard of 99.99% accuracy, was published in Nature in October 2004.

As the Human Genome Project gained momentum it found itself in competition with a private venture aimed at securing the sequence of human DNA for commercial profit — i.e., the research community would be charged for access to the data. Sulston was adamant that our genome belonged to us all and with Francis Collins — then head of the US National Human Genome Research Institute — he played a key role in establishing the principle of open access to such data, preventing the patenting of genes and ensuring that the human genome was placed in the public domain.

One clear statement of this intent was that, on entering the Sanger Centre, you were met by a continuously scrolling read-out of human DNA sequence as it emerged from the sequencers.

In collaboration with Georgina Ferry, Sulston wrote The Common Thread, a compelling account of an extraordinary project that has, arguably, had a greater impact than any other scientific endeavour.

For me and my family John’s death was a heavy blow. My wife, Jane, had worked closely with him since inception of the Sanger Centre and not only had his scientific influence been immense but he had also become a staunch friend and source of wisdom. At the invitation of John’s wife Daphne, a group of friends and relatives gathered at their house after the funeral. As darkness fell we went into the garden and once again it rang to the sound of chatter and laughter from young and old as we enjoyed one of John’s favourite party pastimes — making hot-air lanterns and launching them to drift, flickering to oblivion, across the Cambridgeshire countryside. John would have loved it and it was a perfect way to remember him.

Then …

When John Sulston set out to ‘map the worm’ the tools he used could not have been more basic: a microscope — with pencil and paper to sketch what he saw as the animal developed. His hundreds of drawings tracked the choreography of the worm to its final 959 cells and showed that, along the way, 131 cells die in a precisely orchestrated programme of cell death. The photomontage and sketch below are from his 1977 paper with Bob Horvitz and give some idea of the effort involved.

Photomontage of a microscope image (top) and (lower) sketch of the worm Caenorhabditis elegans showing cell nuclei. From Sulston and Horvitz, 1977.

 … and forty years on

It so happened that within a few days of John’s death Achim Trubiroha and colleagues at the Université Libre de Bruxelles published a remarkable piece of work that is really a descendant of his pioneering studies. They mapped the development of cells from egg fertilization to maturity in a much bigger animal than John’s worms — the zebrafish. They focused on one group of cells in the early embryo (the endoderm) that develop into various organs including the thyroid. Specificially they tracked the formation of the thyroid gland that sits at the front of the neck wrapped around part of the larynx and the windpipe (trachea). The thyroid can be affected by several diseases, e.g., hyperthyroidism, and in about 5% of people the thyroid enlarges to form a goitre — usually caused by iodine deficiency. It’s essential to determine the genes and signalling pathways that control thyroid development if we are to control these conditions.

For this mapping Trubiroha’s group used the CRISPR method of gene editing to mutate or knock out specific targets and to tag cells with fluorescent labels — that we described in Re-writing the Manual of Life.

A flavor of their results is given by the two sets of fluorescent images below. These show in real time the formation of the thyroid after egg fertilization and the effect of a drug that causes thyroid enlargement.

Live imaging of transgenic zebrafish to follow thyroid development in real-time (left). Arrows mark chord-like cell clusters that form hormone-secreting follicles (arrowheads) during normal development. The right hand three images show normal development (-) and goiter formation (+) induced by a drug. From Trubiroha et al. 2018.

John would have been thrilled by this wonderful work and, with a chuckle, I suspect he’d have said something like “Gosh! If we’d had gene editing back in the 70s we’d have mapped the worm in a couple of weeks!”

References

International Human Genome Sequencing Consortium Nature 431, 931–945; 2004.

John Sulston and Georgina Ferry The Common Thread: A Story of Science, Politics, Ethics and the Human Genome (Bantam Press, 2002).

Sulston, J.E. and Horvitz, H.R. (1977). Post-embryonic Cell Lineages of the Nematode, Caenorhabitis elegans. Development Biology 56, 110-156.

Trubiroha, A. et al. (2018). A Rapid CRISPR/Cas-based Mutagenesis Assay in Zebrafish for Identification of Genes Involved in Thyroid Morphogenesis and Function. Scientific Reports 8, Article number: 5647.

Bonkers Really … but …

 

This is just in case you spotted the headline in January 2018: ‘Scientists Counted All The Protein Molecules in a Cell And The Answer Really Is 42. This is so perfect.’ 

Them scientists eh! The things they get up to!! The scallywags in this case were Brandon Ho & chums from the University of Toronto and Signe Dean, the journalist who came up with the headline, was referring, of course, to Douglas Adams’s “Answer to the Ultimate Question of Life …” in The Hitchhiker’s Guide to the Galaxy — though it may be noted that Ho’s paper includes neither the number 42 nor mention of Douglas Adams.

The cult that has evolved around this number is both amusing and bizarre, not least because Adams himself explained that he dreamed 42 up out of the blue. In a different context a while ago (talking about how the way you get to work might affect your life expectancy) I recounted happy evenings spent carousing in The Baron (well, having a quiet jar or two) with Douglas Adams and friends from which it was clear that he was not into abstruse mathematics, astrology or the occult. He just had a vivid imagination.

Anything for a catchy headline but

Aside from the whimsy, is there anything interesting in this paper? Well, yes. Ho & Co studied a type of yeast (Saccharomyces cerevisiae) that is mighty important because it’s been a foundation for brewing and baking since ancient times. So no merry sessions in The Baron of Beef without it! Its cells are about the same size as red blood cells (5–10 microns in diameter) but you can actually see them sometimes as films on the skin of fruit. It’s played a huge role in biology as a ‘model organism’ for studying how we work because the proteins it makes that are essential for life are pretty well identical to those in human cells — so much so that you can swap those that control cell growth and division between the two. Yeast proteins work just fine in human cells and vice versa.

 

Yeast on the skin of a grape. Photo: Barbara W. Beacham

 

The question Ho & Co asked was ‘how many protein molecules are there in one cell?’ In the age when you can sequence the DNA of practically anything at the drop of a hat, you might think we’d know the answer already but in fact it’s not been at all clear. Accordingly, what these authors did was to pull together all the relevant studies that have been done to come up with an absolute figure. The answer that emerged was that the number of protein molecules per yeast cell is 4.2 x 107 — which, of course, can also be written as 42 million. Eureka! We have our headline!! Albeit, as the authors noted, with a two-fold error range.

Does anyone care?

Now you’re just being awkward. You should be grateful to be made to picture for a moment tens of millions of proteins jiggling around in little sacs so small you could get tens of thousands of these cells on the head of a pin. And somehow, in that heaving molecular city, each protein manages to carry out its own task so that the cell works. It is quite staggering.

Mention of tasks leads to the other question Ho et al looked at: how many copies are there of the different types of protein? We know from its DNA sequence that this yeast has about 6,000 genes (Saccharomyces Genome Database). So that’s at least 6,000 different proteins. Not surprisingly, it turns out that about two thirds of them are in the middle in terms of abundance — i.e. there’s between 1,000 and 10,000 molecules of each sort per cell. The rest are either low abundance (up to about 800 molecules per cell) or at the high end — 140,000 to 750,000, i.e. somewhere in the region of half a million copies of each type of protein.

Does this distribution make sense in terms of what these proteins do?

You know the answer because if it didn’t the Toronto team wouldn’t have got their work published but, indeed, proteins present in large numbers are, for example, part of the machinery that makes new proteins (so they’re slaving away all the time) whereas, those present in small numbers do things like repair and replicate DNA and drive cells to divide — important jobs but ones that are only intermittently needed.

These results aren’t going to turn science on its head but it is awe-inspiring when a piece of work really brings us face-to-face with stunning complexity of biology. And if it takes a bonkers headline to catch our eye, so be it!

Reference

Ho, B. et al. (2018). Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome. Cell Systems. Published online: January 23, 2018.

Desperately SEEKing …

These days few can be unaware that cancers kill one in three of us. That proportion has crept up over time as life expectancy has gone up — cancers are (mainly) diseases of old age. Even so, they plagued the ancients as Egyptian scrolls dating from 1600 BC record and as their mummified bodies bear witness. Understandably, progress in getting to grips with the problem was slow. It took until the nineteenth century before two great French physicians, Laënnec and Récamier, first noted that tumours could spread from their initial site to other locations where they could grow as ‘secondary tumours’. Munich-born Karl Thiersch showed that ‘metastasis’ occurs when cells leave the primary site and spread through the body. That was in 1865 and it gradually led to the realisation that metastasis was a key problem: many tumours could be dealt with by surgery, if carried out before secondary tumours had formed, but once metastasis had taken hold … With this in mind the gifted American surgeon William Halsted applied ever more radical surgery to breast cancers, removing tissues to which these tumors often spread, with the aim of preventing secondary tumour formation.

Early warning systems

Photos of Halsted’s handiwork are too grim to show here but his logic could not be faulted for metastasis remains the cause of over 90% of cancer deaths. Mercifully, rather than removing more and more tissue targets, the emphasis today has shifted to tumour detection. How can they be picked up before they have spread?

To this end several methods have become familiar — X-rays, PET (positron emission tomography, etc) — but, useful though these are in clinical practice, they suffer from being unable to ‘see’ small tumours (less that 1 cm diameter). For early detection something completely different was needed.

The New World

The first full sequence of human DNA (the genome), completed in 2003, opened a new era and, arguably, the burgeoning science of genomics has already made a greater impact on biology than any previous advance.

Tumour detection is a brilliant example for it is now possible to pull tumour cell DNA out of the gemisch that is circulating blood. All you need is a teaspoonful (of blood) and the right bit of kit (silicon chip technology and short bits of artificial DNA as bait) to get your hands on the DNA which can then be sequenced. We described how this ‘liquid biopsy’ can be used to track responses to cancer treatment in a quick and non–invasive way in Seeing the Invisible: A Cancer Early Warning System?

If it’s brilliant why the question mark?

Two problems really: (1) Some cancers have proved difficult to pick up in liquid biopsies and (2) the method didn’t tell you where the tumour was (i.e. in which tissue).

The next step, in 2017, added epigenetics to DNA sequencing. That is, a programme called CancerLocator profiled the chemical tags (methyl groups) attached to DNA in a set of lung, liver and breast tumours. In Cancer GPS? we described this as a big step forward, not least because it detected 80% of early stage cancers.

There’s still a pesky question mark?

Rather than shrugging their shoulders and saying “that’s science for you” Joshua Cohen and colleagues at Johns Hopkins University School of Medicine in Baltimore and a host of others rolled their sleeves up and made another step forward in the shape of CancerSEEK, described in the January 18 (2018) issue of Science.

This added two new tweaks: (1) for DNA sequencing they selected a panel of 16 known ‘cancer genes’ and screened just those for specific mutations and (2) they included proteins in their analysis by measuring the circulating levels of 10 established biomarkers. Of these perhaps the most familiar is cancer antigen 125 (CA-125) which has been used as an indicator of ovarian cancer.

Sensitivity of CancerSEEK by tumour type. Error bars represent 95% confidence intervals (from Cohen et al., 2018).

The figure shows a detection rate of about 70% for eight cancer types in 1005 patients whose tumours had not spread. CancerSEEK performed best for five types (ovary, liver, stomach, pancreas and esophagus) that are difficult to detect early.

Is there still a question mark?

Of course there is! It’s biology — and cancer biology at that. The sensitivity is quite low for some of the cancers and it remains to be seen how high the false positive rate goes in larger populations than 1005 of this preliminary study.

So let’s leave the last cautious word to my colleague Paul Pharoah: “I do not think that this new test has really moved the field of early detection very far forward … It remains a promising, but yet to be proven technology.”

Reference

D. Cohen et al. (2018). Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 10.1126/science.aar3247.

Lorenzo’s Oil for Nervous Breakdowns

 

A Happy New Year to all our readers – and indeed to anyone who isn’t a member of that merry band!

What better way to start than with a salute to the miracles of modern science by talking about how the lives of a group of young boys have been saved by one such miracle.

However, as is almost always the way in science, this miraculous moment is merely the latest step in a long journey. In retracing those steps we first meet a wonderful Belgian – so, when ‘name a famous Belgian’ comes up in your next pub quiz, you can triumphantly produce him as a variant on dear old Eddy Merckx (of bicycle fame) and César Franck (albeit born before Belgium was invented). As it happened, our star was born in Thames Ditton (in 1917: his parents were among the one quarter of a million Belgians who fled to Britain at the beginning of the First World War) but he grew up in Antwerp and the start of World War II found him on the point of becoming qualified as a doctor at the Catholic University of Leuven. Nonetheless, he joined the Belgian Army, was captured by the Germans, escaped, helped by his language skills, and completed his medical degree.

Not entirely down to luck

This set him off on a long scientific career in which he worked in major institutes in both Europe and America. He began by studying insulin (he was the first to suggest that insulin lowered blood sugar levels by prompting the liver to take up glucose), which led him to the wider problems of how cells are organized to carry out the myriad tasks of molecular breaking and making that keep us alive.

The notion of the cell as a kind of sac with an outer membrane that protects the inside from the world dates from Robert Hooke’s efforts with a microscope in the 1660s. By the end of the nineteenth century it had become clear that there were cells-within-cells: sub-compartments, also enclosed by membranes, where special events took place. Notably these included the nucleus (containing DNA of course) and mitochondria (sites of cellular respiration where the final stages of nutrient breakdown occurs and the energy released is transformed into adenosine triphosphate (ATP) with the consumption of oxygen).

In the light of that history it might seem a bit surprising that two more sub-compartments (‘organelles’) remained hidden until the 1950s. However, if you’re thinking that such a delay could only be down to boffins taking massive coffee breaks and long vacations, you’ve never tried purifying cell components and getting them to work in test-tubes. It’s a process called ‘cell fractionation’ and, even with today’s methods, it’s a nightmare (sub-text: if you have to do it, give it to a Ph.D. student!).

By this point our famous Belgian had gathered a research group around him and they were trying to dissect how insulin worked in liver cells. To this end they (the Ph.D. students?!) were using cell fractionation and measuring the activity of an enzyme called acid phosphatase. Finding a very low level of activity one Friday afternoon, they stuck the samples in the fridge and went home. A few days later some dedicated soul pulled them out and re-measured the activity discovering, doubtless to their amazement, that it was now much higher!

In science you get odd results all the time – the thing is: can you repeat them? In this case they found the effect to be absolutely reproducible. Leave the samples a few days and you get more activity. Explanation: most of the enzyme they were measuring was contained within a membrane-like barrier that prevented the substrate (the chemical that the enzyme reacts with) getting to the enzyme. Over a few days the enzyme leaked through the barrier and, lo and behold, now when you measured activity there was more of it!

Thus was discovered the ‘lysosome’ – a cell-within-a cell that we now know is home to an array of some 40-odd enzymes that break down a range of biomolecules (proteinsnucleic acidssugars and lipids). Our self-effacing hero said it was down to ‘chance’ but in science, as in other fields of life, you make your own luck – often, as in this case, by spotting something abnormal, nailing it down and then coming up with an explanation.

In the last few years lysosomes have emerged as a major player in cancer because they help cells to escape death pathways. Furthermore, they can take up anti-cancer drugs, thereby reducing potency. For these reasons they are the focus of great interest as a therapeutic target.

Lysosomes in cells revealed by immunofluorescence.

Antibody molecules that stick to specific proteins are tagged with fluorescent labels. In these two cells protein filaments of F-actin that outline cell shape are labelled red. The green dots are lysosomes (picked out by an antibody that sticks to a lysosome protein, RAB9). Nuclei are blue (image: ThermoFisher Scientific).

Play it again Prof!

In something of a re-run of the lysosome story, the research team then found itself struggling with several other enzymes that also seemed to be shielded from the bulk of the cell – but the organelle these lived in wasn’t a lysosome – nor were they in mitochondria or anything else then known. Some 10 years after the lysosome the answer emerged as the ‘peroxisome’ – so called because some of their enzymes produce hydrogen peroxide. They’re also known as ‘microbodies’ – little sacs, present in virtually all cells, containing enzymatic goodies that break down molecules into smaller units. In short, they’re a variation on the lysosome theme and among their targets for catabolism are very long-chain fatty acids (for mitochondriacs the reaction is β-oxidation but by a different pathway to that in mitochondria).

Peroxisomes revealed by immunofluorescence.

As in the lysosome image, F-actin is red. The green spots here are from an antibody that binds to a peroxisome protein (PMP70). Nuclei are blue (image: Novus Biologicals)

Cell biology fans will by now have worked out that our first hero in this saga of heroes is Christian de Duve who shared the 1974 Nobel Prize in Physiology or Medicine with Albert Claude and George Palade.

A wonderful Belgian. Christian de Duve: physician and Nobel laureate.

Hooray!

Fascinating and important stuff – but nonetheless background to our main story which, as they used to say in The Goon Show, really starts here. It’s so exciting that, in 1992, they made a film about it! Who’d have believed it?! A movie about a fatty acid!! Cinema buffs may recall that in Lorenzo’s Oil Susan Sarandon and Nick Nolte played the parents of a little boy who’d been born with a desperate disease called adrenoleukodystrophy (ALD). There are several forms of ALD but in the childhood disease there is progression to a vegetative state and death occurs within 10 years. The severity of ALD arises from the destruction of myelin, the protective sheath that surrounds nerve fibres and is essential for transmission of messages between brain cells and the rest of the body. It occurs in about 1 in 20,000 people.

Electrical impulses (called action potentials) are transmitted along nerve and muscle fibres. Action potentials travel much faster (about 200 times) in myelinated nerve cells (right) than in (left) unmyelinated neurons (because of Saltatory conduction). Neurons (or nerve cells) transmit information using electrical and chemical signals.

The film traces the extraordinary effort and devotion of Lorenzo’s parents in seeking some form of treatment for their little boy and how, eventually, they lighted on a fatty acid found in lots of green plants – particularly in the oils from rapeseed and olives. It’s one of the dreaded omega mono-unsaturated fatty acids (if you’re interested, it can be denoted as 22:1ω9, meaning a chain of 22 carbon atoms with one double bond 9 carbons from the end – so it’s ‘unsaturated’). In a dietary combination with oleic acid  (another unsaturated fatty acid: 18:1ω9) it normalizes the accumulation of very long chain fatty acids in the brain and slows the progression of ALD. It did not reverse the neurological damage that had already been done to Lorenzo’s brain but, even so, he lived to the age of 30, some 22 years longer than predicted when he was diagnosed.

What’s going on?

It’s pretty obvious from the story of Lorenzo’s Oil that ALD is a genetic disease and you will have guessed that we wouldn’t have summarized the wonderful career of Christian de Duve had it not turned out that the fault lies in peroxisomes.

The culprit is a gene (called ABCD1) on the X chromosome (so ALD is an X-linked genetic disease). ABCD1 encodes part of the protein channel that carries very long chain fatty acids into peroxisomes. Mutations in ABCD1 (over 500 have been found) cause defective import of fatty acids, resulting in the accumulation of very long chain fatty acids in various tissues. This can lead to irreversible brain damage. In children the myelin sheath of neurons is damaged, causing neurological defects including impaired vision and speech disorders.

And the miracle?

It’s gene therapy of course and, helpfully, we’ve already seen it in action. Self Help – Part 2 described how novel genes can be inserted into the DNA of cells taken from a blood sample. The genetically modified cells (T lymphocytes) are grown in the laboratory and then infused into the patient – in that example the engineered cells carried an artificial T cell receptor that enabled them to target a leukemia.

In Gosh! Wonderful GOSH we saw how the folk at Great Ormond Street Hospital adapted that approach to treat a leukemia in a little girl.

Now David Williams, Florian Eichler, and colleagues from Harvard and many other centres around the world, including GOSH, have adapted these methods to tackle ALD. Again, from a blood sample they selected one type of cell (stem cells that give rise to all blood cell types) and then used genetic engineering to insert a complete, normal copy of the DNA that encodes ABCD1. These cells were then infused into patients. As in the earlier studies, they used a virus (or rather part of a viral genome) to get the new genetic material into cells. They choose a lentivirus for the job – these are a family of retroviruses (i.e. they have RNA genomes) that includes HIV. Specifically they used a commercial vector called Lenti-D. During the life cycle of RNA viruses their genomes are converted to DNA that becomes a permanent part of the host DNA. What’s more, lentiviruses can infect both non-dividing and actively dividing cells, so they’re ideal for the job.

In the first phase of this ongoing, multi-centre trial a total of 17 boys with ALD received Lenti-D gene therapy. After about 30 months, in results reported in October 2017, 15 of the 17 patients were alive and free of major functional disability, with minimal clinical symptoms. Two of the boys with advanced symptoms had died. The achievement of such high remission rates is a real triumph, albeit in a study that will continue for many years.

In tracing this extraordinary galaxy, one further hero merits special mention for he played a critical role in the story. In 1999 Jesse Gelsinger, a teenager, became the first person to receive viral gene therapy. This was for a metabolic defect and modified adenovirus was used as the gene carrier. Despite this method having been extensively tested in a range of animals (and the fact that most humans, without knowing it, are infected with some form of adenovirus), Gelsinger died after his body mounted a massive immune response to the viral vector that caused multiple organ failure and brain death.

This was, of course, a huge set-back for gene therapy. Despite this, the field has advanced significantly in the new century, both in methods of gene delivery (including over 400 adenovirus-based gene therapy trials) and in understanding how to deal with unexpected immune reactions. Even so, to this day the Jesse Gelsinger disaster weighs heavily with those involved in gene therapy for it reminds us all that the field is still in its infancy and that each new step is a venture into the unknown requiring skill, perseverance and bravery from all involved – scientists, doctors and patients. But what better encouragement could there be than the ALD story of young lives restored.

It’s taken us a while to piece together the main threads of this wonderful tale but it’s emerged as a brilliant example of how science proceeds: in tiny steps, usually with no sense of direction. And yet, despite setbacks, over much time, fragments of knowledge come together to find a place in the grand jigsaw of life.

In setting out to probe the recesses of metabolism, Christian de Duve cannot have had any inkling that he would build a foundation on which twenty-first century technology could devise a means of saving youngsters from a truly terrible fate but, my goodness, what a legacy!!!

References

Eichler, F. et al. (2017). Hematopoietic Stem-Cell Gene Therapy for Cerebral Adrenoleukodystrophy. The New England Journal of Medicine 377, 1630-1638.

 

A Musical Offering 

It’s generally accepted that Johann Sebastian Bach was one of the greatest, if not the greatest, musical composer of all time. In well over 1000 compositions he laid down the framework upon which rested virtually all Western music of the following 200 years. Of these works, The Musical Offering, written in 1747, is a collection of pieces based on a single theme that has been described as the most significant piano composition in history.

Along the way to becoming a unique composer, Bach married twice and sired twenty children, only ten of whom survived into adulthood. Those figures highlight another way in which JSB was something of a freak because, in 1750 when he died aged 65, the average life expectancy in Europe was under 40 years. For that reason cancers, being primarily being diseases of old age, were much less prominent then than now when, on average, we live to be over 80 and cancers account for about one in three deaths.

It’s safe to say that in the 18th century neither Bach nor anyone else knew anything of cancer yet alone that our genetic material carries tens of thousands of genes – a kind of molecular keyboard upon which cellular machinery plays to produce an output of proteins that distinguishes one cell type from another but is also continuously varying, even within individual cells. Bach would have been fascinated by this fluctuating molecular mosaic that, through the wonders of modern sequencing methods, we can display as ‘heat maps’ showing which genes are turned on (being expressed) and to what level.

Musical genes. Left: a heat map showing the pattern of genes being expressed at a given time in several different types of cell. Red: high expression level; green low expression. On the right is the same information transformed into musical notation using the Gene Expression Music Algorithm, GEMusicA (from Staege 2016).

With commendable vision a chap by the name of Martin Staege has come up with an alternative way of looking at the rather mind-blowing picture conveyed by heat maps. Staege is in the Martin Luther University of Halle-Wittenberg – appropriately as Bach’s eldest son studied at the University of Halle. His idea is that gene expression patterns can be transformed into sounds characterized by their frequency (pitch) and tone duration. In other words you can make genes play tunes – and what’s more compare the notes from different cell samples (e.g., normal and tumour cells) so that you can ‘hear’ the differences in gene expression.

Remarkable or what?!

Unsurprisingly, gene tunes sound more Alban Berg than Magic Flute, prompting the redoubtable Dr. Staege to go one step further by producing an algorithm that fits gene themes as best it can to more singable pieces – so you get a kind of difference melody. I don’t think Beethoven or Wagner would see this biological music as a threat and they might, like me, ask ‘what’s the point?’

To which, I guess, the answers are ‘It’s clever and fun’. It’s also yet another way of showing the power of DNA as an information storage medium, and making the point that in this guise it may, in due course, make a massive impact on our lives – much more mundane than musical genes but hugely more useful.

References

Staege, M. S. (2016). Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature. Stem Cells International
Volume 2016, Article ID 7674824, 10 pages http://dx.

Staege, M. S. (2015). A short treatise concerning a musical approach for the interpretation of gene expression data. Sci. Rep. 5, 15281.