Secret Army: More Manoeuvres Revealed

 

I don’t know about you but I find it difficult to grasp the idea that there are more bugs in my body than there are ‘me’ cells. That is, microorganisms (mostly bacteria) outnumber the aggregate of liver, skin and what-have-you cells. They’re attracted, of course, to the warm, damp surfaces of the cavities in our bodies that are covered by a sticky, mucous membrane, e.g., the mouth, nose and especially the intestines (the gastrointestinal tract).

The story so far

Over the last few years it’s become clear that these co-residents — collectively called the microbiota — are not just free-loaders. They’re critical to our well-being in helping to fight infection by other microrganisms (as we noted in Our Inner Self), they influence our immune system and in the gut they extract the last scraps of nutrients from our diet. So maybe it makes them easier to live with if we keep in mind that we need them every bit as much as they depend on us.

We now know that there are about 2000 different species of bacteria in the human gut (yes, that really is 2,000 different types of bug) and, with all that diversity, it’s not surprising that the total number of genes they carry far exceeds our own complement (by several million to about 20,000). In it’s a small world we noted that obesity causes a switch in the proportions of two major sub-families of bacteria, resulting in a decrease in the number of bug genes. The flip side is that a more diverse bug population (microbiome) is associated with a healthy status. What’s more, shifts of this sort in the microbiota balance can influence cancer development. Even more remarkably, we saw in Hitchhiker Or Driver? That the microbiome may also play a role in the spread of tumours to secondary sites (metastasis).

Time for a deep breath

If all this is going on in the intestines you might well ask “What about the lungs?” — because, and if you didn’t know you might guess, their job of extracting oxygen from the air we inhale means that they are covered with the largest surface area of mucosal tissue in the body. They are literally an open invitation to passing microorganisms — as we all know from the ease with which we pick up infections.

In view of what we know about gut bugs a rather obvious question is “Could the bug community play a role in lung cancer?” It’s a particularly pressing question because not only is lung cancer the major global cause of cancer death but 70% lung cancer patients have bacterial infections and these markedly influence tumour development and patient survival. Tyler Jacks, Chengcheng Jin and colleagues at the Massachusetts Institute of Technology approached this using a mouse model for lung cancer (in which two mutated genes, Kras and P53 drive tumour formation).

In short they found that germ-free mice (or mice treated with antibiotics) were significantly protected from lung cancer in this model system.

How bacteria can drive lung cancer in mice. Left: scheme of a lung with low levels of bacteria and normal levels of immune system cells. Right: increased levels of bacteria accelerate tumour growth by stimulating the release of chemicals from blood cells that in turn activate cells of the immune system to release other effector molecules that promote tumour growth. The mice were genetically altered to promote lung tumour growth (by mutation of the Kras and P53 genes). In more detail the steps are that the bacteria cause macrophages to release interleukins (IL-1 & IL-23) that stick to a sub-set of T cells (γδ T cells): these in turn release factors that drive tumour cell proliferation, including IL-22. From Jin et al. 2019.

As lung tumours grow in this mouse model the total bacterial load increases. This abnormal regulation of the local bug community stimulates white blood cells (T cells present in the lung) to make and release small proteins (cytokines, in particular interleukin 17) that signal to neutrophils and tumour cells to promote growth.

This new finding reveals that cross-talk between the local microbiota and the immune system can drive lung tumour development. The extent of lung tumour growth correlated with the levels of bacteria in the airway but not with those in the intestinal tract — so this is an effect specific to the lung bugs.

Indeed, rather than the players prominent in the intestines (Bs & Fs) that we met in Hitchhiker Or Driver?, the most common members of the lung microbiome are Staphylococcus, Streptococcus and Lactobacillus.

In a final twist Jin & Co. took bacteria from late-stage tumours and inoculated them into the lungs of mice with early tumours that then grew faster.

These experiments have revealed a hitherto unknown role for bacteria in cancer and, of course, the molecular signals identified join the ever-expanding list of potential targets for drug intervention.

References

Jin, C. et al. (2019). Commensal Microbiota Promote Lung Cancer Development via γδ T Cells. Cell 176, 998-1013.e16.

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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.

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.

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.

Hitchhiker Or Driver?

 

It’s a little while since we talked about what you might call our hidden self — the vast army of bugs that colonises our nooks and crannies, especially our intestines, and that is essential to our survival.

In Our Inner Self we noted that these little guys outnumber the human cells that make up the body by about ten to one. Actually that estimate has recently been revised — downwards you might be relieved to hear — to about 1.3 bacterial cells per human cell but it doesn’t really matter. They are a major part of what’s called the microbiome — a vast army of microorganisms that call our bodies home but on which we also depend for our very survival.

In our personal army there’s something like 700 different species of bacteria, with thirty or forty making up the majority. We upset them at our peril. Artificial sweeteners, widely used as food additives, can change the proportions of types of gut bacteria. Some antibiotics that kill off bacteria can make mice obese — and they probably do the same to us. Obese humans do indeed have reduced numbers of bugs and obesity itself is associated with increased cancer risk.

In it’s a small world we met two major bacterial sub-families, Bacteroidetes and Firmicutes, and noted that their levels appear to affect the development of liver and bowel cancers. Well, the Bs & Fs are still around you’ll be glad to know but in a recent piece of work the limelight has been taken by another bunch of Fs — a sub-group (i.e. related to the Bs & Fs) called Fusobacterium.

It’s been known for a few years that human colon cancers carry enriched levels of these bugs compared to non-cancerous colon tissues — suggesting, though not proving, that Fusobacteria may be pro-tumorigenic. In the latest, pretty amazing, installment Susan Bullman and colleagues from Harvard, Yale and Barcelona have shown that not merely is Fusobacterium part of the microbiome that colonises human colon cancers but that when these growths spread to distant sites (i.e. metastasise) the little Fs tag along for the ride! 

Bacteria in a primary human bowel tumour.  The arrows show tumour cells infected with Fusobacteria (red dots).

Bacteria in a liver metastasis of the same bowel tumour.  Though more difficult to see, the  red dot (arrow) marks the presence of bacteria from the original tumour. From Bullman et al., 2017.

In other words, when metastasis kicks in it’s not just the tumour cells that escape from the primary site but a whole community of host cells and bugs that sets sail on the high seas of the circulatory system.

But doesn’t that suggest that these bugs might be doing something to help the growth and spread of these tumours? And if so might that suggest that … of course it does and Bullman & Co did the experiment. They tried an antibiotic that kills Fusobacteria (metronidazole) to see if it had any effect on F–carrying tumours. Sure enough it reduced the number of bugs and slowed the growth of human tumour cells in mice.

Growth of human tumour cells in mice. The antibiotic metronidazole slows the growth of these tumour by about 30%. From Bullman et al., 2017.

We’re still a long way from a human therapy but it is quite a startling thought that antibiotics might one day find a place in the cancer drug cabinet.

Reference

Bullman, S. et al. (2017). Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science  358, 1443-1448. DOI: 10.1126/science.aal5240

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.

Cancer GPS?

The thing that pretty well everyone knows about cancers is that most are furtive little blighters. They kill one in three of us but usually we don’t they’re there until they are big enough to make something go wrong in the body or to show up in our seriously inadequate screening methods. In that sense they resemble heart problems of one sort or another, where often the first indication of trouble is unexpectedly finding yourself lying on the floor.

Meanwhile, out on the highways and byways you are about 75 times less likely to be killed in an accident than you are to succumb to either cancers or circulation failure. Which is a way of saying that in the UK about 2000 of us perish on the roads each year. That it’s ‘only’ 2000 is presumably because here your assailant is anything but furtive. All you’ve got to do is side-step the juggernaut and you’ll probably live to be – well, old enough to get cancer.

Did you know, by the way, that ‘juggernaut’ is said to come from the chariots of the Jagannath Temple in Puri on the east coast of India. These are vast contraptions used to carry representations of Hindu gods on annual festival days that look as though walking pace would be too much for them. So, replace the monsters on our roads with real juggernauts! Problem largely solved!!

Flagging cancer

But to get back to cancer or, more precisely, the difficulty of seeing it. After centuries of failing to make any inroads, recent dramatic advances give hope that all is about to change. These rely on the fact that tissues shed cells – and with them DNA – into the circulation. Tumours do this too – so in effect they are scattering clues to their existence into blood. By using short stretches of artificial DNA as bait, it’s possible to fish out tumour cell DNA from a few drops of blood. That’s a pretty neat trick in itself, given we’re talking about fewer than 100 tumour cells in a sea of several billion other cells in every cubic millimeter of blood.

There are two big attractions in this ‘microfluidics’ approach. First it’s almost ‘non-invasive’ in needing only a small blood sample and, second, it is possible that indicators may be picked up long before a tumour would otherwise show up. In effect it’s taking a biochemical magnifying glass to our body to ask if there’s anything there that wouldn’t normally be present. Detect a marker and you know there’s a tumour somewhere in the body, and if the marker changes in concentration in response to a treatment, you have a monitor for how well that treatment is doing. So far, so good.

And the problem?

These ‘liquid biopsy’ methods that use just a teaspoonful of blood have been under development for several years but there has been one big cloud hanging over them. They appear to be exquisitely sensitive in detecting the presence of a cancer – by sequencing the DNA picked up – but they have not been able to pinpoint the tissue of origin. Until now.

Step forward epigenetics

Shuli Kang and colleagues at the University of California at Los Angeles and the University of Southern California have broken this impasse by turning to epigenetics. We noted in Twenty More Winks that an epigenetic modification is any change in DNA, other than in the sequence of bases (i.e. mutation), that affects how an organism develops or functions. They’re brought about by tacking small chemical groups (commonly methyl (CH3) groups) either on to some of the bases in DNA itself or on to the proteins (histones) that act like cotton reels around which DNA wraps itself. The upshot is small changes in the structure of DNA that affect gene expression. You can think of DNA methylation as a series of flags dotted along the DNA strand, decorating it in a seemingly random pattern. It isn’t random, of course, and the target for methylation is a cytosine nucleotide (C) followed by a guanine (G) in the linear DNA sequence – called a CpG site because G and C are separated by one phosphate (p). Phosphate links nucleosides together in the backbone of DNA.

Cancer cells often display abnormal DNA methylation patterns – excess methylation (hypermethylation) in some regions, reduced methylation in others – that contributes to their peculiar behavior. It’s possible to determine the methylation profile of a DNA sample (by a method called bisulfite sequencing).

Kang & Co. developed a computer program to analyse methylation profiles from solid tumours and healthy samples in public databases and compare them to patient DNA of unknown tissue origin.

The peaks represent CpG clusters that characterize normal cells (top) and a variety of cancers. The key point is that the different patterns identify the tissue of origin (from Kang, S. et al., 2017).

The program’s called CancerLocator and in this initial study it was used to test samples from patients with lung, liver or breast cancer. In the modest words of the authors, CancerLocator ‘vastly outperforms’ previous methods – mind you, they struggle to even to distinguish most cancer samples from non-cancer samples. Nevertheless, CancerLocator’s a big step forward, not least because it can detect early stage cancers with 80% accuracy.

It’s also reasonable to expect major improvements as methylation sequencing becomes more extensive and higher resolution reveals more subtle signatures. What’s more, in principle, it should be able to detect all types of cancers – meaning that, after all so many centuries we may at last have a way of side-stepping the juggernaut.

References

Kang, S. et al. (2017). CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biology DOI 10.1186/s13059-017-1191-5.