Food Fix For Pharma Failure

 

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

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

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

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

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

Global expansion

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

Including cancer

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

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

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

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

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

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

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

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

Is our journey really necessary?

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

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

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

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

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

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

References

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

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

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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.

Same Again Please

 

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

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

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

How big is the problem?

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

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

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

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

The results of the survey by Nature from Baker 2016.

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

Tackling the problem in cancer

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

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

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

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

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

To date ten of these replication studies have been published.

How are we doing?

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

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

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

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

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

A closer look at failure

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

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

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

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

Where does that leave us?

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

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

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

Can we do better?

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

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

References

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

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

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

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

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

Caveat Emptor

 

It must be unprecedented for publication of a scientific research paper to make a big impact on a significant sector of the stock market. But, in these days of ‘spin-off’ companies and the promise of unimaginable riches from the application of molecular biology to every facet of medicine and biology, perhaps it was only a matter of time. Well, the time came with a bang this June when the journal Nature Medicine published two papers from different groups describing essentially the same findings. Result: three companies (CRISPR Therapeutics, Editas Medicine and Intellia) lost about 10% of their stock market value.

I should say that a former student of mine, Anthony Davies, who runs the Californian company Dark Horse Consulting Inc., mentioned these papers to me before I’d spotted them.

What on earth had they found that so scared the punters?

Well, they’d looked in some detail at CRISPR/Cas9, a method for specifically altering genes within organisms (that we described in Re-writing the Manual of Life).

Over the last five years it’s become the most widely used form of gene editing (see, e.g., Seeing a New World and Making Movies in DNA) and, as one of the hottest potatoes in science, the subject of fierce feuding over legal rights, who did what and who’s going to get a Nobel Prize. Yes, scientists do squabbling as well as anyone when the stakes are high.

Nifty though CRISPR/Cas9 is, it has not worked well in stem cells — these are the cells that can keep on making more of themselves and can turn themselves in other types of cell (i.e., differentiate — which is why they’re sometimes called pluripotent stem cells). And that’s a bit of a stumbling block because, if you want to correct a genetic disease by replacing a defective gene with one that’s OK, stem cells are a very attractive target.

Robert Ihry and colleagues at the Novartis Institutes for Biomedical Research got over this problem by modifying the Cas9 DNA construct so that it was incorporated into over 80% of stem cells and, moreover, they could switch it on by the addition of a drug. Turning on the enzyme Cas9 to make double-strand breaks in DNA in such a high proportion of cells revealed very clearly that this killed most of them.

When cells start dying the prime suspect is always P53, a so-called tumour suppressor gene, switched on in response to DNA damage. The p53 protein can activate a programme of cell suicide if the DNA cannot be adequately repaired, thereby preventing the propagation of mutations and the development of cancer. Sure enough, Ihry et al. showed that in stem cells a single cut is enough to turn on P53 — in other words, these cells are extremely sensitive to DNA damage.

Gene editing by Cas9 turns on P53 expression. Left: control cells with no activation of double strand DNA breaks; right: P53 expression (green fluorescence) several days after switching on expression of the Cas9 enzyme. Scale bar = 100 micrometers. From Ihry et al., 2018.

In a corresponding study Emma Haapaniemi and colleagues from the Karolinska Institute and the University of Cambridge, using a different type of cell (a mutated line that keeps on proliferating), showed that blocking P53 (hence preventing the damage response) improves the efficiency of genome editing. Good if you want precision genome editing by risky as it leaves the cell vulnerable to tumour-promoting mutations.

Time to buy?!

As ever, “Let the buyer beware” and this certainly isn’t a suggestion that you get on the line to your stockbroker. These results may have hit share prices but they really aren’t a surprise. What would you expect when you charge uninvited into a cell with a molecular bomb — albeit one as smart as CRISPR/Cas9. The cell responds to the DNA damage as it’s evolved to do — and we’ve known for a long time that P53 activation is exquisitely sensitive: one double-strand break in DNA is enough to turn it on. If the damage can’t be repaired P53’s job is to drive the cell to suicide — a perfect system to prevent mutations accumulating that might lead to cancer. The high sensitivity of stem cells may have evolved because they can develop into every type of cell — thus any fault could be very serious for the organism.

It’s nearly 40 years since P53 was discovered but for all the effort (over 45,000 research papers with P53 in the title) we’re still remarkably ignorant of how this “Guardian of the Genome” really works. By comparison gene editing, and CRISPR/Cas9 in particular, is in its infancy. It’s a wonderful technique and it may yet be possible to get round the problem of the DNA damage response. It may even turn out that DNA can be edited without making double strand breaks.

So maybe don’t rush to buy gene therapy shares — or to sell them. As the Harvard geneticist George Church put it “The stock market isn’t a reflection of the future.” Mind you, as a founder of Editas Medicine he’d certainly hope not.

References

Ihry, R.J. et al. (2018). p53 inhibits CRISPR–Cas9 engineering in human pluripotent stem cells. Nature Medicine, 1–8.

Haapaniemi, E. et al. (2018). CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response. Nature Medicine (2018) 11 June 2018.

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.

Another Fine Mess

 

Did you guess from the title that this short piece is about the seeming inability of the British Government to run well, most things but especially IT programmes? Of course you did! Provoked by the latest National Health Service furore. In case you’ve been away with the fairies for a bit, a major cock-up in its computer system has just come to light whereby, between 2009 and 2018, it failed to invite 450,000 women between the ages of 68 and 71 for breast screening. Secretary of State for Health, Jeremy Hunt (our man usually on hand with a can of gasoline when there’s a fire), told Parliament that “there may be between 135 and 270 women who had their lives shortened”. Cue: uproar, headlines: HUNDREDS of British women have died of breast cancer (Daily Express), etc.

Logo credit: Breast Cancer Action

I’ve been reluctant to join in because I’ve said all I think is worth saying about breast cancer screening in two earlier pieces (Risk Assessment and Behind the Screen). Reading them again I thought they were a reasonable summary and I don’t think there’s anything new to add. However, this is  a cancer blog and it’s a story that’s made big headlines so I feel honour-bound to offer a brief comment — in addition to sympathizing with the women and families who have been caused much distress.

My reaction was that Hunt was misguided in mentioning specific numbers — not only because he was asking for trouble from the press but mainly because the evidence that screening itself saves lives is highly questionable. For an expert view on this my Cambridge colleague David Spiegelhalter, who is Professor for the Public Understanding of Risk, has analysed the facts behind breast screening with characteristic clarity in the New Scientist.

Anything to add?

I was relieved on re-reading Risk Assessment to see that I’d given considerable coverage to the report that had just come out (2014) from The Swiss Medical Board.  They’d reviewed the history of mammography screening, concluded that systematic screening might prevent about one breast cancer death for every 1000 women screened, noted that there was no evidence that overall mortality was affected and pointed out that false positive test results presented the risk of overdiagnosis.

In the USA, for example, over a 10-year course of annual screening beginning at 50 years of age, one breast-cancer death would have been prevented whilst between 490 and 670 women would have had a false positive mammogram calling for a repeat examination, 70 to 100 an unnecessary biopsy and between 3 and 14 would have been diagnosed with a cancer that would never have become a problem.

Needless to say, this landed the Swiss Big Cheeses in very hot water because there’s an awful lot of vested interests in screening and it’s sort of instinctive that it must be a good thing. But what’s great about science is that you can do experiments — here actually analysing the results of screening programmes — and quite often the results turn to be completely unexpected, as it did in this case where the bottom line was that mammography does more harm than good.

This has led to the recommendation that the current programmes in Switzerland should be phased out and not replaced.

So we’re all agreed then?

Of course not. In England the NHS recommendation remains that women aged 50 to 70 are offered mammography every three years — which is just as well or we’d have Hunt explaining the recent debacle as new initiative. The American Cancer Society “strongly” recommends regular screening mammography starting at age 45 and the National Cancer Institute refers to “experts” that recommend mammography every year starting at age 25 for women with mutations in their BRCA1 or BRCA2 genes.

The latter is really incredible because a study published in the British Medical Journal in 2012 found that these mutations made the carriers much more vulnerable to radiation-induced cancer. Specifically, women with BRCA 1/2 mutations who were exposed to diagnostic radiation (i.e. mammography) before the age of 30 were twice as likely to develop breast cancer, compared to those with normal BRCA genes.

They are susceptible to radiation that would not normally be considered dangerous because the two BRCA genes encode proteins involved in the repair of damaged DNA — and if that is defective you have a recipe for cancer.

Extraordinary.

So it’s probably true that the only undisputed fact is that we need much better ways for detecting cancers at an early stage of development. The best hope at the moment seems to be the liquid biopsy approach we described in Seeing the Invisible: A Cancer Early Warning System? but that’s still a long way from solving a general cancer problem, well illustrated by breast mammography.

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

Much Ado About … Some Things

Given that the ‘festive season’ is approaching, maybe we should try to find something joyous to say about cancer. It’s not difficult. Over the last 60 years (1950-2013) the 5-year Relative Survival Rates for white Americans for breast and prostate cancers have gone from about 50% to over 90% (99.6% in fact for prostate). A number of other types (e.g., testicular cancer) are now largely curable, if treated early enough. Similar trends have occurred in most developed countries – all this through advances in surgery and radiotherapy but, most of all, because of new drugs.

Big Pharma

It’s big business. According to the Financial Times, annual spending on cancer drugs hit $100 billion worldwide in 2014 and is projected to exceed $150 billion by 2020. As you would hope, this expenditure on drug development and production has resulted in a gradual rise in available cancer drugs, represented below by the number of new cancer drugs approved each year by the American Food and Drug Administration (FDA).

Number of new cancer drugs approved each year by the American Food and Drug Administration from 1949 to 2016 (from Hope Cristol, The American Cancer Society, 2016).

Data compiled from drugs@fda.gov, National Cancer Institute, FDA Orange Book, FDA.gov, and centerwatch.com. Reporting and analysis by Sabrina Singleton, ACS research historian.

We should note that the FDA equivalent on this side of the Atlantic is the European Medicines Agency (EMA) and they tend to follow similar licensing patterns. Thus in 2016 a total of 74 new drug approvals were granted by the FDA and the EMA — 19 by the EMA only, 19 by only the FDA, with 36 approved by both. Of the drugs approved by the EMA in 2016, 17 had received prior FDA approval (i.e. in 2015 or earlier). However, only six drugs registered in the US in 2016 had prior EMA approval, indicating that drug companies tend to apply for approval in the US first before registering their products in the EU.

So rejoice and be merry — and drink to the triumph of science!!

It’s not unbounded joy, of course, because global cancer incidence continues to rise and a number of cancers (e.g., lung, liver and pancreas) remain refractive to all approaches thus far with survival rates stuck below 20%.

A Winter’s Tale

But what’s this? A further, wintry blast of reality from The British Medical Journal no less. It comes from Courtney Davis and her friends at King’s College London and the London School of Economics and Political Science (LSE) who looked at the track record of cancer drugs approved by the EMA between 2009 and 2013. Over this period the EMA approved the use of 48 new cancer drugs.

Charge your glass

It might be a good idea to sit down with a stiff drink at this point and remind ourselves that there are only two aims for cancer drugs: they must either extend the life of the patient or improve their quality of life.

What Dr. D & chums found was — and here, to be absolutely clear, we should quote exactly what they said — “… that most drugs entered the market without evidence of benefit on survival or quality of life. At a minimum of 3.3 years after market entry, there was still no conclusive evidence that these drugs either extended or improved life for most cancer indications. When there were survival gains over existing treatment options or placebo, they were often marginal.”

To be precise, it was 57% (39 of the 68 drugs) that entered the market with no evidence that they improved survival or quality of life.

Cripes!

What does this mean – and how can it be?

Well, first up, clearly a lot of money has been spent by drug companies and health services for absolutely no benefit to patients. Unsurprisingly the authors of the study called on the EMA to “increase the evidence bar for the market authorisation of new cancer drugs.” Which I take to mean ‘get some meaningful data before you stick stuff out there.’ But here’s where things get tricky. If your aim is to extend life, how can you prove a drug works other than by giving it to a significant number of patients and waiting a long time to see what happens?

The way round this has been for clinical trials to use indirect or “surrogate” measures of drug efficacy. The idea is that these endpoints show whether a drug has biological activity and thus might be of clinical use. However, they are not reliable measures of improved quality of life or survival.

So this report leaves us with a long-standing problem. On the one hand there is the understandable drive to get new drugs to patients asap but, on the other, there is the fact that only human beings can model how well a drug works in us. However good your in vitro systems may be and however closely mice may resemble men, they’re not the real thing.

One thing we could do that the report suggests, is to integrate the development and commercialization of cancer drugs at least across the two biggest markets of America and Europe so that the FDA and the EMA don’t appear to be operating in parallel worlds.

All told then, perhaps we should supplant our earlier merriment with the chilling thought that, even after so many years of perspiration and inspiration, cancers still present an immense challenge.

References

Davis, C. et al. (2017). Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European Medicines Agency: retrospective cohort study of drug approvals 2009-13. BMJ 2017;359:j4530 doi: 10.1136/bmj.j4530 (Published 2017 October 03).

SEER Cancer Statistics Review (CSR) 1975-2014, updated June 28, 2017.

Cristol, H. (2016). Evolution and Future of Cancer Treatments, The American Cancer Society.