Breaking Up Is Hard To Do

 

Thus Neil Sedaka, the American pop songster back in the 60s. He was crooning about hearts of course but since then we’ve discovered that for our genetic hearts — our DNA — breaking up is not that tough and indeed it’s quite common.

A moving picture worth a thousand words

When I’m trying to explain cancer to non-scientists I often begin by showing a short movie of a cell in the final stages of dividing to form two identical daughter cells. This is the process called mitosis and the end-game is the exciting bit because the cell’s genetic material, its DNA, has been duplicated and the two identical sets of chromosomes are lined up in the middle of the cell. There ensues a mighty tug-of-war as cables (strands of proteins) are attached to the chromosomes to rip them apart, providing a separate genome for each new cell when, shortly after, the parent cell splits into two. When viewed as a speeded-up movie it’s incredibly dramatic and violent — which is why I show it because it’s easy to see how things could go wrong to create broken chromosomes or an unequal division of chromosomes (aneuploidy). It’s the flip side if you like to the single base changes (mutations) — the smallest damage DNA can suffer — that are a common feature of cancers.

In Heir of the Dog we showed pictures of normal and cancerous chromosomes that had been tagged with coloured markers to illustrate the quite staggering extent of “chromosome shuffling” that can occur.

Nothing new there

We’ve known about aneuploidy for a long time. Over 20 years ago Bert Vogelstein and his colleagues showed that the cells in most bowel cancers have different numbers of chromosomes and we know now that chromosomal instability is present in most solid tumours (90%).

Knowing it happens is one thing: being able to track it in real time to see how it happens is another. This difficulty has recently been overcome by Ana C. F. Bolhaqueiro and her colleagues from the Universities of Utrecht and Groningen who took human colorectal tumour cells and grew them in a cell culture system in the laboratory that permits 3D growth — giving rise to clumps of cells called organoids.

Scheme representing how cells grown as a 3D clump (organoid) can be sampled to follow chromosomal changes. Cells were taken from human colon tumours and from adjacent normal tissue and grown in dishes. The cells were labelled with a fluorescent tag to enable individual chromosomes to been seen by microscopy as the cells divided. At time intervals single cells were selected and sequenced to track changes in DNA. From Johnson and McClelland 2019.

Genetic evolution in real time

As the above scheme shows, the idea of organoids is that their cells grow and divide so that at any time you can select a sample and look at what’s happening to its DNA. Furthermore the DNA can be sequenced to pinpoint precisely the genetic changes that have occurred.

It turned out that cancer cells often make mistakes in apportioning DNA between daughter cells whereas such errors are rare in normal, healthy cells.

It should be said that whilst these errors are common in human colon cancers, a subset of these tumours do not show chromosomal instability but rather have a high frequency of small mutations (called microsatellite instability). Another example of how in cancer there’s usually more than one way of getting to the same end.

Building bridges …

The most common type of gross chromosomal abnormality occurs when chromosomes fuse via their sticky ends to give what are called chromatin bridges (chromatin just means DNA complete with all the proteins normally attached to it). Other errors can give rise to a chromosome that’s become isolated — called a lagging chromosome, it’s a bit like a sheep that has wandered off from the rest of the flock. As the cell finally divides and the daughter cells move apart, DNA bridges undergo random fragmentation.

… but where to …

Little is known about how cells deal with aneuploidy and the extent to which it drives tumour development. This study showed that variation in chromosome number depends on the rate at which chromosomal instability develops and the capacity of a cell to survive in the face of changes in chromosome number. More generally for the future, it shows that the organoid approach offers an intriguing opening for exploring this facet of cancer.

Reference

Bolhaqueiro, A.C.F. et al. (2019). Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids. Nature Genetics 51, 824–834.

Lengauer, C. et al., (1997). Genetic instability in colorectal cancers. Nature 386, 623-627.

Johnson, S.C. and McClelland, S.E. (2019). Watching cancer cells evolve. Nature 570, 166-167.

Sticky Cancer Genes

 

In the previous blog I talked about Breath Biopsy — a new method that aims to detect cancers from breath samples. I noted that it could end up complementing liquid biopsies — samples of tumour cell DNA pulled out of a teaspoon of blood — both being, as near as makes no difference, non-invasive tests. Just to show that there’s no limit to the ingenuity of scientists, yet another approach to the detection problem has just been published — this from Matt Trau and his wonderful team at The University of Queensland.

This new method, like the liquid biopsy, detects DNA but, rather than the sequence of bases, it identifies an epigenetic profile — that is, the pattern of chemical tags (methyl groups) attached to bases. As we noted in Cancer GPS? cancer cells often have abnormal DNA methylation patterns — excess methylation (hypermethylation) in some regions, reduced methylation in others. Methylation acts as a kind of ‘fine tuner’, regulating whether genes are switched on or off. In the methylation landscape of cancer cells there is an overall loss of methylation but there’s an increase in regions that regulate the expression of critical genes. This shows up as clusters of methylated cytosine bases.

Rather helpfully, a little while ago in Desperately SEEKing … we talked about epigenetics and included a scheme showing how differences in methylation clusters can identify normal cells and a variety of cancers and how these were analysed in the computer program CancerLocator.

The Trau paper has an even better scheme showing how the patterns of DNA decoration differ between normal and cancer cells and how this ‘methylscape’ (methylation landscape) affects the physical behaviour of DNA.

How epigenetic changes affect DNA. Scheme shows methylation (left: addition of a methyl group to a cytosine base in DNA) in the process of epigenetic reprogramming in cancer cells. This change in the methylation landscape affects the solubility of DNA and its adsorption by gold (from Sina et al. 2018).

Critically, normal and cancer epigenomes differ in stickiness — affinity — for metal surfaces, in particular for gold. In a clever ploy this work incorporated a colour change as indicator. We don’t need to bother with the details — and the result is easy to describe. DNA, extracted from a small blood sample, is added to water containing tiny gold nanoparticles. The colour indicator makes the water pink. If the DNA is from cancer cells the water retains its original colour. If it’s normal DNA from healthy cells the different binding properties turns the water blue.

By this test the Brisbane group have been able to identify DNA from breast, prostate and colorectal cancers as well as from lymphomas.

So effective is this method that it can detect circulating free DNA from tumour cells within 10 minutes of taking a blood sample.

The aim of the game — and the reason why so much effort is being expended — is to detect cancers much earlier than current methods (mammography, etc.) can manage. The idea is that if we can do this not weeks or months but perhaps years earlier, at that stage cancers may be much more susceptible to drug treatments. Trau’s paper notes that their test is sensitive enough to detect very low levels of cancer DNA — not the same thing as early detection but suggestive none the less.

So there are now at least three non-invasive tests for cancer: liquid biopsy, Breath Biopsy and the Queensland group’s Methylscape, and in the context of epigenetics we should also bear in mind the CancerLocator analysis programme.

Matt Trau acknowledges, speaking of Methylscape, that “We certainly don’t know yet whether it’s the holy grail for all cancer diagnostics, but it looks really interesting as an incredibly simple universal marker for cancer …” We know already that liquid biopsies can give useful information about patient response to treatment but it will be a while before we can determine how far back any of these methods can push the detection frontier. In the meantime it would be surprising if these tests were not being applied to age-grouped sets of normal individuals — because one would expect the frequency of cancer indication to be lower in younger people.

From a scientific point of view it would be exciting if a significant proportion of ‘positives’ was detected in, say, 20 to 30 year olds. Such a result would, however, raise some very tricky questions in terms of what, at the moment, should be done with those findings.

Reference

Abu Ali Ibn Sina, Laura G. Carrascosa, Ziyu Liang, Yadveer S. Grewal, Andri Wardiana, Muhammad J. A. Shiddiky, Robert A. Gardiner, Hemamali Samaratunga, Maher K. Gandhi, Rodney J. Scott, Darren Korbie & Matt Trau (2018). Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nature Communications 9, Article number: 4915.

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.

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

Please … Not Another Helping

 

You may have seen the headlines of the: “Processed food, sugary cereals and sliced bread may contribute to cancer risk” ilk, as this recently published study (February 2018) was extensively covered in the media — the Times of London had a front page spread no less.

So I feel obliged to follow suit — albeit with a heavy heart: it’s one of those depressing exercises in which you’re sure you know the answer before you start.

Who dunnit?

It’s a mainly French study (well, it is about food) led by Thibault Fiolet, Mathilde Touvier and colleagues from the Sorbonne in Paris. It’s what’s called a prospective cohort study, meaning that a group of individuals, who in this case differed in what they ate, were followed over time to see if diet affected their risk of getting cancers and in particular whether it had any impact on breast, prostate or colorectal cancer. They started acquiring participants about 20 years ago and their report in the British Medical Journal summarized how nearly 105 thousand French adults got on consuming 3,300 (!) different food items between them, based on each person keeping 24 hour dietary records designed to record their usual consumption.

Foods were grouped according to degree of processing. The stuff under the spotlight is ‘ultra-processed’ — meaning that it has been chemically tinkered with to get rid of bugs, give it a long shelf-life, make it convenient to use, look good and taste palatable.

What makes a food ‘ultra-processed’ is worked out by something called the NOVA classification. I’ve included their categories at the end.

Relative contribution of each food group to ultra-processed food consumption in diet (from Fiolet et al. 2018).

And the result?

The first thing to be said is that this study is a massive labour of love. You need the huge number of over 100,000 cases even to begin to squeeze out statistically significant effects — so the team has put in a terrific amount of work.

After all the squeezing there emerged a marginal increase in risk of getting cancer in the ultra-processed food eaters and a similar slight increase specifically for breast cancer (the hazard ratios were 1.12 and 1.11 respectively). There was no significant link to prostate and colorectal cancers.

Which may mean something. But it’s hard to get excited, not merely because the effects described are small but more so because such studies are desperately fraught and the upshot familiar.

One problem is that they rely on individuals keeping accurate records. Another problem here is that the classification of ‘ultra-processed’ is somewhat arbitrary — and it’s also very broad — leaving one asking what the underlying cause might be: ‘is it sugar, fat or what?’ Furthermore, although the authors tried manfully to allow for factors like smoking and obesity, it’s impossible to do this with complete certainty. The authors themselves noted that, for example, they couldn’t allow for the effects of oral contraception.

The authors are quite right to point out that it is important to disentangle the facets of food processing that bear on our long-term health and that further studies are needed.

I would only add ‘rather you than me.’

Perforce in these pages we have gone on about diets good and bad so there is no need to regurgitate. Suffice to say that my advice on what to eat is the same as that of any other sane person and summarized in Dennis’s Pet Menace — and it’s not been remotely affected by this new research which, in effect, says ‘junk food is probably bad for you in the long run.’ But let’s leave the last word to Tom Sanders of King’s College London: “What people eat is an expression of their life-style in general, and may not be causatively linked to the risk of cancer.” 

Reference

Fiolet, T. et al. (2018). Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ 2018;360:k322 http://dx.doi.org/10.1136/bmj.k322

NOVA classification:

The ultra-processed food group is defined by opposition to the other NOVA groups: “unprocessed or minimally processed foods” (fresh, dried, ground, chilled, frozen, pasteurised, or fermented staple foods such as fruits, vegetables, pulses, rice, pasta, eggs, meat, fish, or milk), “processed culinary ingredients” (salt, vegetable oils, butter, sugar, and other substances extracted from foods and used in kitchens to transform unprocessed or minimally processed foods into culinary preparations), and “processed foods” (canned vegetables with added salt, sugar coated dried fruits, meat products preserved only by salting, cheeses, freshly made unpackaged breads, and other products manufactured with the addition of salt, sugar, or other substances of the “processed culinary ingredients” group).

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.

The answer to … everything is …

42, as all fans of Douglas Adams and The Hitchhiker’s Guide to the Galaxy will instantly tell you. In the years before he produced his best-seller, a chance contact with Footlights had drawn me into spending many merry evenings with Douglas in The Baron of Beef public house, more or less opposite St John’s College, where he was studying – sporadically, he would doubtless have said – English.

Had a piece of work that’s just come out in The British Medical Journal been published 40-odd years earlier I suspect I would have mentioned it at one of those gatherings – early on before rational thought took alcohol-fuelled flight. It’s interesting because it says we can put off dying from the things that kill most of us (heart failure and cancer) by what Jason Gill, Carlos Celis-Morales and their pals in the University of Glasgow call ‘active commuting’. By that they mean cycling to work is good. Physical inactivity (e.g., spending happy evenings in the pub) is bad.

Had I mentioned it, rather than coming up with an entirely whimsical response to the “ultimate question of life”, Douglas would have spotted that the key to hanging on to life is “on your bike”. Just think: if Jason & Chums had got a move on, history would have been changed. Pondering all their evidence over several pints of The Baron’s best, it’s hard to imagine Douglas coming up with any title other than The Biker’s Guide to the Galaxy.

But hang on: isn’t this just another pretty useless survey?

Maybe – but for several reasons it’s hard to write it off.

First, there have been quite a few studies over the years showing that cycling is good for you.

Second, this is one was huge – so more likely to be meaningful. Using the UK Biobank data it looked for links between death and the way in which more than a quarter of a million people got to work.

Third, and the thing that really caught my eye: the key finding stuck out like the proverbial sore thumb. Usually in surveys of things that might affect our health any trends are difficult to spot: eating X makes you live 10% longer or be 5% less likely to get Y … bla, bla, bla. But here you didn’t need to peer: cycling (a ‘long distance’) to work makes you 40% less likely to die – from anything!

Below is just one bit of their data: I’ve re-drawn it with the cycling result in red but it hardly needs that to highlight the difference between it, walking (blues) and the ‘non-actives’ (green: car or public transport). It’s true, a bit of biking can help (orange: mixed mode cycling) but the really clear benefit comes from cycling (lots) – though they don’t actually say how many miles per day counts as ‘long-distance cycling.’ Modes of transport and distances were self-reported and the latter just divided into ‘long’ and ‘short’.

How you get to work impacts your life expectancy. The figure shows the risk of death from all causes as hazard ratios (ratio of the hazard rates of death): the reference (hazard ratio 1) is travel by car or public transport (green). (From Celis-Morales, C. et al., 2017).

So what of heart failure and cancer?

Perhaps not surprisingly then, commuting by cycling was also associated with a markedly lower risk both of getting heart disease or cancer and of dying therefrom. To give one specific figure: cycling to work lowers the chance of developing cancer by 45%.

It can’t be the lycra

These are horrible studies to undertake, partly because they rely on human beings telling the truth but also because of what are called ‘confounding factors.’ For example, if someone plays a lot of sport and eats sensibly, you might guess they’d be relatively healthy, regardless of how they get to work. Conversely for smoking. However, Celis-Morales & Co did their best to allow for such things and therefore to come up with results that mean something.

But, if you take their findings at face value there remains a key question that the authors do not mention: what is it about biking that’s such a life-saver (assuming you don’t get knocked-off and squashed)? It’s a real puzzle because walking is generally held to be very good for you whilst cycling is the most energy-efficient means of transport devised by man. Both activities use nearly all of your muscles, albeit that biking really works out your glutes and quadriceps, but because bikes are so efficient you use less energy.

Counting the calories

You can do the sums – i.e. work out how many calories used walking, running or cycling on Wolfram Alfra. It’s just confirmed that my daily bike commute does indeed use about half the number of calories required for the same walk.

If you take your commute as training you would suppose that expending more energy (i.e. walking rather than biking) would strengthen your heart and cardiovascular system – and indeed this study shows commuters who did more than 6 miles a week at ‘typical walking pace of three miles an hour’ slightly lowered their risk of cardiovascular disease. But cycling was far more beneficial.

As to cancer, beyond the simplistic notion that fitness = strengthening your immune system and hence capacity resist abnormal cell growth, it’s hard to see a mechanism for biking being so much better than anything else.

So, never mind the science …

Away with Ford Prefect and latter-day variants, automotive  or otherwise! On your bike!! And if you can do it with a friend on a tandem, so much the better!!! Though if you’re going to do it à deux, it might be worth recalling that the Jatravartids had the wisdom to invent the aerosol deodorant before the wheel.

Reference

Celis-Morales, C. et al. (2017). Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. British Medical Journal 357 doi: https://doi.org/10.1136/bmj.j1456