Be amazed

 

Back in May 2018 we reported the first output from the Pan-Cancer Atlas, a massive undertaking that evolved from The Cancer Genome Atlas, itself a huge project aiming to set up a genetic data-base for three cancer types: lung, ovarian, and glioblastoma.

The next instalment from the Pan-Cancer Analysis of Whole Genomes (PCAWG) has just appeared featuring the analysis of a staggering 2,658 whole-cancer genomes and their matching, normal tissues across 38 tumour types and it has reminded us, yet again, of nature’s capacity to surprise. The first finding was that, on average, cancer genomes contained four or five driver mutations when coding and non-coding genomic elements were combined. That’s roughly consistent with the accepted estimate over the last few decades. What was unexpected, however, was that in around 5% of cases no drivers were identified, suggesting that there are more of these mutations to be discovered. Also somewhat surprising is that chromothripsis, the single catastrophic event producing simultaneously many variants in DNA, is frequently an early event in tumour evolution.

The analyses also revealed several mechanisms by which the ends of chromosomes in cancer cells are protected from telomere attrition and that variants transmitted in the germline can affect subsequently acquired patterns of somatic mutation.

A glimpse of the data

The panorama of driver mutations includes the summary below of tumour-suppressor genes with biallelic inactivation (i.e., mutation of one allele (copy) followed by gene deletion of the remaining allele) in 10 or more patients. Familiar tumour suppressors are prominent on the left hand side, as expected. These include TP53 (the guardian of the genome) and the tumour suppressors CDKN2A and CDKN2B (cyclin-dependent kinase inhibitors 2A and 2B) that regulate the cell cycle.

Tumour-suppressor genes for which both copies of the gene (alleles) are inactivated in 10 or more patients. GR = genomic rearrangement, i.e. chromosome breakage. From The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium.

Aneuploidy in the genome of a tumour without known drivers. Each row is an individual tumour: the boxes show chromosome loss (blue) or gain (red). The cancer is a rare kidney tumour (chromophobe renal cell carcinoma). From The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium.

Two tumour types had a surprisingly high fraction of patients without identified driver mutations: 44% for a rare type of kidney cancer (chromophobe renal cell carcinoma) and 22% in a rare pancreatic neuroendocrine cancer. It turned out (as shown in the above figure) that there was a striking loss or gain of chromosomes — called aneuploidy — in the cells of these cancers. This suggests that wholesale loss of tumour suppressor genes or gain of oncogenic function was providing the ‘drivers’ for these cancers.

The genomic cancer message

We should first acknowledge the mind-boggling effort and organization involved in collecting thousands of paired samples, sequencing them and analyzing the output. However, the value of these massive projects is beginning to emerge — and the news is mixed.

One critical trend is that genomic analysis is re-defining the way cancers are classified. 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.

Perhaps 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.

On the other hand, the intention of precision medicine is to match patients to therapies on the basis of genomics and, notwithstanding the above point, the consortium notes that “A major barrier to evidence-based implementation is the daunting heterogeneity of cancer chronicled in these papers, from tumour type to tumour type, from patient to patient, from clone to clone and from cell to cell. Building meaningful clinical predictors from genomic data can be achieved, but will require knowledge banks comprising tens of thousands of patients with comprehensive clinical characterization. As these sample sizes will be too large for any single funding agency, pharmaceutical company or health system, international collaboration and data sharing will be required.”

See for yourself

The PCAWG landing page (http://docs.icgc.org/pcawg/) provides links to several data resources for interactive online browsing, analysis and download of PCAWG data.

Reference

The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes.

 

Brainstorming

 

It’s the first day of a New Year and, as is well known, Scottish folk world-wide make a big celebration of yesterday (Hogmanay), New Year’s Day and indeed quite often the next few days for good measure. Even in the far north-west of England as a youngster with more or less black hair (deemed to be important for some reason) I was trundled round the neighbours in one of the rituals — ‘first-footing’, i.e. being the first guest of the new year, despite our family having no Scottish connections that I knew of.

Scots Wha Hae

Most such jollifications seem to require mournful dirges accompanying incomprehensible lyrics by Robert Burns. To be fair I should note that Max Bruch and Hector Berlioz, wonderful composers both, saw fit to include a musical reference to ‘Scots Wha Hae’ in the Scottish Fantasy and in the concert overture Rob Roy. Mind you, Berlioz himself described his overture as “long and diffuse” and it was so badly received that he burned the score the night of its premier.

However, there is something else that Scots make quite a fuss about, given half a chance, and here perhaps we can agree they have a point. It’s the number of notable scientists and physicians their country has produced. Wikipedia’s List of Scottish engineers and scientists runs to over 150 names — remarkable for a population that even today is only about five million. The listed luminaries feature some household names: Alexander Graham Bell, James Watt, James Clerk Maxwell, Lord Kelvin and Joseph Lister just to be going on with.

But there’s a slightly unnerving thing about Wikipedia’s List in that, long though it is, there are some serious omissions. I spotted this the other day when I was searching for a bit of background about one of the heroes of this New Year’s story. The first missing star I noted was John Hunter, generally thought to have carried out the first surgical removal of a malignant melanoma (skin cancer) in 1787. Worse still, I found no mention of William Macewen: it was his first successful removal of a brain tumour (in 1879) that makes him directly relevant to our story. He was a truly remarkable figure. Thought of as the ‘Father of neurosurgery’, he was a pioneer in  surgery of the brain and other organs. But the really outstanding thing about Sir William Macewen CB., FRS., FRCS, to give him his full handle, was his approach to surgery. Thus, for example, in treating brain tumours he applied his profound knowledge of anatomy to work out from the patient’s symptoms the precise location of the abnormal growth so he knew where to take surgical aim. Amazing!

Very slow progress

Nearly 60 years after Macewen’s pioneering surgery the American composer George Gershwin would have appreciated his genius as treatments had made little progress by the 1930s when Gershwin succumbed to a brain tumour (specifically a glioblastoma multiforme). It took until 1958 for the first useful drug treatment for brain tumours to emerge and until the mid-1970s for radiation therapy come into use. Indeed it was only the introduction of CT scans towards the end of the 20th century that permitted tumour localisation without needing Macewen’s extraordinary gifts.

Something very odd

In parallel with these advances has emerged the evidence for an unexpected feature of brain tumours. You might guess that brain tumours would start in the brain but it turns out that most do nothing of the sort. The vast majority (about 90%) are secondary cancers: that is, they arise when tumour cells spread from another part of the body — commonly breast or lung. In other words most brain tumours are metastases — and they are mighty important. About 24,000 people in the United States discover they have these abnormal growths every year and they cause about 18,000 deaths. The rates are much the same in the UK where deaths from brain and related tumours number just over 5,000.

But also familiar …

Those who follow developments on cancer will know that metastasis is one of the hottest potatoes. Until very recently we had no idea of the molecular goings on that turn a cell in a primary tumour into a wanderer that can leave its site of origin, get into the bloodstream, get out at some other location and there establish a new, secondary colony. The mists are beginning to lift as the wonders of modern biology are applied to this pressing problem.

Step forward one of the main movers and shakers in the field who is the modern hero of today’s piece: David Lyden of the Gale and Ira Drukier Institute for Children’s Health, Weill Cornell Medicine, New York.

So topical is this issue of metastasis that I’m relieved to note that the contributions of the Lyden group have featured regularly in these pages (Keeping Cancer CatatonicScattering the Bad Seed and Holiday Reading (4) – Can We Make Resistance Futile). A succinct summary of those contributions would be: (1) cells in primary tumours release ‘messengers’ into the circulation that ‘tag’ metastatic sites before any cells actually leave the tumour, (2) the messengers that do the site-tagging are small sacs — mini cells — called exosomes, and (3) they find specific addresses by carrying protein labels that home in to different organs — we represented that in the form of a tube train map in Lethal ZIP Codes.

In One More Small Step the same team looked closely at exosomes and found that a wide variety of tumour cell types secrete two sizes of exosomes (big and small! — see blog for details!!). Amazingly these sacs carry about 1000 different types of protein — suggesting that they might have powerful effects.

Breaking the barrier

With that in mind Lyden’s group have now turned their attention to how tumour cells find their way to the brain. How do they achieve the feat of crossing the ‘blood-brain barrier’ — the layer of (endothelial) cells that encloses the brain and controls the types of molecules that can move to and from circulating blood — and are exosomes involved? In other words, are they little bags of trouble that play a role in helping most brain tumours to grow?

Answer ‘yes’ of course, or we wouldn’t have spent so long getting up to speed on the subject. Gonçalo Rodrigues, Lyden & Co. set up a brain slice culture system and pre-treated the slices with exosomes from human breast cancer metastatic cells that were known to spread preferentially to different tissues (brain, lung or bone).

Photos of brain slices showing how exosomes help to provide a niche for human breast cancer metastatic cells to invade, attach and grow. These are fluorescence microscopy images: brain blood vessels (vasculature) are red; cancer cells are green (GFP). Left: no pre-treatment; Right: pretreatment with exosomes. White arrowheads show vasculature-associated cancer cells. White bar = 100 micronsFrom Rodrigues et al. 2019.

The photos show a typical experiment using brain-seeking exosomes. There is a huge increase in the number of green cancer cells attaching to the brain slice as a result of exosome pre-treatment (right) by comparison with no exosome addition (left). Corresponding experiments with exosomes that direct migration to lung or bone show no effect: cancer cell attachment remains low (as in the left hand photo).

How do they do it?

The group took their studies a stage further by looking at the 1000 or so proteins in the exosomes for any that seemed to specify migration to the brain — in other words, to act as addresses of the kind we described in Lethal ZIP Codes. They came up with one in particular: a protein called CEMIP  (if you’re interested that stands for ‘cell migration inducing hyaluronidase 1’. It’s an enzyme that chops up long chains of sugars (called hyaluronic acid). These chains form scaffolds to support proteins in various tissues including the brain — and their disruption may play a role in cancer cell movement).

The levels of CEMIP are higher in exosomes that promote brain metastasis but not in those associated with lung or bone metastatic cells. Thus pre-conditioning the brain microenvironment with CEMIP+ exosomes drives invasion. When they are depleted invasion and tumour cell association with the brain vasculature is disrupted. This remarkable new work has revealed how exosomes help wandering tumour cells to storm the blood-brain barrier. Immediately this opens the possibility of isolating exosomes from small samples of blood and screening them for proteins — i.e. using them as a ‘biomarker’ for metastatic cancer targets. But of course the great goal is to be able to interfere with their actions, an intervention that could dramatically cut the incidence of brain tumours. What a triumph that would be!!

We began with a Scottish tradition. Let’s end with another by raising a mental glass to scientists all over the world who, step by perspiring step are inching towards the goal of controlling cancer. Keep it up guys — and back to your benches!!

Reference

Rodrigues et al. (2019). Tumour exosomal CEMIP protein promotes cancer cell colonization in brain metastasis. Nature Cell Biology 21, 1403–1412.

 

 

The Power of Flower

 

We know we don’t ‘understand cancer’ — for if we did we would at least be well on the way to preventing the ten million annual deaths from these diseases and perhaps even stymieing their appearance in the first place. But at least, after many years of toil by thousands of curious souls, we might feel brave enough to describe the key steps by which it comes about.

Here goes!

Our genetic material, DNA, carries a code of four different units (bases) that enables cells to make twenty-thousand or so different types of proteins. Collectively these make cells — and hence us — ‘work’. An indicator of protein power is that we grow from single, fertilized cells to adults with 50 trillion cells. That phenomenal expansion involves, of course, cells growing and dividing to make more of themselves — and, along the way, a bit of cell death too. The fact that there are nearly eight billion people on planet earth testifies to the staggering precision with which these proteins act.

Nobody’s perfect

As sports fans will know, the most successful captain in the history of Australian rugby, John Eales, was nicknamed ‘Nobody’ because ‘Nobody’s perfect’. Well, you might care to debate the infallibility of your sporting heroes but when it comes to their molecular machinery, wondrous though it is, perfect it is not.

Evidence: from the teeming eight billion there emerges every year 18 million new cancer cases (that’s about one in every 444). And cancers are, of course, abnormal cell growth: cells growing faster than they should or growing at the wrong time or in the wrong place — any of which means that some of the masterful proteins have suffered a bit of a malfunction, as the computer geeks might say.

How can that happen?

Abnormal protein activity arises from changes in DNA (mutations) that corrupt the normal code to produce proteins of greater or lesser activity or even completely novel proteins.

These mutations may be great or small: changes in just one base or seismic fragmentation of entire chromosomes. But the key upshot is that the cell grows abnormally because regulatory proteins within the cell have altered activity. Mutations can also affect how the cell ‘talks’ to the outside world, that is, the chemical signals it releases to, for example, block immune system killing of cancer cells.

Clear so far?

Mutations can change how cells proliferate, setting them free of normal controls and launching their career as tumour cells and, in addition, they can influence the cell’s environment in favour of unrestricted growth.

The latter tells us that cancer cells cooperate with other types of cell to advance their cause but now comes a remarkable discovery of a hitherto unsuspected type of cellular collaboration. It’s from Esha Madan, Eduardo Moreno and colleagues from Lisbon, Arkansas, St. Louis, Indianapolis, Omaha, Dartmouth College, Zurich and Sapporo who followed up a long-known effect in fruit flies (Drosophila) whereby the cells can self-select for fitness to survive.

Notwithstanding the fact that flies do it, the idea of a kind of ‘cell fitness test’ is novel as far as human cells go — but it shouldn’t really surprise us, not least because our immune system (the adaptive immune system) features much cooperation between different types of cell to bring about the detection and removal of foreign or damaged cells.

Blooming science

So it’s been known for over forty years that Drosophila carries out cell selection based on a ‘fitness fingerprint’ that enables it to prevent developmental errors and to replace old tissues with new. However, it took until 2009 before the critical protein was discovered and, because mutant forms of this protein gave rise to abnormally shaped nerve cells that looked like bunches of flowers, Chi-Kuang Yao and colleagues called the gene flower‘.

Cells can make different versions of flower proteins (by alternative splicing of the gene) the critical ones being termed ‘winner’ and ‘loser’ because when cells carrying winner come into contact with cells bearing loser the latter die and the winners, well, they win by dividing and filling up the space created by the death of losers.

The effect is so dramatic that Madan and colleagues were able to make some stunning movies of the switch in cell populations that occured when they grew human breast cancer cells engineered to express different version of flower tagged with red or green fluorescent labels.

Shift in cell populations caused by two types of flower proteins. 

Above are images at time zero and 24 h later of co-cultures of cells expressing  green and red proteins (losers and winners). From Madan et al. 2019.

Click here to see the movie on the Nature website.

Winner takes almost all

The video shows high-resolution live cell imaging over a 24 hour period compressed into a few seconds. Cells expressing the green protein (hFwe1 (GFP)) were co-cultured with red cells (hFwe2 (RFP)). Greens are losers, reds winners. As the movie progresses you can see the cell population shifting from mainly green to almost entirely red, as the first and last frames (above) show.

How does flower power work?

Flower proteins form channels across the outer membrane of the cell that permit calcium flow, and it was abnormal calcium signalling that caused flowers to bloom in Drosophila nerves. It would be reasonable to assume that changes in calcium levels are behind the effects of flower on cancer cells. Reasonable but wrong, for Madan & Co were able to rule out this explanation. At the moment we’re left with the rather vague idea that flower proteins mediate competitive interactions between cells and these determine whether cells thrive and proliferate or wither and die.

Does this really happen in human cancers?

Madan and colleagues looked at malignant and benign human tumours and found that there was more ‘winner’ flower protein in the former than the latter and that ‘loser’ levels were higher in normal cells next to a tumour than further away. Both consistent with the notion that tumour cells express winner and this induces loser in nearby normal cells leading to their death. What’s more they reproduced this effect in mice by transplanting human breast cancer cells expressing winner.

So there we are! After all this time a variant on how cancer cells can manipulate their surroundings to promote the development of tumours. Remarkable though this finding is, in a way that is familiar it’s just the beginning of this story. We don’t know how flower proteins work in giving cancers a helping hand and we don’t know how effective they are. Until we answer those questions it would be premature to try to come up with therapies to block their effect.

But it is a moment to sit back and reflect on the astonishing complexity of living organisms and their continuing capacity to surprise.

Reference

Madan, E. et al. (2019). Flower isoforms promote competitive growth in cancer. Nature 572, 260-264.

Yao, C-K., et al., (2009). A synaptic vesicle-associated Ca2+ channel promotes endocytosis and couples exocytosis to endocytosis. Cell 138, 947–960.

What’s New in Breast Cancers?

 

One of the best-known things about cancer is that it’s good to catch it early. By that, of course, we don’t mean that you should make an effort to get cancer when you’re young but that, if it does arise it’s a good idea to find out before the initial growth has spread to other places in the body. That’s because surgery and drug treatments are very effective at dealing with ‘primary’ tumours — so much so that over 90% of cancer deaths are caused by cells wandering away from primaries to form secondary growths — a process called metastasis — that are very difficult to treat.

The importance of tumour spreading is shown by the figures for 5-year survival rates. Overall in the USA it’s 90% but this figure falls to below 30% for cancers that have metastasized (e.g., to the lungs, liver or bones). For breast cancer the 5-year survival rate is 99% if it is first detected only in the breast (most cases (62%) are diagnosed at this stage). If it’s spread to blood and lymph vessels in the breast the 5-year survival rate is 85%, dropping to 27% if it’s reached distant parts of the body.

What’s the cause of the problem?

The other thing most people know about cancers is that they’re caused by damage to our genetic material — DNA — that is, by mutations. This raises the obvious notion that secondary tumours might be difficult to deal with because they have accumulated extra mutations compared with those in primaries. And indeed, there have been several studies pointing to just that.

Very recently, however, François Bertucci, Fabrice André and their colleagues in various institutes in France, Switzerland and the USA have mapped in detail the critical alterations in DNA that accumulate as different types of breast cancers develop from early tumours to late, metastatic forms. As is the way these days, their paper contains masses of data but the easiest form of the message comes in the shape of ‘violin plots’. These show the spread of results  — in this case the number of mutations per length of DNA.

Metastatic tumours have a bigger mutational load than early tumours. These plots are for one type of breast tumour (HR+/HER2−) and show results for 381 metastases and 501 early tumours. Red dots = median values: these are the “middle” values rather than an average (or mean) and they show a clear upwards shift in burden as early tumours evolve into metastases. From Bertucci et al., 2019.

The violin plots above are for one subtype of breast cancer (HR+/HER2−). Recall that breast tumours are often defined by which of three types of protein can be detected on the surface of the cells: these are ‘receptors’ that have binding sites for the hormones estrogen and progesterone and for human epidermal growth factor. Hence they are denoted as hormone receptors (HRs) and (human) epidermal growth factor receptor-2 (HER2). Thus tumours may have HRs and HER2 (HR+, HER2+) or various receptors may be undetectable. Triple negative breast cancer (TNBC) is an absence of receptors for both estrogen and progesterone and for HER2.

The plots clearly show an increase in mutation load with progression from early to metastatic tumours (on average from 2.4 to 3.8 mutations per megabase of DNA). Looking at individual genes, nine ‘drivers’ emerged that were more frequently mutated in HR+/HER2− metastatic breast cancers (we described ‘driver’ and ‘passenger’ mutations in Taking Aim at Cancer’s Heart).

So what?

For now these findings give us just a little more insight into what goes on at the molecular level to turn a primary into a metastatic tumour. The fact that some of the acquired driver mutations are associated with poor patient survival offers some guidance as to treatment options.

Don’t get carried away

It’s a familiar story in this field: another small advance in piecing together the jigsaw that is cancer. It doesn’t offer any immediate advance in treatment — mainly because most of the nine ‘driver’ genes identified are tumour suppressors — i.e. they normally act as brakes on cell growth. Mutations knock out that activity and at the moment there is no therapeutic method for reversing such mutations. (The other main class of cancer promoters is ‘oncogenes‘ in which mutations cause hyper-activity).

But such steps are important. The young slave girl in Uncle Tom’s Cabin gave us the phrase “grew like Topsy” — meaning unplanned growth. Cancer growth is indeed unplanned and a bit like Topsy but it’s driven by molecular forces and only through untangling these can we begin to design therapies in a rational way.

Reference

Bertucci, F. et al. (2019). Genomic characterization of metastatic breast cancers. Nature 569, 560–564.

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.

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.

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.

Keeping Up With Cancer

 

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

What’s new?

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

Yearly global cancer deaths from 1990 to 2016.

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

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

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

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

Any real surprises?

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

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

Global trends

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

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

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

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

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

What can we do?

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

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

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

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

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

If only …

Reference

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

Fantastic Stuff

 

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

How was it done?

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

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

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

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

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

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

 

What’s next?

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

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

References

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

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

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.