Genes — Which Do We Need?

 

It’s widely known that cancers reflect cellular control going awry as a result of change in our genetic material — DNA. Beyond surgery and radiotherapy, cancer treatment uses drugs that either kill cells non-specifically or target mutated proteins. The latter give specificity for tumour cells but currently there are few such drugs. For mutations that inactivate tumour suppressor genes we have as yet no treatment, although one hope is that we will be able to replace these damaged genes with normal versions.

But there’s a problem with drug or gene replacement tactics for any genetic disease because, fundamentally, we don’t understand what we’re doing. Ideally we’d introduce the offending mutation into humans, look at its effect then follow up with our therapy and track what happens. We can’t do that, of course, and, although we can do equivalent experiments in model organisms like the fruit fly or the mouse, models are not the same as humans.

Nature’s experiments

A quite different approach is to note that under the cloak of evolution Nature has been doing these experiments for us. That’s to say, natural human genetic variation has given rise to a vast array of mutations across the population and all we need to do is find them and see what effect they have had on the biology of the individual. The “all” in the previous sentence is a weighty word because to sift out these variants requires DNA sequencing on a grand scale. Fortunately, as followers of this blog will know, such power in the shape of massively parallel sequencing is now available (see Family Tree of Breast Cancer).

The Genome Aggregation Database (gnomAD) has just published (May 2020) its latest efforts in the shape of DNA sequences of 125,748 exomes (protein-coding DNA) and 15,708 whole human genomes. It’s a simply staggering achievement, the aim being to find out what the differences between our individual genetic codes mean in terms of our health. These variants are the differences that make individual genomes unique and they include single nucleotide polymorphisms (‘SNPs’, pronounced ‘snips’ — one nucleotide (base) differing from the reference DNA sequence), insertions (additional nucleotides inserted in a DNA sequence), deletions (missing nucleotides), substitutions (multiple nucleotides altered relative to the reference sequence) and structural variants (large sections of a chromosome or entire chromosomes duplicated, deleted or rearranged).

Cataloguing genetic variation in humans.  The genome aggregation database (gnomAD) includes 15,708 whole-genome sequences and 125,748 exomes and the study catalogued the complete range of naturally occurring DNA variants.

Representation of 141,456 human DNA sequences. This way of presenting a vast amount of data is called UMAP (Uniform Manifold Approximation and Projection): the sequence of each individual is a dot, the individuals comprising six global and eight sub-continental ancestries. The pseudo colours mark clusters of related DNA sequence. Note that this ‘map’ does not relate to location: it is merely a visual representation of a lot of data. The horizontal bar indicates the number of individuals by population and sub-population in the gnomAD study with the same colour code as in the upper figure. From Karczewski et al. 2020.

It turns out that there are rather a lot of them. After filtering to minimise the errors that come with high-throughput sequencing, nearly 15 million high-quality variants were identified in the exome dataset and 230 million in the whole genome screens. In the protein-coding sequences alone there were over 400,000 variants predicted to block the function of the protein.

Where is this massive study taking us?

These naturally arising mutations provide a potentially valuable window on our genomes that we can look through to answer our title question: which of our genes are essential for survival and which can we manage without?

What gnomAD did was to construct a ‘spectrum of tolerance’ for each protein-coding gene in the human genome. This is potentially important because, for example, if a gene that is not essential for life acquires a disease-causing mutation, blocking the gene might cure the disease without killing the patient.

The clearest example of using natural in vivo models of human gene inactivation to inform therapeutic strategy has come from the LRRK2 gene. Variants in LRRK2 can change the activity of the protein it encodes so as to significantly increase the risk of Parkinson’s disease. From the gnomAD screen it turned out that variants in LRRK2 that blocked its normal activity were not strongly associated with evident disease. In other words, we can do without LRRK — and if it picks up a harmful mutation we can try to knock it out, secure in the knowledge that it’s not essential for survival.

So thank you Nature for doing the experiment we can’t do — tinkering with our own genes to see what happens.

References

Karczewski, K. J. et al. (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443.

Whiffin, N., Armean, I.M., Kleinman, A. et al. (2020). The effect of LRRK2 loss-of-function variants in humans. Nature Medicine. https://doi.org/10.1038/s41591-020-0893-5.

Cummings, B. B. et al. (2020). Transcript expression-aware annotation improves rare variant interpretation. Nature 581, 452–458.

Minikel, E. V. et al. (2020). Evaluating drug targets through human loss-of-function genetic variation. Nature 581, 459–464.

Collins, R. L. et al. (2020). A structural variation reference for medical and population genetics. Nature 581, 444–451.

Cardiff Crock of Gold?

 

One of the oddities of science is that we are aware that we know little and understand less and yet manage to be surprised at frequent intervals when some bright spark discovers something new. So, surprised most of us indeed were by a paper from Andrew Sewell and colleagues at Cardiff University who have tracked down a hitherto unknown sub-population of white blood cells that may turn out to be extremely useful.

Before we get to the really exciting bit we need a follow-up word on CRISPR-Cas9, because that was what the Cardiff group used, and a clear picture of how the immune system works in cancer.

CRISPR-Cas in short

This method adapts a bacterial defence system for detecting and destroying invading viruses. It uses RNA guides to locate specific bits of DNA inside a cell, enabling molecular scissors to cut that section of DNA. This can disable a specific gene or allow a new gene to be inserted — described in Sharpening CRISPR and Re-writing the Manual of Life.

However, as well as being able to knock out genes or insert new ones, CRISPR has another feature. By using designer guide RNAs, CRISPR can scan the entire range of the genome. This DNA scanning feature can be scaled up to screen many genomic sites in parallel in one experiment. Synthesis of short fragments of nucleic acids (oligonucleotides) is carried out automatically using computer-controlled instruments (oligonucleotide synthesizers). The scale is astonishing: high-throughput DNA synthesis platforms can produce libraries of oligos (millions of them), each encoding a different guide RNA sequence and hence a different DNA target. Oligo libraries can be cloned into a lentiviral (a retrovirus) vector system for delivery to cells. This generates parallel, high-throughput, loss-of-function of specific genes from which their function can be inferred.

The immune system and cancer

The immune system can recognise cancer cells as abnormal and kill them. This happens because cancer cells (and cells infected by pathogens) break down proteins made within the cell and display those fragments on their surface. Thus cancer cells can ‘present’ their own antigens thereby stimulating an immune response that leads to their elimination by the immune system. Antigens on the cell surface bind to killer T cells (aka cytotoxic T cells) via the T-cell receptor (a complex of proteins on the T cell surface). This provokes the release of perforin that makes a pore, or hole, in the membrane of the infected cell. Cytotoxins then pass into the cell through this pore, destroying it. Almost all cell types can present antigens in some way and the loss of ‘antigen presentation’ is a major escape mechanism in cancer. It allows tumour cells to become ‘invisible’ and avoid immune attack by anti-tumour white blood cells.Scheme showing a cytotoxic T cell, (a type of lymphocyte aka a killer T cell, cytolytic T cell or CD8+ T cell), that kills cancer cells, interacting via its TCR with an antigenic peptide attached to an MHC molecule on the surface of a target cell. Granzymes are enzymes that cause apoptosis in targets cells.

What is the major histocompatibility complex?

Antigen-presenting cell (APCs) display antigen on their surface attached to major histocompatibility complexes (MHCs). MHCs are essential for the adaptive immune system to work, i.e. the sub-system of the immune response that eliminates pathogens. Human MHCs are also called the HLA (human leukocyte antigen) complex to distinguish it from other vertebrates. They’re encoded by a group of genes that are highly polymorphic — meaning that there are many different variant forms of the genes (alleles). The upshot of this is that no two individuals have exactly the same set of MHC molecules, with the exception of identical twins. This is the cause of transplant rejection wherein an immune response is switched on against HLA antigens expressed on APCs transferred along with the transplanted organ.

And now for the exciting news

The CRISPR screen used by Andrew Sewell and colleagues turned up a new type of T cell — one that differs from conventional T cells by presenting fragments of tumour proteins attached not to HLA proteins but to a different a receptor called MR1. The difference is critical because MR1 doesn’t vary between humans, unlike the highly variable HLAs. This appears to be why, in laboratory experiments, T cells with the MR1-seeking receptor can mediate killing of most types of human cancer cells without damaging healthy cells.

What they did was to take a sample of peripheral blood and select lymphocytes that proliferated in the presence of a cancer cell line (derived from a human lung cancer). They found that this cell clone kills a wide range of cancer cells in culture — so they used the CRISPR screening method to track down what the clone was targetting on cancer cells. Answer: MR1.

The novel T cell clone kills a broad range of tumour cells but does not kill cancer cell lines lacking MR1 or a range of healthy cells from various tissues. From Crowther et al., 2020.

The Cardiff group were further able to show that T-cells of melanoma patients modified to express this new TCR could destroy not only the patient’s own cancer cells, but also other patients’ cancer cells in the laboratory, regardless of HLA type (see Self Help – Part 2 and Gosh! Wonderful GOSH for how adoptive cell transfer works).

Transfer of the clone carrying the novel T cell receptor redirects patient T cells to recognize their own melanoma cells. Normal cells are unaffected. Black dots: + MR1; Grey dots: – MR1. From Crowther et al., 2020.

The data show (left) two T cell populations from two patients with metastatic melanoma. T cells transduced with the T cell receptor that binds MR1 recognized their own melanoma cells and killed them. Normal cells were unaffected regardless of MR1 expression.

These findings describe a TCR that exhibits pan-cancer cell recognition via the invariant MR1 molecule. Engineering T cells from patients that lacked detectable anti-cancer cell activity rendered them capable of killing the patients’ melanoma cells. However, these cells did not attack healthy cells so this method of genetic engineering, coupled with adoptive cell transfer, offers exciting opportunities for pan-cancer, T cell–mediated immunotherapy.

This discovery is most timely because, although CAR-T therapy is personalised to each patient, it targets only a few types of cancers and thus far has not worked for solid tumours.

CRISPR and related technologies are leading us into a new world in which Chinese scientists have already made the first CRISPR-edited human embryos and the first CRISPR-edited monkeys and, very recently in the first human trial of cells modified with CRISPR gene-editing technology, shown that the treatment is safe and lasting. This work, by You Lu at the West China Hospital in Chengdu, took immune cells from people with aggressive lung cancer and disabled the PD-1 gene. The PD-1 protein normally attenuates the immune system to prevent it attacking its own tissues but, as this reduces its anti-cancer capacity, knocking out PD-1 should overcome that restriction.

These advances are remarkable but we are still at the very beginning of gene therapy for cancer and the promise is almost limitless.

Reference

Crowther, M.D., Sewell, J.D. et al., (2020). Genome-wide CRISPR–Cas9 screening reveals ubiquitous T cell cancer targeting via the monomorphic MHC class I-related protein MR1. Nature Immunology  21,  178–185.

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.

 

Blocking the Unblockable

 

It’s very nearly 40 years since the first human ‘cancer gene’ was identified — in 1982 to be precise. By ‘cancer gene’ we mean a region of DNA that encodes a protein that has a role in normal cell behaviour but that has acquired a mutation of some sort that confers abnormal activity on the protein.

The discovery of RAS ‘oncogene’ activation by DNA and protein mutation stimulated intense activity in unveiling the origins of cancer at the molecular level that has continued to this day. It’s been an exciting and sobering story and RAS has emerged as perhaps the best example you could have of the paradox of cancer. On the one hand it seems startlingly simple: on the other it’s been impenetrably complex.

The simple bit first

Relatively quickly it was shown that there were three closely related RAS genes (KRAS, HRAS & NRAS): they all encode a small protein of just 189 amino acids and they all act as a molecular switches. That means RAS proteins can bind to a small regulator molecule (it’s GTP (guanosine triphosphate) — one of the nucleotides found in DNA and RNA). When that happens RAS changes shape so that it can interact with (i.e. stick to) a variety of effector proteins within the cell. These trigger signalling cascades that ultimately control the activity of genes in the nucleus that control cell proliferation, cell cycle progression and apoptosis (cell death). The switch is flicked off when GTP is converted to GDP — so RAS looses its effector binding capacity.

The other simple bit is that RAS turned out to be one end of the spectrum of DNA damage that can activate an oncogene: the smallest possible change in DNA — mutation of just one base changed one amino acid in the RAS protein and hence its shape. Result: permanently switched on RAS: it’s always stuck to GTP.

Cell signalling. Cells receive many signals from messengers that attach to receptor proteins spanning the outer membrane. Activated receptors turn on relays of proteins. RAS proteins are key nodes that transmit multiple signals. The coloured blocks represent a RAS controlled pathway (a relay of proteins, A, B, C, D) that ‘talk’ to the nucleus, switching on genes that drive proliferation. The arrows diverging from RAS indicate that it regulates many pathways controlling such processes as actin cytoskeletal integrity, cell proliferation, cell differentiation, cell adhesion, apoptosis and cell migration.

Oncogenic RAS and human cancers

We’ve noted that RAS signalling controls functions critical in cancer development and it’s therefore not surprising that it’s mutated, on average, in 22% of all human tumours with pancreatic cancer being an extreme example where 90% of tumours have RAS mutations (the form of RAS is actually KRAS). Those facts, together with the seeming simplicity of its molecular action, put RAS at the top of the target table for chemists seeking cancer therapies. We’ve tried to keep up with events in Mission Impossible, Molecular Dominoes and Where’s that tumour? but the repeated story has been that the upshot of the expenditure of much cash, inspiration and perspiration has, until fairly recently, been zippo. Lots of runners but none that made it into clinical trials. However, that has slowly begun to change over the last ten years and now at least five KRAS-modulating agents are in clinical trials.

A few months back Kevin Lou, Kevan Shokat and colleagues at the University of California published a study of a small molecule, ARS-1620, showing that it inhibited mutant KRAS in lung and pancreatic cancer cells. They screened for other interactions that contribute to the KRAS-driven tumour state and identified two sets of such effectors, one enhancing the engagement of ARS-1620 with its target and others that regulated tumour survival pathways in cells and in vivo. Targetting these synergised with ARS-1620 in suppressing tumour growth.

The RAS switch. Scheme of normal RAS action (top): replacement of a bound guanosine diphosphate (GDP) molecule with guanosine triphosphate (GTP) flips the switch so that RAS can interact with other proteins to turn on downstream signalling pathways that control cell growth and differentiation. Oncogenic RAS (with a single amino acid change at position 12 (Glycine to Valine) blocks the breakdown of bound GTP so the switch is always ‘on’. The new small molecule inhibitor characterized by Canon et al., AMG 510, interacts with KRASG12C to block GTP binding. The switch remains ‘off’ and the cancer-promoting activity of mutant KRAS is inhibited.

More recently Jude Canon at Amgen Research, together with colleagues from a number of institutes, described another small molecule, AMG 510, that also recognises the mutant form of KRAS with high specificity, hence impairing cell proliferation. In mice carrying human pancreatic tumours AMG 510 caused permanent tumour regression — provided the mice had functioning immune systems. In mice lacking T cells (i.e. ‘nude’ mice) the tumours re-grew but combining AMG 510 with immunotherapy (an antibody against anti-PD1) gave complete tumour regression. AMG 510 stimulated the expression of inflammatory chemokines that promoted infiltration of the tumours by T cells and dendritic cells (sometimes called ‘antigen-presenting cells’, these cells process antigens and present fragments thereof on their surface to T cells and B cells to promote the adaptive immune response). In preliminary trials four patients with non-small cell lung cancer showed significant effects — either tumour shrinkage or complete inhibition of growth.

So maybe at long last the enigma of RAS is being prised open. The efficacy of AMG 510 against lung cancers is particularly heartening as there remains little in the way of therapeutic options for these tumours.

References

Canon, J. et al. (2019). The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223.

Lou, K. et al. (2019). KRASG12C inhibition produces a driver-limited state revealing collateral dependencies. Science Signaling 12, Issue 583, eaaw9450. DOI: 10.1126/scisignal.aaw9450

Non-Container Ships

 

A question often asked about cancer is: “Can you catch it from someone else?” Answer: “No you can’t.” But as so often in cancer the true picture requires a more detailed response — something that may make scientists unpopular but it’s not our fault! As Einstein more or less said “make it as simple as possible but no simpler.”

No … but …

So we have to note that some human cancers arise from infection — most notably by human immunodeficiency viruses (HIV) that can cause acquired immunodeficiency syndrome (AIDS) and lead to cancer and by human papillomavirus infection (HPV) that can give rise to lesions that are the precursors of cervical cancer. But in these human cases it is a causative agent (i.e. virus) that is transmitted, not tumour cells.

However, there are three known examples in mammals of transmissible cancers in which tumour cells are spread between individuals: the facial tumours that afflict Tasmanian devils, a venereal tumour in dogs and a sarcoma in Syrian hamsters.

Not to be outdone, the invertebrates have recently joined this select club and we caught up with this extraordinary story in Cockles and Mussels, Alive, Alive-O! It’s a tale of clams and mussels and various other members of the huge family of bivalve molluscs — (over 15,000 species) — that began 50 years ago when some, living along the east and west coasts of North America and the west coast of Ireland, started to die in large numbers. It turned out that the cause was a type of cancer in which some blood cells reproduce in an uncontrolled way. It’s a form of leukemia: the blood turns milky and the animals die, in effect, from asphyxiation. In soft-shell clams the disease had spread over 1,500 km from Chesapeake Bay to Prince Edward Island but the really staggering fact came from applying the power of DNA sequencing to these little beach dwellers. Like all cancers the cause was genetic damage — in this case the insertion of a chunk of extra DNA into the clam genome. But amazingly this event had only happened once: the cancer had spread from a single ‘founder’ clam throughout the population. The resemblance to the way the cancer spreads in Tasmanian devils is striking.

Join the club

In 2016 four more examples of transmissible cancer in bivalves were discovered — in mussels from British Columbia, in golden carpet shell clams from the Spanish coast and in two forms in cockles. As with the soft-shell clams, DNA analysis showed that the disease had been transmitted by living cancer cells, descended from a single common ancestor, passing directly from one animal to another. In a truly remarkable twist it emerged that cancer cells in golden carpet shell clams come from a different species — the pullet shell clam — a species that, by and large, doesn’t get cancer. So they seem to have come up with a way of resisting a cancer that arose in them, whilst at the same time being able to pass live tumour cells on to another species!!

Map of the spread of cancer in mussels. This afflicts the Mytilus group of bivalve molluscs (i.e. they have a shell of two, hinged parts). BTN = bivalve transmissible neoplasias (i.e. cancers). BTN 1 & BTN2 indicates that two separate genetics events have occurred, each causing a similar leukemia. The species involved are Mytilus trossulus (the bay mussel), Mytilus chilensis (the Chilean blue mussel) and Mytilus edulis (the edible blue mussel). The map shows how cancer cells have spread from Northern to Southern Hemispheres and across the Atlantic Ocean. From Yonemitsu et al. (2019).

Going global

In the latest instalment Marisa Yonemitsu, Michael Metzger and colleagues have looked at two other species of mussel, one found in South America, the other in Europe. DNA analysis showed that the cancers in the South American and European mussels were almost genetically identical and that they came from a single, Northern hemisphere trossulus mussel. However, this cancer lineage is different from the one previously identified in mussels on the southern coast of British Columbia.

Unhappy holidays

It seems very likely that some of these gastronomic delights have hitched a ride on vessels plying the high seas so that carriers of the cancer have travelled the oceans. Whilst one would not wish to deny them the chance of a holiday, this is serious news because of the commercial value of seafood.

It’s another example of how mankind’s advances, in this case being able to build things like container ships with attractive bottoms, for molluscs at least, can lead to unforeseen problems.

This really bizarre story has only come light because of the depletion of populations of clams and mussels in certain areas but it certainly carries the implication that transmissible cancers may be relatively common in marine invertebrates.

Reference

Yonemitsu, M.A. et al. (2019). A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe. eLife 2019;8:e47788 DOI: 10.7554/eLife.47788.

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.

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.

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.

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.