Category Archives: Science Paper Reviews

Reviews of Scientific Papers.

How HPV driven cancers get their mutations…

Hi there!

It’s been a long time since I last blogged, but that is because I’ve been swimming round in data, which has incidentally led to the findings that were published in this paper , which I will describe in this post.

HPV and the link to cancer.

HPV (Human Papillomaviruses) consist of a family of viruses that infect keratinocytes (skin cells) that line the outside of the body and the inner cavities – some of them just cause warts (and genital warts) but some of them are capable of driving the formation of cancer. These types, which are called “High-risk” strains, are the ones that are targeted for prevention by HPV vaccines.

High-risk HPV strains differ from low-risk strains in terms of cancer-causing ability because of proteins they make during their life cycle. Cells need to be actively dividing to permit HPV replication and in order to do this, the virus uses two proteins, called E6 and E7 , to block and degrade two proteins in human cells, called TP53 and pRb, which are two potent tumour suppressors (genes that prevent tumour formation).

Normally, E6 and E7 are only active for a brief while during the virus’ life cycle, which culminates in the production of more viruses that restart the cycle all over again, but before HPV driven cancers form something very strange happens; by complete accident the viral genome gets inserted and integrated into human DNA in infected cells, or infected cells get locked into a state where E6 and E7 are produced all the time. Suddenly you’ve got cells with TP53 and pRb off all the time, leaving behind cells that can grow abnormally. We see this when women have cervical scrapings looked at, and see “dysplastic” cells that have grown clumpy and abnormal.

However, these dysplastic cells are not cancerous – and haven’t acquired all the hallmarks of cancer. For this to happen there need to be additional changes to the DNA sequence (Mutations) of the genes in dysplastic cells that can confer those properties. Well known examples of things that cause mutations include tobacco smoke; for quite a while it had been an open question as to where HPV-driven tumours got their mutations from.

Suspicions are aroused: could the APOBEC family of proteins be making these mutations? 

One of my major research interests is to see what genes are expressed more and what genes are turned off in HPV driven cancers, and when defining a signature for these tumours I compared them to normal tissue and HPV negative tumours that arise in the same tissue (while cervical cancers usually all tend to be HPV-driven, there are head and neck cancers caused by HPV and those caused by chronic tobacco and alcohol exposure) and one of the genes that I found expressed at high levels in HPV-positive tumours was APOBEC3B.

APOBEC3B is one of many proteins of the APOBEC cytosine deaminases family. These act either on RNA or DNA when it is a single stranded state, and take part in the body’s immune response against viruses by messing up the RNA/DNA from the viruses. They work by changing cytosines, one of the four bases that make up DNA to uracil (a base that is normally only found in RNA) which then gets converted to a thymine or a guanine (two other bases that make up DNA); so if you get lots of these changes in viral DNA you fundamentally break them so they can’t do any of the things they usually do, and it had been known for a while that you could find HPV with messed up DNA in precancerous lesions with patterns of change associated with APOBEC proteins.

This led us to wonder if APOBEC proteins could end up accidentally changing human DNA just like it would change viral DNA and therefore generate the necessary DNA sequence changes to cause cancer; and at the same time we started wondering that a couple of papers came out showing that there were human cancers in which mutations looked like they were being generated by APOBEC enzymes, very likely APOBEC3B (We could tell it was likely APOBEC 3B because it is known to change cytosines that are preceded by a thymine and followed by guanine or adenine or thymine, so if the sequence was TCA or TCG or TCT it would be converted to TGA/TTA or TGG/TTG or TTT/TGT ). There is an alternative process that can also generate TCG->TGG/TTG mutations, so in order to specifically measure APOBEC activity we ended up using the others, which we referred to in the paper as TCW to TKW (TCW->TKW, where K = G or T and W = A or T).

Those previous papers also noted that cervical cancers had lots of mutations that showed the APOBEC signature, but the question remained – was this down to it being the cervix? or was it down to these tumours being HPV+? We decided to take a look in head and neck cancers as well where we could compare HPV+ and HPV- tumours that arose in similar tissues to see if there was truly an association with HPV, and hence we did the work reported in the paper…

HPV positive tumours have a vastly higher fraction of mutations belonging to the APOBEC signature.

First, we ended up looking at levels of APOBEC mutagenesis and how much of all the mutations in tumours were attributable to them using publicly available data for 40 HPV+ head and neck tumours and 253 HPV- head and neck tumours. To do this we used multiple approaches – including looking at TCW->TKW mutations and also trying to break down all the mutations we see in these tumours into patterns of mutations, as was done by these people at the Sanger Institute , and also looking at enrichment for the TCW->TKW mutation pattern locally. All the approaches we used showed the same thing – HPV+ tumours had a vastly higher proportion of mutations most likely caused by APOBEC enzymes.

Figure1:APOBEC mutations are highly enriched in HPV+ HNSCs

Multiple measures of APOBEC activity showed a strong association with HPV status but not age or smoking; APOBEC, age and smoking were the three processes we identified as driving the signatures using the Sanger Institute’s approach. The more the numbers are shifted to the right the stronger the association with the factor listed on the left. 

We found signatures previously associated with APOBEC, smoking and age, and showed that APOBEC activity was not associated with the latter two, which was as expected. Having identified an association with HPV driven tumours we wanted to know if this was a general antiviral response or something HPV specific…so we took a look at patterns of mutations in liver cancers caused by hepatitis B and C viruses and found no evidence for APOBEC mediated mutations being significantly enriched in these tumours.

Of drivers and passengers

Most tumours have hundreds and thousands of mutation, but only a few actively contribute to the acquisition and maintenance of the hallmarks of cancer. So, having initially identified high proportions of APOBEC-mediated mutations in HPV driven cancers when looking across the exome (all protein coding genes in general) we decided to ask if the enrichment we saw in all genes was also maintained when we restricted our searching to genes known previously to drive cancer or those that share features associated with drivers, like occurring at a frequency greater than expected by chance. Our analyses confirmed that APOBEC-mediated mutations were again enriched in the HPV+ head and neck, and cervical cancers compared to the HPV- HNSCs.


Differences between HPV negative HNSCC and HPV+ tumours (HNSCC and Cervical cancer) are maintained when looking at all protein-coding genes (whole exome) and likely driver mutations (MutSig).

Then we went on to look at which driver genes happened to be most mutated by APOBEC proteins, and found a gene called PIK3CA (one of the components of a protein complex called PI3 kinase) towards the very top of the list. PIK3CA has previously been reported as being vital to the sustenance of many HPV positive tumours in particular and head and neck cancers in general, and drugs are being developed to target it. Interestingly, we observed that in the HPV+ tumours 22/25 PIK3CA mutations recorded were of the APOBEC type, while this wasn’t the case for the HPV negative tumours.

This then led to yet another question – can the levels of APOBEC activity explain a preference for APOBEC mutations in HPV-positive tumours? Now for driver genes there are two things that may govern what kinds of mutations we see – how much of a growth advantage a mutation in a driver gene gives that cell and the mutation itself. My supervisor, Tim Fenton, who worked on PI3 kinases previously, knew that there were two regions in PI3 kinase amongst which mutations regularly occurred (one or the other) and then realised that one of them contained a TCW sequence that APOBEC proteins could act on while the other one did not.

The PIK3CA gene makes a protein called p110-alpha, and proteins have different distinct elements in their structure, called domains. One region, called the helical domain, is often mutated at two TCW sequences while the other region, called a kinase domain, is not, and both mutations confer similar growth advantage, and if you look across multiple tumour types, overall you tend to see a 50-50 split between the two. This enabled us to account for growth advantage and directly see if APOBEC activity, which we had already measured by looking at all protein-coding genes, and a preference for APOBEC-induced mutations in the helical domain, were linked.

Since PIK3CA is mutated in multiple types of cancers, I was able to grab some data from The Cancer Genome Atlas project and measure how strongly there was a skew towards acquiring helical domain mutations compared to the kinase domain mutations and just look at what APOBEC activity looked like in each of those types of tumours. The results were quite robust – the higher the APOBEC activity in a cancer type, the stronger the preference for helical domain mutations compared to kinase domain mutations.


Figure 3. A – as you move from left to right (tumour types are arranged from left to right based on median APOBEC activity), you see helical domain mutations (black bars) become strongly preferred compared to kinase domain mutations (yellow bars). B – plotting the median TCW->TKW fraction (APOBEC activity) against the proportion of PIK3CA mutations that are helical hotspot mutations shows a strong correlation.

So yeah, people had been wondering why in bladder cancers, for example, you saw such a strong preference for helical hotspot mutations – we basically addressed that long-standing question with these analyses.

Explanatory factors

So the one other thing we did was to look at what might be driving this process, and surprisingly we found no correlation between how much E6 and E7 was being expressed in these tumours and APOBEC activity, or for that matter between APOBEC3B gene expression and APOBEC activity, and did find a strong link with how many mutations in total these tumours had. The work has led us to hypothesize it may be something like DNA damage induced by HPV, that generates the substrate for APOBEC3B to act upon, that drives the process.


Our work suggests that HPV positive tumours evolve in a trajectory where they incorporate HPV DNA into their own, leading to sustained E6/E7 expression, followed by APOBEC activity until a driver mutation occurs, after which clones expand and show the APOBEC signature when their DNA is sequenced while in HPV negative HNSCC smoking and alcohol do this job, and if PIK3CA is the gene mutated the HPV positive tumours tend to have helical domain hotspot mutations because APOBEC proteins are responsible for them…

Additional stuff

The journal did a Q&A that expands on some of the work in the paper, and you may find it here .

There is a press release from UCL here.



A Bird’s Eye View of Cancer Research…

I often get asked what cancer is when people find out I do cancer research for a living and the whys and the wherefores thereof inevitably follow in conversation. The complexities of the disease often mirror the complexities of the bodies they plague and therefore I decided it might be good to get a few things written down that people could be pointed to in an effort to make things a little more lucid and also to serve as a compilation of resources people could delve into if they so desired. So here it is…

Omnis cellula e cellula

All known living organisms are made of cells, in most cases, one cell on its own is an organism, obtaining food from the environment, breathing, growing, multiplying, and carrying out a whole assortment of other life processes that are of interest to biologists, but I digress.

Cancer is fundamentally a disease of organisms that are made of communities of cells – these cells too do all of the above, but some of the more complex varieties of multicellular organisms show specialisation – brains and lungs and guts and genitals – all for the same purposes, to obtain energy, to stay alive and to reproduce; not that there is some grand predisposition to doing this with foresight, merely that those that stumble onto what is passable in the examinations posed by the brutal machinations of nature get to go on.

Starting from one cell,multicellular organisms expand to have several tissues, a whole paraphernalia of different types of cells. Some cells die out, some stay on but don’t divide unless required to make more cells, some double in number until they stumble across a battery of conditions – other cells, other molecules that tell them to stop growing, or those that induce them to multiply in a frenzy but then stop when balance has been restored.

This behaviour of cells, and the organs they form, and then the organ systems that they comprise, and the organisms themselves, emerge from interactions of the environment, both consisting of organisms and other things that appear prosaic but are nonetheless significant influences on the fates of organisms with the delightfully messy workings of the molecules within cells – from the DNA that contains all the genetic material of a cell that simply must be passed on from generation to generation to facilitate survival of the species.

Dawkins in his magnum opus; “The Selfish Gene”, popularised the notion of organisms serving as mere means for the continued survival of genes, but I shall go one step further and put it to you it isn’t just individual genes that are selfish, it is entire genomes (a collection of all the DNA in a cell/organism). Promiscuity is favoured by nature when rivals are less promiscuous and when it is possible to brutally stifle threats posed by the competition, and cancer essentially is this being taken to a horrifying extreme when genomes find ways to be malignantly selfish at the expenses of the other cells that are also integral to the survival of the organism, bringing with it much suffering and often, death…for alas! Cancer cells lack the foresight to know that their fate is tied inextricably with that of their hosts.

Of DNA, RNA and proteins…

Genomes are made of DNA, and this is the medium by which information gets passed on from generation to generation except in the case of a few viruses, but we don’t really consider viruses to be living things because they cannot reproduce on their own. But in order to help them do this, cells orchestrate a variety of biochemical functions through the medium of RNA that is transcribed from DNA, which by itself can affect other RNA or for some genes gets turned into proteins. RNA, proteins and DNA then interact with the outside environment, other DNA and proteins to give rise to the chemistry that leads to the formation of cells and organisms. This complexity is described elsewhere on the blog [1] [2] [3]  and I will add links and notes at the end so you’ll be able to explore further if it interests you.

On the road towards understanding cancer…we discover cancer causing genes have cellular origins. 

Towards the dusk of the first decade of the 1900s, Peyton Rous made a discovery that would shape perspectives towards cancer research for a long long time to come – he found that cancers in chicken could be transmitted akin to other known viral diseases – and the causative agent he isolated came to be known as Rous Sarcoma Virus (RSV). This immediately led people to believe that cancer was essentially a viral disease, until Michael Bishop and Harold Varmus made a revolutionary discovery – that the genes that caused cancer had a cellular origin – at some point, the virus had by sheer accident incorporated this gene while it was packaging itself up, and had no problems transmitting it because it could spread before the chickens died of cancer.

They got their hands on two strains of RSV, one with cancer causing properties, and one without, and consequently one could identify the viral gene that caused cancer as the one that was present in the former but not the latter. They made a probe of a molecule called RNA, which I shall describe later, to look for similar genes in cells, and then they found that it bound to the DNA of cells from different species, and in every species it happened to be found in the same place in the genomes of the cells (genomes are made of DNA); that gene that had been rampaging through chickens when transmitted through the virus was also found in normal cells, and when switched on in very high levels due to lots of replicating viruses, caused cells to lose control of how they grew and to take part in a frenetic orgy of cellular division, it was, in effect, an oncogene.

Then they looked elsewhere for genes with similar properties and began to identify more and more, which in cancer cells had defects in DNA that affected the function of the proteins they produced and consequently acted to launch the cells into rapidly increasing their cell numbers. On the other hand, people began to find proteins which, when altered, lost the ability to stop cells from dividing uncontrolled, these genes came to be known as tumour suppressors [4] and one of the breakthroughs in learning about the function of these genes came was the elucidation of Knudson’s “Two-hit” hypothesis…

Knudson’s two hit theory of cancer causation came about after the discovery of tumour suppressors, specifically a gene called Rb1.

Looking at genes implicated as either oncogenes or tumour suppressors people began to stumble across changes in DNA sequence compared to the sequences in normal cells that affected the proteins the genes made. These mutations, as we describe them, established the foundations of cancer genetics and genomics.

The hallmarks of cancer.

As people found more and more oncogenes and tumour suppressors they wondered what cancer was, for here was a set of diseases stemming from various tissue types that all appeared to consist of cells that grew rapidly and in many cases spread through the body, albeit at different rates. A seminal paper by Hanahan and Weinberg defined the hallmarks of cancer – traits that any disease that qualifies as a cancer *must* possess…

The Hallmarks of Cancer and examples of potential therapeutic methods to target them. From Hanahan and Weinberg’s seminal paper ‘The Hallmarks of Cancer: The Next Generation’, link in references.

These include the ability to multiply abnormally without requiring external signals, and if external signals that stop normal cells from dividing are present, not pay heed to them, the failure to undergo apoptosis (a form of cell death), immortalisation, which is the ability to divide indefinitely in permissive conditions unlike normal cells, angiogenesis, where tumours induce the formation of blood vessels so they can establish a bloody supply and finally, and most critically, metastasis; the ability to spread through the body and colonise other sites in the body, which is incidentally what is thought to kill patients. 
There was a recent update to the classical set of hallmarks described about and three new hallmarks entered the fray – altered cell metabolism; changes in how cancer cells generate energy, inflammation; a molecular response to wounds and injuries in normal cells that goes wrong and promotes cancer metastasis and genome instability – being prone to mutations and other structural aberrations that generate the complexities of cancer genomes, which I describe later [5]…

More than just mutations, and how we came to find out…

People who had been studying families of proteins called transcription factors noticed that they could fundamentally alter the way the RNA of different genes in the genome was produced – they could alter when they were produced, and how much was produced. This could then affect other proteins that controlled how cells divided and interacted with the environment, in some cases, transcription factors were found to be mutated, such as p53, which is also known as the guardian of the cell because of its critical role in stopping errant cells from progressing to cancer [6], which explains why so many tumours modify p53 function so they can get round it,and with this came the idea that cancers would exhibit differences in gene expression relative to normal tissue and this would then contribute to the achievement of the hallmarks of cancer. See [7] for a description of microarrays and case studies of how looking at expression profiles helped understand cancers.

People also realised that you could get changes in expression patterns independent of transcription factors… Cancer cells are host to a wide variety of large, structural, distortions of the genome, and compared to normal cells, which have 46 chromosomes, cancer cells accumulate a variety of aberrations, ranging from small deletions and duplications of bits of chromosomes to gains and losses of whole chromosomes, or in some cases whole sets of chromosomes (The ubiquitously used cancer cell line, HeLa, has 88 chromosomes).

Changes in copy number of genes through these aberrations could also have effects on gene expression profiles. Finally, people who’d been studying epigenetic processes, which involve cells inheriting expression patterns for instance and then modifying them through modifications of DNA without changes in sequence, such as DNA methylation and Hydroxymethylation or the histones around which DNA is wound [8], and began to develop techniques to characterise epigenetic changes in tumours, and we therefore ended up in a situation where we had a whole panel of analyses we could do on tumours.

The Cancer Genome Atlas

While several other tumour sequencing projects were underway at the likes of the Wellcome Trust Sanger Institute, The Cancer Genome Atlas really set things going ahead with their project on the deadly brain cancer; Glioblastoma Multiforme, with sequencing for mutations, microarray analysis for Copy Number Variation and gene expression and microarray analysis for DNA methylation. They essentially found that there are four groups of glioblastomas based on patterns in gene expression and were able to correlate these with different cellular origins and different ways in which those expression profiles were achieved [9].

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Heatmap from one of the first TCGA papers that profiled glioblastoma. Four subtypes of glioma were described based on expression profiles. They were able to classify samples in an independent dataset and also versions of glioblastomas grown in mice (technically called xenografts).

They also found a subset of tumours that had very high levels of methylation driven by mutations in a gene called IDH1, which leads to too much methylation as a direct consequence, as demonstrated in a landmark paper in the journal Nature, where they put in mutant IDH1 into astrocytes and showed it induced high methylation levels like those seen in gliomas of that type…

Since then, the TCGA has published multiple studies on breast cancer, ovarian cancer, renal clear cell carcinoma,lung adenocarcinoma and colorectal cancer to name a few [10] and is collecting data for more tumour types, and from 12 datasets so far a pan-cancer analysis was released recently [11]. This has inspired the formation of the even more ambitious International Cancer Genome Sequencing Consortium which aims to widely expand the scope and the size of the type of approaches taken by the TCGA to profile the most striking molecular features of tumours and to then relate them to clinical information.

Things are not quite so simple – the problem of heterogeneity.

One would think that by understanding the make-up of tumours and figuring out what drives them, it would be easy to target altered genes, proteins and pathways with specific drugs to achieve cures, however, tumours have such unstable genomes and often contain so many cells by the time they’re detected that they are capable of a great degree of evolution, which may become reflected in resistance to the drugs used to target them. Indeed, studies starting two years ago began to show two features of tumours, firstly; they evolved through time, and therapy often had an influence on which dominant properties were seen in a patient’s disease as they relapsed. They either saw that the most dominant clone before treatment acquired new mutations and then evolved to resist therapy or a previously minor clone expanded.

At around the same time, evidence was found to strongly support the notion that cancers not only evolved in a linear manner but could evolve in parallel. Both those studies were carried out on leukaemias and the same was shown to be true of solid tumours. An analysis of a kidney tumour looking at multiple regions of the primary tumour and metastases (new outgrowths of the tumour derived from cells that had spread from the primary tumour) highlighted branched evolution and also observed that different parts of the primary tumour showed different patterns of gene expression associated with  survival [12].

Intratumour heterogeneity is extensive in kidney cancer and sequencing multiple biopsies enabled reconstruction of evolutionary patterns.

A recent study looking at multiple regions from a series of glioblastomas also found the same striking pattern; there was evidence that all four of the glioma expression subtypes discovered by the TCGA were found in that tumour [13]. These studies have made one thing abundantly clear; that understanding and classifying tumours into subgroups may be of limited utility when the range of evolutionary invention achieved by tumours permits them to acquire different patterns of alterations for the most part in response to therapy. We will learn a lot, indubitably, from large scale analyses of the kinds already being carried out, and only recently we began to uncover what processes might contribute to the formation of mutations and how to find signatures for mutations from all that data being generated by sequencing tumour after tumour after tumour, so chances are we will have a comprehensive collection of molecular profiles to tuck into, soon, on an unprecedented scale. 

Finding chinks and causes for optimism…

Another way weaknesses might be found in tumours involves approaches based on what we call synthetic lethality and collateral lethality. Synthetic lethality is when, because a gene is mutated or altered otherwise, another gene becomes essential while it was not essential if the other gene was intact. A classic example of this is PARP inhibition. PARP is an enzyme that repairs breaks in DNA, but can be dispensed with if the BRCA genes are intact. A significant proportion of Ovarian and Breast cancers, especially those that run in families, show a characteristic loss of BRCA1 or BRCA2, and this makes them especially vulnerable to the blockade of PARP.

Explanation of synthetic lethality to PARP inhibitors. People with one copy of BRCA lost in normal cells can present with tumours that have lost both. Normal cells have BRCA to compensate for the loss of the PARP gene but cancer cells don’t, and blocking PARP can kill them while sparing normal cells as a consequence.

One way of finding synthetic lethal interactions is to combine knockdown experiments, where little RNA sequences are introduced into cells to block and degrade the RNA of target genes and not permit protein to be formed from that RNA and combining that information with mutation, expression and other “-omics” data as we call them. Even without -omics data in attendance, knockdown experiments themselves can reveal certain genes that come to be required specifically in cancer, and in that way we can identify targets for chemists to then develop specific drugs against. Of course, the other approach would be to target things that appear to transcend cancers, and examples include targeting a protein called CD44 that cancer cells appear to universally express to avoid being destroyed by the immune system or to use drugs that target fundamentally common features of tumours such as DNA methylation [14].

Finally, knowledge of tumour evolution itself may be employed to find weaknesses, as described elsewhere on the blog, the mechanism of resistance may by itself predispose tumours to weaknesses, and this could be as simple as withdrawing the drug (letting go of the brakes suddenly when the driver’s still got the foot on the pedal to compensate for jammed brakes till that point), as discussed here [15].

Dealing with heterogeneity may be rendered possible by bypassing resistance mechanisms where cancers find alternate pathways to get to where they need to be to survive and expand by hitting points that are altered in cancer, but have no known alternatives for tumours to route their functioning through. Indeed, this has been shown experimentally by targeting Myc, which when activated is a potent oncogene or by targeting BIM in glioblastoma where tumour cells evolve resistance by finding other ways to prevent BIM from being turned on when the pathway they usually use to do this is blocked with drugs.

For all the complexities of cancer, there might still be ways in which we will figure out how to target and attack them successfully, and one of the keys to that I think the sense of scale and community that cancer research projects these days are marked by. I think the enduring impact of the Human Genome Project [16] was not just the sequencing of the human genome, but ensuring that data was openly accessible to anybody who wanted to use it for their research or look at it for general interest; the TCGA and ICGC have put in place similar policies to govern how their data is accessed, and by allowing researchers to integrate the research they do locally with data that wouldn’t have been generated without big projects like them it is possible to achieve so much more. And maybe we’ll figure out what cancer is, and then determine what they can and can’t be, someday, soon… 

Links to posts on the workings of DNA, RNA and proteins. [1]  (Central Dogma of Molecular Biology)

[2] (Transcription)

[3] (Includes descriptions of microRNAs, RNA species that can block other RNAs from being converted to protein as per the central dogma, thereby affecting gene expression).

Oncogenes, Tumour Suppressors and the Hallmarks of Cancer
[4] (Contains an exposition of the two-hit theory of cancer causation and links to further material on the topic, as well as nuance about how some tumour suppressors behave differently)

[5] (Updated version of the classic paper; The Hallmarks of Cancer by Hanahan and Weinberg. May be paywalled).

[6] (Explains how p53 functions in different contexts, basically)

Understanding Cancers and large scale analyses
[7] (Nature Scitable article on gene expression and cancer).

[8] (An introduction to epigenetic processes).

[9] (The TCGA glioblastoma paper that documented four expression subtypes).

[10] (a list of papers from The Cancer Genome Atlas, most papers are openly accessible and readable should you want to, but they’re science heavy and really written for people with an in-depth understanding of cancer research. You may be able to search for materials and commentary related to them to get a more popular perspective, also look for TCGA press releases on the same site).

[11] (Blogpost on the Nature blogging network containing links to further material, commentary and analysis papers from the TCGA pan-cancer project.)

Heterogeneity, synthetic lethality and the future

[12] (Blogpost on intratumour heterogeneity)

[13] (Paper documenting intratumour heterogeneity in glioblastoma multiforme)

[14] (Blogpost discussing broad spectrum effects of low doses of the DNA methylation blocker decitabine) .

[15] (Blogpost exploring reports of how cancer cells evolved resistance to a drug and how this could be used to target the tumour).

[16] (Long blogpost on the Human Genome Project, written by yours truly and therefore recommended if you are a glutton for a few thousand words more having worked your way through to the end of the article).

That’s all from me now!


A window into acquired resistance to targeted therapies – through the eyes of a MEK inhibitor.


Cancer cells, like all other cells in multicellular organisms, are often dependent on inputs from the environment outside the cell for signals that drive growth and survival, among other things. Since abnormal growth and a failure to die like normal cells do are hallmarks of cancer, it makes sense to try and block signalling pathways that contribute to these features using drugs specific to the proteins in these pathways. This is the fundamental premise behind targeted therapies.

The work I’m going to focus on in this article happens to do with inhibition of MEK, which connects external signals to a set of transcription factors that promote the expression of genes related to cell growth and survival.

The Map-kinase signalling pathway. External growth factor receptor kinases are coupled to transcription of genes promoting cell survival and proliferation by means of the Ras-Raf-MEK-ERK signalling cascade. Kinases are proteins that add an inorganic phosphate group to other proteins or in some cases, lipids. 

This pathway is of interest because b-Raf is found to have a very particular mutation, V600E, in a majority of malignant melanomas – something so characteristic of the disease that there is a drug that specifically targets the mutant version of this protein, but responses are often short-lived because cells learn to get round the blockade of the protein. Interestingly, this pathway is also involved in colorectal cancer, often involving the same mutations or a mutation in the protein that comes before b-Raf, called k-ras, which is a very potent oncogene.

Simon Cook and his group at the Babraham Institute tried to figure out how cancer cells that depended on this pathway would come to acquire resistance to a MEK inhibitor (which is downstream of B-raf and k-ras). To do this, they took two colorectal cancer cell lines with a b-Raf mutation and two with a k-ras mutation and cultured them in the presence of ever increasing concentrations of a MEK inhibitor : AZD6244. They fundamentally found that resistant cells seemed to acquire amplifications in the number of copies of b-Raf or k-Ras, thus serving to maintain the same signal intensity downstream of MEK in the presence of the drug as they would have in the absence thereof.

Resistance to a MEK inhibitor in a B-raf mutant cell line is explained by amplification of B-raf at the DNA level, which is reflected at the protein level and increased activation of ERK1 (P-ERK1/2). The graph at the bottom right shows that knocking down b-Raf levels using RNA interference reverses resistance to the MEK inhibitor.

They also found that in Ras mutant cell lines, amplification of k-Ras was to blame for the phenotype. The problem with that is Ras is a pain in the rear to develop drugs against, but with b-Raf there are inhibitors available and it should be possible to resensitize resistant cells to the MEK inhibitor by hitting it at both points in the pathway.

The trouble with this MEK inhibitor is that it leads to cell cycle arrest being the major response as opposed to cell death, so it would be sensible to see if, for already dampened levels of ERK activation through MEK inhibition, it should be possible to increase the proportion of cancer cells that actually fuck off and die instead of just waiting for the drug to wear off.

Apoptosis is a process mediated by a combination of pro-apoptotic proteins and anti-apoptotic proteins and when a threshold is reached in terms of dominance of pro-apoptotic proteins it sets of a cascade of signalling events that leads to the destruction of the cells. One of the reasons cell cycle arrest is favoured over cell death is the hyperactivity of anti-apoptotic proteins such as Bcl2. There is a protein called BH3 which can bind to and disable Bcl2, rendering cells much more susceptible to apoptosis if the MEK pathway is hit, so they looked at combining a drug that mimics the structure of BH3 with the MEK inhibitor and promptly found that apoptosis was greatly enhanced and the emergence of resistance delayed .

Finally, of course, it is worth considering the fact that in malignant melanoma, drug holidays, where treatment is not administered for a while, has been shown to reverse resistance well in line with what we’d expect – the overexpression of oncogenes is associated with oncogene induced senescence and what might maintain the activity of the pathway in the presence of the drug might activate the pathway too much when the drug is taken away – like how a car might crash if you suddenly took the brakes off while the pedal was still pressed to the same extent as when driving with the brakes on. This means that drug resistance is favoured only because of a selective pressure imposed by the drug and might actually be a detriment in competition with drug sensitive cells when the drug is absent. Take the drug away when the cells that are most dependent form the bulk of the tumour, and they really do crash dramatically

That’s all from me until next time.


PS – additional papers…



Paper Review: Identification and functional validation of HPV-mediated hypermethylation in head and neck squamous cell carcinoma

Hello everyone.

It has been bloody long since I last blogged because I have been battling illness and getting stuck into research at the same time. I’m going to review a paper I did some work towards in this post. I have written about DNA methylation in the past and the research groups I worked with/continue to work with were focusing on the various genetic and epigenetic alterations that characterise  head and neck squamous cell carcinoma (HNSCC), which is the sixth most common type of cancer.

HNSCC can be thought of as two distinct cancers with vastly different prognosis and aetiologies; a vast majority are caused by heavy smoking overlapping with heavy drinking and an increasing proportion is caused by HPV infection, transmissible through oral sex. (HPV, by the way, is the same virus that drives cervical cancer). HPV infection per se is insufficient to cause the cancers associated with it – there have to be additional genetic and epigenetic modifications on top. HPV positive HNSCC has excellent survival relative to HPV negative HNSCC, by the way.

In this study, we obtained clinical samples of both HPV positive and HPV negative HNSCC – some were fresh frozen upon surgical resection/biopsy, a lot were FFPE samples (Formalin fixed,paraffin embedded) and we also profiled cell lines using Illumina 450k methylation arrays, which give a read-out of methylation at 483,000 CpG sites (A cytosine followed by a guanine) across the human genome for less than a large pizza per sample.

The FFPE samples were used as a training set and the Fresh frozen samples and cell lines were used as a validation set. We found quite a few interesting things…

[1] HPV positive HNSCC exhibits a greater degree of DNA methylation (Hypermethylation) than HPV negative HNSCC, especially in genes that are known to be silenced by PRC2 complexes in stem cells. PRC2 complexes consist of multiple proteins that co-operate to produce the H3k27 histone mark. We find the same genes being silenced by DNA methylation instead in HPV positive HNSCC. This is also strongly associated with differences in expression; More the methylation, less the expression, as it should be.

HPV positive HNSCC exhibits hypermethylation relative to HPV negative HNSCC. Blue represents high methylation and yellow represents low methylation.

HPV positive HNSCC exhibits hypermethylation relative to HPV negative HNSCC. Blue represents high methylation and yellow represents low methylation.

[2] A subset of HPV positive HNSCC showed very high degrees of methylation – which is called a CpG Island Methylator Phenotype, and was associated with significantly worse survival.

CIMP phenotype (Cluster 1a) is associated with very high methylation, HPV positivity and significantly worse survival compared to HPV positive tumours with comparatively less methylation (Cluster 1b) as shown in the Kaplan Meier curve at the right.

CIMP phenotype (Cluster 1a) is associated with very high methylation, HPV positivity and significantly worse survival compared to HPV positive tumours with comparatively less methylation (Cluster 1b) as shown in the Kaplan Meier curve at the right.

[3] If you put the viral oncogenes E6 and E7 into a cell line that was derived from HPV negative HNSCC, you tend to see that E6 induces hypermethylation. This wouldn’t be surprising because p53, which is blocked by E6, is known to regulate DNMT1, a DNA methyltransferase that is involved in the maintenance of methylation.

[4] If you use probes on the array that are significantly different between HPV positive HNSCC and HPV negative HNSCC, and compare them to publicly available data for cervical and lung cancer by a process called multidimensional scaling, you find that HPV negative HNSCC is closely related to lung cancer while HPV positive HNSCC is closer to cervical cancer, suggesting that HPV modulates the methylation patterns that make cervical cancer closer to HNSCC of this type.

Multidimensional scaling shows HPV negative HNSCC (HPV0) to be more closely related to lung cancer and HPV positive HNSCC to be similar to cervical cancer.

Multidimensional scaling shows HPV negative HNSCC (HPV0) to be more closely related to lung cancer and HPV positive HNSCC to be similar to cervical cancer.

[5] The relationship between methylation and expression is valid and as predicted even in a panel of HNSCC cell lines, as I demonstrated using qPCR, where we get RNA, make DNA, and then do PCR to find out how many cycles it takes to get past a particular threshold of fluorescence.

Genes that are significantly hypermethylated in HPV positive HNSCC are relatively overexpressed in HPV negative HNSCC as expected (The relationship between most methylation and expression is inverse)

Genes that are significantly hypermethylated in HPV positive HNSCC are relatively overexpressed in HPV negative HNSCC as expected (The relationship between most methylation and expression is inverse)

[6] We found that DNMT1 and DNMT3a , which are enzymes involved in maintaining and establishing DNA methylation, are expressed more in HPV positive HNSCC cell lines relative to HPV negative HNSCC cell lines as a group.

DNMT1 and DNMT3a are significantly overexpressed in a panel of HPV positive HNSCC cell lines vs HPV negative HNSCC cell lines.

DNMT1 and DNMT3a are significantly overexpressed in a panel of HPV positive HNSCC cell lines vs HPV negative HNSCC cell lines.

So basically, we started off with two subsets of a type of cancer, identified that the methylation patterns between them are different, that this has functional ramifications and clinical implications. It would be very interesting if someone ended up looking at hitting methylation in HPV positive cancers with anti-methylation drugs to see if that high level of methylation is just an artefact of how HPV positive HNSCC develops or whether there are therapeutic opportunities to be had.

Journal Reference 
Matthias Lechner, Tim Fenton, James West, Gareth Wilson, Andrew Feber, Stephen Henderson, Christina Thirlwell,Harpreet K Dibra, Amrita Jay, Lee Butcher, Ankur R Chakravarthy, Fiona Gratrix, Nirali Patel, Francis Vaz, Paul O’Flynn, Nicholas Kalavrezos, Andrew E Teschendorff, Chris Boshoff and Stephan Beck, Identification and functional validation of HPV-mediated hypermethylation in head and neck squamous cell carcinoma, Genome Medicine 2013, 5:15 doi:10.1186/gm419


Gatenby’s Gambit.

Pardon the unconventional title. I haven’t blogged for long but now I have some time to write a blog post I am going to write about some research that seeks to change the way we employ chemotherapy to control tumours.

We have loads of targeted and broad-spectrum chemotherapeutic agents available for treating cancer and quite often cancer cells evolve resistance to chemotherapeutic agents; they may acquire mutations that render the drugs ineffective [1] or begin to express drug efflux pumps that can just chuck chemotherapeutic agents outside [2] or activate repair pathways that can unhook crosslinks induced by classical chemotherapeutic agents like cis-platin [3]. These phenotypes come to dominate the otherwise heterogeneous landscape of tumours because the administration of chemotherapy imparts a selective pressure in favour of resistant clones.

Robert Gatenby’s group at Florida began working with the premise that the evolution of resistance is inevitable, but that the expansion and dominance of resistant clones is not. Instead of using chemotherapy at extremely high doses they sought to use drugs at concentrations low enough to maintain a population of non-resistant cancer cells that could then compete with and inhibit the growth of resistant clones.

Non-resistant cells can do this in the absence of high-dose chemotherapy that eliminates all of them because it takes energy to maintain resistance through  some routes (efflux pumps or repair) at least. The researchers in question first established that there was an energy cost associated with the expression of efflux pumps. They found that in low-glucose conditions, cells negative for PGP (an efflux pump) grew nearly as well in low-glucose conditions as they did in high-glucose conditions, but PGP +ve (MCF7/Dox in the figure) took a hit in proliferation.


They then put resistant and non-resistant cells together in culture in the presence and the absence of verapamil, a substrate for PGP that results in increased energy expenditure and  found that the proportion of PGP+ cells was vastly reduced relative to PGP – cells when verapamil was included.

After carrying out studies of how quickly the cells doubled in culture, how sensitive they were to energy restriction (low glucose) and a metabolic inhibitor (2-deoxyglucose) that could compete for and block glucose utilisation they developed a therapeutic strategy that used verapamil and 2-deoxyglucose and went on to test if they could delay disease progression in computer simulations using non-resistant clones to suppress the growth of resistant clones. Especially interesting was the fact they could use non-chemotherapeutic doses of verapamil to reverse fitness, turning PGP expression into a growth disadvantage.


They found that adaptive chemotherapy (where chemotherapy is used in doses that does not eliminate all of the susceptible cancer cells, reducing toxicity in the process)  in the presence of 2-deoxyglucose and verapamil could significantly delay time to disease progression (increasing tumour burden in the presence of the drug). The work provides an interesting perspective on how chemotherapy might best be used and challenges the assumption that maximal doses are optimal.

Primary Reference – (paywalled)

Other References

[2] (paywalled)


Oh, trouble, stemness is thy name.

OK, just a few days ago, three major papers turned up that put a previously controversial idea about the way tumours are organised on a formidable footing, at least in cases of melanomas, gliomas and colorectal tumours in a mouse model.

We have been looking at how many cells are required to transmit a tumour from one mouse to another isogenic (genetically identical) mouse for a long time, and experiments in the fifties and early sixties led to the observation that you had to inject a certain number of cells to induce a tumour in half the injected animals, and this was way greater than one cell. However, there was also the fact that cancers were known to be clonal (originating from one cell) to contend with.

Putting the two ideas together led to the simple conclusion that not all transplanted tumour cells could induce the disease – you had to introduce so many tumour cells that one of them would be a cancer “stem cell” and could induce the tumour. This led to the formulation of the Cancer Stem Cell Hypothesis – that within a tumour there would be a small subpopulation of cells that could, if not eliminated completely, led to a return of the disease. This concept had become enshrined in the principles of radiotherapy but of course questions were raised in the molecular biology community, especially with respect to their presence in solid tumours. Skeptics took it upon themselves to point out that one could simply be looking at immune rejection or loss of viability as the reason why not all cells successfully transmitted the tumours.

The onus, then, was on proponents of the CSC hypothesis to show that there was such a thing as a resident subpopulation of cancer stem cells that led to recurrences. That evidence, it would appear, has finally come up, and in exquisite detail.

Cedric Blanplain and his group used a model of chemical carcinogenesis in mice where a chemical called DMBA is used to initiate a skin tumour and a substance called TPA is used to encourage the growth of the tumour in the site treated with DMBA. Eventually, that process results in a benign tumour called a papilloma which, after persistent TPA treatment, turns malignant. Papillomas are composed of a mass of terminally differentiated cells and an expanding population of undifferentiated, basal-epithelial cells. The number of the former stays fairly constant throughout but the latter expands.

Design of the TAM induced construct used to visualise clonal expansion. As you can see, the expression of the Yellow Fluorescent Protein is conditional to treatment with Tamoxifen, and individual basal cells can easily be identified. From Blanplain et al (See text for link)

To address whether there was a distinct CSC population that maintained the tumour, they used a very clever genetic engineering method to label individual cells – a construct that expressed Yellow Fluorescent Protein when the cells carrying them were exposed to Tamoxifen. They found that some of the labelled cells in the basal compartment expanded and also formed the non-basal differentiated structures in the tumour. In effect, they demonstrated that a subpopulation of cells could give rise to the entire tissue heterogeneity of tissue in the tumour – there was clear evidence for stemness in a population of tumour cells. They additionally found that the stem cell compartment in the tumours gave rise to progenitors capable of multiple fates and finally differentiated cells.
They were able to use microscopy to quantitatively evaluate what was happening and found that CSCs were dividing twice a day – extremely quickly compared to progenitors, which were dividing once in two days. Also, following Tamoxifen treatment, after three weeks only 20% of the originally YFP expressing cells were still expressing it (The basal ones were selectively labelled initially by using a basal specific promoter to drive gene expression)

Then of course we’ve had two more major studies. Parada and his group, again publishing in Nature showed that we had a similar thing going on with glioblastoma multforme, which has an abysmal prognosis. His group found that chemotherapy could wipe out most of the non-stem compartment but there was always a recurrence driven by a stem-like compartment of cells which escaped the effects of said chemotherapy. I find the work in question all the more intriguing because they started off with the hypothesis that these tumours were actually driven by modified versions of human adult neural stem cells in the Subventricular zone of the brain.

That hypothesis was of course well grounded in evidence – for they had identified what combination of mutations always resulted in tumours (using a conditional knock-out of genes that were known to be essential mutations in glioblastoma), and had been able to track those initiating cells to that location. Exploiting this, they used a transgenic construct encoding Green Fluorescent Protein and a Thymidylate Kinase (TK) protein driven by a Nestin promoter,which is active in adult neural stem cells (but not differentiated ones). TK expressing cells, in the presence of Ganciclovir, die if they are cycling, and this allows them to be ablated.

Telozolomide treatment kills proliferating cells, and this leads to subsequent repopulation, which is mediated by previously quiescent CSCs kicking off into division. (e) Reference – Parada et al, see text for link.

When they treated glioblastomas in these mice, they found that treatment with Telozolomide, an agent used to kill glioblastoma cells in clinical practise, was able to kill a mass of cells that was proliferating rapidly. Combining this with Ganciclovir resulted in enhanced cell kill by eliminating some of the stem cell compartment as well, but recurrence, they postulated, was inevitable because they found most of these cells to be quiescent (resting) and thus immune to drugs that hit proliferating cells (like TMZ). Having eliminated proliferating cells, though, the question was what would drive repopulation – would this be a random occurrence with any of the remaining cells kicking off? Or would the postulated CSCs be responsible? The reasoning they used to work this out was that the uptake of CldU and IdU, which are uracil analogues taken up only by proliferative cells, would be extraordinarily biased towards the GFP expressing CSC compartment if that were the source of repopulation following a pulse of TMZ treatment, and they promptly found it was indeed the case.

Treatment of Mice carrying the Nestin-TK-GFP constructs with Ganciclovir results in dramatically improved survival that is not seen in mice not carrying the construct when treated with the drug, or mice carrying the construct not treated with the drug. In some mice surviving after 10 weeks of treatment tumours have shrunk into low-grade lesions following the elimination of the CSC compartment.

And then came their piece de resistance’ – they showed that the only way to ensure long term survival in glioblastoma-afflicted mice was to eliminate the stem cell compartment altogether with ganciclovir treatment, and this led to massive survival benefits in GCV treated mice, and in some cases their brains only had low-grade lesions, benign vestiges of originally aggressive, malignant disease. A clincher if ever there was one for the CSC hypothesis.

The third paper, which I will not bore you with now, can be read here and features Hans Clevers and his group’s work showing that colorectal adenomas also depend on a CSC population that phenocopies normal intestinal crypt stem cells in terms of known surface markers. The great similarity of these CSCs with their normal adult counterparts worries me greatly, for it opens up the possibility that CSCs may be normal adult stem cells going rogue following a series of hits. Any therapy that is designed to hit these must also take care not to hit the normal stem cell compartments in tissues that are exposed.

Of course, there are still other solid tumours for which the CSC model has to be verified, but given that we have exquisite approaches like those described above to tease them out evidence either way shouldn’t be long coming.

What this means for therapy…
We will clearly have to make eliminating cancer stem cells a priority in dealing with cancer, while radiotherapy has the inherent potential to eliminate these most chemotherapy probably does not, and even with the advent of targeted therapies we will need to ensure that we don’t leave stem cells behind.
We have had some thinking heading this way already with the understanding that stem-like cells, as CSCs are otherwise known, may be sensitive to inhibitors of the Sonic hedgehog pathway which is hyperactivated in them, and there is some degree of preclinical evidence showing that this may be worthwhile investigating…

That is all from me this time round.


Of Platelets, cancer cells, EMT and Metastasis.

Hello again!

This time I’ll be writing about a paper that recently appeared in the journal Cancer Cell. The paper is something of a landmark because it has showed how the mere interaction of platelets with cancer cells is sufficient to induce activation of EMT (Epithelial Mesenchymal Transition). It details how the presence of a protein called TGFß derived from platelets, in combination with direct contact between cancer cells and platelets, can activate the NF-kB and Smad signalling pathways that confer invasiveness to cancer cells, thus making metastasis possible.

TGF-beta signalling pathway,courtesy Cell Signaling Technologies.

NF-kB Signalling, courtesy Cell Signaling Technologies.

The paper (see journal reference at the bottom for a link) is elegant because of the way critical questions were asked and addressed… the main questions were if platelets could stimulate metastasis and if so, by what means and methods.

The first question was quite easy to address. They took colon carcinoma cells from a cell line called MC38GFP and breast cancer cells from a cell line called Ep5, they grouped cells from each of those lines into two groups each. One of each group was treated with platelets while the other wasn’t, and the cells were then injected into mice to look for the frequency of metastasis. and bingo, the platelet treated cells showed a higher frequency of metastasis.


Number of metastatic foci.

Treatment with Platelets tends to vastly increase the number of metastatic foci compared to untreated cells.

There you go, first question answered. The researchers then examined whether platelet treatment induced an EMT-like phenotype, and they did this by looking at the mRNA and protein concentrations of various genes and their products that are markers of EMT, such as MMP-9 (a matrix metalloproteinase) and found that the expression of such marker genes and proteins is highly upregulated. Some of the markers used were Snail, Vimentin, Fibronectin and PAI-1 for a mesenchymal phenotype, and E-cadherin expression as a marker of epithelial phenotypes (or the loss thereof).

(D) Relative fold change in mRNA expression in MC38GFP or Ep5 cells treated with buffer or platelets for 40 hr (n = 3). Values are normalized to Gapdh expression. (E) Detection of E-cadherin protein levels by immunoblotting of lysates ofMC38GFPorEp5cells treated as in (D). Amounts of platelets equal to those used to treat cells were also loaded as control (no cells). b-tubulin was used as loading control. (F) Zymography for MMP-9 in the conditioned medium of MC38GFP or Ep5 cells treated as in (D). Amounts of platelets equal to those used to treat cells were also loaded as control (no cells). (G) MC38GFP and Ep5 cells were added at the top of transwells coated with Matrigel and treated with buffer or platelets. The total number of cells that invaded to the bottom of the transwell was counted after 48 hr (n = 3). For (A), (B), (D), and (G) bars represent the mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001 were determined by Student’s t test.

The question then would be how platelets could induce EMT, could TGFß be to blame? To find out they carried out bioinformatics analysis of data from microarray experiments looking for differentially expressed genes when cells were treated with platelets. They found that expression of EMT genes was upregulated and there was increased expression of genes in the TGFß pathway.

Gene upregulation data from microarray analysis when platelet stimulation is induced. Gene enrichment analysis using Gene Ontology functional categorization is presented in the table. You can see how the upregulated genes are all associated with EMT and with TGF-beta dependent activation processes.

This indicated to them that platelets induced the EMT phenotype through the TGFß pathway. They then wanted to find out if TGFß alone was responsible or if direct contact with platelets also had something to do with said upregulation.

To do this, they set up a rather cute experiment where they used a PAI-1 reporter gene to assay TGFß activation (this is possible because PAI-1 is downstream in that pathway and can be used as a surrogate marker)

They assayed levels of activation when platelets AND TGFß were added as opposed to TGFß alone. They observed more PAI-1 activation in the former than the latter, thus indicating that the presence of platelets and TGFß together act synergistically in activating the pathways involved. The central role of TGFß in the process was confirmed by blocking TGFß using an inhibitory antibody and a small molecule, which led to loss of PAI-1 expression.

The question then, of course, was why the presence of both platelets and TGFß would have more effect in EMT activation compared to TGFß alone. What other pathways would be activated such that the added effect could be accounted for?

To test this, they used reporter based assays for multiple pathways involved in cancer, and found that the releasate from platelets (platelet free) activated the JNK pathway, while the presence of cells and the releasate activated the JNK and NF-kB pathways.

Now this is where things got really interesting. Firstly, they confirmed whether NF-kB was actually able to account for the results of cell-inclusive treatment experiments by using Ep5 cells with mutant NF-kB and a reporter with reference to a control with a normal NF-kB and the corresponding reporter.

They found that mutant cells didn’t show NF-kB activation when treated with platelets, and that these cells had the same metastatic potential as cells that were treated with TGFß alone, thus fully implicating this pathway in platelet contact induced EMT.

These results were further validated when treatment with an NF-kB inhibitor also downregulated the expression of several key markers of EMT, such as MMP-9, in cells with intact NF-kB signalling.

They found that inhibiting cells with the NF-kB inhibitor switched off a reporter in that pathway, but not one in the other pathway, indicating that TGFß activation was independent of NF-kB activation, while the effects were synergistic.

And finally, here’s the piece de resistance of the paper; they showed that blocking TGFß secretion in megakaryocytes and platelets or wrecking the TGFß pathway was enough to prevent metastasis, they did this using mice in which TGFß expression was knocked out where seeding with cancer cells prevented metastasis to the lungs. They also showed that even cells pre-treated with wild type platelets for a while before introduction into TGFß fl/fl mice wasn’t enough to trigger metastasis.

This basically confirms that platelet derived TGFß signalling is absolutely necessary, while NF-kB signalling in synergy with it renders tumour cells potentially more metastatic, but isn’t per se sufficient for metastasis.

They note that this could have therapeutic implications, since TGFß inhibition in platelets doesn’t have physiological effects on normal cells, and if this could be replicated in humans it might serve as a brilliant therapeutic strategy.

However, as always, blocking metastasis, it would appear, would only really be useful in tumours where metastasis hasn’t occurred, how established metastatic disease should be dealt with is still a very open, and problematic question.

Journal Reference: Labelle et al, Direct Signaling between Platelets and Cancer Cells Induces an Epithelial-Mesenchymal-Like Transition and Promotes Metastasis, Cancer Cell.

That is all from me this time round.