Month: April 2015

Sanger Science

Keeping pace with changing parasite genetics

23 April 2015
By Roberto Amato

Malaria-infected Red Blood Cell. NIAID, Flickr

Malaria-infected Red Blood Cell.
Credit: NIAID, Flickr

Plasmodium falciparum parasites are responsible for the majority of over 500,000 malarial deaths every year. An adaptive foe, these parasites can hide from the body’s immune system, cope with changes in the Anopheles (mosquito) vector, and develop resistance to antimalarial drugs, at a frightening rate.

Genomics is one of the most powerful tools available to observe these evolutionary processes in action in the parasites. Much of our early work studying natural genetic variation in Plasmodium parasites came about in collaboration with many different researchers around the world as part of the MalariaGEN P. falciparum Community Project. To date, this collaboration has built a catalogue of 1 million single nucleotide polymorphisms (SNPs) in more than 6,000 falciparum samples collected directly from malaria patients in Africa, Asia, Latin America and Oceania.

Using this rich data resource – the largest collection of Plasmodium genomes in the world – we are starting to understand the complex genetics of Plasmodium parasites. For example, the intricate genetic architecture underpinning resistance to the frontline drug, artemisinin.

But this is just the beginning! We are still far from a comprehensive and precise understanding of how this parasite evolves in in the wild and how we should respond to these constant changes. There are of course limitations with our current methods, but beyond that our view of genetic variation is primarily based on SNPs, leaving out other forms of variation such as indels. We are also not yet able to accurately detect changes in key regions of the Plasmodium genome including, for example, the hypervariable var genes, which contribute to the parasites’ ability to evade our immune system.


To generate a more complete, fine-grained view of genetic variation in Plasmodium parasites, we need solid reference genomes, good baseline data and reliable analytical methods. In short, we need to set the scene and lay solid foundations for future analyses.

These technical challenges are the key focus for the pilot phase of the Pf3k project, a global collaboration led by researchers at the Wellcome Trust Sanger Institute, the University of Oxford and the Broad Institute. Established within the past year, the Pf3k Consortium aims to analyse 3,000 P. falciparum samples from the major malaria-endemic regions of the world.

The overall aim is to provide a high-resolution view of natural variation in P. falciparum including those regions of the genome that are inaccessible using standard methods.

At the moment, we are very busy generating thousands of whole genomes from field samples that can act as high-quality reference genomes and assessing various methods to genotype them.

This is a big leap forward with respect to the current gold standard of using one reference, 3D7 v3, which is the whole genome sequence of a single parasite. This limits our ability to access the genome, particularly in regions that differ from the reference.


One good example is the challenges in genotyping crt, a clinically-significant gene involved in choloroquine resistance – and possibly with a role in emerging artemisinin resistance. This gene is so important that it remains one of the first places researchers tend to look.

The current reference has a very specific version of crt which is quite different from what we see in most genomes in Southeast Asia. And crt in Southeast Asia is again different from what we observe in other parts of the world. This geographical diversity makes aligning sequences from various parts of the world challenging; having reference genomes drawn from different populations will allow us to more readily compare like with like and, ultimately, increase the accuracy with which we can spot variants.

The Pf3k Consortium has prepared an initial data set comprising 2,375 samples sequenced here at the Sanger Institute as well as 137 samples from our colleagues at the Broad Institute in Boston, USA. This represents the full pilot set of samples, collected in major malaria-endemic regions in Africa and Asia.

Reflecting our commitment to the early and open release of data, earlier this month, the Pf3k Consortium made this large data set public, including sample information, accession numbers, analysis BAMs and preliminary genotypes. As with previous Pf3k data releases, these data are made available under Fort Lauderdale conditions and can be downloaded or explored using a user-friendly web application designed by colleagues at the Medical Research Council Centre for Genomics and Global Health.

For more information on genetic distance, click image. Credit: Roberto Amato

For more information on genetic distance, click image. Credit: Roberto Amato

Our attention is now focused on evaluating the methods used to generate this baseline data. Often optimised for human genomes, we need to understand to what degree these methods can be used straight off-the-shelf to analyse the Plasmodium genome, which differs in many ways from ours.

It may sound surprising but even some basic concepts, like allele frequencies and genetic distance, are not straightforward when dealing with Plasmodium genomes. When samples come directly from a patient, we’re not getting a single parasite – we get a population of parasites. Depending on a variety of ecological and epidemiological factors, these populations may be so inbreed as to actually appear as a single genome (clonal sample) or may be very diverse (mixed infections).

A funny consequence of mixed infection is that some Plasmodium genomes look like they have an extra set of chromosomes at certain positions! To further increase the complexity, in areas where other Plasmodium parasites are co-endemic, these populations might even be made of different species.

As we improve the resolution and accuracy of our analyses of genetic variation, we’ll be able to delve deeper into key scientific questions like how populations of Plasmodium parasites are evolving, migrating to different locations and developing drug resistance.

Roberto Amato is a Research Associate in Statistical Genomics who is involved in the analysis of natural genetic variation in the Plasmodium parasites that cause malaria. His primary focus is on developing new statistical methods to understand the evolution of these parasites at a population level, in order to shed light on the underlying genetics of antimalarial drug resistance. Based in the Wellcome Trust Sanger Institute’s Malaria Programme, Roberto works closely with colleagues at the Wellcome Trust Centre for Human Genetics at University of Oxford and supports several global collaborations including the MalariaGEN P. falciparum Community Project and Pf3k.


  • Miotto O, Amato R, et al. (2015). Genetic architecture of artemisinin-resistant Plasmodium falciparum. Nature GeneticsDOI:10.1038/ng.3189
  • Miotto O, et al (2013). Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia. Nature GeneticsDOI:10.1038/ng.2624
  • Claessens A, Hamilton WL, et al (2014). Generation of antigenic diversity in Plasmodium falciparum by structured rearrangement of Var genes during mitosis. PLOS Genetics DOI:10.1371/journal.pgen.1004812

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Credit: Justine Desmond, Wellcome Images
Sanger Science

Helping computers to read symptoms

15 April 2015
By Anika Ollerich

While human readers best understand data that is provided as free text, computers can better analyze data that is represented with ontologies. In order for computers to learn how free text corresponds to ontologies, a text corpus (such as the HPO corpus) is required, based on which a computer can learn this relationship. Credit: Tudor Groza

While human readers best understand data that is provided as free text, computers can better analyze data that is represented with ontologies. In order for computers to learn how free text corresponds to ontologies, a text corpus (such as the HPO corpus) is required, based on which a computer can learn this relationship. Credit: Tudor Groza

Every one of us has to visit our GP from time to time. Ideally, we describe our symptoms and are prescribed medicines corresponding to the diagnosis.

Unfortunately, it’s not always possible to identify the cause of a disease and all a GP can do is try to relieve the symptoms.

The symptoms and underlying causes of rarely occurring hereditary diseases are particularly hard to define, which prevents successful treatment or prevention.

To help with the identification of causes for these diseases, numerous biological as well as computational efforts are ongoing. While in a biological experiment the scope is very narrow and precise, focusing perhaps on one gene or set of symptoms, computational projects analyse large amounts of data from several sources. The sheer amount of information can mean that computational projects miss smaller details.

Some of the computational algorithms either work on assumptions derived from biological experiments or take the output of these projects into consideration. The lab robots Adam and Eve, developed by researchers at Aberystwyth University, are an example of a computational approach used to derive a biological hypothesis.

In order for Adam and Eve to be able to execute their algorithms, knowledge needs to be prepared in format that can be understood by computers, which is not necessarily readable for humans. Due to this necessity, the meaning of things, such as the symptoms of diseases, are gathered into groups according to the context in which they are used.

These groupings are called domain-specific ontologies. Probably the best known example of a domain-specific ontology is the Gene Ontology, a collaborative effort by the computational biology community to create a standardised vocabulary for annotating gene function.

The Human Phenotype Ontology is an ontology to encode human-specific symptoms of diseases. The process of encoding includes a textual description of the symptom as well as logical representation that can be used by computers to perform further deductions. For computers to be able to read records written by a GP and derive deductions, it would not only need the ontology, it would also have to know how symptoms are expressed in the text.

One way of teaching a computer the language of specific entities, in our case symptoms, is to generate a corpus. A text corpus is nothing more than a highlighted text.

In our study, we generated such a corpus for the Human Phenotype Ontology by highlighting symptoms in a number of medical research papers. This corpus can then be used to assess how good an algorithm is at recognising symptoms. However, this usually provides only a quantified assessment of performance.

Thus, we also provide a variety of test cases that can elucidate the shortcomings of an algorithm, i.e. which bits of natural language cause the algorithm to struggle to identify a symptom.

Once an algorithm is sufficiently trained, it can then be applied to read the millions of abstracts and full-text papers contained in PubMed and provide summaries for diseases, genes, drugs or any other entity relevant to symptoms of diseases. These summaries have a wide range of applications such as the prediction of disease gene candidates, drug repurposing, and many more.

Anika Oellrich is a Senior Bioinformatician working as part of the International Mouse Phenotyping Consortium (IMPC) project. Her research work focuses on aspects of phenotype mining, in large data sets as well as scientific literature. Having investigated the different representations of phenotypes, she applies this knowledge to data integration and human genetic disorders with the aim of improving the understanding about the molecular mechanisms underlying human diseases.


  • Groza T, et al. Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora. The Journal of Biological Databases and CurationDOI:10.1093/database/bav005
  • Ashburner M, et al (2000). Gene Ontology: tool for the unification of biology. Nature GeneticsDOI:10.1038/75556
  • Hoehndorf R, et al (2012). Linking PHARMGKB to phenotype studies and animal models of disease for drug repurposing. Pacific Symposium on Biocomputing 2012. DOI:10.1142/9789814366496_0038

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Herds of zebra and impala gathering on Masai Mara plain. Credit: Roomtorun
Influencing Policy

Being part of the herd saves lives

13 April 2015
By Rebecca Gladstone

Barbara Bellingham, Wellcome Images

Vaccinating the population saves lives.
Credit: Barbara Bellingham, Wellcome Images

Streptococcus pneumoniae AKA the pneumococcus is a notorious bacteria that can cause countless types of infections from rapidly fatal meningitis and septicaemia, to pneumonia and common ear infections. The pneumococcus is in fact the leading cause of child death worldwide killing up to 1,000,000 children each year.

A number of new pneumococcal vaccines have been licensed to protect children against pneumococcal infections, which are now being introduced around the world. However, the full potential of these life-saving vaccines could be wasted if we fail to vaccinate enough people.

What’s getting up your nose?

Pneumococci can be found harmlessly living in the noses of around 30 per cent of healthy kids in the UK without causing an infection. This colonisation is not completely benign, though, as it is a way of hitchhiking through the population from person to person, resulting in new infections in the most at risk.

Crucially, a pneumococcal vaccine not only protects the individual against infection by readying the immune system but also prevents colonisation of their noses and subsequent spread of the pneumococcus through the healthy population.

Vaccination needs you and your kids

The reduction in spread between vaccinated individuals also means that infections in unvaccinated individuals can happen less often. This phenomenon is called herd protection.

There is a major caveat, though; enough children need to be vaccinated so that there are not enough unvaccinated individuals to continue spreading between. This is like removing enough stepping-stones from a stream crossing to leave the pneumococcus stranded.

Herd protection works for a number of infectious diseases where the bacteria or virus spreads between people, like measles and polio. With herd protection the minority are protected by the majority. The critical fact is that not everyone can be vaccinated for legitimate medical reasons and these people need to be shielded by having the population around them vaccinated.

Babies under a few months old can get pneumococcal infections like meningitis with devastating consequences yet are too young to be vaccinated, so herd protection is their strongest defence. Additionally, since a pneumococcal vaccine has been given routinely to children in the UK, another group of people vulnerable to pneumococcal infections such as pneumonia have benefited: the elderly. By vaccinating enough children we can protect their grandparents and elders too.

Unfortunately, unvaccinated individuals are not distributed evenly in populations. If the pneumococcus gets to a community where vaccination is low, the bacteria can be harboured in the unvaccinated cluster, moving from person to person like a fugitive searching for its next susceptible victim. The key is that although an unvaccinated child might not get sick themselves, they can pass it to others who could succumb to pneumococcal infection.

An international fugitive

The pneumococcus respects no political borders and is found all around the world. Lower income countries often have higher rates of colonisation and infection and can least afford the vaccines. Herd protection plays a key role here as it reduces the number of vaccines they need to purchase; just enough to block its spread.

Pneumococcal vaccination is complicated further by the >90 different types that exist. We can only vaccinate against a few of them: the most infectious and those most resistant to antibiotics. As we stop the types targeted by the vaccine from circulating and causing infections some of the remaining types partially take their place.

The pneumococcus is a moving target. For this reason we need to constantly monitor which types are causing infection or circulating in healthy individuals all around the world. This is why the Global Pneumococcal Sequencing project is sequencing the biggest ever collection of pneumococcal genomes from all over the world, trying to use genetics to understand changes in the pneumococcal population as it responds to the introduction of vaccines.

NB. I refer to herd protection here rather than herd immunity as technically in the case of the pneumococcal vaccine no active immunity is gained by the unvaccinated, instead the surrounding herd protects them.

Rebecca Gladstone is a Senior Bioinformatician in the Pathogen Genomics group at the Wellcome Trust Sanger Institute, where she is currently working on a global collection of 20,000 pneumococcal genomes to assess pneumococcal vaccine impact. Rebecca is interested in learning how to better share the research findings with the public. She Tweets as @becctococcus.

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Sanger Life

A world without smell

04 April 2015
By Darren Logan

A family tree showing the incidence of anosmia in five generations of the same family.

A family tree showing the incidence of anosmia in five generations of the same family.

We generally do not think about our sense of smell too often. Yet it contributes hugely to the multi-sensory perception of our world.

For example, approximately 80 per cent of the flavour of our food relies entirely on smell; without it we are restricted to just five basic tastes.

Most of us will have experienced this when suffering a heavy cold, when our noses are completely blocked, meals are bland and unappetizing.

In addition, we routinely use smell to warn of danger and signal safety, to attract partners and customers (think ‘new car smell’) and to evoke memories.

In fact, olfaction, the technical term for our sense of smell, is sufficiently important to us that the molecular odour receptors in our nose are encoded by the largest single family of genes in our genomes.

So, what happens if you lose your sense of smell or if you were born without it? Having had a research interest in sensory genetics for a number of years, I knew that such a condition, called anosmia, existed. However, like most of us, I had never really considered the profound and disconcerting affect the absence of smell may have on everyday life.

I recently visited Fifth Sense, a charity supporting people with smell and taste disorders. One member, Sarah Kathleen Page, a photographer who was born with anosmia, summed up life without smell:

“We cannot smell the gas leak slowly filling up our home, we cannot smell our newly born daughter, we cannot fully enjoy the sensory pleasures most people take for granted. We hope for a doctor that believes us when we say we cannot smell and long for a friend that never forgets. Without someone to say, ‘I understand, I believe you and I will do my best to help’ we live in a very lonely world.”

To better understand this disorder, my laboratory has begun a study to find the genetic basis of being born without the ability to smell. Along with colleagues at the Monell Chemical Senses Center in Philadelphia, we launched a social media campaign to find families with people who can and cannot smell across multiple generations. We received responses from across the world.

In one case, we found a family with anosmia across five generations; each time, the condition was inherited from parent to child. After carefully mapping out the family trees, we carried out a standardized smell test on each family member to confirm the diagnosis. We then collected saliva and are now sequencing their genomes.

Using a strategy developed at the Wellcome Trust Sanger Institute to successfully identify the genes involved in other rare diseases, we will compare the genetic variation from those within the same family who can and cannot smell. This approach helps narrow down the search for the precise gene responsible.

Identifying the genetic causes of anosmia is only the first step in a long journey. Like many rare genetic conditions, it is unrealistic to expect that new treatments or cures will automatically follow. However, we aim to press on and investigate how these genes contribute to the complex neural circuits that connect our noses and brains. Only then can we evaluate them as potential targets for clinical intervention.

Darren Logan joined the Wellcome Trust Sanger Institute faculty in 2010. His team – Genetics of Behaviour – combines comparative genomics, reverse genetics, behavioural testing, and neural activation studies to identify and investigate genes involved in the signalling, sensing and processing of olfactory cues that influence behaviour. They aim to understand the role of these genes in instructing normal perception and behaviours and their dysfunction in neurological disorders.

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Sanger Science

Following cancer’s journey

01 April 2015
By David Wedge

Subclonal structure within 10 metastatic lethal prostate cancers. Subclones shown as phylogenetic trees and oval plots. Patients with polyclonal seeding (A34, A22, A31, A32 and A24) on right. <strong><br>Click to view larger image</strong><br>Credit: DOI:10.1038/nature14347

Subclonal structure within 10 metastatic lethal prostate cancers. Subclones shown as phylogenetic trees and oval plots. Patients with polyclonal seeding (A34, A22, A31, A32 and A24) on right.
Click to view larger image

Credit: DOI:10.1038/nature14347

In my last post I explained how the numerous different tumour cells found in prostate and other cancers can make it difficult for clinicians to decide on appropriate therapy. In this post, I will explore how this heterogeneity in primary tumours evolves over time, how cancer progresses as tumours spread to other organs (metastasise) and how metastatic tumours respond to treatment.

In a study published in Nature today, we sequenced the whole genomes of 46 metastatic tumours and five primary prostate tumours from 10 men. As cancer cells multiply and spread, they acquire mutations. By identifying these mutations across multiple metastases from the same man, we were able to track the movement of these cells throughout the body.

As I described in the previous post, cancers are made up of separate populations of genetically related cells, which we call subclones. The cells within each of these subclones are descended from a single cell that had a set of mutations in its DNA, or ‘genotype’, that gave it a growth or survival advantage within its environment.

We were particularly interested in the interaction between the genotype and the environment. The environment is made up of many different factors, which change over time. When cancerous cells first appear, their environment is constituted primarily of normal, healthy cells and they have to compete with these cells for energy, space and resources.

As a tumour grows, cells within its interior no longer directly interact with normal cells and they compete mainly with other cancerous cells. The environment changes again if cancer cells escape into the bloodstream and metastasise, seeding tumours in new tissues. The environment may also change as a result of exposure to chemicals, including the chemotherapeutic and targeted drugs that are used to treat cancers.

What did we find?

  • Metastases within a single patient arose from a single cell

This is interesting because, as I described in my last post, prostate cancers are commonly made up of several genetically distinct tumours.
We do not know whether the metastases we studied were derived from multifocal primary tumours but, if they were, it appears that only one region of each primary tumour, with a specific genotype, was able to escape into the bloodstream and to metastasise.

  • The malfunctioning of tumour-suppressor genes is an early event that enables tumours to grow

A set of genes, known as tumour suppressors, are known to prevent or inhibit the development of cancer. These genes can be prevented from functioning by mutations or deletions in DNA. Where we saw these mutations they were almost always found in all of the samples taken from one patient, suggesting that they occurred early in cancer development, before metastatic spread.

  • Male sexual development hormones are hijacked by metastatic tumours

The region of DNA that encodes for the androgen receptor gene, which is important for male sexual development at birth and puberty, is found on the X-chromosome. We found that all metastases had replicated this region of the chromosome, resulting in increased production of the corresponding protein.

Prostate cancers are dependent on the androgen receptor to stimulate growth and all of the patients had been treated with androgen deprivation therapy to inhibit this growth. It is fascinating that the response of the metastases was, in all cases, to ramp up the production of the androgen receptor in response to therapy rather than to acquire new mutations that might provide an alternative route to unrestrained growth. This response was seen even though the tumours had spread from the prostate to other parts of the body.

  • Tumours in other organs had cells from two or more prostate cancer subclones

Most excitingly of all, from our analysis of the subclones we could see that several of the metastases were made up of mixtures of subclones, with similar sets of subclones found in many different metastases.

It is usually assumed that metastases grow from a single tumour cell. However, if the metastases had grown from a single cell, it would be possible to represent all subclones in each metastasis in a single family tree.

Since we see multiple subclones spread across multiple metastases, there must have been seeding of two or more cells between metastatic sites.

Our findings raise a number of further questions:

  • Are the subclones competing with each other or are they actually cooperating, as suggested by the high frequency of polyclonal seeding?
  • Does polyclonal seeding occur in other tumour types and in earlier-stage cancers?
  • Do the multiple cells that seed a metastasis travel through the bloodstream as a single clump or do they travel separately?

We hope to investigate these questions in future studies.

This research was funded by Cancer Research UK

David Wedge is a Senior Staff Scientist in the Cancer Genome Project at the Wellcome Trust Sanger Institute, working on heterogeneity and evolution within prostate and other cancers.


  • Cooper CS, Eeles R, Wedge DC, Van Loo P, et al (2015). Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Nature GeneticsDOI:10.1038/ng.3221

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Cluster of prostate cancer cells. Credit: Anne Weston, IRI, CRUK, Wellcome Images.
Sanger Science

Is the playing field level in prostate cancer?

01 April 2015
By David Wedge

Figure shows the relationships between subclones in each patient. Each line is associated with a subclone; the length of each line represents the number of genetic variations in the subclone and the thickness shows the proportion of the sample made up of that subclone. Credit: doi:10.1038/ng.3221

Figure shows the relationships between subclones in each patient. Each line is associated with a subclone; the length of each line represents the number of genetic variations in the subclone and the thickness shows the proportion of the sample made up of that subclone. Credit: doi:10.1038/ng.3221

Many types of cancer can arise from a field effect, a poorly understood but often observed change in normal tissue that predisposes it to the development of tumours.

In our research, we wanted to find evidence for a field effect within the normal tissue that frequently lies between different tumours in prostate cancer. If we could find the field effect, we hoped to be able to identify a genetic basis for it and, by doing so, learn more about how tumours spread in the prostate.

The field effect was first discovered in 1953, in a type of mouth cancer called oral squamous cell carcinoma, and has since been identified in most tissues, including lung, breast, colon, oesophagus, bladder and prostate. It results in the occurrence of multiple tumours within a single organ.

For some types of cancer, including prostate cancer, we are unsure what causes the field effect, but it’s thought that it is probably genetic (arising from mutations in DNA) or epigenetic (arising from other changes that affect the ability of a cell to produce RNA and proteins, such as changes in methylation of regions of the DNA).

Prostate cancer is very commonly multifocal, meaning that multiple tumours develop in the prostate. These tumours tend to appear at around the same time and are separated by healthy tissue. If a field effect is operative, we would, over time, expect to see the healthy tissue between and around the tumours acquire mutations and become cancerous.

We sequenced the genomes of cells taken from multiple tumour sites and healthy prostate tissue in three men with prostate cancer. By comparing the different genomes, we were able to identify groups of cells with different mutations, known as subclones, in each sample. Subclones compete with one another to expand fastest and take over a tumour, so we often find multiple subclones in one organ.

To learn more about how the field effect might be operating in prostate cancers, we investigated the relationships between these subclones within different regions of the prostate. What we found surprised us. What we thought was normal tissue distant from the tumours actually contained clusters of mutated cells, suggesting that a subclone had expanded. However, none of the mutations were known drivers of cancer and the normal cells shared no more than 10 mutations with the cancerous cells in the tumours.

It seems that the field effect, whatever it is, causes mutations in normal prostate tissue but does not necessarily lead to cancer in this tissue. No driver mutations were shared by the normal and cancer cells, suggesting that the field effect in prostate cancer may not be a genetic effect. Further study will be needed to find out whether, for example, it is an epigenetic effect and whether the field effect is exacerbated by any environmental or lifestyle factors.

Another interesting finding was that tumours contained genetically distinct subclones from different regions of the prostate, suggesting that individual cancer cells can travel across regions of apparently normal tissue.

Fascinatingly, although the different tumours found within a single prostate were almost completely independent, they each contained a similar genetic defect, a deletion of a small part of DNA resulting in the fusion of two genes, TMPRESS2 and ERG. This gene fusion is common, occurring in around half of all prostate cancers and appears to have occurred independently multiple times in different regions of a single prostate.

Although cells in different locations of the prostate are evolving almost completely independently, they are subject to the same selective pressures and it seems that the TMPRESS2-ERG fusion gives such a large advantage to cells that many cells that have acquired this aberration may clonally expand simultaneously.

What does this tell us about treatment and diagnosis of prostate cancer? Firstly, since prostate cancers may be composed of multiple unrelated tumours, it is important to biopsy multiple regions of the tumour, and possibly of the apparently normal prostate, when diagnosing prostate cancers.

Treatment of prostate cancers is often performed on targeted tumours only. Our study suggests that it may be necessary to look at the entire prostate when designing therapy as the field effect may have an impact on wider regions of tissue.

This research was funded by Cancer Research UK

David Wedge is a Senior Staff Scientist in the Cancer Genome Project at the Wellcome Trust Sanger Institute, working on heterogeneity and evolution within prostate and other cancers.


  • Cooper CS, Eeles R, Wedge DC, Van Loo P, et al (2015). Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Nature GeneticsDOI:10.1038/ng.3221

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