Intellectual Property and the Knowledge Economy

I work as a Senior Patent Attorney at a leading semiconductor design company.  Previously I worked as a Patent Attorney in a UK Patent and Trademark Attorney firm, and before this I spent some time working as a Railway Engineer.  I studied at the University of Oxford achieving a Masters in Engineering Science and am also delighted to have been named as an ‘inventor‘ myself on a patent application.  Patents are fascinating but this area of professional expertise is not well known so I’ll explain further.

Britain continues to grow into a knowledge economy where ideas are often developed and commercialised in the UK but manufactured abroad.  In order for businesses to protect their investments and products whilst maintaining their commercial edge; intellectual property (IP) rights are used.    From patents protecting new pharmaceutical drugs and electronics; trademarks and registered designs protecting new fashion ranges to trade secrets and confidential information protecting the latest Formula 1 cars.   IP is protecting innovation developed by companies in the UK every day.  Although all the types of intellectual property may be used to protect innovations in STEM areas, patents are often particularly valuable to companies.

The first patent laws were created in the sixteenth century to try to encourage inventors and businesses to share their knowledge and discoveries publicly.

Prior to governments issuing patents, most business knowledge was controlled by powerful Guilds and only available to their members.  This resulted in developments being constrained by the limited information available.  The hope was that the monopoly right patents provided, would encourage inventors to publish how to perform the invention.  This in turn would enable others to build on the information provided within the patent and result in furthering scientific progress.

The monopoly right provided by a patent lasts for up to 20 years and enables the patent owner to prevent others from working the invention and so recoup the investment costs required to devise the invention.  One method of doing this is to use the patent to protect the market in which the patent owner is selling goods by preventing competitors from doing the same thing.  A good example of a market where this is still a key use of patents is in pharmaceuticals where a company may use its patent to prevent any generic versions of a drug from being made available.

This approach is particularly suited to areas where any one product is only covered by one patent.  However, in some technical areas, for example telecommunications, there may be thousands of patents which could cover a single product.  In these areas competitors tend to implicitly accept that they all infringe each other’s patents and so have agreements not to sue one another or more explicitly agree to cross-licence each other’s portfolios.

The recent smart phone patent wars were caused by a new entrant to the telecommunications market disrupting these agreements.

They also showed businesses that their patent portfolios could be treated as tangible assets. This has led to a rise in so called Patent Assertion Entities (PAEs) who buy patent portfolios from struggling or bankrupt companies and then assert them against companies who are manufacturing or selling products in the hope of extracting licence fees and royalties.

Whether it is to protect investment or develop new products and markets, Patents provide a vital tool to enable UK businesses to prosper, facilitating both the sharing and safeguarding of knowledge in a global economy.

Adeline-Fleur Smith
Senior Patent Attorney at ARM

 

 

Science Issue: The Mathematics in Our Lives

It has been more than 20 years since I set foot in Murray Edwards, excited to have made it to Cambridge. I had chosen to study Maths, the subject that I most loved at school. Soon after I found out that university Maths was quite different to school Maths – more abstract and going at a faster pace – but it was still the right degree for me. Mathematics is a world of symmetry and structure I can immerse myself in, a language allowing me to understand the world in ways I would not have imagined.

After my BA in Cambridge, and driven by my desire to apply Maths to real life situations, I pursued an MSc in Mathematical Modelling and Scientific Computing at the University of Oxford and subsequently a DPhil there. My DPhil research was about the mathematical modelling of sonic booms, the loud bangs created when the aeroplane breaks the sound barrier and flies faster than sound. Understanding them leads to strategies for minimizing annoyance from them in inhabited areas and it requires advanced knowledge of Maths, Physics and Engineering.

I am still fascinated by sonic booms and I have recently created this TED Ed animation to share my fascination with the world – it has been watched more than one million times by now.

I have also given several popularized talks on sonic booms and other applications of maths in the last decade and two years ago, with a team of many young scientists, we co-founded the Mediterranean Science Festival to share science and maths with the world in interactive and entertaining ways.

After my PhD, I worked at the Centre for Mathematical Medicine in Nottingham, on the mathematical modelling of cancer therapies, such as magnetic hyperthermia where a tumour can be burnt by using an external magnet to raise its temperature. Cancer modelling is an important area of mathematical biology which in the last decade has led to many clinical breakthroughs in the fight of cancer.

Leaving the UK, and curious about the corporate world, I worked for some time at the Boston Consulting Group (management consulting firm) in Greece. BCG advises client companies at the CEO-level and hires a diverse range of people. However, all consultants have in common an inquisitive, curious mind, and strong analytical thinking, just like scientific training provides.

Returning to Cyprus in 2010 I joined the university world again. I teach, which I really enjoy, and have also resumed my research on applied Maths. In my main current research project, in collaboration with the Cambridge Engineering Department, we employ stochastic (probabilistic) mathematical methodologies to quantify the important role that uncertainty plays in the way our cells operate and sustain life.

Moreover, in December 2016 I led the organization of the 1st Study Group with Industry in Cyprus. In this weeklong workshop, the 125th in the European series, 50 expert mathematical modellers from 17 different countries worked intensively in teams on tackling four Cypriot industry challenges. From identifying the appropriate algorithm that automatically generates instructions for constructing a lego-like toy, to predicting the spreading of pollutants in an aquifer supplying drinking water, to optimising urban bus routes, these diverse challenges called for a multitude of mathematical methodologies which the teams of modellers enthusiastically pursued, producing very useful results.

Maths has enabled me to work on exciting, diverse real-life problems and has taken me to a path I would not have imagined. I wholeheartedly recommend studying Maths to anyone thinking of it – the possibilities are endless!

Katerina Kaouri
Alumna

Science issue: They just keep moving the line

Flow chemistry equipment

One of the things that is challenging about scientific research is that the problems needing to be solved are constantly evolving. Solutions which were previously considered to be adequate may become inadequate due to changing priorities, meaning that they need to be readdressed.

One such issue which I have become interested in is making peptides.

Peptides are long chains made by joining amino acids residues by amide bonds. Peptides, and proteins (which is the name used for long peptides) are vital components of many of the processes of life, and in recent years there has been ever increasing interest in the use of peptides as potential for treatments for a wide range of diseases.

In 1984 R. B. Merrifield was awarded the Nobel Prize for his excellent work developing a technique to make peptides known as “Solid Phase Peptide Synthesis” or SPPS. The discovery of SPPS revolutionized peptide synthesis, enabling scientists to routinely make increasingly complex peptides, and is to this day the most commonly used method for peptide synthesis. However, SPPS requires large excesses of both the amino acids you are joining together and the chemicals used to form the linkage. As the earth’s resources become increasingly depleted this waste becomes less and less acceptable, meaning that new ways to make peptides must be developed. In order to do this, we as scientists need to be as creative and innovative as possible to come up with new solutions for old problems. One potential solution to the challenge of peptide synthesis is the emerging field of flow chemistry. In flow chemistry, machines are assembled which use pumps to pump streams of reagents through thin tubing. By doing things like meeting two streams containing different reagents together, heating or shining light on the tubing, or flowing the reaction stream through a bed of solid reagents we can effect reactions with very fine control, which has been shown to be very beneficial.

My initial work in this area focused on making a type of naturally occurring molecules known as cyclooligomeric depsipeptides.

The cyclooligomeric depsipeptides synthesized with the dipeptidol monomer units highlighted.

These molecules have repeating dipeptidol units derived from amino acids which are cyclized around to form a ring and have been seen to have interesting bioactivity. By using flow chemistry we able to make these molecules with significantly less effort, as one set up of the machines could be used to make all the amide bonds in the molecule with only minor revision to form the final ring closures. Additionally, we were able to significantly improve the yields for these reactions when compared to previous syntheses. As well as making three natural products (beauvericin, bassinolide and enniatin C) we were able to make three related compounds which have never been made before. This family of molecules can now be tested to see if they have any interesting bioactivity.

There is a way to go until we will know if flow chemistry can augment or even replace the current methods for commercial peptide synthesis, but this work definitely supports the idea that readdressing problems from the past can lead to improvements for our future.

Dr Zoe E. Wilson
Academic Fellow in Organic Chemistry

To read more about our synthesis of the cyclooligomeric depsipeptides see: Daniel Lücke, Toryn Dalton, Steven V. Ley and Zoe E. Wilson*, “Synthesis of natural and unnatural cyclooligomeric depsipeptides enabled by flow chemistry”, Chem. Eur. J., 2016, 22 (12), 4206 – 4217. DOI: 10.1002/chem.201504457

“I recently presented my research at the ACS Fall Meeting in Philadelphia, Pennsylvania, USA. For this I recorded a 3 minute summary talk with the ASC Scientific video lab where I discuss this research in more detail.”

Take a look:

[wpvideo cosEMUxD]

 

Science issue: The Science of Women in Science

17b-ellen-robertson-photoNewsThere are women in science. And then there is the science of women in science. Exploring and applying this science is important to me as a social psychologist, from the USA.

Why do we need a science of women in science? Even though women now participate in the workforce almost equally to men, 46.8% in the USA in 2015 (United States Department of Labor, 2016), they are still missing from many STEM (science, technology, engineering, and mathematics) fields. In the USA in 2015, women made up only 15.4% of architecture and engineering professionals, 25.6% of computer and mathematical professionals, 29.8% of chemists and materials scientists, 24.5% of environmental scientists and geoscientists, and 37.6% of all other physical scientists (United States Bureau of Statistics, 2015).

One way of interpreting these statistics is that women are inherently worse at science than men, and unfortunately this is a common interpretation. However, research suggests that this is not the case. Melissa Hines’ (2004) in-depth work on gender development has shown that only very few and very specific cognitive abilities seem to be inherently different, such as three-dimensional, but not two-dimensional, mental rotation (better in men) and verbal fluency (better in women). In short, cognitive differences which do seem to be inherent are too specific and the gender difference too small to account for the much more dramatic difference in engagement in STEM fields.

So why are there more men in STEM than women?

Levine, et al (2015) summarise the primary barriers to women’s achievement in STEM fields as follows:

  1. Lack of female role models: if girls and women don’t see other women in science, they struggle to imagine themselves in science, and are discouraged from pursuing it;
  2. Women’s self-perceptions: gender stereotypes often make women see themselves as less capable than men in the sciences, which can undermine their success and further discourage them from pursuing science;
  3. Interactions with teachers, parents, and colleagues: if people believe the stereotypes and treat women as if they are less capable at science, women may be accepted less frequently into science positions, and taken less seriously even when they are accepted. Besides having professional consequences for these women, this may furthermore reinforce their own feelings of inability.

Why is this research necessary?

First of all, it’s important for the women among us to be aware that our barriers aren’t biological, but social. This brings our attention to things in our environment that try to limit us, and allows us to overcome them. Secondly, this research makes us all, men and women, realise that every word and every action play a role in determining other women’s opportunities in life.

Each of us might be treating men and women differently when it comes to science, and we might even be underestimating women’s abilities.

Therefore, it becomes the responsibility of all of us to contribute actively to a more equal society.

Ellen Robertson
PhD Student

References:

Hines, M. Brain gender (2004). Oxford, UK: Oxford University Press.

Levine, M., Serio, N., Radaram, B., Chaudhuri, S., and Talbert, W. Addressing the STEM Gender Gap by Designing and Implementing an Educational Outreach Chemistry Camp for Middle School Girls. Journal of Chemical Education. 2015, 92, 1639−1644.

United States Bureau of Statistics. (2015). Women in the labor force: a databook. Washington D.C.: BLS Reports.

United States Department of Labor. (2016). Women in the Labor Force. Retrieved from https://www.dol.gov/wb/stats/facts_over_time.htm#labor

Science issue – Big Data: Friend or Foe?

16b-rebecca-stanley-standingWhen studying phenomena outside the controlled conditions of the lab there are a whole host of variables that can affect your results. How an animal population behaves one season to the next, or why one set of patients responds well to a drug while others don’t, can come down to a number of factors that are difficult to control for in experimental design. This is why sample size is such a key factor in research, ensuring that you have enough data points, covering a variety of different scenarios, can help to smooth out background noise and identify the key trends you’re looking for.

The era of “Big Data” we are now living in has helped with this problem of sample size as the vast amount of data available allows us to investigate different problems and ideas using rich and varied datasets.

Big Data is a exactly that, extremely large datasets – the threshold is a moving target but is generally considered to be in the petabytes, for context, one petabyte worth of MP3-encoded music would take 2,000 years to play! The types of data we can now bring together is only limited by the type of sensors we have; from all the different apps on a smartphone to heart monitors, weather stations and CCTV cameras. This vast and varied data requires new ways of thinking and new analytical tools to reveal relationships between variables and conduct predictive analysis of what might happen in the future.

The applications of this analysis are endless, from translation: Google Translate is based on Big Data statistical analysis of text, to personalised medicine: exploring healthcare data to see which drugs are most effective in which types of people.

I work for a company called Carbon Credentials and our mission is to use data to drive sustainable practices. By connecting carbon emissions data to operational data from buildings you can work out what is consuming the most energy and why. Is it a cultural issue: do people just need to get better at switching their equipment off before they leave for the day? Or is it an operational issue with the building: is the air conditioning and heating on at the same time? (This happens more often that you’d think). We work with large datasets from universities, businesses and hospitals to create tailored sustainability performance programs to optimise the use of buildings to reduce energy wastage and increase user comfort.

This type of data analysis may seem overwhelming, complicated or just dull! But it is revolutionising our lives for better or worse. We can expand scientific research, improve healthcare and reduce carbon emissions but our personal information is no longer our own – if you want to use pretty much any app on your phone you have to give access to your location, photos, contacts and more.

How many sites do you go on that make you accept the “cookies” that personalise your content and ads?

A University of Cambridge research group hit headlines in 2015 for creating an online tool that can predict your relationship status, intelligence levels, political & religious beliefs and sexual preferences just by analysing your Facebook likes. We offer up a lot of this information readily to Facebook but this analysis demonstrates how what you like, what you search for and what you buy can generate a profile which can determine factors such as what news articles will appear in searches. How this information is being used is helping to confirm existing biases and narrow our experience of the online world.

In a world that is increasingly polarising to the extremes of left and right I think it’s important that we consider how data can improve and connect the world rather than isolate and divide it.

Rebecca Stanley
Alumna

Science Issue – Pharmacogenomics: Getting Personal in Immuno-oncology

15b-graphicNewsNot all patients have the same genetic make up – it is estimated that differences between individuals account for 20-95% of variability in drug dispositions and effects. This means that the benefits of a single drug may only be evident in a subset of patients, rather than being uniform across the whole population.

Pharmacogenomics is a field of science that uses information about an individual’s genes, proteins and environment to predict an individual’s response to certain drugs, and create a personalized treatment.

Finding unique patterns in an individual’s genetic makeup through “biomarker” analysis, allows the identification of groups of patients who have the same molecular variation of the disease, and thus, may benefit from a certain drug or combinations of drugs.  Identification of the right group of drugs for a patient leads to 1) a higher probability of successful treatment, 2) reduces the probability of negative side effects.

For example, in breast cancer, there are a number of gene expression signatures that have been developed that can be used to estimate prognosis for an individual patient based on assessment of the tumour. In patients who have a form known as “HER-2+ breast cancer”, overexpression of the HER-2 gene leads to a higher than normal expression of the HER-2 receptor in breast cancer cells. The drug, trastuzumab, was been developed specifically to interfere with the receptor. A number of next-generation drug combinations have recently become available, allowing treatments to be further personalised with a greater chance of successful treatment.

More recently, pharmacogenomics has been applied to an exciting new field known as immuno-oncology (I/O). These treatments use an individual’s own immune system to help fight cancer through use of a single I/O drug or combinations of drugs. Normally, white blood cells known as “T-cells” in an individual’s immune system recognize cancer cells as foreign and attack and kill the problem cells. Certain types of cancer exhibit molecules on their surface, known as PD-L1. Interaction with the T-cell surface molecule (known as PD-1) protects the cancer cell from being attacked by T-cells. However, this attack can be reactivated by drugs that block the PD-L1/ PD-1 interaction. To date, single agent anti-PD-1 / PD-L1 has demonstrated survival benefit in five cancer types – bringing hope to many patients who previously had very few treatment options.

The next wave of innovation is to develop effective I/O combination treatments to address patients who do not benefit from monotherapies, and in this regard, it is critical to co-develop a biomarker that will guide treatment selection. AstraZeneca, Bristol-Myers Squibb, Merck and Roche are key players in the Pharmaceutical industry who are leading I/O combination therapy development. The scientific and medical communities are excited to see data in the coming years, with new presentations at the cancer conference, American Society Of Clinical Oncology, in June 2017.

As the options for personalized medicines expand, biomarkers will also play an important role beyond the lab and the clinic.

They will be vital in ensuring patients receive the most effective combination of treatments at a time when healthcare budgets are under significant pressure.

Eleanor Fung
Director – Global Product & Portfolio Strategy at AstraZeneca


Further reading:
Molecular Oncology 2012, 6: 140
Semin Oncol. 2016 43:501.
J Immunother Cancer. 2016; 4:48
Lancet Oncol. 2016;17:e347-62
www.cancer.gov

Science issue: The extraordinariness of the ordinary

cosmetic-bottlesNewsI spent a while recently flipping the lid of a cosmetics bottle open and shut. This was the kind of lid that you typically get on shampoo bottles, ketchup, honey and so on, where the bottle top, hinge and lid are all fabricated as a single piece of the same material.  This particular one was sealed simply by a stopper on the inside of the lid popping securely into a hole on the bottle top.

Try to find one of these lids yourself, and take a careful look at it, thinking about the properties that are needed to make it work. The lid needs to be hard enough that it can’t be scratched easily, as well as both strong and tough enough that it won’t break if you drop the bottle on the floor. It needs to be elastic enough that the stopper itself can pop in and out of its hole, making a secure seal. The hinge also needs some elasticity so that it can stretch a little to open and close, but it must not be prone to fatigue (progressive weakening and eventually breakage) when it is opened and shut many times. It needs to be possible to manufacture the whole lid in a single piece, including the hinge, because there are no joints. This is likely to have been done by injecting fluid polymer into a mould with more than one entry point for the fluid, perhaps one on the lid side and one on the bottle top side with the flows merging completely where they meet.

The combination of properties that make a specific formulation of a particular polymer suitable for this application did not arise by accident, or just ‘picking something off a shelf’; it is a careful process of optimisation, getting the right balance between all these aspects of the physical properties of the material. And that cannot be done without a proper understanding of why materials behave as they do, right down to the atomic level.

sports-gearWithout the discoveries and advances in materials that were made from the mid-twentieth century onwards, we would not have our smartphones, our electric vehicles, our high-performance sports gear. However we have to fabricate all of our modern materials from the range of elements offered to us by the Periodic Table (a relatively small number of elements, considering how much we are able to do with them). Understanding how materials fit together at the atomic scale also allows us to appreciate the skill and effort that has gone into designing and manufacturing even quite mundane items.

I came into Materials Science out of a fascination with materials at this atomic level; the way that atoms organise themselves into the structures, materials and artefacts that we see and use in the world around us. My particular specialism has been electron microscopy and diffraction, which has given me the opportunity to see materials at a scale far below the resolution of the human eye.  Materials of all kinds are extraordinarily beautiful at the microscopic level, and this atomic world is full of wonder and possibility: where might discoveries in this area lead us next?

Dr Erica Bithell
Bye Fellow

Science issue: A machine that can learn to speak to you

1b-malica-gasic-photo
News
Have you ever talked to Siri and asked yourself how one builds such a system? Some time ago, when I was pursuing my MPhil degree in Cambridge, Prof. Steve Young demonstrated a spoken dialogue system during a talk. I was fascinated by the idea that one could make a computer speak and understand human speech. I thought I must get into this research and so I applied for a PhD at the Department of Engineering’s Dialogue Systems Group.

A spoken dialogue system normally has three parts: speech understanding, which decodes the meaning from the user’s speech; dialogue management, which tries to come up with a good response; and speech generation, which turns the answer into natural speech. All of these modules can be data-driven: machine learning methods allow us to build systems that become better at their tasks the more data they have.

This is very exciting because in today’s world we are generating data at the biggest pace ever.

There are two distinct kinds of machine learning methods that we use for this research. One is called supervised learning.  This is how we learn ourselves when we have a teacher to provide examples. The system simply tries to imitate the teacher.  Another is called reinforcement learning, and one can think of it as learning from interaction. In this approach, the system can explore different possibilities.  Whenever it makes a good decision, it gets a reward from the user.  Over time, it tries to maximise that reward.  Just like a child learns from trial and error.

This kind of learning through interaction in the context of dialogue systems really intrigues me. The problem is that such learning methods normally need a huge number of interactions before the system starts to behave reasonably well. So I’ve been working on ways to speed up this process, so that the system can learn directly from talking to a human. And indeed I was the first researcher to show that this is possible.

Applications for this technology include every area where we currently see human-computer interaction, and it will make such interaction possible in the future in areas where we can’t imagine it today.  Currently, I am particularly interested in applications in the health sector.  To support such systems, we need to develop algorithms capable of supporting much more complex interactions than what is possible today.  But if successfully built, such systems would have a huge benefit for society.

Dr Milica Gašić
Lecturer in Dialogue Systems, Department of Engineering
Fellow, Murray Edwards College

See my interview for The Naked Scientists: http://www.thenakedscientists.com/HTML/interviews/interview/1001757/

Or check out my website:

http://mi.eng.cam.ac.uk/~mg436/

References

Gašić and S. Young “Gaussian Processes for POMDP-based dialogue manager opimisation”, IEEE Transactions on Audio, Speech and Language Processing, 2014

Gašić, F. Jurcicek, B. Thomson, K. Yu and S. Young. “On-line policy optimisation of spoken dialogue systems via live interaction with human subjects”, ASRU, Hawaii, 2011

Science issue: Using evidence to inform public policy

12B Amanda Roper (head and shoulders)News‘Evidence’ is at the heart of scientific and historical endeavour. I had not really thought of the two together until I began making public policy.  I studied history and have spent much of my career working with experts in epidemiology, economics, engineering, law, veterinary science, nuclear physics….  While the list is endless, the issues I have worked on, from counter-terrorism to tackling bovine tuberculosis in cattle, all come back to the evidence and how it is used.

So, how has a self-confessed historian sneaked into writing a STEMM blog? Two words: transferable skills.

Any form of studying can give more than knowing who Emilie du Chatelet was (early eighteenth century mathematician, translated Isaac Newton’s Principia) or being able to tell the difference between a bacteria and a virus.  Among the fundamental transferable skills are clear written and oral communication, understanding complexity and having some proficiency with a computer.  For working at a higher level there are some which are worth cultivating and the ones I have found most important to making an impact in public policy are:

  • Knowing the context – public policy is not developed in a bubble and the bigger picture surrounding an issue such as the economy, overall direction of animal health policy or the political priorities of the Government of the day can be important in pointing to or narrowing down possibilities. History is all about the context and influences at a point in time. Science is influenced by what came before and what’s of value to a society (sometimes before we know it is of value). Public policy decisions are made within a similar environment.
  • Being analytical but pragmatic – a healthy dose of intellectual curiosity combined with understanding different forms of information means I have been able to take an economists evidence, talk it through with epidemiologists, test the information by asking questions and then combine the conclusions with what can be done on the ground to develop options. Data tells part of the story and something may work well in scientific conditions but it might not be possible to put it into practice.
  • Seeing other perspectives – there is rarely a single view, especially among scientists and lawyers, and this is a Good Thing. Being able to synthesise, assess and make decisions is made harder by not having one, agreed, set of evidence to start from, but the outcome is often better. There is also the wider perspective of those a policy might affect: what might be a logical, evidence-based conclusion can result in public outcry.
  • Communicating and influencing effectively – every profession or area of expertise (including policy making) has its own technical language or jargon. It’s how tribes of professions (and humans) work – the included and the excluded. What’s important is being able to break through those barriers to communicate complexity in a clear way while listening to and being aware of your audience. It’s an ability to translate even though everyone is usually speaking the same language!

All of these skills can come from high level study of history, engineering, a social science or a science and then underpin a high level role in using evidence to inform policy. My top tips for making the most of them is to have a good level of self-awareness – what are you good at, what do you find difficult, how do you react to challenge – and then work out what is most valuable for what you want to achieve.

Amanda Roper
Alumna
Head, Parliamentary and Ministerial Relations at Defra (Department for Environment, Food and Rural Affairs)

Science issue: Uncertainties in breast screening

News

11B Ann-Louise-Kinmonth (portrait)

I recently received a routine breast screening invitation, and found myself weighing up the risks and benefits of attending. The accompanying leaflet “Helping you to decide” (pictured) says;” About 12,000 women in the UK dies of breast cancer every year. Survival from the disease has been improving over time, and now about 3 out of 4 women diagnosed with breast cancer are alive 10 years later.”

Key advances include treatment and preventive strategies, including screening. Surely to be part of this good news story I must book my appointment?

11B Ann Louise Kinmonth (NHS breast screening).docx
“Helping you to decide”

Screening aims to identify apparently healthy people who may be at risk of disease, where treatment is more effective if applied earlier. The UK National Screening Committee reviews emerging evidence and applies criteria to weigh population benefits against harms. They recommend systematic population screening by mammography (breast imaging) to prevent breast cancer deaths.

The evidence they review comes from many disciplines; epidemiology, biology, and radiology, statistics and the social sciences.

Large epidemiological studies of breast cancer among  carefully characterised women over time (cohort studies),  and  trials of treatment, tell us about the natural history, enable  estimates of likely cost-effectiveness of treatments  and monitor performance of breast screening programmes in preventing disease progression and early death.

11B Ann Louise Kinmonth (200 women...).docx
What might be expected to happen to 200 women who either do or do not attend breast screening every three years between ages 50 and 70, and then are followed up until aged 80. Graphic by Mike Pearson, from information in NHS Screening leaflet.

Recently statistitians and social scientists have begun to turn these estimates into quantitative, pictograms accessible to the women deciding whether to attend or not. They pay attention  separately to benefits and harms of attending and of not attending  screening. These analyses show  the extent  to which screening is identifying, as cases for treatment, women who would  remain well if they did not attend for screening.

The pictogram shows that 15 of every  200 women are expected to get breast cancer over  20 years; If screened and  treated  3 would be expected to die early from  breast cancer. If not screened, 4 would be expected to die early from breast cancer. But the pictogram also shows  that for each life “saved” thee women are estimated to be labelled as having cancer and treated unnecessarily.

This over treatment seems to be due to limitations in predicting which lesions found will progress without treatment. Breast cancer can be divided into myriad different diseases with genetic and epigenetic variations. More work is needed to identify these, and how they will progress, building on recent discoveries of subgroups of women at high genetic risk of cancer or cancers responsive to particular treatments.

Women scientists have led many of these advances. Perhaps you too may be excited by the possibility of work in this area; maybe you will help to identify biomarkers distinguishing breast lesions that will or will not progress, or better treatments of later cases so that breast screening can be reserved for high risk groups of women or stopped altogether.  Maybe you will be interested in understanding better the psychological costs of screening.

While some women will prefer to minimise their risks from breast cancer, by participation in screening, others will prefer to avoid any risk of over -treatment, by staying away.

Maybe whatever we decide, we should eat less and exercise more to reduce the risk of a whole range of cancers.

Ann Louise Kinmonth CBE
Alumna

Ann Louise Kinmonth CBE (New Hall 1969) held the Foundation Chair of General Practice University of Cambridge, and was a principal investigator of the ADDITION Cambridge trials to investigate intensive treatment of screen detected diabetes. She is Clinical Director of Studies and Fellow St Johns College Cambridge

Further reading

Breast Cancer Focus Nature 2015 Vol. 527 No. 7578_supp ppS101-S12

Understanding Uncertainty.org. . A visualisation of the information in NHS Breast Cancer Screening leaflet; Submitted by david on Wed, 10/06/2015 –

Moss, Sue. M. et al. Effect of mammographic screening from age 40 years on breast cancer mortality in the UK Age trial at 17 years’ follow-up: a randomised controlled trial. Lancet Oncol. http://dx.doi.org/10.1016/S1470-2045(15)00128-X (2015).