How Science Trumps Trump: The Future of US Science Funding


By Johannes Buheitel, PhD

I was never the best car passenger. It’s not that I can’t trust others but there is something quite unsettling about letting someone else do the steering, while not having any power over the situation yourself. On Tuesday, November 8th, I had exactly this feeling, but all I could do was to sit back and let it play out on my TV set. Of course, you all know by now, I’m talking about the past presidential election, in which the American people (this excludes me) were tasked with casting their ballots in support for either former First Lady and Secretary of State Hillary Clinton or real estate mogul and former reality TV personality Donald Trump. And for all that are bit behind on their Twitter feed (spoiler alert!): Donald Trump will be the 45th president of the United States of America following his inauguration on January 20th, 2017. Given the controversies around Trump and all the issues he stands for, there are many things that can, have been  and will be said about the implications for people living in the US but also elsewhere. But for us scientists, the most pressing question that is being asked left and right is an almost existential one: What happens to science and its funding in the US?

The short answer is: We don’t know yet. Not only has there been no meaningful discussion about these issues in public (one of the few exceptions being that energy policy question  by undecided voter-turned-meme Ken Bone), but, even more worryingly, there is just not enough hard info on specific policies from the future Trump administration to go on. And that means, we’re left to just make assumptions based on the handful of words Mr. Trump and his allies have shared during his campaign. And I’m afraid, those paint a dire picture of the future of American science.

Trump has not only repeatedly mentioned in the past that he did not believe in the scientific evidence around climate change (even going as far as calling it a Chinese hoax), but also reminded us of his position just recently, when he appointed  known climate change skeptic Myron Ebell to the transition team of the Environmental Protection Agency (EPA). He has furthermore endorsed the widespread (and, of course misguided) belief that vaccines cause autism. His vice president, Mike Pence, publicly doubted  that smoking can cause cancer as late as in 2000, and called evolution “controversial”.

According to specialists like Michael Lubell from the American Physical Society, all of these statements are evidence that “Trump will be the first anti-science president we have ever had.” But what does this mean for us in the trenches? The first thing you should know is that science funding is more or less a function of the overall US discretionary budget, which is in the hand of  the United States Congress, says  Matt Hourihan, director of the R&D Budget and Policy Program for the American Association for the Advancement of Science (AAAS). This would be a relief, if Congress wasn’t, according to Rush Holt, president of the AAAS, on a “sequestration path that […] will reduce the fraction of the budget for discretionary funding.” In numbers, this means that when the current budget deal expires next year, spending caps might drop by another 2.3%. Holt goes on to say that a reversal of this trend has always been unlikely, even if the tables were turned, which doesn’t make the pill go down any easier. Congress might raise the caps, as they have done before, but this is of course not a safe bet, and could translate to a tight year for US science funding.

So when the budget is more or less out of the hands of Donald Trump, what power does he actually possess over matters of research funding? Well, the most powerful political instrument that the president can implement is the executive order. But also this power is not unlimited and could for example not be used to unilaterally reverse the fundamentals of climate policy, said David Goldston from the Natural Resources Defense Council (NRDC) during a Webinar hosted by the AAAS shortly after the election. Particularly, backing out of the Paris agreement, as Trump has threatened to do, would take at least four years and requires support by Congress (which, admittedly, is in Republican hand). And while the president might be able to “scoop out” the Paris deal by many smaller changes to US climate policy, this is unlikely to happen, at least not to a substantial degree, believes Rush Holt. The administration will soon start to feel push-back by the public, which, so Holt during the AAAS Webinar, is indeed not oblivious about the various impacts of climate change, like frequent droughts or the decline of fisheries in the country. There was further consensus among the panelists that science education funding will probably not be deeply affected. First, because this matter usually has bipartisan support, but also because only about 10% of the states’ education funding actually comes from the federal budget.

So, across the board, experts seem to be a reluctantly positive. Whether this is just a serious case of denial or panic control, we don’t know, but even Trump himself has been caught calling for  “investment in research and development across a broad landscape of academia,” and even seems to be a fan of space exploration. Our job as scientists is now, to keep our heads high, keep doing our research to the best of our abilities but also to keep reaching out to the public, invite people to be part of the conversation, and convincing them of the power of scientific evidence. Or to say it with Rush Holt’s words: “We must make clear that an official cannot wish away what is known about climate change, gun violence, opioid addiction, fisheries depletion, or any other public issue illuminated by research.”


Immunotherapy: Using Your Own Cells to Fight Cancer – Part 2


By Gesa Junge, PhD


Part 1 of this post described passive immunotherapies like antibodies and cytokines, but there are also active immunotherapies, which re-target our immune system towards cancer cells, for example cancer vaccines. These can be preventative vaccines, offering protection against cancer-associated viruses such as Hepatitis B (liver cancer) or Human Papilloma Virus (HPV, cervical cancer). The link between HPV and cervical cancer was first described in 1983, and a vaccine was approved in 2006. By 2015, the incidence of HPV infections in women under 20 had decreased as much as 60% in countries that had 50% vaccination coverage, although it may still be too early to tell what the impact on HPV-associated cancer incidence is. There are also other factors to consider, for example screening programmes are also likely to have a positive impact on HPV-associated cancers.

Vaccines can also be therapeutic vaccines, which stimulate the immune system to attack cancer cells. To date, the only cancer vaccine approved in the US is Provenge, used for the treatment of metastatic prostate cancer. For this therapy, a patient’s white blood cells are extracted from the blood, incubated with prostatic acid phosphatase (PAP, a prostate-specific enzyme) and granulocyte macrophage colony stimulating factor (GM-CSF) in order to produce mature antigen presenting cells which are then returned to the patient and search and destroy tumour cells.

Many other therapeutic cancer vaccines are in development, for example OncoVax, which is an autologous vaccine made from a patient’s resected tumour cells. OncoVax has been in development since the 1990s and is currently in phase III trials. Another example is GVAX, an allogenic whole-cell tumour vaccine currently being studied in phase I and II trials or pancreatic and colorectal cancer. As an allogenic vaccine, it is not made from the patient’s own blood cells (like an autologous vaccine), and it does not target specific antigens but rather increases the production of cytokines and GM-CSF.

Another therapy which is based on re-programming the patient’s immune system is adoptive T-cell transfer. As with some cancer vaccines, a patient’s T-cells are isolated from the blood, and the cells with the greatest affinity for tumour cells are expanded in the lab and the re-infused in the patient. A recent modification of this technique is the use of chimeric antigen receptor (CAR) T-cells, where the T-cell receptors are genetically engineered to be more tumour-specific before re-infusion. This approached was especially promising in chronic lymphocytic leukaemia, where some patients experienced remissions of a year and longer. Later, CAR T-cells were also tested in acute lymphocytic leukaemia, where response rates were as high as 89%.

Finally, a new class of cancer drugs called immune checkpoint inhibitors has been making headlines recently, some of which are now approved for the treatment of cancer. Immune checkpoints are part of the mechanism by which human cells, including cancer cells, can evade the immune system. For example, the programmed cell death (PD) 1 receptor on immune cells interacts with PD1 ligand (PDL1) on cancer cells, which inhibits the killing of the cancer cell by the immune cell. Similarly, CTLA-4 is a receptor on activated T-cells which downregulates the immune response.

The first checkpoint inhibitor was an antibody to CTLA-4, ipilimumab, which was approved for the treatment of melanoma in 2011. PD1 antibodies such as pembrolizumab and nivolumab were only approved in 2014, and the only PDL1 antibody (atezolizumab) in 2016, so it is difficult to tell what the long-term effects of checkpoint inhibitor treatment will be. Numerous checkpoint inhibitors are still undergoing trials, most of the advanced (phase III) ones being targeted to PD1 or PDL1. However, there are other compounds in early trials (phase I or II) that target KIR (killer-cell immunoglobulin-like receptor) which are primarily being studied in myeloma, or LAG3 (lymphocyte activation gene 3), in trials for various solid tumours and leukaemias.

Immunotherapies all come under the umbrella of biological therapies. Biologics are produced by organisms, usually cells in a dish, unlike synthetic drugs, which are manufactured using a chemical process in the lab. This makes biologicals more expensive to manufacture. Ipilimumab therapy, for example, can cost about $100 000 per patient, with pembrolizumab and nivolumab being only slightly less expensive at $48 000 – $67 000. This puts considerable financial strain on patients and insurance companies. From a safety perspective, biologicals can cause the immune system to overreact. This sounds odd, as the whole point of immunotherapy is to activate the immune system in order to fight tumour cells, but if this response gets out of control, it can lead to potentially serious side effects as the immune system attacks the body’s organs and tissues.

All of these therapeutic approaches (antibodies, interleukins, vaccines, and checkpoint inhibitors) are usually not used alone but in combination with each other or other chemotherapy, which makes it difficult to definitively say which drug works best. But it is safe to say that collectively they have improved the lives of a lot of cancer patients. If you are interested in finding out more about the fascinating history of immunotherapy, from the discovery of the immune system to checkpoint inhibitors, check out the CRI’s timeline of progress on immunology and immunotherapy here.


Immunotherapy: Using Your Own Cells to Fight Cancer – Part 1


By Gesa Junge, PhD


Our immune system’s job is to recognize foreign, unfamiliar and potentially dangerous cells and molecules. On the one hand, it helps us fight infections by bacteria and viruses, while on the other hand it can leave us with annoying and potentially dangerous allergic reactions to harmless things like peanuts, pollen or pets. Tumor cells are arguably very harmful to our health, and yet the immune system does not always eliminate them. This is partially because cancer cells are our own cells, and not a foreign, unfamiliar intruder.

The immune system can recognize cancer cells; this was first postulated in 1909 by Paul Ehrlich and subsequently found by several others. However, detecting cancer cells may not be enough to prevent tumor growth. Recent research has shown that while detection can lead to elimination of cancer cells, some cells are not killed but enter an equilibrium stage, where they can exist undisturbed and undergo changes, and finally the cells can escape, if they have changed in a way that allows them to grow undetected by the immune system. This process of elimination, equilibrium and escape is referred to as “cancer immunoediting” and is one of the most active research areas in cancer, particularly in regard to cancer therapy.

Immunotherapy is a form of cancer therapy that harnesses our immune system to kill cancer cells, and there are various approaches to this. Probably the most established forms of immunotherapy are antibodies, which have been used for almost two decades. They generally target surface markers of cancer cells; for example, rituximab is an antibody to CD20, or trastuzumab, which targets HER2. CD20 and HER2 are cell surface proteins highly expressed by leukaemia and breast cancer cells, respectively, while normal, healthy cells have lower expression, making the cancer cells more susceptible. Rituximab was approved for Non-Hodgkins Lymphoma in 1997, the first of now nearly 20 antibodies to be routinely used in cancer therapy. In addition to this, there are several new antibodies undergoing clinical trials for most cancers. These are mainly antibodies to tumour-specific antigens (proteins that may only be expressed by e.g. prostate or lung cancer), and checkpoint inhibitors such as PD1 (more on that in part 2).

Initially, antibodies were usually generated in mice; however, giving murine antibodies to humans can lead to an immune response and resistance to the mouse antibodies when they are administered again later. Therefore, antibodies had to be “humanised”, i.e. made more like human antibodies, without losing the target affinity, and this was only made possible by advances in biotechnology. The first clinically used antibodies, such as rituximab, were chimeric antibodies, in which the variable region (which binds the target) is murine and the constant region is human, making them much better tolerated. Trastuzumab is an example of a humanised antibody, where only the very end of the variable region (the complementarity-determining region, CDR) is murine, and the rest of the molecule is human). And then there are fully human antibodies, such as panitumumab, an anti-EGFR antibody used to treat colorectal cancer. There is actually a system to labeling therapeutic antibodies: -ximab is chimeric, -zumab is humanised and –umab is human.

Antibodies can also be conjugated to drugs, which should make the drug more selective to its target and the antibody more effective in cell-killing. So far there are only very few antibody-drug conjugates in clinical use, but one example is Kadcyla, which consists of trastuzumab conjugated to emtansine, a cytotoxic agent.

Other examples of immunotherapy are cytokines such as interferons and interleukins. These are mediators of the immune response secreted by immune cells which can be given intravenously to help attack cancer cells, and they are used for example in the treatment of skin cancer. Interleukin 2 (IL-2) was the first interleukin to be approved, for the treatment of advanced melanoma and renal cancer, and research into new interleukins and their therapeutic potential is still going strong. Especially IL-2 and IL-12, but also several others are currently in clinical studies for both and various other indications, such as viral infections and autoimmune diseases.

In addition to passive immunotherapies like antibodies and cytokines, there are also active immunotherapies which re-target our immune system towards cancer cells, for example cancer vaccines. More on this, and on new drugs and their issues in part 2.




Putting The "In" in Industry: Top Tips for a Successful Transition


By Esther Cooke, PhD


The plight of early career researchers was magnified last month (October 2016) by demoralizing news features in Nature entitled “Young scientists under pressure,” and “Young, talented and fed-up….” New research shows that annual increases in science-related doctorates, coupled with flat-lining or faltering funding opportunities and full-time faculty positions, is creating stiffer competition and lower success rates for young scientists in academia. Unsurprisingly, more and more PhDs are exploring alternative avenues, notably within pharmaceutical/biotechnology companies.

Vice President of Diagnostic Development at Illumina, Karen Gutekunst, PhD shared insights with the Scripps Consulting Club on how to successfully flee to pharma.

Gutekunst completed her doctorate in molecular genetics at the Georgia Institute of Technology. Her first whiff of R&D in industry came from a headhunt call about a job in New Jersey. Although not ready to leave Atlanta, Gutekunst liked the idea of applying “tech” to real medical problems. She landed her first industry position at Roche and stayed with the company for 18 years, working in project management, development, and regulatory affairs. She then spent two years with Clarient before landing her current job. During the workshop, Gutekunst reflected on personal experiences to highlight key pieces of advice for grad students and postdocs considering a similar path:


  1. Be open to possibilities. We often hold back from opportunities because they don’t fully satisfy our criteria, or for FOMO – that is, fear of missing out – on the “perfect” job that could be just around the corner. Gutekunst advises to keep an open mind: “Don’t be afraid to branch out, as no decision you make is forever.” New opportunities will present, and things will happen in the future that you can’t plan for. It’s all good experience (even the application process) and could be an important step towards that dream job. To be successful in industry, Gutekunst admits, “You have to be willing to change direction on the fly.” Adaptability is a must.


  1. Broaden your skillset. Don’t be an out-and-out lab rat. Gutekunst emphasizes that transferable skills are just as important as research skills. “You need to be a well-rounded person to grow and succeed in industry. It’s not just about how smart you are,” she says. All of the applicants will be smart, so it comes down to how well you will fit in with the culture. Unlike academia, where for the most part you work independently on your own project, research in an industrial setting is much more collaborative – people work together for the good of the company. Look for creative ways to demonstrate communication skills, leadership and project management skills, problem solving ability, and teamwork.


  1. Learn the lingo. Familiarize yourself with insiders’ jargon and acronyms that you might run into during interviews, such as GLP, GMP and GCP (good laboratory, manufacturing and clinical practices, respectively). Gain an understanding of company frameworks and the processes of production, development, and life cycle management, bearing in mind that these may differ between small and large companies. Gutekunst suggests that you tailor your research: “If you’re interested in marketing, understand what product requirements are and why they’re important.” Once you’ve mastered the language, speak it with passion – you’ll need to be able to convince someone to give you a job!


  1. Network, network, network. This one gets drummed into us all of the time, and that’s because it really is important. “You never know what might come of a conversation,” says Gutekunst. Maintain good relations with your colleagues and collaborators, attend conferences, join clubs and societies, and get stuck into professional networking sites like LinkedIn. Be proactive in asking questions and reaching out to people; be willing to stick your neck out. Made connections already? Hold on to them! Speaking from experience, Gutekunst adds, “Connections lead to random phone calls, and random phone calls lead to jobs.”


  1. Don’t wait! When asked about the best time to make the transition, Gutekunst responds, “If you want to go into industry, I’d try to get in as quickly as you can.” The earlier you are in your career, the easier it is to get over the hump of academic stereotypes. It comes back to adaptability; employers are looking for candidates who will adjust quickly to their way of doing things, i.e. before the rhythms of academic research become ingrained. If you’re sure it’s the right direction, don’t wait for that next paper or fellowship – you’ll always put one more hurdle in front of you. Work with what you have, and get in!


  1. Believe in yourself. It’s as simple as that. Have confidence and don’t be intimidated!A career in industry is absolutely attainable for academic PhDs, but a smooth transition requires careful planning and consideration, with some gumption and flexibility thrown in the mix. Check with your graduate students or postdoc services office for more information and resources. If you’re struggling to make the call, the most important thing is to trust your instincts and strive to do what you love – you’ll be happier!


Correlation versus Causation: the Eternal Struggle

By John McLaughlin


Scientists are always warning the public – and each other – not to confuse correlation with causation. Whenever a study is published linking our favorite food to cancer, heart attacks, or other health problems, we are cautioned to take these findings with a grain of salt because identifying causes in a complex sea of correlations is a daunting task.


Despite the challenge, a major task of researchers is to uncover causes – whether it’s the simple mechanism of a protein’s activity within the cell, or a population-level analysis of interactions among genes that increase the risk of disease.


This raises the question: what exactly does it mean for X to cause Y? The concept of causality has existed for a long time, predating the scientific revolution by many centuries. Aristotle explained causation by dividing it into four separate aspects. Take the simple example of a wooden table: its material cause is the wood of which it is composed, its efficient cause is the carpenter who crafted it, its formal cause is the particular shape which makes it a table rather than something else, and its final cause is the purpose for which it was created, maybe to hold a lamp.


Scientists today don’t operate with such a multifaceted theory of causation. Although the meaning of ‘cause’ is usually taken for granted in everyday life, when pressed for a precise definition a biologist would likely explain cause and effect in terms of probabilities. According to probabilistic theories of causation, a cause both precedes its effect and increases its probability, all other things being equal. For instance, we know that smoking causes heart disease; this does not imply that everyone who smokes will suffer heart problems, but it does mean that smokers have a higher probability than non-smokers of developing heart disease, all other factors being held equal.


In order to scientifically study causal relationships, a critical requirement is the ability to intervene in a system and manipulate separate variables. Luckily, researchers can often alter experimental variables and examine counterfactual scenarios, which take the form ‘if X causes Y, then if X does not occur, Y will not occur’. Model organism biologists pride themselves on this skill. Working in the lab, if I’d like to determine whether a particular mutation is the cause of an interesting phenotype, I can compare flies that are genetically identical in all respects except for the mutation in question. By eliminating the confounding variables in this way, a direct causal link can be established.


What, then, is the relationship between causation and correlation? Two correlated variables or events share a mutual connection that can be observed as a positive or negative relationship. At first glance, a correlation between two variables may suggest to us a causal relationship, but this conclusion does not necessarily follow. Fires and fire trucks are often correlated, but obviously it is not the fire trucks that cause fires. To demonstrate this point, just take a look at the ridiculous spurious correlations that can occur between events that are not causally linked.


To make the issue more confusing, even if we do know with certainty that x causes y, it does not therefore imply that these variables will be correlated. Imagine a mixed community of smokers and non-smokers: cigarette smoking is a known cause of heart disease, but in this hypothetical population all of the smokers exercise while the non-smokers do not. If the heart-healthy benefits of the smokers’ exercise perfectly counteract their increased risk of heart disease, then there will be no correlation between smoking and heart disease at the population level.


In a game of billiards, the precise ordering of cause and effect is obvious to the observer. In the real world, discovering causal relationships is often a slow and arduous process, but it’s what scientists signed up to do.