28Apr

Delivering Clinical Insights with Targeted Biomarker Assays

April 28, 2020 Metabolon

Metabolomics reveals biological insights otherwise unseen, making it a crucial component of drug development. For a successful metabolomics study, both small molecule discovery and the ability to dig deeper into specific biomarkers of interest are needed to uncover actionable insights that propel new therapeutic developments. A specific combination of technology and expertise is required to identify these biomarkers of interest and develop assays that are sensitive enough to explore them fully.

At Metabolon, we understand the crucial role targeted small molecule assays play in drug development, and we’ve established best-in-class expertise. Metabolon has developed quantitative assays for more than 900 biochemicals in over 20 different matrices, including more than 150 qualified and/or validated targeted biochemical assays for pharmaceutical and biotechnology research and development. These assays focus on specific metabolites or metabolic pathways and can be used to track biomarkers and enhance biological understanding across preclinical and clinical research.

Our approach speaks directly to the questions biopharma research directors are seeking to answer, such as understanding their therapeutic’s mechanism of action, establishing pharmacodynamics, gauging safety and efficacy, defining patient segmentation, and conducting post-market surveillance. Once the critical biomarkers that are most important to researchers are identified through discovery, a more precise and accurate quantitation of the biomarkers is sought. Metabolon’s strategic services offer biopharma clients global discovery followed with a direct targeted approach to dig deeper into promising and relvant biomarkers.

Metabolon’s targeted assay program is uniquely situated to fit into this workflow.

Targeted biomarker assay development is a natural progression following discovery using Metabolon’s first-in-class metabolomics platform. Metabolon’s discovery platform uses statistical approaches to assess the relative quantity of thousands of biomarkers. Our targeted assay program picks up from there, taking the discovery work and developing a more precise, accurate measurement of those few molecules that are of most interest. We apply LC-MS/MS based methods to determine absolute quantities of important biomarkers, using labeled internal standards as well as calibrators.

Our expertise in the measurement of endogenous biomarkers is unique and second to none. Most bioanalytical services offer expertise in exogenous biomarker measurements, such as the drug of interest or compounds that are exogenously introduced into a system. Metabolon targeted assays measure endogenous biomarkers naturally found at various levels within biological systems. Since endogenous biomarkers are sometimes present at the lower limits of analytical detection, the sensitivity of the assay becomes paramount. Furthermore, modest changes in biomarker levels may have biological relevance; Metabolon’s highly accurate targeted assays are required to tease out even modest changes in analyte levels. Our team of Ph.D. level method development scientists have a degree of experience in endogenous biomarker assay development that few places, if any, in the world can match. Our scientists are highly efficient and experienced in endogenous small molecule analysis, bringing additional data and actionable insights to drug development and clinical trials. No other bioanalytical company can translate metabolomic data and biomarker discovery into valuable biological insight using targeted LC-MS/MS analysis in the way that we can.

Finally, Metabolon’s capability is unique because we can operate under a research or clinical setting. In addition to our targeted work meeting specific client study demands, we also develop assays specifically used in clinical testing. Some of the pre-developed targeted assays offered by Metabolon include:

Fatty acid metabolism panel
Short-chain fatty acid panel
Bile acid panel
Cholesterol metabolism panel
Stratum corneum panel
Sebum lipid panel
accuGFR™ panel (to estimate glomerular filtration rate)
7 alpha-hydroxy-4-cholesten-3-one (C4) panel
Metal ion panel

LC-MS/MS-based assays can be customized based on client needs and developed under the quality system for research use only (RUO) or validated under Good Clinical Practice (GCP) and Clinical Laboratory Improvement Amendments (CLIA). Metabolon is ISO 9001: 2015 certified for analytical and diagnostic testing of biological specimens and accredited by the College of American Pathologists for diagnostic testing on human specimens.

Nowhere else will you find a laboratory like Metabolon’s that combines a world-class metabolomics discovery platform with a highly sophisticated analytical chemistry laboratory doing targeted analysis the way we do it. We have spent nearly 20 years building the best metabolomics competency in world to enable, accelerate and support drug development through biomarker discovery, understanding mechanism of action, patient stratification and more.

We proudly cover the whole breadth of the biopharma pipeline, from discovery to targeted analysis to clinical trial support, because your success is our goal.

Is your study yielding all the insights it could be? Download our free infographic to learn more about the five risks of drug development and how you can avoid them with metabolomics, and contact us today at hello@metabolon.com to get started.

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16Apr

Front lines of COVID-19

April 16, 2020 Metabolon

Metabolon scientists are collaborating with researchers on world-leading programs to use multi-omic deep phenotyping technologies, including metabolomics, to advance human health. A primary focus right now is improving our collective understanding of the SARS-CoV-2 virus and the COVID-19 disease during this unprecedented pandemic. Metabolon has a strong history of working closely with scientists at the Institute for Systems Biology (ISB) like Dr. James R. Heath, Dr. Leroy Hood, Dr. Andrew Magis, Dr. Nathan Price, Dr. John Earls and Dr. Sean Gibbons.

Now, we are teaming up with ISB again to understand and treat coronavirus. Longitudinal blood samples are being collected from patients with varying COVID-19 disease severity to track their health over time. One goal for the project is to understand better which patients are at the highest risk of severe infection so that we can most effectively deploy medical resources. The work will also help to develop a deeper understanding of effective immune and organ system responses during the onset and recovery of affected patients.

The findings will be vital in helping to treat patients effectively and design optimal vaccines and therapies in response to viral outbreaks in the future. Similar to the SARs outbreak in 2003, this important work will also help us understand if there are any long-term health implications for those that recover from COVID-19.

Previous studies of viral infections suggest that patients’ metabolic profiles become altered during disease progression. These metabolic changes influence how a patient responds both through the degree of immune response and clearance of the virus from the lungs and other organs. Even in the early stages of the most recent COVID-19 pandemic, it was clear that pre-existing conditions such as coronary heart disease, diabetes, asthma, and hypertension have an impact on outcomes. As the virus has spread globally, no clear patterns of effects with age, gender and these pre-existing conditions have emerged. Given that therapeutic options are limited, and vaccine development remains in progress, there remain gaps in our understanding of the mechanisms of the virus to host interactions and the trajectory of the infection cycle that metabolomics studies looking across bodily systems will help reveal.

Metabolon is the industry leader in the ability to reveal metabolic perturbations across all biochemical pathways including amino acids, carbohydrates, lipids, nucleotides, microbiota metabolism, energy, cofactors and vitamins, xenobiotics, and novel metabolites. Given that metabolism represents the integration of genetic and non-genetic factors such as microbiome and lifestyle, it is a uniquely poised modality to assess health outcomes and severity of the disease, particularly relevant to understanding the trajectory of COVID-19 infection. Within this context, Metabolon has been given special dispensation by the state of North Carolina to continue all laboratory activity during this crisis due to our capacity to analyze human samples under both clinical and research environments. We are focused on maintaining our high levels of service while protecting our staff and maintaining the physical distancing protocols. We are committed to fast track mission-critical projects linked to COVID-19 research to support this vital work.

Our dedicated team of Study Directors, including experts with years of deep, infectious disease and drug development experience, are available now to address your questions, lead metabolomics study design and draft final biological interpretation reports. Please contact us today via metabolon.com/contact-us to learn how our experts may be able to support expediting your COVID-19 research.

References:

Wang, T et al. Comorbidities and multi-organ injuries in the treatment of COVID-19. The Lancet. 2020;395(10228): e52.

Wu et al. Altered lipid metabolism in recovered SARS patients twelve years after infection. Scientific Reports. 2017:7(9110).

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16Dec

Metabolon reveals a new ally for drug research and development programs: Picking program winners and demonstrating their value

December 16, 2019 Metabolon

Today’s biopharma companies are faced with extreme competition to secure support from investors, approval from regulators, and buy-in from payers. How can a company and its new molecule shine above the noise to overcome these challenges? Enter metabolomics, the study of metabolites, the small-molecule end products of metabolism collectively known as the metabolome.

Metabolon’s superior methodology and vast metabolite library allow for the capture of a complete story about your molecule at every stage, providing valuable input that builds a robust story about your data to shape a comprehensive Global Value Dossier, or what we call a Program Development Dossier.

Obstacles to clinical success

The barriers to program success for biopharma, whether success comes in the form of an asset sale or an approved therapy, are immense. The cost of securing approval for a new drug is now estimated at $2.6 billion, which has increased 145 percent over the last 10 years[1] due in large part to an extremely high failure rate. But what is at the heart of the failure rate? When scientists and biopharma leaders reflect on the source of high drug attrition rates, they cite many factors including: failure of preclinical development models to predict treatment efficacy, safety issues or difficulty differentiating between responder and non-responder patients. A commonality to all these issues and their associated risks is that critical pieces of information are lacking or unclear, leading to a foggy decision-making landscape.

How can clarity emerge from the fog?

Are there better ways to clarify decisions along the R&D continuum to increase the probability that a molecule directed against a target will have efficacy in humans without compromising safety? Typically, this information is sought through two extremes:

Profiling approaches (e.g. RNASeq, proteomics, etc.) which create overwhelming amounts of data.
Assays that were selected based on the putative mechanism and target biology.

The former is difficult to wade-through to discern meaningful information and the latter relies on picking the right markers in advance, which frequently does not occur. Therefore, data that occupies a middle ground between these extremes is needed – something that is comprehensive, meaningful and readily interpreted. Metabolomics data provides a view of these properties and helps to enrich decision making from discovery through the clinic. Hence, small and large biopharmaceutical companies are constantly on the look-out for approaches that deliver this type of data.

Solutions, elixirs, promises

Biopharma companies are routinely bombarded by technologies that promise “the next great thing” that will “revolutionize drug discovery.” Clearly some of these can help incrementally, some will have little utility and some will provide true promise. The question is, how can you discern the best options for your program? One rubric for this is to reflect on what we described above, selecting the method that will deliver clarity through the fog – data that is comprehensive, meaningful and readily interpreted, and data that dynamically assesses a living system and links it to physiological changes induced by disease and drug response. These qualities describe metabolomics data.

Metabolomics provides comprehensive and meaningful insight into each stage of the drug development process, allowing drug developers to attain signals for both safety and efficacy (as well as their associated biomarkers). This capability informs decision-making across the drug development continuum. Importantly, successes driven by these insights accumulate along the way and across programs, suggesting strong potential for metabolomics to aid in reducing drug attrition. In isolated programs, these insights help build confidence, discharge risk and illuminate the value of the program. Regardless to the challenge at hand for biopharma, the Program Development Dossier approach presents the missing link to deliver winning drug programs.

Metabolomic insights empower effective decision-making

Metabolomics enriches R&D decision-making because of the fundamental properties of what it surveys – the metabolome – all the small biochemicals (metabolites) that circulate in the body or in a cell. Metabolomics is particularly important in creating clarity for drug research and development because nearly all the influences that effect physiological processes (disease, target biology, off target activity) affect the metabolome [Image 1]. Hence, measuring with metabolomics provides a consensus report for molecule action in the context of the model being used. Finally, because the metabolome and metabolic pathways are so extensively mapped, this consensus report is readily interpretable.

Image 1:

Of course, the fundamental strengths of metabolomics are not new. What is new is that many biopharmaceutical companies have embraced the broadening view that metabolomics is a first-line tool for drug development. This video features leaders sharing their experience with metabolomics and Metabolon.

Enter the Program Development Dossier

Staples of drug research and development are to gain clarity on the effects of the target-molecule combination and obtain biomarkers that can accompany the program into clinical development. The importance of these insights and biomarkers is escalated when pursuing novel molecules and targets or entering a highly competitive space. As described above, metabolomics provides a powerful synergy to the standard data for gaining these important staples.

As companies build their Program Development Dossier – a comprehensive data package that includes detailed scientific insight into the molecule’s mechanism of action (MoA) and relevant clinical biomarkers – layering in informative data and biomarkers from metabolomics will help to build the most robust story to secure funding and move one step closer to approval. Ideally, studies begin in efficacy models, continue through preclinical development, and continue into first in human studies. In this way, the most translatable markers and understanding of the target/molecule combination will occur [Image 2].

Image 2:

Dossier data revealed through Metabolon’s proprietary untargeted metabolomics solution provides the framework to make a stronger, more confident and more valuable case for a molecule in a shorter timeframe and can travel through the drug development process. This dossier is equipped with translatable biomarkers, clarity on how the molecule/target combination is unique. With this type of Program Development Dossier, both large and small biopharma can have access to credible, highly relevant data that clearly demonstrates the potential value of their development program. In addition, this data can help organizations build an infrastructure in the form of translatable biomarkers that can be leveraged for future programs.

Some common questions that are addressed in a Program Development Dossier fueled by metabolomics are:

Which model is most translatable/relevant?
Is there a clear pharmacodynamic (PD) and efficacy signal from my animal model?
Do I have blood biomarkers for monitoring PD and efficacy, including in non-rodent models?
Will the biomarkers translate to humans?
Are there any obvious liabilities with the molecule?
Do I have the data to support a clear, compelling MoA that translates from animal models to humans?
Is the molecule likely to achieve commercial success based on the competitor landscape?
Do the biomarkers have potential for selecting sub-groups for trials or identifying responders?
What is the clearest path for the next development milestone?

Insights from the dossier

Project teams are increasingly using metabolomics to enrich their drug research and development programs to solidify stakeholder support. Importantly, this use is agnostic to the target class (e.g. GPCR, enzyme, nuclear receptor) as it is recognized that nearly all areas of target biology on route to efficacy will impact metabolic pathways. Below are several abstractions from various organizations’ metabolomics-driven Program Development Dossier initiatives. [Image3].

Image 3:

Metabolon reveals a new ally for drug research and development programs: Picking program winners and demonstrating their value

Case studies of the Program Development Dossier in action

Markers of acute renal toxicity enable screening less toxic chemotypes

While in early development of a molecule directed at a novel target for lowering cholesterol, metabolomics screening revealed markers associated with acute renal failure in mice. Conventional markers did not clearly signify the issue or explain it. The metabolomic biomarkers provided confidence that the effects were clearly off-target and these markers were used to screen other chemotypes to show that the toxicity was isolated to a particular chemotype. This insight provided a potential way forward for the program. This example highlights the peril of relying solely on established markers as they were ineffective in reflecting and explaining the toxicity. In contrast, metabolomic fingerprinting provided clarity and a way forward [REF 2 and 3].

Simple blood biomarkers for assessing response add to the development toolbox in NASH

Endpoints for nonalcoholic steatohepatitis (NASH) trials have many disadvantages including reliability. Simple blood biomarkers are highly desired. The need is amplified since there are so many novel targets in the NASH pipeline – quickly knowing if the program is heading in the right direction (i.e. hitting the target, signs of efficacy) is critical. Leveraging metabolomics, investigators at Gilead bolstered the confidence that they were on the right track in their successful phase II trial of GS-0976 by discovering several metabolite markers of efficacy. These biomarkers can accompany future development for reading out the pharmacodynamic activity or response of GS-0976 [Ref 4 and 5].

Biomarkers of acute inflammatory organ condition in early phase clinical trial delineated by metabolomics

Using metabolomics in clinical development can be beneficial to subtype individuals who respond but also may indicate who will experience adverse events. Metabolomics data showed a clear association with subjects that exhibited severe abdominal pain and inflammation of a specific organ. Over a dozen metabolite markers from two different classes – one related to the primary organ dysfunction and the rest to the activity associated with the inflammation clearly distinguished the group experiencing the adverse event. These metabolites can be used to monitor subjects in subsequent studies for early signs of the adverse event.

Distinguishing mechanism to differentiate molecule in crowded space

One route to finding molecules with novel MoAs is through phenotypic screening. One sponsor had discovered a potent angiogenesis inhibitor with similar potency to an approved drug. Despite it successfully moving through development, the MoA was unknown and Pharmacodynamic biomarkers were lacking. Metabolomics showed that the underlying MoA was clearly distinct from the approved drug and unique to any angiogenesis inhibitor described on the market. This provided a powerful leg-up in distinguishing their molecule from the competitors and identifying PD biomarkers that could accompany it into development.

Summary of what it can do

These examples highlight the importance of adding metabolomics to drug discovery and development programs. The clarifying enrichment and translatable biomarkers provided are clear assets embedded within the Program Development Dossier. The Program Development Dossier is a new companion to ultimately help pick the winners, determine how best to advance them and to demonstrate the value of the program. To learn more about how this approach can help your program read our whitepaper or or contact us today to get started.

References:

[1] DiMasi, Joseph A., et al. “Innovation in the pharmaceutical industry: New estimates of R&D costs.” Journal of Health Economics Volume 47, (2016): 20-33. https://www.sciencedirect.com/science/article/abs/pii/S0167629616000291?via%3Dihub

[2] Zgoda-Pols, Joanna R., et al. “Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists.” Toxicology and applied pharmacology 255.1 (2011): 48-56.

[3] Wang, Ganfeng, and Walter A. Korfmacher. “Development of a biomarker assay for 3‐indoxyl sulfate in mouse plasma and brain by liquid chromatography/tandem mass spectrometry.” Rapid Communications in Mass Spectrometry: An International Journal Devoted to the Rapid Dissemination of Up‐to‐the‐Minute Research in Mass Spectrometry 23.13 (2009): 2061-2069.

[4] Loomba, Rohit, et al. “GS-0976 reduces hepatic steatosis and fibrosis markers in patients with nonalcoholic fatty liver disease.” Gastroenterology 155.5 (2018): 1463-1473.

[5] Charlton, Michael, et al. ” Serum Acylcarnitines Are Biomarkers of Magnetic Resonance Imaging‒Proton Density Fat Fraction Response in NASH Patients Treated With the ACC Inhibitor Firsocostat (GS-0976).” Presented at EASL: The International Liver Congress™ 2019, April 10–14, 2019, Vienna, Austria

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11Dec

Tier 1 Metabolite Identifications: A Compass to Rich Research Insights

December 11, 2019 Metabolon

Research is like a treasure hunt. Without the right navigation tools, precious time is spent on trial and error exploration to find the right path, increasing the cost as well as the risk of program failure. Metabolomics provides uniquely valuable guidance for navigating a biological system, but the signal-to-noise ratio from vast amounts of data can cloud your ability to see the actionable insight.

Tier 1 Metabolite Identifications: A Compass to Rich Research Insights

In untargeted metabolomics, the critical step to separating noise from insight is accurate metabolite identification (often also called annotation). As pointed out by Schrimpe-Rutledge et al, “metabolite annotation is the crucial link between acquired data and meaningful biological information.”

Phrased another way, the biological insight from a study is only as good as the metabolite annotation – if you want the highest quality metabolomic insight you need the highest quality annotation. In recognition of this fact, Sumner et al proposed a schema for stratifying the quality of metabolomic annotations. Tiers, or levels, ranging from 1 to 5 are commonly used to convey metabolite identification confidence. Tiers refer to the level of detail for each metabolite, and thus the assigned confidence for accurate identification. Tier 5, the lowest level of identification, offers a unique feature in the metabolite, but lacks the information required for confirmation. Through increasing precision measurement, additional unique characteristics are discoverable enabling a definitive identification of the molecule to be made. It is not until the appropriate level of detail is reached in Tier 1 that definitive compound identification based on multiple orthogonal measurements and comparison to data from an authentic standard can be achieved. Therefore, Tier 1 identifications represent the highest level of confidence in the annotation. Tiers 2 through 5, on the other hand, represent decreasing levels of confidence based on less rigorous or more ambiguous criteria.

While most metabolomic practitioners operate primarily with annotations that only meet the standards of Tiers 3-5, Metabolon’s Precision Metabolomics™ workflow is uniquely designed to deliver Tier 1-2 identifications for detected metabolites. This unique level of confidence in the annotations is made possible by Metabolon’s use of a chemocentric approach to metabolomics that uniquely detects metabolite features and matches them against Metabolon’s vast in-house library. Metabolon built this library through the analysis of >5,000 authentic standards run in-house using our methods and instruments. This approach to untargeted metabolomics means that all metabolite annotations meet the stringent criteria required for a Tier 1 or 2 identification.

Our method stands in stark contrast to the more traditional metabolomics workflow in which the individual ion features, the instrument signal from a mass spectrometer, undergo statistical analysis. Only the most significant are subjected to an attempt at a high Tier identification. Metabolon’s approach leverages this vast library to ensure accurate annotation of not only the metabolites, which show statistically significant changes in a study, but also those which remain unchanged, and therefore add crucial insight into the underlying biology.

The field of untargeted metabolomics continues to expand due to its growing track record of providing a crucial understanding of biological processes including aging, disease, and the role of the microbiome in health. Metabolon sets the standard in delivering high-quality metabolomic data. By harnessing the power of our extensive library and delivering metabolite measurements with Tier 1 identifications, Metabolon leads the way to unlocking the information stored in the metabolome and revealing the contained biological story.

To learn more about how metabolomics can help you uncover actionable insights with our Precision Metabolomics platform, contact us at hello@metabolon.com.

References:

Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, Mclean JA. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. Journal of The American Society for Mass Spectrometry. 2016;27(12):1897–905.

Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007Dec;3(3):211–21.

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18Nov

Metabolomics Brings New Insights to Type 2 Diabetes Research

November 18, 2019 Metabolon

Diabetes, a serious metabolic condition that results from higher than normal blood sugar levels, is a nationwide epidemic. According to the Diabetes Research Institute Foundation, more than 30 million Americans and 422 million people worldwide have been diagnosed with diabetes.

Type 2 diabetes (T2D) is the most common form of diabetes representing about 90% of diabetic cases, according to the Diabetes Research Institute Foundation. While adults are especially at risk for T2D, the disease can also affect children and young adults. T2D causes an impaired response to insulin; blood sugar levels are not adequately controlled, resulting in too much glucose in circulation and not enough glucose in tissues. Measuring changes in metabolites – small molecules present in the blood – using metabolomics could provide clues into how the disease develops and, subsequently, new treatments. Metabolomics enables simultaneous assessment of hundreds of compounds present in living systems, making it a critical tool that advances our understanding of disease mechanism and opens doors to identify novel targets and treatments.

Metabolon scientists are hard at work searching for new insights to aid in understanding mechanisms contributing to the development of T2D. Most recently, our technology contributed to the identification of a key gut microbiome-derived compound linked to insulin resistance. The study by Koh et al., identified an important connection between microbially produced compound and insulin resistance that helps us better understand how the gut microbiome affects their host’s physiology.

The human body is home to trillions of microorganisms known as the microbiome that live on our skin, in our mouth, nose and gastrointestinal tract. These microorganisms are vital to human health as they produce vitamins, contribute to food digestion and prevent pathogen colonization. Numerous studies have reported that diseases such as obesity and diabetes are associated with altered gut microbiota community structure. To identify factors that may be linked to abnormal activity of gut bacteria and trigger changes contributing to these metabolic diseases, in the Koh et al., study metabolomics was performed on plasma from subjects with T2D. Metabolomic profiling of these samples revealed that the microbiome-derived metabolite, imidazole propionate (ImP), was elevated in patients with T2D. Additionally, ImP-exposed mice showed impaired glucose tolerance. Researchers were able to mechanistically identify that ImP inhibited insulin signaling at the levels of insulin receptor substrate, which occurred through activation of mTORC1pathway. The damaged insulin signaling contributed to insulin resistance.

This work identifies an important connection between microbial activity in the gut, metabolite signaling and effects on host physiology. Metabolomics has provided another puzzle piece that will help researchers better understand how diabetes develops and how it can be treated. Improved understanding of gut microbiota metabolism linked to generation of ImP may also reveal new targets and lead to new treatments aimed to improve insulin signaling.

This study is just one example of how metabolomic profiling supports evolving perspectives by providing biological insights that drive scientific advancement. Metabolomics reveals biological insights otherwise unseen. By providing a better understanding of biology, we can unravel the mystery of human health and disease, and with a global capacity to conduct research and compare data, Metabolon can make connections where others can’t.

Disease processes are complex and we need new understandings to drive discoveries. These groundbreaking innovations can only occur through a deep understanding of the manifestation of living system – something only Metabolon can provide.

To learn more about how partnering with Metabolon can help you uncover actionable insights contact us at hello@metabolon.com.

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13Nov

Adding Metabolomics to Genomics to tackle challenges in Lung Cancer

November 13, 2019 Metabolon

The field of lung cancer research has experienced many breakthroughs in recent years, particularly in precision medicine where oncologists can match genetic mutations to targeted therapies. However, many perplexing questions remain. Why does cancer impact some people and not others? Why do some people respond well to treatments and others don’t? For all the progress, we still have a long way to go.

Over the past two decades, numerous advances in lung cancer treatment have been achieved through genomics. And, while this science has made significant headway, some of the biggest current challenges in lung cancer may be well served by augmenting genomics with other approaches. Because metabolites both influence and are influenced by genetics, proteins and microbiomes, metabolomics can be used in conjunction with genomics to create a more complete understanding of health and treatment response. Specifically, metabolomics is poised to help drive solutions to questions such as: Which people will respond to immunotherapies like immune checkpoint blockade (ICB) – and why others don’t? How do we best understand treatment resistance (particularly in more rare types of lung cancer)? And, how can cancer be detected at earlier stages when the treatments are more tractable?

Notably, although many patients experience durable responses to ICB, the majority do not respond. While the precise factors and predictive biomarkers have remained elusive, some clues exist. In particular, the microbiome has been convincingly linked to response to immunotherapies such as immune checkpoint blockade including in certain types of lung cancers[1].

However, the precise mechanisms and even specific microbial strains driving differences in response to immunotherapies remain elusive. Given that metabolomics is increasingly being recognized as the pivotal approach for determining microbiome function, its use will likely be key to finding biomarkers in response/non-response immunotherapy.

In the context of lack of response to immune checkpoint blockade and determining the factors that may limit or drive response, another layer to consider is what’s happening beyond the tumor. Here is where the branches of genomics and metabolomics can really come together and work in harmony to uncover more insights. What’s happening in the person’s immune system overall is just as important as what’s happening in the tumor. Metabolomics can help us understand both of these activities. Metabolites in the circulation or the tumor microenvironment will likely be important when combined with gene signatures or profiles of the immune cell types that are present in the tumor.

Additionally, metabolomics provides implications for early detection of cancer by combining with other liquid biopsy approaches. In the future we may be able to study the microbiome of patients and see who may be a good candidate for certain therapies as well as who may be most at risk for developing cancer. Or, a circulating metabolite, combined with other diagnostic criteria may help to identify cancer at an earlier stage of the disease.

With cancer, early detection often leads to the best results for curing the disease, but small-cell and non-small-cell cancers are often detected late.

A recent study involving lymphangioleiomyomatosis (LAM) provides a powerful example of how metabolomics can advance lung disease research. LAM is a slow metastasizing neoplasm that is typically due to tuberous sclerosis complex 2 (TSC2) gene mutations resulting in mTORC1 activation in proliferative smooth muscle–like cells in the lung. Metabolon collaborated with researchers at Harvard Medical School to better understand these drivers and, through metabolomics, identified a signature that served as a biomarker. Researchers were able to target the disease based on that signature and identified a novel therapeutic target for lung disease – TSC2 as a negative regulator of COX-2 expression and prostaglandin biosynthesis. This approach, could be applied to lung cancers to better identify drivers of disease, especially in different subtypes such as non-responders.

There is still much to learn about lung cancer and related therapies. Metabolomics reveals biological insights otherwise unseen by other ‘omic technologies, and by combining this powerful technology with the great insights we’ve already seen through genomics, we can accelerate our understanding of lung cancer, and thus, its treatment.

Metabolon can help you take the research to even deeper levels and uncover more actionable insights. Contact us at hello@metabolon.com to get started.

References:

Routy B, Chatelier EL, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors. Science. 2017Feb;359(6371):91–7.

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23Oct

Charting a Path to Chronotherapy: An Atlas of Circadian Metabolism

October 23, 2018 Metabolon

A circadian rhythm is any biological process driven by endogenous cellular clocks that follows an oscillation of about 24 hours. Disruptions in circadian rhythms resulting from our modern lifestyle are associated with familiar afflictions ranging from jet lag to mood and sleep disorders.

The ability to anticipate daily fluctuations in the environment is a critical adaption across all living organisms – our waking (and sleeping) world revolves around the circadian cycle of the Earth’s rotation. While light exposure is a dominant signal for synchronizing circadian clocks with the environment, post-industrial technology has loosened restrictions on human activity that were previously imposed by the solar day. The resulting shift in sleep-wake cycles parallel alterations to the timing of physical activity and dietary intake, which are potent zeitgebers in their own right. Disruptions in circadian rhythms resulting from our modern lifestyle are associated with familiar afflictions ranging from jet lag to mood and sleep disorders.

The correlation of these cycles with human health and disease is well documented, and recent discoveries have reinforced their biological importance. In 2017, the Nobel Prize in Physiology or Medicine recognized Jeffrey C. Hall, Michael Rosbash and Michael W. Young “for their discoveries of molecular mechanisms controlling the circadian rhythm,” including the identification of multiple proteins involved in the activation and synchronization of known clock genes.¹

Unraveling the mechanisms driving circadian rhythms affirms their central role in physiology, and it has sparked further research to determine how external factors that interfere with our clocks might also influence the pathology of disease. Lifestyle risk factors such as diet and exercise are believed to play a role in the susceptibility of diseases such as diabetes, obesity, cardiovascular disease and cancer. Could the elevation or reduction in disease risk be mediated through our internal biological clocks?

The task of describing a phenotype associated with the oscillation of a specific biomarker is complex, and it requires monitoring an elaborate network of metabolites across multiple tissues at various intervals of the circadian cycle. Recently, Paolo Sassone-Corsi’s group at the University of California, Irvine used a global metabolomics approach developed by Metabolon to study circadian metabolism in a range of mouse tissues under varying nutrient conditions.² Dyar and Lutter, et al. examined the metabolic effects of a high-fat diet (HFD) to study how chronic nutrient stress affects naturally oscillating metabolic processes. This work leverages the study of metabolomics as a critical tool to better understand cellular physiology and to reveal connections between external inputs and pathology. The simultaneous evaluation of a comprehensive panel of multi-tissue metabolites – over multiple time intervals, and under varying environmental conditions – allows a glimpse of the communication pathways between various organs that are essential to whole-organism homeostasis. In this case, the combined power of metabolomics with sophisticated analysis tools provides novel insight into the underpinnings of metabolic processes linked to phenotypic differences in mice, and it validates the use of this approach to support new discoveries in human health and disease.

Temporal and tissue-specific metabolite signatures: The effects of nutrient stress
Several recent studies by the Sassone-Corsi team highlight the effect of nutritional abundance on circadian metabolism and demonstrate its relevance to the development and management of metabolic disease.³ Building on their previous work, they used a systems biology approach to examine several tissues in the context of energy balance. By comparing the patterns of metabolism under a normal chow diet with conditions of nutrient stress imposed by HFD, they assembled a spatial and temporal atlas of circadian mouse metabolism. The atlas maps hundreds of circadian metabolites, revealing the metabolic connections that control daily oscillations in processes that are often mediated by distal organ systems. Furthermore, the study showed that external factors such as chronic nutrient stress can alter communication and coordination between tissue clocks, resulting in metabolic changes associated with pathology.

Detection included a wide range of metabolite classes from 8 tissue types (i.e., serum, liver, skeletal muscle, brain, brown and white fat, and sperm). Alterations in the relative abundance of several metabolites were characteristic of known tissue-specific pathology. For example, carbohydrates comprised 53% of total altered liver metabolites of mice fed normal chow compared with only 8% in the HFD group. Lipid metabolites exhibited an inverse proportion, with 11% altered in mice on normal chow versus 52% in HFD-fed mice. The accumulation of lipids in liver relative to carbohydrates is suggestive of HFD-induced hepatic steatosis and may have relevance to the progression of NASH. A similar shift in lipid accumulation in skeletal muscle, a prominent glucose sink, suggests the potential for development of insulin resistance. Supporting these observations, several epidemiological studies have correlated an increased risk for insulin resistance and fatty liver with night shift work.^{4, 5}

To create a visual atlas of the metabolites under study, the group applied algorithms that plotted the significant temporal correlations according to metabolite class and tissue type. The resulting atlas revealed both temporal and tissue-specific signatures of metabolic pathways over the 24-hour cycle. When examining correlations according to metabolite class, serum lipids showed the greatest degree of synchronization with other metabolites under normal chow, consistent with a role for the vasculature in integrating biochemical networks. However, under HFD nutrient stress, these correlations were lost or significantly reduced, affirming the impact of energy balance on circadian misalignment.

Bench to bedside: Actionable insights
In addition to elucidating key spatial and temporal elements of energy metabolism, the work by the Sassone-Corsi team provides a model for examining the relationship between other external factors and normal coherent networks across tissues. Their results evoke the rallying cry of translational science – how might these observed differences in metabolism be converted to clinical interventions? The proposed model is not limited to examining the effect of nutritional behavior on metabolic disease, or other behavioral interventions such as exercise. Integrated analysis of the circadian metabolome using tools like Metabolon’s global metabolomics platform offers potential for further discovery in disease pathways to reveal novel biomarkers and therapeutic targets, as well as fine-tuning clinical diagnostics.

Diagnostic measures and drug dosing are typically scheduled irrespective of circadian metabolism. Constraints of the clinician’s timetable, ensuring appropriate time intervals between medication doses, or the requirements of sample collection (e.g., fasting plasma or morning urine) dictate scheduling rather than coordinating with biological clocks. Many commonly prescribed drugs work by targeting the products of circadian genes, and since their half-lives are often less than 6 hours, timing of administration might have a significant impact on their action or influence potential side effects.⁶ Metabolite comparisons across multiple tissues may provide insight that has been missing from studies of single biomarkers that represent only one tissue at a specific time point of the circadian metabolome.

The relationship between coordination of peripheral clocks and pathology remains mostly unknown, but the circadian atlas presented by Dyar and Lutter, et al. demonstrates how global metabolomics is beginning to fill this information gap. Exploiting known oscillations might permit actionable insights including optimization of other external behaviors, improving the accuracy of diagnostics, and targeting specific time points to administer therapeutics. In the future, this same approach could be used on human samples to unlock biological discoveries hidden within the temporal dysregulation of metabolic processes, and provide insights to develop personalized chronotherapy.

[1] Press release, NobelPrize.org. Nobel Media AB 2018. Updated 2018. https://www.nobelprize.org/prizes/medicine/2017/press-release. Accessed October 10, 2018.

[2] Dyar KA, Lutter D, et al. Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell. 2018;174:1571-1585. doi: 10.1016/j.cell.2018.08.042

[3] Asher G, Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell. 2015; 161:84-92. doi: 10.1016/j.cell.2015.03.015

[4] Guo Y, Rong Y, et al. Shift work and the relationship with metabolic syndrome in Chinese aged workers. PloS One. 2015; 0120632. doi: 10.1371/journal.pone.0120632

[5] Konturek PC, Brzozowski T, Konturek SJ. Gut clock: implication of circadian rhythms in the gastrointestinal tract. J Physiol Pharmaco. 2011; 62:139-150.

[6] Zhang R, Lahens N, et al. A circadian gene expression atlas in mammals. PNAS. 2014; 111:16219-16224. doi: 10.1073/pnas.1408886111

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10Sep

Metabolomics Approaches for NASH and NAFLD R&D

September 10, 2018 Metabolon

Metabolomics gives us valuable methods to compare preclinical model trials to human models of NASH. In this post, we share a case study with Gilead Sciences. More study is needed, but metabolomics may one day prove to outperform more invasive methods of assessing liver fibrosis, revealing opportunities to incorporate metabolomics into diagnostic tools.

Nonalcoholic fatty liver disease (NAFLD) is a condition in which fat builds up in the liver. Nonalcoholic steatohepatitis (NASH) is a type of NAFLD, and if you have NASH, you have inflammation and liver cell damage, along with fat in your liver. With an estimated 60 to 80 percent of the obese population suffering from fatty liver disease, NASH and NAFLD range across a spectrum of severity, with the worst cases frequently leading to the most common type of liver cancer: hepatocellular carcinoma.

Currently, there are no treatments approved by the U.S. Federal Drug Administration (FDA), leaving liver biopsy as the “gold standard” diagnostic and liver transplantation as the only remedy for treating these diseases.

“The use of human cohorts allows us to compare and contrast the metabolic signature to the preclinical models, which helps identify similarities,” said Kari Wong, PhD, a senior study director at Metabolon. “This can be helpful in the identification of biomarkers or even mechanism and natural history of disease.”

The role of metabolomics in the study of NAFLD and NASH is instrumental in the identification and assessment of metabolites. Metabolites have been used throughout history 1) as a method of assessing health, 2) aiding in the understanding of tissue function throughout the 20th century, and 3) are used in present-day clinical settings where metabolites, such as glucose and cholesterol, provide insight into an individual’s current health.

Dysfunctional metabolism lies at the center of NASH/NAFLD and includes lipid metabolism, cholesterol metabolism, inflammation, redox status and mitochondrial function, which can be assessed via metabolic screening.

As a global leader in metabolomics, Metabolon uses its proprietary Precision Metabolomics™ technology to provide a more holistic approach due to the ability to screen for thousands of metabolites in just one biological sample. The significance of this approach is apparent in the success experienced during Gilead’s utilization of metabolomics in its experiments using an acetyl CoA carboxylase inhibitor in a well-established animal model.

Hurdles to studying NASH

An unclear collection of risk factors, complicated by multiple molecular pathways, are involved in the progression of NASH. These risk factors include external inputs like diet, lifestyle, and environment, as well as others including genetic factors, family history and ethnicity. The molecular pathways involved include derangement in lipid metabolism, alteration in oxidative stress, changes in microbiome metabolism and inflammation.

Other hurdles include a lack of robust preclinical models generated by manipulating genes and diet either alone or in combination. The translatability of these models also remains in question.

The use of metabolomics in Gilead’s study of NASH/NAFLD in animal models

Gilead’s use of metabolomics provides a good example of how preclinical research can be applied in the study of NASH. Using two different mouse models Gilead has been able to investigate steatosis and fibrosis separately with the intention of understanding both the mechanism of disease as well as candidate treatments.

During a recent webinar on metabolomics, Jamie Bates, PhD, research scientist at Gilead Sciences, said, “Our strategy on the preclinical side, is to evaluate mechanisms in these two animal models and also understand the mechanism of action a little bit better with metabolomics and transcriptomics.”

The preclinical studies featured a model of mice treated with fast food diets and then treated for five to six months with liver-targeted acetyl CoA carboxylase inhibitor (ACCi) — an essential enzyme in de novo lipogenesis (DNL). The control group was age matched and fed a normal chow (lean) diet.

Overall findings showed that inhibition of ACCi allowed for hepatic steatosis, while improving the function of mitochondria within the cell and reducing redox stress. These findings showed that the fast food diet induced hepatic steatosis which was at least partially rescued by treatment with ACCi. In addition, levels of membrane phospholipids, including phosphatidylethanolamine (PE) and phosphatidylcholine (PC), were reduced in the NASH group (consistent with NAFLD and NASH patients ), and both PEs and PCs were reduced in the fast food diet model and increased upon ACCi treatment.

Going forward

Metabolomics gives us valuable methods to compare preclinical model trials to human models of NASH. More study is needed, but metabolomics may one day prove to outperform more invasive methods of assessing liver fibrosis, revealing opportunities to incorporate metabolomics into diagnostic tools.

[1] Puri P et al, Hepatology 2007 46:1081

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16Apr

Research shows that acetaminophen has unexpected effects on regulation of sex hormones

April 16, 2018 Metabolon

A new study showed the potentially negative impact of the widely used, over-the-counter drug acetaminophen (paracetamol) on sex hormones, which has significant implications for reproductive health. It also highlights the power of genomic and metabolomic profiling to better understand drug mechanisms of action and metabolism even in common drugs such as acetaminophen.

A new metabolomics and genomics-informed study conducted by researchers at Human Longevity, Inc. (HLI), Metabolon, University of California at San Diego, JCVI and King’s College shows that use of acetaminophen (Tylenol^®) has unexpected effects on the regulation of sex hormones. The study was recently published in EBioMedicine.

The study showed the potentially negative impact of the widely used, over-the-counter drug acetaminophen (paracetamol) on sex hormones, which has significant implications for reproductive health. It also highlights the power of genomic and metabolomic profiling to better understand drug mechanisms of action and metabolism even in common drugs such as acetaminophen.

The researchers first looked at 455 active adults over age 18 who were divided into two groups—a 208-person machine-learning training set and a 247-person test set. As a validation set, the team included 1,880 European ancestry twins from the TwinsUK cohort and 1,235 individuals of African American and Hispanic ancestry from the Insulin Resistance Atherosclerosis Study. Metabolomic analysis of more than 700 metabolites was conducted by Metabolon.

The team identified depletion of sulfated sex hormones associated with acetaminophen use in all the study populations. The results showed that use of acetaminophen is roughly equivalent to 35 years of aging on sulfated hormone levels, which could affect placental health and general reproductive health. While there are many factors that go into reproductive health, this new study identified an area of acetaminophen’s effects that weren’t previously known, offering some insight into previous studies suggesting that acetaminophen exposure during gestation may impact development of masculine and behavioral characteristics.

These sulfated sex hormones, also referred to as neurosteroids, have many effects in the brain. There are no currently approved drugs that are specifically designed to target neurosteroid metabolism. Drugs that target this pathway could potentially treat many diseases of the nervous system, for example chronic pain and depression.

“These findings are significant for they showcase how the body is impacted by seemingly innocuous everyday medications like Tylenol,” said Amalio Telenti. “There are hundreds of other drugs that no one has done this research for. We delineate a general strategy that should be applied broadly in the study of medications in common use.”

First author Isaac Cohen, PharmD candidate at UC San Diego, added, “This study reveals significant new opportunities for pharmaceutical research to better understand common medications and their effects on human health and patient safety. Our research also emphasizes how the integration of genomics and metabolomics should enable better outcomes for clinical trials.”

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28Feb

Why do some people respond to cancer therapy while others do not?

February 28, 2018 Metabolon

Why do some people respond to a cancer therapy and some do not? While cancer is a genetic disease, response to therapy is not as systematic or clear-cut as simply matching a mutation to treatment. Other factors are clearly involved. Recent work in both mice and humans suggests that one answer may involve the microbiome.

Martin Hornshaw, PhD
Director, Scientific Marketing — Metabolon, Inc.

Respond or not respond, that is indeed the therapeutic question.

Microbiomes

What might the difference be that enables a positive response to treatment? One possibility is that patients’ gut microbiomes (the community of microorganisms living in the gut) might differ and in a way that affects their response to therapy. Indeed, cancer patients do have differences in their microbiomes that correlate with responses to immunotherapy as recently described in two papers in Science. A third paper also looks at microbiome differences in cancer patients, but the authors performed global metabolomics to explain mechanistically what might be different between those who respond and those who do not respond to treatment. The work in these papers suggests the gut microbiome as a possible route to enhancing cancer immunotherapy.

Matters of the gut
Some general conclusions from two of the studies were that there were significant differences in the diversity and composition of the gut microbiome of patients who responded well to treatment (responders) compared with those who did not (non-responders) and that taking antibiotics during treatment negatively affects outcomes. Wargo et al.¹ identified that systemic and anti-tumor immunity were enhanced in responders who had the “favorable” gut microbiome and in germ-free mice with cancer who had received fecal transplants from responders. They further observed impaired immune response in mice that had received fecal transplants from non-responders. Perhaps a better way of looking at this is that resistance to treatment was caused by an abnormal gut microbiome composition, which likely affected the ability to mount an immune response.

The second team led by Laurence Zitvogel² found that patients on antibiotics, which disrupt the gut microbiome, did not live as long. They also transferred the gut microbiome by fecal transplant from responders to germ-free mice with cancer. These mice fared better on treatment than corresponding mice who received fecal transplants from patients who did not respond well to therapy. Zitvogel’s team took things a step further by studying oral supplementation with bacteria they had noted were enriched in responders, such as Akkermansia muciniphila. The Akkermansia ‘treatment’ resulted in an enhanced immune response into the tumor beds, the normal tissue where the tumors make their horrible home.

Of note is that the first study was in melanoma patients and the second in patients with epithelial tumors such as lung, kidney and bladder cancers. We can conclude that the composition of your gut microbiome seems to be incredibly important as to whether you respond to immunotherapy for at least several types of cancer.

Adding metabolomics

What was not studied in these two very significant papers was the metabolome. Notably, the metabolome has become a valuable tool for understanding microbiome-associated phenotypes across a host of areas. How the microbiome, through the metabolome, might relate to clinical outcomes in cancer immunotherapy brings me to another study.

Frankel et al.³ described some intriguing, but preliminary, observations in 39 metastatic melanoma patients who either responded favorably to treatment or progressed to disease.

Cancer checkmate? Not quite, checkpoint.

As with the first two papers described above, this study sought to investigate the clinical responses to immune checkpoint therapy (ICT) and understand why ICT, which stimulates the immune system, sometimes show great efficacy, but only sometimes. Associations of clinical responses (responder/non-responder) to the different treatments with gut microbiota taxonomy profiles, gut metabolite levels and patient dietary and antibiotic histories were evaluated. Each patient had metagenomic shotgun sequencing performed on fecal specimens collected prior to treatment. Aliquots of the same fecal samples were also analyzed by global metabolomics.

Differences in microbiomes and metabolites and response to checkpoint inhibitors

In terms of differences in microbiome, there were subtle differences between responders and those who progressed. All ICT responders had a microbiome enriched with the same species, Bacteroides caccae, for example. There were also gut microbiome differences in terms of enriched bacterial species between the different regimens of treatment. The study was too small to address the impact of antibiotics.

In addition to differences in gut microbiomes, the researchers found significant differences in responders’ metabolites. By far, the greatest difference was in a plant xenobiotic 15:2 anacardic acid observed in the fecal samples, which is an alkyl derivative of the plant hormone salicylic acid. In ICT responders, it was greatly elevated compared to those who suffered progressive disease.

Anacardic acid is not known to be a bacterial metabolite, and as far as we know it comes from plant-based products. It is interesting that five of the six patients with the highest levels of anacardic acid reported consuming cashews, which are known to produce anacardic acid (mango does, as well). It has been noted that anacardic acids stimulate immune cells, neutrophils and macrophages, which may then go on to recruit T-cells to tumor metastases and thus enhance ICT. This gives one plausible mechanism for why some patients respond to ICT and others do not. Of course, there could be more than one mechanism.

We have a way to go, but these results are intriguing.

It is obvious that these results warrant further investigation, not just in terms of repeat studies, but also as to a possible mechanism for gut microbiome action in stimulating the immune system to attack cancer cells. In addition, the surprising and unexpected finding of highly elevated levels of anacardic acid in responder gut samples merits study as a potential therapeutic intervention.

Global metabolomics should be performed with the goal of identifying and quantifying a wide range of metabolites derived from host metabolism, the microbiome, diet and so on. If the researchers³ had only performed targeted assays of known microbial metabolites, such as secondary bile acids or short chain fatty acids, this discovery would not have been made.

Interestingly, Wargo et al. did attempt an indirect analysis of metabolism using whole genome sequencing data of the gut microbiome metagenome¹. They inferred from that analysis that there were likely differences between responder and non-responder microbiomes metabolically. However, they did not perform direct analyses of the global metabolome of the gut, which would have been the best way to assess differences in the metabolomes of responders and non-responders.

Similarities & differences

While there were similarities, for example the two studies involving melanoma^1,3 both identified that Faecalibacterium were enriched in some responders, all three studies identified different bacteria as being enriched in the gut microbiomes of responders. Why? What does this mean? Is there commonality in the metabolomic output of these different bacteria, or are they working differently in terms of an immune-stimulatory effect? Does any healthy gut microbiome prevent dysfunction of the gut barrier that might then prevent immunosuppression? Are there many or few bacterial routes to positive impact on cancer treatment of the gut microbiome?

There are many interesting and important questions to answer and routes to positive microbiome health to be explored. A metabolomics approach that studies a breadth of metabolism generally gives very strong insight into fundamental biology. This will likely uncover clues as to what is similar and what is unique about the gut metabolism and immune-response of these cancer patients, which may one day lead to improved treatments. I am extremely encouraged that we may soon see more effective cancer treatments based on a combination of immunotherapy plus microbial and/or metabolite supplements.

References
1.   V. Gopalakrishnan et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients Science (2017) epub Nov 2 DOI: 10.1126/science.aan4236
2.   B Routy et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors Science (2017) epub Nov 2 DOI: 10.1126/science.aan3706
3.   AE Frankel et al. Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Therapy Efficiency in Melanoma Patients. Neoplasia (2017) 19, 848-855

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