Researchers have discovered a new clue in explaining how cancer cells with identical genomes can respond differently to the same therapy. In a Nature Communications paper published on 21st March 2019, researchers reveal for the first time that the number of mitochondria in a cell is, in great part, associated with how the cancer responds to drug therapy.

Cancer is the second-leading cause of mortality worldwide, with approximately one in six deaths across the globe attributed to the disease. While treatments for cancer continue to improve as technology advances, researchers and clinicians have been unsuccessful in explaining the diversity of responses in cancer cells to treatments of oncological disease.

In many cases, cancer cells with matching genetic makeup will respond differently to the same treatment. Mount Sinai and IBM researchers combined computational and biological methods to uncover a clue to this behaviour.

Cells die when met with bacteria, malnourishment, or viruses. Our bodies also eliminate billions of cells each day – a process known as “programmed cell death” or apoptosis – to promote normal function. Mitochondria, often referred to as the powerhouse of the cell because of their ability to produce cellular energy, can also act as a catalyst in the activation of programmed cell death, and certain anti-cancer drugs work by activating this process.

This function encouraged researchers to explore the hypothesis that cancer cells with identical genetic makeup, but different quantities of mitochondria, may have varying susceptibility to death if exposed to the same drugs that promote apoptosis.

In exposing various types of cells to six concentrations of a pro-apoptotic drug and measuring the abundance of mitochondria within the surviving cells, the researchers discovered that surviving cells had a greater amount of mitochondria than untreated cells. This strongly suggests that cells with fewer mitochondria are more likely to respond to certain drug treatments.

To analyse this data, researchers used a mathematical framework called DEPICTIVE (an acronym for DEtermining Parameter Influence on Cell-to-cell variability Through the Inference of Variance Explained) to quantify variability in the survival or death of cells due to mitochondrial abundance. Overall, the framework determined that the variability of mitochondria explained up to 30 percent of the varying responses to the pro-apoptotic drug.

“Enhancing our understanding of the relationship between mitochondria variability and drug response may lead to more effective targeted cancer treatments, allowing us to find new ways to tackle the problem of drug resistance,” said Pablo Meyer, PhD, Adjunct Assistant Professor of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, Team Leader of Translational Systems Biology at IBM Research, and co-corresponding author of the publication. “The outcomes of this study were truly multidisciplinary, and only made possible by the strong scientific collaboration established between Mount Sinai and IBM.”


Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the US, with approximately 16 million Americans currently affected. 

Researchers at the University of Michigan have reported on the ability of Parametric Response Mapping (PRM), a relatively new technique, to identify small airway abnormality in COPD.

Currently, it is difficult to identify abnormalities in small airways non-invasively, as the tiny bronchioles that are initially damaged are around two millimetres in internal diameter, and as such are too small to be imaged using computed tomography. 

PRM was developed at Michigan Medicine, in a study led by Dr Brian Ross, Professor of Radiology and Biological Chemistry, and Dr Craig Galban, Associate Professor of Radiology. The technique measures lung density during inhalation and exhalation. 

Using lung tissue from patients undergoing transplantation, and donated healthy lung tissue, researchers mapped the samples back to CT scans taken before surgery. The team was able to non-invasively identify small airway loss, narrowing and obstruction.

Senior author Dr MeiLan Han, a lung specialist and Professor of Internal Medicine at the University of Michigan, said, “Now we have confidence in our ability to identify airway disease when imaging COPD patients. PRM is already clinically available and used by University of Michigan clinical teams to assess patients with COPD. This is what we mean by bench to bedside medicine.”

The study was conducted in patients with a severe form of the disease. A NHLBI funded study, COPDGene, found that the PRM-defined small airway abnormalities have been detected on CT scans of patients with milder disease and could help to predict patients who will lose lung function.

Prof Han said, “We still need to validate the type of airway disease the PRM technique identifies in patients with milder disease. That type of lung tissue is more difficult to obtain, but we are working on techniques that would allow us to use smaller amounts of lung tissue to make such studies feasible.” 

The study was published in the American Journal of Respiratory and Critical Care Medicine.


Drug repurposing can often reduce the time and cost it takes to discover and develop a new drug, especially for novel antimicrobial therapies.

Helicobacter pylori is a Gram-negative, helically shaped, stomach pathogen associated with human gastric mucosa. After entering the body, they can cause chronic gastritis, peptic ulcers and gastric carcinoma.

Gastric cancer is developed by around 2.9 percent of people affected by H. pylori, and with some studies suggesting a link between the pathogen and colorectal cancer, it is important that an effective treatment is found.

Researchers in the Infectious Diseases Division at the Warren Alpert Medical School of Brown University, conducted a pilot screening using a broth micro-dilution assay they developed through a previous high-throughput screening method. 

The team found that anthelmintics, such as niclosamide, oxyclozanide, rafoxanide, and closantel, inhibited the growth of the pathogen strain 60190. 

The use of multiple antimicrobial agents can decrease bacterial resistance and re-establish the clinical efficacy of certain antibiotics. Describing how this can occur, the researchers mentioned how the compounds have an inhibitory role on adhesion, with niclosamide inhibiting the secretion of IL-8, despite acidic conditions where it remained stable.

Dr Nagendran Tharmalingam was the first author of the study.

The team used a number of methods in their analyses, including scanning electron microscopic observations, light microscopy, cytotoxicity testing and in vivo efficacy. Antibacterial susceptibility, kinetic, adhesion and invasion, motility and gene expression assays were also conducted, with researchers also investigating the emergence of resistance and membrane potentials.

Using a Two-way ANOVA, followed by a Bonfererroni post-test, the team carried out statistical analyses of their data.

The research team found that niclosamide administration decreased transmembrane pH, indicating that that anti-pathogen activity was related to the disruption of its proton motive force. Niclosamide was effective in infection models, and could be developed further to fight infection by the pathogen. 

Results should be confirmed using more strains of the pathogen.

The study was published in the journal Scientific Reports.


University of Otago scientists have discovered a way to view the immune cell ‘landscape’ of bowel cancer tumours, paving the way towards more individualised medicine and treatment for many other diseases in future.

The scientists have shown the incredible diversity of immune cells that are inside a colorectal tumour. Immune cells are known to protect against cancer growth and this work provides new information on the types of cells present and how they might be beneficial for the patient.

Lead researcher Associate Professor Roslyn Kemp explained they are using a new technology called high-dimensional mass cytometry to identify cells in the tumours of people with bowel cancer.

“It can be thought of as taking a higher resolution ‘photo’ of the inside of the tumour. The photo may reveal new types of cells that may or may not be targetable by drugs, or reveal different composition of immune cell populations in individuals that could be used to predict the course of the disease.”

Associate Professor Kemp said results of their study have shown there is huge diversity in the type of immune cells that infiltrate the tumour, which means that any one, or more likely a combination of many immune cells, could have an effect on patient outcomes.

She mentioned how the technique could be used to study a number of different diseases.

“It demonstrates the use of a new technology to study the immune response in much more detail than other methods currently used, providing new types of information for patients,” Professor Kemp explained.

“It is a step towards personalised medicine, sometimes referred to as precision medicine, since each patient’s tumour could be looked at with this amount of detail.”

The next step is to carry out a similar study with a slightly different technique to further investigate where all the cells are in the tumour and how that might affect cell function and relationships between types of cells and patient data like stage of disease and patient survival.

The paper was recently published in the Journal of Immunology.


Researchers have demonstrated how machine learning can analyse sequences of proteins providing a wealth of information on the structure of proteins, their function and their evolutionary features.

Sequences of molecules, called amino acids, make up proteins. These amino acids determine the function and structure of the protein, however determining which areas of the sequence is responsible for various properties is challenging.

“Answering this question could have significant implications for pharmaceutical development,” explained co-author Dr Jérôme Tubiana, former PhD student in the Physics Laboratory at l’École Normale Supérieure (ENS), Paris, France.

“For example, it could help with the design of new proteins that have desired functions, or with predicting the future sequence evolution of proteins in living organisms, such as pathogens, and identifying appropriate drug targets.”

The research team used Restricted Boltzmann Machines (RBM), applying them to 20 protein families. These artificial neural networks can provide a wealth of information on protein function and structure. The team found that the connections between artificial neurons in the RBM can be interpreted, and relate to the structure, function or phylogeny of the protein.

the team also identified how to use their RBM model to design new protein sequences by composing and increasing or decreasing the neural networks at will.

“Our RBM model shows how machine-learning techniques can solve complex data recognition and draw conclusions from data in an interpretable way,” said co-author Simona Cocco, CNRS Director of Research at the ENS Physics Laboratory.

“This runs counter to the more complex, black-box models that are traditionally used in data science, as statistical analyses provided by these tools are largely uninterpretable. The interpretability of our method is a major benefit to scientists – it bears the promise of allowing them to generate proteins with desired functions in a controlled way.”

“It will now be interesting to apply our model to proteins in pathogens,” added senior author Rémi Monasson, also CNRS Director of Research at the ENS Physics Laboratory, and Deputy Director of the Henri Poincaré Institute (CNRS/Sorbonne University), France.

“Pathogens, particularly viruses, can often escape drugs through mutations that make treatments ineffective. Our method could be used to predict the mutational escape paths that are accessible to the functional protein from its current sequence, and help identify which combination of protein sites should be targeted by drugs to block all paths.”

The study was published in the journal eLife.


A drug candidate for cancers that are associated with Epstein-Barr virus (EBV) has been identified.

Researchers at The Wistar Institute created a drug candidate for cancer associated with EBV, describing inhibitors of the protein EBNA1, Epstein-Barr nuclear antigen 1. EBNA1 is a DNA-binding protein that is critical for virus replication and for the continuous proliferation of infected cells.

The identified drug candidate has shown efficacy in preclinical models.

“EBNA1 is found in all EBV-associated tumours and does not look like any other protein in the human body,” said Dr Paul M. Lieberman, Hilary Koprowski Endowed Professor, leader of the Gene Expression & Regulation Program at Wistar, and corresponding author on the study.

“These characteristics, along with the protein’s particular structure, make EBNA1 a very attractive therapeutic target.”

Dr Lieberman and his colleagues worked on a class of compounds based on the 3D structure of the virus. They looked at small molecule inhibitors that block the ability of EBNA1 to bind with DNA.

The research team used mouse models of EBV-associated cancers to establish the efficacy of the drug compounds. They transplanted tumour cells, or patient-derived tumour samples into immunocompromised mice, and discovered a dramatic reduction in tumour growth in all conditions. The tumour growth inhibition was greater that that achieved through gamma irradiation or chemotherapy – standard care for patients affected by EBV-associated cancers.

“It has taken the lab nearly a decade to go from concept to identifying a clinical candidate,” said Dr Troy E. Messick, first and co-corresponding author on the study and senior staff scientist in the Lieberman Lab. “We are excited about the activity of these inhibitors in a number of preclinical studies and look forward to the next steps of development.”

Tests conducted by the scientists showed a favourable pharmacological profile, with pharmacological inhibition having profound effects on the gene expression of both EBV and host-cell genes. No evidence of drug resistance was identified.

The study was published in the journal Science Translational Medicine.


British researchers have discovered that an epigenetic protein called EZH2 delays the development of acute myeloid leukemia (AML) but then switches sides once the disease is established to help maintain tumor growth. The study suggests that targeting EZH2 could therefore be an effective treatment for AML, an aggressive blood cancer expected to kill over 10,000 people in the US alone this year.

EZH2 is an epigenetic protein that can control the activity of hundreds of genes by chemically modifying the histone proteins that package up the cell’s DNA. Increases in EZH2 activity are thought to promote the development of a variety of human tumors, including breast and prostate cancers, and several clinical trials are currently investigating whether drugs that prevent EZH2 from modifying histones could be used as cancer treatments.

Whether EZH2 also promotes the development of blood cancers like AML is unclear, however. Some evidence suggests that the epigenetic protein many actually prevent AML and other myeloid malignancies.

A team of researchers led by Professor Brian Huntly at the Cambridge Institute for Medical Research, UK, found that mice lacking EZH2 developed AML much faster than usual, indicating that the protein does indeed delay the development of AML. However, once AML had fully developed and established itself in the mice, deleting the EZH2 gene or inhibiting the EZH2 protein with a drug disrupted tumor growth and significantly prolonged the animals’ survival. Inhibiting EZH2 also prevented the growth of AML cells isolated from patients.

Prof Huntly and colleagues found that inhibiting EZH2 has conflicting effects on the development and maintenance of AML because the protein regulates almost completely different sets of genes at early and late stages of the disease. For example, during the initial stages of AML, loss of EZH2 causes cells to increase production of a transcription factor called Plag1 that accelerates the development of leukemia. But inhibiting EZH2 at later stages of AML has no effect on Plag1 levels.

“Our findings uncover novel and dramatically opposing functions of EZH2 during AML that appear dependent upon the phase of disease, with EZH2 functioning as a tumor suppressor in AML induction and as a facilitator of disease in established AML,” Prof Huntly said. “To our knowledge, this is the first description of an epigenetic regulator having both tumorsuppressive and oncogenic function in different phases of the same cancer. In addition, our work validates EZH2 as a therapeutic target with the potential to treat several different subtypes of AML.”

The study was published in the Journal of Experimental Medicine.


Pancreatic cancer is a grim diagnosis, with a five-year survival rate of less than 9 percent. To improve these odds, researchers at UPMC and the University of Pittsburgh School of Medicine sought genetic signatures in the largest study of its kind that could be used to better match drugs to patients and for early detection.

The study involved sifting through the genomes of thousands of tumors, sampled from all over the world. In 17 percent of cases, there was a genetic flag that indicated the tumor should be susceptible to existing chemotherapy drugs. The researchers also found supporting evidence for heritable genes, including some in the BRCA family associated with breast cancer, that can predispose whole families toward pancreatic cancer.

“People have been looking for such markers for a long time, and our study shows that it’s possible to break pancreatic cancer patients into different treatment buckets,” said senior author Dr Nathan Bahary, Oncologist at UPMC Hillman Cancer Center and Associate Professor of Medicine at Pitt.

One reason why pancreatic cancer is so deadly is that the majority of patients often are identified late in their disease course and frequently present with inoperable tumors at the time of diagnosis. For some of these patients, it may be possible to shrink the tumor with existing chemotherapy drugs, but in a disease where 75 percent of patients die within a year of diagnosis, time is of the essence, and unfortunately, there’s no way to know in advance which patients will respond to which drugs.

“Every pancreatic cancer is different, and performing molecular profiling of each patient’s tumor could help determine the best treatment options,” said lead author Dr Aatur Singhi, surgical pathologist at UPMC and Assistant Professor of Pathology at Pitt. “Rather than blindly giving patients the same chemotherapy, we want to tailor a patient’s chemo to their tumor type. A one-size-fits-all approach isn’t going to work. Therefore, we would like to make molecular profiling standard-of-care for patients with pancreatic cancer.”

Prof Singhi and Prof Bahary’s study characterised the genome of 3,594 pancreatic tumor samples from patients around the world, provided by collaborators at Foundation Medicine.

“We believe that this is the largest study in pancreatic cancer conducted using comprehensive genomic profiling to identify a broad set of genomic alterations, and ultimately, therapeutic targets, in this difficult-to-treat disease,” said Dr Siraj Ali, Senior Director of Clinical Development at Foundation Medicine.

Besides shrinking tumors with personalised chemotherapy, another way to increase pancreatic cancer survival rates is through increased pancreatic cysts screening, Prof Singhi said, but the problem is that pancreatic cysts are incredibly common, and not all lead to cancer.

Previously, Prof Singhi and colleagues developed a clinical molecular test known as PancreaSeq to evaluate common pancreatic cysts and identify which cases may progress to cancer. The discovered biomarkers can be added to the PancreaSeq platform, already being used by several institutions, including UPMC.

The study was published in the journal Gastroenterology.

Adverse drug reactions can be costly. Prolonged hospital stays and clinical investigations cost as much as $30.1 billion every year — and clinical facilities carry much of that burden. And cost is just the tip of the iceberg.  This is highlighted by an FDA website which cites troubling statistics such as the 100k deaths and over 2 million serious adverse drug reactions experienced each year:  Adverse Drug Reactions:  Prevalence and Incidence

In extreme cases, a drug’s regulatory approval may even be revoked, rendering a developer’s multi-billion-dollar investment , while maybe achieving some learnings, overall unprofitable and unsuccessful.

In response to these challenges, a growing number of clinical organizations are using drug and patient data to move from reactive drug safety toward a more proactive global pharmacovigilance system, in which adverse drug reactions can be anticipated before the drugs in question are ever prescribed.

Evolving Standards for Data Collection

The introduction of consistent systems for organizing electronic medical records marked the beginning of an industry-wide shift toward data standardization. Although this digital transformation has already brought many benefits in the realm of patient care, significant work remains to be done. Experts estimate that at least 6 percent of hospital patients experience at least one adverse drug reaction  per visit — and many of those adverse drug reactions could be prevented by a more accessible database of pharmaceutical reports.

Bringing Together Drug Data

As more clinical providers adopt consistent policies for the reporting and organizing of drug data, this information can be correlated with manufacturers’ product updates and new regulations — as well as pharmacokinetic and genomic data gathered from millions of patients worldwide. When this data is brought together in a unified format, clinicians will gain access to a wealth of actionable insights about likely adverse drug reactions, enabling them to shorten patients’ hospital stays and provide safer care.

Tomorrow’s Global Pharmacovigilance System

Comprehensive and standardized data collection is only the first step. To accurately predict adverse drug reactions, clinical providers need to undergo a shift in thinking: from reactive drug safety protocols toward a proactive pharmacovigilance system. In such a system, data from other healthcare facilities, drug manufacturers, regulatory authorities, and other sources will be fed through predictive analytics algorithms, which will forecast potential adverse drug reactions and recommend alternative prescriptions. With the help of this emerging global data network, healthcare professionals can make strides toward more effective prevention of adverse drug reactions. As more providers shift toward a proactive pharmacovigilance paradigm, healthcare facility operators will enjoy lower operating costs and decreased risk — while billions of future patients can anticipate safer hospital visits.


Marnix Wieffer
Drug Safety Information Specialist

As drug safety information specialist, I support pharmaceutical companies in identifying potential drug toxicity concerns and adverse effects. During my first 3 years at Elsevier, I have been working with both academic and corporate customers in northern Europe. As senior marketing manager, I am currently responsible for Elsevier’s drug safety messaging, internal training and market engagement.

In December 2018, the FDA announced it has formally recognized a public database containing genomic information, a huge milestone in the evolving field of precision medicine. Now researchers can use this information to validate their tests instead of having to generate their own data. In 2017 30% of new drugs approved by the FDA were classed as ‘precision medicines’, a figure we’ll no doubt see increase when we reflect on 2018.

However, while precision medicine has had many positive impacts so far, the cost of scaling precision therapies to a larger patient population is still putting many medicines out of reach of both payers and patients. Here we take a look at three areas to address to bring down the costs of precision medicine and extend the benefits to even more patients:  

  • Disparate data sources: Scattered data inevitably mean researchers end up spending the majority of their time formatting and linking it together. Electronic Health Records (EHRs), for example, currently don’t have all of the information included, such as direct-to-consumer genetic testing a patient might have done, which would allow physicians to prescribe precision medicines more effectively. As technology continues to mature and data repositories for life sciences continue to advance, patient information must also be better integrated to allow researchers to access medical data faster.
  • Making use of the data: Even after the data has been collated and integrated, such vast quantities of information can still be unmanageable, and so very challenging for researchers to process. In order to overcome this, researchers need intelligent systems with deep information analytics and AI capable of harmonizing data for analysis, helping them to make better decisions and understand all possible outcomes.
  • High R&D costs: Developing precision medicines require companion diagnostics and genetic testing, which can increase the cost during R&D. Companion diagnostics assess whether a patient is a good candidate for the treatment. Development of companion diagnostics must be paired with the drug development process, which requires considerable collaboration with the FDA and other regulatory agencies.

While there are hurdles such as these outlined that need to be overcome for precision medicine to really shine, it is certainly helping pharmaceutical researchers and developers make leaps and bounds for previously untreatable diseases, and is likely to make huge progress in 2019. Find out how Elsevier’s suite of world class solutions can help your business continue to innovate.


Sr. Manager, Pharma and Biotech Segment

Nicki Catchpole 
Sr. Manager, Pharma and Biotech Segment

As a professional with over 14 years of experience in strategy development and partnership management across a variety of industries, Nicki’s latest role as a Senior Manager, Segment Marketing at Elsevier applies her skills to the area of drug discovery and development in the Pharma and Biotech industry.

In this capacity she is focused on understanding biopharmaceutical R&D challenges and turning them into opportunity to further Elsevier’s ability to serve industry executives and the professionals who innovate in the drug discovery and development space.  Prior to joining Elsevier, Nicki held senior alliance and strategy roles in the Legal, Tax & Accounting, Life Sciences and Cyber Security industries.

Nicki resides in New York City and holds a BA in English Literature and Mandarin Chinese from Carleton College in Northfield, MN.

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