One in eight women will be diagnosed with breast cancer during her lifetime. The earlier cancer is detected, the better the chance of successful treatment and long-term survival. However, early cancer diagnosis is still challenging as testing by mammography remains cumbersome, costly, and in many cases, cancer can only be detected at an advanced stage. A team based in the Dept. of Biomedical Engineering at McGill University’s Faculty of Medicine has developed a new microfluidics-based microarray that could one day radically change how and when cancer is diagnosed.
For years, scientists have worked to develop blood tests for cancer based on the presence of the Carcinoembryonic Antigen (CEA), a protein biomarker for cancer identified over 40 years ago by McGill’s Dr. Phil Gold. This biomarker, however, is also found in healthy people and its concentration varies from person to person depending on genetic background and lifestyle. As such, it has not been possible to establish a precise cut-off between healthy individuals and those with cancer.
‘Attempts have been made to overcome this problem of person-to-person variability by seeking to establish a molecular ‘portrait’ of a person by measuring both the concentration of multiple proteins in the blood and identifying the signature molecules that, taken together, constitute a characteristic ‘fingerprint’ of cancer,’ explains Dr. David Juncker, the team’s principal investigator. ‘However, no reliable set of biomarkers has been found, and no such test is available today. Our goal is to find a way around this.’
Dr. Mateu Pla-Roca, the study’s first author, along with members of Juncker’s team, began by analysing the most commonly used existing technologies that measure multiple proteins in the blood and developing a model describing their vulnerabilities and limitations. Specifically, they discovered why the number of protein targets that can be measured simultaneously has been limited and why the accuracy and reproducibility of these tests have been so challenging to improve. Armed with a better understanding of these limitations, the team then developed a novel microfluidics-based microarray technology that circumvents these restrictions. Using this new approach, it then became possible to measure as many protein biomarkers as desired while minimising the possibility of obtaining false results.
Juncker’s biomedical engineering group, together with oncology and bioinformatics teams from McGill’s Goodman Cancer Research Centre, then measured the profile of 32 proteins in the blood of 11 healthy controls and 17 individuals who had a particular subtype of breast cancer (oestrogen receptor-positive). The researchers found that a subset of six of these 32 proteins could be used to establish a fingerprint for this cancer and classify each of the patients and healthy controls as having or not having breast cancer.
‘While this study needs to be repeated with additional markers and a greater diversity of patients and cancer subsets before such a test can be applied to clinical diagnosis, these results nonetheless underscore the exciting potential of this new technology,’ said Juncker.
Looking ahead, Juncker and his collaborators have set as their goal the development of a simple test that can be carried out in a physician’s office using a droplet of blood, thereby reducing dependence on mammography and minimizing attendant exposure to X-rays, discomfort and cost. His lab is currently developing a hand-held version of the test and is working on improving its sensitivity so as to be able to accurately detect breast cancer and ultimately, many other diseases, at the earliest possible stage. McGill University

The European Respiratory Society (ERS) and the European Centre for Disease Prevention and Control (ECDC) have published their jointly developed European Union Standards for Tuberculosis Care (ESTC). The 21 patient-centred standards aim to guide healthcare workers to ensure optimal diagnosis, treatment and prevention of TB in Europe – with nearly 74,000 reported TB cases in the EU/EEA in 2010 clearly showing that TB remains a public health challenge across the region. The new EU-specific guidelines were developed by a panel of 30 experts and aim to bridge current gaps in the case management of TB that were identified in a recent survey. In the process, the ERS has taken the lead in developing the clinically related standards and ECDC has developed the public health related standards. The ESTC are based on the same recommendations as the International Standards for TB Care (ISTC), but feature additional supplements and replacement information relevant for healthcare providers in the EU. The new guidelines include the following: All people showing signs, symptoms, history or risk factors linked with TB should be examined for TB. All people diagnosed with TB should undergo drug susceptibility testing in a laboratory setting, to rule out drug-resistance and help combat the growing number of multidrug-resistant cases of TB (MDR-TB). Patients with, or highly likely to have, TB caused by drug-resistant organisms (especially MDR-TB) should be treated for at least 20 months, with the recommended intensive phase of treatment being 8 months. It should be ensured that all newly admitted patients who are suspected of having infectious TB are subject to respiratory isolation until their diagnosis is confirmed or excluded following an appropriate infection control plan. The guidelines also include an additional section on how policymakers and healthcare professionals can adopt and introduce the recommendations to a healthcare setting.

A book containing misprints may cause annoyance for the reader, but typos in an individual’s genetic blueprint (DNA) can mean serious disease or even death. The search for genetic correlates for the wide range of diseases plaguing humankind has inspired a wealth of research falling under the heading of genome-wide association studies (GWAS).
According to Sudhir Kumar, director of the Center for Evolutionary Medicine and Informatics at Arizona State University’s Biodesign Institute, however, results from many such studies become less useful when gene variants or alleles implicated in disease in a given population fail to be discovered in subsequent independent studies. ‘Often, we do not discover the same set of mutations for the same disease in different populations,’ he says. ‘This is a huge problem in genomic medicine.’
Kumar and colleagues Joel T. Dudley, Rong Chen, Maxwell Sanderford, and Atul J. Butte, have developed a statistical method to remedy this problem by using evolutionary information. It is capable of significantly enhancing the likelihood of identifying disease-associated alleles that show better consistency across populations, improving the reliability of GWAS studies. The method makes use of phylogenetics – the comparative study of species genomes through long-term evolutionary history.
The new method is now available to use via the web, so that researchers worldwide can apply it as an aid to discovering disease-associated mutations that are more consistently reproducible and therefore useable as diagnostic markers. Kumar refers to this new approach, combining standard comparative genomic studies with phylogenetic data as phylomedicine, a rapidly developing field that promises to streamline genomic information and improve its diagnostic power.
‘We can take this method and apply it to all the data that has been published,’ Kumar says. ‘It will lead to new discoveries that were sitting right there, but nobody knew about.’
The new method boosts the discovery of reproducible mutations by integrating evolutionary history of humans with contemporary genomic information. Applying the new rankings to a large GWAS study improved the discovery of reliable mutation correlates of complex diseases, which will advance personalised medicine based on each patient’s genomic code.
The basic idea behind GWAS is simple: compare the genomes of two populations of subjects, one with disease trait and a control group without the disease. Next, identify the disparities at each position of the genome in the two populations. Find the alleles occurring in the diseased population that are less frequent in the healthy population and you have just pinpointed the gene mutations associated with disease.
Or have you?
As Kumar explains, matters are not so simple. The mutations studied in such studies are known as SNPs (for single-nucleotide polymorphisms). This simply means that for a given gene sequence, one of the four nucleotides (A,T,C and G) found commonly in the population is replaced by something else. For example, the majority of healthy subjects may carry the ‘A’ at a particular position in the genome, but disease individuals may be more likely to carry a ‘C’ at the same position. If the difference between the groups is striking, the SNP may be associated with the disease trait.
Human genomes are vast structures – consisting of some 3 billion base pairs of nucleotides. Most are littered with SNPs and teasing out which ones sit there without apparent effect and which may translate to disease is often a vexing affair. For some diseases, a one-to-one correspondence between gene mutation and disease exists. Generally, these afflictions, known as monogenic diseases, have particular characteristics. They result from a mutation in just a single gene, rather than multiple genes. They are early-onset diseases, taking their toll when the patient is still young.
Monogenic diseases, which include cystic fibrosis, Tay sachs disease, sickle cell anemia and Huntington’s disease are usually not the targets for genome-wide association studies, because the relationship between gene mutation and occurrence of the disease is straightforward and reliable.
By contrast, so-called complex diseases tend to occur later in life, are triggered by mutations occurring at multiple sites along the genome and often have a significant environmental– that is, non-genetic – component. Finding the alleles responsible for such diseases, which include hypertension, rheumatoid arthritis, Alzheimer’s disease, type II diabetes and countless others through GWAS studies has often been a bewildering endeavor, as alleles identified in one study population frequently fail to turn up in different studies with different populations.
GWAS studies compute the odds of an allele along the genome being disease-related and translate this into a statistic known as the P value. Alleles with the lowest P value are least likely to have occurred by random chance. In the current research, a meta-analysis is conducted using results from thousands of previous GWAS studies and phylogenetics are applied to unearth evolutionary trends in the data.
‘Every position in the human genome among the billions of base pairs has evolved over time,’ Kumar says. ‘As the genome evolves, some positions permit change frequently while others do not.’ The positions least likely to change with time and across mammalian species are known as evolutionarily conserved positions. The group conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. ‘Even if a GWAS variant does not have a functional role in a disease, evolutionary information is still very relevant, because every position in the human genome has an evolutionary signature that gives us prior information on how alleles at that position are likely to vary in modern human populations,’ says Dudley, the study’s lead author.
An analysis of existing data found that most of the presumptively disease-related alleles uncovered in the GWAS studies occurred at relatively slow-evolving, highly conserved sites. According to Kumar, this fact accounts for the poor reproducibility of many putative disease alleles across different populations, as alleles occurring at conserved sites tend to be rare. As Kumar explains ‘You can keep finding rare alleles like this all day, but they would have limited clinical utility in a broader population.’
The new ranking system, known as E-ranking, incorporates phylogenetic information from multi-species studies of mammals, and applies it to human GWAS data. The effect is to remove the inherent sampling bias for rare alleles, allowing the more common alleles occurring at fast-evolving sites in the genome to be more readily discovered. ‘Our method removes this bias, which gives a boost to high-frequency common variants that are more likely to reproduce across populations due to the evolutionary history of the genomic position where they are found,’ says Dudley. Arizona State University

In a series of studies Penn researchers demonstrated that, while tests created for AD are effectively diagnosing the condition when it’s clear cut, additional tests are needed to address the many cases with mixed pathology.
‘With the emergence of disease-modifying treatments for AD and other neurodegenerative diseases, it will be of utmost importance to accurately identify the underlying neuropathology in patients,’ said senior author John Q. Trojanowski, MD, PhD, professor of Pathology and Laboratory Medicine and co-director of the Center for Neurodegenerative Disease Research at Penn.
In one study, the Penn team compared results of a test looking at levels of tau and amyloid beta (Aß) in the spinal fluid, using two different types of analytical platforms. They determined that values from the two platforms could effectively be transformed into equivalent units, and these values accurately distinguished AD from FTLD. A cutoff of 0.34 for the t-tau:Aß1-42 ratio had 90 – 100 percent sensitivity and 91-96.7 percent specificity to differentiate FTLD cases, respectively.
In another study, the team looked at patient cases with more than one underlying neurodegenerative disease and compared the accuracy of the biomarkers using clinical and neuropathological diagnosis. They determined that cerebral spinal fluid (CSF) Aß and tau assays provided a valid diagnosis of AD but, in mixed pathology cases where Alzheimer’s was present along with other diseases (confirmed by autopsy), the testing strategies classified the diagnosis as AD alone.
‘We need to develop better CSF diagnostic panels for the early diagnosis of neurodegenerative dementias, including those due to mixed neurodegenerative disease pathologies that commonly co-occur with Alzheimer’s,’ said senior author Murray Grossman, MD, professor of Neurology and director of the Penn FTLD Center. Perelman School of Medicine

Cancer therapies targeting specific molecular subtypes of the disease allow physicians to tailor treatment to a patient’s individual molecular profile. But scientists are finding that in many types of cancer the molecular subtypes are more varied than previously thought and contain further genetic alterations that can affect a patient’s response to therapy.
A UNC-led team of scientists has shown for the first time that lung cancer molecular subtypes correlate with distinct genetic alterations and with patient response to therapy. These findings in pre-clinical models and patient tumour samples build on their previous report of three molecular subtypes of non-small cell lung cancer and refines their molecular analysis of tumours.
Study senior author, Neil Hayes, MD, MPH, associate professor of medicine, says, ‘It has been known for about a decade of using gene expression arrays that ‘molecular subtypes’ exist. These subtypes have molecular ‘fingerprints’ and frequently have different clinical outcomes. However, the underlying etiologies of the subtypes have not been recognised. Why do tumours form subtypes?
‘Our study shows that tumour subtypes have different underlying alterations of DNA as part of the difference. These differences are further evidence of the importance of subtypes and the way we will use them. For example, the mutations are different which may imply much more ability to target than previously recognised. Also, we are starting to get a suggestion that these subtypes may reflect different cells of origin that rely on different cancer pathways. This is further unlocking the diversity of this complex disease.’ Hayes is a member of UNC Lineberger Comprehensive Cancer Center.
The team first defined and reported in 2006 on three lung cancer molecular subtypes, named according to their genetic pattern – bronchoid, squamoid and magnoid.
In this paper they sought to determine if distinct genetic mutations co-occur with each specific molecular subtypes. They found that specific genetic mutations were associated with each subtype and that these mutations may have independent predictive value for therapeutic response. Lineberger Clinical/Translational Developmental Research Award.