One measure of the dramatic progress in next-generation sequencing is that scientists no longer want to talk about advances in instrumentation. They would prefer to talk about other issues, including sample prep and analysis.These timely topics and more will be discussed at the upcoming “Genomic Research” conference, organized by Select Biosciences.
“The sequencing instrument companies have done a very good job speeding up the sequencing machines,” says John Langmore, Ph.D., chief scientific officer of Rubicon Genomics. “And now it’s up to everyone to speed up the sample preparation, speed up the analysis, and also focus the sample preparations on specific clinical samples that are of utility, and focus the informatics on getting the right information from the samples out quickly.”
Scott Diehl, Ph.D., agrees that the collection of raw sequence data is no longer the rate-limiting step of NGS research. Dr. Diehl—director of the Center for Pharmacogenomics and Complex Disease Research at the University of Medicine & Dentistry of New Jersey—believes that the biggest upcoming challenges of clinical research will be (1) gaining the cooperation of the huge numbers of test subjects necessary to understand complex multifactorial diseases and (2) balancing these subjects’ right to privacy with the need to analyze their genomes and medical histories in great detail.
“It’s really a cultural challenge as much as a technological challenge in order to gain the fruits of these amazing advanced technologies,” he says.
Dr. Diehl points to the decades-old needle-in-a-haystack problem of finding single disease-causing mutations amidst an entire genome. Beginning in the 1980s, researchers successfully uncovered the mutations responsible for such disorders as cystic fibrosis, muscular dystrophy, neurofibromatosis, and Huntington disease.
“We said, ‘OK, this works great for simple single-gene diseases; let’s apply it to more complex conditions,’” Dr. Diehl says.
“And to make a long story short, initially using family-based linkage studies and then later using population-based case-control studies of various diseases, we found lots of genes, but...even adding them all up, they only explain a relatively modest portion of the inherited component of the diseases. And so this leads to the conclusion that there must be hundreds of needles we need to find to fully explain the causes of complex diseases.”
To understand how dozens or hundreds of genes interact with each other and the environment in conditions such as diabetes, cancer, heart disease, and mental illness, Dr. Diehl says, “We ultimately need to build up registries of millions of people who are tracked over decades in order to tackle the biostatistical analysis.” The recent genotyping study of 100,000 Kaiser Permanente members is a step in this direction, he says.
Engaging the public on such a large scale will require a massive education and outreach campaign, including frank disclosure of possible threats to privacy. “We need to build safety and privacy protection mechanisms, but sooner or later major genetic databases are likely to get hacked and put out onto the web,” Dr. Diehl concedes.
While Dr. Diehl hopes to analyze large populations of subjects, Marek Malecki, M.D., Ph.D., associate professor at Western University of Health Sciences, is working on populations of cells within an individual—cancer cells, in particular.
A basic problem with cancer is that different tumor cells develop different mutations, leading to distinct subpopulations within the tumor. “Tumor growth is propelled by many different clones with different properties,” Dr. Malecki notes, “and when you deal with a heterogeneous tumor, then basically it’s very hard to apply a one-size-fits-all therapy or diagnosis.”
But what if you could capture, sequence, and treat each specific clonal subpopulation? That is one of Dr. Malecki’s long-term goals. To sort out different populations of normal cells and tumor cells from clinical samples, he and his team have created a panel of BioTags and OncoTags—synthetic antibodies that recognize specific structural variants of cell-surface proteins (e.g., EGFR) often mutated in cancer cells.
Dr. Malecki’s vision is that once the tumor cells are sorted into different clonal populations, they could all be sequenced via NGS and subjected to test treatments. Treatments that appear effective against isolated cancer cell populations could then be administered to the patient as targeted personalized medicine.
One advantage of an OncoTag-based approach, Dr. Malecki says, is that it preserves cells’ viability better than fluorescence-activated cell sorting (FACS) and other existing methods, thus facilitating clonal expansion for profiling of the genome, microRNA, and transcriptome, as well as for testing of targeted therapies. Regardless, the sequencing of the many subpopulations of a tumor is only feasible because of high-throughput NGS.