June 15, 2018 (Vol. 38, No. 12)

Diana Gitig, Ph.D.

NGS Advancements Pave the Way for New Drug-Resistance Research

Next-generation sequencing (NGS) platforms, along with immunotherapies like checkpoint inhibitors, have brought the promise of precision medicine for cancer patients closer to reality.

Terence Rhodes, M.D., director of immuno-oncology at Utah-based Intermountain Healthcare, thinks that Tumor Mutational Burden (TMB), or the number of mutations in a tumor, is starting to come of age as a biomarker that can predict which patients will respond best to immunotherapy.

“TMB is a byproduct that has evolved out of precision genomics and next-generation sequencing that has already been established in the oncology world,” he says. In TMB, sets of 150–300 genes were examined from cancer patients. The particular genes that comprised each set depended on the company that made the assay. But it almost doesn’t matter, he says, as doctors started correlating TMB, i.e., the number of mutations rather than a specific type of mutation that respond to immunotherapy.

“If there is a high TMB, the chances of immunostimulatory antigens being expressed on the outside of tumor cells increase,” he explains. Astonishingly, TMB is a better predictor of response to PD-1 inhibitors like Keytruda® (Pembrolizumab) and Opdivo® (Nivolumab) than to the presence of PD-1. Since PD-1 is detected via immunohistochemistry and it is such an imperfect marker (patients without it can still respond to anti-PD-1 therapy), Dr. Rhodes believes that it will soon fall out of favor.

“In terms of NGS applications in drug discovery and development, NGS can be used to understand disease biology and to target key pathways driving the disease. It can also be useful in uncovering mechanisms associated with drug resistance and rational drug combinations,” according to Prashun Mishra, Ph.D., founder and CEO of Agility Pharmaceuticals. “Moreover, NGS-based gene panels can be used as companion diagnostics assays.”

Tropomycin receptor kinase (TRK) fusion genes and fibroblast growth factor receptor 2 (FGF-R2) are druggable targets that were identified through NGS, and a number of companies are now searching for drugs to target them, adds Dr. Mishra. NGS can facilitate high-throughput screens of chemical libraries and small molecules: Each molecule in the library can be tagged with a unique identifying oligonucleotide. This makes determining which ones bind to the target NGS a relatively simple endeavor.

Understanding Drug Resistance

NGS has also uncovered the mechanism behind the development of drug resistance, an inevitability in a disease as complex as cancer. It revealed that treating melanoma patients with Zelboraf® (Vemurafenib), a drug that inhibits their mutated B-Raf, selects for tumor cells harboring the more aggressive N-Ras mutation. This finding allows doctors to start treating their melanoma patients right at the outset with a drug combination including Cotellic® (cobimetinib), a MEK inhibitor that acts downstream in the N-Raf pathway. Presumably, as NGS technologies become more accurate, less expensive, and more widely used, they may reveal other unexpected relationships like this that will also help patients.

Jordi Rodon-Ahnert, M.D., associate professor in the Department of Genomic Medicine and clinical co-director of the Department of Precision Oncology Decision Support Team at the University of Texas MD Anderson Cancer Center, is concerned that cancer immunotherapy is too hyped—and that we have not even reached the peak of that hype yet.

“CAR-T cells targeting CD-19 cured leukemia in kids, which is a super-aggressive disease that was highly refractive to everything,” said Dr. Rodon-Ahnert. “PD-1 inhibitors gave response rates never seen before, in many different tumor types. They were really very impressive.”

So the hype is hardly a surprise; it is following the same trajectory as most promising new innovations, as outlined in Gartner’s Hype Cycle. Nevertheless, “it makes clinical research go in a single direction without thinking,” he warns. “We need to think outside of the box and not just do what Pharma says, even though it is fashionable.”

The heady promise of immunotherapy makes it easy to forget that not every drug, nor every drug combination, will yield results as tremendous as those that started the frenzy. He cited Epacadostat as an apt cautionary tale. The IDO1 inhibitor, made by Incyte, failed to deliver any benefit as a combination drug in Phase III clinical trials against metastatic melanoma and non-small cell lung cancer—causing Incyte’s stock to plummet.

This may be because the Phase II randomization aspect of clinical studies was kind of skipped; the Phase III trials were undertaken based only on a Phase I cohort, according to Dr. Rodon-Ahnert. “Researchers need to push back and make thoughtful decisions, and not just do what everyone else is doing,” he maintains.

Current immunotherapy agents plotted on Gartner’s Hype Cycle, showing their placement along the five key phases of a technology’s life cycle: the Technology Trigger, the Peak of Inflated Expectations (hype), the Trough of Disillusionment, the Slope of Enlightenment, and the Plateau of Productivity. Starting in 2014, many have surmised that a new Technology Trigger and Peak of Inflated Expectations has begun. [Anderson Cancer Center]

Combatting the Hype

But there are steps being taken to combat this hype. Somak Roy, MBBS, assistant professor of pathology at University of Pittsburgh Medical Center and the chair of the Working Group at the Association of Molecular Pathology (AMP), says that AMP recently released three guideline reports. These reports aim to provide the cancer genomics community with the appropriate tools and guidance to improve the entire NGS workflow and better incorporate the latest technological innovations in molecular pathology. All three reports have been published in The Journal of Molecular Diagnostics.

“Before these guidelines, each lab was left on its own so validation was variable and inefficient. AMP can help labs reduce the time it takes to optimize their validation. Many of the guidelines are commonsensical and have been implemented on a piecemeal basis. But AMP can provide labs with a straightforward, standardized checklist”—a technique promoted by Atul Gawande in his book, The Checklist Manifesto, that has been proven to optimize a disparate range of fields, from surgery to commercial air flight.

AMP recommends that when data files get transferred between the DNA sequencer and the data center and the cloud service provider and the server that someone verify that the transferred files are identical to the originals, as files are known to get truncated during transfers without personnel being aware of the truncation—a problem that has very obvious and troubling ramifications.

Another recommendation is to give each sample identifiers that relate to the location, as well as to the patient and the sample, since different labs in different locations can use the same sample identifiers. This safeguard can help ensure that sample information is maintained from start to finish, throughout the entire pipeline of analysis beginning with sample collection and ending with a patient report.

“NGS and especially bioinformatics is so rapidly evolving, it is hard to keep up,” notes Dr. Roy. This is as true for his team developing guidelines as it is for doctors treating patients, and researchers searching for molecular targets and the drugs to target them.

Quality Control of Cell-Free DNA for Next-Gen Sequencing

Next-generation sequencing (NGS) of cell-free DNA (cfDNA) is a major tool for diagnosing genetic mutations from blood and other body fluids, but the technology can be expensive. NGS, and, by extension, liquid biopsies, can be optimized by incorporating quality control steps during library preparation.

Performing quality control on nucleic acid samples lets users identify degraded, fragmented, and low-purity samples that are likely to produce suboptimal libraries and yield poor sequencing performance, saving researchers time and money.

Illumina has integrated Advanced Analytical Technologies’ (AATI) primary nucleic acid quality control platform, the Fragment Analyzer, into its NGS library prep workflows. The instrument has two major advantages over traditional chip-based systems, notes Brian Bodemann, Ph.D., an AATI account manager.

First, it can accurately quantify nucleic acids, he explains. Wrong concentrations of certain molecules can have disastrous results. If the adaptor stoichiometry is off and proper clean up steps are not completed, for example, adaptors can form strings of repetitive DNA sequences that can bind to the flow cell and stymie data collection.

“This will cause a $10,000 sequencing run to produce poor data,” says Dr. Bodemann. “If the problem goes unnoticed, this can affect the sequencing data quality from all the other samples,” adding that the Fragment Analyzer can flag these low-quality samples and prevent them from slowing down sequencing.

The instrument also qualifies cfDNA more effectively, maintains Dr. Bodemann. On chip-based systems, high molecular weight (HMW) contaminating DNA may not fully separate from the common nucleosomal peaks. Because the Fragment Analyzer uses capillary electrophoresis, any HMW DNA can be fully separated from cfDNA.

“By providing accurate quantification and qualification of the nucleosomes, the Fragment Analyzer provides scientists with a simpler, faster and more easily interpretable route to results,” says Dr. Bodemann.

“Every moment you spend troubleshooting your sequencing data is time you’re wasting on making patients and doctors wait for results.”

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