NEW YORK—RNA and DNA sequencing are familiar standbys in drug discovery and development, but they’re being put to new use together now in a new way: drug repurposing. After engineering a novel platform, a multi-disciplinary team at Mount Sinai used it to canvass FDA-approved cancer drugs for multiple myeloma patients outside of those specifically indicated for multiple myeloma. The pilot trial consisted of 64 patients with late-stage and drug-resistant multiple myeloma who had exhausted other treatment options.
The team sequenced the DNA and RNA of multiple myeloma patients in search of cancer-driving mutations, which they matched against a database of approved drugs that target the identified mutations. To speed their search, they secured their own supercomputer to enable faster processing.
“Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles had not been clinically assessed before now,” said researcher Dr. Alessandro Lagana, an assistant professor of genetics and genomic sciences at the Institute for Next Generation Healthcare and the Icahn Institute for Genomics and Multiscale Biology at the Icahn School of Medicine at Mount Sinai. “We expect RNA sequencing will play a larger role in the precise delivery of targeted drugs in oncology.”
The point of adding RNA sequencing, rather than relying simply on DNA sequencing, is a matter of both multiple myeloma’s nature and a search for a more complete look at a patient’s cancer, according to researcher Dr. Samir Parekh, director of Translational Research in Myeloma at The Tisch Cancer Institute at the Icahn School of Medicine at Mount Sinai and an associate professor of medicine (hematology and medical oncology) and oncological sciences.
“DNA sequencing in general is useful for tumors like lung cancer, where there is a very clear segmentation of the disease into portions that are driven by certain DNA mutations and that have a very clear treatment alternative that can be coupled to that mutation,” he explains. “In heme malignancies like myeloma, that's usually not the case, and there haven't been clear mutations that can be paired with treatments. What RNA tells us is what is driving the tumor from a more comprehensive perspective. RNA-seq quantifies messenger RNA, which is produced from the DNA. So the change in messenger RNA for a particular gene analyzed by our pipeline informs us about activation of certain oncogenic pathways that have drugs associated with them, and certain biomarkers which have been found to be useful—again, at the RNA level— and which can't be picked up by targeted DNA sequencing.”
For this trial, Parekh tells DDNews that the recommended treatments generated by their platform were individual therapies, but says that they are moving toward combination therapies. There’s more work needed to get to that point, however, “Because we have to incorporate drug interactions and things like that, but we are starting to look at combinations using a bulk sequencing and single-cell sequencing approach, which tells us at the individual cell level what is driving the RNA changes, and we use similar methodology to predict drugs.”
The reason this approach was possible is due to Mount Sinai’s particular resources, he adds, such as their large myeloma patient population and “a very strong program in genomics that does drug repurposing using the RNA seq data.” Parekh says he thought multiple myeloma was “an obvious unmet need that could be easily matched together” with Mount Sinai’s strengths.
And it turned out he was right.
“The results that we published are basically the initial pilot trial results that show that there was some benefit—quite unexpected, actually,” Parekh comments. “And it made a tremendous difference in the patients getting the treatments because some of them were going to hospice, some of them were essentially running out of options … they experienced benefit in terms of quality of life and duration of life, and for patients with myeloma, every day can matter. We had patients that stayed alive to see their kids get married, graduate, so all of that was really rewarding as physicians.”
There’s more work in store to further pursue the potential of this approach. The team hopes to explore other heme malignancies and eventually move into solid tumors, Parekh tells DDNews. In addition, Mount Sinai noted that it had already received funding for a next-generation clinical trial that will combine the precision medicine platform used in this trial with machine learning algorithms to try and refine treatment predictions based on patient response to treatment and a physician’s opinion regarding the suggested treatment.
There’s also potential for this platform in preclinical testing, according to Parekh, who explains that in some models, their analysis “actually leads to studies that are done in vitro where we take either the patient cells or other in-vitro models like cell lines, and treat them with the same drug that will ultimately go into the patient. And our systems are now giving back readouts that inform us on how to prioritize the different options coming out of the drug repurposing. For example, one of the challenges we are facing is the approach gives us sometimes half a dozen or a dozen options; which is the best option for a particular patient still needs to be prioritized, and the in-vitro work could really help us do that.”
And for a drug company, he says, “Having this sort of approach would really make clinical trials more efficient by enriching the trials for patients that are likely for response. So you don’t just go to an unselected population, you can go in a more biologically guided way and pick patients that respond, and you get a read on whether the drug is working or not.”
