2 Clarke Drive
Cranbury, NJ 08512
© 2022 MJH Life Sciences™ and OncLive - Clinical Oncology News, Cancer Expert Insights. All rights reserved.
Samir Parekh, MBBS, highlights ongoing research regarding personalized medicine for multiple myeloma.
Samir Parekh, MD
The perception of multiple myeloma is changing, as researchers have discovered that what was once thought of as one disease is actually quite heterogenic, adding a greater need for genomic testing, explained Samir Parekh, MBBS.
“We are moving from an era where we looked at myeloma under the microscope and thought of it as one disease. With these resolution techniques like next-generation sequencing, we are able to understand the genomics better. We have found most patients have 4 or 5 different clones of disease that all behave differently with chemotherapy and need to be tracked to understand how to treat patients better,” said Parekh, an associate professor of medicine, hematology and medical oncology at the Icahn School of Medicine at Mount Sinai Hospital.
“In the future, every patient should have the tumor examined with genomics, in addition to having the pathology examined under a microscope,” added Parekh.
In an interview during the 2019 OncLive® State of the Science Summit™ on Multiple Myeloma, Parekh highlighted ongoing research regarding personalized medicine for multiple myeloma.
OncLive: Could you discuss the understanding of genomics in multiple myeloma?
Parekh: We have specific collaborators looking at genomics in multiple myeloma with an expertise in modeling big data sets by network analyses. Using the Multiple Myeloma Research Foundation (MMRF) CoMMpass data set of more than 1000 patients, we've been able to find new prognostic patterns in myeloma, new therapeutic targets, and new subclasses that may also have therapeutic implications.
We are now expanding this work with the help of a company that spun off Mount Sinai Hospital, called Sema4, in which we are sequencing 1000 patients at Mount Sinai Hospital to act as a comparison cohort to understand what drives relapse and how we can therapeutically target it.
A majority of our patients at Mount Sinai Hospital are referred from our site practices after exhausting multiple lines of treatment and facing dire consequence if they don't have a new treatment option. A lot of these patients can go on to clinical trials, but some of them are not eligible. For these specifications, we designed a clinical trial where we would rapidly sequence their myeloma cells. Using [our DAPHNE] software, we are able to find treatment options for these patients, by looking at the DNA and RNA.
We published a paper in Precision Medicine last year where, to our surprise, we found the majority of the patients had drugs that were able to be repurposed from other cancers. We tested these drugs, some of which were used in patients with skin cancer, kidney cancer, and lung cancer treatment. They worked in those with multiple myeloma, and patients were able to be salvaged and go on to significant milestones. This was very gratifying. Based on this, we are planning to sequence a larger cohort of patients systematically to understand how these treatments affect subclones and clonal evolution, so that we can plan the next wave of treatments for patients with relapsed/refractory myeloma.
There are also some new implications of genomics in immunology. One of our major collaborations is with the world-famous vaccine expert Nina Bhardwaj, MD, PhD, who runs a program at Mount Sinai Hospital. Using genomics, we can identify antigens that are created from mutations in the tumor. Then, we can explore these antigens that drive immunogenicity against tumor by personalizing a cancer vaccine approach. We have a good manufacturing practice facility on our floor that makes these vaccines, and then we can deliver them to patients with myeloma. We already tested this in several patients with promising results. We are going to expand these efforts into the early or smoldering myeloma patient population.
What is the significance of using genomics in multiple myeloma and what prognostic factors are you currently using to determine outcomes and predict a response?
We are using genomics in multiple ways to identify new drivers to understand myeloma better, to find treatments for patients, and to find the impact of genomics of the tumor on the tumor microenvironment. All of these things will help us treat patients better.
In terms of prognosis, specifically, we are looking RNA expression patterns to build a new map of myeloma and find parts of the map that are associated with the [poor-risk] myeloma. If you go to that area and if a patient expresses a particular set of genes, we can predict who is going to do better or worse.
In terms of immunology, you can use neoantigens to treat patients and track them, because these tumor mutations elicit responses that can be the most direct measure of the immunotherapies that are being used in myeloma, including immunomodulatory agents and monoclonal antibodies.
How can the field take an even further personalized approach for each patient?
At this point, personalization of treatments is occurring at a clinical level where age, comorbidities, and prior treatments are taken into account. Genomics have played a big part in certain solid tumors, such as lung cancer, through a subdivide in disease to categories with drugs that are readily available.
In hematological malignancies, this is less common. Part of the challenge is there haven't been a lot of drugs targeting the genomic changes that are common in these tumors. Our approach has been to look beyond the DNA into the RNA expression. We found the RNA expression can help us predict drugs that may be applicable to a lot more patients; this is the way [treatment] is going to evolve.
Mount Sinai Hospital is part of national study run by the MMRF called the MyDRUG study, in which patients are going to be allocated in the different arms based on specific genomic alternations. This is a real effort at trying to move from a one-size-fits-all approach to a personalized approach in myeloma.