New CoMMpass Study findings for August:
- a new high-risk gene signature,
- how to predict which patients will respond to certain therapies, and
- how the bone marrow environment may play an important role in myeloma progression
Our CoMMpass Study is the largest genomic database of any cancer. We are still collecting that data, and will continue to do so until the year 2023. 62% of the patients originally enrolled in CoMMpass are still being followed in the study and continue to donate their data; we thank them for providing this important information, which is fueling more discoveries about myeloma every day.
In the latest data analysis from the CoMMpass Study, researchers at TGen (the Translational Genomics Institute in Phoenix AZ) and MMRF have identified a group of genomic markers, called the PR group, which is associated with poor disease outcomes. Patients in this group experience
- decreased time to progression (meaning shorter remissions on therapy) , and
- lower overall survival
than other patients. Using this data, the researchers have identified an 87 gene “signature” that is strongly associated with poor outcomes for patients who have this signature. The ability of this signature, called the CoMMpass-87 index, to potentially predict outcomes confirms the importance of every patient getting their genome sequenced. If patients are sequenced and are found to have this signature, this may indicate that
- their myeloma is in a high risk category, and that
- they may be good candidates to receive a therapy that is targeted to certain mutated genes in their signature.
This type of precision approach will ensure patients are treated with exactly the right treatment for their specific type of myeloma. Stay tuned for much more information on this work at the American Society of Hematology (ASH) meeting in early December!
In other CoMMpass news, two papers were recently published by groups who used the data from our CoMMpass Study to help predict how well patients will respond to certain treatments, and to investigate how myeloma cells change as patients progress through different stages of the disease.
The first paper [https://www.nature.com/articles/s41467-018-05348-5 ] published by a group of researchers from the Netherlands, describes development of a computer learning model to predict which treatments might provide the most benefit to specific patients. They examined the treatment history, genomic sequencing, and outcomes information of patients from the CoMMpass Study to identify gene markers that were associated with patients that had a good response to certain therapies, for example Revlimid or Velcade.
The ability to identify patients who will respond well, or not respond, to these and other myeloma therapies is very important. Giving a patient a treatment that they will not respond to
- may allow their disease to progress while on that therapy, and also
- may expose patients to unnecessary drug side effects.
If the model developed by these researchers is validated by additional testing, in the future this could help doctors prescribe the most appropriate treatments to their patients, since they will already know which agents will be most effective against their type of myeloma.
The second paper [https://www.nature.com/articles/s41375-018-0206-x ], published by a group from Australia, focusses on genomic data from patients who were sequenced at each of the 3 stages of myeloma: MGUS, smoldering myeloma (SMM), and symptomatic myeloma (MM). They were interested in tracking the number and appearance of cancer clones, or families of cancer cells, that cause myeloma. With the help of CoMMpass data, the researchers found that the same families of clones were found in the same patient at all 3 stages of their disease, indicating that their cancer cells did not change very much while these patients progressed from one stage to the next. This could mean that the microenvironment where the cancer cells grow, inside the bone marrow, may cause the cancer cells to change, and this may have a larger role in myeloma progression than previously thought. This also may suggest that early intervention in MGUS and SMM patients may eliminate these early clones and perhaps prevent progression altogether. The MMRF, in collaboration with the Perelman Family Foundation, has already committed $4M to better understand the role that genomics play in early disease progression, and how the microenvironment can affect early disease progression. Find out more about our Prevention Project here.{ https://themmrf.org/we-are-curing-multiple-myeloma/the-mmrf-prevention-project }
