Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma
Two key abstracts were included in this morning’s myeloma session that used data from the MMRF CoMMpass StudySM to expand our understanding of myeloma patients’ specific genomic profiles (Abstracts 60 and 61).
Brian Walker, PhD from the Myeloma Institute, University of Arkansas for Medical Sciences presented his work (ABSTRACT 60) using data from the MMRF CoMMpass Study. He set out to identify genes that have the potential to cause the development of multiple myeloma (also known as oncogenes) by looking specifically for mutations (ie, a defect or error) in genes. The main findings that are important to know:
- After analyzing the dataset, Dr. Walker found 26 potential oncogenes: 11 of which have been previously identified (confirming other scientists’ work) and 9 that are new.
- He was also able to assess that mutations in these oncogenes happen early in the course of the development of myeloma.
- Ultimately, knowing which oncogenes are mutated, physicians can identify new targets for myeloma therapy.
The complete baseline cohort from the MMRF CoMMpass dataset was also used by our colleagues at TGen (ABSTRACT 61) to seek out whether fusion transcripts occur in patients with myeloma. Fusion transcripts are sometimes created when a chromosomal translocation occurs in myeloma cells (that is, when segments of 2 chromosomes switch positions). If present, they may help us to understand how myeloma cells develop and grow. What we learned from today’s presentation is:
- 903 different fusion transcripts were identified
- New findings included fusion transcripts that activate genes involved in cellular pathways that promote myeloma cell growth and survival (such as NF-kB) and inactivate genes that suppress myeloma cell growth
These abstracts both confirm how CoMMpass data can be used to identify new targets for drug discovery and development; stay tuned for more updates including data from new clinical trials that will be presented over the next couple of days!
Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis
This evening’s myeloma session gave us four more presentations based on data from the MMRF CoMMpass Study (Abstracts 267, 268, 270). In this session, talks focused on identifying patients with high-risk myeloma by their genomic profile to guide therapy choice and ways that myeloma cells become resistant to treatments.
Michael Chapman from the University of Cambridge (ABSTRACT 267) identified a 7-gene signature that predicts response to Velcade- and Revlimid-based therapy using RNA sequencing data from the CoMMpass Study. The signature successfully predicted which patients would benefit from Velcade and Revlimid therapy with respect to response and survival. The signature may be a potential tool to use for precision medicine.
The mechanisms contributing to the poor clinical outcome in patients who have the amplification of chromosome 1q was presented by Alessandro Lagana, PhD and colleagues from Medicine at Mount Sinai (ABSTRACT 268). Dr. Lagana looked at the role of a specific enzyme (ADAR) that is responsible for RNA editing (an important function for regulating normal protein production in cells) since the gene for this enzyme is located on chromosome 1 which is commonly amplified in myeloma patients. RNA samples from newly diagnosed MM patients derived from the MMRF CoMMpass Study were tested to investigate the implications of ADAR activity and they found:
- ADAR copy number was amplified in 19% of the samples tested and was high in those patients who had amplification of chromosome 1
- High ADAR expression is associated with activation of interferon (a protein that is produced by cells in response to infection or tumors)
- High RNA editing activity is associated with resistance to proteasome inhibitors such as bortezomib
Finally, Santiago Barrio Garcia, PhD from the University Hospital Wuerzburg in Germany (ABSTRACT 270) presented his work on the mechanisms of resistance to immunomodulatory drugs (IMiDs) like Revlimid. Using genomic sequencing data from newly diagnosed patients from the CoMMpass study, Dr. Garcia and colleagues identified mutations in genes that are critical for the anti-tumor effects of IMiDs. Indeed, these mutations occurred more frequently in patients after treatment, most often in the gene that codes for the protein cereblon—a key modulator of the anti-tumor effects of IMiDs.