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Unlocking the Mysteries of Multiple Myeloma: A Closer Look at Secretion Patterns
Posted: Mar 26, 2024
Unlocking the Mysteries of Multiple Myeloma: A Closer Look at Secretion Patterns image

The following research was conducted by: 

Jorge Arturo Hurtado Martinez, MD1, Patricia Alejandra Flores Pérez, MD1, Karla Mariana Castro Bórquez, MD1, Nathan W. Sweeney, PhD2, Andrea Isabel Robles Espinoza, MD1, Andrea Jimena Cuevas Vicencio, MD1, Eduardo Franco Hernandez, MD1, Marilú Nájera Flores, MD1, Ana M. Sahagun Sanchez Aldana, MS1, Jennifer M. Ahlstrom, BS1 and Jay R. Hydren, PhD1, (1)HealthTree Foundation, Lehi, UT, (2)Tempus, Chicago, IL

In the complex landscape of multiple myeloma (MM), a rare subset called Non-Secretory Multiple Myeloma (NSMM) has caught the attention of researchers. Traditionally defined by the absence of detectable M-Spike in serum or urine, recent advancements in diagnostic methods have unveiled a more nuanced understanding. This study delves into the evolution of MM secretion patterns and their impact on monitoring and prognosis.

Unveiling the Complexity of Nonsecretory Myeloma 

Non-Secretory Multiple Myeloma (NSMM), constituting 1-2% of all MM cases, was historically characterized by the absence of detectable M-Spike. However, modern diagnostic tools have revealed that a portion of these cases is more accurately classified as oligo-secretory, where myeloma proteins are present in limited amounts. Understanding these secretion patterns is crucial for effective monitoring and management.

The Study's Approach

Using data from the HealthTree Cure Hub, researchers analyzed real-world information from 140 MM patients. They classified patients into different secretion patterns: Secretory (SC), Light Chain Only Oligosecretory (LCO), Heavy Chain Oligosecretory (HCO), and True Non-Secretory (TNSC). Changes in secretion patterns over time were documented by comparing diagnoses with the latest lab follow-up.

Key Findings

  • Of the 140 patients analyzed, 48% were classified as Secretory (SC), 28.5% as Light Chain Only Oligosecretory (LCO), 10% as Heavy Chain Oligosecretory (HCO), and 13.5% as True Non-Secretory (TNSC) at their last follow-up.
  • Notably, 11.5% of patients transitioned to less secretory types over time, with varying speeds of evolution.
  • The study did not find a retrospective impact of secretion pattern evolution at the onset of the disease, challenging previous assumptions about its association with worse prognosis at relapse.
  • However, accurate documentation of secretion patterns in patient records remains a challenge, with one in four patients having inaccurate or missing information.

Conclusions and Future Directions

This study sheds light on the dynamic evolution of MM secretion patterns, challenging assumptions about the additional risk conferred by NSMM at the onset. The findings emphasize the need for improved documentation and awareness in clinical records. As research continues, these insights aim to contribute to a better understanding of MM and pave the way for enhanced diagnostic and therapeutic strategies, offering hope for improved outcomes for patients grappling with this complex condition.


Background: Non-secretory multiple myeloma (NSMM), 1-2% of all multiple myeloma (MM) cases, was traditionally defined by the absence of detectable M-Spike in serum or urine. Advancements in diagnostic methodologies have revealed that a proportion are more accurately categorized as oligo-secretory. Disease evolution significantly impacts monitoring, progression, and prognosis. This study examines MM secretion patterns (SP) and evolution by comparing current classification with medical notes. 

Methodology: Using HealthTree Cure Hub, we analyzed real-world data from 140 patients. SP evolution was assessed as Secretory (SC), Light Chain Only Oligosecretory (LCO), Heavy Chain Oligosecretory (HCO), and True Non-secretory (TNSC) at diagnosis and last lab follow-up. TNSC patients showed no myeloma activity on the M-protein and light chain assay. LCO patients exhibited activity only on their light chain assay. HCO patients met the IMWG criteria for Non-Measurable Myeloma. SC patients didn't meet the criteria for the other categories. Changes to less secretory patterns were recorded based on office notes, indicating a relapse diagnosis. 

Results: Patients were 65±8.5 yr, and 56.5% were female. Of the 122 patients with mSMART stage available, 13% were high-risk, and 6% were double-hit. At their last follow-up, the SPs of the 140 patients analyzed were as follows: 48% SC, 28.5% LCO, 10% HCO, and 13.5% TNSC patients. When comparing SPs at the time of MM diagnosis, 16 patients (11.5%) were found to have changed to a less secretory type. This change took a mean of 66.5 months from the time of diagnosis, with SC-to-HCO (43.25 mo) and HCO-to-TNSC (34.5 mo) being the fastest and SC-to-TNSC being the slowest (96.5 mo). Among the 73 patients with LCO, HCO, or TNSC patterns, 75.3% had their SP accurately reported, 12.3% had it incorrectly classified, and 12.3% were not classified. The median TTNTs of the observed events at the last follow-up using the SP at diagnosis were: SC (39 mo), LCO (50 mo), HCO (23 mo), and TNSC (33 mo) (p = 0.63). The median TTNTs, when using the current SP, were: SC (32 mo), LCO (50 mo), HCO (31.5 mo), and TNSC (33 mo) (p = 0.44). After two years of follow-up, the TTNT survival rates using the SP at diagnosis were: SC (53.3%), LCO (65.9%), HCO (38.5%), and TNSC (50%). The TTNT survival rates, when using the current SP, were: SC (50.7%), LCO (67.5%), HCO (42.9%), and TNSC (57.9%).

Conclusions: Our study elucidates the dynamic evolution of MM SPs, with 11.5% of patients transitioning to less secretory types. The study findings suggest that the NSMM at onset does not confer additional risk. Interestingly, we found no retrospective impact of SP evolution at disease onset, which has been linked to a worse prognosis at relapse. However, one in four patients had inaccurate or missing SP information in their office notes, highlighting the need for improved documentation and awareness.

Interested in more myeloma research findings from our HealthTree team? 

Read here: HealthTree Research Publications

The author Jay Hydren, PhD, CSCS

about the author
Jay Hydren, PhD, CSCS

I’m a clinical researcher, with over 14 years of experience investigating various aspects of human health, nutrition and physiology. My PhD encompassed the broad topics of nutrition and integrative physiology with particular focus on age related diseases and vascular health. My most recent work focuses on accelerating a cure and treatments for Multiple Myeloma. I’m also working to improve patient experiences and decision-making processes for cancer treatment and care. To complement these critical research efforts, I enjoy hiking and skiing in Utah and surrounding states, along with training my dog and digital photography. 

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