Can you identify high-risk MM more effectively?

The biology of multiple myeloma (MM) is very heterogeneous, and prognosis among patients varies substantially. Survival can vary from a few weeks to 20 years. The range of therapy options has become more extensive over time; however, not all patients with MM benefit equally from these options.

In this second article from the current editorial theme on high-risk MM, we summarize an outstanding session about high-risk MM held at the Virtual Edition of the 25th European Hematology Association (EHA) Annual Congress. Nikhil Munshi, Multiple Myeloma Hub Steering Committee member,¹ and Jill Corre² discussed the current approach for risk assessment in myeloma, the need for a more robust model to identify patients with high-risk disease, and its clinical implications.

What is high-risk myeloma?¹

High-risk myeloma accounts for around 25% of patients with MM and is characterized by a short survival of 2–3 years, mostly due to drug resistance and early relapse. Extramedullary disease in soft tissue and higher levels of circulating tumor cells have also been associated with shorter survival. Other clinical factors affecting survival are the response to therapies, or lack of it, the early relapse after transplantation (irrespective of cytogenetics), and the disease stage by the International Staging System (ISS).

Genetic analyses are frequently used to identify patients with higher risk. It is essential to understand MM biology and behavior (i.e., how they react to anti-tumor therapies or how they continue to grow) to differentiate patients at higher risk of progression or death. A summary of cytogenetic abnormalities (CAs) associated with poor prognosis is provided in Table 1.

Table 1. The incidence of recognized cytogenetic abnormalities associated with poor prognosis¹

 The determination of a valid threshold to assess the prognostic impact of these CAs is a recurrent topic of discussion, but crucial for comparison between studies and to obtain clinically meaningful data. For example, a del(17p) level of ≥ 60% in plasma cells has been associated with shorter progression-free survival (PFS) and overall survival (OS) compared with a level of < 60% (p < 0.001).

We talked recently with Nikhil Munshi about the genomics of high-risk MM; listen to his key points on this topic here:

How can we assess the risk?²

In the era of multiple treatment options, risk assessment is considered vital to choose the best therapeutic approach based on individual features, not only at the time of diagnosis but also at the first relapse. This assessment may include several prognostic factors related to the following:

• Patient characteristics, e.g., age and comorbidities
• Tumor load, e.g., cytopenia
• Intrinsic cellular features, e.g., genetic abnormalities, plasma cell proliferation index
• Other factors, e.g., hypoalbuminemia, renal failure, extramedullary disease, or the depth and duration of treatment response

A combination of different risk factors may result in ultra-high-risk MM. To date, the most important prognostic factors are considered the genetic abnormalities in tumor plasma cells and the quality of the response to treatment.

Genetic abnormalities in MM²

Two main pathways are widely accepted as primary events in MM:

• IgH translocations—seen in almost all myeloma cell lines and up to 50% of patients with MM
• Hyperdiploidy—trisomies are not random, mainly occurs in odd chromosomes (3, 5, 7, etc.), and seen in more than 50% of patients

These primary events are thought to cause secondary events, including TP53 inactivating mutations and KRAS mutations, resulting in more aggressive disease.

Additionally, the range of mutational load per patient is wide, with an average of 58.46 (range, 10–500) mutations. There is no single unifying mutation, yet the mutational landscape greatly varies with low recurrence, KRAS and NRAS being the most mutated genes. A minimum of four gene mutations occurs in > 10% of patients with MM.

As plasma cells are non-proliferating cells, it is hard to obtain them at metaphase. Therefore, advanced cytogenetic techniques are required to identify genetic abnormalities in MM. Fluorescence in situ hybridization (FISH) has been frequently used to define genetic abnormalities, allowing them to identify prognostic subgroups. A more advanced technique, next-generation sequencing (NGS), allows more comprehensive testing by identifying mutations, translocations, and the copy-number variation.

Is it time for an update for the current high-risk definition?²

The International Myeloma Working Group (IMWG) defines del(17p), t(4;14), and t(14;16) as high-risk genetic factors. The incidence and characteristics of these and related evidence are summarized in Table 2.

Table 2. Genetic factors associated with high-risk MM by the IMWG¹

 Del1p32 and 1q gain abnormalities are also considered to be high-risk genetic factors associated with shorter OS. Del1p32 occurs in up to 10% of patients and has shown similar impact as del(17p), while 1q gain affects almost 35% of patients with newly diagnosed MM (more patients at relapse), but to date, it is considered to have a lower impact on prognosis. We will analyze this further, considering recently published data, in the next article for the current editorial theme.

Proposed Intergroupe Franchophone du Myélome (IFM) risk-stratification model

The debate about established genetic factors leads to an update of the criteria used to define the cytogenetic risk more effectively.

In 2019, the IFM published a new model based on six independent variables (measured by FISH in isolated cells), and considered more convenient as it does not require a comprehensive technique and can be done via FISH only.³ Below is a summary of the cytogenetic prognostic index, but a more detailed article about the development and validation of the model can be found here.

A specific coefficient was assigned to these variables based on weighted prognostic value (Table 3):

Table 3. Coefficient assigned to each variable³

 The prognostic scores (the sum of coefficients of present abnormalities among six variables) are defined as follows (Table 4):

Table 4. IFM Cytogenetic Prognostic Index³

Among patients with del(17p) and those with t(4;14), 94% and 29% had high-risk disease, respectively. High-risk patients had shorter survival compared with those with standard or low risk (p < 0.001).

Response to treatment was evaluated based on minimal residual disease (MRD) by next-generation flow (NGF) or NGS. PFS analysis showed that negative MRD resulted in similar PFS irrespective of the risk group, while patients with positive MRD and high-risk disease had significantly shorter PFS compared with those classified as standard risk (p < 0.001 and p < 0.0001 for NGS and NGF, respectively).

Moreover, early relapse after first-line treatment (before 18 months) has been associated with shorter OS (p < 0.001). Interestingly,

• 70% of early relapse events were observed in patients with standard-risk disease
• patients who experienced an early relapse and classified as low risk at diagnosis had shorter OS compared with those with high-risk disease who relapsed > 18 months after frontline therapy

 The IFM risk-stratification model identifies high risk as del(17p) > 55% of plasma cells, some t(4;14) abnormalities, and a combination of different intermediate factors, such as poor responses or early relapses. However, the impact of other genetic factors, like TP53 mutations and 1q amplification combined with ISS III, still needs further evaluation.

Conclusion

Risk assessment based on a few genetic abnormalities is not considered enough to classify patients or define personalized therapeutic approaches. The IFM model adds response/relapse status and a combination of intermediate factors to the current risk definition. The innovation in this model is the use of a favorable factor (trisomy 5), and chromosome 1 and its weighted value.

The lack of knowledge on the biology of myeloma and its impact on risk assessment is well recognized, but treatment strategies have been evolving in line with an increased understanding of disease biology. The advances in genomic identification are vital, not only to identify new therapeutic targets for improved treatment results but also to review the definition of risk and better evaluate prognosis in patients with MM.

In clinical practice, regular MRD assessments may help change treatment strategies to target the remaining tumor cells. Besides, cytogenetic and clonality assessments in routine practice may be useful at the time of diagnosis, maintenance, and first relapse after the initial treatment to observe the change from diagnosis using a more comprehensive technique like NGS. There is no doubt that further evaluation is needed to understand the cytogenetics and the biology of myeloma. Risk is considered dynamic and subject to change with new data and technological developments available.