Early Prognostic Value of Monitoring Serum Free Light Chain in Patients with Multiple Myeloma Undergoing Autologous Stem Cell Transplantation

The role of serum-free light chain ratios in the prediction of poor prognosis in multiple myeloma patients: a systematic review and meta-analysis

BACKGROUND

The association between the serum free light chain (sFLC) ratio and the prognosis of multiple myeloma (MM) patients is controversial.

AIM

The purpose of this study is to explore the relationship between the sFLC ratio and the prognosis of MM patients through meta-analysis.

METHODS

Online public databases were searched to find relevant studies. The retrieval time is limited from the establishment of the database to July 2021. The overall survival (OS) and progression-free survival (PFS) rates were compared. The results were described using hazard ratio (HR) and a 95% confidence interval (CI). Qualitative studies were also included.

RESULTS

A total of 9 studies involving 2864 participants were included. A pooled analysis based on four studies including newly-diagnosed MM patients, demonstrated that an abnormal sFLC ratio was associated with poor outcomes of OS (HR = 1.82, 95% CI: 1.15-2.90) and PFS (HR = 1.87, 95% CI: 1.20-2.90). Three qualitative studies showed that an abnormal sFLC ratio was related with poor outcomes of OS (studies all included newly diagnosed MM patients) and PFS (two studies included newly-diagnosed MM patients and one study included non-newly-diagnosed MM patients). Two studies stated that the sFLC ratio is not associated with OS (both studies included non-newly-diagnosed MM patients) and one study reported that the sFLC ratio is not associated with PFS (study included non-newly-diagnosed MM patients).

CONCLUSION

sFLC ratio could be used to predict adverse outcomes in newly-diagnosed MM patients, but is not suitable for non-newly-diagnosed MM patients.

Clinical Application of Biomarkers for Hematologic Malignancies

Over the last decade, significant advancements have been made in the molecular mechanisms, diagnostic methods, prognostication, and treatment options in hematologic malignancies. As the treatment landscape continues to expand, personalized treatment is much more important.

With the development of new technologies, more sensitive evaluation of residual disease using flow cytometry and next generation sequencing is possible nowadays. Although some conventional biomarkers preserve their significance, novel potential biomarkers accurately detect the mutational landscape of different cancers, and also, serve as prognostic and predictive biomarkers, which can be used in evaluating therapy responses and relapses. It is likely that we will be able to offer a more targeted and risk-adapted therapeutic approach to patients with hematologic malignancies guided by these potential biomarkers. This chapter summarizes the biomarkers used (or proposed to be used) in the diagnosis and/or monitoring of hematologic neoplasms.;

Prognostic and predictive biomarker developments in multiple myeloma

New approaches to stratify multiple myeloma patients based on prognosis and therapeutic decision-making, or prediction, are needed since patients are currently managed in a similar manner regardless of individual risk factors or disease characteristics. However, despite new and improved biomarkers for determining the prognosis of patients, there is currently insufficient information to utilise biomarkers to intensify, reduce or altogether change treatment, nor to target patient-specific biology in a so-called predictive manner. The ever-increasing number and complexity of drug classes to treat multiple myeloma have improved response rates and so clinically useful biomarkers will need to be relevant in the era of such novel therapies. Therefore, the field of multiple myeloma biomarker development is rapidly progressing, spurred on by new technologies and therapeutic approaches, and underpinned by a deeper understanding of tumour biology with individualised patient management the goal. In this review, we describe the main biomarker categories in multiple myeloma and relate these to diagnostic, prognostic and predictive applications.

Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers

Background

To assess the quantification of tumor burden in multiple myeloma (MM) patients using whole-body magnetic resonance imaging (MRI) and to identify the correlation between MRI parameters and prognostic biomarkers.

Methods

We retrospectively analyzed 95 newly diagnosed MM patients treated at our hospital from June 2018 to March 2020. All patients underwent whole-body MRI examination, including diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging (mDIXON), and short TI inversion recovery (STIR) sequences. The MRI presentation was used to determine MM infiltration patterns and calculate apparent diffusion coefficient (ADC) and a fat fraction (FF). The one-way ANOVA and Kruskal-Wallis test were used to compare the differences of these values between DS, ISS, and R-ISS stages in different MM infiltration patterns. Spearman correlation test was used for correlation analysis of ADC and FF against prognostic biomarkers, and two independent sample t-test was used to evaluate the differences of ADC and FF in different free light-chain ratio groups.

Results

The MRI presentation was classified into normal pattern (36 patients; 37.9%), diffuse (27 patients; 28.4%), and focal (32 patients; 33.7%) infiltration patterns. Statistically significant ADC and FF differences between different DS, ISS, and R-ISS stages were observed in normal/diffuse infiltration patterns but not in focal infiltration patterns. The ADC and FF of the normal/diffuse infiltration pattern showed correlations with hemoglobin, β2-microglobulin, bone marrow plasma cells, flow cytometry of bone marrow cells, and serum monoclonal protein. In contrast, ADC in focal infiltration patterns was negatively correlated with β2-microglobulin and C-reactive protein. The FF of patients with a normal/diffuse infiltration pattern was higher in the low free light-chain ratio group than that in the high free light-chain ratio group (P=0.023).

Conclusions

Our observations indicate that quantitative whole-body functional MRI examination may serve as an effective complement to imaging diagnosis based on morphology and provide further information on the tumor burden of patients with MM.

Current and future biomarkers for risk-stratification and treatment personalisation in multiple myeloma

Multiple myeloma, an incurable malignancy of the plasma cells in the bone marrow, has a complex pathogenesis due to clonal heterogeneity. Over the years, many clinical trials and researches have led to the development of effective myeloma treatments, resulting in survival prolongation. Molecular prognostic markers for risk-stratification to predict survival, and predictive markers for treatment response are being extensively explored. This review discusses the current risk-adaptive strategies based on genetic and molecular risk signatures that are in practice to predict survival and describes the future prognostic and predictive biomarkers across the fields of genomics, proteomics, and glycomics in myeloma. Gene expression profiling and next generation sequencing are coming to the forefront of risk-stratification and therapeutic-response prediction. Similarly, proteomic and glycomic-based platforms are gaining momentum in biomarker discovery to predict drug resistance and disease progression.

[Evaluation and comparison of prognostic value of serum free light chain ratio/difference in patients with newly diagnosed multiple myeloma]

目的

比较评估初诊时血清游离轻链比值（rFLC）及差值（dFLC）对多发性骨髓瘤（MM）患者预后的价值。

方法

收集整理2012年1月至2016年3月有FLC检测记录的479例初治MM患者的相关临床资料，采用四分位间距法将rFLC、dFLC进行分组。将rFLC分别为3组：≤14.828、14.828~364.597、≥364.597。将dFLC分为3组：≤112.85 mg/L、112.85~2 891.83 mg/L、≥2 891.83 mg/L。随后分别对不同分组进行预后分析比较。使用Kaplan-Meier进行无进展生存（PFS）和总生存期（OS）比较，使用Cox回归进行单因素、多因素预后相关性分析。

结果

不同截断范围rFLC或dFLC患者的OS及PFS差异均有统计学意义。rFLC≤14.828组OS明显优于其他两组（未达到对61个月对47个月，P=0.019）；PFS与rFLC 14.828~364.597组相比，差异无统计学意义（P=0.227），与rFLC≥364.597相比，差异有统计学意义（P=0.024）。dFLC≤112.85 mg/L组与其他两组相比PFS、OS差异均有统计学意义。单因素、多因素分析显示rFLC仅与患者OS显著相关，而dFLC与患者OS、PFS均显著相关。4年OS率比较，rFLC≤14.828组4年OS率达90.84%，明显高于其他两组（59.29%、62.26%）；dFLC≤112.85 mg/L组4年OS率达89.97%，明显高于其他两组（41.32%、71.95%）（P<0.05）。

结论

对于初治MM患者，不同截断范围的rFLC、dFLC对患者生存预后影响不同。其中rFLC≤14.828或dFLC≤112.85 mg/L，其生存预后差异更为明显，且具有更低的死亡风险，危险比也更低。以此截断值判断患者预后较为理想。

Novel biomarkers in multiple myeloma

Significant advancements have been made in the molecular mechanisms of myelomagenesis, diagnostic methods, prognostication, and the treatment options in multiple myeloma (MM) over the last decade. Despite these, MM remains a heterogeneous disease with differing outcomes. As myeloma treatment landscape continues to expand, personalized treatment that provides maximum benefit to a specific patient becomes more important. In the last few years, serum monoclonal proteins including the serum-free light chain assays, imaging, and cytogenetics have been used to predict the outcomes of MM patients receiving different types of therapies. With the development of novel technologies, more sensitive detection of residual disease using flow cytometry and next-generation sequencing has been possible. In addition, liquid biopsies using circulating tumor cells, tumor DNA, and novel immune biomarkers are potentially being investigated. These novel potential biomarkers not only accurately detect the mutational landscape of different cancers compared to standard methods but also serve as prognostic and predictive biomarkers for disease relapse and response to therapy. It is likely that we will be able to offer more targeted and risk-adapted therapeutic approach to patients with MM at different stages of their disease guided by these potential biomarkers.

Using Both Lactic Dehydrogenase Levels and the Ratio of Involved to Uninvolved Free Light Chain Levels as Risk Factors Improves Risk Assessment in Patients With Newly Diagnosed Multiple Myeloma

BACKGROUND

This study aimed to evaluate the prognostic value of the ratio of involved to uninvolved free light chain (rFLC) levels and lactic dehydrogenase (LDH) levels in the risk stratification of patients with multiple myeloma (MM).

MATERIALS AND METHODS

Clinical data of 283 patients with newly diagnosed MM were retrospectively analyzed.

RESULTS

In the traditional chemotherapy group, patients with an rFLC < 100 had a better prognosis than those with an rFLC ≥ 100 (40 months versus 6 months, P = 0.022), as did patients with an LDH ≤ upper limit of normal (ULN) compared to those with an LDH > ULN (29 months versus 6 months, P = 0.023). In patients who underwent novel drug-combined therapy, no significant difference was observed between the rFLC < 100 group and the rFLC ≥ 100 group (54 months versus median not reached, P = 0.508). However, patients with an LDH ≤ ULN had a better prognosis than those with an LDH > ULN (60 months versus 21 months, P = 0.004). Using an rFLC ≥ 100 and an LDH ≥ ULN as adverse risk factors, patients were classified into 3 groups: group 1 (no adverse risk factors), group 2 (1 adverse risk factor) and group 3 (2 adverse risk factors). The median overall survival (OS) of groups 1, 2 and 3 was 52 months, 34 months and 15 months, respectively (P = 0.001).

CONCLUSIONS

rFLC and LDH levels were sensitive prognostic factors in MM patients, combining them could improve the risk stratification and treatment choice of patients in clinical practice.

Established and Novel Prognostic Biomarkers in Multiple Myeloma

Multiple myeloma (MM) is an incurable plasma cell malignancy characterized by notable interpatient heterogeneity. There have been important advances in therapy and overall survival, but some patients with high-risk features still have poor survival rates. Therefore, accurate identification of this subset of patients has been integral to improvement of patient outcome. During the last few years, cytogenetics, gene expression profiling, MRI and PET/CT, as well as serum free light chain assays have been used as accurate biomarkers to better characterize the diverse course and outcome of the disease. With the recent advances of massive parallel sequencing techniques, the development of new models that better stratify high-risk groups are beginning to be developed. The use of multiparameter flow cytometry and next-generation sequencing have paved the way for assessment of minimal residual disease and better prognostication of post-therapeutic outcomes. Circulating tumor cells and circulating tumor DNA are promising potential biomarkers that demonstrate the spatial and temporal heterogeneity of MM. Finally, more prognostic markers are being developed that are specific to immunotherapeutic agents. In this review, we discuss these traditional and novel biomarkers that have been developed for MM and also those that can predict disease progression from precursor stages. Together, these biomarkers will help improve our understanding of the intrapatient and interpatient variabilities and help develop precision medicine for patients with high-risk MM.