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Medically reviewed by Dr. Shani S. Saks, D.O. on August 2, 2023

In this study of multiple myeloma, patients who experienced a complete response had longer survival periods and higher 5-year survival rates compared to non-complete response patients. The development of a unique prediction model based on transcriptome data allowed for the identification of individuals with favorable or unfavorable treatment responses, enabling more precise treatment selection and improved clinical outcomes in multiple myeloma patients receiving their first therapy.

Multiple myeloma (MM) is a significant hematologic malignancy afflicting adults, with an incidence of approximately 6.3 per 100,000 people per year. As an incurable plasma disease, MM causes severe complications, including hypercalcemia, renal failure, anemia, and bone lesions. 

Chromosomal instability is a frequent characteristic of MM, resulting in copy number and structural alterations to the genome. Numerous chromosomal gains or losses, structural variations, and mutations in cancer-driver genes have been identified in MM genomic profiles.

A previous study identified recurrent mutations in genes such as KRAS, NRAS, BRAF, and FAM46C that can impact treatment response. In addition, frequently mutated genes, such as TTN and MUC16, have been identified in MM. 

In this study, over 200 multiple myeloma patients from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study who received a combination of bortezomib, lenalidomide, and dexamethasone as first-line therapy were subjected to a comprehensive genomic and transcriptomic landscape analysis. The study was published in the journal Clinical and Experimental Medicine.

Study Population

For this investigation, whole-exome sequencing (WES) data were obtained from the MMRF-COMPASS project for 959 MM patients who received first-line treatment with bortezomib, lenalidomide, and dexamethasone. In addition to clinical data, the transcriptome data of 787 patients were also obtained from the same source. After careful selection, 280 patients with MM were enrolled, and their paired transcriptome data were collected for analysis.

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Differential Treatment Response and Survival Outcomes
Complete response (CR) patients had substantially longer survival times and higher 5-year survival rates than non-CR patients in this study.

Somatic Mutations and Recurrently Mutated Genes
The analysis of somatic mutations revealed that all samples contained mutations, with the top 11 most commonly mutated genes accounting for the vast majority (92.9%) of cases. KRAS, NRAS, IGLL5, TENT5C, and BRAF, which are known to be frequently mutated, showed mutation frequencies consistent with previous findings. In addition, frequently mutated genes such as TTN and MUC16 were discovered.

Prognostic Significance of TP53 Mutation

Notably, TP53 mutation was associated with poor prognosis and treatment resistance in non-CR patients, whereas CR patients exhibited distinct TP53 mutational hotspots. Analysis of mutational signatures revealed differences in mutagenic factors related to treatment response, with apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) activity serving as the primary driver of mutations in MM.

Multiple Gene Mutations Affect Treatment and Prognosis

Multiple genes, including IGLL5, MUC16, and TP53, exhibited substantially different mutation rates between CR and non-CR groups, indicating their potential as biomarkers for stratifying treatment response. Investigation revealed disruptions in oncogenic pathways such as RTK-RAS, WNT, NOTCH, and Hippo in both groups, with certain disruptions affecting a greater proportion of patients in the CR group. A comprehensive examination of gene combinations revealed encouraging prognostic values for CR patients and identified potential biomarkers for personalized treatment.

Therapeutic Targets and Biological Process Alterations in CR Patients

Substantial disruptions in the NF-kappa B signaling pathway, cell adhesion molecules, malaria, and legionellosis have been discovered, indicating potential therapeutic targets for MM patients. In addition, gene set enrichment analysis (GSEA) revealed alterations in biological processes between CR and non-CR patients, such as FLT3 activity and the TGF-β pathway being suppressed, and the MAPK pathway activated and reactive oxygen species (ROS) clearance enhanced in CR patients, which may contribute to treatment response.

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A New Predictive Model for Initial Therapy for MM Patients

Using least absolute shrinkage and selection operator (LASSO) regression analysis, a predictive model based on transcriptomic data that included 20 genes with promising predictive accuracy was developed. The model showed promise in identifying patients with favorable or unfavorable treatment responses, allowing for more precise treatment selection and enhanced clinical outcomes.

Source:

Zheng, B., Yi, K., Zhang, Y., Pang, T. Y., Zhou, J., He, J., Lan, H., Xian, H., & Li, R. (2023). Multi-omics analysis of multiple myeloma patients with differential response to first-line treatment. Clinical and Experimental Medicine, 1-14. https://doi.org/10.1007/s10238-023-01148-4