Comparison of Transcriptomic Changes in Younger and Older Multiple Myeloma Patients from the MMRF-CoMMpass Study RNA-Seq Data

Merve Keskin^*
*Correspondence: Merve Keskin, Department of Genetics, University of Aziz Sancar, Istanbul, Turkey, Email:

Keywords

Multiple myeloma • Differentially expressed genes • RNA-Seq analysis • Age related differences • Transcriptome changes

Introduction

Multiple Myeloma (MM) is a malignancy of bone marrow terminally differentiated plasma cells, a disease mostly affects the elderly, diagnosed at a median age approximately 66-70 years and characterized by diverse and complex tumor genetics. It is extremely rare in those less than 30 years of age with a reported frequency of 0.02% to 0.3% [1]. Aging is a risk factor for many diseases and cancer is one of these, however the age-related biological characteristics of cancer are still not elucidated at the molecular level. Comparing the clinical and genetic features of patients presenting at an early age with the presentation of older age is important to better understand the pathogenesis of the disease and to understand whether it is necessary to evaluate different treatment options in different age groups [2]. In a recent study that analyzed multiple cancer genome, transcriptome and methylome data from the TCGA (The Cancer Genome Atlas) cohort to understand the biology of cancers in younger versus older individuals, six TCGA tumors showed an age-associated outcome and molecular landscape. The study also demonstrated that aging-associated dysfunction is tumor type specific and suggested that could lead to different therapeutic strategies [2].

In another comprehensive study, using the TCGA dataset with a multi-omics approach, the largest age-related genomic differences were found in gliomas and endometrial cancer, and the study identified age-related global transcriptomic changes that were partly regulated by age-associated DNA methylation changes [3]. Independent studies comparing clinical features of younger and older MM patients have reported varying results, with some reporting no difference in presenting features of young and old MM patients [4], while other studies reported bone lytic lesions, renal impairment, hypercalcemia, light-chain myeloma and extramedullary disease are higher in younger patients [1,5,6]. Most studies concluded that survival was significantly better in the younger patients compared with older patients [7-10] although one of the studies claimed that this difference was lost when adjusted for differences in life expectancy [4].

Age related cytogenetic anomalies in MM were studied in a large cohort study comparing young and elderly patient groups and conventional cytogenetic analysis showed no difference in the frequency of any cytogenetic abnormality [11], however, another study reported that translocation t(11; 14) was more prevalent in younger myeloma patients and it is related to poor outcome [6]. Another study investigating mutational signatures showed that mutational signatures did not substantially differ between age groups, except for the higher rate of APOBEC (SBS2 and 5) in the group>80 years and older and also found that simple and complex structural variants and the prevalence of chromothripsis increased with age [2]. However, there is no information available yet about the differences between young and old MM patients at transcriptome level, which is important to understand the possible different pathogenic processes and determine the need for different treatment strategies.

The aim of this study is to compare the RNA-Seq data of the MMRF (Multiple Myeloma Research Foundation) CoMMpass study in young and old patient groups, to identify differentially expressed genes and examine the biological pathways and drug interactions associated with these genes.

Materials and Methods

Data collection

All data in this study were generated as part of the Multiple Myeloma Research Foundation (MMRF) Personalized Medicine Initiatives. The MMRF CoMMpass cohort (NCT01454297) includes 1143 multiple myeloma patients from 84 clinical centers located in the United States, Canada, Spain, and Italy; in which tumor samples were characterized using whole genome, exome, and RNA sequencing at diagnosis and progression, and clinical data was collected every three months. The cohort was conducted in accordance with the Declaration of Helsinki and all patients provided written informed consent.

In this study, 634 newly diagnosed MM patients from CoMMpass study, whose bone mar-row sample CD138⁺ cells RNA-Seq data available were included. RNA-Seq STAR method un-stranded counts of 634 patients in the IA17 cohort of the MMRF CoMMpass trial were employed to detect DEGs. The RNA-seq and clinical data were downloaded on 1 July 2022, the source files “MMRF_CoMMpass_IA17_star_geneUnstranded_counts. tsv”and“MMRF_CoMMpass_IA17_PER_PATIENT_V2.tsv”.

Study design

The study design and the workflow of this study is summarized in Figure 1. All R codes used to analyze the data. (Figure 1).

Count normalization: All statistical analyses in this study were performed with the statistics software R and R packages developed by Bioconductor project. The expression level of each gene was summarized and normalized using the DESeq2 R/Bioconductor package which by default uses the median of ratios method [12].

Differential gene expression analysis: Differential gene expression analyses were performed using DESeq2 pipeline [12]. p values were adjusted with the default option of the DESeq2 package. Genes were considered differentially expressed if they had an adjusted p value <0.05 and a | log₂ fold change| >0.58.

Gene ontology and gene set enrichment analysis: Gene Ontology (GO) pathway and Kyoto Encyclopedia of Genes and Genes (KEGG) Gene-Set Enrichment Analysis (GSEA) were performed using R package cluster Profiler [13] to investigate the biological function and signaling pathway of DEGs, with a p value <0.05 as cut-off value for significance.

Gene-drug interaction analysis: The interactions of the top 20 significant DEGs with prescription drugs were identified using the Drug Gene Interaction Database (DGIdb) v4.2.0.

Results

Patient characteristics

A total of 634 patients in the MMRF CoMMpass cohort with bone marrow CD138+cells RNA-Seq data were included in this study. Patients were aged 45-93 years (median 64) and were categorized as under 50: young or 50 years and over: old. The characteristics of the patient groups are summarized in Table 1.

Differential gene expression

Globally, RNA-Seq differential gene expression analysis showed 523 genes differentially expressed with p<0.05 and |log₂ FC | >0.58 in young patients, relative to older patients. Overall, 366 of them were upregulated and 157 were downregulated. Normalized RNA-Seq counts of all genes with labeled DEGs are shown in Figure 2. Full list of the significant DEGs is included in Supplementary Data 1 (Figure 2).

Top 20 significant DEGs ranked by adjusted p values were all upregulated in younger patients. The plot of normalized counts of the top 20 DEGs is shown in Figure 3. Among these genes, BCL2A1 and FGR were proto-oncogenes; FCGR1A, FCER1G and TLR2 were related to immune regulation. The description and possible relation to oncogenic pathways of the top 20 significant DEGs are summarized in S1Table.

GO term and KEGG analysis

Further analysis of KEGG gene-set enrichment and GO term enrichment were performed to find the overview functions of differentially expressed genes. GO enrichment analysis revealed that significant pathways were mostly associated with immune regulation. Totally, 220 GO bio-logical pathway terms were enriched with adjusted p values between 3.78 × 10-8-0.04, and q values between 3.29 × 10-8 -0.04. The top 50 significant GO terms are shown in Figure 4. Full list of significant GO terms with p and adjusted p values are included in Supplementary Data 2. Among these, the top five significant GO terms were leukocyte migration, myeloid leukocyte migration, T cell activation, regulation of lymphocyte activation and cytokine-mediated signaling pathway. The category net plot showing the related DEGs with these pathways are summarized in Figure 5.

By KEGG Gene Set Enrichment Analysis (GSEA), only one gene set, “cytokine-cytokine receptor interaction”) pathway was enriched (adjusted p value 0.01 and q value 0.007) in young MM patient DEGs. GSEA plot of the enriched cytokine-cytokine receptor interaction pathway is shown in Figure 6.

Gene-drug interactions

To explore the possible treatment options for younger MM patients, we performed a drug-gene interaction analysis of the top 20 significant DEGs with prescription drugs using DGIdb search tool. The name of drugs with associated genes is listed in S2 Table.

Discussion

In this study, RNA-Seq data analysis of young and older patient groups showed that the DEGs in young patient group were mostly related to immune regulation. The only enriched pathway in GSEA was the "cytokine-cytokine receptor interaction". Although these findings are not surprising considering age-related physiological immunity changes and the plasma cell origin of the disease, it is not yet known whether the immune pathway regulation changes are associated with different pathological processes in different age groups in MM. In a recent review re-view [14], age-associated molecular differences in various cancer types were examined indicating that immune-related pathways were enriched in different age groups in several cancers. A decline in T cell abundance by age, higher mutation burden, increased expression, and decreased promoter methylation of immune checkpoint genes were reported about immune regulation pathways. A better understanding of age-related immune regulation in MM could reveal which patients will benefit from specific immunotherapy. Among the top 20 significant DEGs detected in this study, FCGR1A, FCER1G and TLR2 were directly associated with immune regulation.

FCGR1A encodes a protein that plays an important role in the immune response and it is highly expressed in various cancer types. In a recent study FCGR1A was found associated with infiltrating levels of CD4⁺ T cells, CD8⁺ T cells, B cells, macrophages, neutrophils, and dendritic cells in four cancers and suggested as a potential prognostic biomarker [15]. FCER1G (Fc fragment Of IgE receptor Ig) is involved in the innate immunity. In a recent study, the expression level of FCER1G is found negatively correlated with myeloma progression, and high FCER1G expression suggested as a favorable biomarker in MM patients [16].

Toll Like Receptors (TLRs) are key immune receptors that recognize conserved molecular patterns expressed by pathogens and damaged cells. TLR activation causes inflammatory responses, modulation of cell cycle, apoptosis, or regulation of cell metabolism. In multiple myeloma, TLRs signaling is dysregulated and the expression of TLR1, TLR2 and TLR6 seem to be higher in MM patients than in healthy donors and TLRs are suggested as future therapy targets in MM [17,18].

Furthermore, another DEG found in this study, PGLYRP1, encodes a protein that has peptidoglycan binding activity and involved in humoral immune response [19]. Further investigation of the roles of these genes involved in immune regulation may shed light on innovations in the immunotherapy of the disease.

In addition to the genes involved in immune regulation, known protooncogenic genes (BCL2, FGR), genes under investigation for association with various cancers (RGL4, MT-RNR1, ETS2, ENPP3, FUT7, NTNG2, PRAM1) and an antiangiogenic antitumoral gene (PTX3) were found in the top 20 significant DEGs in young patients.

B-cell lymphoma 2-related protein A1 (BCL2A1) is one of the anti-apoptotic BCL2 proteins, which is a target of nuclear factor jB (NF-jB) gene and it is expressed in the hematopoietic system in physiologic conditions. However, BCL2A1 is overexpressed in a variety of cancer cells [20]. In a recent study, it is reported that disease relapse in MM is related to in-creased NFκB pathway activity, which causes increased BCL2A1 expression [21]. Considering the upregulated BCL2A1 expression in young MM patients, it can be hypothesized that BCL2A1 may be a target for treatment with the current anti-cancer drug obatoklax mesylate currently used in acute myeloid leukemia and lymphoma.

The proto-oncogene FGR belongs to the Src family of tyrosine kinases; in physiological conditions it is expressed in hemopoietic cells. FGR overexpression promotes the development and rapid progression of Acute Myeloid Leukemia (AML), ovarian and colorectal cancers [22]. In the drug-gene interaction analysis conducted in this study, drugs related to the pathways regulated by BCL2 and FGR genes and used in the treatment of various cancer types were identified. Further research is needed to understand the possible benefits of using these drugs in young MM patients.

RGL4, is known to be a regulator of the Ras pathway involved in cell proliferation. Although it has been suggested to contribute to the development of some T-cell malignancies [23], its role in certain cancer types and multiple myeloma has not been elucidated. MT-RNR1 over-expression can be associated with increased mtDNA levels in malignant transformation of myeloma cells. But its role in pathogenesis and as a possible treatment target remain unclear [24]. ETS2 is a downstream effector of the RAS/ RAF/ERK pathway, which plays a critical role in cell proliferation also in the development of tumors. Its over-expression is reported as a poor prognostic factor in Acute Myeloid Leukemia [25]. ENPP3 (ectonucleotide pyrophosphatase/phosphodiesterase 3) is known to be expressed in renal tubules, activated basophils and mast cells. Among cancers, it is expressed by renal cell carcinoma cells and anti-ENPP3 therapy is being investigated [26].

FUT7 is related to e-selectin ligand synthesis and its over-expression in MM was found to be related to significant inferior progression-free survival [27]. NTNG2 gene encodes a membrane binding netrin family protein, reported as related to various cancers and is shown as potential new tumor marker and therapeutic target Hao et al. [28] but its role in multiple myeloma pathogenesis has not been elucidated yet.

PRAM1 encodes an adaptor protein is expressed and regulated during normal myelopoiesis. While it is known to be related to promyelocytic leukemia, it is not considered as a general oncogene [29-34].

There is a need to investigate the effects of expression differences of these genes in MM, which have various roles in various cancers.

Conclusion

Only one gene, PTX3, was found in the top DEGs with antitumoral effects. PTX3 (Long Pentraxin 3) encodes a natural FGF antagonist that causes an anti-angiogenic effect. Moreover, an increased FGF/PTX3 ratio occurs during MM evolution. In a recent study, PTX3 overexpression was found associated with significantly reduced tumor burden in MM models. In this study, by comparing the transcriptome data of young and old MM patients, differentially expressed genes with oncogenic and immunological effects were identified. Replication of these research findings with independent data and supporting the information about the differentially expressed genes with functional studies may shed light on new treatment strategies.

Declarations

Ethical approval and consent to participate

Not applicable for studies not involving humans.

Human and animal ethics

Not applicable for studies not involving humans and animals.

Acknowledgements

All data in this study were generated as part of the Multiple Myeloma Research Founda-tion Personalized Medicine Initiatives. The author thanks Sema Sirma Ekmekci (Istanbul University Aziz Sancar Institude of Experimental Medicine Department of Genetics) and Beyza Oluk (Istanbul University Medical Faculty Department of Hematology for encouraging work on multiple myeloma.

References

1. Kazandjian, Dickran. “Multiple Myeloma Epidemiology and Survival: A Unique Malignancy.” Semin Oncol 43(2016): 676-681.

   [Crossref] [Google Scholar]

2. Shah, Yajas, Akanksha Verma, Andrew Marderstein and Jessica White, et al. “Dengue and Dengue Hemorrhagic Fever: Management Issues in an Intensive Care Unit.” Cell Rep 37(2021): 110100.

   [Crossref] [Google Scholar]

3. Chatsirisupachai, Kasit, Tom Lesluyes, Luminita Paraoan and Peter Van Loo, et al. “An Integrative Analysis of the Age-Associated Multi-Omic Landscape across Cancers.” Nat Commun 12(2021): 2345.

   [Crossref] [Google Scholar]

4. Corso, Klersy, Lazzarino and Bernasconi. “Multiple Myeloma in Younger Patients: The Role Of Age as Prognostic Factor.” Ann Hematol 76(1998): 67-72.

   [Crossref] [Google Scholar]

5. Duek, Trakhtenbrot, Avigdor and Nagler, et al. “Multiple Myeloma Presenting in Patients Younger than 50 Years of Age: A Single Institution Experience.” Acta Haematol 144(2021): 58-65.

   [Crossref] [Google Scholar]

6. Pydi, Venkateswar Rao, Stalin Chowdary Bala, Siva Prasad Kuruva and Rachana Chennamaneni, et al. “Multiple Myeloma in Young Adults: A Single Centre Real World Experience.” Indian J Hematol Blood Transfus 37(2021): 679-683.

   [Crossref] [Google Scholar]

7. Jurczyszyn, Artur, Hareth Nahi, Irit Avivi and Alessandro Gozzetti, et al. “Characteristics and Outcomes of Patients with Multiple Myeloma Aged 21–40 Years versus 41-60 Years: A Multi-Institutional Case-Control Study.” Br J Haematol 175(2016): 884-891.

   [Crossref] [Google Scholar]

8. Boyle, Eileen, Louis Williams, Patrick Blaney and Cody Ashby, et al. “Influence of Aging Processes on the Biology and Outcome of Multiple Myeloma.” Blood 136(2020): 8-9.

   [Crossref] [Google Scholar]

9. Ludwig, Heinz, Vanessa Bolejack, John Crowley and Joan Bladé, et al. “Survival and Years of Life Lost in Different Age Cohorts of Patients with Multiple Myeloma.” Clin Oncol 28(2010): 1599-1605.

   [Crossref] [Google Scholar]

10. Pál, Ildikó, Árpád Illés and László Váróczy. “Multiple Myeloma of the Young-A Single Center Experience Highlights Future Directions.” Pathol Oncol Res 26(2020): 419-424.

    [Crossref] [Google Scholar]

11. Ludwig, Heinz, Brian Durie, Vanessa Bolejack and Ingemar Turesson, et al. “Myeloma in Patients Younger than age 50 Years Presents with More Favorable Features and Shows Better Survival: An Analysis of 10 549 Patients from the International Myeloma Working Group.” Blood 111(2008): 4039-4047.

    [Crossref] [Google Scholar]

12. Love, Michael, Wolfgang Huber and Simon Anders. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol 15(2014): 550.

    [Crossref] [Google Scholar]

13. Yu, Guangchuang, Li-Gen Wang, Yanyan Han and Qing-Yu He. “clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters.” OMICS J Integr Biol 16(2012): 284-287.

    [Crossref] [Google Scholar]

14. Chatsirisupachai, Kasit, Cyril Lagger and João Pedro de Magalhães. “Age-Associated Differences In The Cancer Molecular Landscape.” Trends Cancer 8(2022): 962-971.

    [Crossref] [Google Scholar]

15. Xu, Ji-li and Yong Guo. “FCGR1A Serves as a Novel Biomarker and Correlates with Immune Infiltration in Four Cancer Types.” Front Mol Biosci 7(2020): 1-14.

    [Crossref] [Google Scholar]

16. Fu, Lin, Zhiheng Cheng, Fen Dong and Liang Quan, et al. “Enhanced Expression of FCER1G Predicts Positive Prognosis in Multiple Myeloma.” J Cancer 11(2020): 1182-1194.

    [Crossref] [Google Scholar]

17. Olaia Akesolo, Berta Buey, Manuel Beltrán-Visiedo and David Giraldos, et al. “Toll-Like Receptors: New Targets for Multiple Myeloma Treatment?” Biochem Pharmacol 199(2022): 114992.

    [Crossref] [Google Scholar]

18. Thakur, Krishan, Nityanand Bolshette, Cristiana Trandafir and Vinayak Jamdade, et al. “Role of Toll-Like Receptors in Multiple Myeloma and Recent Advances.” Exp Hematol 43(2015): 158-167.

    [Crossref] [Google Scholar]

19. Romanova, Elena, Tatiana Sharapova, Georgii Telegin and Alexei Minakov, et al. “A 12-mer Peptide of Tag7 (PGLYRP1) Forms a Cytotoxic Complex with Hsp70 and Inhibits TNF-Alpha Induced Cell Death.” Cells 92(2020): 1-13.

    [Crossref] [Google Scholar]

20. Vogler. “BCL2A1: The Underdog in the BCL2 Family.” Cell Death Differ 19(2012): 67-74.

    [Crossref] [Google Scholar]

21. Spaan, Ingrid, Anja van de Stolpe, Reinier Raymakers and Victor Peperzak. “Multiple Myeloma Relapse Is Associated with Increased NFκB Pathway Activity and Upregulation of the Pro-Survival BCL-2 Protein BFL-1.” Cancers 13(2021): 4668.

    [Crossref] [Google Scholar]

22. Perez, Ivette, Sandra Berndt, Rupesh Agarwal and Manuel Castro, et al. “A Model for the Signal Initiation Complex between Arrestin-3 and the Src Family Kinase Fgr.” JMB 434(2022): 167400.

    [Crossref] [Google Scholar]

23. Leonardi, Peter, Ezra Kassin, Inmaculada Hernandez-Muñoz and Roberto Diaz, et al. “Human rgr: Transforming Activity and Alteration in T-cell Malignancies.” Oncogene 21(2002): 5108-5116.

    [Crossref] [Google Scholar]

24. Ruiz-Heredia, Yanira, Alejandra Ortiz-Ruiz, Mehmet Samur and Vanesa Garrido, et al. “Pathogenetic and Prognostic Implications of Increased Mitochondrial Content in Multiple Myeloma.” Cancers 13(2021): 3189.

    [Crossref] [Google Scholar]

25. Fu, Lin, Huaping Fu, Qingyun Wu and Yifan Pang, et al. “High Expression of ETS2 Predicts Poor Prognosis in Acute Myeloid Leukemia and May Guide Treatment Decisions.” J Transl Med 15(2017): 159.

    [Crossref] [Google Scholar]

26. Thompson, John, Robert Motzer, Ana Molina and Toni Choueiri, et al. “Phase I Trials of Anti-ENPP3 Antibody-Drug Conjugates in Advanced Refractory Renal Cell Carcinomas.” Clin Cancer Res 24(2018): 4399-4406.

    [Crossref] [Google Scholar]

27. Natoni, Smith, Keane and Ellistrim, et al. “E-Selectin Ligands Recognised by HECA452 Induce Drug Resistance in Myeloma, Which is Overcome by the E-Selectin Antagonist, GMI-1271.” Leukemia 31(2017): 2642-2651.

    [Crossref] [Google Scholar]

28. Hao, Wenjun, Meng Yu, Jiaxing Lin and Bitian Liu, et al. “The Pan-Cancer Landscape of Netrin Family Reveals Potential Oncogenic Biomarkers.” Sci Rep 10(2020): 1-17.

    [Crossref] [Google Scholar]

29. Moog-Lutz, Christel, Erik Peterson, Pierre Lutz and Steve Eliason, et al. “PRAM-1 Is a Novel Adaptor Protein Regulated by Retinoic Acid (RA) and Promyelocytic Leukemia (PML)-RA Receptor α in Acute Promyelocytic Leukemia Cells.” JBC 276(2001): 22375-22381.

    [Crossref] [Google Scholar]

30. Ronca, Roberto, Sara Taranto, Michela Corsini and Chiara Tobia, et al. “Pentraxin 3 Inhibits the Angiogenic Potential of Multiple Myeloma Cells.” Cancers 13(2021): 2255.

    [Crossref] [Google Scholar]

31. Freshour, Sharon, Susanna Kiwala, Kelsy Cotto and Adam Coffman, et al. “Integration Of The Drug–Gene Interaction Database (Dgidb 4.0) with Open Crowdsource Efforts.” Nucleic Acids Res 49(2021): 1144-1151.

    [Crossref] [Google Scholar]

32. Gilder, Andrew, Letizia Natali, Danielle Van Dyk and Cristina Zalfa, et al. “The Urokinase Receptor Induces a Mesenchymal Gene Expression Signature in Glioblastoma Cells and Promotes Tumor Cell Survival in Neurospheres.” Sci Rep 8(2018): 2982.

    [Crossref] [Google Scholar]

33. Tsao, Betty and Yun Deng. “Constitutive Genes and Lupus.” SLE (2011): 47-61.

    [Crossref] [Google Scholar]

34. Wang, Jin, Shihai Xu, Wen Lv and Fei Shi, et al. “Uridine Phosphorylase 1 is A Novel Immune-Related Target and Predicts Worse Survival in Brain Glioma.” Cancer Med 9(2020): 5940-5947.

    [Crossref] [Google Scholar]