Landscape and clinical significance of long noncoding <scp>RNAs</scp> involved in multiple myeloma expressed fusion transcripts
Authors: Ane Amundarain, Luis V. Valcárcel, Raquel Ordoñez, Leire Garate, Estíbaliz Miranda, Xabier Cendoya, Arantxa Carrasco‐Leon, María José Calasanz, Bruno Paiva, Cem Meydan, Christopher E. Mason, Ari Melnick, Paula Rodriguez‐Otero, José I. Martín‐Subero, Jesús San Miguel, Francisco J. Planes, Felipe Prósper, Xabier Agirre
Journal: American Journal of Hematology (2021)

Multiple myeloma (MM) is a hematologic neoplasm characterized by a clonal expansion of malignant plasma cells (PCs) in the bone marrow, showing clinical, genetic, and epigenetic heterogeneity. Chromosomal translocations are one of the hallmarks of MM, and mainly involve the immunoglobulin heavy chain locus (IGH). These translocations usually result in the placement of various oncogenes under the control of IGH, leading to the up-regulation of genes that provide a selective growth advantage to MM cells.1 Five recurrent IGH translocations have been described in MM; however, in many cases, the second gene involved is not defined in routine clinical analyses. Besides, recent studies have reported novel recurrent fusion partners and novel non-IGH fusions beyond well-known translocations.2, 3 Nevertheless, these approaches did not consider the normal counterpart of B-cells, which may provide new insights regarding the role of fusion transcripts (FT) in MM. Furthermore, MM is also associated with deregulation of long noncoding RNAs (lncRNA), a group of genes with increasing relevance in cancer.4 Various studies suggest the involvement of lncRNAs in chromosomal translocations; however, this has not been assessed in MM. Here, to define the landscape of expressed FTs in MM, we analyzed the strand-specific RNA-seq (ssRNA-seq) data of 35 samples obtained from 6 different B-cell subpopulations (5 naïve, 7 centroblast, 7 centrocyte, 8 memory, 5 tonsillar PC, and 3 bone marrow [BM] PC samples) obtained from 11 healthy donors (8 tonsil and 3 BMPCs) and PCs from 37 MM patients, paying particular attention to FTs involving lncRNAs (lncFT). Using the STAR-Fusion algorithm, we initially identified 2169 FTs. After applying several computational filtering steps, we defined 1454 FTs expressed in B-cells and MM samples (Figure S1). The highest numbers of FTs were detected in healthy donor PCs (tonsillar plasma cells [TPC] and BMPC) (Figure S2A-B), and based on the biological relevance of IG genes in B-cells and malignant PC, detected FTs were classified into IG and REST (none of the associated genes corresponded to an IG gene) categories (Figure S2C–E). The 82.5% of FTs detected in healthy PCs occurred with IG genes, harbored very few reads per transcript, and were only supported by junction reads without any spanning reads covering the non-IG partner gene. Therefore, FTs that were not supported by at least one spanning read were filtered out, resulting in the final detection of 208 expressed FTs in normal B-cells and MM cells (Figure S1A). To validate our results and identify FTs consistently detected, we also applied ARRIBA and STAR-SEQR algorithms to our cohort with the same filters described above. One hundred and fifty-eight FTs were detected by at least two algorithms and were selected for further analyses after a quality check step (Appendix S1; Figures S1A, S2F–I; Table S1). These expressed FTs were detected in every cell population, with a significantly higher number of FTs in MM cells (median of 3 ± 2.97) (Wilcoxon p-value <.001) (Figure S2G). A similar number of reads per FT was detected in all cell subpopulations (analysis of variance [ANOVA] p-value .382), suggesting that FTs are expressed consistently at low levels (Figure S2G), and most of the expressed FTs occurred between two non-IG partners (Figure S2H). Characteristic features of B-cells include IG gene rearrangement and active transcription of IG genes, leading to the transcription of thousands of similar transcripts from these loci,5 which could be misidentified as FTs in these cells. Thus, cell-specific features should be considered when implementing an adequate FT detection pipeline for each cell type to exclude false positive events. Furthermore, the presence of FTs in normal B-cells indicates that FTs are not exclusive to tumor cells, suggesting that FTs may contribute to transcriptional diversity in healthy tissues. From the 158 FTs, we filtered out those detected in at least one normal B-cell sample to focus on MM-specific FTs, leading to the identification of 79 expressed FTs (61 unique) (Figure S1A, Table S2), 29.5% of which had not been previously described (Figure 1A). At least one expressed FT was identified in 75.7% of the MM samples (Figure 1B), indicating that some FTs emerge specifically after malignant transformation. A Human Phenotype ontology analysis of coding genes involved in these 61 unique FTs showed a significant enrichment of genes associated with B lymphocyte dysfunction phenotypes (p-adj <.05), suggesting that important genes for B-cell abnormalities and MM pathogenesis may be more prone to FT formation. Most of the MM-expressed FTs showed an overall low read count, with some exceptions (Figure 1C). As previously described,2 85.3% of MM FTs were patient-specific, but 9 were recurrently expressed FTs (Figure 1D). A total of 88.5% of MM-specific FTs were derived from the fusion between 2 non-IG partners and IG FT percentages were lower than those previously reported2, 3 probably due to our smaller cohort size (Figure 1E). Nevertheless, we identified the IGH-NSD2 expressed FT derived from t(4;14) in two patients and two MM cell lines (Figure S3A–C), and described a novel FT between the genes GBE1 and KIF20B in one MM cell line (Figure S3D–F). Recent studies have shown the implication of lncFTs in MM, such as lncFTs with PVT1, but a complete characterization of the lncFT transcriptome is still pending.3 We observed that the 27.9% of MM-specific expressed FTs were lncFTs (Figure 1F), some of them leading to the overexpression of the associated lncRNA (Figure 1G,H), as in the case of FTs involving oncogenes.2, 3 Interestingly, a relevant fraction of MM-specific FTs occurred between two adjacent genes in the same DNA strand, being defined as transcription read-throughs (RT) (Figure 1I), a novel class of MM-specific FT. A total of 64.3% of RTs involved a lncRNA as a fusion partner gene (Figure 1J), some of them were detected in both MM patient samples and cell lines with a low expression and were validated in cell lines through real-time quantitative reverse transcription PCR (qRT-PCR) and Sanger sequencing (Figure S3G–I). Furthermore, we found recurrences for three RTs with lncRNAs, such as the FT between AC092691.1 and LSAMP (Figure 1K), showing an increased expression of the AC092691.1 in comparison to normal PCs and MM samples without the FT (Figure 1L). The presence of functional oncogenic lncFTs and RTs has been reported for other tumor types,6 suggesting that lncFTs could be important in MM, but additional studies will be needed to determine their role in MM. Finally, we analyzed whether lncFTs might have an impact on the outcome of MM patients by analyzing expressed FTs in 599 MM patients included in the MMRF CoMMpass data set release IA15. We used the intersection of STAR-Fusion and ARRIBA, identifying 556 expressed lncFTs. Interestingly, we found that 35% of MM-specific unique lncFTs defined in our cohort were present in the CoMMpass data set. We observed various FTs between lncRNAs and IG genes (IGK-FAM230C, IGH-LINC-PINT), suggesting that MM patients with IG translocations could involve both coding and noncoding partner genes, and that lncRNAs could explain some of the MM cases in which the associated IG gene in translocations remained unknown. We validated the robustness of our algorithm by comparing the number of patients in which we detected the IGH-NSD2 FT with those patients in which t(4;14) was detected by whole-genome sequencing (WGS), identifying IGH-NSD2 FT in 75 of the 79 samples positive by WGS, and additionally, detecting the expression of this FT in 2 other MM samples where WGS for t(4;14) was negative (Fisher's exact test p-value = 6.7e-88). To assess whether the lncFTs could be associated with prognosis in MM, we selected those lncFTs that were detected in more than 2% of MM patients (Figure S4A), and we evaluated the combination of lncFTs and the defined high-risk genetic markers1 (International Staging System [ISS] stage, t(4;14), t(14;16), t(14;20), del(17p), deletion of CDKN2C, del(1p), amp(1q), and mutations of TP53) using a multivariate coxph model and BIC to select the optimal number of variables. We discovered that the expression of 3 lncFTs (TEX35-AL37796.1, AL050309.1-KLF8, and PVT1-IGL), together with the ISS stage and TP53 mutations resulted in a significantly lower progression-free survival (PFS) (global p < .0001) and the model stratifies the MM patients according to the number of events they have (an event consists of having any of the five risk factors) into four risk groups (Figure 1M,N). Similarly, expression of 1 lncFT (TEX35-AL37796.1) together with the ISS stage, del(17p) or amp(1q) also resulted in statistically significant worse overall survival (OS) (global p < .0001), identifying five groups with significant differences in their OS (Figure 1O,P). An ANOVA test comparing the models derived from high-risk genetic factors only or combining them with lncFTs resulted in a significant improvement for the combination of both risk factors for PFS (p-value = 6.3e−5, Figure S4B) and OS (p-value = .019, Figure S4C). These findings should be validated in other MM cohorts, but our results suggest that lncFTs in MM could contribute to a better patient stratification, impacting in patient management in terms of treatment choice or contributing to the identification of specific subgroups of patients suitable for personalized therapies. In summary, this study provides the first comprehensive landscape of expressed lncFTs and RTs in MM, demonstrating that FTs may also be expressed in normal B-cells and that expression of recurrent lncFTs may have a significant impact in PFS and OS in MM patients. This research was funded by grants from Instituto de Salud Carlos III and co-financed by European Regional Development Fund-FEDER "A way to make Europe" (PI16/02024, PI17/00701 and PI19/01352, CIBERONC CB16/12/00489, Spanish Ministry of Economy, Industry and Competitivity (RTHALMY SAF2017-92632-EXP), with support from 616/C/2019 Fundació La Marató de TV3, Departamento de Salud-Gobierno de Navarra 40/2016 and Fundación Ramón Areces (PREMAMM). The study was also supported by Cancer Research UK [C355/A26819] and FC AECC and AIRC under the Accelerator Award Programme, the Multiple Myeloma Research Foundation Networks of excellence 2017 Immunotherapy Program Grant Award, the International Myeloma Foundation (Brian van Novis), the Qatar National Research Fund award 7-916-3-237, and Paula and Rodger Riney Foundation.Ane Amundarain was supported by FPU (FPU17/02733) grant from Ministerio de Ciencia, Innovación y Universidades, Gobierno de España. Luis V. Valcárcel and Arantxa Carrasco-Leon were supported by PFIS (FI17/00297 and FI16/00275, respectively) award from Instituto de Salud Carlos III (ISCIII). Xabier Cendoya was supported by Basque Government with the grant promoting doctoral theses to young predoctoral researchers (PRE_2018.2.0297). These data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org). The authors declare no competing financial interests. Conception and design: Ane Amundarain, Luis V. Valcárcel, Felipe Prósper, Xabier Agirre. Development of methodology: Ane Amundarain, Luis V. Valcárcel, Raquel Ordoñez, Felipe Prósper, Xabier Agirre. Acquisition of data and assistance with experiments: Ane Amundarain, Luis V. Valcárcel, Raquel Ordoñez, Leire Garate, Estíbaliz Miranda, Xabier Cendoya, Arantxa Carrasco-Leon, María José Calasanz, Cem Meydan, Christopher E. Mason, Ari Melnick, Francisco J. Planes, Felipe Prósper, Xabier Agirre. Analysis and interpretation of data: Ane Amundarain, Luis V. Valcárcel, Leire Garate, Estíbaliz Miranda, Xabier Cendoya, Bruno Paiva, Cem Meydan, Ari Melnick, Paula Rodriguez-Otero, José I. Martín-Subero, Jesús San Miguel, Francisco J. Planes, Felipe Prósper, Xabier Agirre. Writing, review, and/or revision of the manuscript: Ane Amundarain, Luis V. Valcárcel, Felipe Prósper, Xabier Agirre and all authors reviewed and approved final version of the manuscript. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Luis V. Valcárcel, Cem Meydan, Christopher E. Mason, Francisco J. Planes. Study supervision: Felipe Prósper and Xabier Agirre. ssRNA-seq data of MM patients is available at GEO under accession number GSE151063, and ssRNA-seq data of healthy samples are available under accession codes GSE114816 and GSE114803. Figure S1. FT detection pipeline and initial homology filtering. Figure S2. FT in healthy B differentiation and MM samples. Figure S3. Validation of expressed FT detected in MM cohort. Figure S4. FT detection in the CoMMpass dataset. Table S1. Spanning and junction reads, similarity score, and GC percentage of FTs detected in B cell differentiation and MM patient samples. Table S2. Expressed FTs detected in MM samples. Appendix S1. Supplementary Data. 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