A ligand-receptor interactome of the bone tumor microenvironment in multiple myeloma bone pain

Background & Aims

Bone pain is a common symptom in multiple myeloma (MM) patients. Despite the availability of multiple pharmacological options, patients report inadequate analgesic control. Mechanisms of MM bone pain (MMBP) are complex, including both peripheral and central components. The peripheral MM bone microenvironment comprises many cell types that could stimulate sensory neurons through ligand-receptor interactions, where ligands are presented on the cellular surface or secreted, and then bind to receptors expressed on the surface of sensory neurons. Ligand-receptor pairs represent druggable targets via an antibody against the ligand or the receptor, receptor antagonist or inhibitor, or interfering with ligand secretion. To assess MM microenvironment diversity and identify pro-nociceptive interactions, we conducted computational analyses of transcriptomic and proteomic datasets. In addition, we identified upstream regulatory kinases, which control the expression and release of these mediators.

Methods

To characterize the molecular changes of the MM cellular microenvironment, we employed gene expression rank-rank hypergeometric overlap (RRHO) and gene set enrichment analysis (GSEA). RRHO analysis uncovers differentially expressed genes (DEGs) whose expression overlaps among different cell types. GSEA analysis considers signaling pathways consisting of related genes instead of comparing individual genes. To find ligand–receptor pairs, transcriptomic datasets of MM bone marrow cell types were analyzed to find DEGs, and transcriptomic datasets of sensory neurons were analyzed to identify genes expressed in sensory neurons. Then, a subset of MM DEGs and sensory neuron genes was selected based on being annotated as ligands or receptors, respectively. Ca2+ imaging of primary sensory neurons was used to validate the neuro-stimulatory effect of 7 selected ligands. We then used enrichment analysis and signaling network construction to investigate the upstream regulators of secreted ligands.

Results

RRHO analysis showed that MM induces cell type-specific changes in bone marrow cells. GSEA analysis also showed limited overlap in activated cellular programs across different MM microenvironment cell types. Interactome analysis resulted in detecting 348 ligands across 12 different MM microenvironment cell types. There was a small overlap in detected ligands across MM microenvironment cell types, and even smaller if considering the direction of their regulation. Of those, selected ligands (IGF1, MIF, NRG2, WNT5A, THBS1, FGF7, and SEMA6A) induced a rise in intracellular Ca2+ levels in primary cell cultures of murine dorsal root ganglia neurons. Kinase enrichment analysis and signaling network construction showed the involvement of MAPK14 and CSNK2A1 in the regulation of potential pro-nociceptive ligands of MM microenvironment cell types. These kinases could be targeted by currently developed kinase inhibitors.

Conclusions

This study investigated the heterogenous cellular microenvironment of MM and how it could influence MMBP through various ligand-receptor pairs. One of the main findings is the set of distinct ligand-receptor pairs that could be involved in MMBP. However, considering the complexity of MMBP, the identification and targeting of shared regulatory molecules could be an alternative option for MMBP. To this end, our computational analysis proposes MAPK14 and CSNK2A1 inhibitors as candidate analgesics in MMBP.

References

M. P. Davies, S. Fingas, A. Chantry, Mechanisms and treatment of bone pain in multiple myeloma. Current Opinion in Supportive and Palliative Care 13, 408-416 (2019)10.1097/spc.0000000000000467).

M. Diaz-delCastillo, A. D. Chantry, M. A. Lawson, A.-M. Heegaard, Multiple myeloma—A painful disease of the bone marrow. Seminars in Cell & Developmental Biology 112, 49-58 (2021); published online Epub2021/04/01/ (https://doi.org/10.1016/j.semcdb.2020.10.006).

A. García-Ortiz, Y. Rodríguez-García, J. Encinas, E. Maroto-Martín, E. Castellano, J. Teixidó, J. Martínez-López, The Role of Tumor Microenvironment in Multiple Myeloma Development and Progression. Cancers 13, 217 (2021).

S. B. Plaisier, R. Taschereau, J. A. Wong, T. G. Graeber, Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Research 38, e169-e169 (2010)10.1093/nar/gkq636).

A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, J. P. Mesirov, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 102, 15545-15550 (2005) doi:10.1073/pnas.0506580102).

E. Armingol, A. Officer, O. Harismendy, N. E. Lewis, Deciphering cell–cell interactions and communication from gene expression. Nature Reviews Genetics 22, 71-88 (2021); published online Epub2021/02/01 (10.1038/s41576-020-00292-x).

A. Wangzhou, C. Paige, S. V. Neerukonda, D. K. Naik, M. Kume, E. T. David, G. Dussor, P. R. Ray, T. J. Price, A ligand-receptor interactome platform for discovery of pain mechanisms and therapeutic targets. Science Signaling 14, eabe1648 (2021) doi:10.1126/scisignal.abe1648).

A. Jain, B. M. Gyori, S. Hakim, S. Bunga, D. G. Taub, M. C. Ruiz-Cantero, C. Tong-Li, N. Andrews, P. K. Sorger, C. J. Woolf, Nociceptor neuroimmune interactomes reveal cell type- and injury-specific inflammatory pain pathways. bioRxiv, 2023.2002.2001.526526 (2023)10.1101/2023.02.01.526526).

D. J B. Clarke, M. V. Kuleshov, B. M. Schilder, D. Torre, M. E. Duffy, A. B. Keenan, A. Lachmann, A. S. Feldmann, G. W. Gundersen, M. C. Silverstein, Z. Wang, A. Ma’ayan, eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Research 46, W171-W179 (2018)10.1093/nar/gky458).

A. Liu, P. Trairatphisan, E. Gjerga, A. Didangelos, J. Barratt, J. Saez-Rodriguez, From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. npj Systems Biology and Applications 5, 40 (2019); published online Epub2019/11/11 (10.1038/s41540-019-0118-z).