HMMR is an independent prognostic indicator in neuroblastoma and loss of HMMR suppresses cell proliferation, migration and clonogenicity.

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Christina Karapouliou, Vinothini Rajeeve, Pedro Cutillas, andrew stoker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5194003/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Neuroblastoma is a childhood cancer with poor survival rates. Approximately 75% of tumours have no identified oncogenic driver and here our aim was for the first time to investigate whether HMMR, a protein with hyaluronic acid (HA)-binding properties, nuclear actions, and oncogene-like roles in other cancers, harbors similar potential roles in neuroblastoma cells. Methods We bioinformatically analysed patient survival data in relation to HMMR expression, followed by CRISPR/Cas9-based disruption of HMMR in KELLY neuroblastoma cells. HMMR’s support of proliferation, motility and clonogenicity were analysed and the dependence on exogenous HA determined. Xenografted tumours with disrupted HMMR were analysed to assess animal survival characteristics. Lastly, phosphoproteomics was used to begin to define the biochemical actions of HMMR in these tumour-derived cells. Results High HMMR expression is shown to be an independent prognostic indicator of poor survival in neuroblastoma patients. Furthermore, HMMR-deficient cells in culture have reduced proliferation, motility and clonogenic capacities compared to parental cells, and HA had variable ability to rescue these. Loss of HMMR also reduces xenografted tumour growth rates. Signaling downstream of MAPK1/2 and MTOR were both disrupted at a phosphoproteomic level after loss of HMMR, while the phospho-status of DNA damage response (DDR) proteins was significantly enhanced. Conclusion This study indicates that high HMMR expression could be a new and potentially useful prognostic marker of poor neuroblastoma survival. Moreover, HMMR has oncoprotein-like properties in neuroblastoma cells, with some actions being HA-regulated. The study also reveals the first data that may implicate HMMR in MTOR and DDR regulation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Neuroblastoma is a developmental cancer of early childhood, arising from sympathoadrenal precursors [1, 2]. Tumour cells are highly heterogenous, occurring in broadly two super enhancer-regulated states of an adrenergic and mesenchymal state [3, 4]. This tumour heterogeneity contributes to the high relapse rate and poor survival of high-risk patients, representing a stubborn clinical challenge. Although the common oncogenic drivers found in adult human cancers are infrequent upon presentation in neuroblastoma [1, 2, 5], several other drivers are known including MYCN gene amplification and activations of the tyrosine kinase ALK, or tyrosine phosphatase PTPN11 [2]. Nevertheless, 75% of tumours have no clear oncogenic drives to date [2, 6]. In our search for potentially new protein drivers we have been examining the potential roles of HMMR, also known as RHAMM and CDS168 [7]. HMMR may be of interest in a neural tumour because it has been implicated in neurite extension processes in neuronal cell lines, including in NG108-15, a hybrid neuroblastoma/glioma line [8] and HMMR loss-of-function generates neurodevelopmental defects in vertebrate embryos [9]. HMMR also has oncogenic roles in several other human cancer systems, sustaining cell proliferation, survival and migration in cells derived from cancers of brain, lung, ovary, prostate, head and neck and breast [10-14]. HMMR can also promote epithelial to mesenchymal transition, chemotherapy resistance and stemness in gastric cancer cells [15]. HMMR is a cell surface hyaluronic acid (HA) receptor [16, 17]. HA and its catabolized products can promote cell proliferation and survival, motility and metastasis in tumour cells [16, 18], interacting with cells through an HMMR/CD44 complex and signaling through ERK, AKT, SRC, Rho GTPases and FAK [7, 12, 16, 19-23]. Interestingly, HHMR also acts in the nucleus, binding to microtubules and centrosomes and regulating mitotic spindles and chromosomal stability through interactions with DYNLL1 complexes, CHICA and BRCA1 [7, 24, 25]. HMMR also localises TPX2 to centrosomes through a c-terminal bZIP domain, maintaining spindle pole assembly [26-30], but operating in a negative feedback loop through the release of TPX2 after HMMR degradation by BRCA1, leading to Aurora kinase A (AURKA) activation and BRCA1 phosphorylation [28, 31, 32]. HMMR is strongly expressed in neuroblastomas and we hypothesised that it may have pro-oncogenic potential corresponding to that seen in other cancer models. Using in silico analysis we examined the relationship between high HMMR expression in human neuroblastoma tumours and prognostic outcomes. At a cellular level we also targeted HMMR for inactivation using CRISPR/Cas9 in the KELLY neuroblastoma cell line. Our analyses show that HMMR is indeed a promoter of several parameters of tumour cell behaviour and that these are variably affected by HA ligands. Lastly, we used phosphoproteomics to explore the potential biochemical roles of HMMR in neuroblastoma cells, confirming that it modulates ERK signaling and revealing potentially novel roles in MTOR and DNA damage response (DDR) pathways. 2. Materials and Methods 2.1 Cell culture KELLY cells (CVCL_2092) were provided by Prof. Frank Speleman, University of Ghent. Cells were STR genotyped in 2015 by LGC Standards. Cells were cultured at 37 o C in RPMI medium + GlutaMAX (ThermoFisher Scientific, Loughborough, UK) supplemented with 10% FBS (Life Technologies) and 100 U . mL -1 Penicillin, 0.1 mg . mL -1 Streptomycin (Sigma-Aldrich, UK). The HA types used were low (Sigma-Aldrich 40583; LMW, 5000-1000 Da), medium (Sigma-Aldrich 75044; MMW, 150000-300000 Da) and high molecular weights (Sigma-Aldrich 51967; HMW, 1.5-1.8 x 10 6 Da). HA was dissolved in media at the final concentration of 400 μg/mL. 2.2 Generation of HMMR knockout (KO) subclones The HMMR gRNA in exon 5 (CGTGTTCTTCTACAGGAACG) was designed using Benchling (San Francisco, CA, USA;RRID:SCR_013955). Plasmid co-expressing Cas9 and gRNA was purchased from VectorBuilder (Neu-Isenberg, Germany) and transfected using Lipofectamine 2000 (Thermo Fisher Scientific, USA). Cells were incubated for 4-6 days with 1mg/ml puromycin, then single cell sorted using a MoFlo XDP sorter. D NA target regions were subjected to PCR amplification using 5’-GCAACAGAGCACAGAGCAAG-3’ and 5’-ACACCAGGCGATTCAGATTC-3’ and sequenced (Source Bioscience, UK). Sequence trace analysis was performed using the ICE ANALYSIS online tool (Synthego, v2.0; Synthego, CA, USA; RRID:SCR_024508). 2.3 Cell Assays Two thousand cells were seeded in 96-well plates and cell viability was measured after 6 days with resazurin (Merck Life Science UK Ltd, Gillingham, UK). For the clonogenic assay, 400 viable KELLY cells were seeded in 6-well plates and incubated for 3 weeks. Cells were fixed using crystal violet (Sigma-Aldrich) in 25% Methanol. Colonies were counted manually using ImageJ software. For migration assays, cells were grown to confluency in 24-well plates and serum starved overnight. A 200µl pipette tip was used to make a scratch in the monolayer of duplicate wells and cells were then incubated in serum-free media with or without added 400μg/ml of HA for 24 hours. Initial and final cell-free scratch areas were measured using the wound healing size plugin in ImageJ software [33]. Differences between the 0 and 24 h was expressed as migration area. 2.4 Mouse xenografts Animals were used under a Home Office project licence, complying with the Guidance on the operation of the Animals (Scientific Procedures) Act 1986. Female NSG mice (Charles River Laboratories, Sulzfeld, Germany) were injected subcutaneously in the flank with 2×10 6 KELLY, KC17, KA5 or KA14 cells, suspended in a 1:1 PBS and Cultrex matrix (R&D Systems Inc., USA). Each injection group consisted of 3 animals per cell line (12 animals total) and injection groups were repeated three times. Mice were randomly assigned to groups and cell injections were performed in a blinded way. Mice were sacrificed when the tumors reached a maximum allowable size. Tumor volumes (in mm 3 ) were determined using [length x width 2 /2]. 2.5 Immunoblotting Cells were processed for immunoblotting as previously described [34]. Primary antibodies (Cell Signaling) were against: CD44 (#3578;RRID:AB_2076463), pERK (#9106; RRID:AB_331768); tERK (#9102;RRID:AB_330744); pAKT (#4060; RRID:AB_2315049); tAKT (#9272;RRID:AB_329827 ), p-Aurora A (#3079;RRID:AB_2061481), GAPDH (#2118;RRID:AB_561053). Anti-HMMR GTX121502 (GeneTex; RRID:AB_11163915) was also used. Protein expression was quantified by densitometry on X-ray films using ImageJ software (RRID:SCR_003070). 2.6 Phosphoproteomic study and analysis Cells were lysed in 8 M urea with phosphatase inhibitors (10 mM Na3VO4, 100 mM b-glycerol phosphate and 25 mM Na 2 H 2 P 2 O 7 (Merck Life Science UK Ltd) as described previously [35]. Four biological replicates were subjected to mass spectrometry as described previously [34]. The data analysis was performed using the Limma R package (version 3.50.1; RRID:SCR_010943) and p-values were corrected using the qvalue package (version 2.26.0; RRID:SCR_001073) from Bioconductor as described previously [36]. To correlate the differentially expressed phosphoproteins of the HMMR-deficient cells, Pearson’s correlation performed using the online platform ‘ggVolcanoR’ [37]. For the HMMR phosphoproteomic signature, proteins with fold changes of 0.5 and p≤0.05 were considered statistically significant and differentially phosphorylated from controls (78 and 79 protein lists). The heatmap was generated using Morpheus (Broad Institute). For the Kinase substrate enrichment analysis (KSEA), peptide data were processed as previously described [38]( Figure 7B ) and also with the KSEAapp tool [39] ( Online Resource File 5 ). Networks were visualised in Cytoscape [40] (RRID:SCR_003032) using STRINGAPP [41] (RRID:SCR_025009) and OMICS VISUALISER [42] plug-ins. Ingenuity Pathways Analysis (IPA) software (Qiagen, USA; RRID:SCR_008653) was used for upstream regulator analysis. The processed phosphoproteomics data are deposited with Mendeley Data and available at https://data.mendeley.com/datasets/6wr2tj8wr9/1. 2.7 Tumour data and pathway analysis Patient tumour data and Kaplan–Meier survival curves were obtained using the R2 genomics platform (http://r2.amc.nl; RRID:SCR_025770). Datasets used were: Roth (n= 504, GEO: GSE7307), Korpershoek (n= 51, GEO: GSE67066), Favier (n= 188), Delattre (n=64, GEO: GSE12460) , Versteeg (n=88, GEO: GSE16476), Kocak (n=649, GEO: GSE45547), SEQC (n=498, GEO: GSE49710), NRC (n=283, GEO: GSE85047) and TARGET-Asgharzadeh (n=249, GEO: GSE85047). Comparison analysis between differentially expressed genes were also obtained from the Oncomine TM Platform (Thermo Fisher; RRID:SCR_007834) using the studies Albino Brain (n=28, GEO:GSE7529) and Janoueix-Lerosey Brain (n=64, GEO: GSE12460). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were conducted using DAVID [43] (RRID:SCR_001881) and ShinyGO 0.80 [44] (RRID:SCR_019213). 2.8 Statistical analysis Statistical analysis and graphing used GraphPad Prism 10.0 (RRID:SCR_002798). One-way and two-way (Dunnett post hoc analysis) ANOVAs were used. Cell proliferation data were statistically analysed as part of an experimental dataset with multiple treatments but here we show only the analysis relevant to this paper. A Cox regression model was used to test for the independent predictive ability of HMMR expression after adjustment for other significant factors: MYCN amplification, age, and INSS stages. 3. Results 3.1 HMMR as an independent prognostic marker High HMMR expression has been associated with cancer progression [45], but is not yet reported for neuroblastomas. HMMR expression was elevated in neuroblastomas compared to normal tissues, benign ganglioblastomas and neural crest-derived tumour pheochromocytoma ( Figure 1A and Online Resource File 1 ). Moreover, HMMR is ranked in the top 1-5% overexpressed genes among those in the HA axis, HA binding molecules and those associated with cell motility ( Online Resource File 1 ). This supports a possible role of the HMMR gene in the establishment or progression of neuroblastoma. To further explore the role of HMMR in neuroblastomas, we examined the correlation between HMMR expression and tumour staging. Higher HMMR expression associated strongly with increased INSS tumour grade ( Figure 1B ). We found that elevated HMMR expression correlated significantly with poor overall survival in patient datasets analysed in the R2 platform ( Figure 1C ). In t-SNEA maps, elevated HMMR expression partially overlaps with MYCN-amplified patient groups, but shows a somewhat differential expression pattern, and it is also expanded to the non-AMP group ( Figure 1D ). A Cox univariate and multivariable logistic regression analysis was performed on SEQC dataset, demonstrating that HMMR expression, but not other HA-related pathway genes, is an independent risk factor for neuroblastoma patients ( Table 1 ). To clarify the potential biological functions of HMMR we examined the genes that show positive correlations with HMMR expression in 4 tumour datasets ( Online Resource File 2 ). With this 2581 gene set, the HMMR co-expression signature again correlated with poor OS survival in neuroblastoma ( Online Resource File 2 ). 3.2 HMMR promotes neuroblastoma cell growth Equipped with the prognostics data, we wished to more directly determine the cellular function of HMMR in neuroblastoma cells. To do this we used CRISPR/Cas9 in KELLY cells (strong HMMR expressors) to create out-of-frame mutations in a region encoding the HMMR N-terminus [46] ( Figure 2A ). Three HMMR knock-out (KO) clones were identified, KA5 (1 bp homozygous insertion, KA14 and KA16 (1 bp homozygous deletions; these may or may not be independent subclones) ( Figure 2B, Online Resource File 3 ). KC17 had no HMMR alteration and was included in functional analyses as a putative wild-type control. HMMR protein was absent from KA5, 14 and 16, but retained in KC17 and parental KELLY ( Figure 2B ) and subclones were morphologically similar to parental KELLY ( Online Resource File 3). HMMR depletion in these cells inhibited their proliferative expansion compared to KELLY and KC17, agreeing with a similar role in other cancer types ( Figure 2C ) [10-12]. Moreover, low density growth assays showed a very significant reduction in colony forming ability of cells lacking HMMR ( Figure 2D ), indicating a loss of clonogenic capacity. 3.3 HA ligand influence over cell proliferation HMMR is known to act as a surface co-receptor for HA, a prevalent glycosaminoglycan in extracellular matrices. HA can control cell proliferation in other cancer types, and different sizes of exogenous HA can have distinct effects [47, 48 ]. To determine if KELLY proliferation is influenced by exogenous HA in an HMMR-dependent manner, cells were treated with either HMW, LMW or MMW HA forms. Most forms of HA resulted in a mild, but statistically significant inhibition of the growth of KELLY and KC17 cells ( Figure 3 ). Similar effects have been seen in glioma cells U251 and LN229 [49]. However, these inhibitory effects were lost in HMMR KO cells, which were already growth-suppressed ( Figure 3 ). These data suggest that control of KELLY proliferation could be at least in part dependent on HMMR as a co-receptor for HA signals. 3.4 HMMR influences cell migration HMMR promotes cell migration in breast cancer models and C3 fibroblasts [12, 17, 50]. We assessed if HMMR mediates migration in KELLY cells, by comparing the behaviour of wild type and mutated HMMR cells in wound healing assays. We also asked again if any effects seen could be further influenced by exogenous HMW HA. Using scratch assays to assess wound closure, it was evident that cells lacking HMMR migrated about half the speed of cells expressing HMMR ( Figure 4 ). When HMW HA was added to HMMR-expressing cells, the rate of migration decreased slightly but not significantly. In contrast, HMMR-deficient cells were re-stimulated to migrate after the addition of HA, back to near control cell levels. These data indicate that although HMMR is required for maximal motility in KELLY cells, its influence can be compensated with exogenous HA through other HA receptors. 3.5 HMMR promotes tumour growth in vivo To test the tumour-supporting potential of HMMR, cells were subcutaneously injected into NSG mice and tumour growth was monitored. The survival of mice harboring HMMR-deficient KA5 and KA14 tumors was significantly prolonged compared to parental KELLY ( Figure 5 ), corroborating the tumour-supporting role of HMMR. Despite the similar behavior of KC17 to the parental KELLY cells in other assays, this cell line showed tumour outgrowth similar to KA14. KC17 therefore does not behave identically to KELLY parental cells and is not therefore an optimal control for this assay. Below, our phosphoproteomic analysis also identified significant differences between KC17, KELLY and the mutant lines. 3.6 Phosphoproteomic analysis In starting to define potential HMMR signaling pathways, we performed mass spectrometry (MS)-based quantitative phosphoproteomics on KELLY, KC17, KA5 and KA14 ( Figure 6A ). In comparison to KELLY cells ( Online Resource File 4 ), phosphorylation significantly increased in 878 (KA5) and 1310 (KA14) peptides ( Online Resource File 4 ; P< 0.05, FDR ), and decreased in 1013 and 1937, respectively. KA5 and KA14 had closely correlating phosphorylation profiles (R=0.71, P<0.0001; Figure 6B ), suggesting their signaling was affected similarly. Although KC17 was used here as an HMMR-expressing control, these cells clustered separately from parental and HMMR-depleted cells in a PCA analysis ( Online Resource File 4 ) indicating that data generated from KC17 should be treated with caution, as the cells are not identical biochemically to these other cells. To define an HMMR-specific signature, we generated a conservative list of 157 peptides (79 upregulated plus 78 downregulated) specifically altered in HMMR-deficient cells compared to KELLY, but not altered in KC17, filtering additionally for Log 2 FC>0.5 or <-0.5 (Figure 6C , p ≤ 0.05, FDR). KEGG analysis showed significant enrichment for pathways including those linked with Erb-B and mTOR ( Figure 6D ). Log 2 -Fold phosphorylation changes of key peptides showed a general decrease, indicating partial downregulation of these overlapping signaling axes ( Figure 7A and Online Resource File 5 ). In particular, RPS6 showed reduced phosphorylation of RPS6KB1 (p70S6K) target sites, while RPS6KB1 showed reduced phosphorylation on amino acids targeted by ERK. Curiously, phosphorylation of MAPK kinase ERK2 (MAPK1) itself was instead increased on Thr190 and Tyr187 in HMMR-depleted cells ( Figure 7A, C, D, antibody recognizes equivalent of P-Thr185 and P-Tyr187 in ERK2). KSEA on the signature set revealed diverse kinases among both the up- and down-regulated pools ( Online Resource File 5 ). The ERK1 (MAPK3) pathway reached statistical significance for clones KA5 and KA14, and ERK2 (MAPK1) was close to significance (p 0.057); both, however showed down-regulated pathways. We also applied KSEA to the differentially phosphorylated peptides in the complete dataset, again observing modest downregulation of ERK pathways (Figure 7B). In assessing the activation status of the direct substrates of ERK, all except ZFPM1 were downregulated for both ERK1 and ERK2, confirming partial downregulation of ERK signaling ( Figure 7E ). IPA analyses also confirmed this ( Online Resource File 4, blue lines, and Online Resource File 6 ). We thus conclude that HMMR depletion counterintuitively increases ERK phosphorylation, but suppresses the activity of the downstream ERK cascade. Given that MTOR signals may be influenced by HMMR, we looked for upstream regulation of AKT. In immunoblots AKT showed a variably increased phosphorylation of activation site S473 in HMMR depleted cells, but this also occurred in KC17 ( Figure 7C,D ). This peptide was not identified in the phosphoproteomic dataset. Our examination of other common substrates in the proteomics data revealed no clear pattern of AKT activation or inactivation. Thus it is most likely that S6KB1 inactivation after loss of HMMR is not due to AKT suppression. 3.7 DNA damage response proteins and HMMR GO term analysis on the HMMR signature peptides revealed processes including double-strand break repair of DNA, DNA repair, and responses to DNA damage stimuli ( Figure 8A ). Furthermore, the HMMR-co-expression dataset also picked up several aspects of DNA damage and repair in a GO analysis ( Online Resource File 2 ). Examination of the phosphoproteomic data for DNA damage response (DDR) proteins revealed several with significantly increased phosphorylation after HMMR depletion ( Figure 8B ). These include p53BP1 and RIF1, which together bind double-strand DNA breaks and influence non-homologous end joining (NHEJ) [51, 52]. These are not known ATM or ATR target sites and their role in modulating DDR is currently unclear. KAP1 (TRIM28) also shows a potential hyperphosphorylation on S473, a stimulatory site targeted by CHK1 and CHK2 after DNA damage [53-56]; this alteration is statistically significant only in KA5. CHK2 itself shows increased phosphorylation on Y390 in KA5 and KA14, but not KC17; Y390 phosphorylation is necessary for CHK2 kinase activity [57]. S260 in CHK2, another autophosphorylation site [58], also shows hyperphosphorylation in KA5 and KA14. In contrast, some hypophosphorylation is seen on CHK1 S316, a potential autophosphorylation site next to ATR target S317 [56]. Direct evidence for activation of ATM, ATR and BRCA1 is not evident in the dataset. Collectively, the phosphoproteomic data point to there being a restricted but significant perturbation in the DDR network after loss of HMMR. 4. Discussion HMMR is has oncogenic potential in several cancer models, with our study being the first in neuroblastoma. High HMMR expression correlates strongly with poor prognosis and could be an independent risk factor for neuroblastoma patients. We also show in cultured KELLY cells that loss of HMMR leads to reduced proliferation, 2D colony formation and 2D migration. Xenograft analysis also suggests that HMMR is required for maximal tumour growth rate. Our initial, unbiased phosphoproteomic study of these cells indicates that HMMR is directly or indirectly influencing the phosphorylation of many proteins including ERK, IRS2, S6KB1 and S6K. Moreover, we have identified a further potential influence of HMRR in the cell’s DDR network. Overall, these data indicate that HMMR could be an unexplored driver of cancer cell behaviour in neuroblastoma cells with a broad signaling influence. From neuroblastoma patient datasets, high HMMR expression can mark tumours as higher risk and HMMR expression represents a risk factor independent of MYCN . Although HMMR is an HA receptor, other HA-signaling genes, including CD44 , do not show prognostic significance, suggesting that HA signaling per se may not be a sufficient influence behind HMMR’s pro-oncogenic actions in these tumours. HMMR-deficient KELLY cells have reduced proliferation rates, agreeing with what is observed in other tumour cell types [13, 14, 59]. HMMR acts alongside CD44 as a cell surface HA receptor to generate ERK signaling, although depending on the size of HA used this signaling can be either promoted or inhibited (reviewed in [48]). In our study, exogenous HA of varying sizes modestly suppressed proliferation in KELLY cells, whereas cells lacking HMMR (already very growth suppressed) were not further growth-suppressed. The likely explanation of these findings is that endogenous HA normally drives ERK signals and proliferation in part through HMMR/CD44 complexes, and that exogenous HA can partially interfere with this. Once HMMR is removed from the system, however, both endogenous and exogenous HA are hampered in their ability to modulate CD44 signaling, even though CD44 levels were normal in HMMR-deficient cells. At clonogenic density the HMMR-deficient cells struggled to self-renew as 2D colonies. Our preliminary work with HMMR-deficient IMR32 cells indicates a similar deficiency (Karapouliou and Stoker, unpublished). This aligns with the proposed stemness-promoting capacity of HMMR in glioblastoma cells [13]. To counter this, however, HMMR expression in neuroblastoma tumours (from R2 analysis) does not correlate well with proposed neuroblastoma stem cell genes such as NOTCH , GPRC5C or TRKB [60]. HMMR’s clonogenic function may relate to its maintenance of ERK signaling [61], but this needs further investigation. HMMR is also required for optimal motility in a wound repair assay of KELLY cells, corroborating similar findings in in other cell types. HMMR cooperates with CD44 to promote cell motility through ERK, FAK and SRC [12, 17, 50], and cells lacking HMMR can cause a deficit in this CD44-mediated signaling [62]. Our phosphoproteomic data reveal similar correlation at least between motility and ERK signaling in neuroblastoma cells. Exogenous, high molecular weight HA slightly decreased motility in KELLY cells, but surprisingly it rescued the migration deficit in HMMR-deficient cells. HA influences motility in other cells both positively and negatively, complicated by the HA size range [17, 48, 63-65]. One hypothesis here is that loss of HMMR blocks CD44’s ability to efficiently bind endogenous HA and the migration signals then falter. Exogenous HMW HA , however, may be able to drive CD44 multimer formation, re-initiating signaling [66]. Using xenografted tumours of our KELLY derivatives, our study supports to possibility that HMMR positively promotes KELLY tumour growth. This would currently concur with other cancer models where HMMR was able to support tumour growth [13, 14]. One caveat was that the HMMR -intact line KC17 also showed slower growth than KELLY, however the proteomic data clearly showed that KC17 was biochemically non-identical to KELLY and the KO lines and so its behaviour in vivo is difficult to interpret. Further studies with other HMMR-depleted neuroblastoma cell lines are therefore needed to further confirm the in vivo properties of these cells. Phosphoproteomics have allowed us to start uncovering some of the potential signaling that HMMR can directly or indirectly modulate in neuroblastoma cells. Oddly, ERK1/2 was hyperphosphorylated in the KO cells, while ERK1/2 downstream signaling was significantly suppressed. The latter might be expected given the documented, stimulatory role of HMMR in ERK stimulation, but we cannot currently explain why ERK phosphorylation itself increases. In KSEA and GO analyses, components of the MTOR network and the overlapping ErbB pathway, were also suppressed. MTOR is a central regulator of cellular responses to growth factors and cell stress, and integrates signals from ERK, AKT and other signals [67]. A reduction in MTOR signals, particularly those through RPS6KB1 and RPS6 that we observe, might explain in part the reduced proliferation and survival profiles of the HMMR-depleted cells. CD44 and MTOR also regulate each other’s actions in AML and breast cancer models [68, 69], indicating that HA signaling can also feeds into this pathway. The observed reduction in IRS2 phosphorylation may also relate to the reduced MTOR signaling, although this is speculative given that the phosphosites we observe are not characterised [70]. IRS2 nevertheless is of interest given that ALK uses IRS2 as an effector in neuroblastoma cells [71]. Lastly, a reduction in ERBB signaling after loss of HMMR is of interest since ERBB signaling is implicated in neuroblastoma [72], and ERBB’s signaling in ovarian tumour cells can utilizes interactions with CD44 and hyaluronan [73]. Whether or not HMMR operates in a complex of CD44 and ERBB2 remains to be determined. Alongside it’s HA receptor role, HMMR has well documented nuclear roles in spindle dynamics and interactions with BRC1 [7]. Given BRCA1’s central role in homologous DNA repair, it was interesting that loss of HMMR in KELLY cells led to hyperphosphorylation of some DDR regulators. These included p53BP1, KAP1, RIF1 and CHK2. Known regulatory phosphosites were altered on CHK2 [57, 58, 74, 75] and KAP1 [53, 55, 56], while numerous phosphopeptides in 53BP1 remain to be functionally understood. This raises the hypothesis that HMMR is a direct or indirect modulator of DDR in neuroblastoma cells, a potentially new role for this protein. Loss of HMMR may disrupts spindle and chromosome dynamics in neuroblastoma cells, as seen in other cancer cells [24, 25], generating mitotic stress and DNA damage. However, we currently see no direct evidence of ATM or ATR activation or altered phosphorylation targets such as T68 in CHK2 or S824 in KAP1. Our observation of RPS6KB1 and RPS6 hypophosphorylation could also relate to DNA damage since RPS6 phosphorylation attenuate DSBs in BRCA1-deficient breast cancer cells [76], while DNA damage suppresses S6K1-mediated RPS6 phosphorylation in various tumour cells [77]. A new role for HMMR in DDR within neuroblastoma cells is therefore proposed, requiring further investigation. To conclude, we show for the first time that high expression of HMMR and its product HMMR are statistically implicated in poor neuroblastoma patient outcomes and also in supporting several cancer cell behaviors in KELLY cells. Potentially new areas of HMMR influence include modulation of protein phosphorylation in the MTOR and DDR pathways, warranting further exploration. HMMR could thus form an unrecognised signaling hub in these tumour cells, opening avenues for future prognostic or therapeutic investigation. Abbreviations DNA damage repair – DDR Gene ontology - GO Hyaluronic acid – HA Knockout – KO Kyoto Encyclopedia of Genes and Genomes - KEGG Non-homologous end joining – NHEJ Kinase substrate enrichment analysis - KSEA Declarations Funding For AWS and CK this research was funded by Neuroblastoma UK (NBUKStoker19) and the Olivia Hodson Cancer Fund, Great Ormond Street Hospital Children’s Charity (SR16A59). The study was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Funding for VR and PRC was from CRUK (C16420/A18066) and MRC (MR/X013766/1). Data Availability The processed phosphoproteomics data are deposited with Mendeley Data and available at https://data.mendeley.com/datasets/6wr2tj8wr9/1. Declarations of Interests. Pedro Cutillas reports a relationship with Kinomica Limited that includes: board membership, consulting or advisory, and equity or stocks. All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. CRediT Author Statememt Christina Karapouliou : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing. Vinothini Rajeeve : Data curation, Formal analysis, Methodology, investigation, Software, Validation ; Writing – review & editing. 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Variable HR (95%CI) P value Univariate HMMR 1.064 (1.049 - 1.078) <0.0001 CD44 0.9957 (0.9945 – 0.9969) <0.0001 HAS1 0.9921 (0.9232 – 1.048) 0.8036 HAS2 0.9766 (0.9217 – 1.018) 0.3474 HAS3 1.267 (1.203 – 1.332) <0.0001 HYAL1 0.7030 (0.5983 – 0.8210) <0.0001 HYAL2 1.033 (1.018 – 1.046) <0.0001 HYAL3 1.170 (1.085 – 1.245) <0.0001 MYCN 7.246 (4.900 – 10.71) <0.0001 INSS (1) 0.01261 (0.0007151 – 0.05674) <0.0001 INSS (2) 0.08056 (0.02458 – 0.1935) <0.0001 INSS (3) 0.3804 (0.2065 – 0.6497) 0.0009 INSS (4s) 0.1193 (0.03639 – 0.2865) <0.0001 Age of diagnosis 1.000 (1.000 – 1.000) <0.0001 Multivariable HMMR 1.035 (1.010 – 1.058) 0.0041 CD44 0.9996 (0.9984 – 1.001) 0.4772 HAS3 1.049 (0.9677 – 1.131) 0.2289 HYAL1 0.9271 (0.8090 – 1.024) 0.2316 HYAL2 1.006 (0.9885 - 1.022) 0.5112 Additional Declarations Competing interest reported. Pedro Cutillas reports a relationship with Kinomica Limited that includes: board membership, consulting or advisory, and equity or stocks. All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Supplementary Files OnlineResource1.pdf Online Resource File 1: A. Oncomine platform analysis of the expression of HMMR in neuroblastomas compared to ganglioneuroblastomas and ganglioneuromas in Janoueix-Lerosey Brain (top) and Albino Brain (bottom) datasets. The number of tumours analysed for each independent study and the p-values are also indicated. B. Comparison of the expression of HA axis genes, genes related to hyaluronic acid binding, and cell motility genes in the same neuroblastoma studies as in A examined in Oncomine. Red and blue colours indicate over- and under- expression respectively. Median rank and p-values are also depicted. OnlineResource2.pdf Online Resource File 2: HMMR co-expression signature is correlated with poor clinicopathological features in neuroblastoma. A. Overlap of the HMMR co-expressed genes in 4 independent neuroblastoma datasets anlysed using R2. Heatmap (B), survival analysis (C), and GO analysis (D) of the HMMR co-expression signature. Graph in D depicts the fold enrichment (fdr<0.05). OnlineResource3.pdf Online Resource File 3: DNA sequence traces of KELLY subclones after CRISPR/Cas9 treatment to target HMMR . Guide RNA target is underlined. Vertical dashed line is predicted cut site. KC17 has no indels; KA5 has homozygous 1bp inserts; KA14 and KA16 both have the same homozygous 1bp deletions. OnlineResource4.pdf Online Resource File 4: Volcano plots (A) and quantification (B) of up- and down-regulated phosphosites in KA5 and KA14 cells, respectively, as compared to parental KELLY cells. Red and blue dots correspond to phosphopeptides that changed significantly respect to control cells (P ≤ 0.05), while black dots represent phosphopeptides outside the filtering criteria (P ≥ 0.05). The number of up- and down-regulated phosphosites in KC17 subclone are also depicted in B. C, PCA analysis of the phosphoproteomics data for KELLY, KC17, KA5 and KA14 cells. OnlineResource5.pdf Online Resource File 5: A. Up- and down- regulated kinases of the HMMR phosphoproteomic signature are depicted as a bar plot for both HMMR KO lines. Only MAKPK1 and MAPK3 pathways, in both KA5 and KA14, are above the fold-change -Log 10 P-value threshold of 1.3. B. Visualisation of the MTOR and ERBb components as a protein network. The outer and inner rings represent the log Fc changes in KA5 and KA14, respectively. Red and blue colour indicate increased or decreased log FC, respectively, with great colour intensity reflecting greater fold changes. C. IPA upstream regulator analysis of the ERK pathway protein components in KA5 (left) and KA14 (right). Data are displayed as a network with ERK illustrated in the center as ‘inhibited’ and surrounded by its downstream targets (nodes). Each molecule is color-coded based on the phosphorylation status in the dataset. Red and green indicate an increase or decrease in phosphorylation respectively. Dotted lines (edges) indicate the indirect relationship between ERK and its targets due to additional intermediate molecules between them (not depicted in the network). Red and blue edges indicate predicted activation and inhibition respectively of the target proteins whereas the yellow and grey lines are depicted when the activity status is contradictory to the prediction or not predicted. Explanation of the color-coding of the predicted relationship is also indicated in the Prediction Legend. OnlineResource6.pdf Online Resource File 6: The ERK1/2 signaling pathway in both HMMR KO clones analysed by IPA. The colour coding is the same as described in Online Resource File 5 and indicated in the Prediction Legend. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5194003","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363332113,"identity":"e257e6b9-b9b8-4f17-8a8b-b85c67bd9674","order_by":0,"name":"Christina Karapouliou","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"","lastName":"Karapouliou","suffix":""},{"id":363332114,"identity":"01a5ba77-7179-44d7-a848-37723b8d5e65","order_by":1,"name":"Vinothini Rajeeve","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Vinothini","middleName":"","lastName":"Rajeeve","suffix":""},{"id":363332115,"identity":"830413cb-a48a-4f19-9a2b-9b94cce1719e","order_by":2,"name":"Pedro Cutillas","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Cutillas","suffix":""},{"id":363332116,"identity":"5e2826f0-56cf-4ef0-922e-13e07d0b22f2","order_by":3,"name":"andrew stoker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie2RMQrCQBBFJwimWUybNPEKEwKCIDlLZCGV4AUsIgHtrONFrAcCsVmx3TJptN3KUt2ksnJjZ7Gv+zCP+cMAWCx/CwF4AA71gZnnnbxTgrw3f1GQhipe7t4bJRbrWPKGYJMACvqu+MSibSmz+UlmelHNAS+5YQ0xp2Cqwplc6WJjArwajCm5rVZeGJed8hyg6DJRwSQh+lpxdmQuFlUsOpaCoy9uSMsDZ4Hp/PC8b5SqE/T2vG3UIwknIjU0G32GdNAjLRaLxWLkDfqxSFHIB2PRAAAAAElFTkSuQmCC","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"andrew","middleName":"","lastName":"stoker","suffix":""}],"badges":[],"createdAt":"2024-10-02 16:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5194003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5194003/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67267143,"identity":"1e4000b4-73b0-4c61-a74d-b59a933aa931","added_by":"auto","created_at":"2024-10-23 06:55:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2193100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHMMR expression and neuroblastoma clinicopathological features. A.\u003c/strong\u003e The expression of HMMR in normal and neuroblastoma tissues examined by R2 genomics platform. Normal tissues (blue) are divided in 9 groups and pheocytochromocytomas/paragangliomas (green) in 2 groups. Neuroblastic tumours (red) are divided in 4 groups, from benign ganglioneuromas, ganglioblastomas to aggressive metastatic neuroblastomas. The authors and sample numbers are also shown. Grade staging (\u003cstrong\u003eB\u003c/strong\u003e) and overall survival (\u003cstrong\u003eC\u003c/strong\u003e) are depicted for the Kocak and SEQC datasets. \u003cstrong\u003eD. \u003c/strong\u003et-SNEA maps analysis on the in Kocak dataset performed in R2, for MYCN- amplified or non-amplified group (left panel), MYCN expression (middle panel) and HMMR expression (right panel).\u003c/p\u003e","description":"","filename":"HMMRFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/23dc2328bc1a58c5352fa24d.png"},{"id":67267144,"identity":"cc792679-2922-4880-a114-c23f4d73758c","added_by":"auto","created_at":"2024-10-23 06:55:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":917626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHMMR depletion suppresses proliferation and clonogenicity. A. \u003c/strong\u003eSchematic of the HMMR protein, with interaction domains shown for microtubules, HA, CHICA and Calmodulin, plus the carboxy-terminal bZIP region. The guide RNA target site used in CRISPR/Cas9 is shown. \u003cstrong\u003eB. \u003c/strong\u003eImmunoblot analysis of HMMR in parental KELLY cells and the clones generated by CRISPR/Cas9. \u003cstrong\u003eC\u003c/strong\u003e, Cell proliferation assay using resazurin, normalized to growth of KELLY cells (n=4-7\u003cstrong\u003e). D\u003c/strong\u003e, clonogenic assay quantified from 6-well plate assays (example plate image shown, crystal violet staining) (n=4). In C and D data are expressed as a mean ± SD; *p\u0026lt;0.05, **p\u0026lt;0.005, ***P ≤ 0.0005.\u003c/p\u003e","description":"","filename":"HMMRFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/7e81fe75682eec7c034d4fcb.png"},{"id":67267459,"identity":"fdf3e1fa-92e0-4b68-9410-e194373bd9a9","added_by":"auto","created_at":"2024-10-23 07:03:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":600500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHA- and HMMR-dependent \u0026nbsp;proliferation. \u003c/strong\u003eParental KELLY, KC17 and HMMR KO clones were treated with HA in various sizes, LMW, MMW and OMW for 6 days and cell proliferation was assessed. Data\u003cstrong\u003e \u003c/strong\u003ewere normalized to untreated (ut) KELLY cells and then expressed as a mean ± SD (n = 3). Two-way anova was performed; *p\u0026lt;0.05, ***P ≤ 0.001, ****P ≤ 0.0001.\u003c/p\u003e","description":"","filename":"HMMRFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/dd9068fe16f5eb69311e6568.png"},{"id":67267461,"identity":"7c639ae3-c106-4a2e-a324-098dd44544d5","added_by":"auto","created_at":"2024-10-23 07:03:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4043520,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHMMR loss suppresses cell migration, but is restored by HA. \u003c/strong\u003eParental KELLY, KC17 and HMMR KO clones were subjected to scratch assays for 24 hours with or without addition of exogenous OMW HA (HA). The areas covered by migrating cells is expressed in µm\u003csup\u003e2\u003c/sup\u003e. Data\u003cstrong\u003e \u003c/strong\u003eare expressed as a mean ± SD (n = 3); **p\u0026lt;0.005, ***P ≤ 0.0005, ****P ≤ 0.0001. Scale bar= 200µm.\u003c/p\u003e","description":"","filename":"HMMRFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/1d61d3d8f9dd2cfb6e5b3356.png"},{"id":67267462,"identity":"f13ebb4a-3618-4779-b313-5533650a8aeb","added_by":"auto","created_at":"2024-10-23 07:03:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":215812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHMMR depletion reduces tumour growth. \u003c/strong\u003eSurvival assay of mice injected with\u003cstrong\u003e \u003c/strong\u003eparental, KC17 and HMMR KO clones. The length of time in days post-injection of animal termination is shown as a Kaplan-Meier plot. Pairwise comparison is made between tumour types using a Log-rank (Mantel-Cox) test and p values are shown (KC17 vs KA14, p=0.54).\u003c/p\u003e","description":"","filename":"HMMRFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/49510dc13c43f4f881c415fe.png"},{"id":67267458,"identity":"924f9efb-35e6-49b4-8d25-02ba57eac76a","added_by":"auto","created_at":"2024-10-23 07:03:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1886851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhosphoproteomic profiling. A.\u003c/strong\u003e Schematic representation of the phosphoproteomic pipeline. KELLY cells expressing HMMR (parental and KC17) or not (KA5 and KA14) were harvested and phosphoproteomic analysis performed. Pie chart shows the number of identified phosphosites; N=4 biological replicates. \u003cstrong\u003eB.\u003c/strong\u003e Correlation plot of phosphopeptide differences seen between KA5 vs KELLY and between KA14 vs KELLY. Each spot is a phosphorylation site. Thresholds were differential phosphorylation from KELLY of Log\u003csub\u003e2\u003c/sub\u003e -0.5/+0.5 and p-value of 0.05 or below. Proteins with statistically significant up- and down- phosphorylation are marked with red and blue, respectively, whereas orange peptides have a statistically significant, opposite correlation. \u003cstrong\u003eC.\u003c/strong\u003e Phosphoproteomic HMMR signature. A Venn diagram showing overlaps between significantly altered phosphopeptides found in both KA5 and KA14 (collectively logFC ≥ 0.5, logFC ≤ 0.5, p ≤ 0.05, from figure 6B), and altered sites in KC17 cells (p ≤ 0.05). The unique 79- and 78- protein in response to HMMR depletion are marked with black diamonds, generating the HMMR phosphoproteomic signature. A heat map of signature in comparison to KC17 is presented. Colours represent fold change over parental proteins expressed as Log2. \u003cstrong\u003eD\u003c/strong\u003e, KEGG enrichment analysis of the HMMR signature; P\u0026gt;1.3 of -Log\u003csub\u003e10\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"HMMRFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/80d2ff93765792fa3ab39b84.png"},{"id":67267464,"identity":"6b9c605a-0e43-44ee-9b29-b7b633c62b10","added_by":"auto","created_at":"2024-10-23 07:03:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1788901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional activity of the HMMR phosphoproteomic signature.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Phosphopeptide differences between cell lines and KELLY, observed in components of the shared MTOR/ErbB pathways. Red and blue show log\u003csub\u003e2\u003c/sub\u003e-fold increased or decreased phosphorylation, respectively; white is no change (* P\u0026lt;0.05, ** P\u0026lt;0.01, *** P\u0026lt;0.001). \u003cstrong\u003eB, \u003c/strong\u003eKSEA analysis of differentially phosphorylated peptides in the whole dataset, showing those most relevant to MTOR signaling. \u003cstrong\u003eC, \u003c/strong\u003eImmunoblot analysis of cell lines for phosphorylated ERK (P-T185/P-Y187 (ERK2) or P-T202/P-Y204 (ERK1)), AKT (S473), and Aurora A as well as HMMR and CD44 (n = 3). \u003cstrong\u003eD,\u003c/strong\u003e Protein signals were normalised to either their respective total proteins or GAPDH, and then expressed as a mean ± SD; *P ≤ 0.05, **p\u0026lt;0.005. \u003cstrong\u003eE,\u003c/strong\u003e Examples of MAPK3 (ERK1) and MAPK1 (ERK2) substrates are shown as protein networks based on STRING and visualised in Cytoscape. Inner and outer rings represent kinase activity status (z score). Red and blue colour indicate increased or decreased kinase activity, respectively, with greater colour intensity reflecting greater z scores.\u003c/p\u003e","description":"","filename":"HMMRFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/5358c900a33bb1924ad6b96a.png"},{"id":67267465,"identity":"895489e6-e9f3-4a67-ae61-d6c089bd3334","added_by":"auto","created_at":"2024-10-23 07:03:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":891070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHMMR and the DNA damage response. \u0026nbsp;A,\u003c/strong\u003e GO enrichment analysis of the HMMR phosphoproteomic signature is shown, with pathways all P\u0026gt;1.3 of -Log\u003csub\u003e10\u003c/sub\u003e. \u003cstrong\u003eB\u003c/strong\u003e, Phosphopeptide differences between cell lines and KELLY, observed in components of DDR pathways (* P\u0026lt;0.05, ** P\u0026lt;0.01, *** P\u0026lt;0.001, **** P\u0026lt;0.0001). Red and blue show log\u003csub\u003e2\u003c/sub\u003e-fold increased or decreased phosphorylation, respectively; white is no change.\u003c/p\u003e","description":"","filename":"HMMRFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/59832300749f9f8dfbf9e193.png"},{"id":67268687,"identity":"40c46467-e0da-49b3-8c8b-a1f96d6d44fd","added_by":"auto","created_at":"2024-10-23 07:11:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15419365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/7e68affb-e73c-44c2-9831-f5c9b5b60656.pdf"},{"id":67267148,"identity":"6e3b9a6c-edd3-4074-82b8-6fc5dc97b0f0","added_by":"auto","created_at":"2024-10-23 06:55:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":309579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 1: A\u003c/strong\u003e. Oncomine platform analysis of the expression of HMMR in neuroblastomas compared to ganglioneuroblastomas and ganglioneuromas in Janoueix-Lerosey Brain (top) and Albino Brain (bottom) datasets. The number of tumours analysed for each independent study and the p-values are \u0026nbsp;also indicated. \u003cstrong\u003eB.\u003c/strong\u003e Comparison of the expression of HA axis genes, genes related to hyaluronic acid binding, and cell motility genes in the same neuroblastoma studies as in A examined in Oncomine. Red and blue colours indicate over- and under- expression respectively. Median rank and p-values are also depicted.\u003c/p\u003e","description":"","filename":"OnlineResource1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/4cf0ea518efeb8f38b3db131.pdf"},{"id":67267151,"identity":"ea48a1c5-b048-4c0c-83da-b3e2f58a0d4e","added_by":"auto","created_at":"2024-10-23 06:55:08","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":455458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 2: \u003c/strong\u003eHMMR co-expression signature is correlated with poor clinicopathological features in neuroblastoma. \u003cstrong\u003eA\u003c/strong\u003e. Overlap of the HMMR co-expressed genes in 4 independent neuroblastoma datasets anlysed using R2. Heatmap (\u003cstrong\u003eB\u003c/strong\u003e), survival analysis \u003cstrong\u003e(C)\u003c/strong\u003e, and GO analysis (\u003cstrong\u003eD\u003c/strong\u003e) of the HMMR co-expression signature. Graph in \u003cstrong\u003eD\u003c/strong\u003e depicts the fold enrichment (fdr\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"OnlineResource2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/e7c252458e10f60c94b2b5b6.pdf"},{"id":67267154,"identity":"8a1313de-1226-4755-9075-6291dbb84bcc","added_by":"auto","created_at":"2024-10-23 06:55:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":602301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 3: \u003c/strong\u003eDNA sequence traces of KELLY subclones after CRISPR/Cas9 treatment to target \u003cem\u003eHMMR\u003c/em\u003e. Guide RNA target is underlined. Vertical dashed line is predicted cut site. KC17 has no indels; KA5 has homozygous 1bp inserts; KA14 and KA16 both have the same homozygous 1bp deletions.\u003c/p\u003e","description":"","filename":"OnlineResource3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/6fef6f8ee21e6a549a0f84a2.pdf"},{"id":67267146,"identity":"0d91f7bc-f610-4a80-9241-e07b91576a1b","added_by":"auto","created_at":"2024-10-23 06:55:07","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":306280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 4: \u003c/strong\u003eVolcano plots \u003cstrong\u003e(A)\u003c/strong\u003e and quantification \u003cstrong\u003e(B)\u003c/strong\u003e of up- and down-regulated phosphosites in KA5 and KA14 cells, respectively, as compared to parental KELLY cells. Red and blue dots correspond to phosphopeptides that changed significantly respect to control cells (P ≤ 0.05), while black dots represent phosphopeptides outside the filtering criteria (P ≥ 0.05). The number of up- and down-regulated phosphosites in KC17 subclone are also depicted in \u003cstrong\u003eB\u003c/strong\u003e. \u003cstrong\u003eC,\u003c/strong\u003e PCA analysis of the phosphoproteomics data for KELLY, KC17, KA5 and KA14 cells.\u003c/p\u003e","description":"","filename":"OnlineResource4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/3981395affc7e7d5557277dc.pdf"},{"id":67267460,"identity":"1fb403ad-201c-41cd-a574-aa8f8d6b3b6c","added_by":"auto","created_at":"2024-10-23 07:03:07","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":972744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 5:\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Up- and down- regulated kinases of the HMMR phosphoproteomic signature are depicted as a bar plot for both HMMR KO lines. Only MAKPK1 and MAPK3 pathways, in both KA5 and KA14, are above the fold-change -Log\u003csub\u003e10\u003c/sub\u003e P-value threshold of 1.3. \u003cstrong\u003eB.\u003c/strong\u003e Visualisation of the MTOR and ERBb components as a protein network. The outer and inner rings represent the log Fc changes in KA5 and KA14, respectively.\u0026nbsp; Red and blue colour indicate increased or decreased log FC, respectively, with great colour intensity reflecting greater fold changes. \u003cstrong\u003eC.\u003c/strong\u003e IPA upstream regulator analysis of the ERK pathway protein components in KA5 (left) and KA14 (right). Data are displayed as a network with ERK illustrated in the center as ‘inhibited’ and surrounded by its downstream targets (nodes). Each molecule is color-coded based on the phosphorylation status in the dataset. Red and green indicate an increase or decrease in phosphorylation respectively. Dotted lines (edges) indicate the indirect relationship between ERK and its targets due to additional intermediate molecules between them (not depicted in the network). Red and blue edges indicate predicted activation and inhibition respectively of the target proteins whereas the yellow and grey lines are depicted when the activity status is contradictory to the prediction or not predicted. Explanation of the color-coding of the predicted relationship is also indicated in the Prediction Legend.\u003c/p\u003e","description":"","filename":"OnlineResource5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/4f406e0c937b15b9ceeb8db0.pdf"},{"id":67267463,"identity":"ce8e1f48-0c2b-4bfb-8bd6-86316930778a","added_by":"auto","created_at":"2024-10-23 07:03:08","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1089725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource File 6: \u003c/strong\u003eThe ERK1/2 signaling pathway in both HMMR KO clones analysed by IPA. The colour coding is the same as described in Online Resource File 5 and indicated in the Prediction Legend.\u003c/p\u003e","description":"","filename":"OnlineResource6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5194003/v1/dfbf8d7b274765b166b5d211.pdf"}],"financialInterests":"Competing interest reported. Pedro Cutillas reports a relationship with Kinomica Limited that includes: board membership, consulting or advisory, and equity or stocks. All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","formattedTitle":"HMMR is an independent prognostic indicator in neuroblastoma and loss of HMMR suppresses cell proliferation, migration and clonogenicity.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeuroblastoma is a developmental cancer of early childhood, arising from sympathoadrenal precursors [1, 2]. Tumour cells are highly heterogenous, occurring in broadly two super enhancer-regulated states of an adrenergic and mesenchymal state [3, 4]. This tumour heterogeneity contributes to the high relapse rate and poor survival of high-risk patients, representing a stubborn clinical challenge. Although the common oncogenic drivers found in adult human cancers are infrequent upon presentation in neuroblastoma [1, 2, 5], several other drivers are known including \u003cem\u003eMYCN\u003c/em\u003e gene amplification and activations of the tyrosine kinase ALK, or tyrosine phosphatase PTPN11 [2]. Nevertheless, 75% of tumours have no clear oncogenic drives to date [2, 6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our search for potentially new protein drivers we have been examining the potential roles of HMMR, also known as RHAMM and CDS168 [7]. HMMR may be of interest in a neural tumour because it has been implicated in neurite extension processes in neuronal cell lines, including in NG108-15, a hybrid neuroblastoma/glioma line [8]\u0026nbsp;and HMMR loss-of-function generates neurodevelopmental defects in vertebrate embryos [9]. HMMR\u0026nbsp;also has oncogenic roles in several other human cancer systems, sustaining cell proliferation, survival and migration in cells derived from cancers of brain, lung, ovary, prostate, head and neck and breast [10-14]. HMMR can also promote epithelial to mesenchymal transition, chemotherapy resistance and stemness\u0026nbsp;in gastric cancer cells [15]. HMMR is a cell surface hyaluronic acid (HA) receptor [16, 17]. HA and its catabolized products can promote cell proliferation and survival, motility and metastasis in tumour cells [16, 18], interacting with cells through an HMMR/CD44 complex and signaling through ERK, AKT, SRC, Rho GTPases and FAK [7, 12, 16, 19-23]. Interestingly, HHMR also acts in the nucleus, binding to microtubules and centrosomes and regulating mitotic spindles and chromosomal stability through interactions with DYNLL1 complexes, CHICA and BRCA1 [7, 24, 25]. HMMR also localises TPX2 to centrosomes through a c-terminal bZIP domain, maintaining spindle pole assembly [26-30], but operating in a negative feedback loop through the release of TPX2 after HMMR degradation by BRCA1, leading to Aurora kinase A (AURKA) activation and BRCA1 phosphorylation [28, 31, 32].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHMMR\u003c/em\u003e is strongly expressed in neuroblastomas and we hypothesised that it may have pro-oncogenic potential corresponding to that seen in other cancer models. Using in silico analysis we examined the relationship between high \u003cem\u003eHMMR\u003c/em\u003e expression in human neuroblastoma tumours and prognostic outcomes. At a cellular level we also targeted \u003cem\u003eHMMR\u003c/em\u003e for inactivation using CRISPR/Cas9 in the KELLY neuroblastoma cell line. Our analyses show that HMMR is indeed a promoter of several parameters of tumour cell behaviour and that these are variably affected by HA ligands. Lastly, we used phosphoproteomics to explore the potential biochemical roles of HMMR in neuroblastoma cells, confirming that it modulates ERK signaling and revealing potentially novel roles in MTOR and DNA damage response (DDR) pathways.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cem\u003e2.1 Cell culture\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKELLY cells (CVCL_2092)\u0026nbsp;were provided by Prof. Frank Speleman, University of Ghent. Cells were STR genotyped in 2015 by LGC Standards. Cells were cultured at 37\u003csup\u003eo\u003c/sup\u003eC in RPMI medium + GlutaMAX (ThermoFisher Scientific, Loughborough, UK) supplemented with 10% FBS (Life Technologies) and 100 U\u003csup\u003e.\u003c/sup\u003emL\u003csup\u003e-1\u003c/sup\u003e Penicillin, 0.1 mg\u003csup\u003e.\u003c/sup\u003emL\u003csup\u003e-1\u003c/sup\u003e Streptomycin (Sigma-Aldrich, UK). The HA types used were low (Sigma-Aldrich 40583; LMW, 5000-1000 Da), medium (Sigma-Aldrich 75044; MMW, 150000-300000 Da) and high molecular weights (Sigma-Aldrich 51967; HMW, 1.5-1.8 x 10\u003csup\u003e6\u003c/sup\u003e Da). HA was dissolved in media at the final concentration of 400 \u0026mu;g/mL.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Generation of HMMR knockout (KO) subclones\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eHMMR\u003c/em\u003e gRNA in exon 5 (CGTGTTCTTCTACAGGAACG) was designed using Benchling (San Francisco, CA, USA;RRID:SCR_013955). Plasmid co-expressing Cas9 and gRNA was purchased from VectorBuilder (Neu-Isenberg, Germany) and transfected using Lipofectamine 2000 (Thermo Fisher Scientific, USA). Cells were incubated for 4-6 days with 1mg/ml puromycin, then single cell sorted using a MoFlo XDP sorter. D\u003c/p\u003e\n\u003cp\u003eNA target regions were subjected to PCR amplification using 5\u0026rsquo;-GCAACAGAGCACAGAGCAAG-3\u0026rsquo; and 5\u0026rsquo;-ACACCAGGCGATTCAGATTC-3\u0026rsquo; and sequenced (Source Bioscience, UK). Sequence trace analysis was performed using the ICE ANALYSIS online tool (Synthego, v2.0; Synthego, CA, USA;\u0026nbsp;RRID:SCR_024508).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Cell Assays\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwo thousand cells were seeded in 96-well plates and cell viability was measured after 6 days with resazurin (Merck Life Science UK Ltd, Gillingham, UK). For the clonogenic assay, 400 viable KELLY cells were seeded in 6-well plates and incubated for 3 weeks. Cells were fixed using crystal violet (Sigma-Aldrich) in 25% Methanol. Colonies were counted manually using ImageJ software. For migration assays, cells were grown to confluency in 24-well plates and serum starved overnight.\u0026nbsp;A 200\u0026micro;l pipette tip was used to make a scratch in the monolayer of duplicate wells and cells were then incubated in serum-free media with or without added 400\u0026mu;g/ml of HA for 24 hours. Initial and final cell-free scratch areas were measured using the wound healing size plugin in ImageJ software [33]. Differences between the 0 and 24 h was expressed as migration area.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Mouse xenografts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnimals were used under a Home Office project licence, complying with the\u0026nbsp;Guidance on the operation of the Animals (Scientific Procedures) Act 1986. Female NSG mice (Charles River Laboratories, Sulzfeld, Germany) were injected subcutaneously in the flank with 2\u0026times;10\u003csup\u003e6\u003c/sup\u003e KELLY, KC17, KA5 or KA14 cells, suspended in a 1:1 PBS and Cultrex matrix (R\u0026amp;D Systems Inc., USA). Each injection group consisted of 3 animals per cell line (12 animals total) and injection groups were repeated three times. Mice were randomly assigned to groups and cell injections were performed in a blinded way. Mice were sacrificed when the tumors reached a maximum allowable size. Tumor volumes (in mm\u003csup\u003e3\u003c/sup\u003e) were determined using [length x width\u003csup\u003e2\u003c/sup\u003e/2].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5 Immunoblotting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCells were processed for immunoblotting as previously described [34]. Primary antibodies (Cell Signaling) were against: CD44 (#3578;RRID:AB_2076463), pERK (#9106;\u0026nbsp;RRID:AB_331768); tERK (#9102;RRID:AB_330744); pAKT (#4060;\u0026nbsp;RRID:AB_2315049); tAKT (#9272;RRID:AB_329827\u003c/p\u003e\n\u003cp\u003e), p-Aurora A (#3079;RRID:AB_2061481), GAPDH (#2118;RRID:AB_561053). Anti-HMMR GTX121502 (GeneTex;\u0026nbsp;RRID:AB_11163915) was also used. Protein expression was quantified by densitometry on X-ray films using ImageJ software (RRID:SCR_003070).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6 Phosphoproteomic study and analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCells were lysed in 8 M urea with phosphatase inhibitors (10 mM Na3VO4, 100 mM b-glycerol phosphate and 25 mM Na\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e (Merck Life Science UK Ltd) as described previously\u0026nbsp;[35]. Four biological replicates were subjected to mass spectrometry as described previously [34]. The data analysis was performed\u0026nbsp;using the Limma R package (version 3.50.1; RRID:SCR_010943) and p-values were corrected using \u0026nbsp; the \u0026nbsp;qvalue \u0026nbsp;package \u0026nbsp; (version \u0026nbsp;2.26.0; RRID:SCR_001073) \u0026nbsp;from \u0026nbsp; Bioconductor as described previously [36]. \u0026nbsp;To correlate the differentially expressed phosphoproteins of the HMMR-deficient cells, Pearson\u0026rsquo;s correlation performed using the online platform \u0026lsquo;ggVolcanoR\u0026rsquo; [37]. For the HMMR phosphoproteomic signature, proteins with fold changes of 0.5 and p\u0026le;0.05 were considered statistically significant and differentially phosphorylated from controls (78 and 79 protein lists). The heatmap was generated using\u0026nbsp;Morpheus (Broad Institute).\u0026nbsp;For the\u0026nbsp;Kinase substrate enrichment analysis (KSEA), peptide data were processed as previously described [38](\u003cstrong\u003eFigure 7B\u003c/strong\u003e) and also with the KSEAapp tool [39] (\u003cstrong\u003eOnline Resource File 5\u003c/strong\u003e). Networks were visualised in Cytoscape [40] (RRID:SCR_003032)\u0026nbsp;using STRINGAPP [41] (RRID:SCR_025009)\u0026nbsp;and OMICS VISUALISER [42] plug-ins.\u0026nbsp;Ingenuity Pathways Analysis (IPA)\u0026nbsp;software (Qiagen, USA;\u0026nbsp;RRID:SCR_008653) was used for upstream regulator analysis. The processed phosphoproteomics data are deposited with Mendeley Data and available at\u0026nbsp;https://data.mendeley.com/datasets/6wr2tj8wr9/1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7 Tumour data and pathway analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatient tumour data and Kaplan\u0026ndash;Meier survival curves were obtained using the R2 genomics platform (http://r2.amc.nl; RRID:SCR_025770). Datasets used were: Roth (n= 504, GEO: GSE7307), Korpershoek (n= 51, GEO: GSE67066), Favier (n= 188), Delattre (n=64, GEO: GSE12460) , Versteeg (n=88, GEO: GSE16476), Kocak (n=649, GEO: GSE45547), SEQC (n=498, GEO: GSE49710), NRC (n=283, GEO: GSE85047) and TARGET-Asgharzadeh (n=249, GEO: GSE85047). Comparison analysis between differentially expressed genes were also obtained from the Oncomine\u003csup\u003eTM\u003c/sup\u003e Platform (Thermo Fisher; RRID:SCR_007834) using the studies Albino Brain (n=28, GEO:GSE7529) and Janoueix-Lerosey Brain (n=64, GEO: GSE12460). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and\u0026nbsp;Gene Ontology (GO) enrichment analyses were conducted using DAVID [43] (RRID:SCR_001881)\u0026nbsp;and ShinyGO 0.80 [44]\u0026nbsp;(RRID:SCR_019213).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.8 Statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis and graphing used GraphPad Prism 10.0 (RRID:SCR_002798). One-way and two-way (Dunnett post hoc analysis) ANOVAs were used. Cell proliferation data were statistically analysed as part of an experimental dataset with multiple treatments but here we show only the analysis relevant to this paper. A Cox regression model was used to test for the independent predictive ability of HMMR expression after adjustment for other significant factors: MYCN amplification, age, and INSS stages.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 HMMR as an independent prognostic marker\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh \u003cem\u003eHMMR\u003c/em\u003e expression has been associated with cancer progression [45], but is not yet reported for neuroblastomas. \u003cem\u003eHMMR\u003c/em\u003e expression was elevated in neuroblastomas compared to normal tissues, benign ganglioblastomas and neural crest-derived tumour pheochromocytoma (\u003cstrong\u003eFigure 1A and Online Resource File 1\u003c/strong\u003e). Moreover, \u003cem\u003eHMMR\u003c/em\u003e is ranked in the top 1-5% overexpressed genes among those in the HA axis, HA binding molecules and those associated with cell motility (\u003cstrong\u003eOnline Resource File 1\u003c/strong\u003e). This supports a possible role of the \u003cem\u003eHMMR\u003c/em\u003e gene in the establishment or progression of neuroblastoma.\u003c/p\u003e\n\u003cp\u003eTo further explore the role of HMMR in neuroblastomas, we examined the correlation between \u003cem\u003eHMMR\u003c/em\u003e expression and tumour staging. Higher \u003cem\u003eHMMR\u003c/em\u003e expression associated strongly with increased INSS tumour grade (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). We found that elevated \u003cem\u003eHMMR\u003c/em\u003e expression correlated significantly with poor overall survival in patient datasets analysed in the R2 platform (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). In t-SNEA maps, elevated \u003cem\u003eHMMR\u003c/em\u003e expression partially overlaps with MYCN-amplified patient groups, but shows a somewhat differential expression pattern, and it is also expanded to the non-AMP group (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). A Cox univariate and multivariable logistic regression analysis was performed on SEQC dataset, demonstrating that \u003cem\u003eHMMR\u003c/em\u003e expression, but not other HA-related pathway genes, is an independent risk factor for neuroblastoma patients (\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e). To clarify the potential biological functions of HMMR we examined the genes that show positive correlations with \u003cem\u003eHMMR\u003c/em\u003e expression in 4 tumour datasets (\u003cstrong\u003eOnline Resource File 2\u003c/strong\u003e). With this 2581 gene set, the \u003cem\u003eHMMR\u0026nbsp;\u003c/em\u003eco-expression signature again correlated with poor OS survival in neuroblastoma (\u003cstrong\u003eOnline Resource File 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 HMMR promotes neuroblastoma cell growth\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEquipped with the prognostics data, we wished to more directly determine the cellular function of HMMR in neuroblastoma cells. To do this we used CRISPR/Cas9 in KELLY cells (strong \u003cem\u003eHMMR\u003c/em\u003e expressors) to create out-of-frame mutations in a region encoding the HMMR N-terminus [46] (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Three \u003cem\u003eHMMR\u0026nbsp;\u003c/em\u003eknock-out (KO) clones were identified, KA5 (1 bp homozygous insertion, KA14 and KA16 (1 bp homozygous deletions; these may or may not be independent subclones) (\u003cstrong\u003eFigure 2B, Online Resource File 3\u003c/strong\u003e). KC17 had no \u003cem\u003eHMMR\u003c/em\u003e alteration and was included in functional analyses as a putative wild-type control. HMMR protein was absent from KA5, 14 and 16, but retained in KC17 and parental KELLY (\u003cstrong\u003eFigure 2B\u003c/strong\u003e) and subclones were morphologically similar to parental KELLY (\u003cstrong\u003eOnline Resource File 3).\u003c/strong\u003e HMMR depletion in these cells inhibited their proliferative expansion compared to KELLY and KC17, agreeing with a similar role in other cancer types (\u003cstrong\u003eFigure 2C\u003c/strong\u003e)\u0026nbsp;[10-12]. Moreover, low density growth assays showed a very significant reduction in colony forming ability of cells lacking HMMR (\u003cstrong\u003eFigure 2D\u003c/strong\u003e), indicating a loss of clonogenic capacity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 HA ligand influence over cell proliferation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHMMR is known to act as a surface co-receptor for HA, a prevalent glycosaminoglycan in extracellular matrices. HA can control cell proliferation in other cancer types, and different sizes of exogenous HA can have distinct effects [47, 48 ]. To determine if KELLY proliferation is influenced by exogenous HA in an HMMR-dependent manner, cells were treated with either HMW, LMW or MMW HA forms. Most forms of HA resulted in a mild, but statistically significant inhibition of the growth of KELLY and KC17 cells (\u003cstrong\u003eFigure 3\u003c/strong\u003e). Similar effects have been seen in glioma cells U251 and LN229 [49]. However, these inhibitory effects were lost in \u003cem\u003eHMMR\u003c/em\u003e KO cells, which were already growth-suppressed (\u003cstrong\u003eFigure 3\u003c/strong\u003e). These data suggest that control of KELLY proliferation could be at least in part dependent on HMMR as a co-receptor for HA signals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 HMMR influences cell migration\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHMMR promotes cell migration in breast cancer models and C3 fibroblasts [12, 17, 50]. We assessed if HMMR mediates migration in KELLY cells, by comparing the behaviour of wild type and mutated \u003cem\u003eHMMR\u003c/em\u003ecells in wound healing assays. We also asked again if any effects seen could be further influenced by exogenous HMW HA. Using scratch assays to assess wound closure, it was evident that cells lacking HMMR migrated about half the speed of cells expressing HMMR (\u003cstrong\u003eFigure 4\u003c/strong\u003e). When HMW HA was added to HMMR-expressing cells, the rate of migration decreased slightly but not significantly. In contrast, HMMR-deficient cells were re-stimulated to migrate after the addition of HA, back to near control cell\u0026nbsp;levels. These data indicate that although HMMR is required for maximal motility in KELLY cells, its influence can be compensated with exogenous HA through other HA receptors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5 HMMR promotes tumour growth in vivo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the tumour-supporting potential of HMMR, cells were subcutaneously injected into NSG mice and tumour growth was monitored. The survival of mice harboring HMMR-deficient KA5 and KA14 tumors was significantly prolonged compared to parental KELLY (\u003cstrong\u003eFigure 5\u003c/strong\u003e), corroborating the tumour-supporting role of HMMR. Despite the similar behavior of KC17 to the parental KELLY cells in other assays, this cell line showed tumour outgrowth similar to KA14. KC17 therefore does not behave identically to KELLY parental cells and is not therefore an optimal control for this assay. Below, our phosphoproteomic analysis also identified significant differences between KC17, KELLY and the mutant lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.6 Phosphoproteomic analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn starting to define potential HMMR signaling pathways, we performed mass spectrometry (MS)-based quantitative phosphoproteomics on KELLY, KC17, KA5 and KA14 (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). In comparison to KELLY cells (\u003cstrong\u003eOnline Resource File 4\u003c/strong\u003e), phosphorylation significantly increased in 878 (KA5) and 1310 (KA14) peptides (\u003cstrong\u003eOnline Resource File 4\u003c/strong\u003e\u003cstrong\u003e; P\u0026lt;\u003c/strong\u003e\u003cstrong\u003e0.05, FDR\u003c/strong\u003e), and decreased in 1013 and 1937, respectively. KA5 and KA14 had closely correlating phosphorylation profiles (R=0.71, P\u0026lt;0.0001; \u003cstrong\u003eFigure 6B\u003c/strong\u003e), suggesting their signaling was affected similarly.\u003c/p\u003e\n\u003cp\u003eAlthough KC17 was used here as an HMMR-expressing control, these cells clustered separately from parental and HMMR-depleted cells in a PCA analysis \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eOnline Resource File 4\u003c/strong\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003eindicating that data generated from KC17 should be treated with caution, as the cells are not identical biochemically to these other cells. To define an HMMR-specific signature,\u0026nbsp;we generated a conservative list of 157 peptides (79 upregulated plus 78 downregulated) specifically altered in HMMR-deficient cells compared to KELLY, but not altered in KC17,\u0026nbsp;filtering additionally for Log\u003csub\u003e2\u003c/sub\u003eFC\u0026gt;0.5 or \u0026lt;-0.5 \u003cstrong\u003e(Figure 6C\u003c/strong\u003e, p ≤ 0.05, FDR). KEGG analysis showed significant enrichment for pathways including those linked with Erb-B and mTOR (\u003cstrong\u003eFigure 6D\u003c/strong\u003e). Log\u003csub\u003e2\u003c/sub\u003e-Fold phosphorylation changes of key peptides showed a general decrease, indicating partial downregulation of these overlapping signaling axes (\u003cstrong\u003eFigure 7A and Online Resource File 5\u003c/strong\u003e). In particular, RPS6 showed reduced phosphorylation of RPS6KB1 (p70S6K) target sites, while RPS6KB1 showed reduced phosphorylation on amino acids targeted by ERK. Curiously, phosphorylation of MAPK kinase ERK2 (MAPK1) itself was instead increased on Thr190 and Tyr187 in HMMR-depleted cells (\u003cstrong\u003eFigure 7A, C, D,\u0026nbsp;\u003c/strong\u003eantibody recognizes equivalent of P-Thr185 and P-Tyr187 in ERK2). KSEA on the signature set revealed diverse kinases among both the up- and down-regulated pools (\u003cstrong\u003eOnline Resource File 5\u003c/strong\u003e). The ERK1 (MAPK3) pathway reached statistical significance for clones KA5 and KA14, and ERK2 (MAPK1) was close to significance (p 0.057); both, however showed down-regulated pathways. We also applied KSEA to the differentially phosphorylated peptides in the complete dataset, again observing modest downregulation of ERK pathways \u003cstrong\u003e(Figure 7B).\u003c/strong\u003e In assessing the activation status of the direct substrates of ERK, all except ZFPM1 were downregulated for both ERK1 and ERK2, confirming partial downregulation of ERK signaling (\u003cstrong\u003eFigure 7E\u003c/strong\u003e). IPA analyses also confirmed this (\u003cstrong\u003eOnline Resource File 4, blue lines, and Online Resource File 6\u003c/strong\u003e). We thus conclude that HMMR depletion counterintuitively increases ERK phosphorylation, but suppresses the activity of the downstream ERK cascade.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven that MTOR signals may be influenced by HMMR, we looked for upstream regulation of AKT. In immunoblots AKT showed a variably increased phosphorylation of activation site S473 in HMMR depleted cells, but this also occurred in KC17 (\u003cstrong\u003eFigure 7C,D\u003c/strong\u003e). This peptide was not identified in the phosphoproteomic dataset. Our examination of other common substrates in the proteomics data revealed no clear pattern of AKT activation or inactivation. Thus it is most likely that S6KB1 inactivation after loss of HMMR is not due to AKT suppression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.7 DNA damage response proteins and HMMR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO term analysis on the HMMR signature peptides revealed processes including double-strand break repair of DNA, DNA repair, and responses to DNA damage stimuli (\u003cstrong\u003eFigure 8A\u003c/strong\u003e). Furthermore, the HMMR-co-expression dataset also picked up several aspects of DNA damage and repair in a GO analysis (\u003cstrong\u003eOnline Resource File 2\u003c/strong\u003e). Examination of the phosphoproteomic data for DNA damage response (DDR) proteins revealed several with significantly increased phosphorylation after HMMR depletion (\u003cstrong\u003eFigure 8B\u003c/strong\u003e). These include p53BP1 and RIF1, which together bind double-strand DNA breaks and influence non-homologous end joining (NHEJ) [51, 52]. These are not known ATM or ATR target sites and their role in modulating DDR is currently unclear. \u0026nbsp;KAP1 (TRIM28) also shows a potential hyperphosphorylation on S473, a stimulatory site targeted by CHK1 and CHK2 after DNA damage [53-56]; this alteration is statistically significant only in KA5. CHK2 itself shows increased phosphorylation on Y390 in KA5 and KA14, but not KC17; Y390 phosphorylation is necessary for CHK2 kinase activity \u0026nbsp;[57]. S260 in CHK2, another autophosphorylation site [58], also shows hyperphosphorylation in KA5 and KA14. In contrast, some hypophosphorylation is seen on CHK1 S316, a potential autophosphorylation site next to ATR target S317 [56]. Direct evidence for activation of ATM, ATR and BRCA1 is not evident in the dataset. Collectively, the phosphoproteomic data point to there being a restricted but significant perturbation in the DDR network after loss of HMMR.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHMMR is has oncogenic potential in several cancer models, with our study being the first in neuroblastoma. High \u003cem\u003eHMMR\u003c/em\u003e expression correlates strongly with poor prognosis and could be an independent risk factor for neuroblastoma patients. We also show in cultured KELLY cells that loss of HMMR leads to reduced proliferation, 2D colony formation and 2D migration. Xenograft analysis also suggests that HMMR is required for maximal tumour growth rate. Our initial, unbiased phosphoproteomic study of these cells indicates that HMMR is directly or indirectly influencing the phosphorylation of many proteins including ERK, IRS2, S6KB1 and S6K. Moreover, we have identified a further potential influence of HMRR in the cell’s DDR network. Overall, these data indicate that HMMR could be an unexplored driver of cancer cell behaviour in neuroblastoma cells with a broad signaling influence.\u003c/p\u003e\n\u003cp\u003eFrom neuroblastoma patient datasets, high \u003cem\u003eHMMR\u003c/em\u003e expression can mark tumours as higher risk and \u003cem\u003eHMMR\u003c/em\u003e expression represents a risk factor independent of \u003cem\u003eMYCN\u003c/em\u003e. Although HMMR is an HA receptor, other HA-signaling genes, including \u003cem\u003eCD44\u003c/em\u003e, do not show prognostic significance, suggesting that HA signaling per se may not be a sufficient influence behind HMMR’s pro-oncogenic actions in these tumours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHMMR-deficient KELLY cells have reduced proliferation rates, agreeing with what is observed in other tumour cell types [13, 14, 59]. HMMR acts alongside CD44 as a cell surface HA receptor to generate ERK signaling, although depending on the size of HA used this signaling can be either promoted or inhibited (reviewed in [48]). In our study, exogenous HA of varying sizes modestly suppressed proliferation in KELLY cells, whereas cells lacking HMMR (already very growth suppressed) were not further growth-suppressed. The likely explanation of these findings is that endogenous HA normally drives ERK signals and proliferation in part through HMMR/CD44 complexes, and that exogenous HA can partially interfere with this. Once HMMR is removed from the system, however, both endogenous and exogenous HA are hampered in their ability to modulate CD44 signaling, even though CD44 levels were normal in HMMR-deficient cells. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt clonogenic density the HMMR-deficient cells struggled to self-renew as 2D colonies. Our preliminary work with HMMR-deficient IMR32 cells indicates a similar deficiency (Karapouliou and Stoker, unpublished). This aligns with the proposed stemness-promoting capacity of HMMR in glioblastoma cells [13]. To counter this, however, \u003cem\u003eHMMR\u003c/em\u003e expression in neuroblastoma tumours (from R2 analysis) does not correlate well with proposed neuroblastoma stem cell genes such as \u003cem\u003eNOTCH\u003c/em\u003e, \u003cem\u003eGPRC5C\u003c/em\u003e or \u003cem\u003eTRKB\u003c/em\u003e [60]. HMMR’s clonogenic function may relate to its maintenance of ERK signaling [61], but this needs further investigation. HMMR is also required for optimal motility in a wound repair assay of KELLY cells, corroborating similar findings in in other cell types. HMMR cooperates with CD44 to promote cell motility through ERK, FAK and SRC [12, 17, 50], and cells lacking HMMR can cause a deficit in this CD44-mediated signaling [62]. Our phosphoproteomic data reveal similar correlation at least between motility and ERK signaling in neuroblastoma cells. Exogenous, high molecular weight HA slightly decreased motility in KELLY cells, but surprisingly it rescued the migration deficit in HMMR-deficient cells. HA influences motility in other cells both positively and negatively, complicated by the HA size range [17, 48, 63-65]. One hypothesis here is that loss of HMMR blocks CD44’s ability to efficiently bind endogenous HA and the migration signals then falter. Exogenous HMW HA , however, may be able to drive CD44 multimer formation, re-initiating signaling [66].\u003c/p\u003e\n\u003cp\u003eUsing xenografted tumours of our KELLY derivatives, our study supports to possibility that HMMR positively promotes KELLY tumour growth. This would currently concur with other cancer models where HMMR was able to support tumour growth [13, 14]. One caveat was that the \u003cem\u003eHMMR\u003c/em\u003e-intact line KC17 also showed slower growth than KELLY, however the proteomic data clearly showed that KC17 was biochemically non-identical to KELLY and the KO lines and so its behaviour in vivo is difficult to interpret. Further studies with other HMMR-depleted neuroblastoma cell lines are therefore needed to further confirm the in vivo properties of these cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhosphoproteomics have allowed us to start uncovering some of the potential signaling that HMMR can directly or indirectly modulate in neuroblastoma cells. Oddly, ERK1/2 was hyperphosphorylated in the KO cells, while ERK1/2 downstream signaling was significantly suppressed. The latter might be expected given the documented, stimulatory role of HMMR in ERK stimulation, but we cannot currently explain why ERK phosphorylation itself increases. In KSEA and GO analyses, components of the MTOR network and the overlapping ErbB pathway, were also suppressed. MTOR is a central regulator of cellular responses to growth factors and cell stress, and integrates signals from ERK, AKT and other signals [67]. A reduction in MTOR signals, particularly those through RPS6KB1 and RPS6 that we observe, might explain in part the reduced proliferation and survival profiles of the HMMR-depleted cells. CD44 and MTOR also regulate each other’s actions in AML and breast cancer models [68, 69], indicating that HA signaling can also feeds into this pathway. The observed reduction in IRS2 phosphorylation may also relate to the reduced MTOR signaling, although this is speculative given that the phosphosites we observe are not characterised [70]. IRS2 nevertheless is of interest given that ALK uses IRS2 as an effector in neuroblastoma cells [71]. Lastly, a reduction in ERBB signaling after loss of HMMR is of interest since ERBB signaling is implicated in neuroblastoma [72], and ERBB’s signaling in ovarian tumour cells can utilizes interactions with CD44 and hyaluronan [73]. Whether or not HMMR operates in a complex of CD44 and ERBB2 remains to be determined.\u003c/p\u003e\n\u003cp\u003eAlongside it’s HA receptor role, HMMR has well documented nuclear roles in spindle dynamics and interactions with BRC1 [7]. Given BRCA1’s central role in homologous DNA repair, it was interesting that loss of HMMR in KELLY cells led to hyperphosphorylation of some DDR regulators. These included p53BP1, KAP1, RIF1 and CHK2. Known regulatory phosphosites were altered \u0026nbsp;on CHK2 [57, 58, 74, 75] and KAP1 [53, 55, 56], while numerous phosphopeptides in 53BP1 remain to be functionally understood. This raises the hypothesis that HMMR is a direct or indirect modulator of DDR in neuroblastoma cells, a potentially new role for this protein. Loss of HMMR may disrupts spindle and chromosome dynamics in neuroblastoma cells, as seen in other cancer cells\u0026nbsp;[24, 25], generating mitotic stress and DNA damage. However, we currently see no direct evidence of ATM or ATR activation or altered phosphorylation targets such as T68 in CHK2 or S824 in KAP1.\u0026nbsp;Our observation of RPS6KB1 and RPS6 hypophosphorylation could also relate to DNA damage since\u0026nbsp;RPS6 phosphorylation attenuate DSBs in BRCA1-deficient breast cancer cells [76], while DNA damage suppresses S6K1-mediated RPS6 phosphorylation in various tumour cells [77].\u0026nbsp;A new role for HMMR in DDR within neuroblastoma cells is therefore proposed, requiring further investigation.\u003c/p\u003e\n\u003cp\u003eTo conclude, we show for the first time that high expression of \u003cem\u003eHMMR\u003c/em\u003e and its product HMMR are statistically implicated in poor neuroblastoma patient outcomes and also in supporting several cancer cell behaviors in KELLY cells. Potentially new areas of HMMR influence include modulation of protein phosphorylation in the MTOR and DDR pathways, warranting further exploration. HMMR could thus form an unrecognised signaling hub in these tumour cells, opening avenues for future prognostic or therapeutic investigation.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDNA damage repair – DDR\u003c/p\u003e\n\u003cp\u003eGene ontology - GO\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHyaluronic acid – HA\u003c/p\u003e\n\u003cp\u003eKnockout – KO\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u0026nbsp;- KEGG\u003c/p\u003e\n\u003cp\u003eNon-homologous end joining – NHEJ\u003c/p\u003e\n\u003cp\u003eKinase substrate enrichment analysis - KSEA\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor AWS and CK this research was funded by Neuroblastoma UK (NBUKStoker19)\u0026nbsp;and the Olivia Hodson Cancer Fund,\u0026nbsp;Great Ormond Street Hospital Children\u0026rsquo;s Charity (SR16A59).\u0026nbsp;The study was also supported\u0026nbsp;by the NIHR Great Ormond Street Hospital Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the NHS,\u0026nbsp;the NIHR or the Department of Health. Funding for VR and PRC was from CRUK (C16420/A18066) and MRC (MR/X013766/1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe processed phosphoproteomics data are deposited with Mendeley Data and available at https://data.mendeley.com/datasets/6wr2tj8wr9/1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of Interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePedro Cutillas reports a relationship with Kinomica Limited that includes: board membership, consulting or advisory, and equity or stocks.\u0026nbsp;All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Statememt\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChristina Karapouliou\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eVinothini Rajeeve\u003c/strong\u003e: Data curation, Formal analysis, Methodology, investigation, Software, Validation ; Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003ePedro Cutillas\u003c/strong\u003e: Data curation, Methodology, Software, Validation, Visualization Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eAndrew Stoker\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eD.A. 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Cristea, N. Solleveld-Westerink, T.v. Wezel, N. Beerenwinkel, R. Eeles, P. Devilee, M.P.G. Vreeswijk, G. Marra and H.v. Attikum, Cancer Research 82, 615-631 (2021) doi: 10.1158/0008-5472.can-21-1845\u003c/li\u003e\n \u003cli\u003eC.-k. Sun, F. Zhang, T. Xiang, Q. Chen, T.K. Pandita, Y. Huang, M.C.T. Hu and Q. Yang, Oncotarget 5, 3375-3385 (2014) doi: 10.18632/oncotarget.1952\u003c/li\u003e\n \u003cli\u003eM. Cam, H.K. Bid, L. Xiao, G.P. Zambetti, P.J. Houghton and H. Cam, Journal of Biological Chemistry 289, 4083-4094 (2014) doi: 10.1074/jbc.m113.530303\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Univariate and multivariable Cox regression of the prognostic covariates in patients with NB (n=490, SEQC patient dataset from R2 Genomics). See Methods for full details.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.064 (1.049 - 1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.9957 (0.9945 \u0026ndash; 0.9969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHAS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.9921 (0.9232 \u0026ndash; 1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.8036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHAS2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.9766 (0.9217 \u0026ndash; 1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.3474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHAS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.267 (1.203 \u0026ndash; 1.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHYAL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.7030 (0.5983 \u0026ndash; 0.8210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHYAL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.033 (1.018 \u0026ndash; 1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHYAL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.170 (1.085 \u0026ndash; 1.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eMYCN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e7.246 (4.900 \u0026ndash; 10.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eINSS (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.01261 (0.0007151 \u0026ndash; 0.05674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eINSS (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.08056 (0.02458 \u0026ndash; 0.1935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eINSS (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.3804 (0.2065 \u0026ndash; 0.6497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eINSS (4s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.1193 (0.03639 \u0026ndash; 0.2865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eAge of diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.000 (1.000 \u0026ndash; 1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.035 (1.010 \u0026ndash; 1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eCD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.9996 (0.9984 \u0026ndash; 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.4772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHAS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.049 (0.9677 \u0026ndash; 1.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.2289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHYAL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e0.9271 (0.8090 \u0026ndash; 1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.2316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0417%;\"\u003e\n \u003cp\u003eHYAL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.6181%;\"\u003e\n \u003cp\u003e1.006 (0.9885 - 1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.3403%;\"\u003e\n \u003cp\u003e0.5112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5194003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5194003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuroblastoma is a childhood cancer with poor survival rates. Approximately 75% of tumours have no identified oncogenic driver and here our aim was for the first time to investigate whether HMMR, a protein with hyaluronic acid (HA)-binding properties, nuclear actions, and oncogene-like roles in other cancers, harbors similar potential roles in neuroblastoma cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe bioinformatically analysed patient survival data in relation to \u003cem\u003eHMMR\u003c/em\u003eexpression, followed by CRISPR/Cas9-based disruption of \u003cem\u003eHMMR\u003c/em\u003e in KELLY neuroblastoma cells. HMMR’s support of proliferation, motility and clonogenicity were analysed and the dependence on exogenous HA determined. Xenografted tumours with disrupted \u003cem\u003eHMMR\u003c/em\u003e were analysed to assess animal survival characteristics. Lastly, phosphoproteomics was used to begin to define the biochemical actions of HMMR in these tumour-derived cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh \u003cem\u003eHMMR\u003c/em\u003eexpression is shown to be an independent prognostic indicator of poor survival in neuroblastoma patients. Furthermore, HMMR-deficient cells in culture have reduced proliferation, motility and clonogenic capacities compared to parental cells, and HA had variable ability to rescue these. Loss of HMMR also reduces xenografted tumour growth rates. Signaling downstream of MAPK1/2 and MTOR were both disrupted at a phosphoproteomic level after loss of HMMR, while the phospho-status of DNA damage response (DDR) proteins was significantly enhanced.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study indicates that high \u003cem\u003eHMMR\u003c/em\u003e expression could be a new and potentially useful prognostic marker of poor neuroblastoma survival. Moreover, HMMR has oncoprotein-like properties in neuroblastoma cells, with some actions being HA-regulated. The study also reveals the first data that may implicate HMMR in MTOR and DDR regulation.\u003c/p\u003e","manuscriptTitle":"HMMR is an independent prognostic indicator in neuroblastoma and loss of HMMR suppresses cell proliferation, migration and clonogenicity.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 06:55:02","doi":"10.21203/rs.3.rs-5194003/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c121a98-15e9-4391-a163-9bd615bfed1e","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-23T06:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 06:55:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5194003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5194003","identity":"rs-5194003","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}