PHGDH inhibition overcomes dabrafenib resistance through metabolic rewiring in BRAF V600E anaplastic thyroid carcinoma

PHGDH inhibition overcomes dabrafenib resistance through metabolic rewiring in BRAF V600E anaplastic thyroid carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PHGDH inhibition overcomes dabrafenib resistance through metabolic rewiring in BRAF V600E anaplastic thyroid carcinoma Mi-Hyeon You, Sung Young Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069883/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 Dabrafenib, a BRAF kinase inhibitor, is used clinically to treat anaplastic thyroid carcinoma (ATC) harboring the BRAF V600E mutation. However, its clinical efficacy is limited due to the rapid emergence of resistance to treatment. In this study, we established a dabrafenib-resistant ATC cell line (8505C-R) and compared its molecular and phenotypic characteristics with those of the parental cells. We evaluated cell proliferation, colony formation, stem cell marker expression, and MAPK pathway activity and identified elevated levels of EGFR and PHGDH in 8505C-R cells as hallmarks of resistance. RNA sequencing revealed that PHGDH inhibition using the selective inhibitor NCT-503 profoundly reprogrammed gene expression networks related to tumor plasticity, stem-like phenotypes, and hyperactivation of the EGFR–MAPK signaling axis. Notably, LRIG1, a negative regulator of EGFR, was differentially expressed upon PHGDH inhibition, suggesting a feedback loop connecting serine metabolism with receptor tyrosine kinase signaling. Functional assays demonstrated that PHGDH inhibition, either alone or in combination with dabrafenib, markedly suppressed colony formation, expression of stemness-associated markers, and MAPK pathway activity. These results reveal a novel resistance mechanism in BRAF-mutant ATCs mediated by PHGDH-dependent serine metabolism and cross-reaction with EGFR–MAPK signaling. Targeting PHGDH effectively suppresses resistance and stemness characteristics, and may provide a novel therapeutic strategy in combination with dabrafenib for aggressive thyroid cancer. Biological sciences/Cancer/Cancer metabolism Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance anaplastic thyroid cancer BRAF V600E mutation Drug resistance PHGDH therapeutic vulnerability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The B-Raf proto-oncogene (BRAF) V600E mutation is frequently observed in thyroid cancer, especially in anaplastic thyroid carcinoma (ATC), and Dabrafenib, a BRAF inhibitor, is approved and used for the treatment of ATC [ 1 ]. However, rapid development of resistance through compensatory activation of the epidermal growth factor receptor (EGFR) pathway has been reported when dabrafenib is used as monotherapy, which limits the clinical efficacy of the treatment [ 2 , 3 ]. EGFR is a tyrosine kinase receptor that induces cell proliferation, invasion, and angiogenesis through the protein kinase R (PKR)-like endoplasmic reticulum kinase (ERK) and protein kinase B (AKT) pathways, and combined inhibition of BRAF and EGFR has been proposed as an effective method for overcoming resistance in various cancers [ 4 , 5 ]. Although the synergy in this combination therapy has been demonstrated in colon cancer and melanoma, research on this topics in thyroid cancer including ATC remains limited [ 4 , 6 – 8 ]. In this study, we observed a significant increase in the expression of phosphoglycerate dehydrogenase (PHGDH), a key enzyme in serine/glycine metabolism, along with sustained activation of phosphorylated EGFR in dabrafenib-resistant ATC cells (8505C-R) [ 9 – 11 ]. Our findings align with recent studies demonstrating that PHGDH overexpression contributes to resistance mechanisms, suggesting that targeting PHGDH may allow to overcome therapeutic resistance [ 9 , 10 , 12 , 13 ]. PHGDH has been implicated in resistance mechanisms in various cancers such as melanoma, particularly through metabolic rewiring of serine biosynthesis [ 10 , 14 ]. However, to our knowledge, this is the first study to elucidate the functional role of PHGDH in anaplastic thyroid cancer (ATC) [ 11 , 15 – 17 ] We observeded therapeutic responses such as a decrease in p-ERK level, inhibition of colony formation, and a decrease in stemness when the PHGDH inhibitor NCT503 was administered alone or in combination with dabrafenib. RNA sequencing (RNA-seq) analysis suggested that PHGDH inhibition affected mitogen-activated protein kinase (MAPK) signaling, metabolic reprogramming, and cell plasticity [ 12 , 13 , 18 ]. In particular, the expression of Leucine-rich repeats and immunoglobulin-like domains 1 (LRIG 1) increased in the resistant state, suggesting that it may act as a negative feedback regulator that suppresses the EGFR–PHGDH axis [ 19 , 20 ]. The increased expression of LRIG1 is linked to the suppression of EGFR signaling and the reduction of pERK signaling, and may act as an important complementary mechanism for the PHGDH inhibition strategy [ 19 – 21 ]. These results provide important evidence for establishing a novel combination treatment strategy in BRAF V600E mutant thyroid cancer by jointly illuminating the functional crosstalk between the PHGDH-EGFR axis and LRIG1-based suppression of resistance. Results Transcriptional profiling reveals PHGDH as a potential key driver in ATC progression and dabrafenib response. PHGDH expression is elevated in malignant anaplastic thyroid carcinoma cells To gain insights into PHGDH expression in ATC and its association with dabrafenib response, we analyzed publicly available single-cell RNA-seq datasets GSE193581, GSE221329, and in-house bulk RNA-seq data. Initial analysis of all cell types in the GSE193581 dataset showed no significant difference in PHGDH expression between papillary thyroid carcinoma (PTC) and ATC (data not shown) [ 22 ]. However, when the analysis was restricted to malignant cells only, we observed a significant upregulation of PHGDH expression in ATC compared to PTC (Fig. 1A). This finding highlights the importance of analyzing tumor-specific cell populations to uncover disease-relevant expression patterns. We also investigated PHGDH expression in a pair of age-, sex-, and stage-matched ATC samples, (ATC09 and ATC12) both from females in their 50s, T4b,>10% mitochondrial content from the GSE193581 dataset, focusing on their BRAF mutation status. A BRAF V600E mutation was present in ATC09, while ATC12 had wild-type BRAF. Although a visual inspection of the plot with zero-expression cells excluded suggested a higher PHGDH level in ATC09 than in ATC12, statistical analysis That included all cells revealed a significantly lower in PHGDH expression in ATC09 than in ATC12 (Fig. 1B). This result suggests a complex interplay between BRAF mutation status and PHGDH expression in ATC. PHGDH expression during differentiation in non-malignant cells Given the reported association between PHGDH expression and stemness, we next explored PHGDH expression patterns during differentiation in non-malignant cells from the ATC samples using trajectory analysis. Analysis of immune cell trajectories from an ATC sample identified three distinct branches of differentiation (Fig. 1C) [ 23 – 25 ]. PHGDH expression remained generally low across most immune cell types, with no clear increasing or decreasing trend along the pseudotime. However, PHGDH expression tended to be lower in the early differentiation stages along the pseudotime. The trajectory plot revealed dendritic cells at the center, with PHGDH expression tending to increase as cells differentiated further, moving away from the center towards cell types such as T cells (Fig. 1C). This suggests that while Its expression does not fluctuate dramatically, PHGDH might have subtle roles in specific differentiation stages of immune cells within the tumor microenvironment. PHGDH upregulation in dabrafenib-resistant ATC and sensitivity to PHGDH inhibition Given that PHGDH has been implicated in cancer stemness and aggressiveness, we hypothesized its involvement in dabrafenib resistance [ 23 – 25 ]. Analysis of bulk RNA-seq data from GSE221329 revealed that PHGDH expression was significantly higher in an ATC cell line (CUTC60) following dabrafenib treatment for 48 hours than in DMSO control, whereas no significant difference was observed in a PTC cell line (CUTC5) (Fig. 1D). This finding aligns with our expectation that PHGDH plays a role in dabrafenib resistance in ATC. Furthermore, our in-house data for cells with acquired dabrafenib resistance demonstrated that they had significantly higher PHGDH expression than the control cells (Fig. 1E). Crucially, treatment with a NCT503 dramatically reduced PHGDH expression in these resistant cells (Fig. 1E). These results strongly suggest that the PHGDH signaling pathway plays a important role in mediating acquired dabrafenib resistance in ATC, positioning PHGDH as a promising therapeutic target to overcome this resistance. Characterization of dabrafenib-resistant ATC cells To model acquired resistance to dabrafenib, we chronically treated two BRAF V600E-mutant ATC cell lines, 8505C and BCPAP, with dabrafenib for 1 month (Fig. 2A).We then compared the resulting resistant sublines (designated 8505C-R and BCPAP-R) were compared with their parental counterparts to evaluate alterations in proliferative capacity and tumorigenic potential (Fig. 2A–F, Supplementary Fig. 1). Upon treatment with dabrafenib (0–3 µM) for 5 days, growth of parental cells was inhibited in a dose-dependent manner, with significant reductions at 0.5–3 µM (Fig. 2B, p < 0.01). In contrast, growth of 8505C-R cells was not statistically inhibited, confirming the establishment of resistance (Fig. 2B, N.S). Colony formation assays further corroborated this phenotype: dabrafenib significantly reduced the clonogenicity of parental 8505C cells (Fig. 2C and 2D; p < 0.01), whereas resistant sublines retained high colony-forming ability despite continued drug exposure (Fig. 2E and 2F, p < 0.01 for BCPAP-R). These findings demonstrate that prolonged dabrafenib exposure induces a stable resistant phenotype in BRAF-mutant ATC cells, accompanied by altered proliferative behavior and enhanced survival [ 26 , 27 ]. EGF signaling is upregulated in dabrafenib-resistant ATC cells and contributes to MAPK pathway reactivation To investigate the mechanisms underlying acquired resistance to dabrafenib, we performed RTK array analysis in BRAF V600E-mutant 8505C and BCPAP cells treated with or without dabrafenib (Fig. 3A). EGF signaling was significantly upregulated in both 8505C-R and BCPAP-R cells compared to parental controls (Fig. 3B; p < 0.001). Time-course analysis of 8505C cells revealed a gradual increase in the levels of ERK and AKT phosphorylation during dabrafenib treatment (Fig. 3C). In dabrafenib-resistant cell lines, increased pEGF levels were accompanied by elevated pERK levels, indicating MAPK pathway reactivation (Supplementary Fig. 2). Thus, RTK array results confirmed that dabrafenib treatment induced upregulated EGF, thereby activating a bypass MAPK pathway. We also assessed other proteins potentially involved in resistance. Western blot analysis revealed significant PHGDH upregulation in dabrafenib-resistant 8505C cells (Fig. 3D, 3E), consistent with its known role in aggressive tumors. Stemness-related proteins (e.g., OCT4) and epithelial–mesenchymal transition (EMT) markers showed no significant differences in resistant versus control cells (Fig. 3, Supplementary Fig. 3). These results suggest that dabrafenib-resistant ATC cells bypass BRAF inhibition by upregulating EGF signaling and reactivating the MAPK pathway, while also engaging PHGDH-driven mitochondrial one-carbon metabolism [ 9 , 12 , 28 ]. EGF upregulation is not associated with thyroid cancer progression To determine whether EGF upregulation is associated with thyroid cancer progression regardless of dabrafenib exposure, we analyzed publicly available transcriptomic datasets. In the GSE33630 dataset, EGF transcript levels were compared across 11 ATC, 49 PTC, and 45 non-tumorous thyroid tissues. No significant differences in EGF expression were observed between the groups (Fig. 4A). Similarly, in the GSE65144 dataset (12 ATC and 13 normal samples), EGF expression was slightly higher in normal tissues, with no significant difference (Fig. 4B). To validate these findings, we assessed EGF protein expression in normal and PTC tissues, and found no significant difference (Fig. 4C–D, N.S). Collectively, these results indicate that EGF upregulation is not associated with thyroid cancer subtype or progression, but instead reflects a response to dabrafenib-induced resistance in BRAF V600E-mutant cells, consistent with our experimental findings. PHGDH-driven metabolic reprogramming in dabrafenib-resistant anaplastic thyroid carcinoma Building upon these findings, we performed a deeper transcriptional analysis focusing on the PHGDH-driven metabolic network, which we specifically curated. This network comprises six interconnected pathways: nucleotide biosynthesis, glutathione redox balance, transsulfuration pathway, folate metabolism, one carbon metabolism, and serine metabolism (Supplementary Table 1). These pathways are intrinsically linked to PHGDH activity, as PHGDH initiates the de novo serine biosynthesis pathway. The serine produced then provides crucial one-carbon units that fuel the folate metabolism and one-carbon metabolism, thereby supporting nucleotide biosynthesis. Serine can also be converted to cysteine via the transsulfuration pathway, and cysteine serves as a precursor for glutathione, which is critical for maintaining cellular redox homeostasis. This network collectively forms a comprehensive metabolic axis relevant to cell proliferation and stress response. Volcano plots comparing dabrafenib-resistant cells (R) to control (C) revealed a significant upregulation of genes within the PHGDH-driven metabolic network (FDR 4), including PHGDH itself (Fig. 5A, top). Conversely, resistant cells treated with a PHGDH inhibitor (RNCT) showed a significant downregulation of genes in comparison with R, indicating successful pathway perturbation (FDR < 0.05, fold change < 4) (Fig. 5A, bottom). A heatmap of these PHGDH-driven metabolic network genes clearly demonstrated distinct and reciprocal expression patterns in the R vs. C and RNCT vs. R comparisons, underscoring the consistent and widespread activation of this network in resistant cells and its subsequent suppression by PHGDH inhibition (Fig. 5B). Gene set enrichment analysis (GSEA) further illuminated the metabolic shifts associated with dabrafenib resistance and PHGDH inhibition. Interestingly, the nucleotide biosynthesis pathway was significantly depleted in in R vs. C, while other pathways such as the glutathione redox balance, transsulfuration pathway, and folate metabolism one carbon metabolism were enriched (Fig. 5C, top; Supplementary Table 2). Strikingly, this pattern was precisely reversed in the RNCT vs. R comparison, where PHGDH inhibition led to a significant enrichment of the nucleotide biosynthesis pathway and a depletion of the other pathways (Fig. 5C, bottom; Supplementary Table 2). This dynamic interplay strongly suggests that the PHGDH-driven metabolic network, particularly its influence on nucleotide biosynthesis, plays a pivotal role in dabrafenib resistance and its circumvention. The regulation of nucleic acid biosynthesis is a well-established mechanism for cancer cells to escape chemotherapy, and our data indicate that this mechanism can be effectively targeted by PHGDH pathway inhibition. To identify key influential nodes within the PHGDH-driven metabolic network, we constructed a protein–protein interaction (PPI) network by using the String database and integrating expression patterns and the community centrality, degree, and betweenness scores (Fig. 5D,E). Unsupervised network analysis revealed several highly influential genes, including MTHFD1, MTHFD2, DHFR, TYMS, PAICS, CTPS1, and CTH, as core components of this network. MTHFD1, MTHFD2, and DHFR are central enzymes in the one-carbon metabolism pathway and folate cycle, which are crucial for the synthesis of nucleotides, amino acids, and S-adenosylmethionine. Their upregulation in resistant cells highlights the heightened metabolic demand for building blocks necessary for rapid proliferation and adaptation. TYMS (thymidylate Synthase) is a critical enzyme in de novo pyrimidine synthesis, directly involved in DNA replication and repair. Its increased expression underscores the enhanced nucleic acid synthesis in resistant cells. PAICS (phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase) and CTPS1 (CTP synthase 1) are also key enzymes in nucleotide biosynthesis. The high betweenness centrality scores for TYMS and CTH indicate their critical positions within the network as "bridges" or central connectors that facilitate information flow between different parts of the metabolic network. Disrupting these nodes, therefore, could have widespread impacts on the entire network, making them attractive targets for therapeutic intervention. PHGDH inhibition reverses changes in lncRNA expression associated with dabrafenib resistance, highlighting potential links to DNA repair Profiling the expression patterns of long non-coding RNAs (lncRNAs) revealed a striking trend. A significant proportion of lncRNAs that were either upregulated or downregulated in R cells compared to C cells exhibited a reverse expression pattern in RNCT cells (Fig. 6A and 6C). A chord diagram visualizing the log2 fold differences in lncRNAs between the R vs. C and RNCT vs. R groups revealed a directional reversal of expression for a substantial number of lncRNAs (Fig. 6B). Such a clear reciprocal expression pattern is noteworthy and suggests a strong regulatory link between PHGDH activity and lncRNA expression in the context of acquired resistance. To gain insight into the potential functional implications of this differential lncRNA expression, we performed over-representation enrichment analysis using the KEGG database of human lncRNAs. Notably, the top-ranking pathways enriched for the lncRNAs that showed significant changes in expression in our comparisons were predominantly involved in DNA repair (Fig. 6D, Supplementary Table 5). These included the mismatch repair, RNA polymerase, DNA replication, homologous recombination, and nucleotide excision repair pathways, although these enrichments did not reach statistical significance at a conventional adjusted p -value of 0.05. These findings strongly suggest that PHGDH activity influences the expression of lncRNAs that may play a role in DNA repair, hinting at a potential mechanism by which PHGDH inhibition could impact the cellular response to DNA damage or genomic instability associated with drug resistance. Further investigation into the specific roles of these lncRNAs in DNA repair and resistance mechanisms is warranted. Potential role of PHGDH in modulating cancer stemness in dabrafenib-resistant ATC Building upon our previous observation suggesting that PHGDH may play a role in regulating the stemness composition of ATC, we sought to investigate the expression patterns of cancer stemness-associated genes in the context of dabrafenib resistance and PHGDH inhibition [23] . To this end, we compiled an extended set of marker genes related to cancer stem cell identity reported by Galiè et al. (2008) and Malta et al. (2018) [ 29 , 30 ]. Additionally, to encompass a broader understanding of stemness, markers indicative of stem cell identity and pluripotency were incorporated from Ramalho-Santos et al., 2002 (Supplementary Table 4) [ 31 ]. The markers represent genes widely recognized for their roles in defining normal and cancer stem cells, as well as those altered in mesenchymal tumor cells compared to mesenchymal stem cells. Using this curated gene set, we performed consensus clustering using our in-house cells. Among the cancer stemness markers, we found four distinct expression patterns (Groups 1–4), indicating their differential transcriptional regulation in response to dabrafenib resistance and PHGDH inhibition (Fig. 7A). Consistent with these findings, similar expression patterns were also evident within our broader stemness marker set (Supplementary Table 4, Supplementary Fig. 4). Group 1 had high expression in control cells but lower expression in R and RNCT cells, suggesting potential involvement of these genes in maintaining non-resistant state or their downregulation upon resistance acquisition. Conversely, Groups 2 and 3 had higher expression in resistant than in C cells, with Group 3 showing some reversal in RNCT cells. Group 4 displayed unique patterns that warrant further investigation. Notably, within these expression patterns, we observed striking differential expression of specific stemness-related genes. The gene for BMP4 (encoding bone morphogenetic protein 4), a known regulator of cell fate and stem cell maintenance, had significantly higher expression in R cells than in C cells, and its expression appeared to be further increased in RNCT cells (Fig. 7B). The gene for TWIST1 (Twist family BHLH transcription factor 1), a key inducer of EMT and stemness, was also significantly upregulated in R compared to C cells; interestingly, its expression level was maintained or even tended to further increase in RNCT cells, suggesting a potentially complex involvement in resistance (Fig. 7B). Finally, the gene for LRIG1 (leucine-rich repeat and immunoglobulin-like domain containing Nogo receptor–interacting protein 1), which has been implicated in regulating stem cell self-renewal and tumor suppression, had significantly higher expression in R than in C cells, and it was maintained or slightly reduced in RNCT cells (Fig. 7B). These clear changes in the expression of genes for BMP4, TWIST1, and LRIG1 suggest that PHGDH- driven metabolic reprogramming may influence specific signaling pathways and molecular programs associated with cancer stemness, thereby potentially contributing to dabrafenib resistance [ 32 ]. In particular, LRIG1 expression was significantly increased by dabrafenib treatment, and to a lesser extent by PHGDH inhibition in comparison with the control (spheroid formation assay sample) (Fig. 7C). This pattern suggests that LRIG1 upregulation is a compensatory mechanism in response to EGF–PHGDH axis activation, supporting the role of LRIG1 as a feedback regulator that alleviates treatment resistance. Dual inhibition of PHGDH and BRAF suppresses tumorigenic potential and stemness in dabrafenib-resistant ATC cells To evaluate the synergistic anti-tumor effects of PHGDH and BRAF inhibition, we conducted colony and spheroid formation assays in 8505C dabrafenib-resistant cells. PHGDH inhibition significantly suppressed colony formation, and this effect was further enhanced by co-treatment with dabrafenib (Fig. 8A and 8B; p < 0.05 ). At a higher Dabrafenib concentrations (1 µM) combined with NCT 503, colony formation was almost completely abolished (data not shown), indicating a potent combinatorial effect. These results suggest a synergistic inhibition of tumorigenic potential in resistant cells (Supplementary Fig. 5A). Concurrently, pERK levels were also more strongly suppressed by combination treatment than by monotherapy (Fig. 8C and 8D); this observation links metabolic targeting to suppression of MAPK signaling and stem-like traits. To further validate these findings, we analyzed spheroid formation after 10 days of 3D culture. The number of large spheroids (> 50 µm) was significantly decreased by NCT503 treatment alone and was further decreased under combination treatment (Fig. 8E and 8F), with all comparisons reaching statistical significance (p < 0.01). Given the link between PHGDH and cancer stemness, we assessed the expression of stemness-related genes. Co-treatment with dabrafenib and NCT503 significantly reduced mRNA and protein levels of key stemness markers, including OCT4 and Nanog, in resistant cells (Fig. 8G–I). Collectively, these results demonstrate that dual inhibition of PHGDH and BRAF suppresses tumorigenic potential and stemness in dabrafenib-resistant ATC cells by attenuating ERK signaling and expression of pluripotency genes. PHGDH is upregulated in metastatic lesions in a melanoma lung metastasis model To establish a metastatic melanoma model, B16F10 cells were injected into the footpads of BALB/c mice, and on day 31, skin (primary site) and lung (metastatic site) tissues were collected and subjected to RNA-seq analysis (Supplementary Fig. 6A). Many genes were differentially expressed in lung metastatic tissues compared to skin (Supplementary Fig. 6B); among them, PHGDH was significantly upregulated (Supplementary Fig. 6C). These results are consistent with our in vitro results and support the possibility of a functional role of PHGDH in the metastatic microenvironment. LRIG1 was also differentially expressed in lung tissues, but the magnitude and statistical significance of the expression difference was lower than those of PHGDH (Supplementary Fig. 6B, and 6C). In vivo, the expression pattern of PHGDH may vary depending on the time of vascular invasion or the stage of metastatic progression, and various physiological factors may act in a complex manner, making it difficult to unambiguously interpret LRIG1 as a negative correlation with resistance. The fact that in dabrafenib-resistant ATC cells, EGF and PHGDH were simultaneously upregulated and LRIG1 expression was rather decreased upon PHGDH inhibition. This suggests that the relationship among PHGDH, LRIG1, and EGF signals is not a simple linear relationship, but is regulated by a complex feedback network. KEGG pathway analysis identified TGF-β signaling, leukocyte endothelial cell migration, proteoglycans, and adherens junctions as activated pathways (Supplementary Fig. 6C), which are closely related to the maintenance of the niche of cancer stem cells, cell-to-cell interaction, and metastatic ability. In particular, the TGF-β pathway is critically involved in securing tumor flexibility and treatment resistance by inducing EMT and stem cell transformation [ 33 , 34 ]. A schematic model of how the EGF–PHGDH axis cooperatively regulates tumor aggressiveness and therapeutic resistance is depicted in Fig. 8J. In ATC cells harboring the BRAF V600E mutation, the expression of both EGF and PHGDH is upregulated when resistance to dabrafenib is induced. PHGDH, a key enzyme in the serine/glycine metabolic pathway, promotes cancer stemness and adaptive resistance, and EGFR signaling reactivates the MAPK pathway (p-ERK) to enhance tumor cell survival and plasticity (Supplementary Fig. 6D). Treatment with the PHGDH inhibitor NCT503 reduces colony and sphere formation, expression of stem cell markers (NANOG, OCT4), and p-ERK levels. LRIG1 is upregulated in the dabrafenib-resistant state, possibly acting as a feedback inhibitory mechanism, but its expression is reduced after NCT503 treatment, suggesting that PHGDH inhibition can also neutralize this feedback regulatory pathway [ 13 , 19 , 20 ]. Although PHGDH inhibition can partially suppress dabrafenib resistance. The plasticity is still maintained, requiring combined therapeutic strategies that simultaneously target metabolic and signaling pathways. This suggests that PHGDH may act as a key regulator of cancer stemness and therapeutic resistance, beyond being just a metabolic enzyme. Discussion Overview of BRAF V600E-driven resistance in thyroid cancer In thyroid cancer, the BRAF V600E mutation is reported mainly in ATC and is found in 20%–50% of thyroid cancers [35, 36]. If this mutation is present, the MAPK pathway is activated [36-39]. Activated MAPK1 (ERK2) promotes the growth and metastasis of cancer cells, and its persistent activation contributes to recurrence even after chemotherapy and surgery [40, 41]. Dabrafenib, which was the focus of this study, is an approved drug and a BRAF kinase inhibitor that suppresses phosphorylation of ERK, which is a MAPK downstream of BRAF [42, 43] , . However, dabrafenib is not currently used alone because it causes drug resistance. In this study, we constructed a dabrafenib resistance model and analyzed the molecular signals that change significantly during resistance acquisition, in line with previous reports of RTK or NRAS-mediated bypass signaling and clonal evolution [17, 26-28]. PHGDH–EGFR axis as a central mechanism of resistance The main finding of this study is that the mitochondrial serine/glycine metabolic pathway mediated by PHGDH is a central mechanism of dabrafenib resistance (Figure 1-3). In particular, PHGDH expression was significantly increased in the 8505C cell line that had acquired dabrafenib resistance (Figure 2), which was associated with the maintenance of cancer stemness (Figure 3-8). Selective inhibition of PHGDH decreased colony formation and stem cell marker expression, and simultaneously suppressed ERK signaling (as evidenced by a decrease in p-ERK), suggesting the possibility of overcoming resistance (Figure 3-7). MAPK reactivation (increased p-ERK) is a major mechanism of dabrafenib resistance, as supported by reports of bypass activation of ERK through RTKs such as EGFR and IGF1R (Figure 3 and 7) [26, 28, 44]. The p-ERK levels were significantly reduced when PHGDH was inhibited by NCT503 treatment, suggesting a functional link between PHGDH and the EGFR–MAPK axis (Figure 4, Figure 8) [23-25]. In other words, not only the EGFR–MAPK pathway but also the PHGDH–serine/glycine metabolic pathway plays an important role in maintaining dabrafenib resistance, and the interaction between them is an important subject of future research (Figure 6-8) [45-47]. In addition, PHGDH expression was increased in metastatic lesions in a melanoma lung metastasis model (Supplement Figure 6), and changes in the expression of genes related to cell mobility, adhesion, and metabolic pathways were confirmed. Changes in TGF-β signaling, leukocyte endothelial cell migration, and proteoglycan pathways in cancer identified through KEGG analyses suggest that, beyond being just a metabolic enzyme, PHGDH contributes to the maintenance of metastatic ability and stemness [48, 49]. These results demonstrate that PHGDH may act as an important functional regulator during melanoma metastasis, suggesting the feasibility of future therapeutic strategies targeting PHGDH [1, 18, 48, 50]. Therapeutic potential of targeting PHGDH PHGDH inhibition alone can partially resolve dabrafenib resistance by reducing cancer stemness and p-ERK level. In addition, the PHGDH expression level has the potential to be used as a biomarker in personalized medicine in the future, and may also be useful for screening patients with poor dabrafenib response. The PHGDH inhibition strategy could be combined with metabolism-based anticancer strategies such as inhibition of folate metabolism and nucleotide synthesis, and provides a basis for exploring novel combination treatment strategies [51]The increase in PHGDH expression identified in this study is closely related to metabolic reprogramming of treatment-resistant thyroid cancer cells, and similar trends have been reported in recent literature [1, 18, 23, 50]. In particular, single-cell transcriptome analysis showed that PHGDH expression gradually increases as thyroid cancer progresses from PTC to ATC, and it is most prominent in the mesenchymal ATC (mATC) subtype [52]. This subtype shows both metabolic plasticity and an aggressive phenotype, suggesting that serine/glycine metabolism, including PHGDH, is involved in the metastasis and dedifferentiation. Although a direct link between PHGDH and stemness has not been established, the results of this study suggest that the correlation between the two is significant [22, 52]. ATC cells are strongly dependent on glutaminolysis, and inhibition of this pathway activates one-carbon metabolism as a compensatory mechanism for maintaining survival [17]. According to this report, inhibition of PHGDH decreases accumulation of the antioxidant glutathione (GSH) and ROS, and has a synergistic effect with existing anticancer drugs (lenvatinib, sorafenib). These findings emphasize that PHGDH is a key metabolic target for overcoming treatment resistance of solid tumors [9, 52]. Therefore, this study suggests that one-carbon metabolic flow centered on PHGDH plays a key role in the dedifferentiation and resistance acquisition of ATC, and that strategies to inhibit it may improving therapeutic responses [17, 23, 53-55]. Clinical implications and future research directions This study demonstrated that PHGDH is not just a metabolic enzyme, but is involved in various tumor biological functions such as resistance to anticancer agents, cancer stem cell maintenance, and MAPK pathway modulation. Therefore, PHGDH can be considered as a multifunctional target that controls cancer resistance and recurrence [9, 55]. Follow-up studies using patient-derived xenograft (PDX) models or an in vivo system are needed to verify whether PHGDH inhibitors overcome dabrafenib resistance. Methods and materials Cell culture In this experiment, BCPAP, 8505C, and SW1736 thyroid cancer cell lineswere used in this study. BCPAP is derived from PTC with a BRAF mutation, while 8505C and SW1736 originate from anaplastic thyroid carcinoma (ATC) with the same mutation. Cells were cultured at 37 °C in RPMI 1640 medium (Gibco, Grand Island, NY, USA)supplemented with 10% fetal bovine serum and 5% CO₂. Subcultures were performed every 2–3 days, and cells were passaged upon reaching 70–80% confluency. Cell line authentication was confirmed by short tandem repeat (STR) profiling. Colony formation and cell proliferation assay Cells were seeded onto 6-well plates at a density of 5 × 10³ cells per well. After 6 days of incubation, non-adherent cells were removed by washing twice with PBS. Attached cells were fixed in 500 µl of 10% formalin for 20 minutes, followed by staining with 500 µl of 0.1% crystal violet for 20 minutes. Plates were gently rinsed with water. Colonies exceeding a defined size threshold were counted at five random fields per well, and the results were statistically analyzed to compare differences between groups. To assess cell viability, an MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay was performed on BCPAP and 8505C cells treated with dabrafenib. MTT reagent was added to each well and incubated at 37 °C for 4 hours. The reaction was terminated by adding 100 µl of detergent solution, followed by a 2-hour incubation at room temperature in the dark. Absorbance was measured at 540 nm using a microplate reader. Spheroid formation assay Tumor sphere formation was assessed using ultra-low attachment 96- and 6-well plates. 8505C cells were seeded at a density of 3 × 10⁴ cells per well (n = 6). The culture medium consisted of RPMI 1640 supplemented with recombinant human EGF, human FGF, B27 supplement, and 1% penicillin/streptomycin (all from Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). Fresh medium was added every 3 days, and cells were monitored for 10 days. On day 10, spheres ≥50 µm were counted in five minutes under a light microscope at 100× magnification. Samples were collected for subsequent analysis, including real-time PCR [23]. mRNA expression analysis Quantitative real-time PCR (qRT-PCR) was performed to measure mRNA levels of PHGDH, Oct-4 (octamer-binding transcription factor 4), and NANOG (Nanog homeobox) in dabrafenib-resistant cell lines cultured under stemness-inducing conditions for 10 days. Total RNA was extracted using TRIzol® reagent (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA), and cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit (Fermentas, Thermo Fisher Scientific, Waltham, MA, USA). Each 25 µl PCR reaction contained 1.0 µl cDNA, 12.5 µl QuantiFast SYBR Green PCR Master Mix, 1.0 µl RNase-free water, and 10 pM of each primer. PCR was conducted on a 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). 18S rRNA served as the internal control. Primer sequences were previously described by Jeon et al. (2020) and used as referenced [23]. Thermal cycling conditions were as follows: initial denaturation at 95 °C for 10 s, followed by 40 cycles of 60 °C for 30 s. Western blot analysis Proteins (20–30 μg) from cell lysates and human samples were separated by SDS-PAGE using NuPAGE gels (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) and transferred to 0.45 μm nitrocellulose membranes (Amersham Bioscience, Piscataway, NJ, USA). Membranes were blocked with Super Blot blocking buffer (Invitrogen) for 10 minutes, followed by incubation with primary antibodies overnight at 4 °C. After washing three times with TBS-T, membranes were incubated with HRP-conjugated secondary antibodies (1:5000) for 1 hour. Detection was performed using enhanced chemiluminescence (Enzo Life Sciences, New York, USA). Primary antibodies used were as follows: pEGF (Tyr1068), EGF, p-ERK (Thr202/Tyr204), ERK, Vimentin, N-cadherin, E-cadherin, pAKT (Ser473), AKT (Cell Signaling Technology, Cat# 2236, 2748, 4903, 6964, 9102, 9101, 5741, 13116, 3195, 9271, 9272), PHGDH (Abcam, ab57030), and OCT3/4 (Santa Cruz Biotechnology, sc-5279, 1:200). Protein levels were normalized to β-actin (Cell Signaling Technology, 4970). All experiments were independently performed in triplicate. Phospho-receptor tyrosine kinase (RTK) analysis Phospho-receptor tyrosine kinase (RTK) array analysis was conducted using dabrafenib-resistant 8505C, BCPAP, SW1736, and corresponding control cell lines. Approximately 300 µg of total protein lysate was incubated with the Human Phospho-RTK Array Kit (R&D Systems, Minneapolis, MN, USA), following the manufacturer's protocol. Signal intensity was detected by enhanced chemiluminescence (ECL), as used in western blot analysis. Animal study A melanoma metastasis model was established using 6-week-old male C57BL/6N mice (OrientBio, Korea). GFP-labeled B16F10 melanoma cells (1 × 10⁶ cells in 20 μL PBS) were injected into the hind footpad. After tumor development (approximately 28–31 days), mice were sacrificed, and both the footpad tumors (primary tumors) and metastatic lung lesions were harvested. Tissue-derived cells were processed for fluorescence-activated cell sorting (FACS; BD FACSAria III) to isolate GFP-positive cells. Sorted GFP⁺ cells were subjected to downstream analyses including single-cell transcriptomics (scRNA-seq), bulk RNA-seq, and functional assays. All animal procedures were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Konkuk University. Mice were housed under sterile conditions and euthanized under appropriate anesthesia at the experimental endpoint. Pharmacological inhibitors Dabrafenib, a BRAF V600E inhibitor, was used to generate drug-resistant cell lines. Dbrafenib (Selleck Chemicals, Houston, TX, USA) were dissolved in 100% DMSO (Sigma-Aldrich, St. Louis, MO, USA). An equivalent concentration of DMSO was used as a vehicle control in all experiments. The PHGDH inhibitor NCT 503 and its inactive analog were obtained from Sigma-Aldrich (St. Louis, MO, USA). RNA sequencing In vitro thyroid cancer model (8505C cell line). To investigate transcriptomic changes associated with drug resistance, total RNA was extracted from 8505C thyroid cancer cells under three conditions: untreated control, dabrafenib-resistant, and dabrafenib-resistant cells treated with the PHGDH inhibitor NCT 503. Bulk RNA sequencing was performed to assess differential gene expression. Notably, PHGDH expression was markedly elevated in resistant cells and reduced upon NCT 503 treatment, suggesting its potential involvement in serine metabolism–mediated resistance mechanisms. In vivo melanoma model (mouse). To examine transcriptomic alterations associated with melanoma metastasis, B16F10 melanoma cells were injected into the footpads of BALB/c mice to establish a spontaneous metastasis model. On day 31 post-injection, primary skin lesions and metastatic lung tissues were collected. Total RNA was isolated and subjected to bulk RNA sequencing. Differential gene expression analysis was conducted to compare primary and metastatic tissues, followed by KEGG enrichment analysis to identify metastasis-associated pathways. Data Acquisition and Processing Raw count data were imported into a DGEList object using the edgeR package in R. Genes with low expression levels were filtered out to improve data quality, and normalization was performed using the trimmed mean of M-values (TMM) method via the calcNormFactors function to account for library size differences. The normalized count data were then transformed to log2-counts per million (logCPM) values using the voom function from the limma package, which models the mean-variance relationship and generates precision weights for each observation [56]. Differential expression analysis was conducted using linear modeling and empirical Bayes moderation, with differentially expressed genes identified at a false discovery rate (FDR) threshold of 0.05. Single-cell RNA sequencing data were processed using the Seurat package in R [57]. Quality control filtering excluded cells with 5,000 detected features and >10% mitochondrial gene expression. Data normalization and scaling were performed using SCTransform, followed by principal component analysis (PCA). Cell type annotation was conducted using SingleR with the Human Primary Cell Atlas reference dataset from the celldex package. Differential gene expression analysis between sample groups was performed using FindMarkers, both on total cells and malignant cells only. Trajectory analysis was performed using Slingshot on PCA coordinates to infer developmental trajectories and pseudotime ordering [58]. Standard statistical thresholds and multiple testing corrections were applied throughout the analysis. Gene expression datasets GSE193581 and GSE221329 were downloaded from the Gene Expression Omnibus (GEO) database. Single-cell RNA-seq data were processed using Seurat (v4.0) in R. Unsupervised machine learning We employed unsupervised machine learning approaches to identify novel gene modules associated with PHGDH metabolic networks and stemness-related pathways. Soft clustering analysis was performed using fuzzy c-means clustering via the means function from the e1071 package, which allows genes to belong to multiple clusters with varying membership degrees, better capturing the complex regulatory relationships in biological networks. Consensus clustering was implemented using R ConsensusClusterPlus package to ensure robust and stable cluster identification across multiple iterations and subsampling procedures. This approach provided confidence measures for cluster assignments and optimal cluster number determination. To identify key influential nodes and analyze their topological significance within the PHGDH-driven metabolic network, a protein-protein interaction (PPI) network was constructed. This network was derived from the STRING database, with associations filtered to include only those with a medium confidence score. The resulting network was then represented and analyzed using the igraph package in R. Community detection within this PPI network was performed utilizing the leading eigenvector community detection algorithm. This approach identifies densely connected groups of nodes that likely represent functional modules. The modularity of these identified communities was also assessed. Influential nodes were subsequently identified by evaluating various network centrality measures. Eigenvector centrality was employed to determine node influence based on connections to other well-connected nodes, and this metric was used to scale node sizes in visualizations. Additionally, degree centrality (number of direct connections) and betweenness centrality (extent to which a node serves as a bridge between network parts) were calculated. Nodes exhibiting high scores in these centrality measures, particularly those demonstrating substantial differences in Eigen scores between resistant and PHGDH-inhibitor treated conditions, were pinpointed as critical influential factors within the network. Functional Enrichment Analysis To elucidate the biological significance of identified gene sets and modules, functional enrichment analyses were performed. Gene Set Enrichment Analysis (GSEA) was utilized to determine whether predefined sets of genes were significantly overrepresented at the extremes of a ranked gene list [59] . Genes were ranked based on a specified statistic. The GSEA algorithm calculates an Enrichment Score (ES) by traversing the ranked list, reflecting the enrichment of a gene set. To assess statistical significance, a Normalized Enrichment Score (NES) was calculated by normalizing the ES against a null distribution generated by permutation testing. Nominal p-values were subsequently adjusted for multiple hypothesis testing using the False Discovery Rate (FDR) method. Over-Representation Analysis (ORA), based on the hypergeometric test, was additionally employed to identify gene sets that were significantly enriched among differentially expressed genes. This method assesses whether the observed overlap between a list of genes of interest and a predefined gene set is greater than what would be expected by chance. Adjusted p-values, typically using FDR correction, were used to account for multiple comparisons. Co-expression network analysis and hub gene identification To identify key genes with differentially co-expression network in dabrafenib resistance and upon PHGDH inhibition, signed scale-free co-expression networks were constructed. Gene expression correlations were calculated to generate correlation matrices. The eigenvalues and eigenvectors of these correlation matrices were then computed using the eigen() function. The absolute difference between the first eigenvectors (representing Eigen scores) of the two conditions was calculated. Genes exhibiting large delta.eigen values were identified as influential hub genes whose network connectivity and importance significantly shifted between conditions. Statistics Categorical variables were expressed as numbers and percentages, while continuous variables were reported as means ± standard deviations (SDs) or medians with interquartile ranges. Comparisons of continuous variables were performed using Student’s t-test, and categorical variables were compared using Fisher’s exact test. One-way ANOVA was used to assess differences among three or more groups. All Pvalues were two-sided, with P < 0.05 considered statistically significant. Graphs were generated using GraphPad Prism version 5.01 (GraphPad Software Inc., San Diego, CA, USA). Declarations Funding: This study was supported by the KonKuk University 2025. Acknowledgement : We thank J-W Oh (Department of Dermatology, Severance Hospital, Cutaneous Biology Research Institute, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea) for providing GEO data analysis. Conflict of interest : The authors declare no competing interests. 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Supplementary Files SupplementaryTable1.xlsx Table 1 SupplementaryTable2.xlsx Table 2 SupplementaryTable3.xlsx Table 3 SupplementaryTable4.xlsx Table 4 SupplementaryTable5.xlsx Table 5 SupplementaryFigures.pdf Supplementary Figure 1. (A-B) Differences in growth curves of dabrafenib-resistant cell lines from BCPAP compared to the control group. When dabrafenib was not treated, the control group grew statistically significantly (** p<0.01) faster. In contrast, when dabrafenib (1 μM) was treated, the resistant cell lines grew statistically significantly (** p<0.01) faster. (C) 10,000 cells were seeded from the control and resistant cell lines in 8505C, and colonies were stained after 7 days. Supplementary Figure 2. (A) RTK array map. (B) In the 8505C cell line, EGF levels and PERK levels were found to increase in the control and resistant cell lines. (C) In the BCPAP cell line, EGF levels and PERK levels were found to increase in the control and resistant cell lines. Supplementary Figure 3. To determine whether epithelial to mesenchymal transition (EMT) occurred in dabrafenib-resistant cell lines, W.B was performed on N-Cadherin, E-cadherin, and Vimentin. The results showed no significant difference compared to the control group. Supplementary Figure 4. Expression Patterns of Stemness Markers in Dabrafenib-Resistant ATC and Response to PHGDH Inhibition. (A) Heatmap illustrating the expression patterns of the extended stemness marker set. The heatmap displays hierarchical clustering of genes within the broad stemness marker set across control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Genes are grouped into six distinct expression patterns (Groups 1-6) by consensus clustering. Expression values are z-scored across rows, with red indicating high expression and blue indicating low expression. (B) Differential expression of selected cancer stem cell-specific markers. Box plots show the expression levels of individual cancer stem cell markers, including ABCG2, BMI1, CD34, CD44, CTNNB1, EPAS1, EZH2, HIF1A, KDM5B, KLF4, MYC, NES, NOTCH1, BMP4, KRT14, PTCH1, TGFBR2, TNC, VEGFA, TET1, TBX3, and LRIG1 in control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Statistical significance between groups is indicated by individual p-values above the bars. Supplementary Figure 5 (A) Cell viability assay (MTT assay) showing the effect of NCT 503 and Dabrafenib (0.5 µM) alone or in combination on cell proliferation over 5 days. Cells were treated with vehicle control (●), NCT 503 alone (○), Dabrafenib alone (▲), or a combination of NCT 503 and Dabrafenib (▼). Cell viability was measured at Day 0, Day 1, Day 3, and Day 5 both 8505c and 8505c-resistant cell line. Data are presented as mean ± SD (n=3). The combination treatment (NCT 503 + Dabrafenib) showed significantly reduced cell proliferation compared to the control group at Day 5 (**p < 0.01), suggesting a synergistic or additive effect of the combined treatment in inhibiting cell growth. Supplementary Figure 6 Transcriptional profiling of primary and metastatic melanoma lesions in a syngeneic mouse model. (A)A melanoma metastasis model was established by injecting B16F10 melanoma cells into the footpad of BALB/c mice. On day 31, RNA sequencing was performed on the skin (primary tumor site) and lungs (metastatic site). (B) Volcano plot showing differentially expressed genes between primary skin and metastatic lung tissues. (C) PHGDH expression was significantly upregulated in metastatic lung lesions compared to the primary tumors. (D) KEGG pathway analysis identified activation of pathways including TGF-β signaling, leukocyte trans endothelial migration, proteoglycans in cancer, and adherents junctions—all strongly associated with metastasis, tissue invasion, and stemness. 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-7069883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483068531,"identity":"1017474b-baec-4766-b821-a7b1e7ebc5b3","order_by":0,"name":"Mi-Hyeon You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDACCRBRISEHog48IF7LGRtjsJYEorUwtqUlNoA4RGnRnd1j+JjnzOH0+WGHHwJtsZPTbSCgxezOGWNjnorDuRtvpxkAtSQbmx0gpOVGjpk00JbcjbMTQFoOJG4jSgtv2+F0w9npH0jSkpYgL51DtC1pxYZzztgYbpDOKTiQYECUX5I3PnhTISEvPzt984cPFXZyBLUwMHAYgCkDsEoDgspBgP0BmJJvIEr1KBgFo2AUjEQAANGQSPQ8d5AeAAAAAElFTkSuQmCC","orcid":"","institution":"KonKuk University","correspondingAuthor":true,"prefix":"","firstName":"Mi-Hyeon","middleName":"","lastName":"You","suffix":""},{"id":483068532,"identity":"9db64da8-5ce5-4bb4-8250-d2f2659064e1","order_by":1,"name":"Sung Young Kim","email":"","orcid":"","institution":"School of Medicine, Konkuk University","correspondingAuthor":false,"prefix":"","firstName":"Sung","middleName":"Young","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-08 03:00:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7069883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7069883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86654966,"identity":"d6c42d1f-c452-4dc2-99f5-254d89a59e55","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":527804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePHGDH expression in anaplastic thyroid carcinoma and its association with dabrafenib response.\u003c/strong\u003e (A) PHGDH expression in malignant cells of Papillary Thyroid Carcinoma (PTC) vs. Anaplastic Thyroid Carcinoma (ATC) from the GSE193581 single-cell dataset. Box plots show the distribution of PHGDH expression in malignant cells from PTC (red) and ATC (blue) samples. Statistical significance was determined by two-sided Wilcoxon rank-sum test (****\u003cem\u003ep\u0026lt;0.0001\u003c/em\u003e). (B) PHGDH expression in matched BRAF-mutated (ATC09) vs. non-BRAF-mutated (ATC12) ATC samples from the GSE193581 single-cell dataset. Box plots show PHGDH expression in ATC09 (BRAF-mutated, red) and ATC12 (non-BRAF-mutated, blue) malignant cells. Statistical significance was determined by two-sided Wilcoxon rank-sum test (****\u003cem\u003ep\u0026lt;0.0001\u003c/em\u003e). (C) Trajectory analysis of ATC sample (GSE193581) showing PHGDH expression along pseudotime. PCA plot displays the trajectory of immune cell differentiation. Cell clusters are color-coded by cell type, and a pseudotime gradient indicates differentiation progression. Dot size represents the level of PHGDH expression, ranging from low (small, grey) to high (large, black). Dendritic cells (DC) are located centrally, with T cells differentiating outwards, and PHGDH expression generally low with a slight increase in some differentiated cell types. (D) PHGDH expression in PTC and ATC cell lines treated with DMSO or Dabrafenib from the GSE221329 bulk RNA-seq dataset. Box plots show PHGDH expression in PTC (CUTC5) and ATC (CUTC60) cell lines treated with DMSO (control) or dabrafenib for 48 hours. PHGDH expression is significantly increased in dabrafenib-treated ATC cells. (E) PHGDH expression in in-house dabrafenib-resistant ATC cells and response to PHGDH inhibition. Box plots show PHGDH expression (relative arbitrary units) in control ATC cell lines (C), dabrafenib-acquired resistant ATC cell lines (R), and resistant cells treated with a PHGDH inhibitor (RNCT). Statistical significance was determined by two-sided Wilcoxon rank-sum test (***\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e, ns stands for non-significant). Note the dramatic reduction of PHGDH in resistant cells upon inhibitor treatment.\u003c/p\u003e","description":"","filename":"MainFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/b4f95cab1864d38eb84eb5eb.png"},{"id":86658527,"identity":"108121bf-95cc-45f8-9028-fe91dd83747e","added_by":"auto","created_at":"2025-07-14 10:26:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3138462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDabrafenib resistant cell line characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Process of generating Dabrafenib-resistant cell lines from BCPAP and 8505C by treating with Dabrafenib (1 mM). (B) Growth curves according to various concentrations of Dabrafenib in the Dabrafenib-resistant cell line 8505C and the control 8505C. (C-D) Colony formation differences after treating 8505C cell line with 1uM Dabrafenib for 7 days were investigated. As a result, R1 showed a statistically significant increase in the number of colonies compared to the control group (**\u003cem\u003ep\u0026lt;0.01\u003c/em\u003e). (E-F) Colony formation differences after treating BCPAP cell line with 1uM Dabrafenib for 7 days were investigated. As a result, R1 showed a statistically significant increase in the number of colonies compared to the control group (**\u003cem\u003ep\u0026lt;0.01\u003c/em\u003e). All data are presented as the mean ± SD.\u003c/p\u003e","description":"","filename":"MainFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/d5064708754cfe4b249b0fc4.png"},{"id":86656319,"identity":"90fe28f5-007a-4e23-8621-dab7f497d6be","added_by":"auto","created_at":"2025-07-14 10:18:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1117799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe molecular signal changes in dabrafenib-resistant cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) RTK array was processed after Dabrafenib treatment for 12 days in 8505C. A. Compared to the control group, the group treated with Dabrafenib for Day 12 showed an increase in EGFR signal. (B) When this increased EGFR was quantified, a statistically significant EGFR level was confirmed compared to the control group (\u003cem\u003e*** p\u0026lt;0.001\u003c/em\u003e). (C) The molecular signal between Day 0 and Day 12 in the 8505C cell line was examined. As a result, it was confirmed that the p-ERK and pAKT levels significantly increased in a time-dependent manner. (D-E) The pERK, PHGDH, and OCT3/4 levels were confirmed in the Dabrafenib resistant cell line generated from 8505C. D. An increase in p-EKR and PHGDH was observed in the Dabrafenib resistant cell line compared to the control group, and OCT 3/4 was not confirmed. (E) When PHGDH was quantified, a statistically significant increase was observed in each Dabrafenib resistant cell line (R1; Resistant 1, R2; Resistant 2, R3; Resistant 3) compared to the Cont. group. The data are presented as the mean ± SD of three independent experiments (\u003cem\u003e** p\u0026lt;0.01).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"MainFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/c1dbcafd749e235a60169504.png"},{"id":86656321,"identity":"e56ece23-fb15-4427-8e2b-221e07595879","added_by":"auto","created_at":"2025-07-14 10:18:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1064049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in EGF expression levels between Normal and PTC or ATC in human samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The expression level of EGF was compared in normal tissues (patient-matched non-tumor control) and anaplastic thyroid carcinoma (ATC) or papillary thyroid carcinoma (PTC). In GSE33630, there were a total of 105 samples, and there was no difference in EGF between normal and tumor tissues. A slight increase was observed in ATC compared to PTC, although statistically insignificant. (B) A similar trend was confirmed in another microarray set, GSE65144. A statistically insignificant EGF level was observed in anaplastic thyroid carcinoma (ATC) compared to normal thyroid tissue (13 samples). (C) The expression of EGF protein was compared in normal human samples and PTC samples. (D) No statistical significance was observed in the normal group and PTC samples when quantified. The data are presented as the mean ± SD of three independent experiments (** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, and ***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"MainFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/bd57641dfddd9e1b6e8041f6.png"},{"id":86654982,"identity":"5f108740-8f8a-457f-be7d-8b3156b5f7c1","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3031058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional Reprogramming of the PHGDH-Driven Metabolic Network in Dabrafenib Resistance. \u003c/strong\u003e(A) Volcano plots illustrating differentially expressed genes (DEGs) within the PHGDH-driven metabolic network. Top: Comparison of dabrafenib-resistant (R) vs. control (C) ATC cells. Genes significantly upregulated (log2 FC \u0026gt; 2, FDR \u0026lt; 0.05) are red, downregulated (log2 FC \u0026lt; -2, FDR \u0026lt; 0.05) are blue, and non-significant are gray. Bottom: Comparison of PHGDH-inhibited resistant cells (RNCT) vs. untreated resistant cells (R). (B) Heatmap showing the expression patterns of genes in the PHGDH-driven metabolic network. The heatmap displays hierarchical clustering of gene expression across control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) samples, demonstrating distinct expression patterns in resistance and reversal by PHGDH inhibition. (C) Gene Set Enrichment Analysis (GSEA) plots for key metabolic pathways. Top: Enrichment scores for R vs C comparison. Bottom: Enrichment scores for RNCT vs R comparison. Running enrichment scores are shown for \"Nucleotide Biosynthesis pathway,\" \"Glutathione Redox Balance,\" \"Transsulfuration Pathway,\" and \"Folate Metabolism One Carbon Metabolism.\" Note the inverse enrichment patterns between the two comparisons. (D) Protein-protein interaction (PPI) network plot visualizing the relationships of key metabolic enzymes. Top: R vs C comparison. Bottom: RNCT vs C comparison. Nodes represent metabolic enzymes, colored by community (C1-C4) and sized by Eigen score. Edges indicate interactions. Key enzymes like MTHFD1, MTHFD2, DHFR, TYMS, PAICS, CTPS1, and CTH are highlighted. (E) PPI network of the PHGDH-driven metabolic network. Nodes represent proteins, colored by degree centrality (from low/green to high/blue) and sized by between centrality (from small/light to large/dark). Lines indicate predicted protein-protein interactions based on STRING DB. TYMS and CTH showing high betweenness centrality, indicating their pivotal roles as network connectors.\u003c/p\u003e","description":"","filename":"MainFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/6bac60abe3f228a692dd7f0b.png"},{"id":86654977,"identity":"88c7c751-62f9-4890-83a8-153b4bd57703","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1496088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePHGDH Inhibition Reverses lncRNA Expression Patterns Associated with Dabrafenib Resistance and Enrichment for DNA Repair Pathways. \u003c/strong\u003e(A) Volcano plots showing differentially expressed lncRNAs in dabrafenib-resistant (R) vs. control (C) ATC cells (top) and PHGDH-inhibited resistant (RNCT) vs. untreated resistant (R) ATC cells (bottom). Red dots represent significantly upregulated lncRNAs, blue dots represent significantly downregulated lncRNAs (Adjusted p-val \u0026lt; 0.05), and gray dots represent non-significant lncRNAs. (B) Chord diagram visualizing the log2 fold changes of lncRNAs between the R vs C (outer ring, colored by log2FC) and NCT vs R (inner connections, colored by log2FC in R vs C) comparisons. The diagram illustrates the reversal of expression for many lncRNAs upon PHGDH inhibition. (C) Heatmap displaying the expression patterns of the top differentially expressed lncRNAs across control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Expression values are z-scored across rows. (D) Table showing the top-ranking KEGG human lncRNA pathways identified by over-representation enrichment analysis based on the differentially expressed lncRNAs (p-val and adjusted p-val are indicated).\u003c/p\u003e","description":"","filename":"MainFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/0c994be6a68fe0c467c576ff.png"},{"id":86654972,"identity":"8e06bcf9-ef74-4595-b3cc-d5920b088850","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":550578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePHGDH and its Association with Cancer Stemness in Dabrafenib Resistance.\u003c/strong\u003e (A) Heatmap of curated cancer stemness markers in dabrafenib-resistant ATC cells. The heatmap displays the expression patterns of 40 curated cancer stemness markers across control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Genes were grouped into four distinct expression patterns (Groups 1-4) by consensus clustering. Expression values are z-scored. (B) Differential expression of selected cancer stemness markers. Box plots show the expression levels of BMP4, TWIST1, and LRIG1 in control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Statistical significance was determined by two-sided t-test (p-values are indicated above bars). (C) LRIG1 expression was significantly increased in resistant cells treated with dabrafenib (R-dabrafenib) and moderately increased in PHGDH inhibitor (NCT 503) treated resistant cells, compared to the resistant control (Cont). The data are presented as the mean ± SD of three independent experiments (** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, and ***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"MainFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/e7c6d912cb1fdd754a22c855.png"},{"id":86654979,"identity":"3aeac5cc-685b-45e9-8ec7-cba9f1d4f456","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":327960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhen EGF and PHGDH were suppressed, colony formation and Tumor sphere formation were regulated.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) PHGDH and EGF are essential for cell proliferation and tumorigenesis. (A, B) Cell proliferation and colony formation were evaluated in Dabrafenib Resistant 8505C cells. (A) The number of colonies formed was evaluated on day 7, and it was confirmed that it was reduced by NCT 503 treatment compared to the control group, and that it was synergistically reduced in the NCT503 (PHGDH inhibitor, 15 μM) + Dabrafenib treatment group. (B) The number of colonies formed was significantly reduced in the NCT503 + Dabrafenib treatment group in Dabrafenib Resistant 8505C cells on day 7 (***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001). A significant decrease was also observed in the NCT503 + Dabrafenib treatment group compared to the Inactive C (15 μM) + Dabrafenib treatment group (* \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). (C-D) We investigated what molecular signal changes occurred at this time. Compared to the control, we could confirm that the pERK signal significantly decreased in the Dabrafenib and NCt503 groups. In addition, we could confirm that the pERK level decreased synergistically in the group treated with Dabrafenib and NCT503 together (***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001). (E, F) Tumor sphere formation was evaluated with the same group composition. (F) The number of tumor spheres larger than 50 μm was quantified in each group, and the 8505 C cells treated with NCT503 (PHGDH inhibitor, 15 μM) + Dabrafenib showed a significant decrease in the number of tumor spheres larger than 50 μm measured on day 10 (***\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001). In addition, there was no statistical difference (ns) between the control group and the Inactive C (15 μM) group. (E, G) After the tumor sphere formation experiment, the levels of PHGDH mRNA were examined in each sample. (G) NCT503 (15 μM) treatment also significantly suppressed PHGDH, and this value was slightly recovered in the NCT503 + Dabrafenib treatment group (** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01). (H, I) After the tumor sphere formation experiment, the levels of PHGDH, OCT4, and Nanog mRNA were examined in each sample. In the results for evaluating stemness, a statistically significant decrease in stemness (OCT 4, Nanog) was observed in the NCT503 + Dabrafenib treatment group (** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01). (J) Schematic summary illustrating that in ATC cells with BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation, PHGDH expression and EGF signaling are increased after Dabrafenib treatment, and stemness genes such as NANOG, OCT4, and VEGFA are activated to acquire drug resistance and survival. PHGDH inhibition (NCT503 treatment) suppresses stemness gene expression and colony formation, but increased expression of EPAS1 and NES suggests adaptive reprogramming in response to metabolic stress. This partially reduces resistance and stemness, but indicates that residual plasticity remains.\u003c/p\u003e","description":"","filename":"MainFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/b5ddceec3e862437d36bb496.png"},{"id":92187036,"identity":"68a63c05-68eb-4c13-981c-04bdefa08940","added_by":"auto","created_at":"2025-09-25 14:24:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12880185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/d29f4931-437f-4e2f-b052-b0e55503a979.pdf"},{"id":86656317,"identity":"40833f5a-0928-4ecc-b752-4ac675647988","added_by":"auto","created_at":"2025-07-14 10:18:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10270,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/0baa2729d759a50765e3afa2.xlsx"},{"id":86654968,"identity":"5e412ceb-a7cc-4aeb-939c-f4f670815868","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10075,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2\u003c/p\u003e","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/f39f8982be9dc696c8c035c4.xlsx"},{"id":86654970,"identity":"6395850f-0060-4ec7-b86a-641e6312c0f3","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13442,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3\u003c/p\u003e","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/3c723a9cecbb5d36037a5059.xlsx"},{"id":86656323,"identity":"c66a34ec-a2fe-418f-b25a-15495bd1dcb3","added_by":"auto","created_at":"2025-07-14 10:18:06","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10493,"visible":true,"origin":"","legend":"\u003cp\u003eTable 4\u003c/p\u003e","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/d8a71ffa2797419d20e248f3.xlsx"},{"id":86654985,"identity":"5aab8ecb-277c-4bea-934a-6f33a352fe39","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":38643,"visible":true,"origin":"","legend":"\u003cp\u003eTable 5\u003c/p\u003e","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/bb1cf0fb0dd42066b9af2d9f.xlsx"},{"id":86654990,"identity":"b5c0f96e-cd7c-4551-8b7e-e2d3c7a3db0e","added_by":"auto","created_at":"2025-07-14 10:10:06","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9613244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Differences in growth curves of dabrafenib-resistant cell lines from BCPAP compared to the control group. When dabrafenib was not treated, the control group grew statistically significantly (** p\u0026lt;0.01) faster. In contrast, when dabrafenib (1 μM) was treated, the resistant cell lines grew statistically significantly (** p\u0026lt;0.01) faster. (C) 10,000 cells were seeded from the control and resistant cell lines in 8505C, and colonies were stained after 7 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) RTK array map. (B) In the 8505C cell line, EGF levels and PERK levels were found to increase in the control and resistant cell lines. (C) In the BCPAP cell line, EGF levels and PERK levels were found to increase in the control and resistant cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether epithelial to mesenchymal transition (EMT) occurred in dabrafenib-resistant cell lines, W.B was performed on N-Cadherin, E-cadherin, and Vimentin. The results showed no significant difference compared to the control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4.\u003c/strong\u003e \u0026nbsp;Expression Patterns of Stemness Markers in Dabrafenib-Resistant ATC and Response to PHGDH Inhibition. (A) Heatmap illustrating the expression patterns of the extended stemness marker set. The heatmap displays hierarchical clustering of genes within the broad stemness marker set across control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Genes are grouped into six distinct expression patterns (Groups 1-6) by consensus clustering. Expression values are z-scored across rows, with red indicating high expression and blue indicating low expression. (B) Differential expression of selected cancer stem cell-specific markers. Box plots show the expression levels of individual cancer stem cell markers, including ABCG2, BMI1, CD34, CD44, CTNNB1, EPAS1, EZH2, HIF1A, KDM5B, KLF4, MYC, NES, NOTCH1, BMP4, KRT14, PTCH1, TGFBR2, TNC, VEGFA, TET1, TBX3, and LRIG1 in control (C), dabrafenib-resistant (R), and PHGDH-inhibitor treated resistant (RNCT) ATC cells. Statistical significance between groups is indicated by individual p-values above the bars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Cell viability assay (MTT assay) showing the effect of NCT 503 and Dabrafenib (0.5 µM) alone or in combination on cell proliferation over 5 days. Cells were treated with vehicle control (●), NCT 503 alone (○), Dabrafenib alone (▲), or a combination of NCT 503 and Dabrafenib (▼). Cell viability was measured at Day 0, Day 1, Day 3, and Day 5 both 8505c and 8505c-resistant cell line. Data are presented as mean ± SD (n=3). The combination treatment (NCT 503 + Dabrafenib) showed significantly reduced cell proliferation compared to the control group at Day 5 (**p \u0026lt; 0.01), suggesting a synergistic or additive effect of the combined treatment in inhibiting cell growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptional profiling of primary and metastatic melanoma lesions in a syngeneic mouse model. (A)A melanoma metastasis model was established by injecting B16F10 melanoma cells into the footpad of BALB/c mice. On day 31, RNA sequencing was performed on the skin (primary tumor site) and lungs (metastatic site). (B) Volcano plot showing differentially expressed genes between primary skin and metastatic lung tissues. (C) PHGDH expression was significantly upregulated in metastatic lung lesions compared to the primary tumors. (D) KEGG pathway analysis identified activation of pathways including TGF-β signaling, leukocyte trans endothelial migration, proteoglycans in cancer, and adherents junctions—all strongly associated with metastasis, tissue invasion, and stemness.\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069883/v1/c446085ad7843af26b2a8b53.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"PHGDH inhibition overcomes dabrafenib resistance through metabolic rewiring in BRAF V600E anaplastic thyroid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe B-Raf proto-oncogene (BRAF) V600E mutation is frequently observed in thyroid cancer, especially in anaplastic thyroid carcinoma (ATC), and Dabrafenib, a BRAF inhibitor, is approved and used for the treatment of ATC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, rapid development of resistance through compensatory activation of the epidermal growth factor receptor (EGFR) pathway has been reported when dabrafenib is used as monotherapy, which limits the clinical efficacy of the treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EGFR is a tyrosine kinase receptor that induces cell proliferation, invasion, and angiogenesis through the protein kinase R (PKR)-like endoplasmic reticulum kinase (ERK) and protein kinase B (AKT) pathways, and combined inhibition of BRAF and EGFR has been proposed as an effective method for overcoming resistance in various cancers [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the synergy in this combination therapy has been demonstrated in colon cancer and melanoma, research on this topics in thyroid cancer including ATC remains limited [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we observed a significant increase in the expression of phosphoglycerate dehydrogenase (PHGDH), a key enzyme in serine/glycine metabolism, along with sustained activation of phosphorylated EGFR in dabrafenib-resistant ATC cells (8505C-R) [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our findings align with recent studies demonstrating that PHGDH overexpression contributes to resistance mechanisms, suggesting that targeting PHGDH may allow to overcome therapeutic resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PHGDH has been implicated in resistance mechanisms in various cancers such as melanoma, particularly through metabolic rewiring of serine biosynthesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, to our knowledge, this is the first study to elucidate the functional role of PHGDH in anaplastic thyroid cancer (ATC) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWe observeded therapeutic responses such as a decrease in p-ERK level, inhibition of colony formation, and a decrease in stemness when the PHGDH inhibitor NCT503 was administered alone or in combination with dabrafenib. RNA sequencing (RNA-seq) analysis suggested that PHGDH inhibition affected mitogen-activated protein kinase (MAPK) signaling, metabolic reprogramming, and cell plasticity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In particular, the expression of Leucine-rich repeats and immunoglobulin-like domains 1 (LRIG 1) increased in the resistant state, suggesting that it may act as a negative feedback regulator that suppresses the EGFR\u0026ndash;PHGDH axis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The increased expression of LRIG1 is linked to the suppression of EGFR signaling and the reduction of pERK signaling, and may act as an important complementary mechanism for the PHGDH inhibition strategy [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese results provide important evidence for establishing a novel combination treatment strategy in BRAF V600E mutant thyroid cancer by jointly illuminating the functional crosstalk between the PHGDH-EGFR axis and LRIG1-based suppression of resistance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTranscriptional profiling reveals PHGDH as a potential key driver in ATC progression and dabrafenib response.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePHGDH expression is elevated in malignant anaplastic thyroid carcinoma cells\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo gain insights into PHGDH expression in ATC and its association with dabrafenib response, we analyzed publicly available single-cell RNA-seq datasets GSE193581, GSE221329, and in-house bulk RNA-seq data. Initial analysis of all cell types in the GSE193581 dataset showed no significant difference in PHGDH expression between papillary thyroid carcinoma (PTC) and ATC (data not shown) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, when the analysis was restricted to malignant cells only, we observed a significant upregulation of PHGDH expression in ATC compared to PTC (Fig.\u0026nbsp;1A). This finding highlights the importance of analyzing tumor-specific cell populations to uncover disease-relevant expression patterns. We also investigated PHGDH expression in a pair of age-, sex-, and stage-matched ATC samples, (ATC09 and ATC12) both from females in their 50s, T4b,\u0026gt;10% mitochondrial content from the GSE193581 dataset, focusing on their BRAF mutation status. A BRAF V600E mutation was present in ATC09, while ATC12 had wild-type BRAF. Although a visual inspection of the plot with zero-expression cells excluded suggested a higher PHGDH level in ATC09 than in ATC12, statistical analysis\u003c/p\u003e\u003cp\u003eThat included all cells revealed a significantly lower in PHGDH expression in ATC09 than in ATC12 (Fig.\u0026nbsp;1B). This result suggests a complex interplay between BRAF mutation status and PHGDH expression in ATC.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePHGDH expression during differentiation in non-malignant cells\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGiven the reported association between PHGDH expression and stemness, we next explored PHGDH expression patterns during differentiation in non-malignant cells from the ATC samples using trajectory analysis. Analysis of immune cell trajectories from an ATC sample identified three distinct branches of differentiation (Fig.\u0026nbsp;1C) [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PHGDH expression remained generally low across most immune cell types, with no clear increasing or decreasing trend along the pseudotime. However, PHGDH expression tended to be lower in the early differentiation stages along the pseudotime. The trajectory plot revealed dendritic cells at the center, with PHGDH expression tending to increase as cells differentiated further, moving away from the center towards cell types such as T cells (Fig.\u0026nbsp;1C). This suggests that while\u003c/p\u003e\u003cp\u003eIts expression does not fluctuate dramatically, PHGDH might have subtle roles in specific differentiation stages of immune cells within the tumor microenvironment.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePHGDH upregulation in dabrafenib-resistant ATC and sensitivity to PHGDH inhibition\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGiven that PHGDH has been implicated in cancer stemness and aggressiveness, we hypothesized its involvement in dabrafenib resistance [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Analysis of bulk RNA-seq data from GSE221329 revealed that PHGDH expression was significantly higher in an ATC cell line (CUTC60) following dabrafenib treatment for 48 hours than in DMSO control, whereas no significant difference was observed in a PTC cell line (CUTC5) (Fig.\u0026nbsp;1D). This finding aligns with our expectation that PHGDH plays a role in dabrafenib resistance in ATC. Furthermore, our in-house data for cells with acquired dabrafenib resistance demonstrated that they had significantly higher PHGDH expression than the control cells (Fig.\u0026nbsp;1E). Crucially, treatment with a NCT503 dramatically reduced PHGDH expression in these resistant cells (Fig.\u0026nbsp;1E). These results strongly suggest that the PHGDH signaling pathway plays a important role in mediating acquired dabrafenib resistance in ATC, positioning PHGDH as a promising therapeutic target to overcome this resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterization of dabrafenib-resistant ATC cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo model acquired resistance to dabrafenib, we chronically treated two BRAF V600E-mutant ATC cell lines, 8505C and BCPAP, with dabrafenib for 1 month (Fig.\u0026nbsp;2A).We then compared the resulting resistant sublines (designated 8505C-R and BCPAP-R) were compared with their parental counterparts to evaluate alterations in proliferative capacity and tumorigenic potential (Fig.\u0026nbsp;2A\u0026ndash;F, Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eUpon treatment with dabrafenib (0\u0026ndash;3 \u0026micro;M) for 5 days, growth of parental cells was inhibited in a dose-dependent manner, with significant reductions at 0.5\u0026ndash;3 \u0026micro;M (Fig.\u0026nbsp;2B, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, growth of 8505C-R cells was not statistically inhibited, confirming the establishment of resistance (Fig.\u0026nbsp;2B, N.S). Colony formation assays further corroborated this phenotype: dabrafenib significantly reduced the clonogenicity of parental 8505C cells (Fig.\u0026nbsp;2C and 2D; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas resistant sublines retained high colony-forming ability despite continued drug exposure (Fig.\u0026nbsp;2E and 2F, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for BCPAP-R).\u003c/p\u003e\u003cp\u003eThese findings demonstrate that prolonged dabrafenib exposure induces a stable resistant phenotype in BRAF-mutant ATC cells, accompanied by altered proliferative behavior and enhanced survival [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eEGF signaling is upregulated in dabrafenib-resistant ATC cells and contributes to MAPK pathway reactivation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the mechanisms underlying acquired resistance to dabrafenib, we performed RTK array analysis in BRAF V600E-mutant 8505C and BCPAP cells treated with or without dabrafenib (Fig.\u0026nbsp;3A). EGF signaling was significantly upregulated in both 8505C-R and BCPAP-R cells compared to parental controls (Fig.\u0026nbsp;3B; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Time-course analysis of 8505C cells revealed a gradual increase in the levels of ERK and AKT phosphorylation during dabrafenib treatment (Fig.\u0026nbsp;3C). In dabrafenib-resistant cell lines, increased pEGF levels were accompanied by elevated pERK levels, indicating MAPK pathway reactivation (Supplementary Fig.\u0026nbsp;2). Thus, RTK array results confirmed that dabrafenib treatment induced upregulated EGF, thereby activating a bypass MAPK pathway. We also assessed other proteins potentially involved in resistance. Western blot analysis revealed significant PHGDH upregulation in dabrafenib-resistant 8505C cells (Fig.\u0026nbsp;3D, 3E), consistent with its known role in aggressive tumors. Stemness-related proteins (e.g., OCT4) and epithelial\u0026ndash;mesenchymal transition (EMT) markers showed no significant differences in resistant versus control cells (Fig.\u0026nbsp;3, Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eThese results suggest that dabrafenib-resistant ATC cells bypass BRAF inhibition by upregulating EGF signaling and reactivating the MAPK pathway, while also engaging PHGDH-driven mitochondrial one-carbon metabolism [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eEGF upregulation is not associated with thyroid cancer progression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo determine whether EGF upregulation is associated with thyroid cancer progression regardless of dabrafenib exposure, we analyzed publicly available transcriptomic datasets. In the GSE33630 dataset, EGF transcript levels were compared across 11 ATC, 49 PTC, and 45 non-tumorous thyroid tissues. No significant differences in EGF expression were observed between the groups (Fig.\u0026nbsp;4A). Similarly, in\u003c/p\u003e\u003cp\u003ethe GSE65144 dataset (12 ATC and 13 normal samples), EGF expression was slightly higher in normal tissues, with no significant difference (Fig.\u0026nbsp;4B). To validate these findings, we assessed EGF protein expression in normal and PTC tissues, and found no significant difference (Fig.\u0026nbsp;4C\u0026ndash;D, N.S).\u003c/p\u003e\u003cp\u003eCollectively, these results indicate that EGF upregulation is not associated with thyroid cancer subtype or progression, but instead reflects a response to dabrafenib-induced resistance in BRAF V600E-mutant cells, consistent with our experimental findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePHGDH-driven metabolic reprogramming in dabrafenib-resistant anaplastic thyroid carcinoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBuilding upon these findings, we performed a deeper transcriptional analysis focusing on the PHGDH-driven metabolic network, which we specifically curated. This network comprises six interconnected pathways: nucleotide biosynthesis, glutathione redox balance, transsulfuration pathway, folate metabolism, one carbon metabolism, and serine metabolism (Supplementary Table\u0026nbsp;1). These pathways are intrinsically linked to PHGDH activity, as PHGDH initiates the de novo serine biosynthesis pathway. The serine produced then provides crucial one-carbon units that fuel the folate metabolism and one-carbon metabolism, thereby supporting nucleotide biosynthesis. Serine can also be converted to cysteine via the transsulfuration pathway, and cysteine serves as a precursor for glutathione, which is critical for maintaining cellular redox homeostasis. This network collectively forms a comprehensive metabolic axis relevant to cell proliferation and stress response. Volcano plots comparing dabrafenib-resistant cells (R) to control (C) revealed a significant upregulation of genes within the PHGDH-driven metabolic network (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change\u0026thinsp;\u0026gt;\u0026thinsp;4), including PHGDH itself (Fig.\u0026nbsp;5A, top). Conversely, resistant cells treated with a PHGDH inhibitor (RNCT) showed a significant downregulation of genes in comparison with R, indicating successful pathway perturbation (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change\u0026thinsp;\u0026lt;\u0026thinsp;4) (Fig.\u0026nbsp;5A, bottom). A heatmap of these PHGDH-driven metabolic network genes clearly demonstrated distinct and reciprocal expression patterns in the R vs. C and RNCT vs. R comparisons, underscoring the consistent and widespread activation of this network in resistant cells and its subsequent suppression by PHGDH inhibition (Fig.\u0026nbsp;5B).\u003c/p\u003e\u003cp\u003eGene set enrichment analysis (GSEA) further illuminated the metabolic shifts associated with dabrafenib resistance and PHGDH inhibition. Interestingly, the nucleotide biosynthesis pathway was significantly depleted in in R vs. C, while other pathways such as the glutathione redox balance, transsulfuration pathway, and folate metabolism one carbon metabolism were enriched (Fig.\u0026nbsp;5C, top; Supplementary Table\u0026nbsp;2). Strikingly, this pattern was precisely reversed in the RNCT vs. R comparison, where PHGDH inhibition led to a significant enrichment of the nucleotide biosynthesis pathway and a depletion of the other pathways (Fig.\u0026nbsp;5C, bottom; Supplementary Table\u0026nbsp;2). This dynamic interplay strongly suggests that the PHGDH-driven metabolic network, particularly its influence on nucleotide biosynthesis, plays a pivotal role in dabrafenib resistance and its circumvention. The regulation of nucleic acid biosynthesis is a well-established mechanism for cancer cells to escape chemotherapy, and our data indicate that this mechanism can be effectively targeted by PHGDH pathway inhibition.\u003c/p\u003e\u003cp\u003eTo identify key influential nodes within the PHGDH-driven metabolic network, we constructed a protein\u0026ndash;protein interaction (PPI) network by using the String database and integrating expression patterns and the community centrality, degree, and betweenness scores (Fig.\u0026nbsp;5D,E). Unsupervised network analysis revealed several highly influential genes, including MTHFD1, MTHFD2, DHFR, TYMS, PAICS, CTPS1, and CTH, as core components of this network. MTHFD1, MTHFD2, and DHFR are central enzymes in the one-carbon metabolism pathway and folate cycle, which are crucial for the synthesis of nucleotides, amino acids, and S-adenosylmethionine. Their upregulation in resistant cells highlights the heightened metabolic demand for building blocks necessary for rapid proliferation and adaptation. TYMS (thymidylate Synthase) is a critical enzyme in de novo pyrimidine synthesis, directly involved in DNA replication and repair. Its increased expression underscores the enhanced nucleic acid synthesis in resistant cells. PAICS (phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase) and CTPS1 (CTP synthase 1) are also key enzymes in nucleotide biosynthesis. The high betweenness centrality scores for TYMS and CTH indicate their critical positions within the network as \"bridges\" or central connectors that facilitate information flow between different parts of the metabolic network. Disrupting these nodes, therefore, could have widespread impacts on the entire network, making them attractive targets for therapeutic intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePHGDH inhibition reverses changes in lncRNA expression associated with dabrafenib resistance, highlighting potential links to DNA repair\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProfiling the expression patterns of long non-coding RNAs (lncRNAs) revealed a striking trend. A significant proportion of lncRNAs that were either upregulated or downregulated in R cells compared to C cells exhibited a reverse expression pattern in RNCT cells (Fig.\u0026nbsp;6A and 6C). A chord diagram visualizing the log2 fold differences in lncRNAs between the R vs. C and RNCT vs. R groups revealed a directional reversal of expression for a substantial number of lncRNAs (Fig.\u0026nbsp;6B). Such a clear reciprocal expression pattern is noteworthy and suggests a strong regulatory link between PHGDH activity and lncRNA expression in the context of acquired resistance. To gain insight into the potential functional implications of this differential lncRNA expression, we performed over-representation enrichment analysis using the KEGG database of human lncRNAs. Notably, the top-ranking pathways enriched for the lncRNAs that showed significant changes in expression in our comparisons were predominantly involved in DNA repair (Fig.\u0026nbsp;6D, Supplementary Table\u0026nbsp;5). These included the mismatch repair, RNA polymerase, DNA replication, homologous recombination, and nucleotide excision repair pathways, although these enrichments did not reach statistical significance at a conventional adjusted \u003cem\u003ep\u003c/em\u003e-value of 0.05. These findings strongly suggest that PHGDH activity influences the expression of lncRNAs that may play a role in DNA repair, hinting at a potential mechanism by which PHGDH inhibition could impact the cellular response to DNA damage or genomic instability associated with drug resistance. Further investigation into the specific roles of these lncRNAs in DNA repair and resistance mechanisms is warranted.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePotential role of PHGDH in modulating cancer stemness in dabrafenib-resistant ATC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBuilding upon our previous observation suggesting that PHGDH may play a role in regulating the stemness composition of ATC, we sought to investigate the expression patterns of cancer stemness-associated genes in the context of dabrafenib resistance and PHGDH inhibition \u003csup\u003e[23]\u003c/sup\u003e. To this end, we compiled an extended set of marker genes related to cancer stem cell identity reported by Gali\u0026egrave; et al. (2008) and Malta et al. (2018) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, to encompass a broader understanding of stemness, markers indicative of stem cell identity and pluripotency were incorporated from Ramalho-Santos et al., 2002 (Supplementary Table\u0026nbsp;4) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The markers represent genes widely recognized for their roles in defining normal and cancer stem cells, as well as those altered in mesenchymal tumor cells compared to mesenchymal stem cells.\u003c/p\u003e\u003cp\u003eUsing this curated gene set, we performed consensus clustering using our in-house cells. Among the cancer stemness markers, we found four distinct expression patterns (Groups 1\u0026ndash;4), indicating their differential transcriptional regulation in response to dabrafenib resistance and PHGDH inhibition (Fig.\u0026nbsp;7A). Consistent with these findings, similar expression patterns were also evident within our broader stemness marker set (Supplementary Table\u0026nbsp;4, Supplementary Fig.\u0026nbsp;4). Group 1 had high expression in control cells but lower expression in R and RNCT cells, suggesting potential involvement of these genes in maintaining non-resistant state or their downregulation upon resistance acquisition. Conversely, Groups 2 and 3 had higher expression in resistant than in C cells, with Group 3 showing some reversal in RNCT cells. Group 4 displayed unique patterns that warrant further investigation.\u003c/p\u003e\u003cp\u003eNotably, within these expression patterns, we observed striking differential expression of specific stemness-related genes. The gene for BMP4 (encoding bone morphogenetic protein 4), a known regulator of cell fate and stem cell maintenance, had significantly higher expression in R cells than in C cells, and its expression appeared to be further increased in RNCT cells (Fig.\u0026nbsp;7B). The gene for TWIST1 (Twist family BHLH transcription factor 1), a key inducer of EMT and stemness, was also significantly upregulated in R compared to C cells; interestingly, its expression level was maintained or even tended to further increase in RNCT cells, suggesting a potentially complex involvement in resistance (Fig.\u0026nbsp;7B). Finally, the gene for LRIG1 (leucine-rich repeat and immunoglobulin-like domain containing Nogo receptor\u0026ndash;interacting protein 1), which has been implicated in regulating stem cell self-renewal and tumor suppression, had significantly higher expression in R than in C cells, and it was maintained or slightly reduced in RNCT cells (Fig.\u0026nbsp;7B). These clear changes in the expression of genes for BMP4, TWIST1, and LRIG1 suggest that PHGDH- driven metabolic reprogramming may influence specific signaling pathways and molecular programs associated with cancer stemness, thereby potentially contributing to dabrafenib resistance [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In particular, LRIG1 expression was significantly increased by dabrafenib treatment, and to a lesser extent by PHGDH inhibition in comparison with the control (spheroid formation assay sample) (Fig.\u0026nbsp;7C). This pattern suggests that LRIG1 upregulation is a compensatory mechanism in response to EGF\u0026ndash;PHGDH axis activation, supporting the role of LRIG1 as a feedback regulator that alleviates treatment resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDual inhibition of PHGDH and BRAF suppresses tumorigenic potential and stemness in dabrafenib-resistant ATC cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the synergistic anti-tumor effects of PHGDH and BRAF inhibition, we conducted colony and spheroid formation assays in 8505C dabrafenib-resistant cells.\u003c/p\u003e\u003cp\u003ePHGDH inhibition significantly suppressed colony formation, and this effect was further enhanced by co-treatment with dabrafenib (Fig.\u0026nbsp;8A and 8B; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). At a higher Dabrafenib concentrations (1 \u0026micro;M) combined with NCT 503, colony formation was almost completely abolished (data not shown), indicating a potent combinatorial effect. These results suggest a synergistic inhibition of tumorigenic potential in resistant cells (Supplementary Fig.\u0026nbsp;5A). Concurrently, pERK levels were also more strongly suppressed by combination treatment than by monotherapy (Fig.\u0026nbsp;8C and 8D); this observation links metabolic targeting to suppression of MAPK signaling and stem-like traits. To further validate these findings, we analyzed spheroid formation after 10 days of 3D culture. The number of large spheroids (\u0026gt;\u0026thinsp;50 \u0026micro;m) was significantly decreased by NCT503 treatment alone and was further decreased under combination treatment (Fig.\u0026nbsp;8E and 8F), with all comparisons reaching statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Given the link between PHGDH and cancer stemness, we assessed the expression of stemness-related genes. Co-treatment with dabrafenib and NCT503 significantly reduced mRNA and protein levels of key stemness markers, including OCT4 and Nanog, in resistant cells (Fig.\u0026nbsp;8G\u0026ndash;I). Collectively, these results demonstrate that dual inhibition of PHGDH and BRAF suppresses tumorigenic potential and stemness in dabrafenib-resistant ATC cells by attenuating ERK signaling and expression of pluripotency genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePHGDH is upregulated in metastatic lesions in a melanoma lung metastasis model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo establish a metastatic melanoma model, B16F10 cells were injected into the footpads of BALB/c mice, and on day 31, skin (primary site) and lung (metastatic site) tissues were collected and subjected to RNA-seq analysis (Supplementary Fig.\u0026nbsp;6A). Many genes were differentially expressed in lung metastatic tissues compared to skin (Supplementary Fig.\u0026nbsp;6B); among them, PHGDH was significantly upregulated (Supplementary Fig.\u0026nbsp;6C). These results are consistent with our in vitro results and support the possibility of a functional role of PHGDH in the metastatic microenvironment.\u003c/p\u003e\u003cp\u003eLRIG1 was also differentially expressed in lung tissues, but the magnitude and statistical significance of the expression difference was lower than those of PHGDH (Supplementary Fig.\u0026nbsp;6B, and 6C). In vivo, the expression pattern of PHGDH may vary depending on the time of vascular invasion or the stage of metastatic progression, and various physiological factors may act in a complex manner, making it difficult to unambiguously interpret LRIG1 as a negative correlation with resistance. The fact that in dabrafenib-resistant ATC cells, EGF and PHGDH were simultaneously upregulated and LRIG1 expression was rather decreased upon PHGDH inhibition. This suggests that the relationship among PHGDH, LRIG1, and EGF signals is not a simple linear relationship, but is regulated by a complex feedback network. KEGG pathway analysis identified TGF-β signaling, leukocyte endothelial cell migration, proteoglycans, and adherens junctions as activated pathways (Supplementary Fig.\u0026nbsp;6C), which are closely related to the maintenance of the niche of cancer stem cells, cell-to-cell interaction, and metastatic ability. In particular, the TGF-β pathway is critically involved in securing tumor flexibility and treatment resistance by inducing EMT and stem cell transformation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA schematic model of how the EGF\u0026ndash;PHGDH axis cooperatively regulates tumor aggressiveness and therapeutic resistance is depicted in Fig.\u0026nbsp;8J. In ATC cells harboring the BRAF V600E mutation, the expression of both EGF and PHGDH is upregulated when resistance to dabrafenib is induced. PHGDH, a key enzyme in the serine/glycine metabolic pathway, promotes cancer stemness and adaptive resistance, and EGFR signaling reactivates the MAPK pathway (p-ERK) to enhance tumor cell survival and plasticity (Supplementary Fig.\u0026nbsp;6D). Treatment with the PHGDH inhibitor NCT503 reduces colony and sphere formation, expression of stem cell markers (NANOG, OCT4), and p-ERK levels. LRIG1 is upregulated in the dabrafenib-resistant state, possibly acting as a feedback inhibitory mechanism, but its expression is reduced after NCT503 treatment, suggesting that PHGDH inhibition can also neutralize this feedback regulatory pathway [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although PHGDH inhibition can partially suppress dabrafenib resistance. The plasticity is still maintained, requiring combined therapeutic strategies that simultaneously target metabolic and signaling pathways. This suggests that PHGDH may act as a key regulator of cancer stemness and therapeutic resistance, beyond being just a metabolic enzyme.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eOverview of BRAF V600E-driven resistance in thyroid cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn thyroid cancer, the BRAF V600E mutation is reported mainly in ATC and is found in 20%\u0026ndash;50% of thyroid cancers\u0026nbsp;[35, 36]. If this mutation is present, the MAPK pathway is activated [36-39]. Activated MAPK1 (ERK2) promotes the growth and metastasis of cancer cells, and its persistent activation contributes to recurrence even after chemotherapy and surgery\u0026nbsp;[40, 41]. Dabrafenib, which was the focus of this study, is an approved drug and a BRAF kinase inhibitor that suppresses phosphorylation of ERK, which is a MAPK downstream of BRAF\u0026nbsp;[42, 43]\u003csup\u003e,\u003c/sup\u003e. However, dabrafenib is not currently used alone because it causes drug resistance. In this study, we constructed a dabrafenib resistance model and analyzed the molecular signals that change significantly during resistance acquisition, in line with previous reports of RTK or NRAS-mediated bypass signaling and clonal evolution\u0026nbsp;[17, 26-28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePHGDH\u0026ndash;EGFR axis as a central mechanism of resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main finding of this study is that the mitochondrial serine/glycine metabolic pathway mediated by PHGDH is a central mechanism of dabrafenib resistance (Figure 1-3). In particular, PHGDH expression was significantly increased in the 8505C cell line that had acquired dabrafenib resistance (Figure 2), which was associated with the maintenance of cancer stemness (Figure 3-8). Selective inhibition of PHGDH decreased colony formation and stem cell marker expression, and simultaneously suppressed ERK signaling (as evidenced by a decrease in p-ERK), suggesting the possibility of overcoming resistance (Figure 3-7). MAPK reactivation (increased p-ERK) is a major mechanism of dabrafenib resistance, as supported by reports of bypass activation of ERK through RTKs such as EGFR and IGF1R (Figure 3 and 7) [26, 28, 44]. The p-ERK levels were significantly reduced when PHGDH was inhibited by NCT503 treatment, suggesting a functional link between PHGDH and the EGFR\u0026ndash;MAPK axis (Figure 4, Figure 8) [23-25]. In other words, not only the EGFR\u0026ndash;MAPK pathway but also the PHGDH\u0026ndash;serine/glycine metabolic pathway plays an important role in maintaining dabrafenib resistance, and the interaction between them is an important subject of future research (Figure 6-8) [45-47]. In addition, PHGDH expression was increased in metastatic lesions in a melanoma lung metastasis model (Supplement Figure 6), and changes in the expression of genes related to cell mobility, adhesion, and metabolic pathways were confirmed. Changes in TGF-\u0026beta; signaling, leukocyte endothelial cell migration, and proteoglycan pathways in cancer identified through KEGG analyses suggest that, beyond being just a metabolic enzyme, PHGDH contributes to the maintenance of metastatic ability and stemness [48, 49]. These results demonstrate that PHGDH may act as an important functional regulator during melanoma metastasis, suggesting the feasibility of future therapeutic strategies targeting PHGDH [1, 18, 48, 50].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTherapeutic potential of targeting PHGDH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePHGDH inhibition alone can partially resolve dabrafenib resistance by reducing cancer stemness and p-ERK level. In addition, the PHGDH expression level has the potential to be used as a biomarker in personalized medicine in the future, and may also be useful for screening patients with poor dabrafenib response. The PHGDH inhibition strategy could be combined with metabolism-based anticancer strategies such as inhibition of folate metabolism and nucleotide synthesis, and provides a basis for exploring novel combination treatment strategies\u0026nbsp;[51]The increase in PHGDH expression identified in this study is closely related to metabolic reprogramming of treatment-resistant thyroid cancer cells, and similar trends have been reported in recent literature\u0026nbsp;[1, 18, 23, 50]. In particular, single-cell transcriptome analysis showed that PHGDH expression gradually increases as thyroid cancer progresses from PTC to ATC, and it is most prominent in the mesenchymal ATC (mATC) subtype [52].\u003c/p\u003e\n\u003cp\u003eThis subtype shows both metabolic plasticity and an aggressive phenotype, suggesting that serine/glycine metabolism, including PHGDH, is involved in the metastasis and dedifferentiation. Although a direct link between PHGDH and stemness has not been established, the results of this study suggest that the correlation between the two is significant [22, 52]. ATC cells are strongly dependent on glutaminolysis, and inhibition of this pathway activates one-carbon metabolism as a compensatory mechanism for maintaining survival\u0026nbsp;[17]. According to this report, inhibition of PHGDH decreases accumulation of the antioxidant glutathione (GSH) and ROS, and has a synergistic effect with existing anticancer drugs (lenvatinib, sorafenib). These findings emphasize that PHGDH is a key metabolic target for overcoming treatment resistance of solid tumors\u0026nbsp;[9, 52]. Therefore, this study suggests that one-carbon metabolic flow centered on PHGDH plays a key role in the dedifferentiation and resistance acquisition of ATC, and that strategies to inhibit it may improving therapeutic responses\u0026nbsp;[17, 23, 53-55].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical implications and future research directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that PHGDH is not just a metabolic enzyme, but is involved in various tumor biological functions such as resistance to anticancer agents, cancer stem cell maintenance, and MAPK pathway modulation. Therefore, PHGDH can be considered as a multifunctional target that controls cancer resistance and recurrence [9, 55]. Follow-up studies using patient-derived xenograft (PDX) models or an in vivo system are needed to verify whether PHGDH inhibitors overcome dabrafenib resistance.\u003c/p\u003e"},{"header":"Methods and materials ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCell culture\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this experiment, BCPAP, 8505C, and SW1736 thyroid cancer cell lineswere used in this study. BCPAP is derived from PTC with a BRAF mutation, while 8505C and SW1736 originate from anaplastic thyroid carcinoma (ATC) with the same mutation. Cells were cultured at 37 \u0026deg;C in RPMI 1640 medium (Gibco, Grand Island, NY, USA)supplemented with 10% fetal bovine serum and 5% CO₂. Subcultures were performed every 2\u0026ndash;3 days, and cells were passaged upon reaching 70\u0026ndash;80% confluency. Cell line authentication was confirmed by short tandem repeat (STR) profiling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eColony formation and cell proliferation assay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded onto 6-well plates at a density of 5 \u0026times; 10\u0026sup3; cells per well. After 6 days of incubation, non-adherent cells were removed by washing twice with PBS. Attached cells were fixed in 500 \u0026micro;l of 10% formalin for 20 minutes, followed by staining with 500 \u0026micro;l of 0.1% crystal violet for 20 minutes. Plates were gently rinsed with water. Colonies exceeding a defined size threshold were counted at five random fields per well, and the results were statistically analyzed to compare differences between groups.\u003c/p\u003e\n\u003cp\u003eTo assess cell viability, an MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay was performed on BCPAP and 8505C cells treated with dabrafenib. MTT reagent was added to each well and incubated at 37 \u0026deg;C for 4 hours. The reaction was terminated by adding 100 \u0026micro;l of detergent solution, followed by a 2-hour incubation at room temperature in the dark. Absorbance was measured at 540 nm using a microplate reader.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpheroid formation assay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor sphere formation was assessed using ultra-low attachment 96- and 6-well plates. 8505C cells were seeded at a density of 3 \u0026times; 10⁴ cells per well (n = 6). The culture medium consisted of RPMI 1640 supplemented with recombinant human EGF, human FGF, B27 supplement, and 1% penicillin/streptomycin (all from Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). Fresh medium was added every 3 days, and cells were monitored for 10 days. On day 10, spheres \u0026ge;50 \u0026micro;m were counted in five minutes under a light microscope at 100\u0026times; magnification. Samples were collected for subsequent analysis, including real-time PCR\u0026nbsp;[23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003emRNA expression analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative real-time PCR (qRT-PCR) was performed to measure mRNA levels of PHGDH, Oct-4 (octamer-binding transcription factor 4), and NANOG (Nanog homeobox) in dabrafenib-resistant cell lines cultured under stemness-inducing conditions for 10 days. Total RNA was extracted using TRIzol\u0026reg; reagent (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA), and cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit (Fermentas, Thermo Fisher Scientific, Waltham, MA, USA). Each 25 \u0026micro;l PCR reaction contained 1.0 \u0026micro;l cDNA, 12.5 \u0026micro;l QuantiFast SYBR Green PCR Master Mix, 1.0 \u0026micro;l RNase-free water, and 10 pM of each primer. PCR was conducted on a 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). 18S rRNA served as the internal control. Primer sequences were previously described by Jeon et al. (2020) and used as referenced\u0026nbsp;[23]. Thermal cycling conditions were as follows: initial denaturation at 95 \u0026deg;C for 10 s, followed by 40 cycles of 60 \u0026deg;C for 30 s.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWestern blot analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteins (20\u0026ndash;30 \u0026mu;g) from cell lysates and human samples were separated by SDS-PAGE using NuPAGE gels (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) and transferred to 0.45 \u0026mu;m nitrocellulose membranes (Amersham Bioscience, Piscataway, NJ, USA). Membranes were blocked with Super Blot blocking buffer (Invitrogen) for 10 minutes, followed by incubation with primary antibodies overnight at 4 \u0026deg;C. After washing three times with TBS-T, membranes were incubated with HRP-conjugated secondary antibodies (1:5000) for 1 hour. Detection was performed using enhanced chemiluminescence (Enzo Life Sciences, New York, USA).\u003c/p\u003e\n\u003cp\u003ePrimary antibodies used were as follows: pEGF (Tyr1068), EGF, p-ERK (Thr202/Tyr204), ERK, Vimentin, N-cadherin, E-cadherin, pAKT (Ser473), AKT (Cell Signaling Technology, Cat# 2236, 2748, 4903, 6964, 9102, 9101, 5741, 13116, 3195, 9271, 9272), PHGDH (Abcam, ab57030), and OCT3/4 (Santa Cruz Biotechnology, sc-5279, 1:200). Protein levels were normalized to \u0026beta;-actin (Cell Signaling Technology, 4970). All experiments were independently performed in triplicate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhospho-receptor tyrosine kinase (RTK) analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhospho-receptor tyrosine kinase (RTK) array analysis was conducted using dabrafenib-resistant 8505C, BCPAP, SW1736, and corresponding control cell lines. Approximately 300 \u0026micro;g of total protein lysate was incubated with the Human Phospho-RTK Array Kit (R\u0026amp;D Systems, Minneapolis, MN, USA), following the manufacturer\u0026apos;s protocol. Signal intensity was detected by enhanced chemiluminescence (ECL), as used in western blot analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnimal study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA melanoma metastasis model was established using 6-week-old male C57BL/6N mice (OrientBio, Korea). GFP-labeled B16F10 melanoma cells (1 \u0026times; 10⁶ cells in 20 \u0026mu;L PBS) were injected into the hind footpad. After tumor development (approximately 28\u0026ndash;31 days), mice were sacrificed, and both the footpad tumors (primary tumors) and metastatic lung lesions were harvested.\u003c/p\u003e\n\u003cp\u003eTissue-derived cells were processed for fluorescence-activated cell sorting (FACS; BD FACSAria III) to isolate GFP-positive cells. Sorted GFP⁺ cells were subjected to downstream analyses including single-cell transcriptomics (scRNA-seq), bulk RNA-seq, and functional assays.\u003c/p\u003e\n\u003cp\u003eAll animal procedures were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) of Konkuk University. Mice were housed under sterile conditions and euthanized under appropriate anesthesia at the experimental endpoint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePharmacological inhibitors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDabrafenib, a BRAF V600E inhibitor, was used to generate drug-resistant cell lines. Dbrafenib (Selleck Chemicals, Houston, TX, USA) were dissolved in 100% DMSO (Sigma-Aldrich, St. Louis, MO, USA). An equivalent concentration of DMSO was used as a vehicle control in all experiments. The PHGDH inhibitor NCT 503 and its inactive analog were obtained from Sigma-Aldrich (St. Louis, MO, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRNA sequencing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e thyroid cancer model (8505C cell line).\u003cbr\u003e\u0026nbsp;To investigate transcriptomic changes associated with drug resistance, total RNA was extracted from 8505C thyroid cancer cells under three conditions: untreated control, dabrafenib-resistant, and dabrafenib-resistant cells treated with the PHGDH inhibitor NCT 503. Bulk RNA sequencing was performed to assess differential gene expression. Notably, PHGDH expression was markedly elevated in resistant cells and reduced upon NCT 503 treatment, suggesting its potential involvement in serine metabolism\u0026ndash;mediated resistance mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vivo\u0026nbsp;\u003c/em\u003emelanoma model (mouse).\u003cbr\u003e\u0026nbsp;To examine transcriptomic alterations associated with melanoma metastasis, B16F10 melanoma cells were injected into the footpads of BALB/c mice to establish a spontaneous metastasis model. On day 31 post-injection, primary skin lesions and metastatic lung tissues were collected. Total RNA was isolated and subjected to bulk RNA sequencing. Differential gene expression analysis was conducted to compare primary and metastatic tissues, followed by KEGG enrichment analysis to identify metastasis-associated pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Acquisition and Processing\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003eRaw count data were imported into a DGEList object using the edgeR package in R. Genes with low expression levels were filtered out to improve data quality, and normalization was performed using the trimmed mean of M-values (TMM) method via the calcNormFactors function to account for library size differences. The normalized count data were then transformed to log2-counts per million (logCPM) values using the voom function from the limma package, which models the mean-variance relationship and generates precision weights for each observation [56]. Differential expression analysis was conducted using linear modeling and empirical Bayes moderation, with differentially expressed genes identified at a false discovery rate (FDR) threshold of 0.05. Single-cell RNA sequencing data were processed using the Seurat package in R [57]. Quality control filtering excluded cells with \u0026lt;200 or \u0026gt;5,000 detected features and \u0026gt;10% mitochondrial gene expression. Data normalization and scaling were performed using SCTransform, followed by principal component analysis (PCA). Cell type annotation was conducted using SingleR with the Human Primary Cell Atlas reference dataset from the celldex package. Differential gene expression analysis between sample groups was performed using FindMarkers, both on total cells and malignant cells only. Trajectory analysis was performed using Slingshot on PCA coordinates to infer developmental trajectories and pseudotime ordering [58]. Standard statistical thresholds and multiple testing corrections were applied throughout the analysis. Gene expression datasets GSE193581 and GSE221329 were downloaded from the Gene Expression Omnibus (GEO) database. Single-cell RNA-seq data were processed using Seurat (v4.0) in R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUnsupervised machine learning\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed unsupervised machine learning approaches to identify novel gene modules associated with PHGDH metabolic networks and stemness-related pathways. Soft clustering analysis was performed using fuzzy c-means clustering via the means function from the e1071 package, which allows genes to belong to multiple clusters with varying membership degrees, better capturing the complex regulatory relationships in biological networks. Consensus clustering was implemented using R ConsensusClusterPlus package to ensure robust and stable cluster identification across multiple iterations and subsampling procedures. This approach provided confidence measures for cluster assignments and optimal cluster number determination. To identify key influential nodes and analyze their topological significance within the PHGDH-driven metabolic network, a protein-protein interaction (PPI) network was constructed. This network was derived from the STRING database, with associations filtered to include only those with a medium confidence score. The resulting network was then represented and analyzed using the igraph package in R. Community detection within this PPI network was performed utilizing the leading eigenvector community detection algorithm. This approach identifies densely connected groups of nodes that likely represent functional modules. The modularity of these identified communities was also assessed. Influential nodes were subsequently identified by evaluating various network centrality measures. Eigenvector centrality was employed to determine node influence based on connections to other well-connected nodes, and this metric was used to scale node sizes in visualizations. Additionally, degree centrality (number of direct connections) and betweenness centrality (extent to which a node serves as a bridge between network parts) were calculated. Nodes exhibiting high scores in these centrality measures, particularly those demonstrating substantial differences in Eigen scores between resistant and PHGDH-inhibitor treated conditions, were pinpointed as critical influential factors within the network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunctional Enrichment Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the biological significance of identified gene sets and modules, functional enrichment analyses were performed. Gene Set Enrichment Analysis (GSEA) was utilized to determine whether predefined sets of genes were significantly overrepresented at the extremes of a ranked gene list\u0026nbsp;[59]\u003c/p\u003e\n\u003cp\u003e. Genes were ranked based on a specified statistic. The GSEA algorithm calculates an Enrichment Score (ES) by traversing the ranked list, reflecting the enrichment of a gene set. To assess statistical significance, a Normalized Enrichment Score (NES) was calculated by normalizing the ES against a null distribution generated by permutation testing. Nominal p-values were subsequently adjusted for multiple hypothesis testing using the False Discovery Rate (FDR) method. Over-Representation Analysis (ORA), based on the hypergeometric test, was additionally employed to identify gene sets that were significantly enriched among differentially expressed genes. This method assesses whether the observed overlap between a list of genes of interest and a predefined gene set is greater than what would be expected by chance. Adjusted p-values, typically using FDR correction, were used to account for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCo-expression network analysis and hub gene identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify key genes with differentially co-expression network in dabrafenib resistance and upon PHGDH inhibition, signed scale-free co-expression networks were constructed. Gene expression correlations were calculated to generate correlation matrices. The eigenvalues and eigenvectors of these correlation matrices were then computed using the eigen() function. The absolute difference between the first eigenvectors (representing Eigen scores) of the two conditions was calculated. Genes exhibiting large delta.eigen values were identified as influential hub genes whose network connectivity and importance significantly shifted between conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were expressed as numbers and percentages, while continuous variables were reported as means \u0026plusmn; standard deviations (SDs) or medians with interquartile ranges. Comparisons of continuous variables were performed using Student\u0026rsquo;s t-test, and categorical variables were compared using Fisher\u0026rsquo;s exact test. One-way ANOVA was used to assess differences among three or more groups. All Pvalues were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Graphs were generated using GraphPad Prism version 5.01 (GraphPad Software Inc., San Diego, CA, USA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the KonKuk University 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement :\u0026nbsp;\u003c/strong\u003eWe thank J-W Oh (Department of Dermatology, Severance Hospital, Cutaneous Biology Research Institute, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea) for providing GEO data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest :\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e: The authors have nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSubbiah V, Kreitman RJ, Wainberg ZA, Cho JY, Schellens JHM, Soria JC\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. 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However, its clinical efficacy is limited due to the rapid emergence of resistance to treatment. In this study, we established a dabrafenib-resistant ATC cell line (8505C-R) and compared its molecular and phenotypic characteristics with those of the parental cells. We evaluated cell proliferation, colony formation, stem cell marker expression, and MAPK pathway activity and identified elevated levels of EGFR and PHGDH in 8505C-R cells as hallmarks of resistance. RNA sequencing revealed that PHGDH inhibition using the selective inhibitor NCT-503 profoundly reprogrammed gene expression networks related to tumor plasticity, stem-like phenotypes, and hyperactivation of the EGFR\u0026ndash;MAPK signaling axis. Notably, LRIG1, a negative regulator of EGFR, was differentially expressed upon PHGDH inhibition, suggesting a feedback loop connecting serine metabolism with receptor tyrosine kinase signaling. Functional assays demonstrated that PHGDH inhibition, either alone or in combination with dabrafenib, markedly suppressed colony formation, expression of stemness-associated markers, and MAPK pathway activity. These results reveal a novel resistance mechanism in BRAF-mutant ATCs mediated by PHGDH-dependent serine metabolism and cross-reaction with EGFR\u0026ndash;MAPK signaling. Targeting PHGDH effectively suppresses resistance and stemness characteristics, and may provide a novel therapeutic strategy in combination with dabrafenib for aggressive thyroid cancer.\u003c/p\u003e","manuscriptTitle":"PHGDH inhibition overcomes dabrafenib resistance through metabolic rewiring in BRAF V600E anaplastic thyroid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:10:01","doi":"10.21203/rs.3.rs-7069883/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":"bd694a8d-1dd3-46ee-bf02-022c9f264094","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51293603,"name":"Biological sciences/Cancer/Cancer metabolism"},{"id":51293604,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"}],"tags":[],"updatedAt":"2025-09-25T14:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:10:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7069883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069883","identity":"rs-7069883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}