Targeting of a novel interplay between MET Tyrosine Kinase and NRF2 enhances sensitivity to Paclitaxel in Triple Negative Breast Cancer

Targeting of a novel interplay between MET Tyrosine Kinase and NRF2 enhances sensitivity to Paclitaxel in Triple Negative Breast Cancer | 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 Research Article Targeting of a novel interplay between MET Tyrosine Kinase and NRF2 enhances sensitivity to Paclitaxel in Triple Negative Breast Cancer Irene Taddei, Claudia Cirotti, Fabienne Lamballe, Olivier Castellanet, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6855159/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 Background Triple-negative breast cancer (TNBC) is a very aggressive and heterogeneous cancer. The lack of effective targeted therapies and frequency of relapse point to the urgent need for identifying molecular vulnerabilities to overcome resistance to chemotherapy. Nuclear Factor Erythroid 2-related factor 2 (NRF2) is a transcription factor that plays a central role in response to oxidative stress. Its hyperactivation contributes to metabolic rewiring and resistance to therapy in several tumors including TNBC. Unfortunately, efficient pharmacological approaches that block NRF2 functions are still missing. Protein Tyrosine kinases (PTKs), often overactivated in cancer and influencing several signalling pathways, are promising candidates to explore for their potential impact on NRF2. Methods The link between Receptor Tyrosine Kinases and NRF2 expression and its impact on the survival probability of TNBC and non-TNBC patients were investigated by bioinformatic analyses using TCGA and GEO databases. MET-NRF2 connection was further confirmed by immunoblotting, immunofluorescence, qRT-PCR and RNAseq experiments through the combinatorial use of murine and human TNBC cellular models. The efficacy of combination treatments with Paclitaxel and specific inhibitors of MET-NRF2 signalling was assessed by viability assays and flow-cytometry analyses on TNBC cellular models as well as on TNBC patient-derived organoids. Results Here, we identify a novel interplay between MET and SRC kinases with NRF2 expression and activity and demonstrate that its targeting enhances the sensitivity to the standard Paclitaxel treatment of TNBC cells and patient-derived organoids. Conclusions Our study shows that PTKs regulate NRF2 expression and activation in TNBC providing a proof of principle for the ability of Tyrosine Kinase Inhibitors (TKIs) to impinge on NRF2 signalling. Our findings also uncover the value of the MET-SRC-NRF2 axis as exploitable vulnerability in NRF2-hyperactivated TNBC, paving the way for the repositioning of TKIs as modulators of NRF2 signalling. Triple Negative Breast Cancer NRF2 MET tyrosine kinase Tyrosine Kinase Inhibitors Therapy resistance Paclitaxel Patient-derived organoids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Triple Negative Breast Cancer (TNBC) is one of the most aggressive invasive breast tumours. TNBC accounts for ∼15–20% of all the breast cancer (BC) cases and is associated to high heterogeneity and elevated metastatic potential ( 1 ). At the molecular level, it is characterized by the absence of Estrogen Receptor (ER) and Progesterone Receptor (PR) expression, as well as the lack of amplification or overexpression of the Human Epidermal Growth Factor Receptor 2 (HER2) ( 2 ). These characteristics prevent TNBC patients from responding to hormone therapy or anti-HER2 agents, limiting treatment options and resulting in a poor prognosis ( 3 ). Indeed, the first line treatment remains chemotherapy followed by mastectomy and breast-conserving surgery, depending on the case and on the size of the tumour. However, despite the initial chemosensitivity, TNBC patients show frequently relapses and metastasizes, with more aggressive phenotypes than the primary tumour ( 4 ). NRF2 (Nuclear factor-erythroid 2-related factor 2) is a Cap’n’collar nuclear transcription factor and it is considered the master regulator of oxidative stress response ( 5 ). As an inducible transcription factor, its basal protein levels are tightly controlled and maintained at low concentrations through the association with its main negative regulator KEAP1 protein (Kelch-like ECH-associated protein 1), which forms the KEAP1-CUL3-RBX1 E3-ubiquitin ligase complex, thus mediating NRF2 ubiquitination and degradation via 26S proteasome ( 6 , 7 ). Oxidative stress and/or electrophiles can modify reactive Cys residues on KEAP1 protein preventing NRF2/KEAP1 interaction thus promoting NRF2 stabilization, nuclear translocation and activation ( 8 ). NRF2 has a cytoprotective role from xenobiotics and oxidative stress and it regulates more than 200 genes involved in detoxification processes, cellular redox homeostasis, autophagy, apoptosis, cell survival and proliferation ( 9 ). Although traditionally considered a tumour suppressor, its dual role in cancer has become increasingly evident over the years ( 10 ). In this regard, during cancer development, NRF2 hyperactivation creates a suitable environment that protects cancer cells from reactive oxygen species (ROS) damage further supporting tumour growth and drug resistance ( 11 ). NRF2 hyperactivation in cancer is well-established in several tumours including BC ( 12 ). Importantly, in BC, elevated NRF2 expression correlates with poorer overall survival (OS) and disease-free survival (DFS), suggesting its potential as a prognostic factor for BC patients ( 13 ). Of note, although the NFE2L2 (NRF2) and KEAP1 genes are rarely mutated in BC, NRF2 is highly expressed suggesting that it can be modulated by other mechanisms ( 14 ). Protein Tyrosine Kinases (PTKs) are involved in several biological processes including cell survival, proliferation, migration and differentation ( 15 ). Among the 90 PTKs, 58 are Receptor Tyrosine Kinases (RTKs) and 32 are non-Receptor Tyrosine Kinases (nRTKs) ( 16 ). Although, TNBC tumours are very heterogeneous, most of them are characterized by aberrant activation of different RTKs such as MET (Hepatocyte Growth Factor [HGF] Receptor) and EGFR (Epidermal Growth Factor Receptor) ( 17 ). Indeed MET overexpression and activation has been reported also in BC development ( 18 ) and it is associated with basal/like phenotype and identified in 52 % of TNBC ( 19 – 21 ). SRC, a nRTKs, was the first proto-oncogene to be discovered and its aberrant activation has been found in several solid tumours, including BC ( 22 ). Notably, the SRC gene is rarely mutated or amplified in cancer and its hyperactivation is mainly due to the constitutive activation of RTKs, which occurs in a large majority of tumours ( 23 , 24 ). In this regard, SRC is considered a common node of various RTKs, including MET, culminating in the deregulation of different downstream signalling pathways ( 25 ). Here, we provide new evidence that MET and SRC PTKs promote NRF2 expression and activation in human and murine TNBC cellular models. We also demonstrate that pharmacological inhibition of MET, SRC or NRF2 enhances the sensitivity of TNBC cellular models and TNBC patient-derived organoids (PDOs) to Paclitaxel treatment, pointing to this newly identified signalling cascade as a valuable target for the development of more effective combinatorial therapies for TNBC patients. Results Elevated MET/EGFR, and NRF2 levels predict poor prognosis in TNBC Receptor tyrosine kinases (RTKs) are frequently overactivated in many tumours, including TNBC, and their constitutive activation results in aberrant signalling and deregulation of many transcription factors ( 26 ). To test whether RTKs may play a role in NRF2 hyperactivation in TNBC, we first took advantage of the TCGA database, to perform a correlation analysis in breast cancer (BC) patients, irrespective of the subtype. As show by the Spearman correlation analysis, the expression levels of NFE2L2 (NRF2 gene name, here referred NRF2 ) positively correlate with the expression levels of several RTKs in BC (Fig. 1 A). Of note, by evaluating the expression levels of different RTKs among the main four BC subtypes (Luminal A, Luminal B, HER2+, TNBC), we found that TNBC patients exhibit higher levels of MET and EGFR (Fig. 1 B), a feature not observed for other RTKs such as AXL , TGFBR1 and PDGFRA (Fig. S1 A). To further investigate this issue, and to get more insights on its relevance in TNBC, we evaluated whether the relative expression levels of different RTKs and NRF2 influence the survival rates of TNBC patients. By using the GSE31519 dataset ( 27 ), we found a significant decrease in overall survival probability in patients who simultaneously express higher levels of MET/NRF2 or EGFR/NRF2 (Fig. 1 C). Of note, a similar analysis performed in non-TNBC patients does not show any significant variation (Fig. S1 B). By contrast, TNBC patients co-expressing high levels of AXL/NRF2, TGFBR1/NRF2 or PDGFRA/NRF2 were not characterized by alterations in overall survival (Fig. 1 D). These data highlight a possible functional link between MET/EGFR and NRF2 in TNBC. MET targeting affects NRF2 signalling in TNBC murine model To further investigate the significance of the interplay between RTKs and NRF2 in TNBC, we focused our studies on the MET receptor. We took advantage of a unique mouse model, the MMTV-R26 Met mice, in which a slight increase in the expression levels of the wild-type form of MET in the mammary gland leads to the spontaneous development of BC ( 28 ). This murine model represents an ideal system to investigate the role of MET in TNBC as all the developed tumours have been previously characterized as TNBC ( 28 ). Furthermore, mammary gland tumour (MGT) cell lines that recapitulate the heterogeneity of the developed TNBC tumours have also been generated and characterised ( 28 ). Immunoblotting analysis performed using a phosphospecific antibody (pY 1234/35 MET), which selectively recognizes the activated form of MET, showed the activation of MET in all four tumorigenic MGT cell lines (MGT-4, MGT-9, MGT-11, and MGT-13) compared to the non-tumorigenic MGT-2 cells (Fig. 2 A). Notably, the tumorigenic MGT cells also showed higher levels of NRF2 expression suggesting that MET hyperactivation sustains NRF2 signalling (Fig. 2 A). Consistent with this hypothesis, pharmacological inhibition of MET with PHA-665752, a specific inhibitor of MET activity, significantly decreased total NRF2 protein levels in MGT-13 cells (Fig. 2 B). In addition, immunofluorescence and subcellular fractionation analyses showed that PHA-665752 treatment inhibits MET activity and more importantly, caused a significant decrease of NRF2 nuclear staining (Fig. 2 C-D). Lastly, MET inhibition hampered NRF2 transcriptional activity, as indicated by the reduced expression of well-known NRF2 target genes such as sequestome-1 (SQSTM1), heme oxygenase 1 (HMOX1), NAD(P)H quinone dehydrogenase 1 (NQO1), glutamate-cysteine ligase catalytic subunit (GCLC) and solute carrier family 2 (SLC2A1) (Fig. 2 E). To further strengthen the link between MET and NRF2-dependent transcriptional activity, we performed a transcriptomic analysis on MGT-13 cells treated or not with PHA-665752. As expected, the treatment efficiently inhibits MET activity (Fig. S2 A). Principal Component Analysis (PCA) shows that biological replicates of control and PHA-665752-treated samples are highly reproducible and segregate distinctly, according to their transcriptomic profile ( Fig. S2 B ). A total of 24,411 genes were analysed and, among them, 662 genes exhibiting marked downregulation (log 2 FC ≤ − 0.7; Table S1 ) and 760 upregulation (log 2 FC ≥ 0.7; Table S2 ) upon PHA-665752 treatment (Fig. 2 F, S2 C). Coherently with MET inhibition, we observed a strong deregulation of pathways related to migration and invasion of cancer cells (i.e. Mucine type O-glycan biosynthesis, ECM-receptor interaction and focal adhesion), and pathways related to inflammation (i.e. IL-17 signalling pathway and cytokine-cytokine receptor interaction) (Fig. S2 D). However, we noticed also an upregulation of pathways involved in resistance mechanisms or pathway linked to tumour suppressor functions (Fig. S2 E). In searching for a link between MET and NRF2, we next performed enrichment analysis aimed at defining those transcription factors (TFs) modulated by MET inhibition. Interestingly, NRF2 was identified as one of the most downregulated TF by the PHA-665752 treatment (Fig. 2 G), further confirming its cooperative role with MET. Despite a moderate downregulation of the TF itself (Log 2 FC = -0.56 compared to Ctrl) ( Fig. S2 F ), the activation z-score of NRF2 (<-2) suggests that its target genes are significantly altered by the treatment. Particularly, 59 NRF2 downstream genes directly regulated by the TF were employed to calculate its activation z-score. Two clusters can be clearly distinguished, with 34 genes downregulated as a consequence of NRF2 downregulation, and 25 upregulated (Fig. 2 H). Overall, these results demonstrate that MET sustains NRF2 signalling in TNBC murine models. Pharmacological inhibition of MET perturbs NRF2 signalling in human TNBC cell lines and reduces their clonogenicity potential The above outcomes drove us to further explore the interplay between MET and NRF2 in human cellular models. We took advantage of the gastric tumour cell line GTL-16 characterized by MET amplification and overexpression and known to be addicted to MET signalling ( 29 ). Indeed, immunoblotting analysis showed that, also in this system, MET inhibition by PHA-665752 strongly reduced NRF2 expression (Fig. S3 A). This was accompanied by a downregulation of several NRF2 target genes, as shown by qRT-PCR (Fig. S3 B), further strengthening the solidity of the newly identified axis MET-NRF2, even in a different cancer type. Next, we focused on human BC cellular models, utilizing cell lines belonging to different subtypes: T47D (Luminal-A), MDA-MB-361 (Luminal-B), SKBR3 (HER2+), MDA-MB-231 and BT-549 (TNBC). As shown by immunoblotting analysis, TNBC cell lines express higher levels of MET and NRF2. (Fig. 3 A). Importantly, immunofluorescence analyses of MDA-MB-231 and BT-549 cells revealed a decrease in NRF2 nuclear intensity upon PHA-665752 treatment ( Fig. 3 B, S3 C). Moreover, NRF2 transcriptional activity is affected upon MET inhibition in both cell lines (Fig. 3 C). To further evaluate whether pharmacological targeting of the MET-NRF2 axis may functionally affect human TNBC cells, we performed clonogenic assays. Pharmacological inhibition of MET with PHA-665752 and NRF2 with ML-385 (a specific inhibitor of NRF2 activity) significantly reduced the number of colonies formed by MDA-MB-231 and BT-549 cell lines (Fig. 3 D-E), but not by MCF10-A (Fig. S3 D), a non-tumorigenic human mammary cell line ( 30 ). Collectively, these data suggest that MET activity regulates NRF2 pathway also in human cellular models and that this axis represents a specific therapeutic target in TNBC. MET and NRF2 targeting enhances sensitivity to Paclitaxel in TNBC cell lines The absence of ER, PR and HER2 prevents TNBC patients from responding to hormone therapy or HER2-targeted drugs, significantly limiting their treatment options to chemotherapy, with or without immunotherapy, radiotherapy, and surgery ( 3 ). Furthermore, high heterogeneity and resistance to chemotherapy characterize many TNBC cases, resulting in poor prognosis ( 3 ). For these reasons, uncovering novel molecular pathways that can be targeted is urgently needed. Data collected until now, drove us to investigate whether MET and NRF2 targeting may increase TNBC sensitivity to chemotherapy, beside reducing clonogenicity potential. To this aim, we performed cell viability assays both in murine and human TNBC models by treating either MGT-13 or BT-549 cells with PHA-665752 (MET inhibitor) or ML-385 (NRF2 inhibitor) in combination with Paclitaxel (PTX), a chemotherapeutic agent commonly used in the clinic for TNBC patients. As shown by the heatmap representing the percentage of survival cells, the combination of PHA-665752 or ML-385 with PTX significantly reduced cell viability in both cell lines (Fig. 4 A-B). Notably, by using Combenefit software ( 31 ) to calculate the Loewe additivity score, we found that these combination treatments exert a synergistic effect (Fig. 4 C-D). Subsequently, we investigated cell death induction upon the proposed combined treatments. Flow cytometry analysis of the percentage of MGT-13 cell death revealed a significant increase upon combined treatment with PHA-665752 and PTX (Fig. 4 E). Likewise, NRF2 inhibition with ML-385 in combination with PTX also significantly increased MGT-13 cell death (Fig. 4 F ) . Overall, these data strengthen the idea that targeting MET/NRF2 axis may enhance chemotherapy sensitivity in TNBC. SRC kinase activates NRF2 in TNBC We recently reported that SRC (nRTKs) triggers p62 phosphorylation, driving the release of NRF2-KEAP1 interaction resulting in NRF2 hyperactivation and ferroptosis resistance in Glioblastoma ( 32 ). Of note, RTKs deregulation results in SRC hyperactivation as the latter is part of the signalling cascade activated downstream many RTKs, including MET ( 26 ). Accordingly, we could show that MET inhibition impinged on SRC phosphorylation in human TNBC cells, as revealed by pY 416 SRC antibody confirming its activation (Fig. 5 A). We therefore asked whether SRC activity could sustain NRF2 transcription factor signalling. Pharmacological inhibition of SRC with Dasatinib (DAS) slightly but significantly reduced total NRF2 protein expression levels in human TNBC cell lines (MDA-MB-231 and BT-549; Fig. 5 B, S4 A). More importantly, immunofluorescence analyses showed that treatment with DAS decreases the nuclear accumulation of NRF2 in both cell lines (Fig. 5 C, S4 B). Subcellular fractionation experiments also confirmed that DAS treatment reduces NRF2 nuclear levels (Fig. 5 D). Coherently, qRT-PCR experiments showed that SRC pharmacological inhibition strongly affects NRF2 transcriptional activity, as indicated by the downregulation of several NRF2 target genes (Fig. 5 E). To further investigate the significance of SRC-NRF2 interplay in TNBC, we analysed SRC expression by querying TCGA datasets of BC samples. Among BC subtypes, TNBC samples exhibited the highest levels of SRC expression (Fig. 5 F). Next, we evaluated the overall survival probability of TNBC patients stratified by different levels of SRC and NRF2 , using the GSE31519 cohort ( 27 ). Interestingly, patients expressing simultaneously higher levels of SRC and NRF2 are characterized by poorer clinical outcomes compared to patients with other expression profiles (Fig. 5 G). In contrast, the same analysis performed in non-TNBC patients revealed no significant differences (Fig. 5 H). All together, these data indicate that SRC kinase sustains NRF2 activity in TNBC and suggest that its targeting may also enhance Paclitaxel efficacy. Targeting PTKs and NRF2 improve chemotherapy efficacy in TNBC patient-derived organoids To validate our findings in TNBC models that are closer to the clinical setting, we employed patient-derived organoids (PDOs). PDOs are human models that recapitulate the genetic and phenotypic feature of the tumour of origin, including the response to treatments ( 33 ). We took advantage of three PDOs, named PDO-21, PDO-43, PDO-46 ( Table S3 ), derived from tumours that do not exhibit amplification or mutations in the SRC , NFE2L2 or KEAP1 genes, previously characterized as faithful TNBC models ( 34 ). We first determined the IC 50 of PHA-665752, DAS, ML-385, and PTX in the three PDOs (Fig. S5 A-B) and then tested the effectiveness of combinatorial treatments with these drugs. We found that the combination of PTX with PHA-665752, ML-385 or DAS significantly reduced viability of PDOs (Fig. 6 A-C), consistent with what was observed in TNBC cell lines. Additionally, the proposed combinations reduce the size of the PDOs that survived to the treatments, although with different efficacy (Fig. 6 D-E, S5 C-D). All together, these data suggest that targeting MET, SRC, or NRF2 can improve the chemotherapy efficacy of PTX and represents a promising therapeutic strategy for ameliorating TNBC therapy. Discussion Triple-Negative Breast Cancer (TNBC) is a highly aggressive and heterogeneous disease, characterized by the absence of target therapy and poor prognosis ( 1 , 3 ). In this regard, the identification of new molecular pathways that drive TNBC resistance to therapy is urgently needed. NRF2 transcription factor, the master regulator of oxidative stress response ( 11 ), is aberrantly activated in several tumors including TNBC and it is associated with radio- and chemoresistance mechanisms( 14 ). NRF2 is considered an “undruggable” protein due to the lack of active sites or allosteric pockets and for this reason studies aimed to uncover those molecular mechanisms that may affect NRF2 expression and activity are urgently needed ( 35 ). Most of the NRF2 inhibitors used for research purposes are natural plant-derived compounds, like polyphenols. Although these are commonly referred to as antioxidants, some of them inhibit NRF2-dependent expression of cytoprotective genes ( 36 ). However, the inhibitory effect of these natural compounds is still controversial and, although highly safe, they have a weak specificity ( 37 , 38 ). So far, ML-385 is the only selective inhibitor currently available that targets the ability of NRF2 to dimerize with MAFG, but unfortunately, it is not approved for clinical use ( 39 ). More recently, it has been reported a novel NRF2 inhibitor, ARE-PROTAC chimeric molecule, which selectively degrades NRF2-MAFG heterodimer via ubiquitin-proteasome system ( 40 ), However its potential use in clinics has not been investigated yet. The identification of the molecular mechanisms and signalling responsible for NRF2 pathway activation in cancer may represent an alternative strategy to uncover novel potential molecular targets to dampen NRF2 and ameliorate the therapeutic response. Frequently, genetic mutation on NRF2 ( NFE2L2 ) or its major negative regulator KEAP1 are responsible for NRF2 hyperactivation in cancer ( 41 ). However, some tumors show NRF2 deregulation independently of these mutations highlighting the ability of cancer cell to rewire signalling pathways to their own advantage ( 10 ). Remarkably, it has been recently reported that cysteine mediated NRF2 activation represents a novel survival mechanism for TNBC ( 42 ). The deregulation of Receptor Tyrosine Kinases (RTKs) and non-Receptor Tyrosine Kinases (nRTKs) is a common feature in various type of cancer, including TNBC. Physiologically, they are tightly regulated because of their essential role in cellular proliferation, survival and migration and their constitutive activation in cancer has been exploited as a potential vulnerability to selectively hit cancer cells ( 17 ). To this purpose, different Tyrosine Kinases Inhibitors (TKIs) have been developed and several of them are widely used in the clinic ( 43 ). Here, we first identify a link between aberrant RTKs signalling and NRF2 upregulation in TNBC and then we demonstrate that its targeting may ameliorate the therapeutic response to standard therapy. We focused our attention on MET which is overexpressed in about 40% of BC patients and in more than 50% of TNBC ( 44 ). Importantly, its overexpression correlates with tumour progression and aggressiveness ( 45 ). Using murine and human TNBC cellular models, we demonstrated that MET sustains NRF2 expression, nuclear localization and transcriptional activity. NRF2 regulates more than 200 target genes ( 5 ) and here we could show that MET inhibition decreases NRF2 transcriptional activity, as observed by the downregulation of the expression of some canonical targets, like GCLC , MAFG, SLC7A11 and GSR . Furthermore, transcriptomic analysis identified NRF2 among those TFs whose activity is significantly decreased upon MET inhibition by PHA-665752 treatment. The connection between MET and NRF2 has never been investigated in TNBC so far. In agreement with our data, it has been shown that MET-NRF2-HO1 axis has a fundamental role in reducing oxidative stress in renal cancer( 46 , 47 ) suggesting the possibility of a similar role also in TNBC. As pointed out before, the idea of NRF2 targeting to overcome cancer cell resistance to therapy is well supported by the literature ( 35 ). Here we show that treatment with NRF2 specific inhibitor ML-385 strongly affects human TNBC cellular clonogenicity potential, thus supporting the hypothesis that NRF2 targeting may represent a valuable strategy to enhance the therapeutic response. Remarkably, the same treatment has no effects on the non-tumorigenic human mammary cell line MCF10-A, highlighting the specificity of targeting NRF2 in cancer cells. Interestingly, it is well known that standard chemotherapeutic agents such as Paclitaxel (PTX) strongly cause ROS accumulation leading to NRF2 activation, probably responsible for cell protection and therefore resulting in tumour chemoresistance ( 48 , 49 ). Importantly, it has been shown that NRF2 targeting with naturally derived extracts increases sensitivity to Paclitaxel in vitro and in vivo prostate cancer models ( 50 ). Here, we demonstrate that ML-385 significantly ameliorates cell sensitivity to Paclitaxel treatment in TNBC cellular models as well as in Patient Derived Organoids (PDOs) established from TNBC patients, which represent a more reliable model that recapitulate the heterogeneity of TNBC. More importantly, our study supports the repositioning of MET inhibitors as a valuable strategy to hit NRF2 signalling and therefore sensitize cancer cells to Paclitaxel standard treatment. Interestingly, we highlight a positive correlation between several RTKs expression and NRF2 and show a significant correlation between high levels of EGFR and NRF2 and a worse prognosis in TNBC patients. Importantly, EGFR is often deregulated in TNBC and several EGFR TKIs have been shown to ameliorate the therapeutic response ( 51 ). In this regard, the co-targeting of both MET and EGFR could represent another valuable strategy to improve NRF2 targeting and counteract TNBC ( 44 , 52 ). The molecular mechanism that allows MET to modulate NRF2 deserves further elucidation. Notably, SRC kinase is an important downstream mediator of several RTKs including MET ( 53 ). SRC is upregulated in TNBC and its activation can cause the phosphorylation of several downstream substrates including transcription factors ( 26 ). Here, we also demonstrate that the pharmacological inhibition of SRC activity, using Dasatinib, reduced NRF2 protein levels, nuclear localization and activity in human TNBC cell lines similarly to what previously reported in GBM cells ( 32 ). Consistently, TNBC patients present high levels of SRC expression and, intriguingly, the simultaneous expression of high levels of SRC and NRF2 result in a worse clinical outcome. Moreover, SRC inhibition sensitize PDO to Paclitaxel similarly, to what observed upon NRF2 and MET inhibition. Given these evidence, we can therefore speculate that, also in our models, MET sustains SRC activity and as a consequence upregulate NRF2 expression and signalling. However, at this stage we cannot exclude that MET as well as other RTKs may also impinge on NRF2 independently of SRC. Future experiments will clarify this issue. Moreover, it will be interesting to extend our studies to other TKIs, to uncover more effective compounds and/or other PTKs that may represent valuable targets to modulate NRF2 and therefore be exploited to ameliorate TNBC sensitivity to standard therapeutic approaches. Conclusions Overall, our work highlights for the first time the existence of a novel functional interplay between MET and NRF2 in TNBC. Importantly, we demonstrate that the targeting of this axis ameliorates the sensitivity to Paclitaxel treatment suggesting that its upregulation may represent a novel signature to improve TNBC patients’ stratification. Materials and Methods Cell culture Mouse cell lines MGT cell lines, derived from MMTV-R26 Met mice ( 28 ), were grown in DMEM/F12 (Dulbecco’s modified Eagle’s media/F12, 1/1, Sigma-Aldrich) supplemented with: 10% foetal bovine serum (FBS, Sigma-Aldrich), 100 U/mL penicillin, 100 mg/mL streptomycin (P/S, Sigma-Aldrich), L-glutamine (2mM, Sigma-Aldrich), glucose (0,25%, Sigma-Aldrich), insulin (10 µg/mL, Sigma-Aldrich), transferrin (10 µg/mL, Sigma-Aldrich), sodium selenite (5 ng/mL, Sigma-Aldrich), hydrocortisone (0,5 µg/mL, Sigma-Aldrich), EGF (20 ng/mL, Sigma-Aldrich), and HGF (10 ng/mL, Thermo Fisher Scientific), at 37° in a 5% of CO₂ atmosphere. All MGT cells are routinely tested and confirmed negative for Mycoplasma contamination. Human cell lines Human non-TNBC (T47D, MDA-MB-361, and SKBR3) and TNBC cell lines (MDA-MB-231) were cultured in RPMI-1640 (Sigma-Aldrich) supplemented with 10% FBS, L-glutamine (2mM,), and P/S. BT-549 (TNBC cell line) were cultured in DMEM with 10% FBS, L-glutamine (2mM), and P/S. MCF10-A cells, a non-transformed human mammary epithelial cell line, were grown in DMEM/F12 supplemented with horse serum (5%, Sigma-Aldrich), EGF (20 ng/mL), hydrocortisone (0,5 µg/mL), cholera toxin (100 ng/mL, Sigma-Aldrich), insulin (10ng/mL), and P/S. All cell lines are cultured at 37° in a 5% of CO₂ atmosphere and negatively tested for Mycoplasma contamination. Breast cancer patient-derived organoids (BC-PDO) culture BC-PDOs were obtained as previously described ( 34 ). Briefly, breast cancer tissue was finely chopped, washed with 10 mL AdDF+++ (Advanced DMEM/F12 containing 1× Glutamax, 10 mM HEPES, and antibiotics), and digested in 10 mL of AdDF+++ containing 4 mg/mL collagenase II and 5 µM RHO/ROCK pathway inhibitor (Y-27632, Tocris) on an orbital shaker at 37°C for 1–2 hours. The digested tissue suspension was mechanically disrupted by pipetting up and down 10 times, passed through a 100 µm filter pre-coated with AdDF+++ containing 0.1% BSA, and centrifuged at 490 × g for 5 minutes. The pellet was resuspended in 10 mL AdDF+++ and centrifuged again.The pellet was incubated with 2 mL red blood cell lysis buffer for 5 minutes at room temperature to eliminate erythrocytes, followed by washing with culture medium and pelleting at 490 × g. The resulting pellet was resuspended in 10 mg/mL of cold Cultrex growth factor-reduced BME type 2, and 40 µL drops of the BME-cell suspension were allowed to solidify on pre-warmed 24-well suspension culture plates at 37°C for 30 minutes. After polymerization, 400 µL of BC organoid medium (AdDF+++, 0.5 mM A8301, 1× B27, 5 ng/mL EGF, 100 nM β-estradiol, 5 ng/mL FGF7, 20 ng/mL FGF10, 10 µM forskolin, 5 nM heregulin β1, 0.5 µg/mL hydrocortisone, 1.25 mM N-acetylcysteine, 10 mM nicotinamide, 100 ng/mL noggin, 100 µg/mL primocin, 10% R-spondin-conditioned medium, 1 mM SB202190, 5 µM Y-27632) was added to each well, and the plates were placed in a humidified incubator at 37°C with 5% CO2. The medium was refreshed every 3 days. Organoids were passaged every 1–2 weeks by incubation with Cultrex Organoid Harvesting Solution for 45 minutes at 4°C to digest the BME, and then dissociated by enzymatic digestion with TrypLE Express (Gibco) for 7–10 minutes at 37°C, followed by pipetting up and down several times. TrypLE Express activity was blocked by adding 10 mL of AdDF+++ and centrifuging at 490 × g. Organoid fragments were resuspended in cold BME and re-seeded as described above at a suitable ratio (1:1 to 1:6), allowing the formation of new organoids. Antibodies and drugs Primary antibodies used are as follows: anti-NRF2 (12721S; Cell Signalling Technology), anti-phospho-SRC (Tyr416) (2101S; Cell Signalling Technology), anti-SRC (2108S; Cell Signalling Technology), anti-phospho-MET (Tyr1234/35) (3126S; Cell Signalling Technology), anti-MET (3127S; Cell Signalling Technology), anti-vinculin (13901T; Cell Signalling Technology), anti-lamin A/C (sc-376248; Santa Cruz Biotechnology), anti-GAPDH (sc-47724; Santa Cruz Biotechnology), anti-β-Actin (3700T, Cell Signalling Technology); PHA-665752 (S1070; TargetMol), Dasatinib (CDS023389; Sigma-Aldrich), ML-385 (S8790; Selleckchem), Paclitaxel (T7191; Sigma-Aldrich); Cisplatin (T1564; TargetMol). Protein extract, nuclei/cytoplasm fractionation and western blot analyses Total protein lysates were prepared using Buffer A (10 mM Hepes [pH 7.9], 10 mM KCl, 1.5 mM MgCl2, 0.5 mM DTT, 0.1% NP-40) supplemented with 10 mg/ml Protease Inhibitor Cocktail-1 (P2714; Sigma-Aldrich), 10 mg/mL TPCK, 1mM phenylmethylsulfonyl fluoride, 25mM NaF, 1mM sodium orthovanadate, 25 mM β-glycerophosphate. Lysates were incubated for 20 min on ice, then sonicated, and centrifuged at 12,000g at 4°C for 20 min. For nuclei and cytoplasm fractionation, cells were lysed in Buffer A (without NP-40) for 20 min on ice. NP-40 was then added to a final concentration of 0.1%. Next, nuclei were separated from the cytoplasm by centrifugation at 12,000g at 4°C for 30 sec. The cytoplasm was harvested and the nuclear pellet was lysed in Buffer A supplemented with 0.05% NP-40 for 20 min on ice, then sonicated and centrifuged at 12,000g for 20 min. For western blot, 30–80 µg of proteins were separated by SDS-PAGE, blotted on nitrocellulose membrane and incubated with specific antibodies. Immunofluorescence Cells were seeded on coverslips and grown at 37°C in a 5% CO₂ atmosphere. After treatments, cells were washed with 1X PBS and then fixed with 4% PFA for 15 min at room temperature (RT), permeabilized using PBS/Triton X-100 0.3% solution for 10 min, blocked with BSA 3% in PBS solution for 1h, and then incubated with primary antibodies (NRF2 1:50) overnight in a humid chamber at 4°C. Secondary antibodies (1:500, Thermo Fisher Scientific) were applied for 1h at RT, and nuclei staining were performed using Hoechst 33342 (Thermo Fisher Scientific). The images were acquired with ZEISS fluorescence microscopy and analysed with ImageJ Fiji version 2.3. Real time PCR Cells were homogenized with TRI Reagent (Themo Fisher Scientific) and RNA was extracted using the manufacturer’s protocol. One microgram of total RNA was retrotranscribed in cDNA using SensiFAST cDNA Synthesis KIT (Bioline). Specific pair of primers were designed and tested with primerBLAST. RT-PCR were performed using the SensiFAST Syber Low-ROX kit (Bioline) QuantStudio 3 RT–qPCR (Applied Biosystems). Data were analyzed using the second-derivative maximum method. The fold change in mRNA levels was compared to the control condition after normalization to the actin housekeeping gene. List of human primers: GENE FORWARD PRIMER REVERSE PRIMER ACTIN 5’-GGCCGAGGACTTTGATTGCA-3’ 5’-GGGACTTCCTGTAACAACGCA-3’ SQSTM-1 5’-GGGAAAGGGCTTGCACCGGG-3’ 5’CTGGCCACCCGAAGTGTCCG-3’ HMOX1 5’-CACAGCCCGACAGCATGCCC-3’ 5’-GCCTTCTCTGGACACCTGACCCT-3’ NQO1 5’-GGTTTGGAGTCCCTGCCATT-3’ 5’-CCTTCTTACTCCGGAAGGGTC-3’ GCLC 5’-CGCACAGCGAGGAGCTTCGG-3’ 5’-CTCCACTGCATGGGACATGGTGC-3’ SLC2A1 5’-TCACTGTCGTGTCGCTGTTT-3’ 5’-GGCCACGATGCTCAGATAGG-3’ 18S 5’-GGCCGTTCTTAGTTGGTGGA-3’ 5’-TCAATCTCGGGTGGCTGAAC-3’ List of murine primers: GENE FORWARD PRIMER REVERSE PRIMER Actin 5’-CACACCCGCCACCAGTTCGC-3’ 5’-TTGCACATGCCGGAGCCGTT-3’ Sqstm-1 5’-GCTCTTCGGAAGTCAGCAAACC-3’ 5’-GCAGTTTCCCGACTCCATCTGT-3’ Hmox1 5’-CACTCTGGAGATGACACCTGAG-3’ 5’-GTGTTCCTCTGTCAGCATCACC-3’ Nqo1 5’-TGGCCGATTCAGAGTGGCATCCT-3’ 5’-TGCATGCGGGCATCTGGTGG-3’ Gclc 5’-ACACCTGGATGATGCCAACGAG-3’ 5’-CCTCCATTGGTCGGAACTCTAC-3’ Slc2a1 5’-GCTTCTCCAACTGGACCTCAAAC-3’ 5’-ACGAGGAGCACCGTGAAGATGA-3’ Transcriptomic experiment and data analysis Total RNA was extracted using Qiazol (Qiagen, IT), purified from DNA contamination through a DNase I (Qiagen, IT) digestion step and further enriched by Qiagen RNeasy columns for gene expression profiling (Qiagen, IT). Quantity and integrity of the extracted RNA were assessed by NanoDrop Spectrophotometer (NanoDrop Technologies, DE) and by Agilent TapeStation (Agilent Technologies, CA), respectively. RNA libraries for sequencing were generated using the same amount of RNA for each sample according to the Illumina Stranded Total RNA Prep kit with an initial ribosomal depletion step using Ribo-Zero Plus (Illumina, CA). The libraries were quantified by qPCR and sequenced in paired-end mode (2x100 bp) with NovaSeq 6000 (Illumina, CA). For each sample generated by the Illumina platform, a pre-process step for quality control was performed to assess sequence data quality and to discard low-quality reads. RNA-seq data were analyzed with the nf-core tool version 3.3, using the “rnaseq” pipeline and default parameters ( 54 ), aligning reads to the reference genome for Mus musculus GRCm38. The output from nf-core was then used as input for the R package DESeq2 ( 55 ) to calculate differential expression. Genes with total raw counts across all samples below 50 were excluded to reduce background noise and improve the robustness of differential expression analysis. Normalized counts were transformed using the variance stabilizing transformation (VST function). Pathway ontology analysis was performed with ShinyGO version 0.8 ( 56 ), using the list of upregulated (log2FoldChange > 0.7 compared to control) or downregulated (log2FoldChange < -0.7) genes with pvalue < 0.05 as input. Transcription factors analysis, including calculation of activation z-score, was conducted with Qiagen IPA ( 57 ), selecting only those transcription factors with activation z-score greater than 2 or lower than − 2. The list of genes affected by NRF2 expression was also derived from IPA. Volcano plot and bar plots were generated with the ggplot package in R, PCA was performed using the plot PCA function from DESeq2, and heatmaps were created with the ComplexHeatmap package. Cell viability assay MGT-13 and BT-549 cells were seeded in 96-well plates at 3,000 cells per well (150µl media/well). After 24h, cells were treated with single or combined drugs at the indicated concentrations. Cell viability was detected using Cell Counting Kit-8 (CCK-8, TargetMol) reagent after 72h of treatment and then the absorbance was read using TECAN Infinitive-200 PRO spectrophotometer. The results represent the mean value of at least three independent experiments done in triplicates. Breast cancer patient-derived organoids (BC-PDO) viability assays For PDO viability assays, organoids were harvested and dissociated as previously described and then resuspended in 2% BME growth medium before seeding in 100 µL volumes on BME-precoated 96-well plates. After 24 hours, organoids were treated with the indicated drugs. After 5 days, the plate was imaged at 10× magnification using an IncuCyte SX5 live-content imaging system (Essen Bioscience) at 37°C with 5% CO2. Viability was assessed using the CellTiter-Glo® Luminescent Cell Viability Assay (Promega) with a microplate reader (Spark, Tecan). Drug dose–response curves were visualized using linear regression analysis in GraphPad Prism 9, and half-maximal inhibitory concentration (IC 50 ) values were determined from the fitted curves. PDOs were classified as small, medium and large on their diameter measured manually drawing lines using ImageJ software. To calculate the organoid diameter range, we subtracted the diameter of the smallest organoid from the diameter of the largest organoid and divided this value by 3. Then, the obtained value was used to determine the diameter range for small, medium and large organoids ( 58 ). Clonogenic assay Human TNBC cells were seeded in 35mm dishes at 1,000 cells per well and incubated at 37°C, 5% CO 2 for colony formation. After 24h, cells were treated with PHA-665752 (2µM) or ML-385 (5µM) for 72h. Each 2 days, the medium was changed until 10–15 days of growing. Then, colonies were fixed and stained with a solution containing 10% (vol/vol) methanol and 0.5% of crystal violet for 15 min for colony visualization. The stained colonies were counted. The results represent the mean value of at least three independent experiments. Cytofluorimetric analysis Cell death analysis was evaluated upon combination treatments for 72h by using a CytoFLEX S (Beckman Coulter) instrument. 1x10 6 cells were collected and centrifuged at 300g for 5 min at 4°C and double-stained with Annexin V-APC-propidium iodide (PI) kit according to the manufacturer’s instructions (eBioscienceTM Annexin V Apoptosis Detection Kits; Thermo Fisher Scientific). PI was replaced with DAPI due to autofluorescence issues observed with some inhibitors. Unstained samples were used as control. Quality control was evaluated using CytoFLEX Daily QC Fluorospheres (Beckman Coulter). FCS files were analyzed using CytExpert version 2.2 software (Beckman Coulter). Cell death was represented as percentage to control condition. Bioinformatics analysis of publicly available data of BC For comparative gene expression analysis between BC subtypes, transcriptomic data (RNA sequencing) were obtained from The Cancer Genome Atlas (TCGA) database. Patients were stratified based on their estrogen receptor (ER), progesterone receptor (PR), and HER2 status. Gene correlation analysis was conducted using Spearman correlation coefficients, with statistical significance defined as p < 0.05. Volcano plot was done using the VolcaNoseR Rstudio package in R studio. The GSE31519 cohort, comprising 579 TNBC patients with corresponding microarray data, was obtained from the Gene Expression Omnibus (GEO) database and used to generate Kaplan-Meier survival curves for TNBC. Patients were classified into high- and low- expression groups based on the median expression levels of the gene of interest. The statistical significance of survival differences was assessed using the log-rank test, and graphical representations were generated using GraphPad Prism software. Statistical analysis All experiments represent the mean ± SEM or ± SD of at least three independent experiments (biological replicates). Drug interactions were evaluated using Combenefit software, which calculates the synergism score using Loewe additivity mathematical model based on viability parameters ( 31 ). Negative values indicate antagonism, while positive values indicate additivity or synergism. The synergism score is calculated based on the combination index (CI) used to evaluate drug interactions. CI 1 means antagonism. Error bars represent SD for immunofluorescence experiments and SEM for all the other techniques. Differences between data populations were assessed according to the two-tailed unpaired t -test (independent samples). For immunofluorescence analysis, an unpaired t test was used when data followed a normal distribution, and the Mann–Whitney test was applied in other situations. For multiple comparisons, we used the ANOVA test. Results were considered significant if P ≤0.05 (*), P ≤0.01 (**), P ≤0.001 (***), P ≤0.0001 (****). All statistical analyses were performed using GraphPad Prism software 8.4.2 version. Abbreviations BC: Breast Cancer TNBC: Triple Negative Breast Cancer NRF2: Nuclear Factor Erythroid 2-related factor 2 PTKs: Protein Tyrosine Kinases TKIs: Tyrosine Kinase Inhibitors ER: Estrogen Receptor PR: Progesterone Receptor HER2: Human Epidermal Growth Factor Receptor 2 KEAP1 : Kelch-like ECH-associated protein 1 OS : Overall Survival DFS: Disease Free Survival RTKs: Receptor Tyrosine Kinanes nRTKs: non-Receptor Tyrosine Kinases PDOs: Patient-Derived Organoids MGT: Mammary Gland Tumour TFs: Transcription Factors PTX: Paclitaxel DAS: Dasatinib PHA: PHA-665752 Declarations Ethics approval and consent to participate For organoid development, tumor biopsies were collected from patients treated at ‘‘Fondazione Policlinico Universitario A. Gemelli IRCCS’’ (FPG), Rome, Italy, from May 2020 to October 2022. The protocol was approved by the Institutional Review Board (Protocol ID: 3642) and conducted in accordance with the Helsinki Declaration. All enrolled patients gave their written informed consent for participation. Relevant clinical data were collected and managed using REDCap electronic data capture tools at FPG (https://redcap-irccs.policlinicogemelli.it). Consent for publication Not applicable Availability of data and materials The dataset generated and analysed during the current study https://urldefense.com/v3/__https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290518__;!!O5Bi4QcV!DnSpwUVkhFP9m6fIVSd0lrfHzAHaOr_b0NUxodpYyxw4-IZ3XNhAcreWudH_GAaWXt8-zaiY8_Ty7E4D8PbGz8KKaw$ is not publicly available before publication, but reviewers can gain access to the GEO dataset using the following token: uvcjuymsnxibtwt. Competing interests The authors declare that they have no competing interests. Funding This work has been supported by research grants from Associazione Italiana per la Ricerca sul Cancro AIRC-IG2021-n.26230 to D Barilà and IG30651 to C Sette; C Cirotti has been supported by AIRC-IG2021-n.26230; I Taddei has been supported by a MUR fellowship to the PhD Program in Cellular and Molecular Biology, Department of Biology, University of Tor Vergata and by AIRC-IG2021-n.26230. Authors' contributions I.T. designed and performed most of the experiments, contributed to data analysis, interpretation and wrote the original draft; C.Ci. contributed to data analysis, interpretation and wrote the original draft. O.C. performed the analysis of overall survival and gene expression analyses of BC patients; F.L. and F.M. contributed to work on MMTV-R26 Met -derived cell lines, designed cell viability experiments, interpretation and edited the original draft; F.F., G.C., F.D.N. and M.F. performed RNA sequencing experiment and data analysis; V.M. and E.C. performed the patients-derived organoids (PDOs) viability experiments and data analysis; A.D.L. provided breast cancer biopsy and relative clinical data; C.S. designed the PDOs experiments, evaluated data and contributed to edited the original draft. D.B. designed the experiments, evaluated the data and wrote the original draft. Acknowledgements We thank all the members of our laboratory for critical reading of the article and for helpful discussion. References Zagami P, Carey LA. Triple negative breast cancer: Pitfalls and progress. npj Breast Cancer. 2022;8:1–20. Garrido-Castro AC, Lin NU, Polyak K. 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Polyclinic","correspondingAuthor":false,"prefix":"","firstName":"Eleonora","middleName":"","lastName":"Cesari","suffix":""},{"id":469790807,"identity":"f9de6b33-cf7c-4b83-9704-c07e978bac51","order_by":11,"name":"Alba Di Leone","email":"","orcid":"","institution":"Agostino Gemelli University Polyclinic","correspondingAuthor":false,"prefix":"","firstName":"Alba","middleName":"Di","lastName":"Leone","suffix":""},{"id":469790808,"identity":"9cb0f946-cdee-49bf-945c-43a6e6a015fc","order_by":12,"name":"Claudio Sette","email":"","orcid":"","institution":"Agostino Gemelli University Polyclinic","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Sette","suffix":""},{"id":469790809,"identity":"390a4a1b-1ffc-46a7-9ed5-0b2c7cc7b977","order_by":13,"name":"Daniela 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13:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6855159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6855159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84695518,"identity":"5fdc1103-3ae6-444c-8f63-c038d1abd7bc","added_by":"auto","created_at":"2025-06-16 10:37:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":933746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh expression levels of PTKs and NRF2 correlate with worst prognosis in TNBC patients. A\u003c/strong\u003e) Volcano plot for Spearman correlation between NRF2 expression and indicated RTKs in BC samples from TCGA-BRCA dataset. Significant positive correlations are shown in red, and negative correlations in blue. Common RTKs are highlighted. \u003cstrong\u003eB)\u003c/strong\u003e Expression levels of \u003cem\u003eMET\u003c/em\u003e and \u003cem\u003eEGFR\u003c/em\u003e across the four different BC subtypes from the TCGA dataset. Kaplan–Meier curves showing the probability of overall survival in TNBC patients with different expression levels of \u003cem\u003eMET/NRF2\u003c/em\u003e (left) and \u003cem\u003eEGFR/NRF2 \u003c/em\u003e(right) \u003cstrong\u003e(C)\u003c/strong\u003e, \u003cem\u003eAXL, TGFBR1, PDGFRA\u003c/em\u003e, and \u003cem\u003eNRF2\u003c/em\u003e \u003cstrong\u003e(D)\u003c/strong\u003e. Statistical analysis: \u003cstrong\u003eB) \u003c/strong\u003eOne-way ANOVA followed by Tukey’s multiple comparison statistical test was performed. \u003cstrong\u003eC-D)\u003c/strong\u003e Survival data derived from GSE31519 dataset for TNBC patients. P values were computed using the Logrank (Mantel Cox) test. ns: not significant; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/cb1f8ee413bea605f54d63b0.png"},{"id":84697242,"identity":"0af4c0cc-4ebb-4384-ba9f-9fe2feffa57c","added_by":"auto","created_at":"2025-06-16 10:53:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1634351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of the MET receptor reduces NRF2 localization and activity in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMMTV-R26\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eMet\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e \u003cstrong\u003ecell lines. A) \u003c/strong\u003eImmunoblotting (top) of pY\u003csub\u003e1234/35\u003c/sub\u003eMET, MET, NRF2 and relative densitometric analysis (bottom) of NRF2 protein levels in all MGT cell lines derived from the \u003cem\u003eMMTV-R26\u003c/em\u003e\u003csup\u003e\u003cem\u003eMet\u003c/em\u003e\u003c/sup\u003e tumours. Actin was used as a loading control. \u003cstrong\u003eB)\u003c/strong\u003e Immunoblotting (left) of pY\u003csub\u003e1234/35\u003c/sub\u003eMET, MET, NRF2 and relative densitometric (right) analysis of NRF2 protein levels in the MGT-13 cell line upon 16h of PHA treatment. Actin was used as a loading control. \u003cstrong\u003eC)\u003c/strong\u003e Immunofluorescence (left) and relative quantification (right) of NRF2 (red) nuclear intensity in MGT-13 cells upon 16h of PHA treatment. DNA (Hoechst, blue). \u003cstrong\u003eD)\u003c/strong\u003e Immunoblotting (top) and relative densitometric analysis (bottom) of NRF2 cytosolic and nuclear fractions in MGT-13 cells upon 16h of PHA treatment. Vinculin and Lamin A/C were used as loading and quality controls. \u003cstrong\u003eE)\u003c/strong\u003e qRT-PCR of NRF2 target genes in MGT-13 cells after 16h of PHA treatment. Actin was used as housekeeping gene.\u003cstrong\u003e F) \u003c/strong\u003eWorkflow of RNA-seq experiments. p-value £ 0.5 and Log\u003csub\u003e2\u003c/sub\u003efoldchange (log₂FC) \u0026gt; 0.7 were used as threshold for analysis.\u003cstrong\u003e G)\u003c/strong\u003e Activation Z-score for transcription factors (CTRL vs PHA). Values lower than -2 (blue bars) suggest an inhibition of the pathway related to the corresponding transcription factor in the PHA treatment. Values over 2 (red bars) suggest an activation of the pathway in PHA samples. \u003cem\u003eNFE2L2\u003c/em\u003e (NRF2) in bold. Data from IPA. \u003cstrong\u003eH)\u003c/strong\u003e Heatmap of z-scores for genes involved in the NRF2 pathway (according to IPA) for CTRL vs PHA (3 samples for each condition, see x axis). Positive values of z-score (red) indicate upregulation within the sample, negative values (blue) downregulation. Results represent the mean at least of three independent experiments (± SEM or ± SD). Statistical analysis: \u003cstrong\u003eA-B-D)\u003c/strong\u003e Unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003eC)\u003c/strong\u003e Mann-Whitney test according to the normal distribution. \u003cstrong\u003eE)\u003c/strong\u003e Multiple \u003cem\u003et-\u003c/em\u003e test. PHA: PHA-665752 1 µM, MET inhibitor. ns: not significant; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/a630c432e848d8326cfea1eb.png"},{"id":84696812,"identity":"9b273e0b-d5d8-4421-8115-8491df258545","added_by":"auto","created_at":"2025-06-16 10:45:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2186339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeting of the MET receptor reduces NRF2 activity in human TNBC cell lines. A)\u003c/strong\u003e Immunoblotting analysis of pY\u003csub\u003e1234/35\u003c/sub\u003eMET, MET and NRF2 in human BC cell lines. Ponceau was used as a loading control. \u003cstrong\u003eB)\u003c/strong\u003e Immunofluorescence (left) and relative quantification analysis (right) of NRF2 (red) nuclear intensity in human TNBC cells upon 16h of PHA treatment. DNA (Hoechst, blue).\u003cstrong\u003e C) \u003c/strong\u003eqRT-PCR of NRF2 target genes in human TNBC cells after 16h of PHA treatment. Actin was used as housekeeping. \u003cstrong\u003eD-E)\u003c/strong\u003e Clonogenic assays (left) and relative quantification analysis (right) on human TNBC cells treated with PHA \u003cstrong\u003e(D)\u003c/strong\u003e and ML-385 \u003cstrong\u003e(E)\u003c/strong\u003e for 72h. Results represent the mean of at least of three independent experiment (± SEM or ± SD). Statistical analysis: \u003cstrong\u003eB)\u003c/strong\u003e Mann-Whitney test according to the normal distribution. \u003cstrong\u003eC)\u003c/strong\u003e Multiple \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003eD-E) \u003c/strong\u003eUnpaired \u003cem\u003et\u003c/em\u003e-test. PHA: PHA-665752 1 µM, MET inhibitor. ML-385: 5 µM, NRF2 inhibitor. ns: not significant; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/7ca0eb9fb2e7ac9e66fe7c00.png"},{"id":84695527,"identity":"11d1e926-4e5e-477e-b3ec-b0d1ff5e9d21","added_by":"auto","created_at":"2025-06-16 10:37:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1310507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMET or NRF2 targeting enhances Paclitaxel sensitivity of TNBC cells. \u003c/strong\u003eCell viability of MGT-13 \u003cstrong\u003e(A) \u003c/strong\u003eand BT-549 \u003cstrong\u003e(B)\u003c/strong\u003e cells exposed to Paclitaxel in combination with PHA-665752 or ML-385. Percentage of cell viability in presence of drugs compared to controls (untreated cells) is indicated using a colour scale (from green to red). \u003cstrong\u003eC-D) \u003c/strong\u003eTop panel: matrix synergy plot representing the synergy/antagonism score of each combination and its statistical significance calculated by Loewe model. Bottom panel: mapped to d-r surface showing the synergy distribution of drug combinations. The synergistic analysis was performed on MGT-13 \u003cstrong\u003e(C) \u003c/strong\u003eand BT-549 \u003cstrong\u003e(D) \u003c/strong\u003ecell lines. \u003cstrong\u003eE-F) \u003c/strong\u003eHistograms of flow cytometry experiments representing the percentage of dead cells upon ANNEXIN V/DAPI\u003csup\u003e+\u003c/sup\u003e staining of MGT-13 cells exposed for 72h to PHA and PTX \u003cstrong\u003e(E) \u003c/strong\u003eor ML-385 and PTX \u003cstrong\u003e(F) \u003c/strong\u003etreatments alone or in combination. Results represent the mean at least of three independent experiment (± SEM). Statistical analysis: \u003cstrong\u003eA-B-E-F) \u003c/strong\u003eOne-way ANOVA statistical test was performed for each combination compared to the drug alone. (In A-B, * indicate the significance respect to PTX treatment). \u003cstrong\u003eC-D) \u003c/strong\u003eLoewe models was used for synergy score calculation by Combenefit software. PHA: PHA-665752: MET inhibitor; ML-385: NRF2 inhibitor. PTX: Paclitaxel. ns: not significant; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/c70a5aa7b1feead5424b3d24.png"},{"id":84696816,"identity":"78871481-6a55-48c5-98f7-a6335b203025","added_by":"auto","created_at":"2025-06-16 10:45:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2180373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSRC expression sustains NRF2 nuclear accumulation and activity in TNBC. A) \u003c/strong\u003eImmunoblotting of pY\u003csub\u003e1234/35\u003c/sub\u003eMET, MET, pY\u003csub\u003e416\u003c/sub\u003eSRC, SRC (left) and relative densitometric analyses (right) of pY\u003csub\u003e416\u003c/sub\u003eSRC normalized on total SRC in human TNBC cell lines after 16h of PHA treatment. GAPDH was used as loading control. \u003cstrong\u003eB)\u003c/strong\u003e Immunoblotting (left) of NRF2, pY\u003csub\u003e416\u003c/sub\u003eSRC, SRC and relative densitometric analysis (bottom) of NRF2 protein levels in MDA-MB-231 cells upon 16h of DAS treatment. GAPDH was used as a loading control. \u003cstrong\u003eC) \u003c/strong\u003eImmunofluorescence (left) and relative quantification (right) of NRF2 (red) nuclear intensity in MDA-MB-231 cells upon 16h of DAS treatment. DNA (Hoechst, blue). \u003cstrong\u003eD)\u003c/strong\u003e Immunoblotting (top) and relative densitometric analysis (bottom) of NRF2 cytosolic and nuclear fractions in MDA-MB-231 cells upon 16h of DAS treatment. Vinculin and Lamin A/C were used as loading and quality controls. \u003cstrong\u003eE)\u003c/strong\u003e qRT-PCR of NRF2 target genes in MDA-MB-231 cells after 16h of DAS treatment. Actin was used as housekeeping gene. \u003cstrong\u003eF)\u003c/strong\u003e Expression levels of \u003cem\u003eSRC\u003c/em\u003e in log\u003csub\u003e2\u003c/sub\u003e across the four different breast cancer subtypes from the TCGA dataset. \u003cstrong\u003eG-H)\u003c/strong\u003e Kaplan–Meier curves show the overall survival probability of TNBC (n=579; \u003cstrong\u003eG\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eand non-TNBC (n=544; \u003cstrong\u003eH\u003c/strong\u003e) patients with different expression levels of \u003cem\u003eSRC\u003c/em\u003e and \u003cem\u003eNRF2\u003c/em\u003e. Results represent the mean of at least three independent experiment (± SEM or ± SD). Statistical analysis: \u003cstrong\u003eA-B-D) \u003c/strong\u003eUnpaired \u003cem\u003et-\u003c/em\u003etest. \u003cstrong\u003eC)\u003c/strong\u003e Mann-Whitney test according to the normal distribution. \u003cstrong\u003eE)\u003c/strong\u003e Multiple \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003eF)\u003c/strong\u003e One-way ANOVA followed by Tukey’s multiple comparison statistical test was performed. \u003cstrong\u003eG-H) \u003c/strong\u003eSurvival data obtained from Gene Expression Omninbus (GEO) Id GSE31519 for TNBC patients and TCGA dataset for non-TNBC patients. P values were computed using the Logrank (Mantel Cox). PHA: PHA-665752 1 µM, MET inhibitor. DAS: Dasatinib 50 nM; SRC inhibitor. ns: not significant; * p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/a75040c38a81271b0c69ae63.png"},{"id":84696820,"identity":"3d37947d-3803-424d-a570-cff62c2654cd","added_by":"auto","created_at":"2025-06-16 10:45:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1676152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMET, SRC or NRF2 targeting improve Paclitaxel efficacy in TNBC patient-derived organoids. \u003c/strong\u003eOrganoids viability assays of PDO-21, PDO-43 and PDO-46 after PHA and PTX \u003cstrong\u003e(A)\u003c/strong\u003e, ML-385 and PTX \u003cstrong\u003e(B)\u003c/strong\u003eDAS and PTX \u003cstrong\u003e(C) \u003c/strong\u003ecombination treatments. \u003cstrong\u003eD)\u003c/strong\u003e 4X digital magnification of bright-field images and relative quantification of organoids size of PDO-21 culture treated as described in \u003cstrong\u003e(A-B-C)\u003c/strong\u003e. Scale bar: 400 µm. \u003cstrong\u003eE) \u003c/strong\u003ePercentage of small, medium and large PDOs treated as in (A-B-C). Results represent the mean of at least three independent experiment (± SEM). Statistical analysis: \u003cstrong\u003eA-B-C)\u003c/strong\u003eOne-way ANOVA statistical test was performed for each combination compared to the drug alone. \u003cstrong\u003eE) \u003c/strong\u003eTwo-way ANOVA followed by Tukey’s multiple comparison test (* respect to CTR, $ respect to inhibitors and # respect to PTX).\u003cstrong\u003e \u003c/strong\u003ens: not significant; *, $, # p\u0026lt;0.05; **, $$, ## p\u0026lt;0.01; ***, $$$, ### p\u0026lt;0.001; ****, $$$$, #### p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/8efe9837a8da6541b50b97c5.png"},{"id":97667251,"identity":"db3e3176-b8dd-468a-9010-0a1aa924444f","added_by":"auto","created_at":"2025-12-08 09:23:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11382305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/5652a18a-bfea-4f6e-af8e-760c243755d7.pdf"},{"id":84695516,"identity":"04143d71-ca02-49c7-9613-3e0aeea4c6c4","added_by":"auto","created_at":"2025-06-16 10:37:07","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":326326,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/44c98f94581ec87f4491607f.tif"},{"id":84698229,"identity":"641c2ad9-aaf1-47dc-aaef-fecc03b0700b","added_by":"auto","created_at":"2025-06-16 11:01:08","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":427756,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/9b79349e8f89a3ee83c6f165.tif"},{"id":84695523,"identity":"b60c36aa-d665-4c74-a1b7-092d610d6f07","added_by":"auto","created_at":"2025-06-16 10:37:08","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":356182,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/3f4c3b905e9c25951d5fc160.tif"},{"id":84695524,"identity":"3e2c6c4c-7229-472e-9bfe-6248fb26fb3a","added_by":"auto","created_at":"2025-06-16 10:37:08","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":414494,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/5bdca0e471a05f53bd4eb140.tif"},{"id":84695531,"identity":"8ede0d51-7c1b-4289-9e60-6ab023eab28f","added_by":"auto","created_at":"2025-06-16 10:37:08","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":982630,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFig5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6855159/v1/582aa73408afb7b7c6400ad8.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting of a novel interplay between MET Tyrosine Kinase and NRF2 enhances sensitivity to Paclitaxel in Triple Negative Breast Cancer ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple Negative Breast Cancer (TNBC) is one of the most aggressive invasive breast tumours. TNBC accounts for \u0026sim;15\u0026ndash;20% of all the breast cancer (BC) cases and is associated to high heterogeneity and elevated metastatic potential (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). At the molecular level, it is characterized by the absence of Estrogen Receptor (ER) and Progesterone Receptor (PR) expression, as well as the lack of amplification or overexpression of the Human Epidermal Growth Factor Receptor 2 (HER2) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These characteristics prevent TNBC patients from responding to hormone therapy or anti-HER2 agents, limiting treatment options and resulting in a poor prognosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Indeed, the first line treatment remains chemotherapy followed by mastectomy and breast-conserving surgery, depending on the case and on the size of the tumour. However, despite the initial chemosensitivity, TNBC patients show frequently relapses and metastasizes, with more aggressive phenotypes than the primary tumour (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNRF2 (Nuclear factor-erythroid 2-related factor 2) is a Cap\u0026rsquo;n\u0026rsquo;collar nuclear transcription factor and it is considered the master regulator of oxidative stress response (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As an inducible transcription factor, its basal protein levels are tightly controlled and maintained at low concentrations through the association with its main negative regulator KEAP1 protein (Kelch-like ECH-associated protein 1), which forms the KEAP1-CUL3-RBX1 E3-ubiquitin ligase complex, thus mediating NRF2 ubiquitination and degradation via 26S proteasome (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Oxidative stress and/or electrophiles can modify reactive Cys residues on KEAP1 protein preventing NRF2/KEAP1 interaction thus promoting NRF2 stabilization, nuclear translocation and activation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). NRF2 has a cytoprotective role from xenobiotics and oxidative stress and it regulates more than 200 genes involved in detoxification processes, cellular redox homeostasis, autophagy, apoptosis, cell survival and proliferation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Although traditionally considered a tumour suppressor, its dual role in cancer has become increasingly evident over the years (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In this regard, during cancer development, NRF2 hyperactivation creates a suitable environment that protects cancer cells from reactive oxygen species (ROS) damage further supporting tumour growth and drug resistance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). NRF2 hyperactivation in cancer is well-established in several tumours including BC (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Importantly, in BC, elevated NRF2 expression correlates with poorer overall survival (OS) and disease-free survival (DFS), suggesting its potential as a prognostic factor for BC patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Of note, although the \u003cem\u003eNFE2L2\u003c/em\u003e (NRF2) and \u003cem\u003eKEAP1\u003c/em\u003e genes are rarely mutated in BC, NRF2 is highly expressed suggesting that it can be modulated by other mechanisms (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProtein Tyrosine Kinases (PTKs) are involved in several biological processes including cell survival, proliferation, migration and differentation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Among the 90 PTKs, 58 are Receptor Tyrosine Kinases (RTKs) and 32 are non-Receptor Tyrosine Kinases (nRTKs) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although, TNBC tumours are very heterogeneous, most of them are characterized by aberrant activation of different RTKs such as MET (Hepatocyte Growth Factor [HGF] Receptor) and EGFR (Epidermal Growth Factor Receptor) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Indeed MET overexpression and activation has been reported also in BC development (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and it is associated with basal/like phenotype and identified in 52 % of TNBC (\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\u003eSRC, a nRTKs, was the first proto-oncogene to be discovered and its aberrant activation has been found in several solid tumours, including BC (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Notably, the \u003cem\u003eSRC\u003c/em\u003e gene is rarely mutated or amplified in cancer and its hyperactivation is mainly due to the constitutive activation of RTKs, which occurs in a large majority of tumours (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In this regard, SRC is considered a common node of various RTKs, including MET, culminating in the deregulation of different downstream signalling pathways (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we provide new evidence that MET and SRC PTKs promote NRF2 expression and activation in human and murine TNBC cellular models. We also demonstrate that pharmacological inhibition of MET, SRC or NRF2 enhances the sensitivity of TNBC cellular models and TNBC patient-derived organoids (PDOs) to Paclitaxel treatment, pointing to this newly identified signalling cascade as a valuable target for the development of more effective combinatorial therapies for TNBC patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eElevated MET/EGFR, and NRF2 levels predict poor prognosis in TNBC\u003c/h2\u003e \u003cp\u003eReceptor tyrosine kinases (RTKs) are frequently overactivated in many tumours, including TNBC, and their constitutive activation results in aberrant signalling and deregulation of many transcription factors (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To test whether RTKs may play a role in NRF2 hyperactivation in TNBC, we first took advantage of the TCGA database, to perform a correlation analysis in breast cancer (BC) patients, irrespective of the subtype. As show by the Spearman correlation analysis, the expression levels of \u003cem\u003eNFE2L2\u003c/em\u003e (NRF2 gene name, here referred \u003cem\u003eNRF2\u003c/em\u003e) positively correlate with the expression levels of several RTKs in BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Of note, by evaluating the expression levels of different RTKs among the main four BC subtypes (Luminal A, Luminal B, HER2+, TNBC), we found that TNBC patients exhibit higher levels of \u003cem\u003eMET\u003c/em\u003e and \u003cem\u003eEGFR\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), a feature not observed for other RTKs such as \u003cem\u003eAXL\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e and \u003cem\u003ePDGFRA\u003c/em\u003e (Fig.\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). To further investigate this issue, and to get more insights on its relevance in TNBC, we evaluated whether the relative expression levels of different RTKs and \u003cem\u003eNRF2\u003c/em\u003e influence the survival rates of TNBC patients. By using the GSE31519 dataset (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), we found a significant decrease in overall survival probability in patients who simultaneously express higher levels of \u003cem\u003eMET/NRF2\u003c/em\u003e or \u003cem\u003eEGFR/NRF2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Of note, a similar analysis performed in non-TNBC patients does not show any significant variation (Fig.\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). By contrast, TNBC patients co-expressing high levels of \u003cem\u003eAXL/NRF2, TGFBR1/NRF2\u003c/em\u003e or \u003cem\u003ePDGFRA/NRF2\u003c/em\u003e were not characterized by alterations in overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). These data highlight a possible functional link between MET/EGFR and NRF2 in TNBC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMET targeting affects NRF2 signalling in TNBC murine model\u003c/h3\u003e\n\u003cp\u003eTo further investigate the significance of the interplay between RTKs and NRF2 in TNBC, we focused our studies on the MET receptor. We took advantage of a unique mouse model, the \u003cem\u003eMMTV-R26\u003c/em\u003e\u003csup\u003e\u003cem\u003eMet\u003c/em\u003e\u003c/sup\u003e mice, in which a slight increase in the expression levels of the \u003cem\u003ewild-type\u003c/em\u003e form of MET in the mammary gland leads to the spontaneous development of BC (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This murine model represents an ideal system to investigate the role of MET in TNBC as all the developed tumours have been previously characterized as TNBC (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Furthermore, mammary gland tumour (MGT) cell lines that recapitulate the heterogeneity of the developed TNBC tumours have also been generated and characterised (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Immunoblotting analysis performed using a phosphospecific antibody (pY\u003csub\u003e1234/35\u003c/sub\u003eMET), which selectively recognizes the activated form of MET, showed the activation of MET in all four tumorigenic MGT cell lines (MGT-4, MGT-9, MGT-11, and MGT-13) compared to the non-tumorigenic MGT-2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Notably, the tumorigenic MGT cells also showed higher levels of NRF2 expression suggesting that MET hyperactivation sustains NRF2 signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Consistent with this hypothesis, pharmacological inhibition of MET with PHA-665752, a specific inhibitor of MET activity, significantly decreased total NRF2 protein levels in MGT-13 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In addition, immunofluorescence and subcellular fractionation analyses showed that PHA-665752 treatment inhibits MET activity and more importantly, caused a significant decrease of NRF2 nuclear staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Lastly, MET inhibition hampered NRF2 transcriptional activity, as indicated by the reduced expression of well-known NRF2 target genes such as \u003cem\u003esequestome-1 (SQSTM1), heme oxygenase 1 (HMOX1), NAD(P)H quinone dehydrogenase 1 (NQO1), glutamate-cysteine ligase catalytic subunit (GCLC)\u003c/em\u003e and \u003cem\u003esolute carrier family 2 (SLC2A1)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). To further strengthen the link between MET and NRF2-dependent transcriptional activity, we performed a transcriptomic analysis on MGT-13 cells treated or not with PHA-665752. As expected, the treatment efficiently inhibits MET activity (Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Principal Component Analysis (PCA) shows that biological replicates of control and PHA-665752-treated samples are highly reproducible and segregate distinctly, according to their transcriptomic profile (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). A total of 24,411 genes were analysed and, among them, 662 genes exhibiting marked downregulation (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.7; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) and 760 upregulation (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;\u0026ge;\u0026thinsp;0.7; \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e) upon PHA-665752 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). Coherently with MET inhibition, we observed a strong deregulation of pathways related to migration and invasion of cancer cells (i.e. Mucine type O-glycan biosynthesis, ECM-receptor interaction and focal adhesion), and pathways related to inflammation (i.e. IL-17 signalling pathway and cytokine-cytokine receptor interaction) (Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD). However, we noticed also an upregulation of pathways involved in resistance mechanisms or pathway linked to tumour suppressor functions (Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE). In searching for a link between MET and NRF2, we next performed enrichment analysis aimed at defining those transcription factors (TFs) modulated by MET inhibition. Interestingly, NRF2 was identified as one of the most downregulated TF by the PHA-665752 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), further confirming its cooperative role with MET. Despite a moderate downregulation of the TF itself (Log\u003csub\u003e2\u003c/sub\u003eFC = -0.56 compared to Ctrl) (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eF\u003c/b\u003e), the activation z-score of NRF2 (\u0026lt;-2) suggests that its target genes are significantly altered by the treatment. Particularly, 59 NRF2 downstream genes directly regulated by the TF were employed to calculate its activation z-score. Two clusters can be clearly distinguished, with 34 genes downregulated as a consequence of NRF2 downregulation, and 25 upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Overall, these results demonstrate that MET sustains NRF2 signalling in TNBC murine models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePharmacological inhibition of MET perturbs NRF2 signalling in human TNBC cell lines and reduces their clonogenicity potential\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe above outcomes drove us to further explore the interplay between MET and NRF2 in human cellular models. We took advantage of the gastric tumour cell line GTL-16 characterized by \u003cem\u003eMET\u003c/em\u003e amplification and overexpression and known to be addicted to MET signalling (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Indeed, immunoblotting analysis showed that, also in this system, MET inhibition by PHA-665752 strongly reduced NRF2 expression (Fig.\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). This was accompanied by a downregulation of several NRF2 target genes, as shown by qRT-PCR (Fig.\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB), further strengthening the solidity of the newly identified axis MET-NRF2, even in a different cancer type. Next, we focused on human BC cellular models, utilizing cell lines belonging to different subtypes: T47D (Luminal-A), MDA-MB-361 (Luminal-B), SKBR3 (HER2+), MDA-MB-231 and BT-549 (TNBC). As shown by immunoblotting analysis, TNBC cell lines express higher levels of MET and NRF2. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Importantly, immunofluorescence analyses of MDA-MB-231 and BT-549 cells revealed a decrease in NRF2 nuclear intensity upon PHA-665752 treatment \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC). Moreover, NRF2 transcriptional activity is affected upon MET inhibition in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate whether pharmacological targeting of the MET-NRF2 axis may functionally affect human TNBC cells, we performed clonogenic assays. Pharmacological inhibition of MET with PHA-665752 and NRF2 with ML-385 (a specific inhibitor of NRF2 activity) significantly reduced the number of colonies formed by MDA-MB-231 and BT-549 cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E), but not by MCF10-A (Fig.\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD), a non-tumorigenic human mammary cell line (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Collectively, these data suggest that MET activity regulates NRF2 pathway also in human cellular models and that this axis represents a specific therapeutic target in TNBC.\u003c/p\u003e\n\u003ch3\u003eMET and NRF2 targeting enhances sensitivity to Paclitaxel in TNBC cell lines\u003c/h3\u003e\n\u003cp\u003eThe absence of ER, PR and HER2 prevents TNBC patients from responding to hormone therapy or HER2-targeted drugs, significantly limiting their treatment options to chemotherapy, with or without immunotherapy, radiotherapy, and surgery (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, high heterogeneity and resistance to chemotherapy characterize many TNBC cases, resulting in poor prognosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). For these reasons, uncovering novel molecular pathways that can be targeted is urgently needed. Data collected until now, drove us to investigate whether MET and NRF2 targeting may increase TNBC sensitivity to chemotherapy, beside reducing clonogenicity potential. To this aim, we performed cell viability assays both in murine and human TNBC models by treating either MGT-13 or BT-549 cells with PHA-665752 (MET inhibitor) or ML-385 (NRF2 inhibitor) in combination with Paclitaxel (PTX), a chemotherapeutic agent commonly used in the clinic for TNBC patients. As shown by the heatmap representing the percentage of survival cells, the combination of PHA-665752 or ML-385 with PTX significantly reduced cell viability in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Notably, by using Combenefit software (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) to calculate the Loewe additivity score, we found that these combination treatments exert a synergistic effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). Subsequently, we investigated cell death induction upon the proposed combined treatments. Flow cytometry analysis of the percentage of MGT-13 cell death revealed a significant increase upon combined treatment with PHA-665752 and PTX (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Likewise, NRF2 inhibition with ML-385 in combination with PTX also significantly increased MGT-13 cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Overall, these data strengthen the idea that targeting MET/NRF2 axis may enhance chemotherapy sensitivity in TNBC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSRC kinase activates NRF2 in TNBC\u003c/h3\u003e\n\u003cp\u003eWe recently reported that SRC (nRTKs) triggers p62 phosphorylation, driving the release of NRF2-KEAP1 interaction resulting in NRF2 hyperactivation and ferroptosis resistance in Glioblastoma (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Of note, RTKs deregulation results in SRC hyperactivation as the latter is part of the signalling cascade activated downstream many RTKs, including MET (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Accordingly, we could show that MET inhibition impinged on SRC phosphorylation in human TNBC cells, as revealed by pY\u003csub\u003e416\u003c/sub\u003eSRC antibody confirming its activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We therefore asked whether SRC activity could sustain NRF2 transcription factor signalling. Pharmacological inhibition of SRC with Dasatinib (DAS) slightly but significantly reduced total NRF2 protein expression levels in human TNBC cell lines (MDA-MB-231 and BT-549; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). More importantly, immunofluorescence analyses showed that treatment with DAS decreases the nuclear accumulation of NRF2 in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB). Subcellular fractionation experiments also confirmed that DAS treatment reduces NRF2 nuclear levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Coherently, qRT-PCR experiments showed that SRC pharmacological inhibition strongly affects NRF2 transcriptional activity, as indicated by the downregulation of several NRF2 target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the significance of SRC-NRF2 interplay in TNBC, we analysed \u003cem\u003eSRC\u003c/em\u003e expression by querying TCGA datasets of BC samples. Among BC subtypes, TNBC samples exhibited the highest levels of \u003cem\u003eSRC\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Next, we evaluated the overall survival probability of TNBC patients stratified by different levels of \u003cem\u003eSRC\u003c/em\u003e and \u003cem\u003eNRF2\u003c/em\u003e, using the GSE31519 cohort (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Interestingly, patients expressing simultaneously higher levels of \u003cem\u003eSRC\u003c/em\u003e and \u003cem\u003eNRF2\u003c/em\u003e are characterized by poorer clinical outcomes compared to patients with other expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). In contrast, the same analysis performed in non-TNBC patients revealed no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eAll together, these data indicate that SRC kinase sustains NRF2 activity in TNBC and suggest that its targeting may also enhance Paclitaxel efficacy.\u003c/p\u003e\n\u003ch3\u003eTargeting PTKs and NRF2 improve chemotherapy efficacy in TNBC patient-derived organoids\u003c/h3\u003e\n\u003cp\u003eTo validate our findings in TNBC models that are closer to the clinical setting, we employed patient-derived organoids (PDOs). PDOs are human models that recapitulate the genetic and phenotypic feature of the tumour of origin, including the response to treatments (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). We took advantage of three PDOs, named PDO-21, PDO-43, PDO-46 (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e), derived from tumours that do not exhibit amplification or mutations in the \u003cem\u003eSRC\u003c/em\u003e, \u003cem\u003eNFE2L2\u003c/em\u003e or \u003cem\u003eKEAP1\u003c/em\u003e genes, previously characterized as faithful TNBC models (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We first determined the IC\u003csub\u003e50\u003c/sub\u003e of PHA-665752, DAS, ML-385, and PTX in the three PDOs (Fig.\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA-B) and then tested the effectiveness of combinatorial treatments with these drugs. We found that the combination of PTX with PHA-665752, ML-385 or DAS significantly reduced viability of PDOs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C), consistent with what was observed in TNBC cell lines. Additionally, the proposed combinations reduce the size of the PDOs that survived to the treatments, although with different efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E, \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eS5\u003c/span\u003eC-D). All together, these data suggest that targeting MET, SRC, or NRF2 can improve the chemotherapy efficacy of PTX and represents a promising therapeutic strategy for ameliorating TNBC therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTriple-Negative Breast Cancer (TNBC) is a highly aggressive and heterogeneous disease, characterized by the absence of target therapy and poor prognosis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In this regard, the identification of new molecular pathways that drive TNBC resistance to therapy is urgently needed.\u003c/p\u003e \u003cp\u003eNRF2 transcription factor, the master regulator of oxidative stress response (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), is aberrantly activated in several tumors including TNBC and it is associated with radio- and chemoresistance mechanisms(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNRF2 is considered an \u0026ldquo;undruggable\u0026rdquo; protein due to the lack of active sites or allosteric pockets and for this reason studies aimed to uncover those molecular mechanisms that may affect NRF2 expression and activity are urgently needed (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Most of the NRF2 inhibitors used for research purposes are natural plant-derived compounds, like polyphenols. Although these are commonly referred to as antioxidants, some of them inhibit NRF2-dependent expression of cytoprotective genes (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, the inhibitory effect of these natural compounds is still controversial and, although highly safe, they have a weak specificity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). So far, ML-385 is the only selective inhibitor currently available that targets the ability of NRF2 to dimerize with MAFG, but unfortunately, it is not approved for clinical use (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). More recently, it has been reported a novel NRF2 inhibitor, ARE-PROTAC chimeric molecule, which selectively degrades NRF2-MAFG heterodimer via ubiquitin-proteasome system (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), However its potential use in clinics has not been investigated yet.\u003c/p\u003e \u003cp\u003eThe identification of the molecular mechanisms and signalling responsible for NRF2 pathway activation in cancer may represent an alternative strategy to uncover novel potential molecular targets to dampen NRF2 and ameliorate the therapeutic response.\u003c/p\u003e \u003cp\u003eFrequently, genetic mutation on NRF2 (\u003cem\u003eNFE2L2\u003c/em\u003e) or its major negative regulator \u003cem\u003eKEAP1\u003c/em\u003e are responsible for NRF2 hyperactivation in cancer (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, some tumors show NRF2 deregulation independently of these mutations highlighting the ability of cancer cell to rewire signalling pathways to their own advantage (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Remarkably, it has been recently reported that cysteine mediated NRF2 activation represents a novel survival mechanism for TNBC (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe deregulation of Receptor Tyrosine Kinases (RTKs) and non-Receptor Tyrosine Kinases (nRTKs) is a common feature in various type of cancer, including TNBC. Physiologically, they are tightly regulated because of their essential role in cellular proliferation, survival and migration and their constitutive activation in cancer has been exploited as a potential vulnerability to selectively hit cancer cells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). To this purpose, different Tyrosine Kinases Inhibitors (TKIs) have been developed and several of them are widely used in the clinic (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we first identify a link between aberrant RTKs signalling and NRF2 upregulation in TNBC and then we demonstrate that its targeting may ameliorate the therapeutic response to standard therapy.\u003c/p\u003e \u003cp\u003eWe focused our attention on MET which is overexpressed in about 40% of BC patients and in more than 50% of TNBC (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Importantly, its overexpression correlates with tumour progression and aggressiveness (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Using murine and human TNBC cellular models, we demonstrated that MET sustains NRF2 expression, nuclear localization and transcriptional activity. NRF2 regulates more than 200 target genes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and here we could show that MET inhibition decreases NRF2 transcriptional activity, as observed by the downregulation of the expression of some canonical targets, like \u003cem\u003eGCLC\u003c/em\u003e, \u003cem\u003eMAFG, SLC7A11\u003c/em\u003e and \u003cem\u003eGSR\u003c/em\u003e. Furthermore, transcriptomic analysis identified NRF2 among those TFs whose activity is significantly decreased upon MET inhibition by PHA-665752 treatment.\u003c/p\u003e \u003cp\u003eThe connection between MET and NRF2 has never been investigated in TNBC so far. In agreement with our data, it has been shown that MET-NRF2-HO1 axis has a fundamental role in reducing oxidative stress in renal cancer(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) suggesting the possibility of a similar role also in TNBC.\u003c/p\u003e \u003cp\u003eAs pointed out before, the idea of NRF2 targeting to overcome cancer cell resistance to therapy is well supported by the literature (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Here we show that treatment with NRF2 specific inhibitor ML-385 strongly affects human TNBC cellular clonogenicity potential, thus supporting the hypothesis that NRF2 targeting may represent a valuable strategy to enhance the therapeutic response. Remarkably, the same treatment has no effects on the non-tumorigenic human mammary cell line MCF10-A, highlighting the specificity of targeting NRF2 in cancer cells.\u003c/p\u003e \u003cp\u003eInterestingly, it is well known that standard chemotherapeutic agents such as Paclitaxel (PTX) strongly cause ROS accumulation leading to NRF2 activation, probably responsible for cell protection and therefore resulting in tumour chemoresistance (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Importantly, it has been shown that NRF2 targeting with naturally derived extracts increases sensitivity to Paclitaxel \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e prostate cancer models (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Here, we demonstrate that ML-385 significantly ameliorates cell sensitivity to Paclitaxel treatment in TNBC cellular models as well as in Patient Derived Organoids (PDOs) established from TNBC patients, which represent a more reliable model that recapitulate the heterogeneity of TNBC. More importantly, our study supports the repositioning of MET inhibitors as a valuable strategy to hit NRF2 signalling and therefore sensitize cancer cells to Paclitaxel standard treatment. Interestingly, we highlight a positive correlation between several RTKs expression and NRF2 and show a significant correlation between high levels of EGFR and NRF2 and a worse prognosis in TNBC patients. Importantly, EGFR is often deregulated in TNBC and several EGFR TKIs have been shown to ameliorate the therapeutic response (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In this regard, the co-targeting of both MET and EGFR could represent another valuable strategy to improve NRF2 targeting and counteract TNBC (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe molecular mechanism that allows MET to modulate NRF2 deserves further elucidation. Notably, SRC kinase is an important downstream mediator of several RTKs including MET (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). SRC is upregulated in TNBC and its activation can cause the phosphorylation of several downstream substrates including transcription factors (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Here, we also demonstrate that the pharmacological inhibition of SRC activity, using Dasatinib, reduced NRF2 protein levels, nuclear localization and activity in human TNBC cell lines similarly to what previously reported in GBM cells (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Consistently, TNBC patients present high levels of \u003cem\u003eSRC\u003c/em\u003e expression and, intriguingly, the simultaneous expression of high levels of \u003cem\u003eSRC\u003c/em\u003e and \u003cem\u003eNRF2\u003c/em\u003e result in a worse clinical outcome. Moreover, SRC inhibition sensitize PDO to Paclitaxel similarly, to what observed upon NRF2 and MET inhibition.\u003c/p\u003e \u003cp\u003eGiven these evidence, we can therefore speculate that, also in our models, MET sustains SRC activity and as a consequence upregulate NRF2 expression and signalling. However, at this stage we cannot exclude that MET as well as other RTKs may also impinge on NRF2 independently of SRC. Future experiments will clarify this issue. Moreover, it will be interesting to extend our studies to other TKIs, to uncover more effective compounds and/or other PTKs that may represent valuable targets to modulate NRF2 and therefore be exploited to ameliorate TNBC sensitivity to standard therapeutic approaches.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our work highlights for the first time the existence of a novel functional interplay between MET and NRF2 in TNBC. Importantly, we demonstrate that the targeting of this axis ameliorates the sensitivity to Paclitaxel treatment suggesting that its upregulation may represent a novel signature to improve TNBC patients\u0026rsquo; stratification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eMouse cell lines\u003c/h2\u003e \u003cp\u003eMGT cell lines, derived from \u003cem\u003eMMTV-R26\u003c/em\u003e\u003csup\u003e\u003cem\u003eMet\u003c/em\u003e\u003c/sup\u003e mice (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), were grown in DMEM/F12 (Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s media/F12, 1/1, Sigma-Aldrich) supplemented with: 10% foetal bovine serum (FBS, Sigma-Aldrich), 100 U/mL penicillin, 100 mg/mL streptomycin (P/S, Sigma-Aldrich), L-glutamine (2mM, Sigma-Aldrich), glucose (0,25%, Sigma-Aldrich), insulin (10 \u0026micro;g/mL, Sigma-Aldrich), transferrin (10 \u0026micro;g/mL, Sigma-Aldrich), sodium selenite (5 ng/mL, Sigma-Aldrich), hydrocortisone (0,5 \u0026micro;g/mL, Sigma-Aldrich), EGF (20 ng/mL, Sigma-Aldrich), and HGF (10 ng/mL, Thermo Fisher Scientific), at 37\u0026deg; in a 5% of CO₂ atmosphere. All MGT cells are routinely tested and confirmed negative for \u003cem\u003eMycoplasma\u003c/em\u003e contamination.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHuman cell lines\u003c/h2\u003e \u003cp\u003eHuman non-TNBC (T47D, MDA-MB-361, and SKBR3) and TNBC cell lines (MDA-MB-231) were cultured in RPMI-1640 (Sigma-Aldrich) supplemented with 10% FBS, L-glutamine (2mM,), and P/S. BT-549 (TNBC cell line) were cultured in DMEM with 10% FBS, L-glutamine (2mM), and P/S. MCF10-A cells, a non-transformed human mammary epithelial cell line, were grown in DMEM/F12 supplemented with horse serum (5%, Sigma-Aldrich), EGF (20 ng/mL), hydrocortisone (0,5 \u0026micro;g/mL), cholera toxin (100 ng/mL, Sigma-Aldrich), insulin (10ng/mL), and P/S. All cell lines are cultured at 37\u0026deg; in a 5% of CO₂ atmosphere and negatively tested for \u003cem\u003eMycoplasma\u003c/em\u003e contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer patient-derived organoids (BC-PDO) culture\u003c/h2\u003e \u003cp\u003eBC-PDOs were obtained as previously described (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Briefly, breast cancer tissue was finely chopped, washed with 10 mL AdDF+++ (Advanced DMEM/F12 containing 1\u0026times; Glutamax, 10 mM HEPES, and antibiotics), and digested in 10 mL of AdDF+++ containing 4 mg/mL collagenase II and 5 \u0026micro;M RHO/ROCK pathway inhibitor (Y-27632, Tocris) on an orbital shaker at 37\u0026deg;C for 1\u0026ndash;2 hours. The digested tissue suspension was mechanically disrupted by pipetting up and down 10 times, passed through a 100 \u0026micro;m filter pre-coated with AdDF+++ containing 0.1% BSA, and centrifuged at 490 \u0026times; g for 5 minutes. The pellet was resuspended in 10 mL AdDF+++ and centrifuged again.The pellet was incubated with 2 mL red blood cell lysis buffer for 5 minutes at room temperature to eliminate erythrocytes, followed by washing with culture medium and pelleting at 490 \u0026times; g. The resulting pellet was resuspended in 10 mg/mL of cold Cultrex growth factor-reduced BME type 2, and 40 \u0026micro;L drops of the BME-cell suspension were allowed to solidify on pre-warmed 24-well suspension culture plates at 37\u0026deg;C for 30 minutes. After polymerization, 400 \u0026micro;L of BC organoid medium (AdDF+++, 0.5 mM A8301, 1\u0026times; B27, 5 ng/mL EGF, 100 nM β-estradiol, 5 ng/mL FGF7, 20 ng/mL FGF10, 10 \u0026micro;M forskolin, 5 nM heregulin β1, 0.5 \u0026micro;g/mL hydrocortisone, 1.25 mM N-acetylcysteine, 10 mM nicotinamide, 100 ng/mL noggin, 100 \u0026micro;g/mL primocin, 10% R-spondin-conditioned medium, 1 mM SB202190, 5 \u0026micro;M Y-27632) was added to each well, and the plates were placed in a humidified incubator at 37\u0026deg;C with 5% CO2. The medium was refreshed every 3 days.\u003c/p\u003e \u003cp\u003eOrganoids were passaged every 1\u0026ndash;2 weeks by incubation with Cultrex Organoid Harvesting Solution for 45 minutes at 4\u0026deg;C to digest the BME, and then dissociated by enzymatic digestion with TrypLE Express (Gibco) for 7\u0026ndash;10 minutes at 37\u0026deg;C, followed by pipetting up and down several times. TrypLE Express activity was blocked by adding 10 mL of AdDF+++ and centrifuging at 490 \u0026times; g. Organoid fragments were resuspended in cold BME and re-seeded as described above at a suitable ratio (1:1 to 1:6), allowing the formation of new organoids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAntibodies and drugs\u003c/h2\u003e \u003cp\u003ePrimary antibodies used are as follows: anti-NRF2 (12721S; Cell Signalling Technology), anti-phospho-SRC (Tyr416) (2101S; Cell Signalling Technology), anti-SRC (2108S; Cell Signalling Technology), anti-phospho-MET (Tyr1234/35) (3126S; Cell Signalling Technology), anti-MET (3127S; Cell Signalling Technology), anti-vinculin (13901T; Cell Signalling Technology), anti-lamin A/C (sc-376248; Santa Cruz Biotechnology), anti-GAPDH (sc-47724; Santa Cruz Biotechnology), anti-β-Actin (3700T, Cell Signalling Technology);\u003c/p\u003e \u003cp\u003ePHA-665752 (S1070; TargetMol), Dasatinib (CDS023389; Sigma-Aldrich), ML-385 (S8790; Selleckchem), Paclitaxel (T7191; Sigma-Aldrich); Cisplatin (T1564; TargetMol).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eProtein extract, nuclei/cytoplasm fractionation and western blot analyses\u003c/h2\u003e \u003cp\u003eTotal protein lysates were prepared using Buffer A (10 mM Hepes [pH 7.9], 10 mM KCl, 1.5 mM MgCl2, 0.5 mM DTT, 0.1% NP-40) supplemented with 10 mg/ml Protease Inhibitor Cocktail-1 (P2714; Sigma-Aldrich), 10 mg/mL TPCK, 1mM phenylmethylsulfonyl fluoride, 25mM NaF, 1mM sodium orthovanadate, 25 mM β-glycerophosphate. Lysates were incubated for 20 min on ice, then sonicated, and centrifuged at 12,000g at 4\u0026deg;C for 20 min. For nuclei and cytoplasm fractionation, cells were lysed in Buffer A (without NP-40) for 20 min on ice. NP-40 was then added to a final concentration of 0.1%. Next, nuclei were separated from the cytoplasm by centrifugation at 12,000g at 4\u0026deg;C for 30 sec. The cytoplasm was harvested and the nuclear pellet was lysed in Buffer A supplemented with 0.05% NP-40 for 20 min on ice, then sonicated and centrifuged at 12,000g for 20 min. For western blot, 30\u0026ndash;80 \u0026micro;g of proteins were separated by SDS-PAGE, blotted on nitrocellulose membrane and incubated with specific antibodies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence\u003c/h2\u003e \u003cp\u003eCells were seeded on coverslips and grown at 37\u0026deg;C in a 5% CO₂ atmosphere. After treatments, cells were washed with 1X PBS and then fixed with 4% PFA for 15 min at room temperature (RT), permeabilized using PBS/Triton X-100 0.3% solution for 10 min, blocked with BSA 3% in PBS solution for 1h, and then incubated with primary antibodies (NRF2 1:50) overnight in a humid chamber at 4\u0026deg;C. Secondary antibodies (1:500, Thermo Fisher Scientific) were applied for 1h at RT, and nuclei staining were performed using Hoechst 33342 (Thermo Fisher Scientific). The images were acquired with ZEISS fluorescence microscopy and analysed with ImageJ Fiji version 2.3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReal time PCR\u003c/h2\u003e \u003cp\u003eCells were homogenized with TRI Reagent (Themo Fisher Scientific) and RNA was extracted using the manufacturer\u0026rsquo;s protocol. One microgram of total RNA was retrotranscribed in cDNA using SensiFAST cDNA Synthesis KIT (Bioline). Specific pair of primers were designed and tested with primerBLAST. RT-PCR were performed using the SensiFAST Syber Low-ROX kit (Bioline) QuantStudio 3 RT\u0026ndash;qPCR (Applied Biosystems). Data were analyzed using the second-derivative maximum method. The fold change in mRNA levels was compared to the control condition after normalization to the actin housekeeping gene.\u003c/p\u003e \u003cp\u003eList of human primers:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGENE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFORWARD PRIMER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREVERSE PRIMER\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eACTIN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GGCCGAGGACTTTGATTGCA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-GGGACTTCCTGTAACAACGCA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSQSTM-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GGGAAAGGGCTTGCACCGGG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;CTGGCCACCCGAAGTGTCCG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHMOX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CACAGCCCGACAGCATGCCC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-GCCTTCTCTGGACACCTGACCCT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNQO1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GGTTTGGAGTCCCTGCCATT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-CCTTCTTACTCCGGAAGGGTC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGCLC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CGCACAGCGAGGAGCTTCGG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-CTCCACTGCATGGGACATGGTGC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSLC2A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-TCACTGTCGTGTCGCTGTTT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-GGCCACGATGCTCAGATAGG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e18S\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GGCCGTTCTTAGTTGGTGGA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-TCAATCTCGGGTGGCTGAAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eList of murine primers:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGENE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFORWARD PRIMER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREVERSE PRIMER\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CACACCCGCCACCAGTTCGC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-TTGCACATGCCGGAGCCGTT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSqstm-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GCTCTTCGGAAGTCAGCAAACC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-GCAGTTTCCCGACTCCATCTGT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHmox1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CACTCTGGAGATGACACCTGAG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-GTGTTCCTCTGTCAGCATCACC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNqo1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-TGGCCGATTCAGAGTGGCATCCT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-TGCATGCGGGCATCTGGTGG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGclc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-ACACCTGGATGATGCCAACGAG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-CCTCCATTGGTCGGAACTCTAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSlc2a1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GCTTCTCCAACTGGACCTCAAAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026rsquo;-ACGAGGAGCACCGTGAAGATGA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic experiment and data analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using Qiazol (Qiagen, IT), purified from DNA contamination through a DNase I (Qiagen, IT) digestion step and further enriched by Qiagen RNeasy columns for gene expression profiling (Qiagen, IT). Quantity and integrity of the extracted RNA were assessed by NanoDrop Spectrophotometer (NanoDrop Technologies, DE) and by Agilent TapeStation (Agilent Technologies, CA), respectively. RNA libraries for sequencing were generated using the same amount of RNA for each sample according to the Illumina Stranded Total RNA Prep kit with an initial ribosomal depletion step using Ribo-Zero Plus (Illumina, CA). The libraries were quantified by qPCR and sequenced in paired-end mode (2x100 bp) with NovaSeq 6000 (Illumina, CA). For each sample generated by the Illumina platform, a pre-process step for quality control was performed to assess sequence data quality and to discard low-quality reads.\u003c/p\u003e \u003cp\u003eRNA-seq data were analyzed with the nf-core tool version 3.3, using the \u0026ldquo;rnaseq\u0026rdquo; pipeline and default parameters (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), aligning reads to the reference genome for Mus musculus GRCm38. The output from nf-core was then used as input for the R package DESeq2 (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) to calculate differential expression. Genes with total raw counts across all samples below 50 were excluded to reduce background noise and improve the robustness of differential expression analysis. Normalized counts were transformed using the variance stabilizing transformation (VST function). Pathway ontology analysis was performed with ShinyGO version 0.8 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), using the list of upregulated (log2FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;0.7 compared to control) or downregulated (log2FoldChange \u0026lt; -0.7) genes with pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as input. Transcription factors analysis, including calculation of activation z-score, was conducted with Qiagen IPA (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), selecting only those transcription factors with activation z-score greater than 2 or lower than \u0026minus;\u0026thinsp;2. The list of genes affected by NRF2 expression was also derived from IPA. Volcano plot and bar plots were generated with the ggplot package in R, PCA was performed using the plot PCA function from DESeq2, and heatmaps were created with the ComplexHeatmap package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCell viability assay\u003c/h2\u003e \u003cp\u003eMGT-13 and BT-549 cells were seeded in 96-well plates at 3,000 cells per well (150\u0026micro;l media/well). After 24h, cells were treated with single or combined drugs at the indicated concentrations. Cell viability was detected using Cell Counting Kit-8 (CCK-8, TargetMol) reagent after 72h of treatment and then the absorbance was read using TECAN Infinitive-200 PRO spectrophotometer. The results represent the mean value of at least three independent experiments done in triplicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer patient-derived organoids (BC-PDO) viability assays\u003c/h2\u003e \u003cp\u003eFor PDO viability assays, organoids were harvested and dissociated as previously described and then resuspended in 2% BME growth medium before seeding in 100 \u0026micro;L volumes on BME-precoated 96-well plates. After 24 hours, organoids were treated with the indicated drugs. After 5 days, the plate was imaged at 10\u0026times; magnification using an IncuCyte SX5 live-content imaging system (Essen Bioscience) at 37\u0026deg;C with 5% CO2. Viability was assessed using the CellTiter-Glo\u0026reg; Luminescent Cell Viability Assay (Promega) with a microplate reader (Spark, Tecan). Drug dose\u0026ndash;response curves were visualized using linear regression analysis in GraphPad Prism 9, and half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) values were determined from the fitted curves. PDOs were classified as small, medium and large on their diameter measured manually drawing lines using ImageJ software. To calculate the organoid diameter range, we subtracted the diameter of the smallest organoid from the diameter of the largest organoid and divided this value by 3. Then, the obtained value was used to determine the diameter range for small, medium and large organoids (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eClonogenic assay\u003c/h2\u003e \u003cp\u003eHuman TNBC cells were seeded in 35mm dishes at 1,000 cells per well and incubated at 37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e for colony formation. After 24h, cells were treated with PHA-665752 (2\u0026micro;M) or ML-385 (5\u0026micro;M) for 72h. Each 2 days, the medium was changed until 10\u0026ndash;15 days of growing. Then, colonies were fixed and stained with a solution containing 10% (vol/vol) methanol and 0.5% of crystal violet for 15 min for colony visualization. The stained colonies were counted. The results represent the mean value of at least three independent experiments.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCytofluorimetric analysis\u003c/h2\u003e \u003cp\u003eCell death analysis was evaluated upon combination treatments for 72h by using a CytoFLEX S (Beckman Coulter) instrument. 1x10\u003csup\u003e6\u003c/sup\u003e cells were collected and centrifuged at 300g for 5 min at 4\u0026deg;C and double-stained with Annexin V-APC-propidium iodide (PI) kit according to the manufacturer\u0026rsquo;s instructions (eBioscienceTM Annexin V Apoptosis Detection Kits; Thermo Fisher Scientific). PI was replaced with DAPI due to autofluorescence issues observed with some inhibitors. Unstained samples were used as control. Quality control was evaluated using CytoFLEX Daily QC Fluorospheres (Beckman Coulter). FCS files were analyzed using CytExpert version 2.2 software (Beckman Coulter). Cell death was represented as percentage to control condition.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis of publicly available data of BC\u003c/h2\u003e \u003cp\u003eFor comparative gene expression analysis between BC subtypes, transcriptomic data (RNA sequencing) were obtained from The Cancer Genome Atlas (TCGA) database. Patients were stratified based on their estrogen receptor (ER), progesterone receptor (PR), and HER2 status. Gene correlation analysis was conducted using Spearman correlation coefficients, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Volcano plot was done using the VolcaNoseR Rstudio package in R studio.\u003c/p\u003e \u003cp\u003eThe GSE31519 cohort, comprising 579 TNBC patients with corresponding microarray data, was obtained from the Gene Expression Omnibus (GEO) database and used to generate Kaplan-Meier survival curves for TNBC. Patients were classified into high- and low- expression groups based on the median expression levels of the gene of interest. The statistical significance of survival differences was assessed using the log-rank test, and graphical representations were generated using GraphPad Prism software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments represent the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM or \u0026plusmn;\u0026thinsp;SD of at least three independent experiments (biological replicates). Drug interactions were evaluated using Combenefit software, which calculates the synergism score using Loewe additivity mathematical model based on viability parameters (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Negative values indicate antagonism, while positive values indicate additivity or synergism. The synergism score is calculated based on the combination index (CI) used to evaluate drug interactions. CI\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates synergism, CI\u0026thinsp;=\u0026thinsp;1 means additivity and CI\u0026thinsp;\u0026gt;\u0026thinsp;1 means antagonism. Error bars represent SD for immunofluorescence experiments and SEM for all the other techniques. Differences between data populations were assessed according to the two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test (independent samples). For immunofluorescence analysis, an unpaired \u003cem\u003et\u003c/em\u003e test was used when data followed a normal distribution, and the Mann\u0026ndash;Whitney test was applied in other situations. For multiple comparisons, we used the ANOVA test. Results were considered significant if \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 (*), \u003cem\u003eP\u003c/em\u003e\u0026le;0.01 (**), \u003cem\u003eP\u003c/em\u003e\u0026le;0.001 (***), \u003cem\u003eP\u003c/em\u003e\u0026le;0.0001 (****). All statistical analyses were performed using GraphPad Prism software 8.4.2 version.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBC:\u0026nbsp;\u003c/strong\u003eBreast Cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNBC:\u0026nbsp;\u003c/strong\u003eTriple Negative Breast Cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNRF2:\u003c/strong\u003e Nuclear Factor Erythroid 2-related factor 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTKs:\u003c/strong\u003e Protein Tyrosine Kinases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTKIs:\u003c/strong\u003e Tyrosine Kinase Inhibitors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eER:\u003c/strong\u003e Estrogen Receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR:\u003c/strong\u003e Progesterone Receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHER2:\u003c/strong\u003e Human Epidermal Growth Factor Receptor 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEAP1\u003c/strong\u003e:\u0026nbsp;Kelch-like ECH-associated protein 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e: Overall Survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDFS:\u003c/strong\u003e Disease Free Survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRTKs:\u003c/strong\u003e Receptor Tyrosine Kinanes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003enRTKs:\u0026nbsp;\u003c/strong\u003enon-Receptor Tyrosine Kinases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePDOs:\u003c/strong\u003e Patient-Derived Organoids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMGT:\u0026nbsp;\u003c/strong\u003eMammary Gland Tumour\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTFs:\u003c/strong\u003e Transcription Factors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePTX:\u003c/strong\u003e Paclitaxel\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAS:\u003c/strong\u003e Dasatinib\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePHA:\u003c/strong\u003e PHA-665752\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor organoid development, tumor biopsies were collected from patients treated at \u0026lsquo;\u0026lsquo;Fondazione Policlinico Universitario A. Gemelli IRCCS\u0026rsquo;\u0026rsquo; (FPG), Rome, Italy, from May 2020 to October 2022. The protocol was approved by the Institutional Review Board (Protocol ID: 3642) and conducted in accordance with the Helsinki Declaration. All enrolled patients gave their written informed consent for participation. Relevant clinical data were collected and managed using REDCap electronic data capture tools at FPG (https://redcap-irccs.policlinicogemelli.it).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated and analysed during the current study https://urldefense.com/v3/__https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290518__;!!O5Bi4QcV!DnSpwUVkhFP9m6fIVSd0lrfHzAHaOr_b0NUxodpYyxw4-IZ3XNhAcreWudH_GAaWXt8-zaiY8_Ty7E4D8PbGz8KKaw$\u003cbr\u003eis not publicly available before publication, but \u003cstrong\u003ereviewers can gain access to the GEO dataset using the following token: uvcjuymsnxibtwt.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been supported by research grants from Associazione Italiana per la Ricerca sul Cancro AIRC-IG2021-n.26230 to D Baril\u0026agrave; and IG30651 to C Sette; C Cirotti has been supported by AIRC-IG2021-n.26230; I Taddei has been supported by a MUR fellowship to the PhD Program in Cellular and Molecular Biology, Department of Biology, University of Tor Vergata and by AIRC-IG2021-n.26230.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.T. designed and performed most of the experiments, contributed to data analysis, interpretation and wrote the original draft; C.Ci. contributed to data analysis, interpretation and wrote the original draft. O.C. performed the analysis of overall survival and gene expression analyses of BC patients; F.L. and F.M. contributed to work on MMTV-R26\u003csup\u003eMet\u003c/sup\u003e-derived cell lines, designed cell viability experiments, interpretation and edited the original draft; F.F., G.C., F.D.N. and M.F. performed RNA sequencing experiment and data analysis; V.M. and E.C. performed the patients-derived organoids (PDOs) viability experiments and data analysis; A.D.L. provided breast cancer biopsy and relative clinical data; C.S. designed the PDOs experiments, evaluated data and contributed to edited the original draft. D.B. designed the experiments, evaluated the data and wrote the original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the members of our laboratory for critical reading of the article and for helpful discussion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZagami P, Carey LA. Triple negative breast cancer: Pitfalls and progress. npj Breast Cancer. 2022;8:1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrido-Castro AC, Lin NU, Polyak K. Insights into molecular classifications of triple-negative breast cancer: Improving patient selection for treatment. Cancer Discov. 2019;9:176\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasci D, Naro C, Puxeddu M, Urbani A, Sette C, La Regina G et al. Recent Advances in Drug Discovery for Triple-Negative Breast Cancer Treatment. Molecules. 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STAR Protoc Cell Press. 2023;4:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triple Negative Breast Cancer, NRF2, MET tyrosine kinase, Tyrosine Kinase Inhibitors, Therapy resistance, Paclitaxel, Patient-derived organoids","lastPublishedDoi":"10.21203/rs.3.rs-6855159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6855159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Triple-negative breast cancer (TNBC) is a very aggressive and heterogeneous cancer. The lack of effective targeted therapies and frequency of relapse point to the urgent need for identifying molecular vulnerabilities to overcome resistance to chemotherapy.\u003c/p\u003e\n\u003cp\u003eNuclear Factor Erythroid 2-related factor 2 (NRF2) is a transcription factor that plays a central role in response to oxidative stress. Its hyperactivation contributes to metabolic rewiring and resistance to therapy in several tumors including TNBC. Unfortunately, efficient pharmacological approaches that block NRF2 functions are still missing. Protein Tyrosine kinases (PTKs), often overactivated in cancer and influencing several signalling pathways, are promising candidates to explore for their potential impact on NRF2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe link between Receptor Tyrosine Kinases and NRF2 expression and its impact on the survival probability of TNBC and non-TNBC patients were investigated by bioinformatic analyses using TCGA and GEO databases. MET-NRF2 connection was further confirmed by immunoblotting, immunofluorescence, qRT-PCR and RNAseq experiments through the combinatorial use of murine and human TNBC cellular models. The efficacy of combination treatments with Paclitaxel and specific inhibitors of MET-NRF2 signalling was assessed by viability assays and flow-cytometry analyses on TNBC cellular models as well as on TNBC patient-derived organoids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Here, we identify a novel interplay between MET and SRC kinases with NRF2 expression and activity and demonstrate that its targeting enhances the sensitivity to the standard Paclitaxel treatment of TNBC cells and patient-derived organoids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Our study shows that PTKs regulate NRF2 expression and activation in TNBC providing a proof of principle for the ability of Tyrosine Kinase Inhibitors (TKIs) to impinge on NRF2 signalling. Our findings also uncover the value of the MET-SRC-NRF2 axis as exploitable vulnerability in NRF2-hyperactivated TNBC, paving the way for the repositioning of TKIs as modulators of NRF2 signalling.\u003c/p\u003e","manuscriptTitle":"Targeting of a novel interplay between MET Tyrosine Kinase and NRF2 enhances sensitivity to Paclitaxel in Triple Negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 10:37:03","doi":"10.21203/rs.3.rs-6855159/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":"2f38d828-2ef3-4e9a-97c3-8199feaa62d7","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T08:54:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 10:37:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6855159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6855159","identity":"rs-6855159","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}