Artemisia absinthium (wormwood) exhibits anticancer properties by inhibiting proliferation and causing cell death in breast cancer. Targeted drug delivery of A. absinthium nanoformulation using N-isopropyl acrylamide, N-vinyl pyrrolidone, and acrylic acid-based polymeric nanoparticles (NVA-AA NPs) was ensured by utilizing features of the tumor microenvironment, although their mechanism of action involved in cytotoxicity remains unknown.The present study employed nano LC-MS/MS to identify differences in secretory protein expression associated with the treatment of breast cancer cell lines  by NVA-AA NPs for the determination of affected pathways and easily accessible therapeutic targets. Different bioinformatics tools were used to identify signature differentially expressed proteins (DEPs) using survival analysis by GENT2 and correlation analysis between their mRNA expressions and sensitivity toward small-molecule drugs as well as immune cell infiltration by GSCA.Analysis by GENT2 revealed 22 signature DEPs with the most significant change in their expression regulation, namely, gelsolin, alpha-fetoprotein, complement component C3, C7, histone H2B type 1-K, histone H2A.Z, H2AX, heat shock cognate 71 kDa protein, heat shock 70 kDa protein 1-like, cytochrome c somatic, GTP-binding nuclear protein Ran, tubulin beta chain, tubulin alpha-1B chain, tubulin alpha-1C chain, phosphoglycerate mutase 1, kininogen 1, carboxypeptidase N catalytic chain, fibulin-1, peroxiredoxins 4, lactate dehydrogenase C, SPARC, and SPARC-like protein 1. Correlation analysis between their mRNA expressions versus immune cell infiltrates showed a positive correlation with antitumor immune response elicited by these NPs as well as a correlation with drug response shown by the GDSC and CTRP drugs in different cancer cells.Our results suggest that NVA-AA NPs were able to invade the tumor microenvironment; transformed the communication network between the cancer cells; affected potential drivers of microtubular integrity, nucleosome assembly, and cell cycle; and eventually caused cell death. According to an estimation by the American Cancer Society, 1.9 million new cancer cases were diagnosed with 609,360 cancer deaths in the United States in 2022 . Similar2)-based hollow nanoshells have been developed which act as sonosensitizers and carriers of free radical generators or hypoxia-selective cytotoxin, synergistically increasing the therapeutic efficiency of SDT against solid tumors (2 nanospheres (mNiS2 NSs) have been designed that significantly improved the PTT effect by inhibiting tumor growth in vitro and in vivo (Sonodynamic therapy (SDT) is a new therapeutic strategy employing ultrasound for the activation of sonosensitizers that produce reactive oxygen species which could destroy tumor cells; however, a hypoxic tumor microenvironment poses certain limitations to SDT. Nanoplatforms are a widely explored biocompatible delivery system for treating different types of cancer that could considerably increase the efficiency of existing treatment by utilizing the tumor microenvironment. Recently, ultrasmall titanium nitride nanodots (TiNs) and titanium dioxide  is a perennial herb that belongs to the family Asteraceae and is found all over the world such as in the Indian subcontinent. This plant has been used in ethnic medicine due to its several medicinal properties such as antiseptic, antibacterial, antifungal, antiparasitic, antihelminthic, and anti-inflammatory. The anticancer effect of A. absinthium methanolic extract has been studied in-vitro on human breast carcinoma cell lines MDA-MB-231 and MCF-7, depicting its antiproliferative effect mediated via the apoptotic pathway (A. absinthium ethanolic extract-loaded polymeric nanoparticles (NVA-AA) on breast cancer cell lines (MCF-7 and MDA-MB-231) targeting the tumor microenvironment. Targeted drug delivery involves the simultaneous administration of anticancer agents or phytoconstituents with various routes of action in order to combat the aforementioned drawbacks of the majority of anticancer medications. Temperature- and pH-sensitive, self-assembled polymeric nanoparticles, made of N-isopropyl acrylamide (NIPAAM), N-vinyl pyrrolidone (VP), and acrylic acid (AA), are biocompatible and have been used in the past with several drugs  for site-specific delivery of different formulations (A. absinthium extract-loaded polymeric nanoparticles (NVA-AAs) was explored against breast cancer with a proteomic analysis approach to find out their mechanism of action. These NPs were able to cause significant cell proliferation inhibition, cell death, and cell cycle arrest at the G0/G1 phase in MCF-7 as well as MDA-MB-231 cell lines  and is mainly responsible for cell–cell communication. Altered patterns of secretion are closely associated with the growth and progression of cancer as these transformed signals aid the cancer cells to metastasize. Altered cancer secretome tends to have an important role in tumor microenvironment communication as well as the development of chemoresistance . MoreoveSecretome profiling of the cell lines through the proteomics approach helps in the identification and selection of secreted proteins into the extracellular milieu to be utilized as biomarkers or drug targets for therapeutic application. The nano liquid chromatography-tandem mass spectrometry (nano LC-MS/MS) approach has been used previously for secretome profiling of different cell lines that helps in the quantification of hundreds to thousands of secretory proteins present in the media . In the 22.1N-isopropylacrylamide (NIPAAM) was procured from Sigma-Aldrich, USA and recrystallized with N-hexane overnight. N-vinyl 2-pyrrolidone (VP) and acrylic acid (AA) were purchased from Across Organics (USA). Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), antibiotic solution (100×), trypsin (with 0.5% EDTA), and ammonium persulfate (APS) were bought from HiMedia, USA, and ferrous ammonium sulfate (FAS) was from SRL Pvt Ltd. India MCF-7 and MDA-MB-231 cell lines were procured from the National Centre for Cell Science (NCCS), Pune, India.2.2. Artemisia absinthium ethanolic extract was added slowly to the aqueous solution of NPs with continuous vortexing and mild sonication to assist its physical entrapment into the hydrophobic core of NPs.NIPAAM-VP-AA (NVA-AA) polymeric nanoparticles were synthesized based on previous studies , 23, 24.2.32. Cells  were grown onto 96-well plates and treated with an IC50 concentration of A. absinthium ethanolic extract-loaded NIPAAM-VP-AA (NVA-AA) polymeric nanoparticles at 176.83 ± 11.8 μg/ml for MCF-7 and 181.39 ± 23.2 μg/ml for MDA-MB-231  molecular subtype, b) metastatic status, c) best or worst prognosis, and d) response rate to different therapies. Two subtypes were further chosen out of the four existing breast cancer subtypes: the first is luminal A subtype which is ER and/or PR+, HER2−, slow growing with a low-grade, high response rate, and the finest prognosis, and the second was triple-negative breast cancer (TNBC) subtype which is ER−, PR−, and HER2−, aggressive in nature with a low response rate and a variable prognosis. Therefore, MCF-7 and MDA-MB-231 were selected for the luminal A and the TNBC subtype, respectively. MCF-7 and MDA-MB-231 cells were grown in DMEM supplemented with 10% FBS at 37°C and 5% COA-MB-231 . After 22.4Samples were dissolved in 6 M of Gn-HCl  buffer. A 50-µg sample was reduced with 5 mM of TCEP and further alkylated with 50 mM of iodoacetamide. It was further digested with trypsin  for 16 h at 37°C. Digests were cleaned using a C18 silica cartridge to remove the salt and dried using a speed vac. The dried pellet was resuspended in buffer A .2.5QExactive Plus equipped with a nano electrospray ion source. One microgram was loaded on a C18 column 50 cm, 3.0 μm EASY-Spray column (Thermo Fisher Scientific). Peptides were eluted with a 0%–40% gradient of buffer B  at a flow rate of 300 nl/min and injected for mass spectrometry analysis. LC gradients were run for 100 min. MS1 spectra were acquired in the Orbitrap at 70,000 resolution. Dynamic exclusion was employed during data-dependent acquisition (DDA) for 10 s excluding all charge states for a given precursor. This method was chosen to ensure that each fragment obtained can be related back to a specific precursor, as the resulting MS/MS spectrum contains only fragments from the precursor. Moreover, at the same pressure of He gas, the ion trap performs both ion collection and fragmentation in the used dynamic exclusion method. MS2 spectra were acquired at 17,500 resolution. All the processed samples were subjected to mass spectrometry run in technical replicates. Raw files containing mass/charge values were produced for each of the samples. Then, these raw files were analyzed through Thermo Proteome Discoverer (v2.2) against the UniProt Human database. For the SEQUEST and Amanda search, the precursor and fragment mass tolerance were set at 10 ppm and 0.5 Da, respectively. The protease used to generate peptides, i.e., enzyme specificity, was set for trypsin/P (cleavage at the C terminus of “K/R: unless followed by “P”) along with a maximum missed cleavage value of 2. Carbamidomethyl on cysteine was considered as fixed modification, while oxidation of methionine and acetylation of N terminus were both considered as variable modifications for database search. Both peptide spectrum match and protein false discovery rate were set to 0.01 false discovery rate (FDR). Based on UniProt accession number, Pfam, KEGG pathways, and GO annotations were assigned for the list of identified proteins. Differentially expressed proteins (DEPs) were identified by calculating their abundance ratio (treated/untreated) as log2 of fold change.All the experiments were performed quantitatively using the RSLC Nano system  coupled to Thermo Fisher-2.62.6.1http://gent2.appex.kr/gent2/) generated by the Affymetrix U133A or U133Plus2 microarray platform [GPL570 platform (HG-U133_Plus_2)] was utilized to find out the signature secretory proteins out of the total identified DEPs from NVA-AA-treated MCF-7 and MDA-MB-231 cell lines  to filter out the most important key proteins (DEPs) affected by the treatment of MCF-7 and MDA-MB-231 cells in comparison to untreated cell lines. Two-sample t-tests were applied, and filtered-out proteins were further verified for their distinct expression pattern across different cancer subtypes varying in estrogen receptor (ER) status. Similarly, survival analysis for the key proteins was further carried out, and Kaplan–Meier (KM) plots with median cutoff were produced to investigate the impact of their gene expression on overall survival (OS) of breast cancer patients, as well as their clinical significance and prognostic value. As MCF-7 and MDA-MB-231 represent luminal A and triple-negative breast cancer (TNBC) subtypes, respectively, therefore, the identified key proteins from MCF-7 and MDA-MB-231 were further compared with the existing expression profile across luminal A and TNBC subtype using GENT2. P-value <0.05 was considered statistically significant for expression as well as survival analysis.A web-based gene expression database called the Gene Expression database of Normal and Tumor tissues 2 (GENT2) (ll lines . Briefly2.6.2http://bioinfo.life.hust.edu.cn/GSCA/#/) (50) of 265 small molecules and 481 compounds toward breast cancer, provided by the Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/) and Cancer Therapeutics Response Portal (CTRP)  datasets, respectively. P-value was adjusted by FDR. Red and blue colors specify positive and negative correlations, respectively, such that a darker color represents a stronger correlation . These analyses would help to predict the sensitivity of MCF-7 and MDA-MB-231 toward NVA-AA NP treatments, which are shared with the list of anticancer drugs provided by the GDSC and CTRP. A positive correlation indicates that a high expression of genes provides a high resistance toward the drug and vice versa.An integrated platform, Gene Set Cancer Analysis (GSCA) (GSCA/#/) , was useP-value was adjusted by FDR. Red and blue colors indicate positive and negative correlations, respectively, with a darker color representing a stronger correlation .GSCA also provides multidimensional genomic data across 33 cancer types from the Cancer Genome Atlas (TCGA). Spearman correlation analysis was applied to estimate the association between signature gene mRNA expression and 24 immune cell infiltrates in BRCA through the ImmuCellAI module of GSCA . Immune 2.6.3https://genecodis.genyo.es/). This tool uses standard hypergeometric or Wallenius’ non-central hypergeometric distribution for enrichment analysis, where hypergeometric or Wallenius tested the raw P-values and corrected them via Benjamini/Hochberg FDR. Different modules, namely, KEGG, Reactome, gene annotation for cellular components, PharmGKB, and DisGeNET, were used to determine the association of these signature genes with other metabolic pathways, cellular components, pharmacogenomics information about drug responses, and other human diseases, respectively. The bar charts were generated, where bar size signifies −log10 and the red intensity signifies the number of genes present  of the selected signature DEPs was done using an online web tool, GeneCodis ( present .2.6.4http://www.pantherdb.org/) online software.The protein–protein interaction (PPI) among significantly differentially expressed signature proteins selected after nano LC-MS/MS analysis was built using the STRING module of Cytoscape version 3.7.1. Proteins of interest were enlisted into the STRING database using their UniProt IDs to generate the interaction between these proteins to highlight the mechanism involved in cytotoxicity caused by NVA-AA treatment and identify secretory protein targets. All the identified proteins were also classified based on their molecular function, biological roles, pathways, cellular components, and protein classes using PANTHER 17.0  Kit. Quantitative PCR of synthesized cDNA was performed on the Bio-Rad real-time PCR cycler using SYBR Green chemistry. The total reaction volume was made 10 μl which included 5 μl of 2× Fermentas Maxima SYBR Green qPCR Master Mix (Thermo Scientific), 0.25 μl of each primer (20 μM) (−ΔΔCT) method. The untreated sample was used as the calibrator/control. HPRT1 served as the normalizer gene.Validation of the expression of selected altered signature DEPs, namely, gelsolin (GSN) (actin-binding protein and tumor suppressor), complement component C3 (C3) (component of innate immune response), and cytochrome c somatic (CYCS) (apoptotic biomarker) due to NVA-AA treatment of MCF-7 and MDA-MB-231 cell lines, was done by quantitation of the corresponding mRNA transcripts using quantitative PCR. The total RNA from the untreated and treated MCF-7 and MDA-MB-231 cell lines was isolated using the RNASure2.8t-test for the identification of differentially expressed secretory proteins among NVA-AA NP-treated and untreated MCF-7 and MDA-MB-231 cell lines using GraphPad Prism software . P-value <0.05 was considered statistically significant.Data were analyzed through unpaired 3Artemisia absinthium ethanolic extract was encapsulated in the hydrophobic core of these polymeric nanoparticles (NVA-AA NPs). Nano LC-MS/MS was employed to quantify the unique proteins secreted in the media of NVA-AA-treated MCF-7 and MDA-MB-231 cell lines. Raw data files obtained from nano LC-MS/MS were analyzed against the human subset of the RefSeq database from NCBI, and the master list of proteins was prepared with their relative quantification. Unique proteins were selected out of the total protein groups and peptide groups identified in Thermo Proteome Discoverer against the Human database based on abundance count (=1). Among these, 65 and 156 proteins were significantly differentially expressed (P < 0.05) in MCF-7 and MDA-MB-231 cell lines  and 110 nm ± 12.6 nm (by TEM)  are involved in apoptosis, e.g., cytochrome c (CYCS), histone H2AX (H2AFX), nucleoside diphosphate kinase A (NME1), and lactotransferrin (LTF); a few are involved in vesicular trafficking, e.g., Rab GDP dissociation inhibitor beta (GDI2), while others are involved in the inhibition of angiogenesis, e.g., pigment epithelium-derived factor (SERPINF1) and thrombospondin-1 (THBS1). Some tumor suppressor genes are also highly upregulated in NVA-AA-treated cells, e.g., inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) and gelsolin (GSN), while proteins involved in the inhibition of cell proliferation and metastasis are also upregulated, e.g., vitamin D-binding protein (GC). However, a few proteins involved in tumorigenesis were downregulated (fold change ≤ −1), e.g., elongation factor 1-alpha 2 (EEF1A2) and elongation factor 1-alpha 1 (EEF1A1). A few proteins belonging to the histone family  had upregulated expression, suggesting that NVA-AA NPs affected the genome integrity of treated MCF-7 cells through nucleosome disassembly as compared with untreated cells. Increased expression of tubulin isoforms  indicates the potential of NVA-AA to hamper the dynamics of microtubule assembly in MCF-7 cells. This could be linked with the downregulation of EEF1A2 and EEF1A1 in the treated cells that are required for cytoskeletal protein rearrangement comprised of actin and tubulins.Similarly, proteins involved in the inhibition of cancer invasion are highly upregulated (fold change ≥ 1) in NVA-AA-treated MDA-MB-231 cells, e.g., lumican (LUM), along with proteins involved in vesicular trafficking, e.g., Rab GDP dissociation inhibitor beta (GDI2). Moreover, proteins involved in proliferation and tumorigenesis were downregulated (fold change ≤ −1) after treatment, e.g., elongation factor 1-alpha 1 (EEF1A1), elongation factor 1-alpha 2 (EEF1A2), peroxiredoxin-4 (PRDX4), GTP-binding nuclear protein Ran (RAN), and alpha-enolase (ENO1), as well as proteins involved in proteosomal degradation, cell adhesion, and migration, e.g., ubiquitin-60S ribosomal protein L40 (UBA52), fibronectin (FN1), and periostin (POSTN). The downregulation of tubulin family genes, TUBA1B, TUBA4A, TUBB, and TUBB8, after NVA-AA NP treatment in MDA-MB-231 suggests a disturbance in tubulin assembly. Heat shock protein HSP 90-beta (HSP90AB1), which is associated with the survival of certain oncogenes, was also significantly downregulated by NVA-AA NPs. Proteins belonging to the family of keratins  were also downregulated in our study.3.23.2.1P-values of these proteins in breast cancer tissues are represented along with their expression after treatment in the affected cell lines in Out of the total proteins, analysis by GENT2 revealed 22 signature DEPs having the most significant change in their expression level after NVA-AA treatment of MCF-7 and MDA-MB-231. Differential expression levels, fold change (log2FC), and the 3.2.2Bubble plots were obtained from GSCA showing the correlation between mRNA of selected signature DEPs and drug response as shown in Thirty CTRP drugs acting as inhibitors of different signaling pathways, namely, compound 23 citrate, GW-405833, PAC-1, indisulam, ISOX, apicidin, belinostat, decitabine, COL-3, CR-1-31B, KPT185, PHA-793887, PL-DI, PX-12, SR-II-138A, ciclopirox, narciclasine, piperlongumine, vorinostat, BRD-K34222889, CD-437, PRIMA-1, chlorambucil, cytarabine hydrochloride, doxorubicin, SB-743921, cerulenin, manumycin A, necrosulfonamide, and vincristine, showed a variable degree of correlation with the mRNA expression of selected signature DEPs . It was observed that the expression pattern of our signature genes was positively correlated  with Tfh, NK, CD4_T, central_memory, cytotoxic, CD8_T, Gamma_delta, NKT, Th1, Exhausted, Th2, iTreg, MAIT, B cell, and Tr1 and negatively correlated  with neutrophil, monocyte, effector_memory, Th17, nTreg, CD8_naive, CD4_naive, macrophage, and DC.The heatmap  in both NVA-AA NP-treated MCF-7 and MDA-MB-231 cell lines, indicating inhibition of cell cycle regulation; however, C3 (P < 0.01) showed a significant upregulation in NVA-AA NP-treated MCF-7 and a non-significant upregulation in NVA-AA NP-treated MDA-MB-231 cell lines, suggesting stimulation of innate immune response by NVA-AA NPs in MCF-7 and MDA-MB-231 cell lines. Moreover, CYCS was upregulated in treated MCF-7 and significantly downregulated (P < 0.05) in treated MDA-MB-231 suggesting induction of caspase-dependent and caspase-independent cell death by NVA-AA NPs.The gene expression analysis of GSN, C3, and CYCS in treated and untreated cell lines was conducted using quantitative real-time PCR Figure 64A. absinthium whole-plant ethanolic extract-loaded NIPAAM-VP-AA (NVA-AA) against the breast cancer cell lines MCF-7 and MDA-MB-231. NVA-AA NPs significantly inhibited cell proliferation and cell viability by inducing apoptosis in the treated cell lines ], modulation of the cell cycle , cellular responses to stimuli , triggering of innate immune response , and neutrophil degranulation .via inhibiting phospholipases C (PLC)/protein kinase C (PKC) signal transduction pathway  is a glycoprotein that functions as a carrier protein for various ligands as well as the regulator of cellular growth of different cell types, which was overexpressed in NVA-AA NP-treated MCF-7 as well as MDA-MB-231 cell lines in our study. According to a study by Sierralta et al., the addition of an active site of AFP consisting of cyclized 9-amino acid peptide (cP) to MCF-7 breast cancer cells and estrogen-dependent ZR75-1 cells inhibited cell proliferation . Therefovia triggering the innate immune system. Thus, our data suggest that cytotoxicity caused by NVA-AA NPs elicited activation of complement cascade due to induced apoptosis.Immune cells are the key players whose pathological implications vary with the heterogeneous pattern of gene expression in cancer initiation, progression, and response to cytotoxic drugs. Complement components are one of the crucial factors in cancer that specifically binds to cells at immune surveillance facilitating the removal of apoptotic cells by macrophages . On the Tubulins are cytoskeletal proteins whose alpha and beta subunits heterodimerize for the microtubule assembly required for cell division. Agents affecting tubulin dynamics have been utilized as an ideal approach for chemotherapeutics , 44. NVAHistone variants including H2AFZ and H2AFX were downregulated in MDA-MB-231, whereas HIST1H2BK was downregulated in both MCF-7 and MDA-MB-231 cell lines after NVA-AA NP treatment. Histones are critical drivers of DNA packaging into the nucleosome which is differentially regulated throughout the cell cycle. H2AFZ is an oncogene that was overexpressed in breast cancer patients with adverse clinical outcomes . SimilarHeat shock proteins (HSPs) are stress-responsive molecules that are required for the stabilization of oncogenes, their transcriptional regulation, protein folding, and cell cycle maintenance . Two sigPhosphoglycerate mutase 1 (PGAM1) is a key metabolic enzyme in the glycolysis pathway whose overexpression is associated with breast cancer. However, Zhang et al. demonstrated the non-metabolic function of PGAM1, which is involved in modulating actin filament organization, cell motility, migration, and enhancing cancer progression . Previouin-vitro investigation by Sheng et al. showed inhibition of cell proliferation, motility, and cell cycle arrest in MDA-MB-231 cell lines by treatment with si-RNA directed against RAN (RAN-si-RNA)  . TherefoKininogen 1 (KNG1) is a crucial component of the blood coagulation system whose expression is lowered in a variety of cancers including breast, ovarian, and prostate. It is known as a metastasis inhibitor, capable of inhibiting angiogenesis, invasion, and migration of human prostate cancer . Downregin vitro, and a low proliferation index was found in fibulin-1-expressing breast cancer tissue samples (Fibulin-1 (FBLN1) is an ECM-associated glycoprotein, localized in the basement membrane and interacting with other components like laminin. FBLN1 has a critical role in epithelial-to-mesenchymal transition (EMT) during cancer due to its capability of regulating cell differentiation, adhesion, migration, and proliferation. High as well as aberrant expression of FBLN1 was assessed in breast carcinoma . However samples . Thus, tA. absinthium extract encapsulated in NVA NPs.Cellular damage caused by oxidative stress and redox status imbalance is closely associated with cancer cell proliferation and survival. Peroxiredoxins 4 (PRDX4) is one of the antioxidant enzymes specifically located in the endoplasmic reticulum (ER) and secreted in the extracellular space. It has been suggested that PRDX4 has a potential role in tumor initiation, progression, chemoresistance against docetaxel, and disease recurrence. High expression of PRDX4 has been reported in a variety of cancers including breast cancer; on the other hand, increased tissue level of PRDX4 was found to be correlated with a better survival rate in breast cancer patients . HoweverSecreted protein acidic and rich in cysteine (SPARC) binds with ECM components and growth factors mediating cell–ECM interaction, modulating different cellular processes, cell microenvironment, adhesion, and drug resistance . ContradLactate dehydrogenase C (LDHC) is an isoform of the lactate dehydrogenase (LDH) family whose elevated expression was observed in serum-derived exosomes as well as tissue of breast cancer patients and associated with adverse clinical outcomes, poor survival, and remission . It is avia modulating direct as well as indirect interactions between other secretory factors  displayed negative correlations with WH-4-023, TGX221, and pazopanib. Thus, their overexpression might increase the sensitivity to NVA-AA treatment similar to WH-4-023, TGX211, and pazapanib treatments to other cancer cells. Moreover, the change in the expression of TUBA1C, PRDX4, LDHC, C7, and KNG1 due to NVA-AA treatment corroborated with the sensitivity provided by CTRP drugs to cancer cells . Thus, dImmune cell infiltration  showed positive correlations with the expressions of the dysregulated signature genes/proteins in NVA-AA-treated MCF-7 and MDA-MB-231 cell lines. These immune cells were reported to be involved in antitumor immune responses and also considered important targets in immunotherapy –90. Howein-vitro study include further experimental validation at the in-vivo level due to variation in the tumor microenvironment, the presence of certain biological barriers at the cell line and organism level, and bioinformatics analysis findings, which may further be comprehended through in-vitro and in-vivo experiments. Remarkably, validating and establishing A. absinthium extract-loaded NVA-AA NPs as the drug carrier ensuring site-specific drug delivery would increase the therapeutic effectiveness and lessen side effects related to the existing anticancer payload associated with currently available treatments. However, the clinical application of NVA-AA NPs against breast cancer requires more vigorous preclinical studies to ensure its efficacy and safety at an individual level. Also, it will be interesting to investigate the effect of these NPs on other cancer types as well.The limitations of our A. absinthium whole-plant extract-loaded NVA-AA polymeric nanoparticles. We can conclude that cytotoxicity by NVA-AA NPs was mainly associated with their capacity to interfere with the cytoskeletal dynamics, cell cycle regulators, cell–cell interaction, intracellular trafficking, cell polarization, and migration of cancer cells, irrespective of the breast cancer subtype.This study has a fundamentally exploratory aim which was focused on the secretome profiling to unravel the altered processes and pathways in MCF-7 and MDA-MB-231 breast cancer cell lines caused due to the cytotoxicity of The datasets presented in this study can be found in online repository named “PRIDE database”, with accession number “PXD042365”.Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.SK did the investigation, data curation, formal analysis, methodology, visualization, validation, writing—original draft, and writing—review and editing. MM was involved in data curation, methodology, validation, and writing—review and editing. IM did the investigation, visualization, and writing—review and editing. SS was involved in the methodology and writing—review and editing. SW did the conceptualization, methodology, supervision, project administration, funding acquisition, validation, visualization, and writing—review and editing. All authors contributed to the article and approved the submitted version.


The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups. Applying thermal proteome profiling to acute B cell childhood leukemia cell lines combined with deep peptide fractionation and a graph-based clustering algorithm allows inference of functional proteoform groups and their association with drug response. Therefore, identification and functional characterization of proteoforms can improve understanding of biological processes in health and disease.Proteins are the functional units expressed from genes and ultimately define the phenotype of cells. Through genomic variation , alternative splicing of transcripts, proteolytic cleavage, post-translational modifications  and protein–protein interactions (PPIs), the complexity of the functional proteome is expanded to millions of proteoforms3, and inference based on peptide-level data from bottom-up proteomics has recently been established using different approaches5. In peptide correlation analysis (PeCorA)4, the pattern across samples of individual peptides mapping to a protein is compared to all other peptides to find differentially abundant peptides that may reflect proteoforms. Correlation-based functional proteoform assessment (COPF)5, however, aims to detect proteoforms supported by multiple peptides using a combination of PeCorA and hierarchical clustering, cutting obtained clusters to obtain a predefined number of proteoforms per protein that are tested for significance and scored by within versus across cluster correlation. However, these approaches, although powerful, can be limited by proteome coverage or by availability and variability of sample conditions that distinguish different proteoforms. Furthermore, proteoforms have been detected representing protein sequence and post-translational modification status differences, but other important variations of functional protein state, including protein complex and metabolite associations, are difficult to distinguish without specific targeted experimental methods and have, therefore, been excluded from identification. Recent initiatives have been proposed to define a human proteoform reference6, and a reference map of proteoforms of human hematopoietic cells has recently been reported7, and additional efforts are underway to address these gaps and improve knowledge of proteoforms.Although global proteoform measurement is critical for achieving full proteome characterization and annotation, its realization is still hampered by technological and analytical limitations. Top-down proteomics enables the precise characterization of proteoforms of individual proteins8 and, more recently, tissues9 by monitoring the changes in the thermal stability of proteins upon drug binding. It is implemented by applying the cellular thermal shift assay (CETSA)10 on a proteome-wide scale using multiplexed quantitative mass spectrometry11. Recent work has shown that TPP can not only inform on drug–target engagement but also on protein–nucleic acid12, protein–protein13 and protein–metabolite interactions14 as well as metabolic pathway activity15 and the functional relevance of post-translational modifications16. Moreover, it has been found that cell-type-specific physiology is reflected in characteristic proteome thermal stability profiles and can be predictive of drug responses17.Thermal proteome profiling (TPP) is a method originally developed for unbiased detection of drug targets in living cells18, we measured thermal stability with unprecedented peptide coverage per gene. This aspect was exploited to infer functionally relevant proteoform groups in an unbiased manner, capturing differently spliced, modified or cleaved proteins expressed from the same gene. We linked differentially thermally stable proteoform groups across cell lines with the developmental stage of the cell of origin and the genetic subtypes of the cALL samples. Moreover, we analyzed differential co-aggregation of pairs of proteoform groups across the different cALL cell lines and linked co-aggregation to disease biology. Lastly, we systematically made use of measured biophysical proteoform states to find biomarkers for cell line sensitivity to 528 oncology and investigational compounds. The results of protein and proteoform group melting can be explored and used for hypothesis generation in a user-friendly online tool at https://www.proteomics.se/deepmeltome/.Here we introduce the application of TPP for the detection of functional proteoform groups. We demonstrate this by applying TPP to detect biologically influenced melting differences without any drug perturbation in 20 different B cell childhood acute lymphoblastic leukemia (cALL) cell lines, representing various disease subtypes defined by characteristic chromosomal rearrangements. In combination with high-resolution isoelectric focusing fractionation (HiRIEF)8 with eight temperatures per sample and multiplexed two cell lines at a time using TMTpro20  at 10% when evaluating our method on simulated datasets , showed a profile similar to the median peptide signal per gene symbol, ELAC2_1 displayed a pattern  24 to find differences across the 20 cALL cell lines  across the profiled cell lines . This indicates that our grouping enables us to detect differences between some proteoforms that are hidden at the protein level. Among the top hits of the analysis, we found a proteoform of p53 (TP53_1), a tumor suppressor protein, and fructose-1,6-bisphosphatase 1 (FBP1_1), a rate-limiting enzyme of gluconeogenesis  with the B cell progenitor origin of the acute lymphoblastic leukemia cell lines27. DNTT diversifies the variable region of the Ig-light chain during the pre-B stage and diversifies Ig-heavy chain variable regions during the pro-B stage29. Thus, higher thermal stability may indicate differences in DNTT–DNA binding dynamics between the developmental stages.Another protein with differential thermal stability across cell lines was the DNA nucleotidylexotransferase proteoform 1 (DNTT_1), a DNA polymerase that adds random nucleotides to the junction of rearranged immunoglobulin chains during B cell maturationles Fig.  that wer30, showed higher thermal stability in cell lines of the TCF3-PBX1 subtype  and were selectively sensitive to the PARP inhibitor talazoparib 27). This suggests that this proteoform is associated with sensitivity to drug treatment that reduces genomic stability, which could implicate nuclear relocalization in the cell lines with observed high thermal stability.We also found that INPP4B_1, a proteoform of INPP4B, a protein and lipid phosphatase that antagonizes the PI3K/Akt signaling pathway15, these examples pinpoint pathway activation status and reveal new candidate biomarkers for therapy.Overall, we detected hundreds of examples of proteoforms with differential thermal stability in the cALL cell lines studied. Because thermal stability reflects the state and activity of proteins in a complementary way to traditional abundance proteomics13. Recently, we exploited this concept to test for differential co-aggregation of protein interactors between two conditions32. Here, we adapted this approach to a robust multigroup comparison  dataset34 showed a slightly inferior predictive power for our deep TPP approach  as significantly differential across cell lines  or complex members have been shown to often coincide, a feature attributed to co-aggregation of the respective interactorsson Fig.  to detecnes Fig. .Fig. 5Di35. Furthermore, MDM2 is often upregulated in different cancers, leading to increased degradation of p53, resulting in uncontrolled cell division36. We, thus, wondered whether cell lines in which MDM2_2 and TP53_1 co-aggregated, which we interpreted as a sign of MDM2 binding to p53 and promoting its degradation, were more susceptible to MDM2 inhibition than other cell lines. Indeed, the two cell lines that featured co-aggregation of MDM2_2 and TP53_1, LC4-1 and P30-OHKUBO, showed higher sensitivity to idasanutlin, an MDM2 inhibitor, compared to other cell lines  profiles27 of cell lines with co-aggregating versus differential CXXC1_2 and SETD1A_3 melting profiles revealed that the gene set ‘p53-Independent DNA Damage Response’ was significantly enriched among upregulated genes in cell lines that featured co-aggregation of this proteoform pair  with all previously determined 1,408 differentially thermally stable proteoforms  . Previously, it was observed that CRKL was thermally destabilized upon treatment with dasatinib, another BCR-ABL inhibitor8. Inversely, thermal stabilization of CRKL appears to be related to active ABL1 signaling, which is in line with a positive correlation of sensitivity to BCR-ABL1 inhibitors , high FIGNL1_1 thermal stability could reflect active engagement in the FIGNL1-containing complex to resolve DNA double-strand breaks. Indeed, correlation of FIGNL1_1 thermal stability with anti-mitotic drug sensitivity  was stronger than for FIGNL1 abundance . Furthermore, the proteoform FIGLN1_2 did not correlate significantly with drug sensitivity  and drug sensitivity, we sought to generalize this principle across a larger drug panel—namely, the 528 drugs used in our previous studyrms Fig. , we retrod) Fig. . CRKL isors Fig. . Moreoveors Fig. . FIGNL1 43. Thus, it appears plausible that high thermal stability of PIP4K2C_2, potentially reflecting a higher fraction of cofactor-bound protein pool, is associated with increased sensitivity to MDM2 inhibition, as the related signaling pathway appears to lead only to cell growth in the absence of p53 function. The fact that the correlation of idasanutlin sensitivity to PIP4K2C abundance is weaker and positive rather than negative  of the terms ‘actin binding’, ‘antigen binding’ and ‘immunoglobulin receptor binding’. This may indicate that high EPS8L2_2 could reflect actin remodeling in response to B cell receptor (BCR) activation45. It was shown previously that nuclear export inhibition suppresses downstream effects of BCR signaling in chronic lymphocytic leukemia46; therefore, it is plausible that eltanexor treatment may be effective in a subset of acute lymphoblastic leukemias relying on BCR signaling for proliferation.Finally, we detected a positive correlation between EPS8L2_2  with eltanexor Fig. , a nucle10. However, it has been realized that these methods can also detect other sources of protein biophysical variation that are difficult to quantify with other proteomics methods, including protein interactions with other biomolecules47. Since the adaptation of the method to infer functional phosphorylation sites16, it has also become clear that TPP bears the potential for detecting post-translationally modified proteoforms. In the present study, we performed TPP with unprecedented peptide coverage and generalized this concept to enable unbiased detection of co-existing functional proteoform groups. The detected events of diversified protein products comprise cases of alternative splicing, proteolytic cleavage, post-translational modifications and variants interacting with metabolites, proteins or DNA.CETSA and TPP were developed with the primary goal of detecting protein targets of drugs4 or peptide correlation combined with hierarchical clustering for detection of functional proteoform groups from bottom-up proteomics datasets, such as full proteome or size-exclusion experiments. However, underlying sample conditions need to reveal proteoform differences at the level of protein abundance for these methods to work. Furthermore, analysis methods either have been designed to specifically detect single outlier peptides, in the case of PeCorA, or require specification of the number of proteoforms expected per protein, in the case of the COPF algorithm5. Our approach, instead, measures peptide thermal stability, which can reflect any form of thermodynamic perturbation of a protein’s makeup or its interactions. This has been shown to reveal various types of functional proteoforms, such as fusion events8, post-translational modifications16, differential12 PPIs and spliced and proteolytically cleaved isoforms. Combining this readout across different cell lines further increases robustness of the approach toward outlier peptides and sensitivity to detect subtle differences between proteoforms. Our FDR-controlled graph-based analysis strategy is able to flexibly decide how many functional proteoform groups a protein has (from a single proteoform to any number). It makes use of peptide profile similarity rather than correlation, the latter being problematic in the context of TPP due to the high inherent correlation of peptide melting curves. One of the limitations of our strategy is that it relies on identifying a minimal number of peptides per protein to detect proteoforms. Because longer proteins give rise to more tryptic peptides, we are more likely to find proteoforms for longer proteins  or IMDM (Sigma-Aldrich) supplemented with either 10% or 20% FBS (Sigma-Aldrich), 20 mM HEPES (Gibco/Life Technologies), 1 mM sodium pyruvate (Sigma-Aldrich), 1× MEM non-essential amino acids (Sigma-Aldrich) and 1× penicillin–streptomycin (Sigma-Aldrich) was preferably used. Cell line provider details, culture conditions and growth media are also described in Supplementary Table 27. Cell lines were grown at 37 °C and 5% CO2 to a cell density of approximately 1–2 million cells per milliliter. Cells were harvested at 500g for 3 minutes and washed twice with HBSS .The 20 childhood B cell precursor acute lymphoblastic leukemia (BCP-ALL) cell lines used in this study were obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen , the Children’s Oncology GroupFreshly washed cells were resuspended to a density of 100 million cells per milliliter in HBSS and distributed as aliquots of 10 million cells into eight 0.2-ml PCR tubes. Tubes were heated in parallel for 3 minutes to 41, 44, 47, 50, 53, 56, 59 and 63 °C, followed by a 3-minute incubation time at room temperature. Afterwards, cells were flash-frozen in liquid nitrogen.g and 4 °C for 40 minutes. Supernatants were transferred to new tubes, and protein concentrations were determined using the DC protein assay according to standard protocols provided by the kit manufacturer (Bio-Rad). Equal volumes of soluble protein supernatants were transferred to new tubes and subjected to in-solution digestion. First, the samples were supplemented with reagents to contain a final concentration of 50 mM TEAB, 0.1% SDS and 5 mM TCEP. Reduction was performed at 65 °C for 30 minutes. Samples were then cooled down to room temperature and alkylated with 15 mM of chloroacetamide for 30 minutes. Proteins were digested overnight with 1:40 Lys-C -to-protein ratio. Consecutively, trypsin (Thermo Fisher Scientific) was added at a 1:70 enzyme-to-protein ratio for an 8-hour incubation at 37 °C. Finally, the same amount of trypsin was added one more time for an overnight incubation. Resulting peptides were labeled by 16-plex TMTpro tags  using the same amount of respective label for each sample. Eight melting points of two randomly selected cell lines were combined in each TMT 16-plex set. The protein amounts were adjusted to contain the same total protein amount for all cell lines throughout the TMT sets. An overview of the sets is given in Supplementary Table Lysis was performed by five freeze–thaw cycles using a 25 °C heating block and liquid nitrogen. Cell debris and precipitated proteins were removed by centrifugation at 21,00018. Sample pools of ~300 µg were subjected to peptide IEF-IPG (isoelectric focusing by immobilized pH gradient) in a pH range of 3–10 and 3.7–4.9, respectively. Dried peptide samples were dissolved in 250 µl of rehydration solution of 8 M urea containing 1% IPG pharmalyte pH 3–10 or 2.5–5, respectively  and allowed to adsorb to the gel bridge strip and the 24-cm linear gradient IPG strips  by swelling overnight. After focusing, the peptides were passively eluted into 72 contiguous fractions with MilliQ water/35% acetonitrile (ACN)/35% ACN + 0.1% formic acid (FA) using an in-house-constructed IPG extraction robot  into a 96-well plate , which were then dried in a SpeedVac. The resulting fractions were dried and kept at −20 °C.The pre-fractionation method was applied as previously described−1, the curved gradient went from 2% B up to 40% B in each fraction, as shown in Supplementary Data m/z) were followed by data-dependent MS/MS  on the top five ions using higher-energy collision dissociation at 30% normalized collision energy. Precursors were isolated with a 1.2-m/z window. Automatic gain control targets were 1 × 106 for MS1 and 1 × 105 for MS2. Maximum injection times were 100 ms for MS1 and 100 ms for MS2. Dynamic exclusion was set to 30-second duration. Precursors with unassigned charge state or charge state 1 were excluded. An underfill ratio of 1% was used.Online LC–MS was performed using a Dionex UltiMate 3000 RSLCnano System coupled to a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific). Each fraction was subjected to MS analysis. Samples were trapped on a C18 guard-desalting column  and separated on a 50-cm-long C18 column . The nano capillary solvent A was 95% water, 5% DMSO and 0.1% FA; solvent B was 5% water, 5% DMSO, 95% ACN and 0.1% FA. At a constant flow of 0.25 μl min49. Spectra were then searched using MSGF+ (v10072)50 and Percolator (version 2.08)51, where search results from eight subsequent fractions were grouped for Percolator target/decoy analysis. All searches were done against the human protein subset of Ensembl 99 in the Galaxy platform52. MS-GF+ settings included precursor mass tolerance of 10 ppm, fully tryptic peptides, maximum peptide length of 50 amino acids and a maximum charge of 6. Fixed modifications were TMTpro 16-plex on lysines and peptide N-termini and carbamidomethylation on cysteine residues, and a variable modification was used for oxidation on methionine residues. Quantification of TMTpro 16-plex reporter ions was done using IsobaricAnalyzer (version 2.0) of the OpenMS project53. Peptide-spectrum matches (PSMs) found at 1% FDR were used to infer gene identities. Protein quantification by TMTpro 16-plex reporter ions was calculated using TMT PSM ratios. The median PSM TMT reporter ratio from peptides unique to a gene symbol was used for quantification. Protein FDRs were calculated using the picked FDR method using gene symbols as protein groups and limited to 1% FDR54.Orbitrap raw MS/MS files were converted to mzML format using msConvert from the ProteoWizard tool suite55 and converted to fold changes relative to the first temperature. Next, to assign similarly melting peptides found to map to a certain gene symbol, a graph for each gene symbol was created connecting all peptides (vertices) with weights (edges) corresponding to their similarity in melting profile. The similarity was computed withdij is the weighted Euclidean distance between two peptides across all cell lines:n at temperature k, and v represents the number of valid comparisons—that is, 56; however, only gene symbols for which at least ten peptides were identified and with at least two peptides per sample were subjected to this analysis (a detected community had to be supported by at least three peptides to be accepted to ensure that outlier peptides did not affect robust proteoform identification). Peptides mapping to gene symbols for which these criteria were not fulfilled were grouped to single proteoforms, and peptides mapping to gene symbols that were included in the community detection were assigned to proteoform groups if the modularity of the detected communities was higher than 1 × 10−13 and the peptide ambiguity ratio was lower than 0.5 . Modularity was computed using the function modularity of the igraph R package. Through the assignment of peptides to communities, functional proteoform groups for each gene symbol were created. Summarization on proteoform group level was performed by summation of non-normalized raw peptide data assigned to individual communities. Obtained proteoform signal intensities were then normalized per temperature using variance stabilizing normalization, and relative fold changes to the lowest measured temperature were formed.Quantitative reporter ion signal for PSMs was summarized on peptide level by summation. Reporter ion signals of all individual temperatures were normalized using variance stabilizing normalizationT represents the temperature; p represents the plateau; and a and b are parameters affecting the slope and inflection point of the curve24. Fits for individual cell lines  were accepted if they had a residual standard deviation of Sres < 0.1 f(Tm) = 0.5), and areas under the melting curve were computed for accepted fits of cell-line-specific proteoform thermal profiles by integration of the fitted sigmoid formulas. Null models were fit using the same sigmoid model (4) for each proteoform across all cell lines for which an alternative model fit was accepted. The null model RSS was computed as F-statistic was computed withpi, υ0 and υ1 representing the number of observations for protein i and the number of parameters of the null and alternative model, respectively24. Proteoforms with an F-statistic above the 90th percentile were considered to have differential melting across cell lines. The rationale of considering this threshold for considering proteoforms differentially thermally stable across cell lines is the observation that, due to the heterogeneity across cell lines, most proteoforms do not reflect the expected distribution under the null hypothesis. However, this is assumed by the NPARC approach, which uses an empirical null model to infer significant deviance from the null. Although not controlling FDR at a fixed threshold, we chose this threshold, because proteoform groups with an F-statistic above the 90th percentile were found to have visually distinct thermal stability differences across cell lines.All functional proteoform groups detected in at least ten cell lines were fitted by a sigmoid function for each cell line individually. The sigmoid was fit using the NPARC R package implementation, which is defined as32. We started by extending the list of highly confident string interactions (combined score ≥ 950) by all possible proteoform interactions—that is, if protein A was previously annotated to interact with protein B and we detected three proteoforms for protein A and two for B, we replaced this entry by all possible 3 × 2 combinations. Next, we tested for co-aggregation of pairs of proteoforms in all individual cell lines using the approach described in ref. 13. All pairs of proteoforms that showed significant co-aggregation (Padj. < 0.1) in at least one of the cell lines were included for the differential analysis across cell lines. The test statistic for differences in co-aggregation across cell lines was determined by computing k, between all annotated pairs of proteoforms A and B for all individual cell lines n, ranking all RSSn and computingTo test for pairs of proteoforms that co-aggregated in some cell lines but melted differentially in others, we adapted our previous approach for testing this between two conditions(n−1) and RSS(2) represent the second highest and the second lowest RSS. In this way, the F-statistic became large only for cases in which at least two cell lines featured small and big differences between the melting curves of the two proteoforms, respectively. We considered F-statistics higher than the 90th percentile for further inspection due to difficult tractability of the underlying null distribution required to calibrate the F-statistic in terms of FDR.Above, RSSF-statistics obtained from the NPARC analysis) with the sDSSs of the respective cell lines for all drugs with a minimal effect (sDSS < 6 for at least one cell line) using the R package limma40. Results obtained for all proteoforms and drugs were jointly adjusted for multiple testing using the Benjamini–Hochberg method57. Proteoform–sDSS associations with an adjusted P value of less than 0.1 were considered significant. The cell line COG-319 was excluded from the analysis because the sDSS for the cell line was an outlier that appeared unspecifically sensitive to most drugs, which negatively affected interpretation of drug sensitivity correlation results.Proteoform thermal stabilities were associated with sDSSs by performing correlation analyses between the area under the melting curves of proteoforms found to differ across cell lines  a dataset in which we generated 15 peptides per protein and (2) one with 50 peptides per protein. For each dataset, we simulated 1,000 negative proteins  with varying melting points (ranging between 50 °C and 60 °C) with peptides that only differed by noise on two levels: (1) a melting point variability with standard deviation of 2 °C and (2) variability of the measured fold changes with standard deviation of 0.1, whereas variations below 0 were forbidden by forcing such cases to a small non-zero value. Additionally, we simulated a total of 200 positive proteins  using the same sources of noise as for the negatives. For each peptide, we simulated eight-fold changes in 20 different cell lines, similar to our measurements in our true dataset.We applied the PepNet algorithm to this dataset and sorted the results by modularity to perform a receiver operating characteristic (ROC) analysis checking whether true-positive proteoforms were ranked higher than true-negative ones.5 to the same dataset for comparison, we multiplied simulated fold changes by a factor of 1,000 because the algorithm expects intensity values rather than fold changes. Obtained results were ranked by the algorithm’s ‘proteoform_score’ and subjected to ROC analysis.To apply the COPF algorithmFor both methods, proteoforms were accepted as correctly detected if two proteoforms were detected when simulated regardless of whether all peptides were correctly assigned to both IDs.13. We ranked protein pairs by increasing average distance and performed a ROC analysis using PPIs annotated by StringDB33 with a combined score of 900 or higher and direct and indirect PPIs within human protein complexes as annotated in ref. 58 as positives and all non-annotated PPI pairs as negatives.To benchmark the capability of deep TPP data to predict PPIs, we computed the average Euclidean distances between all pairs of quantified proteins as suggested previously34 measured in HeLa cells. Intensity values measured in the different fractions were converted to fold changes by dividing by the highest value per protein across fractions. The dataset, which comprised 42 fractions, was then downsampled to eight fractions to have a similar number of observations per protein to the TPP dataset. We then employed the same procedure as for the deep TPP dataset to compute average Euclidean distances between pairs of proteins. The results were also sorted by increasing distances, and the same annotation of positives and negatives was used for ROC analysis.To compare the capability of SEC–MS data to predict PPIs, we downloaded the interphase dataset in ref. 59). The sex of the cell line donors was included as a covariate in the design formula, when testing for a difference in conditions.Differential RNA-seq analysis was performed using DESeq2 . Pelleted cells were resuspended in HBSS, and 75-µl cell suspensions (10 million cells) were aliquoted to 0.2-ml tubes. Samples were then heated in a temperature range of 37–70 °C in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) for 3 minutes, followed by 3-minute cooling at room temperature and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze–thaw cycles and centrifuged at 21,000g for 40 minutes at 4 °C. The cleared supernatants were transferred to new tubes, denatured in LDS sample buffer (Thermo Fisher Scientific) and analyzed by western blotting.COG-355 and ALL-PO cell suspensions were centrifuged at 300Cleared protein supernatants were denatured in LDS sample buffer (Thermo Fisher Scientific), resolved by SDS–PAGE using NuPAGE 4–12%, Bis-Tris gel  and NuPAGE MES SDS Running Buffer  and transferred to nitrocellulose membranes . SeeBlue Plus2 Pre-stained Standard was used as protein ladder . Afterwards, the membranes were blocked with 5% non-fat dry milk in TBST (Thermo Fisher Scientific) and incubated with primary antibodies for the appropriate target. TMPO/LAP2 , PSAP  and Saposin-C  antibodies were used for western blotting to detect corresponding targets. After overnight primary incubation at 4 °C, blots were rinsed using TBST and incubated with the appropriate HRP-conjugated secondary antibodies . All antibody incubations were diluted in 5% non-fat dry milk in TBST. Protein bands were developed with Clarity ECL Substrate Chemiluminescent HRP substrate (Bio-Rad) in a iBright CL1000 Imaging System . Bands were quantified using iBright Analysis Software version 4.0.1 (Thermo Fisher Scientific). Images of the full uncropped blots are provided with annotation in Supplementary Fig. Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41589-023-01284-8.Supplementary InformationSupplementary Table 1, Figs. 1–17 and References.Reporting SummarySupplementary Data 1Table of detected proteoforms.Supplementary Data 2Differential melting from the NPARC analysis.Supplementary Data 3Table of PPI analysis.Supplementary Data 4Table of drug sensitivity–proteoform correlations.Supplementary Data 5HiRIEF gradient lengths.


Osteosarcoma is the most common bone sarcoma in children and young adults. While universally delivered, chemotherapy only benefits roughly half of patients with localized disease. Increasingly, intratumoral heterogeneity is recognized as a source of therapeutic resistance. In this study, we develop and evaluate an in vitro model of osteosarcoma heterogeneity based on phenotype and genotype. Cancer cell populations vary in their environment-specific growth rates and in their sensitivity to chemotherapy. We present the genotypic and phenotypic characterization of an osteosarcoma cell line panel with a focus on co-cultures of the most phenotypically divergent cell lines, 143B and SAOS2. Modest environmental  or chemical perturbations dramatically shift the success and composition of cell lines. We demonstrate that in nutrient rich culture conditions 143B outcompetes SAOS2. But, under nutrient deprivation or conventional chemotherapy, SAOS2 growth can be favored in spheroids. Importantly, when the simplest heterogeneity state is evaluated, a two-cell line coculture, perturbations that affect the faster growing cell line have only a modest effect on final spheroid size. Thus the only evaluated therapies to eliminate the spheroids were by switching therapies from a first strike to a second strike. This extensively characterized, widely available system, can be modeled and scaled to allow for improved strategies to anticipate resistance in osteosarcoma due to heterogeneity. Bulk sequencing characterizations described recurrent targetable copy number alterations, but effective targeting of these gains remains largely unrealized4. The impact of immunotherapy in osteosarcoma to date has also been modest7. Clinically, osteosarcoma is treated with surgical resection both with neoadjuvant and adjuvant chemotherapy with methotrexate, doxorubicin, and cisplatin (MAP). At present, we lack the tools to change therapy in a patient-specific manner except upon treatment failure and disease progression, though ongoing circulating tumor DNA work may show prognostic and predictive relevance with several ongoing studies. Tumor necrosis is seen in surgical resection specimens after neoadjuvant chemotherapy and correlates with long-term outcome, however alterations in therapy based on necrosis rate have not improved outcomes9.With prevalent loss of TP53 and other tumor suppressors, osteosarcoma has a complex genotype profile which varies widely from patient to patient11. The vast majority of localized patients and oligometastatic patients achieve a complete response after chemotherapy and surgery. First strikes like surgery and MAP chemotherapy greatly reduce and eliminate the dominant population, but by this definition second strikes are needed to eliminate the minor, resistant population12. Historically, a first strike of surgery alone has been inadequate for optimal outcomes, most commonly due to rapid development of pulmonary metastases. Some of these patients could be salvaged with chemotherapy and thoracotomy, strongly suggesting that chemotherapy is able to eradicate microscopic metastases and thus be an effective second strike to surgery alone in about half of osteosarcoma patients11. A renewed focus on tumor heterogeneity with the dynamics of small, resistant populations is increasingly being realized in the lab15. Meaningful perturbations of these small populations are more difficult to investigate with typical clinical trial designs18. Furthermore, single cell sequencing of clinical samples is revealing that resistant, subclinical clones present at diagnosis can be selected for with standard first-line chemotherapy ultimately leading to resistant and fatal disease19.Our approach conceptually to osteosarcoma heterogeneity is to consider two populations: (1) a dominant, large population of cells that are mainly in the primary tumor, sensitive to MAP chemotherapy, or amenable to surgery and (2) a minor, resistant population in or outside the resected tumor and insensitive to MAP chemotherapy which leads to relapse. With the ability to often reduce the viable numbers of osteosarcoma cells by two orders of magnitude or more with systemic therapy, and with the often quick and multifocal pulmonary recurrences in the era of amputation alone for therapy, subclinical populations have long been known to be the determinants of outcome in osteosarcoma20.With the acceptance of MAP chemotherapy into the first strike for all pediatric and Adolescent and Young Adult (AYA) patients, focus has shifted to identifying agents that can improve upon the first strike or become an effective second strike. Unfortunately, early phase trial metrics such as response rate and progression free survival rate depend more on the therapeutic sensitivity of the dominant population. Thus, we lack adequate clinical endpoints and methods to identify all efficacious second strikes. There have been increasing insights into small dynamic populations that resist chemotherapy, undergo epigenetic and phenotypic changes, and can adapt to the lung microenvironment and lead to metastases21. We thus aimed to use co-culture of multiple osteosarcoma cell lines to model osteosarcoma heterogeneity including minor populations. We first characterized genomic features of the cell lines through RNAseq to determine RNA expression and single nucleotide variations (SNVs), and through copy number arrays to assess copy number variation (CNV). As the genomic characterization was highly consistent with prior publications, we then shifted to analysis of the phenotypic features of osteosarcoma cells in different environments, including manipulation of pH and nutrients and exposure to chemotherapeutic agents. Finally, by coculturing the fastest growing and slowest growing cell lines at different ratios, we explored therapeutic schedules and combinations of agents towards complete response where either the fast-growing or slow growing population predominated.Cell lines can be thought of as having significantly reduced heterogeneity compared to clinical disease, both within and across patients22. RNA, DNA, and miRNA from each of the osteosarcoma cell lines were isolated for expression, gene fusions, single nucleotide variations (SNVs), and copy number variation (CNV) analysis, using human osteoblasts as a control , 143B was able to outgrow and therefore outcompete SAOS2, rendering it undetectable even when deprived of nutrients  > 0.5), while also being differentially active against 143B versus SAOS2 6, RAS pathway gains (foraging and intake of carbohydrates and amino acids)2, an abnormal cell cycle with CMG helicase wild type status (DNA replication and never genes)41, and MYC (resistance)42 capture common and recurrent genetic changes in osteosarcoma. Until we find agents to meaningfully alter these pathways, multiple strategies will be needed to tackle the resulting complex phenotype. We believe robust and high throughout preclinical systems will be necessary to prioritize such multistep strategies. We propose that this model of heterogeneity can help experimentally evaluate concepts related to a therapeutic strategy based on evolutionary tumor dynamics.Despite the enthusiasm for sequencing, we believe incorporating tumor phenotype into treatment planning holds at least the same promise for translating towards successful trials in osteosarcoma. We recognize the importance of heterogeneity at presentation as reflected in our model and do support continued evaluations of genomic sequencing at diagnosis and resection to better characterize clinical heterogeneity. We thus present a proposed synthesis of the genomic and phenotypic worldview of shared osteosarcoma changes. Copy number variations, which vary across patients but are remarkably stable over the course of therapy and progression, suggest that much of the evolution after diagnosis is epigenetic43. The identities of the lines were confirmed using STR analysis. Cells were maintained in DMEM + 15% FBS and antibiotics. Basic growth characteristics and kinetics including doubling time and log-phase were determined at several starting cell concentrations.In this study we evaluated 6 established osteosarcoma cell lines . U2OS, MG-63, SAOS2, 143B, and MNNG were obtained from ATCC. OS252 was a gift from Richard Gorlick at MD Anderson44, was utilized to explore monoculture and co-culture growth characteristics under environmental stress or in the presence of chemotherapeutic agents. Cell viability was quantified using Cell-Titer Glo 2.0 or Real Time Glo . Following addition of CT-Glo to cell cultures, cells were agitated for 30 min on a shaker at room temperature. Luminescence was measured using a Cytation 3 plate reader (Bio-Tek Instruments) at room temperature or at 37 °C for CT-Glo or RT-Glo, respectively. Raw data were transferred to Microsoft Excel workbooks, where subsequent background subtraction and normalization analyses were conducted.Screening methodology, described in prior publications3) were seeded onto 384-well plates, with subsequent additions of drug treatments (when applicable) and CT-Glo, using a Precision XS liquid handling station . Cells were grown 24 h prior to the addition of any chemical agents and were subjected to drug or vehicle control for 72 h before being assayed with CT-Glo. All drug screens were completed in 2D using cell monolayers. For high throughput characterization of cell growth across multiple cell lines concurrently, several plates for each cell line were seeded with a series of cell concentrations ranging from 1,800 to 47,000 cells/cm2 . Viability was quantified every 24 h until growth curves plateaued at around 96 or 120 h. Each experimental or control condition was split into a minimum of 4 technical replicates. Biological replicates were averaged following normalization.Cells  and RNA-sequencing libraries were prepared using the Nugen Ovation Human FFPE RNA-seq Multiplex System according to the manufacturer’s protocol . Library size and quality were assessed using the Agilent BioAnalyzer  and sequencing was performed using the NextSeq 500 v2 sequencer (150 cycles) to generate a targeted 80 million 75-base paired-end reads. Raw sequence data were demultiplexed using the Illumina bcl2fastq2 software . Paired-end RNAseq reads were preprocessed for quality assessments and adapter trimming and aligned to the human reference genome hs37d5 using TopHat v2.0.13 default setting45. Gene-level expression was quantified using HTSeq v0.6.146 based on the RefSeq gene model downloaded from USCS Table Browser. Normalized read counts were obtained via calculating library size factors and scaling raw read counts using the Bioconductor R package DESeq2 v.1.6.347. microRNA-Sequencing was performed using the Illumina TruSeq Small RNA Sample Preparation Kit, which enables the sequencing of small RNAs between 17 and 35 nucleotides. Briefly, universal RNA adapters were ligated to 1 ug of total RNA, which was used to generate single-strand cDNA following the manufacturer’s protocol . Following PCR amplification and gel purification, the final libraries were reviewed on an Agilent BioAnalyzer DNA 1000 chip and were then sequenced on the Illumina NextSeq 500 sequencer to generate approximately 10 million sequencing reads per sample.Cell lines at 80% confluence were subjected to RNA-sequencing for gene expression analysis. Total RNA was prepared from each osteosarcoma cell line using the http://broadinstitute.github.io/picard/) and Genome Analysis ToolKit 49. Sequence variants were annotated to determine genic context  using ANNOVAR50. Additional contextual information was incorporated, including allele frequency in other studies such as 1000 Genomes, the NHLBI Exome Sequence Project, in silico functional impact predictions, and observed impacts from databases like ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/) and the Collection of Somatic Mutations in Cancer (COSMIC).To detect driver mutations in the RNAseq data, sequence reads were aligned to the reference human genome (hs37d5) with STAR and duplicate identification, insertion/deletion realignment, split and trim, quality score recalibration, and variant identification were performed with the Picard toolkit (Copy number variation (CNV) and loss-of-heterozygosity (LOH) status were obtained with the CytoScan HD Assay , which was performed on each cell line starting with 250 ng of DNA. DNA was extracted using the Blood and Cell Culture DNA Midi Kit . The CytoScan HD assay uses 750,000 SNP probes and 1.9 million non-polymorphic probes to report genome-wide copy number aberrations at a resolution of 25–50 Kb. In addition, the assay can measure genome-wide LOH, including copy-neutral LOH. The data generated from the assay were normalized, copy number status calculated, and the data reviewed for quality using the Chromosome Analysis Suite (ChAS) v3.0 .31 Media was changed every ~ 3 days. Plates were incubated in an Incucyte SX5 where brightfield and fluorescent scans were taken every 24 h.Twelve different conditions were assessed at notated concentrations ) with an initial spheroid size of 1000 cells/well . Media containing 4.5 g/L glucose, 2.5 mM L-glutamine, pH 7–7.4 and antibiotics was deemed “normal” media. Subsequent conditions included no glucose (0 g/L), no glutamine, and no glucose or glutamine. Due to prior work and the importance of tumor microenvironmental conditions the lab was interested in varying pH on the cells and the effects of the small molecules tested, As a results we included conditions with high pH (8.0), low pH (6.5), high pH with no glucose, low pH with no glucose, high pH with no glutamine, low pH with no glutamine, high pH with no glucose or glutamine, and finally low pH with no glucose or glutamine media.24. Briefly, we utilize a system to evaluate combinations with the aim of rapidly translating that data into clinical trials24. We selected FDA-approved agents or agents with strong preliminary data for OS, clinically achievable dosages, and tolerable dosing schedules. Treated experimental wells were normalized to untreated wells for a given cell line. In the case of these earlier agents without consistent human or animal drug data, we chose 3 fixed concentrations for each agent. Drugs were then selected based on differential sensitivity to either the 143B or the SAOS2 cell line for further use.Drug screening with cells in log phase was conducted using phamocokinetic guided dose levels when available and high-throughput cell viability assays as previously describedThe 143B and SAOS2 cell lines were transduced with GFP lentivirus  and mCherry lentivirus (Cat#: LVS(VB220407-1474cpn), VectorBuilder, Chicago, IL, USA) respectively, according to manufacturer’s protocol. The GFP/mCherry containing cells were selected with 5 µg/ml of puromycin. Cells were then plated on Greiner 96-well U-bottom, Cellstar® cell-repellent plates  using 3.5% Matrigel  to form spheroids. Spheroids were characterized with an initial spheroid size of 1,000 cells/well comprised of SAOS2 alone, 143B alone and SOAS2 comprising 10, 50, or 90%, with 143B cells comprising the remainder to 100%.Spheroids again at notated SAOS2 compositions ) with a total cell number of 20,000 cells/well to start with a larger spheroid were treated for 96 h with panobinostat or trametinib or both in normal media or media with no glutamine at clinically achievable levels based on available pharmacokinetics from clinical trials. Cells were then treated for 72 h with Courmermycin-A1 (CA1), a Cdc45-MCM-GINS helicase inhibitor (CMGi) that helps prevent cell recovery after treatment, and then the whole cycle was repeated. Plates were incubated in an Incucyte ZOOM system where brightfield and fluorescent scans were taken every 12 h.Supplementary Information 1.Supplementary Information 2.


Sebaceous horn, also known as a cutaneous horn, is a relatively rare skin lesion that typically appears as a cone-shaped, hard, and keratinous growth on the skin's surface. While it can occur on various body parts, it most commonly appears on the face, ears, nose, and back of the hands. This image shows a sebaceous horn on the lateral aspect of the left thigh, a rare place to occur. The first step in diagnosing a sebaceous horn is a thorough clinical examination of by a clinician. They will visually be inspecting the lesion, noting its size, shape, colour, and texture. A detailed medical history from the patient may include information about the lesion's duration, any changes over time, and associated symptoms such as pain, itching, or bleeding. Dermoscopy can help identify specific features, such as vessels, scales, and other characteristics, that aid in diagnosing sebaceous horns and ruling out other conditions If there is uncertainty about the diagnosis or if the sebaceous horn is large, has irregular features, or shows any signs of malignancy, a biopsy may be recommended. Sebaceous horns can sometimes resemble other skin conditions or tumors, including actinic keratosis, squamous cell carcinoma, verruca vulgaris, or seborrheic keratosis. Therefore, it is important to consider these possibilities and use the diagnostic tools mentioned above to differentiate sebaceous horns from other lesions. Once the diagnosis is confirmed, the treatment plan can be developed. Treatment options for sebaceous horns may include surgical excision, cryotherapy, and laser therapy.


Enhanced glycolysis (Warburg effect) driven by the BRAF oncogene, dysregulated GAPDH expression, and activation of the PI3K/AKT/mTOR signaling pathway may significantly contribute to the resistance-targeted therapy of BRAF-mutated melanomas. Therefore, we aimed to study for the first time the anti-tumor activity of the GAPDH inhibitor GP-2250 in BRAF-mutated melanoma cell lines and benign melanocytes. We employed three melanoma cell lines and one primary melanocyte cell line , which were exposed to different GP-2250 doses. GP-2250’s effects on cell proliferation and viability were evaluated by means of the BrdU and MTT assays, respectively. The RealTime-Glo Annexin V Apoptosis and Necrosis Assay was performed for the evaluation of apoptosis and necrosis induction. RT-PCR and western blotting were implemented for the determination of AKT and STAT3 gene and protein expression analyses, respectively. The melanoma cell lines showed a dose-dependent response to GP-2250 during BrDU and MTT testing. The RealTime-Glo Annexin V assay revealed the heterogenous impact of GP-2250 on apoptosis as well as necrosis. With respect to the melanoma cell lines Ma-Mel-86a and SH-4, the responses and dosages were comparable to those used for the MTT viability assay. Using the same dose range of GP-2250 administered to melanoma cells, however, we observed neither the noteworthy apoptosis nor necrosis of GP-2250-treated benign melanocytes. The gene expression profiles in the melanoma cell lines for AKT and STAT3 were heterogenous, whereby AKT as well as STAT3 gene expression were most effectively downregulated using the highest GP-2250 doses. Immunoblotting revealed that there was a time-dependent decrease in protein expression at the highest GP-2250 dose used, whereas a time- as well as dose-dependent AKT decrease was predominantly observed in Ma-Mel-61a. The STAT3 protein expression of Ma-Mel-86a and SH-4 was reduced in a time-dependent pattern at lower and moderate doses. STAT3 expression in Ma-Me-61a was barely altered by GP-2250. In conclusion, GP-2250 has anti-neoplastic effects in BRAF-mutated melanoma cell lines regarding tumor cell viability, proliferation, and apoptosis/necrosis. GP-2250 is able to downregulate the gene and protein expression of aberrant tumorigenic pathways in melanoma cell lines. Since GP-2250 is a GAPDH inhibitor, the substance may be a promising combination therapy for tumors presenting the Warburg effect, such as melanoma. Mutations in BRAF, particularly the V600E, are frequently harbored in patients with cutaneous melanoma. These genetic alterations are considered to be one of the main drivers of the malignant transformation of melanocytes, even though we and other research groups have shown that these mutations are also very frequently found in benign melanocytic nevi ,2,3. CurGP-2250  is a relThe substance is an oxathiazinane  and has inhibitory effects on glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression and activity, selectively resulting in oxidative stress, mitochondrial dysfunction, and programmed cell death in cancer cells .As shown in in vitro as well as in vivo experiments, GP-2250 is able to downregulate the viability of pancreatic carcinoma cells, which is also accompanied by the induction of apoptosis as well as necrosis. The same study group reported on the anti-tumor activity of GP-2250, investigating other malignancies as well ,14,15,16As reported by Guo and co-workers , GAPDH bWe aimed to study for the first time the anti-tumor activity of GP-2250 in BRAF-mutated melanoma cell lines and benign melanocytes, also addressing the expression of parts of the PI3K/AKT/mTOR signaling pathway.In Using the RealTime-Glo Annexin V Apoptosis and Necrosis Assay and the full range of GP-2250 dosage, we observed neither the noteworthy apoptosis nor necrosis of benign melanocytes at 2 h, 6 h, and 24 h .In melanoma cells, GP-2250 induced programmed cell death when tested after 24 h using the RealTime-Glo Annexin V Apoptosis and Necrosis Assay. The doses of GP-2250 were comparable to those used in the MTT test (150 µmol/L and higher). In the Ma-Mel-61a cell line, GP-2250 induced both apoptosis and necrosis, but in a heterogeneous pattern . As showUsing gene expression profiling, we aimed to study the components of the PI3K/AKT/mTOR pathway, which is involved in resistance development in BRAF-mutant melanomas. As shown in Immunoblotting was performed for the protein expression analysis of AKT and STAT3 in all three melanoma cell lines . Cells wIn all cell lines, there was a time-dependent decrease in protein expression at the highest GP-2250 dose used, whereas a time- as well as dose-dependent AKT decrease was predominantly observed in Ma-Mel-61a. Regarding STAT3 expression, the protein expression of Ma-Mael-86a and SH-4 was reduced in a time- dependent manner at lower and moderate doses . STAT3 e2 concentration of about 10%, which may predispose melanoma to stronger hypoxia with O2 concentrations smaller than 1%. Hence, we speculate that GP-2250 might induce apoptosis and necrosis in tumor cells with high GPDH activity and strongly hypoxic conditions [In the present study, we have shown that all three melanoma cell lines showed a dose-dependent response to GP-2250 during BrdU testing to a different extent, demonstrating the anti-proliferative effect of the substance on melanoma cells. Moreover, a dose-dependent response to GP-2250 was found in all three melanoma lines during the MTT viability assay, demonstrating its cytotoxic effects on melanoma cells. The RealTime-Glo Annexin V Apoptosis and Necrosis Assay revealed the heterogeneous impact of GP-2250 on apoptosis as well as necrosis. With respect to the melanoma cell lines Ma-Mel-86a and SH-4, the responses and dosages were comparable to those used for the MTT viability assay previously. Using the same dose range of GP-2250 administered to melanoma cells, however, we observed neither the noteworthy apoptosis nor necrosis of GP-2250-treated benign melanocytes at 2 h, 6 h, and 24 h. This result may indicate that normal melanocytes are much less prone to GAPDH-inhibiting effects than malignant melanoma, particularly GAPDH, which is not rate limiting in normal cells ,22. Natunditions ,22,23.We exclusively studied cell lines with BRAF V600E) mutation. Importantly, BRAF mutations in melanoma affect metabolism via the hypoxia-inducible factor 1 (HIF-1), which is the key oxygen sensor and principal regulator of hypoxia-mediated gene response, along with HIF-2. BRAF (V600E) mutation was found to directly regulate HIF-1 expression in cutaneous melanoma ,24. More00E mutatBy contrast, the gene expression profiles in the melanoma cell lines for AKT and STAT3 were heterogeneous, whereas the downregulation of AKT as well as STAT3 gene expression was most effectively downregulated using the highest GP-2250 doses. Western blotting revealed that in all cell lines investigated, there was a time-dependent decrease in protein expression at the highest GP-2250 dose used, whereas a time- as well as dose-dependent AKT decrease was predominantly observed in Ma-Mel-61a. The STAT3 protein expression of Ma-Mel-86a and SH-4 was reduced in a time-dependent pattern at lower and moderate doses. STAT3 expression in Ma-Me-61a was barely altered by GP-2250. The discrepancies between the gene and protein expression analyses observed may be due to relatively short time intervals of measurements. It is possible that the measurements at 36 h and 48 h had resulted in more consistent protein and gene analyses. As discussed by Gavish et al. , the com2 in a humidified atmosphere to 60–80% confluency.Three human BRAF-mutated CM cell lines were studied in this work: Ma-Mel-61a (source: RRID:CVCL_C291), Ma-Mel-86a (source: RRID:CVCL_A221), and SH-4 (source: RRID:CVCL_1692). All cell lines originated from CM metastases. Based on the material transfer agreement, the cell lines have been previously provided to J.C. Becker by D. Schadendorf . As a control, we used a commercially available cell line with normal human primary epidermal melanocytes . The cell lines MaMel61a and MaMel86a were maintained in RPMI-1640, supplemented with 10% Fetal Bovine Serum (FBS) and Penicillin/Streptomycin, each at 100 U/mL . Cell lines SH4 were maintained in DMEM (LGC Standards GmbH) supplemented with 10% Fetal Bovine Serum (FBS) and Penicillin/Streptomycin, each at 100 U/mL. The culture media for melanocytes was Dermal Cell Basal Medium additionally supplemented with Adult Melanocyte Growth Mix (LGC Standards GmbH). All cells were maintained as a monolayer at 37 °C with 5% COPowdered GP-2250  was stored at room temperature and freshly prepared every two weeks by dissolving it in double-distilled water. It was set to a physiological pH, sterile-filtered and stored, protected from light.MTT assays were performed on all three CM cell lines in order to analyze calorimetrically the antineoplastic effect of the substance GP2250. Cells were seeded individually to obtain a sub-confluent monolayer in a 96-well plate format and incubated for 24 h prior to treatment. To examine its dose–response relationship, cells were incubated with individual increasing concentrations (from 50 up to 4500 µmol/L) depending on the cell line and ddH2O as the control for 24 h. Following the stated exposition times, 10 µL of MTT -2,5-diphenyltetra-zoliumbromid) reagent (5 mg/mL) was added and incubated for 2 h before violet Formazan crystals were dissolved in 100 µL of DMSO (dimethyl sulfoxide). The viability was analyzed using a microplate absorbance reader  by determining the optical density at 560 nm (reference wavelength 720 nm). The assay was performed using 8 replicates in three independent experiments with consecutive passages.Cells were seeded individually to obtain a sub-confluent monolayer in a 96-well plate format and incubated for 24 h prior to treatment. In order to examine the dose–response of substance GP-2250 regarding its anti-proliferative activity, cells were incubated with individual increasing concentrations of GP-2250 (from 50 up to 3500 µmol/L) depending on the cell line and with ddH2O as the control for 6 h, and submitted for BrdU proliferation assay (5-bromo-2-deoxyuridine)-ELISA  according to the manufacturer’s instructions. The incubation time of 6 h was appropriate for the BrdU proliferation assay in previous experiments. Based on the incorporation of the thymidine analogue BrdU during DNA synthesis, the amount of synthesized DNA was detected using a microplate absorbance reader  at 450 nm with a reference wavelength of 550 nm. BrdU assays were performed with 8 replicates of three independent experiments with consecutive passages.2. After 24 h, the supernatant was discarded and the cells with GP-2250 were stimulated at the concentration of 0.0–2000 µmol/L with a total of 100 µL/well. Analysis was performed with RealTime-Glo Annexin V Apoptosis and Necrosis Assay  according to the manufacturer’s protocol. This assay measures the exposure of phosphatidylserine on the outer leaflet of the cell membrane during the apoptotic process, and the loss of membrane integrity during secondary necrosis. Annexin V binding is detected with a simple luminescence signal, and necrosis is detected with a fluorescent DNA binding dye. The luminescence signal and the fluorescence signal were measured on the GloMax Discover Detection System GM 3000 + 3030 (Promega) after 2 h, 6 h and 24 h.The cells were seeded in a sterile, white 96-well tissue culture plate at a concentration of 10,000 or 30,000 cells/100 µL volume/well, depending on the cell line. All cell lines were incubated for 24 h at 37 °C and with 5% CO® RSC SimplyRNA Tissue Kit (Promega) according to the manufacturer’s protocol. Quantitative analysis was performed using real-time RT-PCR in accordance with the MIQE Guidelines using the PowerSYBR Green PCR Master Mix on a QuantStudio 5 . Primer Express software, v3.0.1  was used to design the PCR primers. Two widely used reference genes, namely ß-2-microglobulin (ß2-MG) and RPL-38, were tested. The latter gene exhibited the most stable expression levels, and this gene was used as the housekeeping gene. Primers were produced by the custom oligonucleotide synthesis service TIB Molbiol . The well-established comparative −ΔΔCT method was used. Target mRNA expression was normalized between test samples based on the corresponding RPL38 mRNA transcript levels in each skin sample.The total RNA was extracted from skin tissue samples using the MaxwellProtein isolation was performed using RIPA (Radioimmunoprecipitation assay) lysis . Subsequently, a BCA (Bicinchoninic acid) assay  was used for protein quantification. After loading equal amounts of protein per lane (30 μg protein), 7 to 20% Protean-TGX (Tris-Glycine eXtended) gels  was electrophoresed at 250 V for 30 to 45 min and transferred to a TransBlot Turbo PVDF (Polyvinylidenfluorid) membrane (BIO RAD) using a TransBlot Turbo system (BIO RAD). Membranes were blocked in EveryBlock Blocking buffer (BIO RAD) according to the manufacturer’s antibody specification protocol for 5 min and incubated overnight at 4 °C with the primary antibodies  at 1:1000 dilution. Thereafter, the membranes were washed using PBST  and incubated with an Anti-rabbit IgG HRP-linked AB 7074; . Band detection was performed using the ChemiDoc MP imaging system (BIO RAD). Western blotting was performed without replicates. The comparative quantification of Western blot results was carried out using the BIO RAD Image lab software, Version 6.1 (BIO RAD).The preliminary data of the present study indicate that GP-2250 has anti-neoplastic effects in BRAF-mutated melanoma cell lines regarding tumor cell viability, proliferation, and apoptosis/necrosis. By contrast, apoptosis/necrosis is not observed in benign primary melanocytes even with the use of the highest concentration. GP-2250 is able to downregulate the gene and protein expression of aberrant tumorigenic pathways in melanoma cell lines. Since GP-2250 is a GAPDH inhibitor, the substance may be a promising combination therapy for tumors presenting the Warburg effect, such as melanoma. Hence, future experiments on a combined treatment using BRAFi/MEKi with GP-2250 in BRAF-mutated melanoma cell lines are warranted. Moreover, it could be intriguing to explore in vivo investigations to complement the findings of the present study.