human melanoma cells  (ATCC)

Journal: bioRxiv

Article Title: Acquired resistance to the PRMT5 inhibitor confers collateral sensitivity to MEK inhibition in MTAP-null non-small cell lung cancer

doi: 10.64898/2026.04.16.719008

Figure Lengend Snippet: Assessment of MTAP status and PRMT5 inhibition in MTAP-isogenic cells. (A) Immunoblot analysis of MTAP and PRMT5 protein levels across a panel of NSCLC cell lines. (B) Immunoblot validation of MTAP expression in MTAP-isogenic murine and human cell lines. (C) Immunoblot analysis of SDMA levels in the A375 MTAP-isogenic cell pair following treatment with increasing concentrations of EPZ015666 or MRTX1719.

Article Snippet: Human NSCLC cell lines H1299 (#CRL-5803, RRID: CVCL_0060, sex: male), H1975 (#CRL-5908, RRID: CVCL_1511, sex: female), A549 (#CCL-185, RRID: CVCL_0023, sex: male), H838 (#CRL-5844, RRID: CVCL_1594, sex: male), H1437 (#CRL-5872, RRID: CVCL_1472, sex: male), and H2126 (#CCL-256, RRID: CVCL_1532, sex: male); the human SCLC cell line H2171 (#CRL-5929, RRID: CVCL_1536, sex: male); human colorectal cancer cell lines RKO (#CRL-2577, RRID: CVCL_0504, sex: unspecified) and DLD1 (#CCL-221, RRID: CVCL_0248, sex: male); the human melanoma cell line A375 (#CRL-1619, RRID: CVCL_0132, sex: female); and the murine melanoma cancer cell lines B16-F10 (#CRL-6475, RRID: CVCL_0159, sex: male) were obtained from the American Type Culture Collection (ATCC).

Techniques: Inhibition, Western Blot, Biomarker Discovery, Expressing

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Decreased ribosome levels observed in DTP cells across different types of cancer (A–C) Polysome profiles of DTP cells across various cancer types and treatment regimens. A375 BRAF V600E melanoma cells were treated with vemurafenib (1 μM) and cobimetinib (500 nM) for 3 days; PC9 EGFR del19 lung adenocarcinoma cells were treated with 1 μM erlotinib for 5 days; QBC939 cholangiocarcinoma treated with 10 μM gemcitabine and 5 μM cisplatin, evaluated using sucrose-gradient ultracentrifugation with a range of 5% to 50%. Par: parental cells subjected to dimethyl sulfoxide (DMSO) treatment, Per: persister cells. All the polysome profiling in each cell model have been performed in n = 3 biological independent experiments, representative profiling images were shown here. (D) Western blotting analysis of key translation regulator c-Myc and ribosomal proteins in different type of cancer DTP cells. β-tubulin was used as a loading control. Representative results were shown, all blots in each cell model were performed in n = 3 independent biological experiments. See also A. (E) Principal-component analysis of the transcriptomic data from four different treatment phases, including untreated samples, regression phase, stable residual phase, and regrowth phase. n = 4 independent mice were analyzed in each group. (F) Transcriptomes analysis of ribosomal proteins alterations of a murine melanoma model at various phases. (G) Transcriptional changes of c-Myc expression in murine melanoma model. Data are shown as mean ± SD ( n = 4 mice). The Mann-Whitney nonparametric t test p values were calculated. (H) The mRNA expression level of c-Myc within biopsies from melanoma patients treated with vemurafenib. Data are shown as mean ± SD from n = 7 paired melanoma patient samples in pre-treatment and on-treatment samples. The Mann-Whitney nonparametric t test p values were calculated. See also . (I) Schematic view of the dynamics of treatment response in relationship with persister cells.

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: Western Blot, Control, Expressing, MANN-WHITNEY

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Translational alterations and specific ribosomes associate with the essentiality of ribosomal proteins in DTP cells (A) Polysome profile of A375 human melanoma cells whose mRNAs were subjected to polysome RNA-seq. See also . (B) Scatterplot of genome-wide transcripts undergoing differential regulation at transcription and translation level. Total mRNAs (transcription) and polysome-bound mRNAs (translatome) were analyzed by using Xtail software, the respective transcripts were classified into three categories, including transcriptional regulation (|total mRNA log 2 fold change| > 1, polysome mRNA log 2 fold change| < 1, p -adjusted <0.05), translational regulation (|total mRNA log 2 fold change| < 1, polysome mRNA log 2 fold change| > 1, p -adjusted <0.05) and homodirectional regulation. See also . (C) The correlation between total mRNA and polysome-bound mRNA abundance of transcripts selectively upregulated at the translational level in persister cells (Transcript TEup ). Left, the respective abundances of Transcript TEup genes in total mRNA and polysome-bound mRNA fractions in A375 parental cells (Par); Right, the respective abundances of Transcript TEup genes in total mRNA and polysome-bound mRNA fractions in A375 persister cells (Per). N = 4 independent biological samples were analyzed in each fraction. See also . (D) Quantitative PCR examination of corresponding transcripts in different fractions of A375 cells, including total mRNA fraction, 80S monosome and polysome fractions. Unpaired t test was performed for statistical analysis. See also . (E) Schematic view of quantitative high-coverage tandem-mass-tag (TMT) mass spectrometry measuring the expression of all ribosomal proteins within parental (Par) and DTP cells (Per). (F) TMT-mass spectrometry-based comparative analysis of ribosomal protein components in parental (Par) and DTP cells (Per). N = 2 biological independent samples were analyzed in TMT experiment. See also . (G and H) Essentiality analysis of ribosomal proteins derived from DTP cells based on The Cancer Dependency Map (DepMap) project. (G) Upregulated propensity of ribosome proteins (RPs) within large subunit. (H) Downregulated propensity of RPs within small subunit. (I) Differential expression analysis of ribosome-bound proteins in parental (Par) and DTP cells (Per) based on TMT mass spectrometry. Red dots, fold change >2, adjusted p value <0.01; Blue dots, fold change <2, adjusted p value <0.01. See also .

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: RNA Sequencing, Genome Wide, Software, Real-time Polymerase Chain Reaction, Mass Spectrometry, Expressing, Derivative Assay, Quantitative Proteomics

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Myc-mediated downregulation of ribosome biogenesis in DTP cells (A) Immunofluorescence of nucleolar proteins, nucleophosmin 1(NPM1, green) and pescadillo (PES1, red), showing different phenotype of ribosome biogenesis center in parental and DTP cells. Representative images were shown from n = 3 biological independent experiments. Scale bars, 10 μm. (B) Quantification of nucleolus number and area per nuclear at single cell level in A375 melanoma cells. Four different regions of interest were randomly selected for image analysis, and a total of 100 cells were analyzed. Unpaired t test Mann-Whitney test was performed for statistical analyses. (C) Image-based machine learning classification by using Cellprofiler to categorize the nucleolar phenotype into positive (more number and higher granularity) and negative (less number and lower granularity) feature. (D and E) The nucleoli of DTP cells exhibited a higher proportion of negative features in two distinct DTP models. (D) A375 melanoma persister cells treated with 1 μM vemurafenib and 500 nM cobimetinib, n = 4 different regions of interest were randomly selected for image analysis, and a total of 100 cells were analyzed. (E) Lung adenocarcinoma PC9 persister cells treated with 1 μM erlotinib. N = 6 different regions of interest were randomly selected for image analysis, and a total of 100 cells were analyzed. Data are shown as mean ± SD. The Mann-Whitney nonparametric t test p values were calculated. (F and G) Lower intensity of cell proliferation marker Ki67 staining showing correlation with negative feature of nucleolar phenotype in A375 melanoma DTP cells. Representative images were shown from n = 3 biologically independent experiments. Ki67 fluorescence intensity was quantified within individual cells ( n = 30) by Cellprofiler software. The Mann-Whitney nonparametric t test p values were calculated. (H) Chromatin immunoprecipitation (ChIP) with c-Myc antibody in A375 melanoma parental (Par) and DTP cells (Per). Genomic DNA was purified and quantitative PCR was conducted to assess the enrichment levels of the corresponding c-Myc binding sequence. The data are presented as the mean ± SD from n = 3 biologically independent experiments. CCND1 enrichment level was used as a positive control. The Mann-Whitney nonparametric t test p values were calculated. (I) Levels of RPL7L and RPL39 proteins were downregulated in DTP cells compared to parental cells, as indicated by TMT mass spectrometry analysis. Data are shown as mean ± SD. The Mann-Whitney nonparametric t test p values were calculated. (J) Knockdown of c-Myc reduced global translational activity in A375 melanoma cells as indicated by polysome profiling. Inset, c-Myc shRNA analysis by western blot. n = 3 biologically independent experiments were performed showing similar results. (K) Knockdown of c-Myc enhanced the responsiveness to vemurafenib and cobimetinib treatment in SK-MEL-28 and WM983B melanoma cells. Representative results were shown from n = 3 biologically independent experiments. Data are shown as mean ± SD. (L) Clonogenic assay of A375 melanoma cells under vemurafenib and cobimetinib treatment with or without c-Myc shRNA-mediated knockdown for 3 weeks. N = 4 biologically independent experiments were performed. Data are shown as mean ± SD. The Mann-Whitney nonparametric t test p values were calculated. Corresponding clonogenic assay example images were shown for each treatment group. Combo, vemurafenib (1 μM) + cobimetinib (500 nM).

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: Immunofluorescence, Single Cell, MANN-WHITNEY, Marker, Staining, Fluorescence, Software, Chromatin Immunoprecipitation, Purification, Real-time Polymerase Chain Reaction, Binding Assay, Sequencing, Positive Control, Mass Spectrometry, Knockdown, Activity Assay, shRNA, Western Blot, Clonogenic Assay

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Mathematical framework of ribosome-induced survival checkpoint (RISK) (A) The “Risk” model description. RNA i (i = 1 or 2), RNA 1 represents stress-related genes featuring increased N6-methyladenosine modification in their 5′UTR (yellow dot on the mRNA), RNA 2 represents proliferation-related genes featuring increased N6-methyladenosine modification in the 3′UTR (gray dot on the mRNA); P i (i = 1 or 2), corresponding protein productions of RNA i ; E = N R , an ensemble of ribosomes; μ, production rate of proteins; γ, degradation rate of proteins; τ i (i = 1 or 2), the ribosome interaction preference weight of each type of mRNA. (B and C) Mathematical model exploration of the relationship between mRNA and protein production in ribosome overloaded or under loaded conditions. (B) RNA 1 = RNA 2 = 150. From left to right: mean ratio of P 1 to total protein at equilibrium, mean number of P 1 as function of the abundance of ribosomes N ribo . Continuous lines, result of the simulation. Dotted line, approximated mean by Wallenius noncentral hypergeometric distribution. (C) From left to right: N ribo = 20 (underloaded), N ribo = 220 (overloaded). Mean number of P 1 as function of RNA 1 for different values of τ 1 : τ 1 = 0.1 (blue curve), τ 1 = 0.3 (red curve), τ 1 = 0.5 (yellow curve), τ 1 = 0.7 (purple curve), τ 1 = 0.9 (green curve). See also . (D) Protein production allocation under differential ribosome quantities. From top to bottom: RNA 1 = RNA 2 = 50% total RNAs, RNA 2 > RNA 1 = 25% total RNAs. Functional relationship of τ 1 , N ribo and resulting ratio of P 1 to total protein at equilibrium. Darkness or lightness of the square color represents the relative protein ratio of P 1 to total protein. (E) Parameter estimation of mathematical model by using experimental data from in vivo A375 melanoma xenograft. Top: evolution of the number of melanoma cells in continuous 200 ppm PLX4720 (BRAFi) and 7 ppm PD0325901 treatment for different initial conditions of %RNA 1 . Dotted lines represent experimental data. Continuous lines represent computational simulated results. Bottom: the corresponding dynamic changes in N ribo over days. (F–H) Evolution of time to reach persistence revealed by the mathematical model, for α S = 0.0037. (F) The time to reach persistence as a function of ribosome adaptation speed α R under different initial percentage of RNA 1 . (G) The corresponding percentage residual persister cells. (H) The time to reach acquired resistance as a function of ribosome adaptation speed α R . See also .

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: Modification, Functional Assay, In Vivo

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Targeting ribosome in combination with SOC treatment disrupts the “Risk” balance (A) Schematic view of SOC treatment (Tx) in combination with ribosome inhibitor (RIBOi). v b , proliferation rate of tumor cells; v d , death rate of tumor cells. (B) Parameter space impacting on the distance between experimental data and simulation results. By altering both ribosome adaptation speed α R and the speed of resistance acquisition ɑ s , decrease of α R can systematically better simulate the experimental results by fixing ɑ s . Red cross, represents the qualitative fitting between experimental data and simulation results. See also . (C) Evolution of tumor volume of melanoma cells under different type of treatment regimens in nude mice harboring BRAF V600E -mutated A375 melanoma xenografts. Dashed lines: data from in vivo experiments. Solid lines: simulated data from the model. N = 8 mice in each group were subjected to treatment. Data are represented by mean ± SD. (D) Waterfall plot of the best response after 30 days treatment in nude mice harboring BRAF V600E -mutated A375 melanoma xenografts. The percentage changes of tumor volumes were calculated based on the tumor volume at 30 days after the initiation of the treatment compared to the original tumor volume at day 1.

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: In Vivo

Journal: iScience

Article Title: Mathematical modeling of ribosome competition informs testable treatment strategies for drug-tolerant cancer persister cells

doi: 10.1016/j.isci.2026.115493

Figure Lengend Snippet: Intermittent treatment significantly slows down the rate of resistance acquisition (A) Schematic view of intermittent treatment regimen. T 1 , duration under treatment; T 2 , duration of drug withdrawal; Q 1 , tumor volume change after initial treatment; Q 2 , tumor volume change after one cycle of intermittent treatment; N, tumor burden. (B) Parameter space of initial percentage of RNA1 and ribosome adaptation speed α R . Here, we show the resulting Q1 (top), Q2 (middle), final number of cells (log10) as a function of both initial %RNA1 and α R . See also . (C) In silico evolution of tumor burden under various intermittent treatment strategies. We start with N0 = 500 cells and alter the drug induction (T1) and drug holiday (T2) to simulate the tumor volume changes during 150 days. (D) Experimental data of different intermittent treatment regimens in nude mice harboring BRAF V600E -mutated A375 melanoma xenografts. Data are presented as mean ± SD, n = 6 mice each group.

Article Snippet: The human cell lines A375, SK-MEL-28, and QBC939, as well as the mouse cell line YUMM1.7, were purchased from the American Type Culture Collection (ATCC).

Techniques: In Silico