Results

T24-RC48 exhibits morphological changes and reduced drug sensitivity

Morphological observation revealed that the RC48-resistant human bladder cancer cell line T24-RC48 exhibited a smaller cell size and a tendency to grow in clusters compared to the parental T24 cells (Fig. 1a-d). The resistance index, calculated as the ratio of IC50 values (resistant IC50 / parental IC50), was 10 (Fig. 1m), indicating significantly reduced sensitivity to RC48 in the resistant cells.

T24-RC48 maintains proliferation capacity under RC48 treatment

Inverted phase-contrast microscopy showed that after 5 days of culture, all cell lines (T24 wild-type and low/middle/high resistance T24-RC48) maintained good growth status, with intact morphology, tight adhesion, clear edges, and no significant apoptotic bodies or debris. Cell density reached approximately 100% (Fig. 1e-h). However, after 5 days of treatment with 200 μg/ml RC48, the density of T24 wild-type and low/middle/high resistance T24-RC48 cells decreased significantly. T24 wild-type cells showed extensive detachment and shrinkage, exhibiting typical apoptotic morphology (Fig. 1i). The low-resistance T24-RC48 cells adhered sparsely, with abnormal morphology making viability difficult to assess (Fig. 1j). The middle-resistance group retained about 50% cell density, with improved but still abnormal morphology (Fig. 1k). The high-resistance group showed the highest cell density among all treated groups, with normal morphology and visible mitotic figures, indicating active proliferation (Fig. 1l).
Without RC48 treatment, all four cell lines exhibited stable proliferation with similar doubling times (Fig. 1n). After treatment with 200 μg/ml RC48, the OD value of wild type T24 dropped sharply from 2.1 to 0.48 (77.1% inhibition). The inhibition rates decreased with increasing resistance: low resistance (OD = 0.55, 72.5% inhibition), middle resistance (OD = 0.61, 60.5% inhibition), and high resistance (OD = 1.53, 19.4% inhibition) (Fig. 1o), confirming a negative correlation between resistance level and drug sensitivity.

T24-RC48 shows altered cell cycle distribution upon RC48 exposure

After 36 hours of RC48 treatment, flow cytometry revealed significant changes in cell cycle distribution. The G2 peak increased markedly in all treated cells, most notably in wild type T24. No visible differences were observed among RC48-resistant cell lines in untreated or treated conditions. However, resistant cells showed distinct G1 and G2 peak positions compared to wild-type cells (Fig. 2a).
G1 phase proportion decreased significantly (P < 0.0001), while G2 phase proportion increased significantly (P < 0.0001) in all cells after treatment. Among them, the overall growth rate of G2 phase in the drug-resistant group was lower than that in the wild type. S phase proportion decreased significantly in wild type cells (P < 0.05), showed no change in low resistance cells, and increased significantly in middle and high resistance cells (P < 0.0001) (Fig. 2b).

Apoptosis resistance is enhanced in T24-RC48

Apoptosis rates increased in all cell lines after 48 hours of RC48 treatment (Fig. 2c-d). In the untreated group, the apoptosis rate of low resistance cells was significantly lower than that of wild type cells (P < 0.001), the apoptosis rate of middle resistance cells was significantly lower than that of low resistance cells (P < 0.001), and the apoptosis rate of high resistance cells was significantly lower than that of middle resistance cells (P < 0.05). In the treatment group, the apoptosis rate of low resistance cells was significantly lower than that of wild type cells (P < 0.0001), the apoptosis rate of middle resistance cells was significantly lower than that of low resistance cells (P < 0.0001), the apoptosis rate of high resistance cells was significantly lower than that of middle resistance cells (P < 0.0001).

Migratory and invasive capacities are elevated in T24-RC48

ImageJ quantification showed that the number of migrating cells increased with resistance level: wild type (752±31.6), low resistance (894±144.3), middle resistance (978±133.9), and high resistance (1252±338.3). Significant differences were observed only between wild-type and high-resistant groups (P < 0.01) (Fig. 2E-f).
Similarly, in invasion assays, high resistance cells showed significantly higher invasion capacity compared to wild type (P < 0.0001), with no significant difference between wild type and low resistance groups (Fig. 2g-h).

Transcriptomic alterations highlight pathways associated with resistance

Differential gene expression analysis showed the highest number of upregulated genes in the high resistance vs. wild type comparison. Reactome pathway analysis revealed enrichment in extracellular matrix organization1 (involving genes like ICAM1 and DSP), GPCR ligand binding, and peptide ligand-binding receptors (Fig. 3a). DO (Disease Ontology) enrichment analysis showed significant association with urinary system disease (Fig. 3b). GO analysis indicated enrichment in angiogenesis2 (BP, in which VEGF was a top hit), extracellular matrix (CC), and receptor regulator activity (MF) (Fig. 3c). KEGG pathways included cytokine-cytokine receptor interaction3 (featuring IL1β and CD274/PD-L1) and cell adhesion molecules4 (Fig. 3d). List of differentially expressed genes by transcriptome sequencing is provided in Document S1.

Proteomic analysis identifies key protein expression changes

The highest number of differentially expressed proteins was found in the high resistance vs Wild type comparison. Domain enrichment analysis showed significant hits in armadillo-type fold and NAD(P)-binding domain (Fig. 3e). GO terms included immune response to tumor cell (BP, involving genes like PD-L1), superoxide dismutase activity (MF, involving genes like SOD2), and nucleus (CC) (Fig. 3f). KEGG pathways were enriched in metabolic pathways (including antioxidant enzymes such as SOD2 and CAT) (Fig. 3g). Proteins like RBPJ5 and PD-L16 were also significantly upregulated in the resistant cells, corroborating the transcriptomic findings. Candidate list of key proteins in proteomic sequencing is provided in Document S1.

Integrated multi-omics analysis uncovers consensus resistance mechanisms

A total of 120 common differentially expressed genes were identified through Venn analysis. Among these, 118 exhibited consistent expression trends, with 76 commonly upregulated and 42 commonly downregulated (Fig. 3h). This core gene set included several key players implicated in resistance, such as PD-L1, RBPJ, SOD2, CAT, DSP, ICAM1, and IL1β, which were selected for further validation. GO enrichment included immune-related processes (BP, e.g., PD-L1), cell adhesion molecule binding (MF, e.g., ICAM1), and cell-substrate junction (CC) (Fig. 3i, j). KEGG pathways included cytokine-cytokine receptor interaction 7 (e.g., IL1β) and transcriptional misregulation8 in cancer (Fig. 3k). List of key genes obtained from combined proteomic and transcriptomic analysis is provided in Document S1.
Notably, the expression of RBPJ and PD-L1 increased with higher resistance levels in both transcriptomic and proteomic analyses.
In addition, we applied advanced bioinformatic approaches to further elucidate the regulatory mechanism underlying drug resistance. Specifically, transcriptomic and proteomic profiles were analyzed across wild-type and low, medium, and high resistance groups. Genes showing significant alterations (ANOVA, P < 0.05) at both transcriptional and protein levels were identified. Transcription factor enrichment analysis was performed using the TRRUST database, and key transcription factors were prioritized based on the concordance of their expression trends across increasing drug resistance levels at both transcript and protein levels. This systematic approach revealed that RBPJ, a Notch pathway protein, exhibited a stepwise increase in expression with escalating resistance—evident in both transcriptomic and proteomic analyses (Fig. 3l). Moreover, RBPJ was identified as a potential upstream regulator of angiogenesis-related processes enriched in transcriptomic GO analysis and immune response to tumor cell pathways highlighted in proteomic GO analysis, suggesting its role in modulating potential resistance-associated genes such as VEGF and PD-L1.

qRT-PCR validation confirms dysregulation of key resistance-related genes

To validate the key resistance-related genes and pathways identified through our integrated transcriptomic and proteomic analyses, we performed quantitative real-time PCR (qRT-PCR) to examine their mRNA expression levels.
PD-L1 expression was significantly upregulated in all resistant groups compared to wild type T24 cells (P < 0.0001) (Fig. 4a). VEGF expression was consistently increased in all resistant cells (P < 0.0001) (Fig. 4b). RBPJ, a key transcriptional regulator of the Notch pathway13, showed no significant change in low resistance cells but was markedly increased in middle and high resistance groups (P < 0.0001) (Fig. 4c). SOD2, encoding manganese superoxide dismutase, was significantly elevated across all resistant groups (P < 0.01 to P < 0.0001) (Fig. 4d). CAT (catalase) expression was significantly downregulated in all resistant groups (P < 0.0001 to P < 0.001) (Fig. 4e). DSP (desmoplakin) exhibited no change in low resistance cells but was significantly upregulated in middle and high resistance cells (P < 0.0001) (Fig. 4f). ICAM1 was significantly increased in all resistant groups (P < 0.05 to P < 0.0001) (Fig. 4g). IL1β showed no change in low resistance cells but was significantly upregulated in middle and high resistance cells (P < 0.01 to P < 0.0001) (Fig. 4h).

Western Blot analysis confirms upregulation of RBPJ protein in RC48-resistant cells

To further validate the protein expression of RBPJ, a key transcriptional regulator of the Notch signaling pathway, we performed Western blot analysis on T24 wild-type and T24-RC48 cells with varying resistance levels. Consistent with the transcriptomic and proteomic sequencing data, RBPJ protein expression was significantly elevated in the high resistance T24-RC48 cells compared to the wild-type T24 (P < 0.0001). A significant increase was also observed in the middle resistance group (P < 0.05), whereas the low resistance group showed no statistically significant difference relative to wild-type cells (Fig. 4i, j). These results corroborate the multi-omics findings and reinforce the role of RBPJ-mediated Notch signaling activation in the acquisition of RC48 resistance in bladder cancer.

Pro-angiogenic capacity is elevated in T24-RC48 and inhibited by Ivonescimab

Under light microscopy at 2 hours, Human Umbilical Vein Endothelial Cells (HUVECs) began to migrate and connect, forming initial network structures in all groups, with no overt differences observed between conditions or cell lines (Fig. 5a-h). By 4 hours, the tubular networks became more pronounced and extensive. Conditioned media from middle and high resistance T24-RC48 cells appeared to promote more robust tube formation compared to that from parental T24 cells under drug-free conditions (Fig. 5i-p). At the 12-hour time point, the tube networks were mature and stable. The enhanced pro-angiogenic effect of conditioned media from high-resistance cells was visually more distinct compared to the parental control (Fig. 5Q-x).
Statistical analysis of the number of junction points quantified these observations. At 2 hours, Ivonescimab significantly reduced junction points only in the high-resistance group compared to the untreated control (P < 0.05), with no significant differences in parental, low, or middle resistance cells (Fig. 5y). At 4 hours, Ivonescimab again significantly decreased junction points in the high-resistance group (P < 0.05), but not in other groups (Fig. 5y). Similarly, at 12 hours, high-resistance T24-RC48 showed a significant reduction in junction points upon Ivonescimab treatment (P < 0.05), while other groups remained unaffected (Fig. 5y). In the absence of Ivonescimab, at 4 hours, conditioned media from middle-resistance T24-RC48 significantly increased junction points relative to parental T24 (P < 0.05), as did high-resistance T24-RC48 (P < 0.05) (Fig. 5z). At 12 hours, high-resistance T24-RC48 conditioned media further enhanced junction points compared to parental T24 (P < 0.01) (Fig. 5z). No other comparisons reached statistical significance.

Discussion

The development of resistance to antibody-drug conjugates like RC48 is a major clinical challenge. Our multi-omics investigation reveals that acquired resistance in bladder cancer is a multifaceted phenotype driven primarily by transcriptional and proteomic adaptations.
Our phenotypic data clearly demonstrate that T24-RC48 exhibit not only reduced drug sensitivity, but also enhanced migratory and invasive capabilities. These traits are consistent with a more aggressive cancer phenotype often linked to treatment resistance and disease progression9. Furthermore, flow cytometry revealed attenuated G2/M arrest and reduced apoptosis upon RC48 treatment in T24-RC48, indicating evasion of RC48's cytotoxic mechanism.
Multi-omics integration pinpointed several consistently dysregulated genes and pathways. Notably, we observed upregulation of VEGF and PD-L1. VEGF overexpression is a well-established mechanism promoting angiogenesis, and it has been implicated in resistance to various targeted therapies10. Similarly, PD-L1 upregulation may enable tumors to evade host immune surveillance11. These findings align with recent studies emphasizing the tumor microenvironment's role in modulating ADC efficacy12.
Additionally, activation of the Notch signaling pathway emerged as a likely contributor to resistance. Notch signaling is known to promote cancer stemness, epithelial-mesenchymal transition (EMT), and chemoresistance13. Our results suggest that Notch activation may help sustain cell survival and proliferative signals.
The significant downregulation of CAT (catalase) and upregulation of SOD2 (superoxide dismutase 2)14 indicate altered redox homeostasis in T24-RC48, which may afford protection against RC48-induced cytotoxicity, as MMAE-mediated apoptosis often involves reactive oxygen species (ROS) generation15.
Our omics data also highlighted the involvement of cell adhesion and inflammation-related genes such as ICAM116 and IL1β, suggesting that inflammatory pathways and cell-matrix interactions may further support survival under drug stress.
Multi-omics and functional analyses consistently identified the upregulation of PD-L1 and VEGF in T24-RC48, which was associated with activation of the Notch signaling pathway. To illustrate the potential crosstalk between these pathways and their collective contribution to RC48 resistance, we propose a signaling network model where Notch activation transcriptionally promotes PD-L1 and VEGF expression, facilitating immune evasion and angiogenesis, respectively (Fig. 6)17-20. This model provides a mechanistic basis for the phenotypic changes, which carries important clinical implications. The co-upregulation of PD-L1 and VEGF supports a combinatorial therapeutic approach using bispecific agents like Ivonescimab (anti-PD-1/VEGF)21. This strategy could potentially reverse resistance by normalizing vasculature and reactivating antitumor immunity. Previous studies have already demonstrated promising results with RC48 in combination with immune checkpoint inhibitors22 or antiangiogenics23, and our data provide a mechanistic rationale for such synergies.
Notably, whole-genome sequencing revealed an average of 3,155,367 single nucleotide polymorphisms (SNPs) and 923,236 insertions/deletions (InDels) per sample. Detailed sequencing analysis data are provided in Document S1. However, no recurrent mutations directly associated with RC48-resistance were identified, suggesting that this category of genomic alterations may not be the primary driver of resistance in T24-RC48.
In conclusion, this study delineates a complex resistance landscape in RC48-treated BLCA, involving Notch signaling, oxidative stress, angiogenesis, and immune evasion. These results not only advance our understanding of RC48 resistance but also pave the way for biomarker-driven combination regimens to overcome resistance and improve patient outcomes.

Figures

a, Schematic representation of directed evolution of resistance to RC48. b-c, Phase-contrast micrographs of parental T24 and low, middle, and high resistance T24-RC48 cells. d-g, Phase-contrast micrograph of parental T24 and low, middle, and high resistance T24-RC48 cells after 120 hours in drug-free middle. h-k, Phase-contrast micrograph of parental T24 and low, middle, and high resistance T24-RC48 cells after 120 hours of treatment with 200 µg/mL RC48. l, Dose-response curves of T24 and T24-RC48 cells treated with RC48 for 72 hours, as determined by the CCK-8 assay. Data are presented as mean ± SD (n=4). m, Growth curves of parental T24 and low, middle, and high resistance T24-RC48 cells under drug-free conditions over a period of 5 days, as assessed by the CCK-8 assay. Data are presented as mean ± SD (n=6). n, Growth curves of the indicated cell lines treated with a designated concentration of RC48 (200ug/ml) over 5 days. Cell viability was measured daily. Data are presented as mean ± SD (n=6).

a, Representative histograms of cell cycle distribution. b, Quantitative analysis of parental T24 and low, middle, and high resistance T24-RC48 cells’ cell cycle distribution. Data are presented as mean ± SD (n=3). c, Representative flow cytometry dot plots of parental T24 and low, middle, and high resistance T24-RC48 cells’ apoptosis. d, Quantitative analysis of apoptosis induction. Data are presented as mean ± SD (n=3). e, Representative images of migrated parental T24 and low, middle, and high resistance T24-RC48 cells in the Transwell migration assay. Data are presented as mean ± SD (n=3). f, Quantitative analysis of cell migration. g, Representative images of invaded parental T24 and low, middle, and high resistance T24-RC48 cells in the Transwell invasion assay. h, Quantitative analysis of cell invasion. Data are presented as mean ± SD (n=3). (ns, not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001)

a, Bubble plot of reactome pathway enrichment analysis of differentially expressed genes (DEGs) from transcriptomics. b, Bubble plot of DO (Disease Ontology) enrichment analysis of DEGs from transcriptomics. c, Bar chart of GO enrichment analysis of DEGs from transcriptomics. d, Bar chart of KEGG pathway enrichment analysis of DEGs from transcriptomics. e, Bar chart of Domain enrichment analysis of differentially expressed proteins (DEPs) from proteomics. f, Bar chart of GO enrichment analysis of DEPs from proteomics. g, Bubble plot of KEGG pathway enrichment analysis of DEPs from proteomics. h, Venn diagram illustrating the overlap between transcriptomic DEGs and proteomic DEPs. i, Bar chart of joint GO enrichment analysis of the co-upregulated genes/proteins identified in both transcriptomic and proteomic analyses (from 3H). j, Bar chart of joint GO enrichment analysis of the co-downregulated genes/proteins identified in both transcriptomic and proteomic analyses (from 3H). k, Bar chart of joint KEGG pathway enrichment analysis of the common genes/proteins identified in both omics analyses (from 3H). l, The expression trends of RBPJ and PD-L1 across different resistance levels, as measured by both transcriptomics (mRNA level) and proteomics (protein level). (ns, not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001)

a, Expression level of PD-L1 in each group. Data are presented as mean ± SD (n=3). b, Expression level of VEGF in each group. Data are presented as mean ± SD (n=3). c, Expression level of RBPJ in each group. Data are presented as mean ± SD (n=3). d, Expression level of SOD2 in each group. Data are presented as mean ± SD (n=3). e, Expression level of CAT in each group. Data are presented as mean ± SD (n=3). f, Expression level of DSP in each group. Data are presented as mean ± SD (n=3). g, Expression level of ICAM1 in each group. Data are presented as mean ± SD (n=3). h, Expression level of IL1β in each group. Data are presented as mean ± SD (n=3). i, Representative Western blot images of RBPJ protein expression in parental T24 and low, middle, high resistance T24-RC48 cells. GAPDH was used as a loading control. j, Quantitative analysis of RBPJ protein expression levels normalized to GAPDH. Data are presented as mean ± SD (n=3). (ns, not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001)

a-d, Representative phase-contrast micrographs of HUVEC tube formation at 2 hours under drug-free conditions. HUVECs were co-cultured with conditioned media from: (a) parental T24, (b) low resistance T24-RC48, (c) middle resistance T24-RC48, and (d) high resistance T24-RC48. e-h, Representative images of HUVEC tube formation at 2 hours in the presence of Ivonescimab (300 μg/mL). HUVECs were co-cultured with conditioned media from: (e) parental T24, (f) low resistance T24-RC48, (g) middle resistance T24-RC48, and (h) high resistance T24-RC48. i-l, Representative images of HUVEC tube formation at 4 hours under drug-free conditions. HUVECs were co-cultured with conditioned media from: (i) parental T24, (j) low resistance T24-RC48, (k) middle resistance T24-RC48, and (l) high resistance T24-RC48. m-p, Representative images of HUVEC tube formation at 4 hours in the presence of Ivonescimab (300 μg/mL). HUVECs were co-cultured with conditioned media from: (m) parental T24, (n) low resistance T24-RC48, (o) middle resistance T24-RC48, and (p) high resistance T24-RC48. q-t, Representative images of HUVEC tube formation at 12 hours point under drug-free conditions. HUVECs were co-cultured with conditioned media from: (q) parental T24, (r) low resistance T24-RC48, (s) middle resistance T24-RC48, and (t) high resistance T24-RC48. u-x, Representative images of HUVEC tube formation at 12 hours in the presence of Ivonescimab (300 μg/mL). HUVECs were co-cultured with conditioned media from: (u) parental T24, (v) low resistance T24-RC48, (w) middle resistance T24-RC48, and (x) high resistance T24-RC48. y, Quantitative analysis of the number of junction points in the HUVEC network following treatment with or without Ivonescimab. z, Quantitative analysis of the number of junction points in HUVECs cultured with conditioned media from different cell lines over time (2h, 4h, 12h) under drug-free conditions. Data are presented as mean ± SD (n=3). (ns, not significant, * P < 0.05, ** P < 0.01)

Schematic representation of the hypothesized mechanism by which Notch pathway activation (via RBPJ-mediated transcription) leads to increased expression of PD-L1 and VEGF, contributing to immune escape and angiogenesis, respectively. These adaptations collectively promote RC48 resistance and tumor aggressiveness.

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