Active JNK2 downregulated β-catenin expression and inhibited its transcriptional activity in a dose-dependent manner.

<p>(A) Activated JNK2 reduced β-catenin protein level in a dose-dependent manner. HEK293T cells were co-transfected with pcDNA3-HA-β-catenin along with different amounts of pcDNA3-Flag-MKK7-JNK2, as indicated. Forty-eight hours after transfection, cells were harvested for immunoblotting analysis to detect the alterations of HA-β-catenin and p-JNK. β-actin served as loading control. (B) Activated JNK2 inhibited β-catenin-mediated transcriptional activity of TCF in a dose-dependent manner. HEK293T cells were co-transfected with pcDNA3-HA-β-catenin, TOPFLASH, Renilla, along with different amounts of pcDNA3-Flag-MKK7-JNK2, as indicated. Forty-eight hours after transfection, cells were harvested for luciferase activity assay. Each bar represents the mean ± standard deviation (SD) for triplicated samples.</p

Activated JNK2 interacts with β-catenin and GSK3β.

<p>(A) Active JNK2 binding to β-catenin and GSK3β was analyzed by immunoprecipitation. β-catenin (HA tagged) was co-transfected with empty vector or active JNK2 (Flag tagged) into HEK293T cells. Immunoprecipitation was performed with a Flag antibody. (B) Mammalian two-hybridization assays showed a strong binding of β-catenin and JNK2 protein. The experiments were triplicated independently. (C) Active JNK2 and β-catenin co-localized in the cell nucleus and cytoplasm. Active JNK2 (Flag tagged) and pEGFP-β-catenin were co-transfected into HEK293T cells. The cells were immunostained with a Flag antibody. Co-localization (yellow fluorescence) of active JNK2 (red fluorescence) and β-catenin (green fluorescence) was detected in the nucleus and cytoplasm.</p

Active JNK2 downregulated β-catenin expression, inhibited its transcriptional activity and reduced GSK3β phosphorylation.

<p>(A) Active JNK2 suppressed β-catenin expression and GSK3β phosphorylation in HEK293T cells. HEK293T cells were transfected with pcDNA3-HA-β-catenin together with pcDNA3-Flag-MKK7-JNK1 or pcDNA3-Flag-MKK7-JNK2. Forty-eight hours after transfection, cells were harvested for immunoblotting analysis to detect the alterations of HA-β-catenin, p-JNK, p-c-Jun, phospho-Ser⁹ GSK3β, and GSK3β. β-actin served as loading control. (B) Active JNK2 reduced GSK3β phosphorylation and downregulated β-catenin expression in human lung cancer cell line A549. A549 cells were co-transfected with pcDNA3-HA-β-catenin and pcDNA3-Flag-MKK7-JNK2. Forty-eight hours after transfection, cells were harvested for immunoblotting analysis to detect the alterations of β-catenin, p-JNK, and phospho-Ser⁹ GSK3β. β-actin served as loading control. (C) Active JNK inhibited β-catenin-mediated transcriptional activity of TCF. HEK293T cells were co-transfected with pcDNA3-Flag-MKK7-JNK1 or pcDNA3-Flag-MKK7-JNK2, pcDNA3-HA-β-catenin, TOPFLASH (TOP) or FOPFLASH (FOP), and Renilla. 48 h after transfection, cells were harvested for luciferase activity assay. Each bar represents the mean ± standard deviation (SD) for triplicated samples.</p

Active JNK2-mediated β-catenin degradation occurred through the proteasome system and GSK3β.

<p>(A) HEK293T cells were co-transfected with pcDNA3-HA-β-catenin and pcDNA3-Flag-MKK7-JNK2 (lane 3 and 4) or empty vector (lane 1 and 2). Forty-four hours after transfection, 25 µM MG132 was added to the indicated samples (lane 2 and 4). Four hours later cells were harvested for immunoblotting analysis to detect the expression of HA-β-catenin and p-JNK. (B) Blocking GSK3β activity by LiCl reduced β-catenin expression inhibition by activated JNK2. pcDNA3-HA-β-catenin was transfected into HEK293T cells along with pcDNA3-Flag-MKK7-JNK2 (lane 3 and 4) or empty vector (lane 1 and 2). Thirty-six hours after transfection, half of the cultures were treated overnight with 30 mM LiCl (lane 2 and 4) and then harvested for immunoblotting analysis to detect the expression of HA-β-catenin, phospho-Ser-9 GSK3β, and p-JNK. (C) Mutant β-catenin was resistant to activated JNK2 induced degradation. Wild-type β-catenin (HA- β-catenin) (lanes 1 and 2) or various β-catenin mutants (HA-S33F β-catenin, lanes 3 and 4; HA-S33Y β-catenin, lanes 5 and 6; HA-S37A β-catenin, lanes 7 and 8) were transfected into HEK293T cells along with pcDNA3-Flag-MKK7-JNK2 (lane 2,4,6,8) or empty vector (lanes 1,3,5,7). 48 hours after transfection, cells were harvested for immunoblotting analysis to determine the protein levels of HA-β-catenin. β-actin served as loading control.</p

SBP1 promoter was hypermethylated in human cancer tissues and colon cancer cell lines, which silenced gene expression and was revered by 5-Aza-2′-Deoxycytidine (DAC).

<p>A, SBP1 promoter was highly methylated in human colon cancer tissues. Genomic DNA from adjacent normal (N) and adenocarcinoma (T) tissue was isolated, converted and amplified for human SBP1 promoter using MS-PCR. # indicated different patients. Unmethylated (U) and methylated (M) bands were detected by DNA separation on 2% agarose gels with ethidium bromide. B, Western blotting analysis showed various expression of SBP1 in human colon cancer cell lines; HCT116 cells with stable transfection of SBP1 overexpressing plasmid was used as positive control. C, SBP1 promoter was hypermethylated in HCT116 cells. Genomic DNA from LS174T, HCT116, SW480, Caco-2 and HT-29 colon cancer cells was isolated, converted and amplified via MS-PCR for human SBP1 promoter followed by gel separation of total DNA on 2% agarose gels (U, unmethylated DNA; M, methylated DNA). D, DAC reversed SBP1 promoter methylation in HCT116 cells. DNA from HCT116 cells treated with 30 µM of DAC for 96 h was isolated and separated on 2% agarose gels after conversion and MS-PCR. E, Luciferase assay showed that DAC increased SBP1 promoter luciferase activity of HCT116 cells with transfection of a vector containing the full length promoter of SBP1 and with a treatment of 30 µM DAC or PBS for 72 hours. pGL4 vector and Renilla was co-transfected as controls (* p<0.01, compared to PBS). F, DAC restored SBP1 protein expression in HCT116 cells with DAC treatment for 72 h, analyzed by Western blotting. G. SBP1 mRNA was restored by DAC in HCT116 cells (* p<0.01, compared to PBS). Each experiment was performed at least 3 times.</p

SBP1 had tumor suppressive functions.

<p>A, HCT116 cells transfected with HA-SBP1 had an increased sensitivity to H2O2 by MTS proliferation assay compared to the empty vector (HA). The cells were treated with H2O2 for 24 hours. B, HCT116 cells transfected with HA-SBP1 increased H2O2-induced apoptosis by Flow cytometry analysis. Cells were treated with 0.3 mM of H₂O₂ for 24 hours. Boxes indicate apoptotic cells (sub-G1). C, SBP1 inhibited cancer cell migration: cell migration of SBP1 overexpressing HCT116 cells and HA-control cells was assessed via transwell chambers. Migrated cells were detected with DAPI staining.</p

BP1 attenuated colorectal cancer cell growth in NIH-III nude mice.

<p>NIH-III nude mice were injected with HCT116 cells stably expressing SBP1 (pIRES2-SBP1) or vector (pIRES2) control. Four weeks after injection, tumor volume (A) and tumor weight (B) was determined.</p

Multi-Targeted Antiangiogenic Tyrosine Kinase Inhibitors in Advanced Non-Small Cell Lung Cancer: Meta-Analyses of 20 Randomized Controlled Trials and Subgroup Analyses

<div><p>Background</p><p>Multi-targeted antiangiogenic tyrosine kinase inhibitors (MATKIs) have been studied in many randomized controlled trials (RCTs) for treatment of advanced non-small cell lung cancer (NSCLC). We seek to summarize the most up-to-date evidences and perform a timely meta-analysis.</p><p>Methods</p><p>Electronic databases were searched for eligible studies. We defined the experimental arm as MATKI-containing group and the control arm as MATKI-free group. The extracted data on objective response rates (ORR), disease control rates (DCR), progression-free survival (PFS) and overall survival (OS) were pooled. Subgroup and sensitivity analyses were conducted.</p><p>Results</p><p>Twenty phase II/III RCTs that involved a total of 10834 participants were included. Overall, MATKI-containing group was associated with significant superior ORR (OR 1.29, 95% CI 1.08 to 1.55, <i>P</i> = 0.006) and prolonged PFS (HR 0.83, 0.78 to 0.90, <i>P</i> = 0.005) compared to the MATKI-free group. However, no significant improvements in DCR (OR 1.08, 1.00 to 1.17, <i>P</i> = 0.054) or OS (HR 0.97, 0.93 to 1.01, P = 0.106) were observed. Subgroup analyses showed that the benefits were predominantly presented in pooled results of studies enrolling previously-treated patients, studies not limiting to enroll non-squamous NSCLC, and studies using MATKIs in combination with the control regimens as experimental therapies.</p><p>Conclusions</p><p>This up-to-date meta-analysis showed that MATKIs did increase ORR and prolong PFS, with no significant improvement in DCR and OS. The advantages of MATKIs were most prominent in patients who received a MATKI in combination with standard treatments and in patients who had previously experienced chemotherapy. We suggest further discussion as to the inclusion criteria of future studies on MATKIs regarding histology.</p></div

DataSheet_1_TMBcat: A multi-endpoint p-value criterion on different discrepancy metrics for superiorly inferring tumor mutation burden thresholds.pdf

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.</p

Table_1_TMBcat: A multi-endpoint p-value criterion on different discrepancy metrics for superiorly inferring tumor mutation burden thresholds.xlsx

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.</p