Nanovaccine Displaying Immunodominant T Cell Epitopes of Fibroblast Activation Protein Is Effective Against Desmoplastic Tumors

Cancer-associated fibroblasts (CAFs), which are dominant cell types in the tumor microenvironment (TME), support tumor growth by secreting cytokines and forming an extracellular matrix (ECM) that hampers the penetration of chemical and biological therapeutics within the tumor and thereby limits their therapeutic efficacy. Here, we report a cancer nanovaccine targeting fibroblast activation protein α (FAP)-expressing CAFs as a potential pan-tumor vaccine. We predicted immunodominant FAP-specific epitope peptides in silico and selected two candidate peptides after in vitro and in vivo screening for immunogenicity and antitumor efficacy. Next, we developed a nanoparticle-based vaccine that displays the two selected epitope peptides on the surface of lipid nanoparticles encapsulating CpG adjuvant (FAPPEP-SLNPs). Immunization with one of two FAPPEP-SLNP nanovaccines led to considerable growth inhibition of various tumors, including desmoplastic tumors, by depleting FAP+ CAFs and thereby reducing ECM production in the TME while causing little appreciable adverse effects. Furthermore, when combined with a chemotherapeutic drug, the FAPPEP-SLNP nanovaccine increased drug accumulation and resulted in a synergistic antitumor efficacy far better than that of each corresponding monotherapy. These findings suggest that our FAPPEP-SLNP nanovaccine has potential for use as an “off-the-shelf” pan-tumor vaccine applicable to a variety of tumors and may be a suitable platform for use in various combination therapies

Presentation_1_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.zip

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Data_Sheet_2_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.xlsx

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Data_Sheet_1_Confounder-adjusted MRI-based predictors of multiple sclerosis disability.pdf

IntroductionBoth aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as “accelerated aging.” Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.MethodsStandardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.ResultsConfounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.ConclusionGBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.</p

Additional file 10 of Metformin regulates expression of DNA methyltransferases through the miR-148/-152 family in non-small lung cancer cells

Additional file 10: Proposed action of metformin on cell growt

Additional file 7 of Metformin regulates expression of DNA methyltransferases through the miR-148/-152 family in non-small lung cancer cells

Additional file 7: Correlation between age, tumor size, and the expression of miR-148/-152 family member

Additional file 9 of Metformin regulates expression of DNA methyltransferases through the miR-148/-152 family in non-small lung cancer cells

Additional file 9: Cox Proportional hazards analysi

Additional file 1 of Risk factors for scabies in hospital: a systematic review

Supplementary Material 1: Table S1. Search strategies according to each databas

Additional file 3 of Metformin regulates expression of DNA methyltransferases through the miR-148/-152 family in non-small lung cancer cells

Additional file 3: Gene Ontology analysis of differentially expressed miRNA

Additional file 6 of Metformin regulates expression of DNA methyltransferases through the miR-148/-152 family in non-small lung cancer cells

Additional file 6: Clinicopathological characteristic