暂无摘要。

Detrimental molecular processes in multiple sclerosis (MS) lead to the cellular accumulation of lipid peroxidation products and iron in the CNS, which represents the main driving force for ferroptosis. Ferroptosis is an iron-dependent form of regulated cell death, with proposed roles in neurodegeneration, oligodendrocyte loss and neuroinflammation in the pathogenesis of MS. Ferroptosis-related gene expression signature and molecular markers, which could reflect MS severity and progression, are currently understudied in humans. To tackle these challenges, we have applied a curated approach to create and experimentally analyze a comprehensive panel of ferroptosis-related genes covering a wide range of biological processes associated with ferroptosis. We performed the first ferroptosis-related targeted RNAseq on PBMCs from highly distinctive MS phenotype groups: mild relapsing-remitting (RR) (n = 24) and severe secondary progressive (SP) (n = 24), along with protein detection of GPX4 and products of lipid peroxidation (MDA and 4-HNE). Out of 138 genes, 26 were differentially expressed genes (DEGs), indicating changes in both pro- and anti-ferroptotic genes, representing a molecular signature associated with MS severity. The top three DEGs, as non-core ferroptosis genes, CDKN1A, MAP1B and EGLN2, were replicated by qPCR to validate findings in independent patient groups (16 RR and 16 SP MS). Co-expression and interactions of DEGs were presented as additional valuable assets for deeper understanding of molecular mechanisms and key targets related to MS severity. Our study integrates a wide genetic signature and biochemical markers related to ferroptosis in easily obtainable PBMCs of MS patients with clinical data and disease severity, thus providing novel molecular markers which can complement disease-related changes in the brain and undergo further research as potential therapeutic targets.

BACKGROUND: We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.
METHODS: The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.
RESULTS: The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.
CONCLUSIONS: Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

BACKGROUND: Our objective was to assess whether modifications to a customized targeted RNA sequencing (RNAseq) assay to include unique molecular identifiers (UMIs) that collapse read counts to their source mRNA counts would improve quantification of transcripts from formalin-fixed paraffin-embedded (FFPE) tumor tissue samples. The assay (SET4) includes signatures that measure hormone receptor and PI3-kinase related transcriptional activity (SETER/PR and PI3Kges), and measures expression of selected activating point mutations and key breast cancer genes.
METHODS: Modifications included steps to introduce eight nucleotides-long UMIs during reverse transcription (RT) in bulk solution, followed by polymerase chain reaction (PCR) of labeled cDNA in droplets, with optimization of the polymerase enzyme and reaction conditions. We used Lin\'s concordance correlation coefficient (CCC) to measure concordance, including precision (Rho) and accuracy (Bias), and nonparametric tests (Wilcoxon, Levene\'s) to compare the modified (NEW) SET4 assay to the original (OLD) SET4 assay and to whole transcriptome RNAseq using RNA from matched fresh frozen (FF) and FFPE samples from 12 primary breast cancers.
RESULTS: The modified (NEW) SET4 assay measured single transcripts (p< 0.001) and SETER/PR (p=0.002) more reproducibly in technical replicates from FFPE samples. The modified SET4 assay was more precise for measuring single transcripts (Rho 0.966 vs 0.888, p< 0.01) but not multigene expression signatures SETER/PR (Rho 0.985 vs 0.968) or PI3Kges (Rho 0.985 vs 0.946) in FFPE, compared to FF samples. It was also more precise than wtRNAseq of FFPE for measuring transcripts (Rho 0.986 vs 0.934, p< 0.001) and SETER/PR (Rho 0.993 vs 0.915, p=0.004), but not PI3Kges (Rho 0.988 vs 0.945, p=0.051). Accuracy (Bias) was comparable between protocols. Two samples carried a PIK3CA mutation, and measurements of transcribed mutant allele fraction was similar in FF and FFPE samples and appeared more precise with the modified SET4 assay. Amplification efficiency (reads per UMI) was consistent in FF and FFPE samples, and close to the theoretically expected value, when the library size exceeded 400,000 aligned reads.
CONCLUSIONS: Modifications to the targeted RNAseq protocol for SET4 assay significantly increased the precision of UMI-based and reads-based measurements of individual transcripts, multi-gene signatures, and mutant transcript fraction, particularly with FFPE samples.