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Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

ABSTRACT: Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

PROVIDER: PRJNA605000 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11029235.fastq.gz	Fastqsanger.gz
	SRR11029236.fastq.gz	Fastqsanger.gz
	SRR11029237.fastq.gz	Fastqsanger.gz
	SRR11029238.fastq.gz	Fastqsanger.gz
	SRR11029239.fastq.gz	Fastqsanger.gz

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Project description:Introduction Immune checkpoint blockade (ICB) is a systemic therapeutic option for advanced hepatocellular carcinoma (HCC). However, low patient response rates necessitate the development of robust predictive biomarkers that identify individuals who will benefit from ICB. A 4-gene inflammatory signature, comprising CD8, PD-L1, LAG-3, and STAT1, was recently shown to be associated with a better overall response to ICB in various cancer types. Here, we examined whether tissue protein expression of CD8, PD-L1, LAG-3, and STAT1 predicts response to ICB in HCC. Methods HCC samples from 191 Asian patients, comprising resection specimens from 124 patients (ICB-naïve) and pre-treatment specimens from 67 advanced HCC patients treated with ICB (ICB-treated), were analyzed for CD8, PD-L1, LAG-3, and STAT1 tissue expression using multiplex immunohistochemistry followed by statistical and survival analyses. Results Immunohistochemical and survival analyses of ICB-naïve samples showed that high LAG-3 expression was associated with shorter median progression-free survival (mPFS) and overall survival (mOS). Analysis of ICB-treated samples revealed that high proportions of LAG-3+ and LAG-3+CD8+ cells pre-treatment were most closely associated with longer mPFS and mOS. Using a log-likelihood model, adding the total LAG-3+ cell proportion to the total CD8+ cell proportion significantly increased the predictive values for mPFS and mOS, compared with the total CD8+ cell proportion alone. Moreover, levels of CD8 and STAT1, but not PD-L1, were significantly correlated with better responses to ICB. After analyzing viral-related and non-viral HCC samples separately, only the LAG3+CD8+ cell proportion was significantly associated with responses to ICB regardless of viral status. Conclusion Immunohistochemical scoring of pre-treatment levels of LAG-3 and CD8 in the tumor microenvironment may help predict ICB benefits in HCC patients. Furthermore, immunohistochemistry-based techniques offer the advantage of being readily translatable in the clinical setting.

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Adapting the 10x Genomics Platform for single-cell RNA-seq in yeast reveals the importance of stochastic gene expression during the lag phase

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