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Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer.

ABSTRACT: Next-generation sequencing has produced a large quantity of DNA or RNA sequences related to the processes occurring within tumors and their microenvironment in a reasonable time and cost. These data have been used to guide the identification of neoantigens and to determine their specific T-cell receptors. Furthermore, adoptive T-cell therapy targeting neoantigens is under development for cancer treatment. In this review, we first provide an overview of sequencing technologies and the updated findings concerning neoantigens related to adoptive T-cell therapy and then summarize the methods and principles underlying the development of next-generation sequencing-based neoantigen-reactive T-cell therapy for cancer.

SUBMITTER: Yin Q

PROVIDER: S-EPMC6488971 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer.

Yin Qinan Q Tang Jiaxing J Zhu Xuekai X

Briefings in functional genomics 20190301 2

Next-generation sequencing has produced a large quantity of DNA or RNA sequences related to the processes occurring within tumors and their microenvironment in a reasonable time and cost. These data have been used to guide the identification of neoantigens and to determine their specific T-cell receptors. Furthermore, adoptive T-cell therapy targeting neoantigens is under development for cancer treatment. In this review, we first provide an overview of sequencing technologies and the updated fin ...[more]

PMID: 29982317

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Project description:BACKGROUND: We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis. RESULTS: The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc http://fgp.huck.psu.edu/NG_Sims/ngsim.pl, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics. CONCLUSION: NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.

Dataset Information

Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer.

Publications

Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer.

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