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RNA-sequencing (RNA-seq) is the state-of-the-art technique for transcriptome analysis that takes advantage of high-throughput next-generation sequencing. Although being a powerful approach, RNA-seq imposes major challenges throughout its steps with numerous caveats. There are currently many experimental options available, and a complete comprehension of each step is critical to make right decisions and avoid getting into inconclusive results. A complete workflow consists of: (1) experimental design; (2) sample and library preparation; (3) sequencing; and (4) data analysis. RNA-seq enables a wide range of applications such as the discovery of novel genes, gene/transcript quantification, and differential expression and functional analysis. This chapter will encompass the main aspects from sample preparation to downstream data analysis. It will be discussed how to obtain high-quality samples, replicates amount, library preparation, sequencing platforms and coverage, focusing on best recommended practices based on specialized literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments.Sanger sequencing [6], but with advances in next-generation sequencing technology (NGS), transcriptomic studies have evolved considerably and RNA-seq [7,8] became the state-of-art for transcriptome analysis.RNA-seq consists of the direct sequencing of transcripts by NGS. Several NGS platforms [9][10][11] are commercially available nowadays. In general, an RNA set of interest is converted to a library of complementary DNA (cDNA) fragments and sequenced in a high-throughput manner. Compared to ESTs, RNA-seq provides better resolution and representativeness, whereas when compared to microarrays, the independence of reference sequences facilitates the discovery of novel genes and isoforms [8].RNA-seq experiments harbors challenges from the experimental design to data analysis. Since a complete comprehension of each step is critical to make right decision, this chapter will encompass essential principles required for a successful RNA-seq experiment, focusing on best recommended practices based on specialized and recent literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments. Experimental designIn order to obtain a successful RNA-seq experiment, it is critical to have a good experimental design. Despite its importance, a proper planning is not always done. There are many experimental options available, and to fully comprehend each step, it is essential to make right decisions, avoiding inconclusive results. These choices depend on extrinsic (e.g., cost, time, samples availability) and intrinsic (e.g., experimental design complexity, transcriptional variability among tissues, samples and organisms) factors. The amount of available resources is usually the main extrinsic limiting factor driving researchers' decisio...

Next-generation sequencing (NGS) technologies represented the next step in the evolution of DNA sequencing, through the generation of thousands to millions of DNA sequences in a short time. The relatively fast emergence and success of NGS in research revolutionized the ield of genomics and medical diagnosis. The traditional medicine model of diagnosis has changed to one precision medicine model, leading to a more accurate diagnosis of human diseases and allowing the selection of molecular target drugs for individual treatment. This chapter atempts to review the main features of NGS technique (concepts, data analysis, applications, advances and challenges), starting with a brief history of DNA sequencing followed by a comprehensive description of most used NGS platforms. Further topics will highlight the application of NGS towards routine practice, including variant detection, whole-exome sequencing (WES), whole-genome sequencing (WGS), custom panels (multi-gene), RNA-seq and epigenetic. The potential use of NGS in precision medicine is vast and a beter knowledge of this technique is necessary for an eicacious implementation in the clinical workplace. A centralized chapter describing the main NGS aspects in the clinic could help beginners, scientists, researchers and health care professionals, as they will be responsible for translating genomic data into genomic medicine.

Background: Tumor mutational burden (TMB) is currently under investigation as a biomarker for predicting response to anti-cancer immunotherapy. TMB can be defined as the number of somatic nonsynonymous mutations per Mb in a cancer genome, therefore DNA artifacts could lead to inaccurate measurements. Cytosine deamination (C:G>T:A) is a well-known phenomenon in formalin-fixed, paraffin-embedded (FFPE) samples, producing errors in next-generation sequencing (NGS). Herein, we measured TMB in FFPE samples using two comprehensive NGS panels for routine diagnostics implementation. Methodology: TMB assessment was performed on FFPE samples by amplicon-based target enrichment on Thermo Fisher's Oncomine™ Tumor Mutation Load (OTML; n=9) and/or hybrid capture-based on Illumina's TruSight™ Oncology 500 Assay (TSO500; n=8), according to manufacturer's instructions. TMB values were compared for uracil-DNA glycosylase (UDG) treatment prior to PCR amplification and enrichment method. Samples were also compared according to their previous results in a clinically-certified NGS and an in-house microsatellite instability (MSI) assay. TMB >=10 mutations/Mb was considered high, and MSI >=20% was considered unstable. Results and Discussion: The concordance between TMB values for OTML and TSO500 was 75% (n=4) and 62.5% (n=8) with and without UDG treatment, respectively. UDG reduced artifactual C:G>T:A for both enrichment approaches, reinforcing its presence in FFPE DNA; however, there was no alterations on TMB results for both methods (TSO500 n=5; OTML n=7). C:G>T:A variants could be successfully filtered by low allelic frequency (<5%) or low quality (<q20) in non-treated samples. For OTML, a high baseline noise was observed in samples with high deamination scores (>60), leading to TMB overestimation (>350), which could not be corrected even after UDG treatment and manufacture's bioinformatic adjustment. Interestingly, the same samples presented low TMB values for TSO500. Samples with low deamination rates were not influenced by UDG treatment or bioinformatics adjustment regardless of enrichment method. TMB values from a certified NGS was completely concordant for TSO500 (n=4) but only 25% for OTML (n=4). MSI was fully concordant between certified NGS and TSO500 (n=4) and between in-house method and TSO500 (n=4) (value not analyzed by OTML assay). No QC parameter predicted differences between high and low deaminated samples. Conclusions: NGS enrichment methods strongly influenced TMB assessment. Hybrid-based capture showed better performance than amplicon-based. UDG treatment and higher allelic frequency cutoff could minimize false positives at variant calling level, although no significant effect could be observed for general TMB values. Future experiments should cover other alternatives to distinct FFPE DNA damages. Citation Format: Michele A. Pereira, Feliciana L. Marinho, Joice P. Silva, Renato Puga, Maíra C. Freire, Mariana R. Monteiro, Rafael L. Guedes, Luiz H. Araujo. Tumor mutational burden is affected by next-generation sequencing enrichment method in highly deaminated samples from solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1996.

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