Exome-wide benchmark of difficult-to-sequence regions using short-read next-generation DNA sequencing.

Next-generation DNA sequencing (NGS) in short-read mode has recently been used for genetic testing in various clinical settings. NGS data accuracy is crucial in clinical settings, and several reports regarding quality control of NGS data, primarily focusing on establishing NGS sequence read accuracy, have been published thus far. Variant calling is another critical source of NGS errors that remains unexplored at the single-nucleotide level despite its established significance. In this study, we used a machine-learning-based method to establish an exome-wide benchmark of difficult-to-sequence regions at the nucleotide-residue resolution using 10 genome sequence features based on real-world NGS data accumulated in The Genome Aggregation Database (gnomAD) of the human reference genome sequence (GRCh38/hg38). The newly acquired metric, designated the 'UNMET score,' along with additional lines of structural information from the human genome, allowed us to assess the sequencing challenges within the exonic region of interest using conventional short-read NGS. Thus, the UNMET score could provide a basis for addressing potential sequential errors in protein-coding exons of the human reference genome sequence GRCh38/hg38 in clinical sequencing.

Prediction and prioritization of neoantigens: integration of RNA sequencing data with whole-exome sequencing.

The importance of neoantigens for cancer immunity is now well-acknowledged. However, there are diverse strategies for predicting and prioritizing candidate neoantigens, and thus reported neoantigen loads vary a great deal. To clarify this issue, we compared the numbers of neoantigen candidates predicted by four currently utilized strategies. Whole-exome sequencing and RNA sequencing (RNA-Seq) of four non-small-cell lung cancer patients was carried out. We identified 361 somatic missense mutations from which 224 candidate neoantigens were predicted using MHC class I binding affinity prediction software (strategy I). Of these, 207 exceeded the set threshold of gene expression (fragments per kilobase of transcript per million fragments mapped ≥1), resulting in 124 candidate neoantigens (strategy II). To verify mutant mRNA expression, sequencing of amplicons from tumor cDNA including each mutation was undertaken; 204 of the 207 mutations were successfully sequenced, yielding 121 mutant mRNA sequences, resulting in 75 candidate neoantigens (strategy III). Sequence information was extracted from RNA-Seq to confirm the presence of mutated mRNA. Variant allele frequencies ≥0.04 in RNA-Seq were found for 117 of the 207 mutations and regarded as expressed in the tumor, and finally, 72 candidate neoantigens were predicted (strategy IV). Without additional amplicon sequencing of cDNA, strategy IV was comparable to strategy III. We therefore propose strategy IV as a practical and appropriate strategy to predict candidate neoantigens fully utilizing currently available information. It is of note that different neoantigen loads were deduced from the same tumors depending on the strategies applied.

De Novo Assembly of the Transcriptome of Turritopsis, a Jellyfish that Repeatedly Rejuvenates.

In most animals, aging is an irreversible process; however the species Turritopsis sp. has been observed to undergo a rejuvenation process as many as 14 times. In the present study, we used multiplexed RNA libraries to obtain the transcriptome from four developmental stages (St) of Turritopsis sp., including (I) immature medusa, (II) dumpling, (III) dumpling with a short stolon, and (IV) polyp, which had recently rejuvenated. A total of 4.02 billion paired-end reads were assembled de novo, yielding 90,327 contigs. Our analyses revealed that significant blast hits were recovered for 74% of the assembled contigs, and 19% were successfully annotated with gene ontology (GO) terms. A BLAST search demonstrated that 32% of the contigs were most similar to Hydra vulgarissequences. Raw reads from each sample were mapped against the contigs to find St-specific genes. This represents the first comprehensive set of de novo transcriptome data for this species, which may provide clues toward a better understanding of cyclical rejuvenation in multicellular animals.

Copy number variant detection using next-generation sequencing in EYS-associated retinitis pigmentosa.

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy and a major cause of blindness. RP is caused by several variants of multiple genes, and genetic diagnosis by identifying these variants is important for optimizing treatment and estimating patient prognosis. Next-generation sequencing (NGS), which is currently widely used for diagnosis, is considered useful but is known to have limitations in detecting copy number variations (CNVs). In this study, we re-evaluated CNVs in EYS, the main causative gene of RP, identified via NGS using multiplex ligation-dependent probe amplification (MLPA). CNVs were identified in NGS samples of eight patients. To identify potential CNVs, MLPA was also performed on samples from 42 patients who were undiagnosed by NGS but carried one of the five major pathogenic variants reported in Japanese EYS-RP cases. All suspected CNVs based on NGS data in the eight patients were confirmed via MLPA. CNVs were found in 2 of the 42 NGS-undiagnosed RP cases. Furthermore, results showed that 121 of the 661 patients with RP had EYS as the causative gene, and 8.3% (10/121 patients with EYS-RP) had CNVs. Although NGS using the CNV calling criteria utilized in this study failed to identify CNVs in two cases, no false-positive results were detected. Collectively, these findings suggest that NGS is useful for CNV detection during clinical diagnosis of RP.

An Immunogram for the Cancer-Immunity Cycle: Towards Personalized Immunotherapy of Lung Cancer.

INTRODUCTION: The interaction of immune cells and cancer cells shapes the immunosuppressive tumor microenvironment. For successful cancer immunotherapy, comprehensive knowledge of antitumor immunity as a dynamic spatiotemporal process is required for each individual patient. To this end, we developed an immunogram for the cancer-immunity cycle by using next-generation sequencing. METHODS: Whole exome sequencing and RNA sequencing were performed in 20 patients with NSCLC (12 with adenocarcinoma, seven with squamous cell carcinoma, and one with large cell neuroendocrine carcinoma). Mutated neoantigens and cancer germline antigens expressed in the tumor were assessed for predicted binding to patients' human leukocyte antigen molecules. The expression of genes related to cancer immunity was assessed and normalized to construct a radar chart composed of eight axes reflecting seven steps in the cancer-immunity cycle. RESULTS: Three immunogram patterns were observed in patients with lung cancer: T-cell-rich, T-cell-poor, and intermediate. The T-cell-rich pattern was characterized by gene signatures of abundant T cells, regulatory T cells, myeloid-derived suppressor cells, checkpoint molecules, and immune-inhibitory molecules in the tumor, suggesting the presence of antitumor immunity dampened by an immunosuppressive microenvironment. The T-cell-poor phenotype reflected lack of antitumor immunity, inadequate dendritic cell activation, and insufficient antigen presentation in the tumor. Immunograms for both the patients with adenocarcinoma and the patients with nonadenocarcinoma tumors included both T-cell-rich and T-cell-poor phenotypes, suggesting that histologic type does not necessarily reflect the cancer immunity status of the tumor. CONCLUSIONS: The patient-specific landscape of the tumor microenvironment can be appreciated by using immunograms as integrated biomarkers, which may thus become a valuable resource for optimal personalized immunotherapy.