RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing

RNA modification writer expression profiles predict clinical outcomes and guide neoadjuvant immunotherapy in non-small cell lung cancer

Background

RNA modifications, including adenosine-to-inosine RNA editing, alternative polyadenylation, m¹A and m⁶A, play a significant role in tumorigenesis and tumor immunity. However, the functions of RNA modification enzymes (writers) in immunotherapy and tumor microenvironment (TME) remain unknown.

Methods

Nonnegative matrix factorization clustering was applied to identify RNA modification clusters in lung adenocarcinoma, one of the most prevalent subtypes of non-small cell lung cancer (NSCLC). CIBERSORT and ESTIMATE algorithms were performed to depict TME characteristics. Additionally, a scoring system called Writer-Score was established to quantify RNA modification patterns and subsequently predict clinical outcomes. We subsequently used RNA sequencing, targeted DNA sequencing and multiplex immunofluorescence to further evaluate the efficacy of Writer-Score in NSCLC patients receiving neoadjuvant immunotherapy.

Findings

We identified three distinct RNA modification clusters and two DEGclusters, which were shown to be strongly associated with a variety of TME features and biological processes. Additionally, the Writer-Score served as an important factor in post-transcriptional events and immunotherapy. The Writer-Score was capable of properly predicting the prognosis of NSCLC patients receiving neoadjuvant PD-1 inhibitor therapy.

Interpretation

Our work systematically analyzed four types of RNA modifications and constructed a scoring system to guide neoadjuvant immunotherapy in NSCLC, which highlighted the writers’ roles in post-transcriptional events, TME and neoadjuvant immunotherapy.

Funding

A full list of funding bodies that supported this study can be found in the Acknowledgements section.

Consistent count region-copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

PURPOSE

Despite the importance of exonic copy number variations (CNVs) in human genetic diseases, reliable next-generation sequencing-based methods for detecting them are unavailable. We developed an expandable and robust exonic CNV detection tool called consistent count region (CCR)-CNV.

METHODS

In total, about 1000 samples of the truth set were used for validating CCR-CNV. We compared CCR-CNV performance with 2 well-known CNV tools. Finally, to overcome the limitations of CCR-CNV, we devised a combined approach.

RESULTS

The mean sensitivity and specificity of CCR-CNV alone were above 95%, which was superior to that of other CNV tools, such as DECoN and Atlas-CNV. However, low covered region and positive predictive value and high false discovery rate act as obstacles to its use in clinical settings. The combined approach showed much improved performance than CCR-CNV alone.

CONCLUSION

In this study, we present a novel diagnostic tool that allows the identification of exonic CNVs with high confidence using various reagents and clinical next-generation sequencing platforms. We validated this method using the largest multiple ligation-dependent probe amplification-confirmed data set, including sufficient copy normal control data. The approach, combined with existing CNV tools, allows the implementation of CCR-CNV in clinical settings.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

BACKGROUND

Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.

RESULTS

In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.

CONCLUSION

These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Transcriptome alteration spectrum in rat lung induced by radiotherapy

Radiation therapy is crucial for curative treatment of lung cancer, which frequently leads to lung injury. Long non-coding RNAs (lncRNAs) are a group of RNAs longer than 200 nucleotides and lack protein-coding capacity. Increasing evidences demonstrate the important roles of lncRNAs in biological processes. However, the mechanism underlying the association of ionizing radiation with alterations in mRNA and lncRNA expression and lung injury remains unclear. In our study, the male Sprague-Dawley (SD) rats were exposed to a dose of 18 Gy of 6 MV X-ray and the transcriptome spectrum was studied. To identify the differentially expressed mRNAs and lncRNAs induced by X-ray, the RNA sequencing data of lung tissues from irradiated and normal rats for 4, 8, and 16 weeks were analyzed, using |log2_ratio| ≥ 1 and q ≤ 0.05 as thresholds for significantly differential expression. The number of differentially expressed mRNAs was 1097 (686 up- and 411 down-) for 4-week radiotherapy group, 3006 (1935 up- and 1071 down-) for 8-week group and 1838 (1178 up- and 660 down-) for 16-week group. There were 606 (279 up- and 327 down-) differentially expressed lncRNAs in 4-week group, 1715 (831 up- and 884 down-) in 8-week group and 1043 (656 up- and 387 down-) in 16-week group. The differentially expressed mRNAs were mainly involved in cell cycle regulation and Fc receptor pathway, while the lncRNA target genes were significantly enriched in cellular stress response and regulation of cell migration. Moreover, compared with the control group, the irradiated group presented higher tissue specificity of lncRNAs. Radiation-induced lung injury, especially the dynamic network of lncRNAs and mRNAs, is worthy of study. Investigation on the regulatory details of related pathways is significant for the prevention of radiation-related lung injury, as well as the improvement of radiation therapy.

Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data

BACKGROUND

Whole genome sequencing (WGS) has increased in popularity and decreased in cost over the past decade, rendering this approach as a viable and sensitive method for variant detection. In addition to its utility for single nucleotide variant detection, WGS data has the potential to detect Copy Number Variants (CNV) to fine resolution. Many CNV detection software packages have been developed exploiting four main types of data: read pair, split read, read depth, and assembly based methods. The aim of this study was to evaluate the efficiency of each of these main approaches in detecting germline deletions.

METHODS

WGS data and high confidence deletion calls for the individual NA12878 from the Genome in a Bottle consortium were the benchmark dataset. The performance of BreakDancer, CNVnator, Delly, FermiKit, and Pindel was assessed by comparing the accuracy and sensitivity of each software package in detecting deletions exceeding 1 kb.

RESULTS

There was considerable variability in the outputs of the different WGS CNV detection programs. The best performance was seen from BreakDancer and Delly, with 92.6% and 96.7% sensitivity, respectively and 34.5% and 68.5% false discovery rate (FDR), respectively. In comparison, Pindel, CNVnator, and FermiKit were less effective with sensitivities of 69.1%, 66.0%, and 15.8%, respectively and FDR of 91.3%, 69.0%, and 31.7%, respectively. Concordance across software packages was poor, with only 27 of the total 612 benchmark deletions identified by all five methodologies.

CONCLUSIONS

The WGS based CNV detection tools evaluated show disparate performance in identifying deletions ≥1 kb, particularly those utilising different input data characteristics. Software that exploits read pair based data had the highest sensitivity, namely BreakDancer and Delly. BreakDancer also had the second lowest false discovery rate. Therefore, in this analysis read pair methods (BreakDancer in particular) were the best performing approaches for the identification of deletions ≥1 kb, balancing accuracy and sensitivity. There is potential for improvement in the detection algorithms, particularly for reducing FDR. This analysis has validated the utility of WGS based CNV detection software to reliably identify deletions, and these findings will be of use when choosing appropriate software for deletion detection, in both research and diagnostic medicine.