Evaluation of Next Generation Sequencing for Detecting HER2 Copy Number in Breast and Gastric Cancers.

Amplicon-based next generation sequencing (NGS) approaches have been preferentially adopted by the clinical laboratories on the basis of a short turnaround time (TAT) and small DNA input needs. However, little work has been done to assess the amplicon-based NGS methods for copy number variation (CNV) detection in comparison with current standard methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). The correlation between NGS based CNV detection and the later standard methods has remained unexplored. We developed an amplicon-based panel to detect human epidermal receptor growth factor (HER2) amplification in formalin-fixed paraffin-embedded (FFPE) tumor tissue samples from 280 breast cancer and 50 gastric cancer patients. Assessment by IHC and FISH was conducted in parallel, and descriptive statistics were used to assess the concordance. The copy number detected by NGS was correlated with either the average HER2 copy number (signals/cell) (r = 0.844; p < 0.001) or the HER2/CEP17 ratio (r = 0.815; p < 0.001). We determined a cut-off value for NGS to categorize HER2 amplification status by using 151 HER2 non-amplified FFPE samples. In breast cancer patients, the cut-off value was 2.910, with 95.35%, 98.67% and 97.29% sensitivity, specificity and concordance, respectively. However, this cut-off value displayed low sensitivity in gastric cancer patients (64.71%), and the following macrodissection procedure was not effective for increasing sensitivity (57.14%). Evaluation of HER2 copy number with NGS in our study was comparable with IHC and FISH in breast cancer patients, but concordance in gastric cancer was only moderate. The greater discordance in gastric cancer may reflect the underlying biological mechanisms, and further study is warranted. NGS-based HER2 assessment may decrease the equivocal HER2 determinations in breast cancer patients assessed by FISH/IHC.

Identification of MET exon14 skipping by targeted DNA- and RNA-based next-generation sequencing in pulmonary sarcomatoid carcinomas.

BACKGROUND: Pulmonary sarcomatoid carcinomas (PSCs) constitutes a heterogeneous group of NSCLCs, which show poor prognosis even with aggressive surgical treatment and postoperative chemotherapy. The detection MET exon14 skipping (METex14 skipping) in PSCs suggests the targeted therapeutic opportunities with MET TKIs. PATIENTS AND METHODS: We detected MET exon14 alterations using both targeted DNA- and RNA-based Next Generation Sequencing (NGS) and elucidated the driver mutation profile of 77 Chinese PSC patients. We also collected and analyzed the demographic features and clinical outcomes of patients harboring METex14 skipping mutation. RESULTS: METex14 skipping was detected in 20.8% of PSCs. A concordance of 96.1% was observed for DNA- and RNA-based NGS. 13 different genomic variants were revealed to induce METex14 skipping, including indels (N = 1) at splice acceptor sites, base substitutions (N = 4) and indels (N = 5) at splice donor sites, indels (N = 2) in the ∼20bp intronic noncoding region adjacent to the splice acceptor site, and indels (N = 1) in the exonic region. Patients harboring METex14 skipping tended to be older than others. In most cases, METex14 skipping were exclusive to other tumor driver alterations, however, we detected one case with METex14 skipping and a concurrent KRAS mutation. In survival analysis, we identified METex14 skipping as an unfavorable factor for Disease Free Survival (DFS) of PSCs. CONCLUSION: Although a high concordance of 96.1% was observed for DNA- and RNA-based NGS in detecting METex14 skipping, RNA-based sequencing appears the most accurate method, because some somatic variants not covering METex14 splices sites might also induce skipping. Without targeted treatment, patients with METex14 skipping had a shorter DFS. Because of the clinical significance of METex14 skipping and emerging effective treatment with MET TKI, the clinical screening for METex14 skipping should be encouraged, particularly in PSC patients who have poor prognosis with no effective treatments.

Performance comparison of NextSeq and Ion Proton platforms for molecular diagnosis of clinical oncology.

PURPOSE: Next-generation sequencing is a powerful approach to detect genetic mutations with which cancer diagnosis and treatment can be tailored to the individual patient in the era of personalized and precision medicine. Ion Torrent Systems Ion Proton and Illumina NextSeq are 2 major targeted sequencing platforms; however, not much work has been done to compare these platforms' performance for mutation detection in formalin-fixed paraffin-embedded (FFPE) materials. METHODS: We benchmarked the performance by using a collection of FFPE samples from 23 patients with different cancers for NextSeq and Ion Proton platforms. We report analysis of sequencing in terms of average coverage depth, read length, and variant detection. RESULTS: Sequencing results by NextSeq and Ion Proton displayed near perfect coverage behavior (>99%) on target region. We analyzed the ability to call variants from each platform and found that Ion Proton sequencing can identify 89% of single nucleotide variants (SNVs) whose mutant allele frequency (MAF) is greater than or equal to 5% detected by the NextSeq pipeline in common analytical regions. The correlation coefficient of MAF for those common SNVs was 1.0046 (R2 = 0.973) between the 2 platforms. To call lower mutant frequency (5%-10%) mutations for NextSeq sequencing, coverage depth should be improved. The concordance of small insertions and deletions between these 2 pipelines was up to 100%. CONCLUSIONS: The 2 sequencing pipelines evaluated were able to generate usable sequence and had high concordance. They are proper for mutation detection in clinical application.

Development and validation of an ultra-high sensitive next-generation sequencing assay for molecular diagnosis of clinical oncology.

Dramatic improvements in the understanding of oncogenes have spurred the development of molecular target therapies, which created an exigent need for comprehensive and rapid clinical genotyping. Next-generation sequencing (NGS) assay with increased performance and decreased cost is becoming more widely used in clinical diagnosis. However, the optimization and validation of NGS assay remain a challenge, especially for the detection of somatic variants at low mutant allele fraction (MAF). In the present study, we developed and validated the Novogene Comprehensive Panel (NCP) based on targeted capture for NGS analysis. Due to the high correlation between SNV/INDEL detection performance and target coverage, here we focused on these two types of variants for our deep sequencing strategy. To validate the capability of NCP in single-nucleotide variant (SNV) and small insert and deletion (INDEL) detection, we implemented a practical validation strategy with pooled cell lines, deep sequencing of pooled samples (>2000X average unique coverage across target region) achieving >99% sensitivity and high specificity (positive predictive value, PPV >99%) for all types of variations with expected MAF >5%. Furthermore, given the high sensitivity and that false positive may exist in this assay, we confirmed its accuracy of variants with MAF <5% using 35 formalin-fixed and paraffin-embedded (FFPE) tumor specimens by Quantstudio 3D Digital PCR (dPCR; Life Technologies) and obtained a high consistency (32 of 35 mutations detected by NGS were verified). We also used the amplification refractory mutation system (ARMS) to verify the variants with a MAF in a broad range of 2-63% detected in 33 FFPE samples and reached a 100% PPV for this assay. As a potential clinical diagnosis tool, NCP can robustly and comprehensively analyze clinical-related genes with high sensitivity and low cost.

Population Genomics Reveals Low Genetic Diversity and Adaptation to Hypoxia in Snub-Nosed Monkeys.

Snub-nosed monkeys (genus Rhinopithecus) are a group of endangered colobines endemic to South Asia. Here, we re-sequenced the whole genomes of 38 snub-nosed monkeys representing four species within this genus. By conducting population genomic analyses, we observed a similar load of deleterious variation in snub-nosed monkeys living in both smaller and larger populations and found that genomic diversity was lower than that reported in other primates. Reconstruction of Rhinopithecus evolutionary history suggested that episodes of climatic variation over the past 2 million years, associated with glacial advances and retreats and population isolation, have shaped snub-nosed monkey demography and evolution. We further identified several hypoxia-related genes under selection in R. bieti (black snub-nosed monkey), a species that exploits habitats higher than any other nonhuman primate. These results provide the first detailed and comprehensive genomic insights into genetic diversity, demography, genetic burden, and adaptation in this radiation of endangered primates.

A global network-based protocol for functional inference of hypothetical proteins in Synechocystis sp. PCC 6803.

Functional inference of hypothetical proteins (HPs) is a significant task in the post-genomic era. We described here a network-based protocol for functional inference of HPs using experimental transcriptomic, proteomic, and protein-protein interaction (PPI) datasets. The protocol includes two steps: i) co-expression networks were constructed using large proteomic or transcriptomic datasets of Synechocystis sp. PCC 6803 under various stress conditions, and then combined with a Synechocystis PPI network to generate bi-colored networks that include both annotated proteins and HPs; ii) a global algorithm was adapted to the bi-colored networks for functional inference of HPs. The algorithm ranked the associations between genes/proteins with known GO functional categories, and assumed that the top one ranked HP for each GO functional category might have a function related to the GO functional category. We applied the protocol to all HPs of the model cyanobacterium Synechocystis, and were able to assign putative functions to 122 HPs that have never been functionally characterized previously. Finally, the functional inference was validated by the known biological information of operon, and results showed that more than 70% HPs could be correctly validated. The study provided a new protocol to integrate different types of OMICS datasets for functional inference of HPs, and could be useful in achieving new insights into the Synechocystis metabolism.

Metabolomic basis of laboratory evolution of butanol tolerance in photosynthetic Synechocystis sp. PCC 6803.

BACKGROUND: Recent efforts demonstrated the potential application of cyanobacteria as a "microbial cell factory" to produce butanol directly from CO2. However, cyanobacteria have very low tolerance to the toxic butanol, which limits the economic viability of this renewable system. RESULTS: Through a long-term experimental evolution process, we achieved a 150% increase of the butanol tolerance in a model cyanobacterium Synechocystis sp. PCC 6803 after a continuous 94 passages for 395 days in BG11 media amended with gradually increased butanol concentration from 0.2% to 0.5% (v/v). To decipher the molecular mechanism responsible for the tolerance increase, we employed an integrated GC-MS and LC-MS approach to determine metabolomic profiles of the butanol-tolerant Synechocystis strains isolated from several stages of the evolution, and then applied PCA and WGCNA network analyses to identify the key metabolites and metabolic modules related to the increased tolerance. The results showed that unstable metabolites of 3-phosphoglyceric acid (3PG), D-fructose 6-phosphate (F6P), D-glucose 6-phosphate (G6P), NADPH, phosphoenolpyruvic acid (PEP), D-ribose 5-phosphate (R5P), and stable metabolites of glycerol, L-serine and stearic acid were differentially regulated during the evolution process, which could be related to tolerance increase to butanol in Synechocystis. CONCLUSIONS: The study provided the first time-series description of the metabolomic changes related to the gradual increase of butanol tolerance, and revealed a metabolomic basis important for rational tolerance engineering in Synechocystis.

Metabolomic and network analysis of astaxanthin-producing Haematococcus pluvialis under various stress conditions.

Various combinations of acetate (Ac), Fe(2+) and high light (HL) stress conditions were evaluated to maximize astaxanthin accumulation and biomass production in Haematococcus pluvialis, and then GC-MS and LC-MS based metabolomics were applied to determine molecular mechanisms responsible for enhancing astaxanthin accumulation under the stress conditions. With the optimized analytical protocols, the GC-MS and LC-MS analyses allowed identification of 93 stable and 24 unstable intracellular metabolites from H. pluvialis, respectively. In addition, a metabolic network was constructed based on GC-MS metabolomic datasets using a weighted correlation network analysis (WGCNA) approach. The network analysis uncovered 2, 1 and 1 distinguished metabolic modules highly associated with HL, Fe(2+) & HL, and Ac & Fe(2+) & HL conditions, respectively. Finally, LC-MS analysis found that AKG, Glu and R5P may be metabolites associated with the Fe(2+) & HL condition. The study provided the first metabolomic view of cell growth and astaxanthin accumulation in H. pluvialis.

Metabolomic analysis of the salt-sensitive mutants reveals changes in amino acid and fatty acid composition important to long-term salt stress in Synechocystis sp. PCC 6803.

Early studies in cyanobacteria have found that few genes induced by short-term salt shock (15-60 min) display a stable induction in the long-term (>1 day) salt-acclimated cells; meanwhile, most of the genes responsive to long-term salt stress were different from those by short-term salt shock, suggesting that different regulatory mechanisms may be involved for short-term and long-term salt stress responses. In our previous work using the model cyanobacterium Synechocystis sp. PCC 6803, sll1734 encoding CO2 uptake-related protein (CupA) and three genes encoding hypothetical proteins (i.e., ssr3402, slr1339, and ssr1853) were found induced significantly after a 3-day salt stress, and the corresponding gene knockout mutants were found salt sensitive. To further decipher the mechanisms that these genes may be involved, in this study, we performed a comparative metabolomic analysis of the wild-type Synechocystis and the four salt-sensitive mutants using a gas chromatography-mass spectrometry (GC-MS) approach. A metabolomic data set that consisted of 60 chemically classified metabolites was then subjected to a weighted correlation network analysis (WGCNA) to identify the metabolic modules and hub metabolites specifically related to each of the salt-stressed mutants. The results showed that two, one, zero, and two metabolic modules were identified specifically associated with the knockout events of sll1734, ssr3402, slr1339, and ssr1853, respectively. The mutant-associated modules included metabolites such as lysine and palmitic acid, suggesting that amino acid and fatty acid metabolisms are among the key protection mechanisms against long-term salt stresses in Synechocystis. The metabolomic results were further confirmed by quantitative reverse-transcription PCR analysis, which showed the upregulation of lysine and fatty acid synthesis-related genes. The study provided new insights on metabolic networks involved in long-term salt stress response in Synechocystis.

Global metabolomic and network analysis of Escherichia coli responses to exogenous biofuels.

Although synthetic biology progress has made it possible to produce various biofuels in more user-friendly hosts, such as Escherichia coli, the large-scale biofuel production in these non-native systems is still challenging, mostly due to the very low tolerance of these non-native hosts to the biofuel toxicity. To address the issues, in this study we determined the metabolic responses of E. coli induced by three major biofuel products, ethanol, butanol, and isobutanol, using a gas chromatography-mass spectrometry (GC-MS) approach. A metabolomic data set of 65 metabolites identified in all samples was then subjected to principal component analysis (PCA) to compare their effects and a weighted correlation network analysis (WGCNA) to identify the metabolic modules specifically responsive to each of the biofuel stresses, respectively. The PCA analysis showed that cellular responses caused by the biofuel stress were in general similar to aging cells at stationary phase, inconsistent with early studies showing a high degree of dissimilarity between metabolite responses during growth cessation as induced through stationary phases or through various environmental stress applications. The WGCNA analysis allowed identification of 2, 4, and 2 metabolic modules specifically associated with ethanol, butanol, and isobutanol treatments, respectively. The biofuel-associated modules included amino acids and osmoprotectants, such as isoleucine, valine, glycine, glutamate, and trehalose, suggesting amino acid metabolism and osmoregulation are among the key protection mechanisms against biofuel stresses in E. coli. Interestingly, no module was found associated with all three biofuel products, suggesting differential effects of each biofuel on E. coli. The findings enhanced our understanding of E. coli responses to exogenous biofuels and also demonstrated the effectiveness of the metabolomic and network analysis in identifying key targets for biofuel tolerance.