A protocol for representative sampling of solid tumors to improve the accuracy of sequencing results.
Ontology highlight
ABSTRACT:
SUBMITTER: Gallegos LL
PROVIDER: S-EPMC8243513 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.
| S-EPMC3211142 | biostudies-literature
Project description:It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.
| S-EPMC5542532 | biostudies-other
Project description:Sequencing microbial DNA from deep subsurface environments is complicated by a number of issues ranging from contamination to non-reproducible results. Many samples obtained from these environments - which are of great interest due to the potential to stimulate microbial methane generation - contain low biomass. Therefore, samples from these environments are difficult to study as sequencing results can be easily impacted by contamination. In this case, the low amount of sample biomass may be effectively swamped by the contaminating DNA and generate misleading results. Additionally, performing field work in these environments can be difficult, as researchers generally have limited access to and time on site. Therefore, optimizing a sampling plan to produce the best results while collecting the greatest number of samples over a short period of time is ideal. This study aimed to recommend an adequate sampling plan for field researchers obtaining microbial biomass for 16S rRNA gene sequencing, applicable specifically to low biomass oil and gas-producing environments. Forty-nine different samples were collected by filtering specific volumes of produced water from a hydraulically fractured well producing from the Niobrara Shale. Water was collected in two different sampling events 24 h apart. Four to five samples were collected from 11 specific volumes. These samples along with eight different blanks were submitted for analysis. DNA was extracted from each sample, and quantitative polymerase chain reaction (qPCR) and 16S rRNA Illumina MiSeq gene sequencing were performed to determine relative concentrations of biomass and microbial community composition, respectively. The qPCR results varied across sampled volumes, while no discernible trend correlated contamination to volume of water filtered. This suggests that collecting a larger volume of sample may not result in larger biomass concentrations or better representation of a sampled environment. Researchers could prioritize collecting many low volume samples over few high-volume samples. Our results suggest that there also may be variability in the concentration of microbial communities present in produced waters over short (i.e., hours) time scales, which warrants further investigation. Submission of multiple blanks is also vital to determining how contamination or low biomass effects may influence a sample set collected from an unknown environment.
| S-EPMC7518088 | biostudies-literature
Project description:The serine/threonine kinase AKT, also known as protein kinase B (PKB), is the major substrate to phosphoinositide 3-kinase (PI3K) and consists of three paralogs: AKT1 (PKB?), AKT2 (PKB?) and AKT3 (PKB?). The PI3K/AKT pathway is normally activated by binding of ligands to membrane-bound receptor tyrosine kinases (RTKs) as well as downstream to G-protein coupled receptors and integrin-linked kinase. Through multiple downstream substrates, activated AKT controls a wide variety of cellular functions including cell proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. In human cancers, the PI3K/AKT pathway is most frequently hyperactivated due to mutations and/or overexpression of upstream components. Aberrant expression of RTKs, gain of function mutations in PIK3CA, RAS, PDPK1, and AKT itself, as well as loss of function mutation in AKT phosphatases are genetic lesions that confer hyperactivation of AKT. Activated AKT stimulates DNA repair, e.g. double strand break repair after radiotherapy. Likewise, AKT attenuates chemotherapy-induced apoptosis. These observations suggest that a crucial link exists between AKT and DNA damage. Thus, AKT could be a major predictive marker of conventional cancer therapy, molecularly targeted therapy, and immunotherapy for solid tumors. In this review, we summarize the current understanding by which activated AKT mediates resistance to cancer treatment modalities, i.e. radiotherapy, chemotherapy, and RTK targeted therapy. Next, the effect of AKT on response of tumor cells to RTK targeted strategies will be discussed. Finally, we will provide a brief summary on the clinical trials of AKT inhibitors in combination with radiochemotherapy, RTK targeted therapy, and immunotherapy.
| S-EPMC7169978 | biostudies-literature
Project description:BACKGROUND:Further advances of targeted cancer therapy require comprehensive in-depth profiling of somatic mutations that are present in subpopulations of tumor cells in a clinical tumor sample. However, it is unclear to what extent such intratumor heterogeneity is present and whether it may affect clinical decision-making. To study this question, we established a deep targeted sequencing platform to identify potentially actionable DNA alterations in tumor samples. METHODS:We assayed 515 formalin-fixed paraffin-embedded (FFPE) tumor samples and matched germline DNA (475 patients) from 11 disease sites by capturing and sequencing all the exons in 201 cancer-related genes. Mutations, indels, and copy number data were reported. RESULTS:We obtained a 1000-fold mean sequencing depth and identified 4794 nonsynonymous mutations in the samples analyzed, of which 15.2% were present at <10% allele frequency. Most of these low level mutations occurred at known oncogenic hotspots and are likely functional. Identifying low level mutations improved identification of mutations in actionable genes in 118 (24.84%) patients, among which 47 (9.8%) otherwise would have been unactionable. In addition, acquiring ultrahigh depth also ensured a low false discovery rate (<2.2%) from FFPE samples. CONCLUSIONS:Our results were as accurate as a commercially available CLIA-compliant hotspot panel but allowed the detection of a higher number of mutations in actionable genes. Our study reveals the critical importance of acquiring and utilizing high sequencing depth in profiling clinical tumor samples and presents a very useful platform for implementing routine sequencing in a cancer care institution.
| S-EPMC4511273 | biostudies-literature
Project description:Intratumoral genetic heterogeneity (ITH) poses a significant challenge to utilizing sequencing for decision making in the management of cancer. Although sequencing of multiple tumor regions can address the pitfalls of ITH, it does so at a significant increase in cost and resource utilization. We propose a pooled multiregional sequencing strategy, whereby DNA aliquots from multiple tumor regions are mixed prior to sequencing, as a cost-effective strategy to boost translational value by addressing ITH while preserving valuable residual tissue for secondary analysis. Focusing on kidney cancer, we demonstrate that DNA pooling from as few as two regions significantly increases mutation detection while reducing clonality misattribution. This leads to an increased fraction of patients identified with therapeutically actionable mutations, improved patient risk stratification, and improved inference of evolutionary trajectories with an accuracy comparable to bona fide multiregional sequencing. The same approach applied to non-small-cell lung cancer data substantially improves tumor mutational burden (TMB) detection. Our findings demonstrate that pooled DNA sequencing strategies are a cost-effective alternative to address intrinsic genetic heterogeneity in clinical settings.
| S-EPMC8312736 | biostudies-literature
Project description:Blood perfusion in tumors can be significantly lower than that in the surrounding normal tissue owing to the leakiness and/or compression of tumor blood vessels. Impaired perfusion reduces oxygen supply and results in a hypoxic microenvironment. Hypoxia promotes tumor progression and immunosuppression, and enhances the invasive and metastatic potential of cancer cells. Furthermore, poor perfusion lowers the delivery of systemically administered drugs. Therapeutic strategies to improve perfusion include reduction in vascular permeability by vascular normalization and vascular decompression by alleviating physical forces (solid stress) inside tumors. Both strategies have shown promise, but guidelines on how to use these strategies optimally are lacking. To this end, we developed a mathematical model to guide the optimal use of these strategies. The model accounts for vascular, transvascular, and interstitial fluid and drug transport as well as the diameter and permeability of tumor vessels. Model simulations reveal an optimal perfusion region when vessels are uncompressed, but not very leaky. Within this region, intratumoral distribution of drugs is optimized, particularly for drugs 10 nm in diameter or smaller and of low binding affinity. Therefore, treatments should modify vessel diameter and/or permeability such that perfusion is optimal. Vascular normalization is more effective for hyperpermeable but largely uncompressed vessels (e.g., glioblastomas), whereas solid stress alleviation is more beneficial for compressed but less-permeable vessels (e.g., pancreatic ductal adenocarcinomas). In the case of tumors with hyperpermeable and compressed vessels (e.g., subset of mammary carcinomas), the two strategies need to be combined for improved treatment outcomes.
| S-EPMC3832007 | biostudies-literature
Project description:BackgroundLong-read sequencing platforms offered by Oxford Nanopore Technologies (ONT) allow native DNA containing epigenetic modifications to be directly sequenced, but can be limited by lower per-base accuracies. A key step post-sequencing is basecalling, the process of converting raw electrical signals produced by the sequencing device into nucleotide sequences. This is challenging as current basecallers are primarily based on mixtures of model species for training. Here we utilise both ONT PromethION and higher accuracy PacBio Sequel II HiFi sequencing on two plants, Phebalium stellatum and Xanthorrhoea johnsonii, to train species-specific basecaller models with the aim of improving per-base accuracy. We investigate sequencing accuracies achieved by ONT basecallers and assess accuracy gains by training single-species and species-specific basecaller models. We also evaluate accuracy gains from ONT's improved flowcells (R10.4, FLO-PRO112) and sequencing kits (SQK-LSK112). For the truth dataset for both model training and accuracy assessment, we developed highly accurate, contiguous diploid reference genomes with PacBio Sequel II HiFi reads.ResultsBasecalling with ONT Guppy 5 and 6 super-accurate gave almost identical results, attaining read accuracies of 91.96% and 94.15%. Guppy's plant-specific model gave highly mixed results, attaining read accuracies of 91.47% and 96.18%. Species-specific basecalling models improved read accuracy, attaining 93.24% and 95.16% read accuracies. R10.4 sequencing kits also improve sequencing accuracy, attaining read accuracies of 95.46% (super-accurate) and 96.87% (species-specific).ConclusionsThe use of a single mixed-species basecaller model, such as ONT Guppy super-accurate, may be reducing the accuracy of nanopore sequencing, due to conflicting genome biology within the training dataset and study species. Training of single-species and genome-specific basecaller models improves read accuracy. Studies that aim to do large-scale long-read genotyping would primarily benefit from training their own basecalling models. Such studies could use sequencing accuracy gains and improving bioinformatics tools to improve study outcomes.
| S-EPMC9749173 | biostudies-literature
Project description:BackgroundTargeted sequencing of cytologic samples has significantly increased in recent years. With increasing numbers of clinical trials for variant specific therapeutics, validating a comprehensive assay for cytologic samples has become clinically important.AimFor this study, a retrospective review of cytologic cell blocks from fine needle aspirations and fluid specimens was performed.MethodsTwo hundred twenty six total cases of solid tumor malignancies were identified, of which 120 cases and 20 lymph node negative controls were sequenced for the Oncomine Comprehensive Assay. Cytology and surgical specimen correlation was performed in a subset of cases. Statistical analysis to determine variant concordance was performed.ResultsWithin the 117 cases sequenced, a total of 347 pathogenic variants were detected. Of the 117 cases, 32 cases (27.4%) would qualify for FDA approved targeted therapy according to the current guidelines, and an additional 23 cases (19.7%) would qualify for clinical trial based on pathogenic variants detected.DiscussionWith over 27% of cases in our cohort qualifying for some form of targeted therapy, our study shows the importance of providing comprehensive molecular diagnostic options. Despite only half of the cytology cases in the review period having enough material to be sequenced, overall approximately 27% of patients in this cohort would have benefitted from this service.
| S-EPMC9972011 | biostudies-literature
Project description:Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) with and without designed internal controls.
2023-05-23 | GSE230475 | GEO