Mid- and long-term outcomes of thoracic endovascular aortic repair in acute and subacute uncomplicated type B aortic dissection.

BACKGROUND: Uncomplicated type B aortic dissection (un-TBAD) has been managed conservatively with medical therapy to control the heart rate and blood pressure to limit disease progression, in addition to radiological follow-up. However, several trials and observational studies have investigated the use of thoracic endovascular aortic repair (TEVAR) in un-TBAD and suggested that TEVAR provides a survival benefit over medical therapy. Outcomes of TEVAR have also been linked with the timing of intervention. AIMS: The scope of this review is to collate and summarize all the evidence in the literature on the mid- and long-term outcomes of TEVAR in un-TBAD, confirming its superiority. We also aimed to investigate the relationship between the timing of TEVAR intervention and results. METHODS: We carried out a comprehensive literature search on multiple electronic databases including PubMed, Scopus, and EMBASE to collate and summarize all research evidence on the mid- and long-term outcomes of TEVAR in un-TBAD, as well as its relationship with intervention timing. RESULTS: TEVAR has proven to be a safe and effective tool in un-TBAD, offering superior mid- and long-term outcomes including all-cause and aorta-related mortality, aortic-specific adverse events, aortic remodeling, and need for reintervention. Additionally, performing TEVAR during the subacute phase of dissection seems to yield optimal results. CONCLUSION: The evidence demonstrating a survival advantage in favor TEVAR over medical therapy in un-TBAD means that with further research, particular trials and observational studies, TEVAR could become the gold-standard treatment option for un-TBAD patients.

AI-Driven Synthetic Biology for Non-Small Cell Lung Cancer Drug Effectiveness-Cost Analysis in Intelligent Assisted Medical Systems.

According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer (International Agency for Research on Cancer, 2018), (Bray et al., 2018). Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of alung cancer patients. The 21st century is an era of life medicine, big data, and information technology. Synthetic biology is known as the driving force of natural product innovation and research in this era. Based on the research of NSCLC targeted drugs, through the cross-fusion of synthetic biology and artificial intelligence, using the idea of bioengineering, we construct an artificial intelligence assisted medical system and propose a drug selection framework for the personalized selection of NSCLC patients. Under the premise of ensuring the efficacy, considering the economic cost of targeted drugs as an auxiliary decision-making factor, the system predicts the drug effectiveness-cost then. The experiment shows that our method can rely on the provided clinical data to screen drug treatment programs suitable for the patient's conditions and assist doctors in making an efficient diagnosis.

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation.

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy.

Machine learning guided aptamer refinement and discovery.

Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics.

A Complete Genome Screening Program of Clinical Methicillin-Resistant Staphylococcus aureus Isolates Identifies the Origin and Progression of a Neonatal Intensive Care Unit Outbreak.

Whole-genome sequencing (WGS) of Staphylococcus aureus is increasingly used as part of infection prevention practices. In this study, we established a long-read technology-based WGS screening program of all first-episode methicillin-resistant Staphylococcus aureus (MRSA) blood infections at a major urban hospital. A survey of 132 MRSA genomes assembled from long reads enabled detailed characterization of an outbreak lasting several months of a CC5/ST105/USA100 clone among 18 infants in a neonatal intensive care unit (NICU). Available hospital-wide genome surveillance data traced the origins of the outbreak to three patients admitted to adult wards during a 4-month period preceding the NICU outbreak. The pattern of changes among complete outbreak genomes provided full spatiotemporal resolution of its progression, which was characterized by multiple subtransmissions and likely precipitated by equipment sharing between adults and infants. Compared to other hospital strains, the outbreak strain carried distinct mutations and accessory genetic elements that impacted genes with roles in metabolism, resistance, and persistence. This included a DNA recognition domain recombination in the hsdS gene of a type I restriction modification system that altered DNA methylation. Transcriptome sequencing (RNA-Seq) profiling showed that the (epi)genetic changes in the outbreak clone attenuated agr gene expression and upregulated genes involved in stress response and biofilm formation. Overall, our findings demonstrate the utility of long-read sequencing for hospital surveillance and for characterizing accessory genomic elements that may impact MRSA virulence and persistence.

Impact of HCV core gene quasispecies on hepatocellular carcinoma risk among HALT-C trial patients.

Mutations at positions 70 and/or 91 in the core protein of genotype-1b, hepatitis C virus (HCV) are associated with hepatocellular carcinoma (HCC) risk in Asian patients. To evaluate this in a US population, the relationship between the percentage of 70 and/or 91 mutant HCV quasispecies in baseline serum samples of chronic HCV patients from the HALT-C trial and the incidence of HCC was determined by deep sequencing. Quasispecies percentage cut-points, ≥42% of non-arginine at 70 (non-R(70)) or ≥98.5% of non-leucine at 91 (non-L(91)) had optimal sensitivity at discerning higher or lower HCC risk. In baseline samples, 88.5% of chronic HCV patients who later developed HCC and 68.8% of matched HCC-free control patients had ≥42% non-R(70) quasispecies (P = 0.06). Furthermore, 30.8% of patients who developed HCC and 54.7% of matched HCC-free patients had quasispecies with ≥98.5% non-L(91) (P = 0.06). By Kaplan-Meier analysis, HCC incidence was higher, but not statistically significant, among patients with quasispecies ≥42% non-R(70) (P = 0.08), while HCC incidence was significantly reduced among patients with quasispecies ≥98.5% non-L(91) (P = 0.01). In a Cox regression model, non-R(70) ≥42% was associated with increased HCC risk. This study of US patients indicates the potential utility of HCV quasispecies analysis as a non-invasive biomarker of HCC risk.

Alpha-CENTAURI: assessing novel centromeric repeat sequence variation with long read sequencing.

MOTIVATION: Long arrays of near-identical tandem repeats are a common feature of centromeric and subtelomeric regions in complex genomes. These sequences present a source of repeat structure diversity that is commonly ignored by standard genomic tools. Unlike reads shorter than the underlying repeat structure that rely on indirect inference methods, e.g. assembly, long reads allow direct inference of satellite higher order repeat structure. To automate characterization of local centromeric tandem repeat sequence variation we have designed Alpha-CENTAURI (ALPHA satellite CENTromeric AUtomated Repeat Identification), that takes advantage of Pacific Bioscience long-reads from whole-genome sequencing datasets. By operating on reads prior to assembly, our approach provides a more comprehensive set of repeat-structure variants and is not impacted by rearrangements or sequence underrepresentation due to misassembly. RESULTS: We demonstrate the utility of Alpha-CENTAURI in characterizing repeat structure for alpha satellite containing reads in the hydatidiform mole (CHM1, haploid-like) genome. The pipeline is designed to report local repeat organization summaries for each read, thereby monitoring rearrangements in repeat units, shifts in repeat orientation and sites of array transition into non-satellite DNA, typically defined by transposable element insertion. We validate the method by showing consistency with existing centromere high order repeat references. Alpha-CENTAURI can, in principle, run on any sequence data, offering a method to generate a sequence repeat resolution that could be readily performed using consensus sequences available for other satellite families in genomes without high-quality reference assemblies. AVAILABILITY AND IMPLEMENTATION: Documentation and source code for Alpha-CENTAURI are freely available at http://github.com/volkansevim/alpha-CENTAURI CONTACT: ali.bashir@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing and Overriding the Structural Mechanism of the Quizartinib-Resistant FLT3 "Gatekeeper" F691L Mutation with PLX3397.

UNLABELLED: Tyrosine kinase domain mutations are a common cause of acquired clinical resistance to tyrosine kinase inhibitors (TKI) used to treat cancer, including the FLT3 inhibitor quizartinib. Mutation of kinase "gatekeeper" residues, which control access to an allosteric pocket adjacent to the ATP-binding site, has been frequently implicated in TKI resistance. The molecular underpinnings of gatekeeper mutation-mediated resistance are incompletely understood. We report the first cocrystal structure of FLT3 with the TKI quizartinib, which demonstrates that quizartinib binding relies on essential edge-to-face aromatic interactions with the gatekeeper F691 residue, and F830 within the highly conserved Asp-Phe-Gly motif in the activation loop. This reliance makes quizartinib critically vulnerable to gatekeeper and activation loop substitutions while minimizing the impact of mutations elsewhere. Moreover, we identify PLX3397, a novel FLT3 inhibitor that retains activity against the F691L mutant due to a binding mode that depends less vitally on specific interactions with the gatekeeper position. SIGNIFICANCE: We report the first cocrystal structure of FLT3 with a kinase inhibitor, elucidating the structural mechanism of resistance due to the gatekeeper F691L mutation. PLX3397 is a novel FLT3 inhibitor with in vitro activity against this mutation but is vulnerable to kinase domain mutations in the FLT3 activation loop.

Characterization of structural variants with single molecule and hybrid sequencing approaches.

MOTIVATION: Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent 'third-generation' sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates. RESULTS: We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly.

Optimizing PCR assays for DNA-based cancer diagnostics.

Somatically acquired DNA rearrangements are characteristic of many cancers. The use of these mutations as diagnostic markers is challenging, because tumor cells are frequently admixed with normal cells, particularly in early stage tumor samples, and thus the samples contain a high background of normal DNA. Detection is further confounded by the fact that the rearrangement boundaries are not conserved across individuals, and might vary over hundreds of kilobases. Here, we present an algorithm for designing polymerase chain reaction (PCR) primers and oligonucleotide probes to assay for these variant rearrangements. Specifically, the primers and probes tile the entire genomic region surrounding a rearrangement, so as to amplify the mutant DNA over a wide range of possible breakpoints and robustly assay for the amplified signal on an array. Our solution involves the design of a complex combinatorial optimization problem, and also includes a novel alternating multiplexing strategy that makes efficient detection possible. Simulations show that we can achieve near-optimal detection in many different cases, even when the regions are highly non-symmetric. Additionally, we prove that the suggested multiplexing strategy is optimal in breakpoint detection. We applied our technique to create a custom design to assay for genomic lesions in several cancer cell-lines associated with a disruption in the CDKN2A locus. The CDKN2A deletion has highly variable boundaries across many cancers. We successfully detect the breakpoint in all cell-lines, even when the region has undergone multiple rearrangements. These results point to the development of a successful protocol for early diagnosis and monitoring of cancer. For online Supplementary Material, see www.liebertonline.com.

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding approximately 18x haploid coverage of aligned sequence and close to 300x clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies.

A geometric approach for classification and comparison of structural variants.

MOTIVATION: Structural variants, including duplications, insertions, deletions and inversions of large blocks of DNA sequence, are an important contributor to human genome variation. Measuring structural variants in a genome sequence is typically more challenging than measuring single nucleotide changes. Current approaches for structural variant identification, including paired-end DNA sequencing/mapping and array comparative genomic hybridization (aCGH), do not identify the boundaries of variants precisely. Consequently, most reported human structural variants are poorly defined and not readily compared across different studies and measurement techniques. RESULTS: We introduce Geometric Analysis of Structural Variants (GASV), a geometric approach for identification, classification and comparison of structural variants. This approach represents the uncertainty in measurement of a structural variant as a polygon in the plane, and identifies measurements supporting the same variant by computing intersections of polygons. We derive a computational geometry algorithm to efficiently identify all such intersections. We apply GASV to sequencing data from nine individual human genomes and several cancer genomes. We obtain better localization of the boundaries of structural variants, distinguish genetic from putative somatic structural variants in cancer genomes, and integrate aCGH and paired-end sequencing measurements of structural variants. This work presents the first general framework for comparing structural variants across multiple samples and measurement techniques, and will be useful for studies of both genetic structural variants and somatic rearrangements in cancer. AVAILABILITY: http://cs.brown.edu/people/braphael/software.html .

Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer.

Paired-end sequencing is emerging as a key technique for assessing genome rearrangements and structural variation on a genome-wide scale. This technique is particularly useful for detecting copy-neutral rearrangements, such as inversions and translocations, which are common in cancer and can produce novel fusion genes. We address the question of how much sequencing is required to detect rearrangement breakpoints and to localize them precisely using both theoretical models and simulation. We derive a formula for the probability that a fusion gene exists in a cancer genome given a collection of paired-end sequences from this genome. We use this formula to compute fusion gene probabilities in several breast cancer samples, and we find that we are able to accurately predict fusion genes in these samples with a relatively small number of fragments of large size. We further demonstrate how the ability to detect fusion genes depends on the distribution of gene lengths, and we evaluate how different parameters of a sequencing strategy impact breakpoint detection, breakpoint localization, and fusion gene detection, even in the presence of errors that suggest false rearrangements. These results will be useful in calibrating future cancer sequencing efforts, particularly large-scale studies of many cancer genomes that are enabled by next-generation sequencing technologies.

A sequence-based survey of the complex structural organization of tumor genomes.

BACKGROUND: The genomes of many epithelial tumors exhibit extensive chromosomal rearrangements. All classes of genome rearrangements can be identified using end sequencing profiling, which relies on paired-end sequencing of cloned tumor genomes. RESULTS: In the present study brain, breast, ovary, and prostate tumors, along with three breast cancer cell lines, were surveyed using end sequencing profiling, yielding the largest available collection of sequence-ready tumor genome breakpoints and providing evidence that some rearrangements may be recurrent. Sequencing and fluorescence in situ hybridization confirmed translocations and complex tumor genome structures that include co-amplification and packaging of disparate genomic loci with associated molecular heterogeneity. Comparison of the tumor genomes suggests recurrent rearrangements. Some are likely to be novel structural polymorphisms, whereas others may be bona fide somatic rearrangements. A recurrent fusion transcript in breast tumors and a constitutional fusion transcript resulting from a segmental duplication were identified. Analysis of end sequences for single nucleotide polymorphisms revealed candidate somatic mutations and an elevated rate of novel single nucleotide polymorphisms in an ovarian tumor. CONCLUSION: These results suggest that the genomes of many epithelial tumors may be far more dynamic and complex than was previously appreciated and that genomic fusions, including fusion transcripts and proteins, may be common, possibly yielding tumor-specific biomarkers and therapeutic targets.

Optimization of primer design for the detection of variable genomic lesions in cancer.

Primer approximation multiplex PCR (PAMP) is a new experimental protocol for efficiently assaying structural variation in genomes. PAMP is particularly suited to cancer genomes where the precise breakpoints of alterations such as deletions or translocations vary between patients. The design of PCR primer sets for PAMP is challenging because a large number of primer pairs are required to detect alterations in the hundreds of kilobases range that can occur in cancer. These sets of primers must achieve high coverage of the region of interest, while avoiding primer dimers and satisfying the physico-chemical constraints of good PCR primers. We describe a natural formulation of these constraints as a combinatorial optimization problem. We show that the PAMP primer design problem is NP-hard, and design algorithms based on simulated annealing and integer programming, that provide good solutions to this problem in practice. The algorithms are applied to a test region around the known CDKN2A deletion, which show excellent results even in a 1:49 mixture of mutated:wild-type cells. We use these test results to help set design parameters for larger problems. We can achieve near-optimal designs for regions close to 1 Mb.