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AbstractMotivationPrincipled computational approaches for tumor phylogeny reconstruction via single cell sequencing (SCS) typically aim to identify the most likely perfect phylogeny tree through combinatorial optimization or Bayesian inference. Because of the limitations of SCS technologies, such as frequent allele dropout and variable sequence coverage, a noise reduction/elimination process may become necessary to infer a tumor phylogeny. Such noise reduction processes may aim to correct fordoi:10.1101/2020.02.07.938852 fatcat:ngd6ndp4wrcxthleaf2dkceayi
more »... e most likely/parsimonious set of false negative/false positive variant calls so as to construct a perfect phylogeny. Since these problems are NP-hard, available principled approaches for tumor phylogeny reconstruction are limited in their ability to scale up for handling emergent SCS datasets. In fact, even when the goal is to infer basic topological features of the tumor phylogeny rather than reconstructing it entirely, available techniques may be prohibitively slow. As a result, fast techniques to deduce, e.g. (i) whether the most likely tree has a linear (chain) or branching topology, or (ii) whether a perfect phylogeny is feasible from single-cell genotype matrix, without explicitly testing for the three gametes rule, are highly desirable.ResultsIn this paper we introduce deep-learning solutions to the above mentioned problems for studying tumor evolution from SCS data. After training with sufficiently many examples: (1) our fully connected neural network for differentiating linear vs branching topologies, can improve the running time of the fastest combinatorial tumor phylogeny reconstruction methods by a factor of ≥ 1000, while achieving an accuracy of ∼ 98% on simulated data including 100 cells and 100 mutations with realistic noise levels (leading to mostly false negatives) of 10 – 15%; (2) similarly, our fully connected neural network for checking whether the input data permits a perfect phylogeny, achieves an accuracy of ∼ 90% on simulated data including 10 cells and 10 mutations, with similar noise levels; (3) finally, our reinforcement learning approach for tumor phylogeny reconstruction can actually eliminate noise and obtain the PP, when false negative/false positive rate ≤ 2%, for a large fraction of evaluation data sets with varying number of cells and mutations, even when trained with fixed size data sets of only 10 cells and 10 mutations - this may be useful for future clinical applications that would employ emerging SCS technologies with lower noise levels.Availabilityhttps://github.com/algo-cancer/PhyloMContactcenk.email@example.com
AbstractMotivationAs multi-region, time-series, and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those withdoi:10.1101/2020.03.09.967257 fatcat:yu3lbpxrnndrxgqlzr3sojam6m
more »... ilar characteristics).ResultsIn this paper, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.AvailabilityCONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.
Supplemental Information Tumor Phylogeny Topology Inference via Deep Learning Erfan Sadeqi Azer, Mohammad Haghir Ebrahimabadi, Salem Malikić, Roni Khardon, S. ... As shown in (Malikic et al., 2019a) , the reported tree has high support from both single-cell and bulk data. ...doi:10.1016/j.isci.2020.101655 pmid:33117968 pmcid:PMC7582044 fatcat:r3vj6gwdvzdw7jtynf4zqb3ct4
 Salem Malikic, Andrew W McPherson, Nilgun Donmez, and Cenk S Sahinalp. Clonality inference in multiple tumor samples using phylogeny. Bioinformatics, 31:1349-1356, 2015. ...  Nilgun Donmez, Salem Malikic, Alexander W Wyatt, Martin E Gleave, Colin C Collins, and S Cenk Sahinalp. Clonality inference from single tumor samples using low coverage sequence data. ...doi:10.1101/234914 fatcat:3vuqpifzavbw3budoaqnioe4ja
Refractory hypothyroidism has been increasingly identified worldwide. Primary hypothyroidism is considered refractory when there is a persistent elevation of thyroid-stimulating hormone (TSH) above the upper limit of normal despite escalating doses of levothyroxine with or without the persistence of hypothyroid symptoms. Further escalation of levothyroxine to supratherapeutic doses could be associated with potential complications such as iatrogenic hyperthyroidism, cardiac failure, and otherdoi:10.7759/cureus.23522 pmid:35494965 pmcid:PMC9038596 fatcat:a2dha46cebgbxmasq7pzejmh44
more »... ditions. Therefore, physicians should rule out non-compliance and pursue a further evaluation to identify etiologies for increased requirements or decreased absorption of levothyroxine in patients not achieving therapeutic doses. Here, we present a 40-year-old Indian male with worsening refractory hypothyroidism that resolved following eradication of his Helicobacter pylori (H. pylori) infection. Herein, we highlight a unique and reversible cause of refractory hypothyroidism. With this case report, we hope to encourage physicians to include H. pylori testing in the evaluation of primary hypothyroidism refractory to treatment.
This is an expected result even for a complicated structure neural network (Salem, et al, 2000a) . ... Non-Linear Model A nonlinear seventh-order model is used to simulate the dynamic behavior of the generating unit connected to a constant voltage bus through two parallel transmission lines (Shamsollahi and Malik ...doi:10.3182/20020721-6-es-1901.00953 fatcat:osodqbgifrhx3ini2mtcoib56a
Motivation: CYP2D6 is highly polymorphic gene which encodes the (CYP2D6) enzyme, involved in the metabolism of 20-25% of all clinically prescribed drugs and other xenobiotics in the human body. CYP2D6 genotyping is recommended prior to treatment decisions involving one or more of the numerous drugs sensitive to CYP2D6 allelic composition. In this context, high-throughput sequencing (HTS) technologies provide a promising time-efficient and cost-effective alternative to currently used genotypingdoi:10.1093/bioinformatics/btv232 pmid:26072492 pmcid:PMC4542776 fatcat:kzbe3utn5baxvhrasmnwjovfnm
more »... echniques. To achieve accurate interpretation of HTS data, however, one needs to overcome several obstacles such as high sequence similarity and genetic recombinations between CYP2D6 and evolutionarily related pseudogenes CYP2D7 and CYP2D8, high copy number variation among individuals and short read lengths generated by HTS technologies. Results: In this work, we present the first algorithm to computationally infer CYP2D6 genotype at basepair resolution from HTS data. Our algorithm is able to resolve complex genotypes, including alleles that are the products of duplication, deletion and fusion events involving CYP2D6 and its evolutionarily related cousin CYP2D7. Through extensive experiments using simulated and real datasets, we show that our algorithm accurately solves this important problem with potential clinical implications. Availability and implementation: Cypiripi is available at http://sfu-compbio.github.io/cypiripi. Contact: firstname.lastname@example.org.
Recent studies on the heritability of methylation patterns in tumor cells, suggest that tumor heterogeneity and progression can be studied through methylation changes. To elucidate methylation-based evolution trajectories in tumors, we introduce a novel computational framework for methylation phylogeny reconstruction, leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), additionally incorporating copy number information inferred independently from matched singledoi:10.1101/2021.03.22.436475 fatcat:vsac7sy53jbi5jc7xgqp6tnode
more »... RNA sequencing (scRNA-seq) data, when available. Our framework consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. We validate our approach with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Additionally, we show that the constructed phylogeny can be used to impute missing entries, which, in turn, may help reduce sparsity issues in scBS-seq data sets.
Intra-tumor heterogeneity presents itself through the evolution of subclones during cancer progression. While recent research suggests that this clonal diversity is a key factor in therapeutic failure, the determination of subclonal architecture of human tumors remains a challenge. To address the problem of accurately determining subclonal frequencies in tumors as well as their evolutionary history, we have developed a novel combinatorial method named CITUP (Clonality Inference in Tumors Usingdoi:10.1093/bioinformatics/btv003 pmid:25568283 fatcat:35rpbac3ibeajeph6diznre2du
more »... hylogeny). An important feature of CITUP is its ability to exploit data from multiple time-point and/or regional samples from a single patient in order to improve estimates of mutational profiles and subclonal frequencies. Using extensive simulations and real datasets comprising tumor samples from two leukemia drugresponse studies, we show that CITUP can infer the evolutionary trajectory of human tumors with high accuracy. keywords: Cancer progression, intra-tumor heterogeneity, combinatorial methods iv To my beloved parents Faiz and Sadeta, and my dear sister Faiza v You can't connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something: your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life.
Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computationaldoi:10.1038/s41467-019-10737-5 pmid:31227714 pmcid:PMC6588593 fatcat:z54irlrrprh5dfkicnodblhgsy
more »... pproach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees.
We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure isdoi:10.1186/s13015-019-0152-9 pmid:31372179 pmcid:PMC6661107 fatcat:7ozycr2qpbhfde74n6vdxfc64e
more »... efined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.
Salbutamol-induced QT interval prolongation is a relatively rare adverse effect of beta2-agonists. We report a case of a two-year-old female patient with no known past medical history, brought by her parents to the ED 30 minutes after ingesting a total dose of 97 mg of salbutamol solution. ECG was done for the patient when she arrived and showed sinus tachycardia with prolonged QTc (509 ms) and normal QRS complex. The patient was admitted to the Pediatric Intensive Care Unit (PICU) withdoi:10.7759/cureus.21904 pmid:35273858 pmcid:PMC8901152 fatcat:vmbqvmxqard2togpkii2cx3cim
more »... nt tachycardia and tachypnea in the initial reassessment. ECG was repeated with normal QT interval after IV Mg sulfate. The patient was observed in PICU for 12 hours with serial ECG and venous blood gas (VBG). IV potassium chloride (KCL) infusion started, and serial VBG showed normal potassium and lactate. The patient was doing well in the next six hours, with normal serial ECG, labs, and vital signs. In conclusion, salbutamol-induced QT prolongation has infrequently been reported in the literature. Although inhaled salbutamol is commonly used in clinical practice, physicians have limited experience with the severe features of its toxicity. Salbutamol is known to cause minimal side effects, which may be under-recognized and progress to serious manifestations such as hypokalemia, QT prolongation, and sudden cardiac death.
This study aimed to evaluate the aetiologies of hyperprolactinaemia in the United Arab Emirates (UAE). This retrospective study used laboratory databases to identify all patients who underwent evaluation for prolactin at Tawam Hospital, Al Ain, UAE, between 2009 and 2015. Of those 2,280 patients, all patients with low or normal prolactin (n = 1,315) were excluded. Subsequently, charts of the remaining patients (n = 965) with hyperprolactinaemia were reviewed and those with incomplete work-upsdoi:10.18295/squmj.2019.19.02.008 pmid:31538011 pmcid:PMC6736269 fatcat:nw73yvjjdfgppn4laqicqc6hfy
more »... insufficient documentation of the hyperprolactinaemia's aetiology were excluded (n = 458). A total of 507 patients were included in the study. The average age at prolactin evaluation was 36 ± 13.2 years and the majority (67.1%) of patients were female. The most common reasons for requesting prolactin were menstrual disorders (29.5%), infertility (18%), evaluation of sellar masses (14.3%), ruling out seizures (13.4 %) and monitoring while on psychiatric medications (8.7%). The most common causes of hyperprolactinaemia were prolactinoma (17%), transient hyperprolactinaemia (14.6%), drug-induced side effects (14.4%), polycystic ovarian syndrome (11.8%) and seizure disorder (7.7%). In females, common aetiologies were prolactinomas, transient and idiopathic hyperprolactinaemia, while sellar masses, seizures, chronic kidney disease and acute illnesses were common aetiologies of hyperprolactinaemia in males. The prolactin level varied between the different aetiologies and a level of >250 ng/mL was suggestive of macro-prolactinoma. A significant proportion of patients with hyperprolactinaemia have transient hyperprolactinaemia. Before further investigations are carried out, prolactin level assessment should be repeated, especially in patients with mild hyperprolactinaemia.
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