Fabrication Technology of Self-Dissolving Sodium Hyaluronate Gels Ultrafine Microneedles for Medical Applications with UV-Curing Gas-Permeable Mold

Microneedles are of great interest in diverse fields, including cosmetics, drug delivery systems, chromatography, and biological sensing for disease diagnosis. Self-dissolving ultrafine microneedles of pure sodium hyaluronate hydrogels were fabricated using a UV-curing TiO2-SiO2 gas-permeable mold polymerized by sol-gel hydrolysis reactions in nanoimprint lithography processes under refrigeration at 5 °C, where thermal decomposition of microneedle components can be avoided. The moldability, strength, and dissolution behavior of sodium hyaluronate hydrogels with different molecular weights were compared to evaluate the suitability of ultrafine microneedles with a bottom diameter of 40 μm and a height of 80 μm. The appropriate molecular weight range and formulation of pure sodium hyaluronate hydrogels were found to control the dissolution behavior of self-dissolving ultrafine microneedles while maintaining the moldability and strength of the microneedles. This fabrication technology of ultrafine microneedles expands their possibilities as a next-generation technique for bioactive gels for controlling the blood levels of drugs and avoiding pain during administration.

Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells

An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.

Improving cellular phylogenies through the integrated use of mutation order and optimality principles

The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data

Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.

Validation of the severe COVID-19 prognostic value of serum IL-6, IFN-λ3, CCL17, and calprotectin considering the timing of clinical need for prediction

Although biomarkers to predict coronavirus disease 2019 (COVID-19) severity have been studied since the early pandemic, no clear guidelines on using them in clinical practice are available. Here, we examined the ability of four biomarkers to predict disease severity using conserved sera from COVID-19 patients who received inpatient care between January 1, 2020 and September 21, 2021 at the National Center for Global Health and Medicine, collected at the appropriate time for prediction. We predicted illness severity in two situations: 1) prediction of future oxygen administration for patients without oxygen support within 8 days of onset (Study 1) and 2) prediction of future mechanical ventilation support (excluding non-invasive positive pressure ventilation) or death of patients within 4 days of the start of oxygen administration (Study 2). Interleukin-6, IFN-λ3, thymus and activation-regulated chemokine, and calprotectin were measured retrospectively. Other laboratory and clinical information were collected from medical records. AUCs were calculated from ROC curves and compared for the predictive ability of the four biomarkers. Study 1 included 18 patients, five of whom had developed oxygen needs. Study 2 included 45 patients, 13 of whom required ventilator management or died. In Study 1, IFN-λ3 showed a good predictive ability with an AUC of 0.92 (95% CI 0.76-1.00). In Study 2, the AUC of each biomarker was 0.70-0.74. The number of biomarkers above the cutoff showed the possibility of good prediction with an AUC of 0.86 (95% CI 0.75-0.97). When two or more biomarkers were positive, sensitivity and specificity were 0.92 and 0.63, respectively. In terms of biomarker testing at times when prognostication may be clinically useful, IFN-λ3 was predictive of oxygenation demand and a combination of the four biomarkers was predictive of mechanical ventilator requirement.

Molecular evidence for SARS-CoV-2 in samples collected from patients with morbilliform eruptions since late 2019 in Lombardy, northern Italy

As a reference laboratory for measles and rubella surveillance in Lombardy, we evaluated the association between SARS-CoV-2 infection and measles-like syndromes, providing preliminary evidence for undetected early circulation of SARS-CoV-2. Overall, 435 samples from 156 cases were investigated. RNA from oropharyngeal swabs (N = 148) and urine (N = 141) was screened with four hemi-nested PCRs and molecular evidence for SARS-CoV-2 infection was found in 13 subjects. Two of the positive patients were from the pandemic period (2/12, 16.7%, March 2020-March 2021) and 11 were from the pre-pandemic period (11/44, 25%, August 2019-February 2020). Sera (N = 146) were tested for anti-SARS-CoV-2 IgG, IgM, and IgA antibodies. Five of the RNA-positive individuals also had detectable anti-SARS-CoV-2 antibodies. No strong evidence of infection was found in samples collected between August 2018 and July 2019 from 100 patients. The earliest sample with evidence of SARS-CoV-2 RNA was from September 12, 2019, and the positive patient was also positive for anti-SARS-CoV-2 antibodies (IgG and IgM). Mutations typical of B.1 strains previously reported to have emerged in January 2020 (C3037T, C14408T, and A23403G), were identified in samples collected as early as October 2019 in Lombardy. One of these mutations (C14408T) was also identified among sequences downloaded from public databases that were obtained by others from samples collected in Brazil in November 2019. We conclude that a SARS-CoV-2 progenitor capable of producing a measles-like syndrome may have emerged in late June-late July 2019 and that viruses with mutations characterizing B.1 strain may have been spreading globally before the first Wuhan outbreak. Our findings should be complemented by high-throughput sequencing to obtain additional sequence information. We highlight the importance of retrospective surveillance studies in understanding the early dynamics of COVID-19 spread and we encourage other groups to perform retrospective investigations to seek confirmatory proofs of early SARS-CoV-2 circulation.

Fabrication of High-Resolution Fine Microneedles Derived from Hydrolyzed Hyaluronic Acid Gels in Vacuum Environment Imprinting Using Water Permeable Mold

Hydrolyzed hyaluronic acid high-resolution fine microneedles of 13 µm in diameter and 24 µm in height were fabricated from hydrolyzed hyaluronic acid gels made in mixtures of water using vacuum environment imprint lithography processes with a water permeable mold. The gas traps of water and volatile solvents in the imprint materials cause transfer failure in the conventional water impermeable molds of quartz and metal. However, the water permeable mold allows the use of 67 wt% dilution water with high solubility to increase the fluidity of the hydrolyzed hyaluronic acid during the patterning of high-resolution fine microneedles for cosmetics and pharmaceuticals. This demonstration sets a new paradigm of functional pure gels for high-resolution nano-patterning applications with various cosmetic and pharmaceutical materials containing dilution water using a water permeable mold.

Clone Phylogenetics Reveals Metastatic Tumor Migrations, Maps, and Models

Simple Summary

Metastasis is the spread of cancer cells across organs and is a major cause of cancer mortality. Analysis of tumor sequencing data provides a means toward the reconstruction of routes of metastatic cell migrations. Our reconstructions demonstrated that many metastases were likely seeded from pre-existing metastasis of primary tumors. Additionally, multiple clone exchanges between tumor sites were common. In conclusion, the pattern of cancer cell migrations is often complex and is highly variable among patients.

Abstract

Dispersal routes of metastatic cells are not medically detected or even visible. A molecular evolutionary analysis of tumor variation provides a way to retrospectively infer metastatic migration histories and answer questions such as whether the majority of metastases are seeded from clones within primary tumors or seeded from clones within pre-existing metastases, as well as whether the evolution of metastases is generally consistent with any proposed models. We seek answers to these fundamental questions through a systematic patient-centric retrospective analysis that maps the dynamic evolutionary history of tumor cell migrations in many cancers. We analyzed tumor genetic heterogeneity in 51 cancer patients and found that most metastatic migration histories were best described by a hybrid of models of metastatic tumor evolution. Synthesizing across metastatic migration histories, we found new tumor seedings arising from clones of pre-existing metastases as often as they arose from clones from primary tumors. There were also many clone exchanges between the source and recipient tumors. Therefore, a molecular phylogenetic analysis of tumor variation provides a retrospective glimpse into general patterns of metastatic migration histories in cancer patients.

TopHap: rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

MOTIVATION

Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites but millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate and fast phylogenetic inference of resolvable phylogenetic features.

RESULTS

We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. We develop a bootstrap strategy that resamples genomes spatiotemporally to assess topological robustness. The application of TopHap to build a phylogeny of 68 057 SARS-CoV-2 genomes (68KG) from the first year of the pandemic produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million SARS-CoV-2 genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major and recent variants of concern.

AVAILABILITY AND IMPLEMENTATION

TopHap is available at https://github.com/SayakaMiura/TopHap.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells

In cancer, somatic mutations occur continuously, causing cell populations to evolve. These somatic mutations result in the evolution of cellular gene expression patterns that can also change due to epigenetic modifications and environmental changes. By exploring the concordance of gene expression changes with molecular evolutionary trajectories of cells, we can examine the role of somatic variation on the evolution of gene expression patterns. We present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles. MOCA outputs cells and genes showing convergent and divergent gene expression patterns in functional genomics.

TopHap: Rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

MOTIVATION

Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of SARS-CoV-2 strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites and millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate phylogenetic inference of resolvable phylogenetic features.

RESULTS

We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. To assess topological robustness, we develop a bootstrap resampling strategy that resamples genomes spatiotemporally. The application of TopHap to build a phylogeny of 68,057 genomes (68KG) produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major variants of concern.

AVAILABILITY

TopHap is available on the web at https://github.com/SayakaMiura/TopHap .

CONTACT

s.kumar@temple.edu.

The durability of immunity against reinfection by SARS-CoV-2: a comparative evolutionary study

Background

Among the most consequential unknowns of the devastating COVID-19 pandemic are the durability of immunity and time to likely reinfection. There are limited direct data on SARS-CoV-2 long-term immune responses and reinfection. The aim of this study is to use data on the durability of immunity among evolutionarily close coronavirus relatives of SARS-CoV-2 to estimate times to reinfection by a comparative evolutionary analysis of related viruses SARS-CoV, MERS-CoV, human coronavirus (HCoV)-229E, HCoV-OC43, and HCoV-NL63.

Methods

We conducted phylogenetic analyses of the S, M, and ORF1b genes to reconstruct a maximum-likelihood molecular phylogeny of human-infecting coronaviruses. This phylogeny enabled comparative analyses of peak-normalised nucleocapsid protein, spike protein, and whole-virus lysate IgG antibody optical density levels, in conjunction with reinfection data on endemic human-infecting coronaviruses. We performed ancestral and descendent states analyses to estimate the expected declines in antibody levels over time, the probabilities of reinfection based on antibody level, and the anticipated times to reinfection after recovery under conditions of endemic transmission for SARS-CoV-2, as well as the other human-infecting coronaviruses.

Findings

We obtained antibody optical density data for six human-infecting coronaviruses, extending from 128 days to 28 years after infection between 1984 and 2020. These data provided a means to estimate profiles of the typical antibody decline and probabilities of reinfection over time under endemic conditions. Reinfection by SARS-CoV-2 under endemic conditions would likely occur between 3 months and 5·1 years after peak antibody response, with a median of 16 months. This protection is less than half the duration revealed for the endemic coronaviruses circulating among humans (5–95% quantiles 15 months to 10 years for HCoV-OC43, 31 months to 12 years for HCoV-NL63, and 16 months to 12 years for HCoV-229E). For SARS-CoV, the 5–95% quantiles were 4 months to 6 years, whereas the 95% quantiles for MERS-CoV were inconsistent by dataset.

Interpretation

The timeframe for reinfection is fundamental to numerous aspects of public health decision making. As the COVID-19 pandemic continues, reinfection is likely to become increasingly common. Maintaining public health measures that curb transmission—including among individuals who were previously infected with SARS-CoV-2—coupled with persistent efforts to accelerate vaccination worldwide is critical to the prevention of COVID-19 morbidity and mortality.

Funding

US National Science Foundation.

An Evolutionary Portrait of the Progenitor SARS-CoV-2 and Its Dominant Offshoots in COVID-19 Pandemic

Global sequencing of genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here we present the heretofore cryptic mutational history and spatiotemporal dynamics of SARS-CoV-2 from an analysis of thousands of high-quality genomes. We report the likely most recent common ancestor of SARS-CoV-2, reconstructed through a novel application and advancement of computational methods initially developed to infer the mutational history of tumor cells in a patient. This progenitor genome differs from genomes of the first coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the United States harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide months before and after the first reported cases of COVID-19 in China. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains that have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic in 2020. There have been multiple replacements of predominant coronavirus strains in Europe and Asia as well as continued presence of multiple high-frequency strains in Asia and North America. We have developed a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).

PathFinder: Bayesian inference of clone migration histories in cancer

SUMMARY

Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes.In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases.

AVAILABILITY AND IMPLEMENTATION

PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.

An evolutionary portrait of the progenitor SARS-CoV-2 and its dominant offshoots in COVID-19 pandemic

We report the likely most recent common ancestor of SARS-CoV-2 - the coronavirus that causes COVID-19. This progenitor SARS-CoV-2 genome was recovered through a novel application and advancement of computational methods initially developed to reconstruct the mutational history of tumor cells in a patient. The progenitor differs from the earliest coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the USA harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide as soon as weeks after the first reported cases of COVID-19. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains, which have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic. There have been multiple replacements of predominant coronavirus strains in Europe and Asia and the continued presence of multiple high-frequency strains in Asia and North America. We provide a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).

Impact of visit-to-visit variability in blood pressure on coronary plaque as assessed by integrated backscatter intravascular ultrasound

Introduction

Visit-to-visit variability (VVV) in blood pressure (BP) has been reported to be a strong predictor of cardiovascular disease. However, the association of VVV in BP and coronary plaque composition has not been fully elucidated.

Purpose

The aim of this study was to examine the relationship between VVV in BP and the tissue characteristics of coronary plaques as assessed by integrated backscatter (IB) intravascular ultrasound (IVUS) in patients with coronary artery disease (CAD).

Methods

One hundred-two consecutive patients with CAD who underwent percutaneous coronary intervention (PCI) using IVUS, and who had at least six clinic visits a year before PCI were included. We measured systolic and diastolic BP (SBP and DBP) at each visit and determined VVV in BP expressed as the standard deviation of the average BP. Gray-scale and IB IVUS examinations were performed for the culprit lesion of a coronary artery just before PCI.

Results

There were no significant associations between the average SBP or DBP and various IVUS parameters. However, VVV in SBP was positively correlated with both the percentage of atheroma volume (PAV) (r=0.227, p=0.023) and percentage of lipid volume (PLV) (r=0.532, p<0.001). VVV in DBP was positively correlated with PLV (r=0.243, p=0.014), while there was no significant correlation between VVV in DBP and PAV. A multiple regression analysis showed that VVV in SBP was independently associated with PAV (p=0.036) and PLV (p<0.001).

Conclusions

Larger VVV in SBP was significantly associated with an increased plaque burden and lipid composition at the culprit lesion of a coronary artery in CAD patients. The improvement of VVV in SBP may contribute to the regression and stabilization of coronary plaques.

Figure 1

Funding Acknowledgement

Type of funding source: None

Different clinical features between left atrial and left atrial appendage thrombus: an insight into more than 16,000 transesophageal echography database

Background

Thrombosis localization inside the left atrium (LA) is very frequently in the left atrial appendage (LAA). Some cases show atrial thrombus outside LAA, whose characteristics have not well elucidated.

Purpose

To reveal the different clinical background between thrombus inside LAA and outside LAA in a large transesophageal echo (TEE) database.

Methods

We searched the TEE database over 8 years of our center to identify patients in whom LA thrombus was pointed out. After initial TEE imaging, LA thrombus was confirmed with repeated TEE by echocardiography specialist or other modality, such as CT images.

Results

In a consecutive 16,142 TEE cases, LA thrombus was suspected in 162 cases (1%), finally 38 cases were identified as LA thrombus. They were divided into two groups; LAA only group and outside LAA group. Atrial fibrillation was concomitant in 34 patients (89%). History of stroke was significantly higher in LAA group (46.2% vs 8.3%, p=0.03) and malignant tumor was significantly higher in outside LAA group (23.1% vs 58.3%, p=0.035). LA thrombus was the first symptom in 3 cases of outside LAA group, following by malignant tumor was newly detected. Oral anticoagulation was prescribed in 23 cases (61%) at TEE study, mostly treated with warfarin, and median 123 days later thrombus dissolution was confirmed in 21 cases (55%). During mean follow up of 893 days, 4 patients experienced ischemic stroke, but two of them were due to self-interruption.

Conclusion

Outside LAA thrombus is minor but more concomitant with malignant tumor. Physician must be aware that screening of cancer is important if outside LAA thrombus is detected.

Funding Acknowledgement

Type of funding source: None

Solventless granulation and spheronization of indomethacin crystals using a mechanical powder processor: Effects of mechanically induced amorphization on particle formation

Our aim was to reveal the effects of mechanically-induced amorphization on the solventless agglomeration and spheronization of drug crystals using a mechanical powder processor. This process can provide spherical particles comprising 100% drug. Indomethacin crystals were mechanically treated using various jacket temperatures and the resulting particles were characterized using particle and crystalline analyses. Also, the adhesive and mechanical properties of amorphous indomethacin were examined. At 20 °C, the indomethacin crystals fragmented and amorphized during processing, indicating that glassy-state indomethacin with no adhesiveness does not contribute to agglomeration or spheronization. At 40 °C, agglomeration occurred due to the transformation of mechanically-induced amorphous phases from non-adhesive glass to an adhesive supercooled liquid at around the glass transition temperature. However, at higher temperatures, the formation of agglomerates was suppressed by recrystallization of the amorphous surface. At 60 °C, the indomethacin crystals compacted and spheronized due to deformation of the particle surface, consistent with results showing that the stiffness of amorphous indomethacin decreased suddenly above 60 °C. The lifespan of the amorphous phase decreased due to enhanced recrystallization as the temperature increased, thereby reducing the degree of spheronization. In conclusion, agglomeration and spheronization are affected by the glass transition temperature and recrystallization of the mechanically-induced amorphous phase.

Epidemiology of long-stay patients in the pediatric intensive care unit: prevalence, characteristics, resource consumption and complications

Background

The impact of pediatric intensive care unit (PICU) utilization and resource consumption among long-stay patients has not been characterized recently. This study aimed to describe the resource consumption and characteristics of long-stay patients in a PICU.

Methods

This was a single-center descriptive cohort study of 1309 patients admitted to a PICU in 2017. The main outcome was ICU length of stay (LOS). Patients were divided into prolonged LOS (PLS) and non-PLS groups if they had an LOS of ≥ 28 or < 28 days, respectively. Two groups were compared to characterize PLS.

Results

Thirty-two (2.4%) patients had a PLS and utilized 33% of PICU bed days. Factors associated with PLS with odds ratio [95% confidence interval (CI)] were being a neonate (7.8 [2.5-25.4], p = <0.001), being an infant (2.9 [1.0-9.0], p = 0.04), admission for a respiratory ailment (7.3 [1.6-44.2], p = 0.003), cardiovascular dysfunction (24.1 [4.8-152.1], p = <0.001), post-cardiac operation (8.0 [1.7-50.1], p = 0.003), post-cardiopulmonary arrest (22.8 [1.7-211.9], p = 0.01), and transfer from another facility (4.2 [1.8-10.7], p = 0.001). PLS patients developed more nosocomial infections and disproportionately received monitoring and therapeutic resources.

Conclusions

A PLS was associated with substantial PICU utilization and complication rates. Future studies should aim to alleviate both institutional and patient-related issues in the affected population harboring possible risk factors for PLS.

Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data

Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.

A new method for inferring timetrees from temporally sampled molecular sequences

Pathogen timetrees are phylogenies scaled to time. They reveal the temporal history of a pathogen spread through the populations as captured in the evolutionary history of strains. These timetrees are inferred by using molecular sequences of pathogenic strains sampled at different times. That is, temporally sampled sequences enable the inference of sequence divergence times. Here, we present a new approach (RelTime with Dated Tips [RTDT]) to estimating pathogen timetrees based on a relative rate framework underlying the RelTime approach that is algebraic in nature and distinct from all other current methods. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. In analyses of an extensive collection of computer-simulated datasets, we found the accuracy of RTDT time estimates and the coverage probabilities of their confidence intervals (CIs) to be excellent. In analyses of empirical datasets, RTDT produced dates that were similar to those reported in the literature. In comparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we found that no method performed the best in every scenario. So, we provide a brief guideline for users to select the most appropriate method in empirical data analysis. RTDT is implemented for use via a graphical user interface and in high-throughput settings in the newest release of cross-platform MEGA X software, freely available from http://www.megasoftware.net.

Pathogen timetrees trace the origins and evolutionary histories of strains in populations, hosts, and outbreaks. The tips of these molecular phylogenies often contain sampling time information because the sequences were generally obtained at different times during the disease outbreaks and propagation. We have developed a new method for inferring divergence times and confidence intervals for phylogenies with tip dates. The new Relative Times with Dated Tips (RTDT) methods showed excellent performance in the analysis of computer-simulated datasets, producing similar or better results in several evolutionary scenarios as compared to other fast, non-Bayesian methods. The new method is available in the cross-platform MEGA software package (version 10.1 and higher) that provides a graphical user interface and allows usage via a command line in scripting and high throughput analysis (www.megasoftware.net).

Delineation of Tumor Migration Paths by Using a Bayesian Biogeographic Approach

Understanding tumor progression and metastatic potential are important in cancer biology. Metastasis is the migration and colonization of clones in secondary tissues. Here, we posit that clone migration events between tumors resemble the dispersal of individuals between distinct geographic regions. This similarity makes Bayesian biogeographic analysis suitable for inferring cancer cell migration paths. We evaluated the accuracy of a Bayesian biogeography method (BBM) in inferring metastatic patterns and compared it with the accuracy of a parsimony-based approach (metastatic and clonal history integrative analysis, MACHINA) that has been specifically developed to infer clone migration patterns among tumors. We used computer-simulated datasets in which simple to complex migration patterns were modeled. BBM and MACHINA were effective in reliably reconstructing simple migration patterns from primary tumors to metastases. However, both of them exhibited a limited ability to accurately infer complex migration paths that involve the migration of clones from one metastatic tumor to another and from metastasis to the primary tumor. Therefore, advanced computational methods are still needed for the biologically realistic tracing of migration paths and to assess the relative preponderance of different types of seeding and reseeding events during cancer progression in patients.

Predicting clone genotypes from tumor bulk sequencing of multiple samples

Motivation

Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.

Results

We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by PhyloWGS, and BayClone2, Canopy and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Overall, the deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.

Availability and implementation

CloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational enhancement of single-cell sequences for inferring tumor evolution

Motivation

Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.

Results

Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs is a challenge, especially for small datasets. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference. In conclusion, tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.

Availability and implementation

BEAM is available on the web at https://github.com/SayakaMiura/BEAM.

Abstract P4-02-05: Apocrine morphology and LAR molecular subtype predict prognosis of TNBC patients with residual disease after neoadjuvant chemotherapy

Background: TNBC molecular subtype classification updated by Lehmann et al. includes 4 subtypes: basal-like 1 and 2 (BL1), (BL2), mesenchymal (M), and luminal androgen receptor (LAR), and as a modifier of these subtypes, an Immunomodulatory (IM) gene expression signature. However, molecular subtypes have not been linked to morphological features of TNBC. Apocrine carcinoma has been proposed as a TNBC category that expresses androgen receptor. LAR-subtype TNBC has a poor response to neoadjuvant systemic therapy (NST). We hypothesized that defining the apocrine-featured TNBC by morphology and molecular subtype predict the prognosis of patients with residual disease after NST. Methods: We created the Pan-Pacific TNBC Consortium dataset, which contains paired samples of matched pre and post-NST TNBC tumors from 4 institutions. All patients received NST and didn't have a pathological complete response (pCR). Three pathologists examined hematoxylin and eosin-stained slides of 86 pre-NST samples and determined (1) the presence of apocrine differentiation, (2) the level of tumor-infiltrating lymphocytes (TILs), (3) the histological grade (HG), and (4) the rate of necrosis. These morphological features were compared among the subtypes. For a sample to be considered apocrine positive, apocrine differentiation had to be identified by 2 or more pathologists. Fisher's exact test was used to test the association of subtypes and morphological features. The log-rank test was used to compare disease-free survival (DFS). Results: Twelve of 24 (50%) apocrine-positive tumor samples were LAR subtype, and12 of 17 (70%) LAR-subtype tumor samples exhibited apocrine differentiation. The other subtypes showed following: BL1, 11/44 (25%); BL2, 0/7 (0%); M, 1/10 (10%); unclassified, 0/8 (0%). The median follow-up time was 22 months. In all populations, 2-year DFS rates were higher in patients with apocrine-positive tumors than in those whose tumors did not exhibit apocrine differentiation (P = .027; 2-year DFS, 85% vs 54%). The LAR subtype was also associated with lower HG, although LAR tumors had a similar prognosis to the other subtypes. In the combined analysis of subtypes and apocrine differentiation, patients with apocrine-positive LAR tumors had a higher 2-year DFS rate than did those with apocrine-negative LAR tumors (P = .044; 2-year DFS, 88% vs. 30%). However, patients with apocrine-positive BL1 tumors had no better DFS than did those with apocrine-negative BL1 tumors (P = .133). TIL levels and the presence of the IM signature were positively associated (P = .01), and apocrine differentiation positivity tended to be negatively associated with TIL level (P = .06). Neither TIL level nor IM signature was associated with survival. Conclusion: Apocrine differentiation was associated with the LAR subtype of TNBC and better prognosis in patients who did not have a pCR. The LAR subtype alone did not predict DFS; however, LAR tumors with apocrine differentiation had a better prognosis than did LAR tumors without apocrine differentiation. Using a combination of morphologic and genomic testing may be helpful in determining the prognosis of patients with apocrine-positive TNBC tumors who have residual disease after NST.

Citation Format: Masuda H, Miura S, Harano K, Wang Y, Hirota Y, Matsunaga Y, Lim B, Lucci A, Parinyanitikul N, Lee HJ, Gong G, Rao A, Seitz RS, Morris SW, Hout DR, Nakamura S, Tripathy D, Harada O, Krishnamurthy S, Ueno NT. Apocrine morphology and LAR molecular subtype predict prognosis of TNBC patients with residual disease after neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-02-05.