Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogenous phenotypes.

COMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data.

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.

To be or not to be stressed: Designing autonomy to reduce stress at work.

BACKGROUND: Many organizations are undertaking efforts to reduce the stress of (oftentimes overworked) employees. Information Technology (IT) (e.g., smartphones) has the potential to be a key instrument for reducing stress. One design-relevant factor considered to reduce stress is the concept of autonomy. Unfortunately, little research exists using autonomy as a characteristic of technology design. OBJECTIVE: Against this background, this study aimed to investigate specific autonomy-related design options with the potential to prevent stress. METHODS: In a factorial survey, this experimental study tested three design options in an overwork scenario: 1) autonomy (no intervention by design), 2) nudge ("nudging" by design), and 3) enforcement (hard stop by design). 51 participants (mean age 38 years, 50% women, mean work experience 18 years) from the Netherlands, United Kingdom, United States of America, and Germany participated in the experiment for 330 seconds on average. To test our hypothesis, we used a two-step approach. First, a multiple linear regression was applied. Second, we carried out a one-way ANCOVA comparing the effects of our design options. RESULTS: Our results indicate that autonomy can be manipulated through technology design and is negatively correlated with stress. Additionally, the design options autonomy and nudge were associated with lower levels of perceived stress than was enforcement. CONCLUSION: The study proposes a careful use of IT and policies that limit the perceived autonomy of employees. Overall, this study offers a set of design recommendations arguing that organizations should implement technology that helps employees prevent overwork and maintain their autonomy.

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC.

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.

Statistical tests for intra-tumour clonal co-occurrence and exclusivity.

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland.

BACKGROUND: In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40-80 % (Volz et al., 2021; Leung et al., 2021; Davies et al., 2021). AIM: This study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. METHODS: We generated whole genome sequences from 11.8 % of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variant's transmission fitness advantage on a national and a regional scale. RESULTS: We estimate B.1.1.7 had a transmission fitness advantage of 43-52 % compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07-1.41] from 01 January until 17 January 2021 and 1.18 [1.06-1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00-1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. CONCLUSION: The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2-3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.

The Bourque distances for mutation trees of cancers.

BACKGROUND: Mutation trees are rooted trees in which nodes are of arbitrary degree and labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular Robinson-Foulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels. RESULTS: We generalize the Robinson-Foulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. We show the basic version of the Bourque distance for mutation trees can be computed in linear time. We also make a connection between the Robinson-Foulds distance and the nearest neighbor interchange distance.

Approach bias retraining through virtual reality in smokers willing to quit smoking: A randomized-controlled study.

Automatic approach biases toward smoking-related cues have been implicated in the development and maintenance of addictive behaviors. Studies aiming at modifying such biases have shown promise in changing maladaptive approach tendencies for smoking cues and reducing smoking behavior. However, training effects tend to be small and partly inconsistent. The present randomized-controlled trial incorporated virtual reality (VR) technology into Approach Bias Modification (ABM) to improve efficacy. One-hundred-eight smokers attended behavioral counseling for smoking cessation and were thereafter randomized to receive VR-ABM or VR-control training. During VR-ABM, participants trained to implicitly avoid smoking-related objects and to approach alternative objects, while no such contingency existed in the VR-control condition. Trainings were administered in six sessions within a two-week period. Assessments were conducted at baseline, post-intervention (three weeks after baseline), and at follow-up (seven weeks after baseline). VR-ABM did not change approach biases, nor other cognitive biases, but it was superior in reducing daily smoking. However, this effect was limited to the two-week training period. Both groups improved in other smoking- and health-related variables across time. Future work should continue to investigate working mechanisms of ABM, in particular crucial training ingredients. VR could prove valuable for public health as the potential of VR-based treatments is large and not fully explored.

Bone quality analysis of jaw bones in individuals with type 2 diabetes mellitus-post mortem anatomical and microstructural evaluation.

OBJECTIVES: With the higher risk of dental implant failure with type 2 diabetes mellitus (T2DM), there is a need to characterize the jaw bones in those individuals. The aim of this post mortem study was to compare jaw bone quality of individuals with T2DM to healthy controls. MATERIAL AND METHODS: Bone cores from the edentulous lower first molar region and the region of mandibular angle were collected from male individuals with T2DM (n = 10, 70.6 ± 4.5 years) and healthy controls (n = 11, 71.5 ± 3.8 years) during autopsy. Within the T2DM, a subgroup treated with oral antidiabetics (OAD) and one on insulin were identified. Bone quality assessment encompassed evaluation of bone microstructure, matrix composition, and cellular activity, using microcomputed tomography (micro-CT), quantitative backscattered electron imaging (qBEI), Raman spectroscopy, and bone histomorphometry. RESULTS: In the mandibular angle, T2DM showed 51% lower porosity of the lingual cortex (p = 0.004) and 21% higher trabecular thickness (p = 0.008) compared to control. More highly mineralized bone packets were found in the buccal cortex of the mandibular angle in insulin-treated compared to OAD-treated T2DM group (p = 0.034). In the molar region, we found higher heterogeneity of trabecular calcium content in T2DM insulin compared to controls (p = 0.015) and T2DM OAD (p = 0.019). T2DM was associated with lower osteocyte lacunar size in the trabecular bone of the molar region (vs. control p = 0.03). CONCLUSIONS: Alterations in microstructure, mineralization, and osteocyte morphology were determined in jaw bone of individuals with T2DM compared to controls. CLINICAL RELEVANCE: Future studies will have to verify if the mild changes determined in this study will translate to potential contraindications for dental implant placements.

[Drug interactions in dermatological systemic treatment]. / Wechselwirkungen in der dermatologischen Systemtherapie.

Severe pharmacological side effects have an occurrence of 5-7% and represent a frequent reason for hospital admission. The prevalence of undesired pharmacological side effects during hospitalization is even higher with approximately 11.5%. The causes are often interactions between drugs due to the polypharmacy of multimorbid older patients. On average, a 65-year-old male patient will simultaneously be taking 5 medications. Due to the increasing use of systemic drugs in dermatology and the simultaneously increasing polypharmacy, knowledge of interactions between medications is essential for dermatologists in order to avoid severe side effects of drugs. This article provides assistance in order to identify patients and medications with a high risk for severe interactions and, therefore, to avoid the occurrence of undesired effects or the reduction of the therapeutic effects of active substances. We would like to point out that this article deals with individual aspects and does not mean that the testing of individual drug interactions with interaction programs can be omitted. It should also not be neglected that in addition to prescription-only drugs, foodstuffs, dietary supplements and herbs can also lead to interactions with medications.

Publisher Correction: Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-19902-7.

Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics.

Clonal diversity is a consequence of cancer cell evolution driven by Darwinian selection. Precise characterization of clonal architecture is essential to understand the evolutionary history of tumor development and its association with treatment resistance. Here, using a single-cell DNA sequencing, we report the clonal architecture and mutational histories of 123 acute myeloid leukemia (AML) patients. The single-cell data reveals cell-level mutation co-occurrence and enables reconstruction of mutational histories characterized by linear and branching patterns of clonal evolution, with the latter including convergent evolution. Through xenotransplantion, we show leukemia initiating capabilities of individual subclones evolving in parallel. Also, by simultaneous single-cell DNA and cell surface protein analysis, we illustrate both genetic and phenotypic evolution in AML. Lastly, single-cell analysis of longitudinal samples reveals underlying evolutionary process of therapeutic resistance. Together, these data unravel clonal diversity and evolution patterns of AML, and highlight their clinical relevance in the era of precision medicine.

Computational Model Reveals a Stochastic Mechanism behind Germinal Center Clonal Bursts.

Germinal centers (GCs) are specialized compartments within the secondary lymphoid organs where B cells proliferate, differentiate, and mutate their antibody genes in response to the presence of foreign antigens. Through the GC lifespan, interclonal competition between B cells leads to increased affinity of the B cell receptors for antigens accompanied by a loss of clonal diversity, although the mechanisms underlying clonal dynamics are not completely understood. We present here a multi-scale quantitative model of the GC reaction that integrates an intracellular component, accounting for the genetic events that shape B cell differentiation, and an extracellular stochastic component, which accounts for the random cellular interactions within the GC. In addition, B cell receptors are represented as sequences of nucleotides that mature and diversify through somatic hypermutations. We exploit extensive experimental characterizations of the GC dynamics to parameterize our model, and visualize affinity maturation by means of evolutionary phylogenetic trees. Our explicit modeling of B cell maturation enables us to characterise the evolutionary processes and competition at the heart of the GC dynamics, and explains the emergence of clonal dominance as a result of initially small stochastic advantages in the affinity to antigen. Interestingly, a subset of the GC undergoes massive expansion of higher-affinity B cell variants (clonal bursts), leading to a loss of clonal diversity at a significantly faster rate than in GCs that do not exhibit clonal dominance. Our work contributes towards an in silico vaccine design, and has implications for the better understanding of the mechanisms underlying autoimmune disease and GC-derived lymphomas.

Promoting smoking abstinence in smokers willing to quit smoking through virtual reality-approach bias retraining: a study protocol for a randomized controlled trial.

BACKGROUND: Automatic processes to approach smoking-related cues have been repeatedly linked to smoking status, intensity of smoking, and cigarette craving. Moreover, recent findings suggest that targeting those tendencies directly by means of approach bias modification (ABM) has merit in changing maladaptive approach tendencies for drug cues and reducing drug consumption. However, training effects tend to be small. Embedding the training into virtual reality (VR) technology could be a promising way to improve training efficacy. The present protocol describes a randomized controlled trial that aims to assess the efficacy of a newly developed VR-ABM as a means of reducing smoking-related approach biases or nicotine consumption in smokers seeking abstinence. METHODS: One hundred daily smokers who are motivated to quit smoking will be recruited into the randomized controlled trial. All participants will attend a brief smoking cessation intervention (TAU) and will be randomly assigned either to the experimental (VR-avoidance training) or the placebo-control group (VR-placebo training). During the VR-avoidance training, participants are implicitly instructed to make an avoidance movement in response to smoking-related objects (e.g., cigarettes) and an approach movement in response to alternative objects (e.g., healthy food). During the VR-placebo training, no such contingency between arm movement and item content exists. Trainings are administered in six sessions within two weeks. Training effects on automatic approach tendencies and smoking behavior are measured immediately after training and at a 7-week follow-up. DISCUSSION: Embedding the training into virtual reality (VR) technology could be a promising new way to improve ecological validity, realism, and immersion and thereby increase ABM training effects. The results of this study can inform future research in the optimization and advancement of treatment for addiction. TRIAL REGISTRATION: Registered with Current Controlled Trials: study ID ISRCTN16006023. Registered on 28 March 2019.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Multimodal X-ray imaging of nanocontainer-treated macrophages and calcium distribution in the perilacunar bone matrix.

Studies of biological systems typically require the application of several complementary methods able to yield statistically-relevant results at a unique level of sensitivity. Combined X-ray fluorescence and ptychography offer excellent elemental and structural imaging contrasts at the nanoscale. They enable a robust correlation of elemental distributions with respect to the cellular morphology. Here we extend the applicability of the two modalities to higher X-ray excitation energies, permitting iron mapping. Using a long-range scanning setup, we applied the method to two vital biomedical cases. We quantified the iron distributions in a population of macrophages treated with Mycobacterium-tuberculosis-targeting iron-oxide nanocontainers. Our work allowed to visualize the internalization of the nanocontainer agglomerates in the cytosol. From the iron areal mass maps, we obtained a distribution of antibiotic load per agglomerate and an average areal concentration of nanocontainers in the agglomerates. In the second application we mapped the calcium content in a human bone matrix in close proximity to osteocyte lacunae (perilacunar matrix). A concurrently acquired ptychographic image was used to remove the mass-thickness effect from the raw calcium map. The resulting ptychography-enhanced calcium distribution allowed then to observe a locally lower degree of mineralization of the perilacunar matrix.

Multiscale bone quality analysis in osteoarthritic knee joints reveal a role of the mechanosensory osteocyte network in osteophytes.

Osteophytes - bony outgrowths on joint structures - are found in healthy individuals but are specifically present in late osteoarthritis (OA). Osteophyte development and function is not well understood, yet biomechanical stimuli are thought to be critical. Bone adapts to mechanical forces via the cellular network of osteocytes. The involvement of osteocytes in osteophyte formation and maturation has not been unravelled. Forty-three osteophytes from tibias of 23 OA patients (65 ± 9 years) were analysed. The trabecular bone structure of osteophytes presented with fewer trabeculae of lower bone mineral density compared to subchondral bone. We identified 40% early stage and 60% late stage osteophytes that significantly differed in their trabecular bone characteristics. Osteophyte bone revealed a higher number of osteocytes and a lower number of empty osteocyte lacunae per bone area than the subchondral bone. We found that OA osteophytes consist of younger bone material comprised of woven and lamellar bone with the capacity to develop into a late stage osteophyte potentially via the involvement of the osteocyte network. Our analysis of OA osteophytes implies a transition from woven to lamellar bone as in physiological bone growth within a pathological joint. Therefore, osteophyte development and growth present a valuable research subject when aiming to investigate the osteogenic signalling cascade.

Computational Analysis of DNA and RNA Sequencing Data Obtained from Liquid Biopsies.

Next-generation sequencing of DNA and RNA obtained from liquid biopsies of cancer patients may reveal important insights into disease progression and metastasis formation, and it holds the promise to enable new methods for noninvasive screening and clinical decision support. However, implementing liquid biopsy sequencing protocols is challenged by capturing circulating tumor cells or cell-free tumor DNA from blood samples, by amplifying genomic DNA and RNA in a reliable and unbiased manner, and by extracting biologically meaningful signals from the noisy sequencing data. In this chapter, we discuss computational methods for the analysis of DNA and RNA sequencing data obtained from liquid biopsies, addressing these challenges.