Tasquinimod suppresses tumor cell growth and bone resorption by targeting immunosuppressive myeloid cells and inhibiting c-MYC expression in multiple myeloma

Background: Immunotherapy emerged as a promising treatment option for multiple myeloma (MM) patients. However, therapeutic efficacy can be hampered by the presence of an immunosuppressive bone marrow microenvironment including myeloid cells. S100A9 was previously identified as a key regulator of myeloid cell accumulation and suppressive activity. Tasquinimod, a small molecule inhibitor of S100A9, is currently in a phase Ib/IIa clinical trial in MM patients (NCT04405167). We aimed to gain more insights into its mechanisms of action both on the myeloma cells and the immune microenvironment. Methods: We analyzed the effects of tasquinimod on MM cell viability, cell proliferation and downstream signaling pathways in vitro using RNA sequencing, real-time PCR, western blot analysis and multiparameter flow cytometry. Myeloid cells and T cells were cocultured at different ratios to assess tasquinimod-mediated immunomodulatory effects. The in vivo impact on immune cells (myeloid cell subsets, macrophages, dendritic cells), tumor load, survival and bone disease were elucidated using immunocompetent 5TMM models. Results: Tasquinimod treatment significantly decreased myeloma cell proliferation and colony formation in vitro, associated with an inhibition of c-MYC and increased p27 expression. Tasquinimod-mediated targeting of the myeloid cell population resulted in increased T cell proliferation and functionality in vitro. Notably, short-term tasquinimod therapy of 5TMM mice significantly increased the total CD11b+ cells and shifted this population toward a more immunostimulatory state, which resulted in less myeloid-mediated immunosuppression and increased T cell activation ex vivo. Tasquinimod significantly reduced the tumor load and increased the trabecular bone volume, which resulted in prolonged overall survival of MM-bearing mice in vivo. Conclusion: Our study provides novel insights in the dual therapeutic effects of the immunomodulator tasquinimod and fosters its evaluation in combination therapy trials for MM patients

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe

Visual analytics for cyber-physical systems development: Blending design thinking and systems thinking

Cyber-physical systems (CPS) are integrations of computational and physical processes. They represent a new generation of systems that interact with humans and expand the capabilities of the physical world through computation, communication, and control. At the same time, actions and interventions associated with this complex systems can have highly unpredictable and unintended consequences. Furthermore, today's practices of CPS design and implementation are not able to support the level of complexity required to detect these consequences. One methodology to approach this complex problem space is systems thinking (ST). Systems thinking emerges as both a worldview and a process in the sense that it informs one's understanding regarding a system and can be used as a problem-solving approach. Systems thinking is an abstraction-oriented analysis approach, specifically designed for heterogeneous complex systems. At the same time, another methodology, design thinking (DT), has enjoyed significantly increased visibility and importance over the last decade. Design thinking is a creative problem-solving approach, which puts human to the center and focuses first on the needs and experiences of the user. This paper aims to illustrate the possibility to use design thinking and systems thinking methodologies together to better deal with the complexity related problems during CPS design and implementation. The study proposes visual analytics as an integrative tool between these two methodologies, by (1) analyzing and understanding CPS development process through systems thinking, and (2) innovating and transforming the process through design thinking. To this end, an example use case is described and the application of the blended methodology explained step by step in relation to the use case. Visual analytics and data visualization are discussed in several steps and the possible benefits highlighted

Cyber-physical systems research and education in 2030: Scenarios and strategies

© 2020 Future cyber-physical systems (CPS), such as smart cities, collaborative robots, autonomous vehicles or intelligent transport systems, are expected to be highly intelligent, electrified, and connected. This study explores a focal question about how these new characteristics may affect the education and research related to CPS in 2030, the date identified by the United Nations to achieve the Agenda for Sustainable Development. To this end, first, we have conducted a trend spotting activity, seeking to identify possible influencing factors that may have a great impact on the future of CPS education and research. These factors were clustered in a total of 12 trends – four certainties; namely connectivity, electrification, data and automation – and eight uncertainties; namely intelligence, data ethics, labour market, lifelong learning, higher education, trust in technology, technological development speed, and sustainable development goals. After that, two of the eight uncertainties are identified and used to construct a scenario matrix, which includes four scenarios. These two uncertainties – the so-called strategic uncertainties – are: fulfilment of sustainable development goals and the nature of the technological development, respectively. These two important uncertainties are considered to build the scenarios due to their potential impact on the research and education of CPS. For instance, sustainable development goals are significant targets for many initiatives, organisations and countries. While 2030 is the deadline to achieve these goals, the relationship between the sustainable development goals related to CPS research and education is not studied well. Similarly, the speed of technological development is seen as a driving force behind future CPS. However, the effect of this speed to CPS research and education environment is not known. Different outcomes of the chosen two uncertainties are, then, combined with the remaining trends and uncertainties. Consequently, four scenarios are derived. The Terminator scenario illustrates a dystopian future where profit is the driving force behind technological progress and sustainable development goals are not accomplished. In contrast, The Iron Giant scenario represents the successful implementation of the sustainable development goals where technological development is the force behind the accomplishment of these goals. The scenario called Slow Progress represents a future where gradual technological improvements are present, but sustainability is still not seen as concerning the issue. The Humanist scenario illustrates a future where slow technological development is happening yet sustainable development goals are successfully implemented. Finally, the scenarios are used to initiate discussions by illustrating what the future of research and education could look like and a list of strategies for future CPS research and education environments is proposed. To this end, we invite educators, researchers, institutions and governments to develop the necessary strategies to enable data-orientated, continuous, interdisciplinary, collaborative, ethical, and sustainable research and education by improving digital fluency, advancing digital equality, contributing to new ways of teaching complex thinking, expanding access to learning platforms and preparing next generations to adapt for a rapidly changing future of work conditions

Digitalizing Swedish industry: What is next?: Data analytics readiness assessment of Swedish industry, according to survey results

Digitalization refers to enabling, improving, and transforming operations, functions, models, processes, or activities by leveraging digital technologies. Furthermore, digitalization is considered one of the most powerful drivers of innovation with the potential to trigger the next wave of innovation. Today, the importance of digitalization is well-understood in Swedish government agencies and industry. Although there are several initiatives working to actively drive change, one question is key: What is the next step? Data analytics is a promising way to turn information into outcomes, enhance decision-making, make data-driven discoveries, minimize risk, and unearth valuable insights that would otherwise remain hidden. This paper presents survey results on data analytics adoption and usage within Swedish industry, to highlight post-digitalization industry needs. To this end, a questionnaire was designed and distributed. Answers from more than 100 respondents from the manufacturing, technology, engineering, telecommunications, and automotive industries in Sweden were collected and analyzed. The assessment results show that Swedish industry has a high resources readiness score. This suggests that the necessary tools, and human resources are in place. Moreover, its cultural readiness level, which focuses on the acceptance of data-driven decision-making, scores between high and very high. At the same time, the information systems readiness level is in between medium and high, except in the telecommunication domain. However, the organizational readiness level is between medium and low, which shows that the organizations are not structured to enable the adaptation of data analytics and the business impacts of data analytics are not in place yet. These findings suggest that the industry should use the advantages of the current cultural, information systems, and resources readiness capabilities and concentrate efforts on exploring the business impacts of data analytics, ensuring the support from executive managers, and implementing data analytics protocols to improve organizational readiness. Moreover, the industry should consider structural changes in organizations, in addition to systematically initiating proper planning, timing, budgeting, and setting of clear key performance indicators/metrics in order to ameliorate the organizational readiness of data analytics

Uncertainty Management in Situation Awareness for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are a result of highly cross-disciplinary processes and are evolving to perform increasingly challenging tasks in dynamically changing environments. This leads to an increasing CPS complexity and therefore the management of uncertainty to ensure the trustworthiness of these systems is needed. Our paper focuses on uncertainty management (UM) both in general and more specifically in the context of CPS situation awareness (SA). The motivation behind this is the important role of SA and its many inherent uncertainties. To this end, firstly, a literature review is conducted to acquire the state of the art of UM. Later, we present findings and observations from the literature review, with two main challenges identified - inconsistent understanding and terminology among a multitude of uncertainty perspectives, and a lack of collaboration among different communities. On this basis, lastly, two case studies are conducted to exemplify the challenges and provide brief ideas on how to deal with them. The whole investigation in the paper suggests an urgent strengthening of common understanding through enhanced collaboration and regulations

Testosterone levels in relation to oral contraceptive use and the androgen receptor CAG and GGC length polymorphisms in healthy young women

BACKGROUND: The combined effect from the androgen receptor (AR) CAG and GGC length polymorphisms on testosterone levels has not been studied in young women. METHODS: Testosterone levels were measured in blood drawn on both menstrual cycle days 5-10 and 18-23 in 258 healthy women, aged = 17 repeats) was significantly associated with lower testosterone levels in OC users during cycle days 5-10 (P-trend =0.014), but not during cycle days 18-23 or in non-users. The interaction between the GGC length polymorphism and OC status was highly significant during cycle days 5-10 (P = 0.002) and near-significant during days 18-23 (P = 0.07). Incident breast cancer was more common in women with two short GGC alleles (log-rank P = 0.003). CONCLUSION: The GGC repeat length was the only significant genetic factor modifying the testosterone levels in current OC users from high-risk families. Homozygosity for the short GGC allele may be linked to the increased risk of early-onset breast cancer after OC exposure in high-risk women