Abstract Background MiT/TFE-rearranged, translocation renal cell carcinoma (tRCC) is characterized by gene fusions involving TFE3 or TFEB, and is a rare, aggressive non-ccRCC, often occurring in children and with few biomarkers or targeted therapy options. TFE3 is the most commonly translocated gene with ASPSCR1, PRCC and SFPQ as fusion partners. GPNMB (Glycoprotein non-metastatic melanoma protein B) is a canonical transcriptional target of MiT/TFE proteins expressed at low levels in normal tissues but upregulated in numerous MiT/TFE-driven tumors (e.g. tRCC), where it is associated with poor prognosis. GPNMB can also be cleaved at its ECD by metalloproteinases and secreted from the surface of cancer cells. Soluble GPNMB can thus be detected in the serum of cancer patients and function as a surrogate biomarker. CD44, the primary receptor for GPNMB, is a tumor-associated antigen linked to cancer invasion, metastases and stemness, and is associated with poor prognosis in many cancers. The aim of the current study was to characterize the biology of GPNMB and CD44 in pre-clinical models of tRCC Methods We used the following human cell line models: As in vitro pre-clinical companion models we analyzed cells with stable doxycycline-inducible expression of WT-TFE3, PRCC-TFE3, SFPQ-TFE3 and NONO-TFE3 fusions using a) the Flp-In T-RExTM system in HEK293 cells, b) rtTA3 (Tet-on) system in HK2 proximal tubular epithelial cells and c) patient derived UOK cells with TFE3-fusion transgenes, comparing them to UOK cells from ccRCC patients. We also generated a novel transgenic knock-in mouse expressing the SFPQ-TFE3 fusion. We crossed SFPQ-TFE3LSL knock-in mice to 1) KSP-Cadherin 16 Cre mice that express Cre recombinase in the distal tubular epithelial cells and collecting ducts of the kidney. 2) Tamoxifen-inducible, Pax8- ERTCre mice diffusely expressing Cre recombinase following kidney development. We also examined tumorigenesis in the previously described PRCC-TFE3LSL; Ksp-Cre mice (W. Marston Linehan, NIH), and in an ASPSCR1-TFE3 PDX mouse model and a LN metastatic lesion from a patient with the ASPSCR1-TFE3 fusion (Dr. John Copland, Mayo Clinic). We are also currently examining blood serum, EDTA plasma and urine samples from non-metastatic and metastatic ccRCC and tRCC cases, obtained courtesy of the Kidney Cancer Program at Johns Hopkins. Results To further characterize GPNMB functionality and value as a cell-surface therapeutic target in tRCC, we engineered cell lines with genomic deletion of GPNMB via CRSIPR-Cas9 editing. Deletion of GPNMB in PRCC-TFE3 cell lines [UOK120/UOK124] significantly decreased clonogenic growth in 2D, spheroid size and viability and tumor xenograft growth in NSG mice and was associated with a decrease in phosphorylation of mTORC1 substrates [p-P70S6K, p-4EBP1]. We then examined expression of CD44, the primary receptor for GPNMB, and a tumor-associated antigen associated with poor prognosis in many cancers. Expression of CD44 and its ligand SPP1/OPN, was significantly increased in bulk RNA-Seq data from multiple transgenic models of SFPQ-TFE3, PRCC-TFE3 and ASPSCR1-TFE3, in human tRCC cases compared to normal kidney, and in SFPQ-TFE3/ PRCC-TFE3 transgenic kidney tumors and an ASPSCR-TFE3 PDX model, by immunoblotting and IHC, with increased membrane localization. shRNA-mediated depletion of CD44 profoundly and specifically decreased clonogenicity of multiple TFE3-fusion lines, with no effect seen in ccRCC lines. Conclusions In conclusion, GPNMB regulates the growth of tRCC cells, potentially via an autocrine mechanism involving its receptor CD44, and targeting GPNMB-CD44 signaling may be of therapeutic benefit in tRCC. DOD CDMRP Funding yes
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 91 citations· ⚡7
The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2 220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations
