Professional Education

  • Master of Science, University of Warsaw, Computer science (2009)
  • Master of Science, University of Warsaw (2010)
  • Doctor of Philosophy, Universite Libre De Bruxelles (2014)

Stanford Advisors

All Publications

  • Gene expression profiling of low-grade endometrial stromal sarcoma indicates fusion protein-mediated activation of the Wnt signaling pathway GYNECOLOGIC ONCOLOGY Przybyl, J., Kidzinski, L., Hastie, T., Debiec-Rychter, M., Nusse, R., van de Rijn, M. 2018; 149 (2): 388–93


    Low-grade endometrial stromal sarcomas (LGESS) harbor chromosomal translocations that affect proteins associated with chromatin remodeling Polycomb Repressive Complex 2 (PRC2), including SUZ12, PHF1 and EPC1. Roughly half of LGESS also demonstrate nuclear accumulation of β-catenin, which is a hallmark of Wnt signaling activation. However, the targets affected by the fusion proteins and the role of Wnt signaling in the pathogenesis of these tumors remain largely unknown.Here we report the results of a meta-analysis of three independent gene expression profiling studies on LGESS and immunohistochemical evaluation of nuclear expression of β-catenin and Lef1 in 112 uterine sarcoma specimens obtained from 20 LGESS and 89 LMS patients.Our results demonstrate that 143 out of 310 genes overexpressed in LGESS are known to be directly regulated by SUZ12. In addition, our gene expression meta-analysis shows activation of multiple genes implicated in Wnt signaling. We further emphasize the role of the Wnt signaling pathway by demonstrating concordant nuclear expression of β-catenin and Lef1 in 7/16 LGESS.Based on our findings, we suggest that LGESS-specific fusion proteins disrupt the repressive function of the PRC2 complex similar to the mechanism seen in synovial sarcoma, where the SS18-SSX fusion proteins disrupt the mSWI/SNF (BAF) chromatin remodeling complex. We propose that these fusion proteins in LGESS contribute to overexpression of Wnt ligands with subsequent activation of Wnt signaling pathway and formation of an active β-catenin/Lef1 transcriptional complex. These observations could lead to novel therapeutic approaches that focus on the Wnt pathway in LGESS.

    View details for DOI 10.1016/j.ygyno.2018.03.007

    View details for Web of Science ID 000432645000025

    View details for PubMedID 29544705

  • Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data JOURNAL OF COMPUTER ASSISTED LEARNING Prieto, L. P., Sharma, K., Kidzinski, L., Rodriguez-Triana, M. J., Dillenbourg, P. 2018; 34 (2): 193–203


    The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time), on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7-0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.

    View details for DOI 10.1111/jcal.12232

    View details for Web of Science ID 000426626700009

    View details for PubMedID 29686446

    View details for PubMedCentralID PMC5909982