Speaker: Denisa Sufaj
10 Marzo 2026 | 14:15
DEIB, Sala Seminari "A. Alario" (Ed. 21)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, March 10th, 2026 at 2:15 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB "Alessandra Alario" Seminar Room (Building 21) organized by the Data Science for Bioinformatics group.The seminar will be held by Denisa Sufaj, a BCG master student, on the following subject: "Modeling the senescence-proliferation continuum in prostate cancer: from transcriptomic axes to discrete cellular states".
Cellular senescence and proliferation are commonly described as mutually exclusive and discrete biological states. However, in cancer, transcriptional programs associated with these phenotypes often coexist and vary in a graded manner, challenging binary classifications. In human prostate cancer, the relationship between proliferative activity and senescence-associated signaling remains insufficiently resolved at single-cell resolution.
In this thesis, we model the senescence–proliferation continuum in human prostate cancer by reconstructing a transcriptomic axis that captures gradual transitions from highly proliferative to senescent-like cellular programs. Using single-cell RNA sequencing data derived from human tumor samples, we integrate pathway-level scoring and data-driven latent representations to define a continuous biological landscape. We then translate this axis into discrete cellular states through statistical modeling approaches, enabling robust classification while preserving the underlying continuum structure.
Our results show that senescence-associated programs do not form a strictly separable compartment but instead emerge along a structured gradient opposing proliferative activity. Beyond canonical markers, we identify a set of genes whose expression dynamics strongly align with the reconstructed axis, highlighting candidate regulators and effectors that shape the senescence–proliferation balance in human prostate cancer cells. Discrete states thus arise as emergent properties of an underlying continuous transcriptomic space.
This work provides a quantitative framework to model senescence in human prostate cancer, bridging continuous biological variation and discrete state assignment, and offering refined molecular insight into tumor cell heterogeneity.