Thesis Defense - Computer Science: Elena Krikun

Please join the Department of Computer Science for the upcoming thesis defense:

Presenter: Elena Krikun

Thesis title: Causal Discovery and Treatment Effect Modeling in Breast Cancer


Abstract: Modeling breast cancer outcomes remains challenging because of extreme molecular heterogeneity and the inability of associative models, including those developed through traditional machine learning, to support counterfactual, intervention-based clinical reasoning. Building on recent advances in causal feature selection, multiomics variable selection, and individual treatment effect estimation, this thesis proposes a hybrid pipeline within a unified computational multiomics framework that integrates high-dimensional data with causal modeling to produce interpretable precision oncology models that extend beyond risk prediction.

The proposed pipeline was developed using the TCGA-BRCA cohort as the discovery set and validated on the independent retrospective METABRIC cohort to assess transportability. To address the curse of dimensionality, the framework applies Markov Blanket-based local causal discovery across seven data modalities and reduces more than 600,000 initial features to a sparse and stable causal core. This causal representation is then used for survival modeling (C-index = 0.8085, 5-year AUC = 0.8676) and individual treatment effect (ITE) estimation for chemotherapy, hormone therapy, and targeted therapy. External validation on METABRIC achieved a C-index of 0.7200 and a 5-year AUC of 0.7639, indicating moderate but clear transportability across cohorts and assay platforms. The final causal core confirmed the integration of clinical, proteomic, and epigenetic signals, and identified a long non-coding RNA as a structurally relevant driver.

The treatment-effect stage used treatment-specific arm definitions reconstructed from clinical records together with a robustness-oriented validation protocol. Chemotherapy showed the strongest and most stable beneficial treatment effect, most notably in the TNBC subgroup, where treatment-effect estimates remained consistently protective across estimators and overlap-adjusted variants. Hormone-therapy estimates showed a consistently protective direction in receptor-positive subgroup analyses, although the magnitude of the effect was attenuated under stricter overlap control, indicating residual confounding and limited positivity in the observational setting. Targeted therapy also showed a protective direction under most evaluated techniques, but given the very small number of treated patients and partial estimator disagreement, these effect estimates should be interpreted as exploratory.


Committee Members:
Dr. Abedalrhman Alkhateeb (supervisor, committee chair), Dr. Saad B. Ahmed, Dr. Maysa Yaseen (Electrical & Computer Engineering)

Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.