Doctoral research · Published foundation
Causal Network Inference in Tumor Microenvironments
I co-developed a data-based method for inferring cell-to-cell networks from tumor measurements and used it to study how oncogenic CCN4 expression is associated with changes in local cellular relationships in breast cancer and melanoma.
Scientific problem
Tumor behavior depends on relationships among cell and tissue states.
Tumors are not collections of malignant cells acting independently. Their behavior depends on communication among tumor, immune, stromal, and vascular cell states. Yet many mechanistic network models begin with a structure assembled manually from selected literature.
During my doctoral research, I co-developed a data-based approach for inferring heterocellular networks from observational tumor measurements. The method represents cell states and tissue features as nodes and estimates directed relationships among them. Edge direction, sign, and support provide a compact account of how the local cellular system is organized.
Published result
Oncogenic expression was studied as a change in system structure.
We applied the method to human breast cancer and melanoma data to study how oncogenic CCN4 expression alters the surrounding cellular network. The resulting models connected tumor state with immune and stromal features and showed how an oncogenic signal can be interpreted as a change in local organization rather than as an isolated molecular marker.
The inferred graph is conditional on the measured variables, data quality, and assumptions of the structure-learning procedure. It identifies relationships that can guide mechanistic modeling and experimental follow-up; it does not by itself establish intervention effects.
Placed within a network of tumor and tissue-state relationships.
Represented as interacting components rather than isolated abundance estimates.
Included in the same directed system as tumor and immune features.
Used to distinguish stronger structural evidence from uncertain relationships.
Methodological extension
BaMANI makes disagreement among plausible structures visible.
The doctoral work motivated an ensemble framework for causal structure learning. Instead of relying on one algorithm and one fitted graph, BaMANI compares evidence across algorithms and resampled datasets, then summarizes edge-level stability.
Causal structure learning can prioritize relationships for mechanistic testing. It does not replace intervention studies or remove dependence on identification assumptions.
Takeaway
Move from isolated biomarkers to interpretable system structure.
The work produced interpretable heterocellular networks in human cancer datasets and a methodological direction for reporting structural uncertainty. The strongest use is to identify relationships that deserve mechanistic testing while keeping uncertainty in the graph visible.