Hagerling Lab research

Targeting Nonrandom Cancer Colonization in Children and Adults

We investigate the biology of metastatic cancer in both children and adults — with a focus on why tumors behave differently depending on where they spread. Using spatial multiomics, patient-derived models, and deep learning, we work to uncover organ-specific vulnerabilities in metastatic tumors and translate those findings into new therapeutic strategies and prognostic tools.


Overview of Hagerling Lab research on organ-specific metastatic cancer

Research Focus

The overarching objective of the current research in the Hagerling lab is to determine whether the distant metastatic site itself must be considered in treatment and drug development. To address this, we apply spatial multiomics and patient-derived xenograft (PDX) models to study both adult and pediatric metastases, aiming to resolve whether metastasis site–specific tumor cell properties and microenvironmental cues hide therapeutic targets.

Patterns of metastatic spread vary between cancer types, a clinical observation that strongly suggests metastasis formation depends on organ-specific intrinsic or acquired traits of tumor cells, as well as contributions from non-malignant accessory cells—such as immune and stromal cells—within the microenvironment. Based on this, we hypothesize that (i) organ-specific capabilities hide cellular vulnerabilities that could be targeted therapeutically, and, (ii) curative strategies for metastatic cancer will require treatments tailored to the metastatic site.

The overarching aim of our research program is to identify organ-specific therapeutic targets and prognostic biomarkers in metastasis. The research is structured into two complementary projects:

Research figure — spatial analysis
Research figure — metastatic spread

Current Projects

  1. The Non-Random Metastatic Behavior of Neuroblastoma Reveals Therapeutic Opportunities
  2. Spatial Multiomics to Decode and Target the Non-Random Metastatic Behavior of Common Adult Malignancies
Spatial multispectral imaging