CEEL: Cancer Ecology & Evolution Lab
Cancer is a complex evolving ecosystem. In the realm of medicine, we define cancer as an ecological phenomenon starting with one rebel cell breaking free from its ecological limits and multiples rapidly disrupting the equilibrium of resident tissue homeostasis, This will eventually lead to the extinction of other species and potential ecosystem collapse with many unpredicted variations. These ecological changes of tumor microenvironment will apply novel selection pressure on cancer cells and dictates which changes in cancer cells offer adaptive advantages. To address the significance of these evolutionary and ecological processes in cancer regarding cancer initiation, progression, and metastasis, we study how various tumors are evolving in their microenvironment from normal to precancer and cancer in clinically meaningful ways. We study how changes in microenvironment of normal, precancer, and cancer cells can change their phenotype adapting to varied microenvironment and how adaptation to it can shape the new ecosystem and evolutionary trajectory of cancer cells. This interplay between tumor cells and the microenvironment plays a fundamental role in the development of an ever-changing tumor ecosystem leading to more genotypic heterogeneity and phenotypic plasticity. We use the integration of spatial single-cell transcriptomics, proteomics, metabolomic and lipidomics, and pathomics machine learning analysis to capture the heterogeneity and plasticity of cancer cells in their natural ecological microenvironment and habitats.
Our projects include:
- Project 1: Ecology and Evolution of Breast Carcinogenesis
- Aim 1: Investigate how microenvironmental selection influences the phenotypic switch in early breast cancer. Single-cell RNA sequencing and single-cell ATAC sequencing will be employed to correlate phenotypes with mutation signatures.
- Aim 2: Utilize novel single-cell barcoding techniques to track clonal dynamics and correlate these with genotypic and phenotypic adaptations.
- Aim 3: Uncover the mechanisms behind metabolic reprogramming in early breast cancer cells.
- Project 2: Metabolic Phenotypes in DCIS for Disease Progression and Upstaging Stratification
- Aim 1: Employ CODEX and Vectra multiplex IHC techniques to define metabolic phenotypes in longitudinal breast cancer patient samples (from DCIS to IDC to metastasis).
- Aim 2: Conduct comprehensive omics analyses on longitudinal patient samples to track evolutionary dynamics and design predictive biomarkers for progression and upstaging.
- Project 3: Coevolution of Tumor and Stroma in Breast Cancer
- Aim 1: Investigate the role of cancer-associated fibroblasts in influencing tumor-stroma evolution and in ductal carcinoma in situ upstaging to invasive ductal carcinoma and metastasis.
- Aim 2: Identify specific epigenetic mechanisms of metabolic reprogramming of cancer-associated fibroblasts due to long-term exposure to the acidic microenvironment.
- Aim 3: Analyze longitudinal patient samples with multiplex IHC staining and develop patient-derived spheroid/organoid cultures to investigate tumor ecosystems.
- Project 4: Evolution of Resistance Phenotypes to PARPi in Ovarian Cancer: Genotypic Heterogeneity vs. Phenotypic Plasticity
- Aim 1: Integrate multi-omics data (DNA sequencing, single-cell RNA sequencing, and proteomics) to unravel the mechanisms of resistance to PARP1 inhibitors.
- Aim 2: Examine the fitness landscape dynamics of cancer cells under PARPi treatment.
- Aim 3: Utilize patient-derived organoid cultures to assess the roles of genetic heterogeneity and phenotypic plasticity in PARPi resistance.