We use single-cell tools to identify cancer cell populations associated with treatment resistance and develop tools to facilitate these studies. Explore our research here.

hero-image

Research Areas

Developmental Biology of Acute Leukemia in Children

B cell acute lymphoblastic leukemia is the most common cancer of childhood and remains one of the most deadly since despite improvements in outcomes, relapse remains a challenge and about only half of children suffering relapse will survive. We are using the knowledge of the normal process of development of healthy B cells to show us how B cell ALL goes wrong. These studies help us to better understand the biology of ALL cells and lead us to new approaches for risk prediction and treatment for kids with ALL. We are investigating how developmental features align with relapse sites with a special focus on the central nervous system.

author-picture

Yuxuan Liu

author-picture

Timothy Keyes

author-picture

Pablo Domizi

author-picture

Crystal Wang

author-picture

Astraea Jager

author-picture

Abhishek Koladiya

Drug Resistance and Response: Chemotherapy, Immunotherapy and More

Relapse occurring after standard and emerging therapies remains hard to cure. Only about half of patients who relapse after ALL relapse will survive their leukemia. We use patient samples and single-cell studies to identify ALL cells predictive of future relapse and then interrogate these cells as to why they were treatment resistant in the first place. Recent work has identified cells resistant to the therapeutic workhorse in ALL, glucocorticoids, and identified dasatinib as an additional agent to overcome this resistance (Sarno et al., Nat Comm 2023).

In the case of patients receiving chimeric antigen receptor T (CAR T) cells for relapsed leukemia, we are discovering cells that are prone to loss of the CAR target, CD19 or CD22 and have identified key transcription factors that lead to loss of this target, leading to antigen loss relapse. These studies will help us better predict what patients are at risk of antigen escape relapse and determine better treatment paradigms for these patients.

author-picture

Jolanda Sarno, Ph.D.

author-picture

Milton Merchant

author-picture

Yuxuan Liu

author-picture

Timothy Keyes

author-picture

Pablo Domizi

author-picture

Astraea Jager

author-picture

Abhishek Koladiya

The Role of Metabolism in Acute Lymphoblastic Leukemia

Deranged cellular metabolism is a hallmark of cancer. Although we are learning much about the role of altered metabolism in acute myeloid leukemia, the role of metabolism in treatment resistance in ALL is poorly understood. Our previous work identified cells with active mTOR signaling (Good et al. Nature Medicine 2018) to be predictive of future relapse when found at diagnosis. Further analysis of this signaling signature has identified a unique metabolic state and new potential therapeutic vulnerabilities.

author-picture

Milton Merchant

author-picture

Lucille Stuani, Ph. D.

author-picture

Yuxuan Liu

author-picture

Crystal Wang

author-picture

Astraea Jager

author-picture

Abhishek Koladiya

Single-cell Lineage Architecture and Immune Environments of Neuroblastoma

The advent of single-cell, high-dimensional imaging technologies have enabled unprecedented analysis of solid tumors without loss of structural and architectural features. We are using MIBI-ToF to analyze primary neuroblastoma tumors. Neuroblastoma is the most common extracranial solid tumor of childhood and has diverse outcomes based on known clinical features. We are studying neuroblastoma at the single-cell level to better understand the immune landscape of neuroblastoma tumors and how tumor cells of different lineages (mesenchymal vs. adrenergic) co-exist within the tumor ecosystem.

author-picture

Marte Brunvoll Kammersgaard

author-picture

Sri Varra

Outcome Prediction from Single-cell Studies

Using single-cell data from cancer samples presents a unique opportunity to understand patient outcomes. Data can be evaluated across scales: at the single-cell level, at the cell population level, at the patient level. We use this data to develop new algorithmic approaches to this type of data with a goal of informing patient outcomes in cancer. This involves machine learning, statistics and neural network models. We also focus on models that are human-interpretable and that provide insight into biology, as opposed to black boxes where the signals driving the results are obscure. We maintain access to these tools through our laboratory Github repository.

author-picture

Yuxuan Liu

author-picture

Timothy Keyes

author-picture

Pablo Domizi

author-picture

Crystal Wang

author-picture

Astraea Jager

author-picture

Abhishek Koladiya

Funders

Giving

giving-image