Uncovering Oncogenic Mechanisms in T-ALL Through Multi-Omics and Advanced Genomic Profiling
Relapsed T-lineage acute lymphoblastic leukemia (T-ALL) is a challenging and life-threatening condition in children, with poor survival rates after relapse. To prevent these relapses, it’s crucial to identify which patients are most at risk. In our recent study (soon to be published in Nature), we analyzed the DNA and RNA of 1,309 childhood T-ALL cases to uncover genetic changes that drive the disease and predict poor outcomes. Our study revealed that over 95% of the cases had key genetic drivers, with more than half occurring in non-coding regions of the genome—areas that don’t directly code for proteins but still play vital roles in regulating gene activity. We identified 15 distinct subtypes of T-ALL, each with its own set of genetic features, developmental stage, and treatment outcomes. We also showed that these subtypes and genetic changes can predict how patients respond to treatment and their likelihood of survival. However, there are still unanswered questions: 1) Around 10% of cases don’t show clear genetic drivers or involve complex changes that are difficult to detect; 2) The link between the cell type where the cancer starts, its genetic changes, and how it’s regulated remains unclear; 3) We lack accurate tools to predict outcomes based on genetic and epigenetic changes.
Project Goal:
The goal of my research is to uncover the genetic and epigenetic factors that drive high-risk types of T-ALL. By understanding these mechanisms, I aim to develop precise tools to classify patients based on their risk and guide them toward more effective, personalized treatments. Over the next three years, I plan to 1) Study the hidden, non-coding genetic changes that contribute to T-ALL; 2) Investigate how changes in the chromatin landscape influence the disease at diagnosis and relapse; 3) Build artificial intelligence (AI) models that can predict a patient’s risk of relapse and survival based on these findings. By understanding the genetic and epigenetic landscape of T-ALL, this research will help classify patients more accurately, predict outcomes, and guide better treatment strategies, ultimately improving care for children with this disease.
