Martin Enge's Group
The Enge lab pursues projects focusing on clonal evolution, gene regulation and cell interaction in cancer using novel single-cell multiomics methodology. Our research aims to study these processes in cells from intact patient or mouse tissue, which allows us to reverse-engineer cancer as a complex disease influenced by both tumor cell heterogeneity and stromal interactions.
Cell types in healthy tissues only take on a finite number of states, since they are generated following a strict developmental program, and characterization of every cell type in human tissues is therefore possible. However, cancer cells carry genetic alterations that may remove such constraints, leading to a fundamental challenge: the potential co-existence of genetically distinct clones, each supporting multiple stable cancer cell states. This motivates the development of multiomics approaches for applications in cancer.
Clonal evolution and cell types in cancer
Pediatric Acute Lymphoblastic Leukemia (ALL) arises from lymphocyte progenitors and is known to maintain a hierarchy of cell differentiation, making it a suitable model disease for studying cancer stem cells. Although cure rates for ALL are currently above 80%, the prognosis of patients with relapsed ALL is dismal, with an overall survival rate of only 30%. We are analyzing primary and relapsed ALL from the same patient, with the aim of determining the clonal structure of the leukemia and characterize the cancer stem cell population in the primary sample of patients which later relapse. To differentiate between genetic and epigenetic changes, we are using a novel method that allows us to obtain genotypic and transcriptomic data from the same single cell, allowing us to trace clonal expansions that are mainly driven by epigenetic factors as well as those driven by genetic alterations.
Akademiska Stråket 1
External web site: www.engelab.org
For full publication list, see google scholar
A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells.
Zachariadis V, Cheng H, Andrews N, Enge M
Mol Cell 2020 11;80(3):541-553.e5
Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns.
Enge M, Arda HE, Mignardi M, Beausang J, Bottino R, Kim SK, Quake SR
Cell 2017 Oct;171(2):321-330.e14