Matthew G. Jones

Matt Jones is an incoming Assistant Professor in the Department of Biology at MIT, an intramural member of the Koch Institute for Integrative Cancer Research, and a core member of the Institute for Medical Engineering and Science (IMES). His lab develops computational tools and experimental technologies to study tumor evolution.

Previously, he earned his PhD in Biomedical Informatics from UCSF and performed postdoctoral work with Professor Howard Chang at Stanford University. During this time, he developed computational approaches for single-cell lineage-tracing technologies and pioneered the use of evolutionary approaches for studying cancer dynamics. During his postdoctoral work, he focused his work on extrachromosomal DNA amplifications: circular, megabase-scale DNA amplifications found across cancers and associated with poor patient survival, drug resistance, and metastasis.  He is also a former UCSF Discovery Fellow and recipient of the NCI K99/R00 Pathway to Independence Award.

From the moment that a tumor is born, it is evolving across several levels: including at the genetic, epigenetic, metabolic, and microenvironmental levels. The central goal of the Jones Lab is to develop innovative computational and technological approaches to uncover the mechanisms of tumor evolution, with the ultimate aim of identifying new therapeutic targets and creating predictive models to monitor tumor initiation and progression.

Currently, our research centers on three interrelated goals: (1) investigating the spatiotemporal dynamics of copy-number alterations (particularly extrachromosomal DNA) in cancer populations; (2) developing new computational methods to trace cellular lineages; and (3) elucidating the principles by which tumors are organized over time. To pursue these aims, we integrate advances in computation and AI with cutting-edge multi-omic approaches (including single-cell, spatial, and long-read technologies), lineage tracing, and high-resolution imaging. Broadly, we expect that our studies will reveal generalizable rules governing tumor progression and treatment resistance, enable the predictive modeling of tumors, and inspire new approaches to intercept tumor progression.