Cancer cells are not static; they evolve like Darwinian organisms to survive therapy. Assistant Professor Matthew Jones at MIT is spearheading a research initiative that treats oncological progression like a high-stakes game of chess. By leveraging machine learning, his team aims to decode the internal logic of tumors, specifically focusing on how they change their genetic and epigenetic structures to bypass medical interventions.

A primary focus of the research involves extrachromosomal DNA (ecDNA). These are circular DNA particles that exist outside the traditional chromosome structure within the cell nucleus. While once considered rare, modern sequencing reveals ecDNA in approximately 25% of aggressive cancers, including brain and lung tumors. These particles allow cancer to adapt and mutate much faster than previously understood, effectively rewriting the biological rule book for tumor survival.

To navigate this complexity, the lab utilizes single-cell lineage tracing. This technology allows researchers to look back through a cell's ancestry to identify the exact moment an aggressive mutation occurred. By integrating these histories into predictive computational models, scientists hope to move toward personalized medicine where clinicians can anticipate a tumor's next move before it happens, potentially identifying new therapeutic targets and improving long-term patient survival rates.

Cancer cells are not static; they evolve like Darwinian organisms to survive therapy. Assistant Professor Matthew Jones at MIT is spearheading a research initiative that treats oncological progression like a high-stakes game of chess. By leveraging machine learning, his team aims to decode the internal logic of tumors, specifically focusing on how they change their genetic and epigenetic structures to bypass medical interventions.

A primary focus of the research involves extrachromosomal DNA (ecDNA). These are circular DNA particles that exist outside the traditional chromosome structure within the cell nucleus. While once considered rare, modern sequencing reveals ecDNA in approximately 25% of aggressive cancers, including brain and lung tumors. These particles allow cancer to adapt and mutate much faster than previously understood, effectively rewriting the biological rule book for tumor survival.

To navigate this complexity, the lab utilizes single-cell lineage tracing. This technology allows researchers to look back through a cell's ancestry to identify the exact moment an aggressive mutation occurred. By integrating these histories into predictive computational models, scientists hope to move toward personalized medicine where clinicians can anticipate a tumor's next move before it happens, potentially identifying new therapeutic targets and improving long-term patient survival rates.