Francisco J. Sánchez-Rivera

Francisco J. Sánchez-Rivera was born and raised in Mayagüez, Puerto Rico. He obtained his bachelor’s degree in Microbiology from the University of Puerto Rico at Mayagüez and his PhD in Biology from MIT. As a PhD student with Tyler Jacks, he was among the first to use CRISPR to rapidly and systematically interrogate cancer drivers in vivo and to identify genotype-specific dependencies in lung adenocarcinoma. As a HHMI Hanna H. Gray Fellow with Scott W. Lowe at MSKCC, he developed and applied CRISPR base editing methods to engineer mutations with high efficiency and precision in cells and tissues of living animals to quantitatively interrogate cancer variants at scale, as well as approaches to chart tumor evolution using CRISPR and single cell RNA sequencing. Sánchez-Rivera joined the MIT faculty in 2022 as an assistant professor in the Department of Biology and a member of the Koch Institute.

The long-term goal of the Sánchez-Rivera laboratory is to elucidate the cellular and molecular mechanisms by which genes and disease-predisposing mutations interact with and within an individual’s genome to influence the development of diseases like cancer. To do so, we employ increasingly sophisticated genome editing technologies to engineer and manipulate the DNA of cells and organisms with single nucleotide precision.

Using cancer as a model genetic disease, and genes that exhibit functional and mutational variation as prototypes, we are pursuing three overarching goals: 1) investigate how genes and mutations interact at the molecular level depending on context, 2) probe how genetic background influences disease initiation and progression, and 3) define mechanisms by which genes and other DNA sequences interact to influence these phenotypes. Understanding these mechanisms is of fundamental and clinical importance as they could be leveraged to design more precise genome-informed cancer therapies. More broadly, we expect that our framework will produce generalizable concepts and approaches that could shed light on the development and treatment of genetic diseases beyond cancer and bridge the gap between correlation and causality.