Francisco J. Sánchez-Rivera

Francisco J. Sánchez-Rivera

Assistant Professor of Biology; Intramural Faculty, Koch Institute

Francisco J. Sánchez-Rivera aims to understand how genetic variation shapes normal physiology and disease, with a focus on cancer.

617-715-3389

Phone

76-361A

Office

fsr@mit.edu

Email

Koch Institute for Integrative Cancer Research

Location

Lab Website
Jamie Rothman

Assistant

617-258-6635

Assistant Phone

Education

  • PhD, 2016, Biology, MIT
  • BS, 2008, Microbiology, University of Puerto Rico at Mayagüez

Research Summary

The overarching goal of the Sánchez-Rivera laboratory is to elucidate the cellular and molecular mechanisms by which genetic variation shapes normal physiology and disease, particularly in the context of cancer. To do so, we develop and apply genome engineering technologies, genetically-engineered mouse models (GEMMs), and single cell lineage tracing and omics approaches to obtain comprehensive biological pictures of disease evolution at single cell resolution. By doing so, we hope to produce actionable discoveries that could pave the way for better therapeutic strategies to treat cancer and other diseases.

Awards

  • V Foundation Award, 2022
  • Hanna H. Gray Fellowship, Howard Hughes Medical Institute, 2018-2026
  • GMTEC Postdoctoral Researcher Innovation Grant, Memorial Sloan Kettering Cancer Center, 2020-2022
  • 100 inspiring Hispanic/Latinx scientists in America, Cell Mentor/Cell Press, 2020

Key Publications

  1. High-throughput evaluation of genetic variants with prime editing sensor libraries. Gould, SI, Wuest, AN, Dong, K, Johnson, GA, Hsu, A, Narendra, VK, Atwa, O, Levine, SS, Liu, DR, Sánchez Rivera, FJ et al.. 2024. Nat Biotechnol , .
    doi: 10.1038/s41587-024-02172-9PMID:38472508
  2. A prime editor mouse to model a broad spectrum of somatic mutations in vivo. Ely, ZA, Mathey-Andrews, N, Naranjo, S, Gould, SI, Mercer, KL, Newby, GA, Cabana, CM, Rideout, WM 3rd, Jaramillo, GC, Khirallah, JM et al.. 2024. Nat Biotechnol 42, 424-436.
    doi: 10.1038/s41587-023-01783-yPMID:37169967
  3. Base editing sensor libraries for high-throughput engineering and functional analysis of cancer-associated single nucleotide variants. Sánchez-Rivera, FJ, Diaz, BJ, Kastenhuber, ER, Schmidt, H, Katti, A, Kennedy, M, Tem, V, Ho, YJ, Leibold, J, Paffenholz, SV et al.. 2022. Nat Biotechnol 40, 862-873.
    doi: 10.1038/s41587-021-01172-3PMID:35165384
  4. Keap1 mutation renders lung adenocarcinomas dependent on Slc33a1. Romero, R, Sánchez-Rivera, FJ, Westcott, PMK, Mercer, KL, Bhutkar, A, Muir, A, González Robles, TJ, Lamboy Rodríguez, S, Liao, LZ, Ng, SR et al.. 2020. Nat Cancer 1, 589-602.
    doi: 10.1038/s43018-020-0071-1PMID:34414377
  5. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Sánchez-Rivera, FJ, Papagiannakopoulos, T, Romero, R, Tammela, T, Bauer, MR, Bhutkar, A, Joshi, NS, Subbaraj, L, Bronson, RT, Xue, W et al.. 2014. Nature 516, 428-31.
    doi: 10.1038/nature13906PMID:25337879

Recent Publications

  1. It's prime time for multiplexed prime editing. Wu, K, Sánchez-Rivera, FJ. 2025. Cell Genom 5, 100852.
    doi: 10.1016/j.xgen.2025.100852PMID:40209678
  2. Using Prime Editing Guide Generator (PEGG) for high-throughput generation of prime editing sensor libraries. Gould, SI, Sánchez-Rivera, FJ. 2025. Methods Enzymol 712, 437-451.
    doi: 10.1016/bs.mie.2025.01.006PMID:40121083
  3. Multiplexed in vivo base editing identifies functional gene-variant-context interactions. Acosta, J, Johnson, GA, Gould, SI, Dong, K, Lendner, Y, Detrés, D, Atwa, O, Bulkens, J, Gruber, S, Contreras, ME et al.. 2025. bioRxiv , .
    doi: 10.1101/2025.02.23.639770PMID:40060482
  4. RNA-binding proteins and glycoRNAs form domains on the cell surface for cell-penetrating peptide entry. Perr, J, Langen, A, Almahayni, K, Nestola, G, Chai, P, Lebedenko, CG, Volk, RF, Detrés, D, Caldwell, RM, Spiekermann, M et al.. 2025. Cell 188, 1878-1895.e25.
    doi: 10.1016/j.cell.2025.01.040PMID:40020667
  5. Genome-wide CRISPR guide RNA design and specificity analysis with GuideScan2. Schmidt, H, Zhang, M, Chakarov, D, Bansal, V, Mourelatos, H, Sánchez-Rivera, FJ, Lowe, SW, Ventura, A, Leslie, CS, Pritykin, Y et al.. 2025. Genome Biol 26, 41.
    doi: 10.1186/s13059-025-03488-8PMID:40011959
  6. Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer. Zucker, M, Perry, MA, Gould, SI, Elkrief, A, Safonov, A, Thummalapalli, R, Mehine, M, Chakravarty, D, Brannon, AR, Ladanyi, M et al.. 2025. Cell 188, 851-867.e17.
    doi: 10.1016/j.cell.2024.11.010PMID:39701102
  7. Precision mutational scanning: your multipass to the future of genetics. Roth, JF, Sánchez-Rivera, FJ. 2025. Nat Methods 22, 13-15.
    doi: 10.1038/s41592-024-02522-0PMID:39562755
  8. Site of breast cancer metastasis is independent of single nutrient levels. Abbott, KL, Subudhi, S, Ferreira, R, Gültekin, Y, Steinbuch, SC, Munim, MB, Honeder, SE, Kumar, AS, Ramesh, DL, Wu, M et al.. 2024. bioRxiv , .
    doi: 10.1101/2024.10.24.616714PMID:39484531
  9. A genome-wide arrayed CRISPR screen identifies PLSCR1 as an intrinsic barrier to SARS-CoV-2 entry that recent virus variants have evolved to resist. Le Pen, J, Paniccia, G, Kinast, V, Moncada-Velez, M, Ashbrook, AW, Bauer, M, Hoffmann, HH, Pinharanda, A, Ricardo-Lax, I, Stenzel, AF et al.. 2024. PLoS Biol 22, e3002767.
    doi: 10.1371/journal.pbio.3002767PMID:39316623
  10. TGF-β and RAS jointly unmask primed enhancers to drive metastasis. Lee, JH, Sánchez-Rivera, FJ, He, L, Basnet, H, Chen, FX, Spina, E, Li, L, Torner, C, Chan, JE, Yarlagadda, DVK et al.. 2024. Cell 187, 6182-6199.e29.
    doi: 10.1016/j.cell.2024.08.014PMID:39243762
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Photo credit: Adam Lerner