Oncology Division
Alphabetical list (active faculty):   
Obi L. Griffith

Obi L. Griffith, PhD

Associate Professor

Department of Medicine

Oncology Division

Stem Cell Biology

Department of Genetics

McDonnell Genome Institute

Research Interests

  • Cancer informatics
  • Clinical statistics
  • Breast cancer


  • 314-747-9248 (office)
  • 314-286-1810 (fax)
  • Division of Oncology
    Mail Stop 8501-0029-10
    Washington University
    660 South Euclid Avenue
    St. Louis, MO 63110
  • Room 10116, Mid Campus Center (lab)


My research career began during the earliest phase of reference genome sequencing and helped to address basic questions such as the relationship between genome size (C-value) and organism complexity and longevity. Subsequently, I contributed to the Mammalian Gene Collection, publishing some of the first full-length sequences for many human genes. I was also part of a small team of bioinformaticians that, under extreme time pressure, sequenced, assembled, finished and published the first whole genome sequence for the severe acute respiratory syndrome (SARS-CoV-1) virus at the height of the 2003 epidemic. As a member of large-scale genome science centers for over 15 years, I have led the genomic characterization of a number of cancer types and subtypes. This has involved both cancer cohort analyses and case studies representing thousands of tumors in total. This work has contributed to the discovery of novel drivers of tumorigenesis, diagnostic and prognostic markers, and drug targets for breast cancer, liver cancer, small cell lung cancer (SCLC), leukemia, lymphoma, and others. I have also made significant contributions to understanding the genetic basis and fidelity of experimental cancer models. This has included identifying the critical drivers of tumor development in several genetically engineered mouse models, quantifying genomic heterogeneity in xenograft models, and comprehensively characterizing the molecular landscape and drug sensitivities of cell line models.

My current research is focused on the development of personalized medicine strategies for cancer using genomic technologies. I was one of two primary informaticians that completed the analysis for the first cancer patient to have whole genome and transcriptome sequencing performed with the goal of identifying personalized treatment options. More recently, I co-led the analysis of whole genome and transcriptome data that identified FLT3 as a target for Sunitinib and resulted in the successful treatment of a patient with adult ALL. I am currently leading the development of the first completely open-source and open-access public domain knowledgebase for expert-crowd-annotation of clinically actionable variants in cancer (civicdb.org). My lab focuses on the identification of molecular markers at the DNA, RNA and protein level that are useful for diagnosis and prognosis of cancer. Using bioinformatics methods and machine learning, we have developed biomarkers, panels and assays that can accurately classify malignant versus benign thyroid neoplasms, predict relapse in ER+ breast cancer, detect high-risk colorectal adenomas, and others.

Through my lab’s research activities and leadership roles in the Variant Interpretation for Cancer Consortium, Cancer Genomics Consortium, ClinGen, and GA4GH I have helped establish widely used standards, guidelines, knowledgebases and informatics resources for the clinical interpretation of cancer somatic variants. I am a strong advocate for open-access science and open-source tool development. All software and resources developed in my lab are completely open and free for academic use. I helped release an end-to-end pipeline for clinical cancer sequencing which automates state-of-the-art methods for sequence alignment, somatic variation detection, RNA sequence analysis, and the integration of these data types into a user-friendly report of the most clinically relevant genome and transcriptome changes in a tumor or group of tumors (github.com/genome). I developed a database and web tool for interrogating the druggable genome (dgidb.org). To accompany workshops that I help teach at CSHL, CBW and elsewhere I have released complete tutorials for RNA-seq analysis on the cloud (rnabio.org), precision medicine bioinformatics (pmbio.org), and genomic visualization (genviz.org).