Malachi Griffith, Ph.D.

Associate Professor
Internal Medicine
Oncology
Genetics

Molecular Genetics and Genomics Program
Biomedical Informatics and Data Science Program
Cancer Biology Program
Computational and Systems Biology Program

  • 314-286-1274

  • The Genome Institute

  • mgriffit@wustl.edu

  • http://griffithlab.org/

  • https://twitter.com/malachigriffith

  • genomics, bioinformatics, data mining, immunogenomics, precision medicine, personalized medicine, cancer vaccines, and cancer research

  • Improving our understanding of human disease biology and the development of personalized medicine strategies using genomics and informatics technologies

Research Abstract:

I currently co-lead a bioinformatics lab with my identical twin brother Obi Griffith. Our lab's research is focused on improving our understanding of cancer biology and the development of personalized medicine strategies using genomics and informatics technologies. We develop bioinformatics methods for the analysis of high throughput sequence data and identification of biomarkers for diagnostic, prognostic and drug response prediction. In collaboration with a diverse group of physician scientists at WASHU and other institutions we are currently leading analyses of diverse sequence data types to profile tumors of individual patients whose conditions are not well addressed by standard-of-care options. We are also leading the analysis of several large-scale genomics projects to help discover genomic signatures relevant to disease initiation, progression and treatment response. In addition to being an Associate Professor of the Department of Medicine, I am an Assistant Director of the McDonnell Genome Institute where our lab is physically located. Finally, I am an instructor for several Bioinformatics workshops delivered annually in various locations including Cold Spring Harbor Laboratories (New York), the Ontario Institute for Cancer Research (Toronto and Montreal), the University of Edinburgh (Scotland), Berlin (Germany) and Cesky Krumlov (Czech Republic).

In addition to many ongoing large scale cancer omics data analysis projects, clinical trial data analyses, and Genomics Tumor Board case studies, the Griffith Lab actively develops methods, algorithms and tools to help others perform these analyses. We have made substantial contributions to open source and open access resources for the scientific community including creation of platforms for alternative expression analysis, regulatory region annotation, mining drug-gene interactions (http://www.dgidb.org), curation of functional mutations (http://www.docm.info), the clinical interpretation of variants in cancer (http://www.civicdb.org) the design of personalized cancer vaccines (http://pvactools.org/), integration of whole genome and RNA-seq data in a regulatory and splicing context (http://regtools.org/), and GenVisR for genomic data visualization (https://github.com/griffithlab/GenVisR).

We have published over 100 papers with major areas of focus in precision medicine, cancer, genomics, immunogenomics, biomarker discovery, sequence analysis, gene regulation and alternative RNA splicing.

To learn more about past and ongoing projects in the Griffith Lab you can browse the following:
https://scholar.google.com/citations?user=fcIQQ3oAAAAJ
https://scholar.google.com/citations?user=2_XMQ_oAAAAJ
https://github.com/griffithlab

Mentorship and Commitment to Diversity Statement:
The GriffithLab (http://www.griffithlab.org) is an interdisciplinary team of biologists, bioinformaticians, computer scientists, and software engineers. Each member of the lab is trained to use computational biology approaches to address research questions relating to cancer biology and precision cancer medicine. We train students and post-docs with a biology background that would like to develop bioinformatics skills. We are equally interested in trainees with a computer science background that would like to apply these skills to studying cancer and improving patient outcomes.

Selected Publications:

Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151.

Uppaluri R, Campbell KM, Egloff AM, Zolkind P, Skidmore ZL, Nussenbaum B, Paniello RC, Rich JT, Jackson R, Pipkorn P, Michel LS, Ley J, Oppelt P, Dunn GP, Barnell EK, Spies NC, Lin T, Li T, Mulder D, Hanna Y, Cirlan I, Pugh TJ, Mudianto T, Riley R, Zhou L, Jo VY, Stachler MD, Hanna GJ, Kass JI, Haddad RI, Schoenfeld JD, Gjini E, Lako A, Thorstad WL, Gay HA, Daly M, Rodig S, Hagemann IS, Kallogjeri D, Piccirillo JF, Chernock RD, Griffith M, Griffith OL, Adkins DR. Neoadjuvant and Adjuvant Pembrolizumab in Resectable Locally Advanced, Human Papillomavirus-Unrelated Head and Neck Cancer: A Multicenter, Phase 2 Trial. Clinical Cancer Research. 2020 Jul 14:clincanres.1695.2020.

De Mattos-Arruda L, Vazquez M, Finotello F, Lepore R, Porta E, Hundal J, Amengual-Rigo P, Ng CKY, Valencia A, Carrillo J, Chan TA, Guallar V, McGranahan N, Blanco J, Griffith M. Neoantigen prediction and computational perspectives towards clinical benefit: Recommendations from the ESMO Precision Medicine Working Group. Annals of Oncology. 2020 May 19:S0923-7534(20)39824-0.

Wagner AH, Walsh B, Mayfield G, Tamborero D, Sonkin D, Krysiak K, Deu-Pons J, Duren RP, Gao J, McMurry J, Patterson S, Del Vecchio Fitz C, Pitel BA, Sezerman OU, Ellrott K, Warner JL, Rieke DT, Aittokallio T, Cerami E, Ritter DI, Schriml LM, Freimuth RR, Haendel M, Raca G, Madhavan S, Baudis M, Beckmann JS, Dienstmann R, Chakravarty D, Li XS, Mockus S, Elemento O, Schultz N, Lopez-Bigas N, Lawler M, Goecks J, Griffith MϮ, Griffith OLϮ, Margolin AA; Variant Interpretation for Cancer Consortium. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nature Genetics. 2020 Apr;52(4):448-457.

Wagner AH, Kiwala S, Coffman AC, McMichael JF, Cotto KC, Mooney TB, Barnell EK, Krysiak K, Danos AM, Walker J, Griffith OLϮ, Griffith MϮ. CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase. JCO Clinical Cancer Informatics. 2020 Mar;4:245-253.

Hundal J, Kiwala S, McMichael J, Miller CA, Xia H, Wollam AT, Liu CJ, Zhao S, Feng YY, Graubert AP, Wollam AZ, Neichin J, Neveau M, Walker J, Gillanders WE, Mardis ER, Griffith OLϮ, Griffith MϮ. pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunology Research. 2020 Mar;8(3):409-420.

Lever J, Jones MR, Danos AM, Krysiak K, Bonakdar M, Grewal JK, Culibrk L, Griffith OLϮ, Griffith MϮ, Jones SJMϮ. Text-mining clinically relevant cancer biomarkers for curation into the CIViC database. Genome Medicine. 2019 Dec 3;11(1):78.

Linette GP, Becker-Hapak M, Skidmore ZL, Baroja ML, Xu C, Hundal J, Spencer DH, Fu W, Cummins C, Robnett M, Kaabinejadian S, Hildebrand WH, Magrini V, Demeter R, Krupnick AS, Griffith OL, Griffith M, Mardis ER, Carreno BM. Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens. Proceedings of the National Academy of Sciences USA. 2019 Nov 19;116(47):23662-23670.

Barnell EK, Waalkes A, Mosior MC, Penewit K, Cotto KC, Danos AM, Sheta LM, Campbell KM, Krysiak K, Rieke D, Spies NC, Skidmore ZL, Pritchard CC, Fehniger TA, Uppaluri R, Govindan R, Griffith MϮ, Salipante SJϮ, Griffith OLϮ. Open-Sourced CIViC Annotation Pipeline to Identify and Annotate Clinically Relevant Variants Using Single-Molecule Molecular Inversion Probes. JCO Clinical Cancer Informatics. 2019 Oct;3:1-12.

Campbell KM, O'Leary KA, Rugowski DE, Mulligan WA, Barnell EK, Skidmore ZL, Krysiak K, Griffith M, Schuler LA, Griffith OL. A Spontaneous Aggressive ERα+ Mammary Tumor Model Is Driven by Kras Activation. Cell Reports. 2019 Aug 6;28(6):1526-1537.e4.

Hundal J, Kiwala S, Feng YY, Liu CJ, Govindan R, Chapman WC, Uppaluri R, Swamidass SJ, Griffith OLϮ, Mardis ERϮ, Griffith MϮ. Accounting for proximal variants improves neoantigen prediction. Nature Genetics. 2019 Jan;51(1):175-179.

Ainscough BJ, Barnell EK, Ronning P, Campbell KM, Wagner AH, Fehniger TA, Dunn GP, Uppaluri R, Govindan R, Rohan TE, Griffith M, Mardis ER, Swamidass SJ, Griffith OL. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nature Genetics. 2018 Dec;50(12):1735-1743.

Wagner AH, Devarakonda S, Skidmore ZL, Krysiak K, Ramu A, Trani L, Kunisaki J, Masood A, Waqar SN, Spies NC, Morgensztern D, Waligorski J, Ponce J, Fulton RS, Maggi LB Jr, Weber JD, Watson MA, O'Conor CJ, Ritter JH, Olsen RR, Cheng H, Mukhopadhyay A, Can I, Cessna MH, Oliver TG, Mardis ER, Wilson RK, Griffith MϮ, Griffith OLϮ, Govindan RϮ. Recurrent WNT pathway alterations are frequent in relapsed small cell lung cancer. Nature Communications. 2018 Sep 17;9(1):3787.

Griffith OL, Spies NC, Anurag M, Griffith M, Luo J, Tu D, Yeo B, Kunisaki J, Miller CA, Krysiak K, Hundal J, Ainscough BJ, Skidmore ZL, Campbell K, Kumar R, Fronick C, Cook L, Snider JE, Davies S, Kavuri SM, Chang EC, Magrini V, Larson DE, Fulton RS, Liu S, Leung S, Voduc D, Bose R, Dowsett M, Wilson RK, Nielsen TO, Mardis ER, Ellis MJ. The prognostic effects of somatic mutations in ER-positive breast cancer. Nature Communications. 2018 Sep 4;9(1):3476.

Campbell KM, Lin T, Zolkind P, Barnell EK, Skidmore ZL, Winkler AE, Law JH, Mardis ER, Wartman LD, Adkins DR, Chernock RD, Griffith M, Uppaluri R, Griffith OL. Oral Cavity Squamous Cell Carcinoma Xenografts Retain Complex Genotypes and Intertumor Molecular Heterogeneity. Cell Reports. 2018 Aug 21;24(8):2167-2178.

Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, Wollam A, Spies NC, Griffith OLϮ, Griffith MϮ. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Research. 2017 Nov 16.

Griffith M*Ϯ, Spies NC*, Krysiak K*, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA, Rieke DT, Kujan L, Barnell EK, Wagner AH, Skidmore ZL, Wollam A, Liu CJ, Jones MR, Bilski RL, Lesurf R, Feng YY, Shah NM, Bonakdar M, Trani L, Matlock M, Ramu A, Campbell KM, Spies GC, Graubert AP, Gangavarapu K, Eldred JM, Larson DE, Walker JR, Good BM, Wu C, Su AI, Dienstmann R, Margolin AA, Tamborero D, Lopez-Bigas N, Jones SJ, Bose R, Spencer DH, Wartman LD, Wilson RK, Mardis ER, Griffith OLϮ. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017 Jan 31;49(2):170-174.

Krysiak K, Gomez F, White BS, Matlock M, Miller CA, Trani L, Fronick CC, Fulton RS, Kreisel F, Cashen AF, Carson KR, Berrien-Elliott MM, Bartlett NL, Griffith M, Griffith OL, Fehniger TA. Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma. Blood. 2017 Jan 26;129(4):473-483.

Skidmore ZL, Wagner AH, Lesurf R, Campbell KM, Kunisaki J, Griffith OLϮ, Griffith MϮ. GenVisR: Genomic Visualizations in R. Bioinformatics. 2016 Oct 1;32(19):3012-4.

Ainscough BJ, Griffith MϮ, Coffman AC, Wagner AH, Kunisaki J, Choudhary MN, McMichael JF, Fulton RS, Wilson RK, Griffith OLϮ, Mardis ER. DoCM: a database of curated mutations in cancer. Nat Methods. 2016 Sep 29;13(10):806-7.

Griffith M*Ϯ, Griffith OL*, Krysiak K, Skidmore ZL, Christopher MJ, Klco JM, Ramu A, Lamprecht TL, Wagner AH, Campbell KM, Lesurf R, Hundal J, Zhang J, Spies NC, Ainscough BJ, Larson DE, Heath SE, Fronick C, O`Laughlin S, Fulton RS, Magrini V, McGrath S, Smith SM, Miller CA, Maher CA, Payton JE, Walker JR, Eldred JM, Walter MJ, Link DC, Graubert TA, Westervelt P, Kulkarni S, DiPersio JF, Mardis ER, Wilson RK, Ley TJϮ. Comprehensive genomic analysis reveals FLT3 activation and a therapeutic strategy for a patient with relapsed adult B-lymphoblastic leukemia. Exp Hematol. 2016 Jul;44(7):603-13.

Krysiak K, Christopher MJ, Skidmore ZL, Demeter RT, Magrini V, Kunisaki J, O`Laughlin M, Duncavage EJ, Miller CA, Ozenberger BA, Griffith M, Wartman LD, and Griffith OL. A genomic analysis of Philadelphia chromosome-negative AML arising in patients with CML. Blood Cancer J. 2016 Apr 8;6:e413.

Last Updated: 3/22/2021 1:39:42 PM

My lab works to overcome the data analysis and interpretation bottleneck for clinical translation of genomic observations.
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