Stefano Monti, Ph.D.


Associate Professor of Medicine and Biostatistics

Affiliate faculty, Bioinformatics Program
Affiliate member, Broad Institute of MIT & Harvard
Affiliate member, Hariri Institute for Computing and Computational Science & Engineering


B.S., Computer Science, University of Udine, Italy, 1991
M.S., Computer Science/AI, Intelligent Systems Program, University of Pittsburgh, 1996
Ph.D., Computer Science/AI, Intelligent Systems Program, University of Pittsburgh, 1999
Postdoctoral Fellow, Machine Learning/AI, Robotics Institute, Carnegie Mellon, 2000

Contact Information

Office: E611
Phone: 617-414-7031

Research Interests

Computational Biology, Cancer Genomics, Statistics and Machine Learning

Selected Publications

Gusenleitner D, Auerbach S, Meila T, Gómez H, Sherr DH, Monti S (2014). Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action. PLoS ONE 9(7):e102579 [Chemical watch review; Voice of America report]

Chen L, Monti S, Juszczynski P, Ouyang J, Chapuy B, Neuberg D, Doench JG, et al. (2013). SYK Inhibition Modulates Distinct PI3K/AKT- Dependent Survival Pathways and Cholesterol Biosynthesis in Diffuse Large B Cell Lymphomas. Cancer Cell, 23(6), 826–838. [Cancer Discovery review]

Hartley SW, Monti S, Liu CT, Steinberg MH, and Sebastiani P (2012) Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction, Frontiers in Genetics, 3:176

Caro P, Kishan  AU, Norberg E, Stanley I, Chapuy B, Ficarro SB, Polak K, Tondera D, Gounarides J, Yin H, Zhou F, Green MR, Chen L, Monti S, Marto JA, Shipp MA, Danial N (2012) Metabolic Signatures Uncover Novel Targets in Molecular Subsets of Diffuse Large B Cell Lymphoma. Cancer Cell, 22(4) 547-560. [Cancer Cell review]

Monti S, Chapuy B, Takeyama K, Rodig SJ, Hao Y, T. Yeda KT, Inguilizian H, Mermel C, Curie T, Dogan A, Kutok JL, Beroukim R, Neuberg D, Habermann T, Getz G, Kung AL, Golub TR, Shipp MA (2012) Integrative Analysis Reveals an Outcome-associated and Targetable Pattern of p53 and Cell Cycle Deregulation in Diffuse Large B-cell Lymphoma, Cancer Cell, 22(3):359-372.

Chiarle R, Zhang Y, Frock RL, Lewis SM, Molinie B, Ho Y, Myers DR,  Choi VW, Compagno M, Malkin DJ, Neuberg D, Monti S, Giallourakis CC, Gostissa M,  and Alt FW (2011) Genome-Wide Translocation Sequencing Reveals Mechanisms of Chromosome Breaks and Rearrangements in B Cells. Cell, 147:(1):107-119.

Green M, Monti S, et al. (2011) Signatures of murine B-cell development implicate Yy1 as a regulator of the germinal center-specific program. PNAS, 108(7): 2873-2878.

Chapman M, et al. (2011) Initial genome sequencing and analysis of multiple myeloma. Nature, 471(7339):467–472.

Green M, Monti S, et al. (2010) Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular Sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood, 116(17): 3268-3277.

The Cancer Genome Atlas (TCGA) Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216):1061-8.

Takeyama K, Monti S, et al. (2008) Integrative Analysis Reveals 53BP1 Copy Loss and Decreased Expression in a Subset of Human Diffuse Large B-cell Lymphomas. Oncogene, 27(3): 318-322.

Polo JM, Juszczynski P, Monti S, et al. (2007) A transcriptional signature with differential expression of BCL6 target genes accurately identifies BCL6-dependent diffuse large B-cell lymphomas. PNAS, 104(9): 3207-3212.

Hayes DN, Monti S, et al. (2006) Gene Expression Profiling Reveals Reproducible Human Lung Adenocarcinoma Subtypes in Multiple Independent DNA Microarray Cohorts. J Clinical Oncology, 24(31): 5079-5090.

Monti, S., Savage, K.J., et al. (2005) Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood, 105(5): p. 1851-1861.

Monti, S., et al. (2003) Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52(1-2): p. 91-118.