# Allegra Petti Assistant Professor ![petti_allegra.jpg](/sites/g/files/omnuum5321/files/styles/hwp_4_5__480x600/public/bbsphd/files/petti_allegra.jpg?itok=ajJl-P1U) location\_on Massachusetts General HospitalSimches Research Center, Room 3.832185 Cambridge StreetBoston, MA 02114 smartphone [617-306-3143](tel:617-306-3143) email laptop\_windows [Lab Website](https://pettilab.mgh.harvard.edu/) laptop\_windows [Lab Website](https://pettilab.mgh.harvard.edu/) laptop\_windows [Publications](https://www.ncbi.nlm.nih.gov/myncbi/allegra.petti.1/bibliography/public/) My lab studies the development and evolution of cancer through analysis of intratumoral genetic, transcriptional, and immunological heterogeneity. Our work lies at the intersection of computational biology, molecular biology, and medicine: Using techniques drawn from applied mathematics, statistics, and computer science, we integrate multidimensional genomic and clinical data to derive biologically relevant and clinically actionable insights into cancer. In particular, we employ state-of-the-art genomic technologies - such as single-cell RNA-sequencing (scRNA-seq), single-cell DNA-sequencing, and spatial transcriptomics - which enable us to understand intratumoral heterogeneity with unprecedented resolution. We work in a variety of tumor types, with a current focus on brain tumors such as glioblastoma (GBM), meningioma, pituitary adenomas, and brain metastases. GBM is a particularly devastating disease, with a median survival of 15 months. Efforts to develop effective new therapies for GBM have been impeded by two defining features of the disease: Its extensive intratumoral heterogeneity and its unique immune microenvironment. GBM is heterogeneous with respect to the genome, tumor cell state, immune microenvironment, and larger spatial domains including a necrotic core, a viable proliferating tumor core, and a periphery comprised of tumor-infiltrated normal brain parenchyma. Compared to other tumor types, GBM is considered “immune cold;” its immune microenvironment is characterized by a paucity of T-cells and an abundance of immunosuppressive myeloid cells that enhance the tumor-promoting capacity of other immune compartments (for example, by driving T-cell exhaustion). The connections between tumor genotype, cell state, and immune environment are not well understood, but are important for designing state-of-the-art treatments for this disease. Ongoing projects address these interactions in the context of GBM and other tumors, and include the following: Elucidating the spatial organization of tumor and immune cells at the infiltrating margin of primary human glioblastomas. The transcriptional heterogeneity of GBM has been well-documented using scRNA-seq, but this destroys information about spatial organization and local intercellular interactions. To address this, we are integrating spatial transcriptomics and single-cell RNA-sequencing to compare tumor cell state and immune cell composition within the tumor core and the tumor edge, a relatively understudied but clinically important region that gives rise to most recurrent tumors. Tools to translate genomic insights into functional and translational studies. The clinical potential of scRNA-seq is largely untapped because it is difficult to physically isolate and study the cell states discovered using this technology. We are integrating CITE-seq (which measures the transcriptome and cell-surface proteome of single-cells), flow cytometry, and high-throughput drug screening to translate our current understanding of intratumoral heterogeneity into clinically actionable information and functional studies of specific cell states. Using mouse models to understand tumor evolution. Using a variety of well-studied mouse GBM models, we are combining scRNA-seq and spatial transcriptomics to characterize the dynamic reconfiguration of tumor and normal brain tissue during tumor formation and treatment response. Mouse tumors are small enough that a single Visium chip affords a birds-eye view of a cross section of the entire tumor as well as extensive surrounding brain tissue. The resulting data, coupled with mathematical tools such as pseudotemporal ordering and multiple regression, is enabling us to reconstruct the time course of molecular events that occurs during tumor formation. Understanding drug resistance in single cells using scRNA-seq, CellTagging, and in vivo tumor models. To understand how tumor cells respond to drug treatment, we must understand which cells are eliminated by treatment, and how the remaining cells are altered by treatment. To this end, we are using single-cell barcoding and scRNA-seq to trace the fate of tumor cells throughout standard-of-care treatment. - ## Program Affiliation [Leder Human Biology](/bbs-faculty/leder-human-biology) - ## Discipline [Cancer Biology](/discipline/cancer-biology) [Cell Biology](/discipline/cell-biology) [Computational](/discipline/computational) [Genomics](/discipline/genomics) [Immunology](/discipline/immunology) [Synthetic & Systems Biology](/discipline/synthetic-systems-biology) [Technology Development](/discipline/technology-development) - ## Organism [Human](/organism/human) [Mouse](/organism/mouse) - ## People [Faculty](/people/faculty) - ## Location [Massachusetts General Hospital](/location/massachusetts-general-hospital)