Aaron Newman, PhD

Our lab builds novel data science tools to better understand the cellular and molecular composition of normal and neoplastic tissues. We are using these tools, together with high throughput sequencing, single cell genomics, and experimental techniques, to study the diversity and clinical significance of 1). cancer cell subtypes involved in tumor initiation, maintenance, and metastasis, and 2). stromal cell subsets in the tumor microenvironment. 

Assistant Professor of Biomedical Data Science

Publications

  • Single-cell transcriptional diversity is a hallmark of developmental potential. Science (New York, N.Y.) Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A. n., Bharadwaj, A. n., Berger, M. J., Ilagan, F. n., Kuo, A. H., Hsieh, R. W., Cai, S. n., Zabala, M. n., Scheeren, F. A., Lobo, N. A., Qian, D. n., Yu, F. B., Dirbas, F. M., Clarke, M. F., Newman, A. M. 2020; 367 (6476): 405–11

    Abstract

    Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

    View details for DOI 10.1126/science.aax0249

    View details for PubMedID 31974247

  • Determining cell type abundance and expression from bulk tissues with digital cytometry. Nature biotechnology Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F. n., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. n., Diehn, M. n., Alizadeh, A. A. 2019

    Abstract

    Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

    View details for PubMedID 31061481

  • Integrated digital error suppression for improved detection of circulating tumor DNA NATURE BIOTECHNOLOGY Newman, A. M., Lovejoy, A. F., Klass, D. M., Kurtz, D. M., Chabon, J. J., Scherer, F., Stehr, H., Liu, C., Bratman, S. V., Say, C., Zhou, L., Carter, J. N., West, R. B., Sledge Jr, G. W., Shrager, J. B., Loo Jr, B. W., Neal, J. W., Wakelee, H. A., Diehn, M., Alizadeh, A. A. 2016

    View details for DOI 10.1038/nbt.3520

  • Robust enumeration of cell subsets from tissue expression profiles NATURE METHODS Newman, A. M., Liu, C., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., Alizadeh, A. A. 2015

    View details for DOI 10.1038/nmeth.3337

  • The prognostic landscape of genes and infiltrating immune cells across human cancers NATURE MEDICINE Gentles, A. J., Newman, A. M. (co-first author) , Liu, C., Bratman, S. V., Feng, W., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., Diehn, M., West, R. B., Plevritis, S. K., Alizadeh, A. A. 2015

    View details for DOI 10.1038/nm.3909