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
(650) 724-7270

Publications

  • 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

  • An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage NATURE MEDICINE Newman, A. M., Bratman, S. V., To, J., Wynne, J. F., Eclov, N. C., Modlin, L. A., Liu, C. L., Neal, J. W., Wakelee, H. A., Merritt, R. E., Shrager, J. B., Loo, B. W., Alizadeh, A. A., Diehn, M. 2014; 20 (5): 552-558

    Abstract

    Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II-IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to ∼0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

    View details for DOI 10.1038/nm.3519

    View details for Web of Science ID 000335710700028

  • FACTERA: a practical method for the discovery of genomic rearrangements at breakpoint resolution BIOINFORMATICS Newman, A. M., Bratman, S. V., Stehr, H., Lee, L. J., Liu, C., Diehn, M., Alizadeh, A. A. 2014