Publications & Posters
DCIS genomic signatures define biology and correlate with clinical outcome: a Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts<span class='publication-meta'>Strand, <i>et al.</i>, <i>BioRxiv</i> July 2021</span>
Summary: Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents a fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct stromal expression patterns and immune cell compositions were identified. We found RNA expression patterns that correlate with later events. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.
- New transcriptomic classification solution reveals 3 major subgroups in DCIS.
- Four stroma-specific signatures identified.
- Outcome analysis identifies pathways involved in DCIS progression.
- CNAs characterize high risk of distant relapse IBC subtypes observed in DCIS.
Strand, et al., BioRxiv July 2021
On Clustering for Cell Phenotyping in Multiplex Immunohistochemistry (mIHC) and Multiplexed Ion Beam Imaging (MIBI) Data<span class='publication-meta'>Seal, <i>et al.</i>, <i>Research Square</i> July 2021</span>
Results: Unsupervised cell phenotyping methods including PhenoGraph, flowMeans, and SamSPECTRAL, primarily used in flow cytometry data, often perform poorly or need elaborate tuning to perform well in the context of mIHC and MIBI data. We show that, instead, semi-supervised cell clustering using Random Forests, linear and quadratic discriminant analysis are superior. We test the performance of the methods on two mIHC datasets from the University of Colorado School of Medicine and a publicly available MIBI dataset. Each dataset contains numerous highly complex images.
Seal, et al., Research Square July 2021
Spatial epitope barcoding reveals subclonal tumor patch behaviors<span class='publication-meta'>Rovira-Clavé, <i>et al.</i>, <i>BioRxiv</i> July 2021</span>
Rovira-Clavé, et al., BioRxiv July 2021
Virus-Dependent Immune Conditioning of Tissue Microenvironments<span class='publication-meta'>Jiang, <i>et al.</i>, <i>BioRxiv</i> May 2021</span>
Jiang, et al., BioRxiv May 2021
Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma<span class='publication-meta'>Risom, <i>et al.</i>, <i>BioRxiv</i> Jan 2021</span>
Risom, et al., BioRxiv Jan 2021
Multiplexed single-cell metabolic profiles organize the spectrum of cytotoxic human T cells<span class='publication-meta'>Hartmann, <i>et al., Nat. Biotechnology</i> Aug 2020</span>
Hartmann, et al., Nat. Biotechnol. 31 Aug 2020: s41587-020-0651-8
Single-nucleus and spatial transcriptomics of archival pancreatic cancer reveals multi-compartment reprogramming after neoadjuvant treatment<span class='publication-meta'>Hwang, <i>et al., BioRxiv</i> Aug 2020</span>
Hwang, et al., BioRxiv 25 Aug 2020: 10.1101/2020.08.25.267336
PREPRINT doi: 10.1101/2020.08.25.267336
Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma<span class='publication-meta'>Ji, <i>et al., Cell</i> Jul 2020</span>
Ji, et al., Cell (2020) July 23
Multiplexed imaging of human tuberculosis granulomas uncovers immunoregulatory features conserved across tissue and blood<span class='publication-meta'>McCaffrey, <i>et al., BioRxiv</i> Jun 2020</span>
PREPRINT – McCaffrey, et al., BioRxiv (2020) June 09
MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure<span class='publication-meta'>Keren, <i>et al., Science Advances</i> Oct 2019</span>
Abstract: Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 μm × 800 μm at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.
Keren, et al., Science Advances 09 Oct 2019: 5(10), eaax5851
Tissue imaging by mass spectrometry: a practical guide for the medicinal chemist<span class='publication-meta'>Johnson, <em>et al., ACS Medical Chemistry Letters</em> Jan 2019</span>
Johnson et al. ACS Medical Chemistry Letters. January, 2019
A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging<span class='publication-meta'>Keren, <em>et al, Cell</em> Sept 2018</span>
Keren et al. Cell, Volume 174, Issue 6, P1373-1387. E19. September 06, 2018
Multiplexed ion beam imaging analysis for quantitation of protein expression in cancer tissue sections<span class='publication-meta'>Rost, <em>et al., Lab. Invest.</em> Aug 2017</span>
Rost et al., Lab Invest. 2017 Aug;97(8):992-1003
SITC 2020 - P48
Advances in Multiplexed Ion Beam Imaging (MIBI) for immune profiling of the tumor microenvironment
SITC 2019 - P61
Segmentation and Classification of Single Cells using Multiplexed Ion Beam Imaging (MIBI)
SITC 2018 - P106
Multiplexed Ion Beam Imaging For Characterization of the Tumor Microenvironment Across Tumor Types
SITC 2018 - P20
A Structured Tumor Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Imaging