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The vascular landscape of human cancer
Benjamin M. Kahn, … , Robert B. Faryabi, Ben Z. Stanger
Benjamin M. Kahn, … , Robert B. Faryabi, Ben Z. Stanger
Published December 1, 2020
Citation Information: J Clin Invest. 2021;131(2):e136655. https://doi.org/10.1172/JCI136655.
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Research Article Angiogenesis Oncology

The vascular landscape of human cancer

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Abstract

Tumors depend on a blood supply to deliver oxygen and nutrients, making tumor vasculature an attractive anticancer target. However, only a fraction of patients with cancer benefit from angiogenesis inhibitors. Whether antiangiogenic therapy would be more effective if targeted to individuals with specific tumor characteristics is unknown. To better characterize the tumor vascular environment both within and between cancer types, we developed a standardized metric — the endothelial index (EI) — to estimate vascular density in over 10,000 human tumors, corresponding to 31 solid tumor types, from transcriptome data. We then used this index to compare hyper- and hypovascular tumors, enabling the classification of human tumors into 6 vascular microenvironment signatures (VMSs) based on the expression of a panel of 24 vascular “hub” genes. The EI and VMS correlated with known tumor vascular features and were independently associated with prognosis in certain cancer types. Retrospective testing of clinical trial data identified VMS2 classification as a powerful biomarker for response to bevacizumab. Thus, we believe our studies provide an unbiased picture of human tumor vasculature that may enable more precise deployment of antiangiogenesis therapy.

Authors

Benjamin M. Kahn, Alfredo Lucas, Rohan G. Alur, Maximillian D. Wengyn, Gregory W. Schwartz, Jinyang Li, Kathryn Sun, H. Carlo Maurer, Kenneth P. Olive, Robert B. Faryabi, Ben Z. Stanger

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Figure 2

The EI is a transcriptional predictor of tumor vascularity.

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The EI is a transcriptional predictor of tumor vascularity.
(A) Schemati...
(A) Schematic showing the separation of stroma and epithelium from 124 PDAC samples by LCM followed by RNA-Seq. (B) EI scores plot, ranging from 0 to 1, derived by applying the classifier to RNA-Seq data generated from 124 stromal PDAC samples. For visualization of hyper- and hypovascular classes, both the EI score (red line) and the 1-EI score (blue line) representing the epithelial and nonepithelial probabilities, respectively, are plotted. (C and D) Representative images showing immunohistochemical staining for CD31 in tumors predicted by the classifier to be hypervascular (C) or hypovascular (D). (E and F) MVD quantification plotted as box plots for 24 tumors classified as either hypervascular or hypovascular (E) or by smoothened Loess regression for all 44 tumors in which CD31 staining was performed (F). (G) Schematic of the congenic tumor clone library generated from PDACs arising in KPCY C57BL/6 mice (16). (H) EI scores plot, ranging from 0 to 1, derived by applying the classifier to RNA-Seq data generated from 19 clonal tumors. (I and J) Representative images showing immunohistochemical staining for endomucin (EMCN) in tumors predicted by the classifier to be hypervascular (I) or hypovascular (J). (K and L) MVD quantification shown as box plots for 15 tumors classified as either hypervascular or hypovascular (K) or by smoothened Loess regression for all 19 tumors in which endomucin staining was performed (includes tumors with an intermediate vascular status) (L). An unpaired Student’s t test was used for comparisons of MVD density between hypervascular and hypovascular tumors (E and K). Pearson’s correlation coefficient and the corresponding 2-tailed probability values were calculated to test the relationship between the EI score and MVD in all tumors (F and L). In the box plots, the center line marks the median, the box limits span the IQR, and the whiskers extend 1.5 times the IQR range. Scale bars: 100 μm.

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ISSN: 0021-9738 (print), 1558-8238 (online)

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