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Multiplexed immunofluorescence delineates proteomic cancer cell states associated with metabolism
Anup Sood, Alexandra M. Miller, Edi Brogi, Yunxia Sui, Joshua Armenia, Elizabeth McDonough, Alberto Santamaria-Pang, Sean Carlin, Aleksandra Stamper, Carl Campos, Zhengyu Pang, Qing Li, Elisa Port, Thomas G. Graeber, Nikolaus Schultz, Fiona Ginty, Steven M. Larson, Ingo K. Mellinghoff
Anup Sood, Alexandra M. Miller, Edi Brogi, Yunxia Sui, Joshua Armenia, Elizabeth McDonough, Alberto Santamaria-Pang, Sean Carlin, Aleksandra Stamper, Carl Campos, Zhengyu Pang, Qing Li, Elisa Port, Thomas G. Graeber, Nikolaus Schultz, Fiona Ginty, Steven M. Larson, Ingo K. Mellinghoff
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Resource and Technical Advance Oncology

Multiplexed immunofluorescence delineates proteomic cancer cell states associated with metabolism

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Abstract

The phenotypic diversity of cancer results from genetic and nongenetic factors. Most studies of cancer heterogeneity have focused on DNA alterations, as technologies for proteomic measurements in clinical specimen are currently less advanced. Here, we used a multiplexed immunofluorescence staining platform to measure the expression of 27 proteins at the single-cell level in formalin-fixed and paraffin-embedded samples from treatment-naive stage II/III human breast cancer. Unsupervised clustering of protein expression data from 638,577 tumor cells in 26 breast cancers identified 8 clusters of protein coexpression. In about one-third of breast cancers, over 95% of all neoplastic cells expressed a single protein coexpression cluster. The remaining tumors harbored tumor cells representing multiple protein coexpression clusters, either in a regional distribution or intermingled throughout the tumor. Tumor uptake of the radiotracer 18F-fluorodeoxyglucose was associated with protein expression clusters characterized by hormone receptor loss, PTEN alteration, and HER2 gene amplification. Our study demonstrates an approach to generate cellular heterogeneity metrics in routinely collected solid tumor specimens and integrate them with in vivo cancer phenotypes.

Authors

Anup Sood, Alexandra M. Miller, Edi Brogi, Yunxia Sui, Joshua Armenia, Elizabeth McDonough, Alberto Santamaria-Pang, Sean Carlin, Aleksandra Stamper, Carl Campos, Zhengyu Pang, Qing Li, Elisa Port, Thomas G. Graeber, Nikolaus Schultz, Fiona Ginty, Steven M. Larson, Ingo K. Mellinghoff

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Figure 3

Protein coexpression clusters in human breast cancer.

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Protein coexpression clusters in human breast cancer.
(A) Consensus clus...
(A) Consensus clustering was used to determine the optimum cluster set. The graph shows the relative change in area under cumulative distribution function for K = 2–15: when K increases, a positive change in the area under the curve decreases. After K = 8, the relative change is minimal and the curve flattens, suggesting that K = 8 is the best close-to-true partition. (B) Heatmap showing expression levels of each protein (log2) within the 8 clusters. IF, immunofluorescence. Rows were arranged following the lateral dendrogram. (C) Consensus hierarchical clustering of protein coexpression patterns in the TCGA breast cancer data set (747 human breast cancers). Protein expression was measured by reverse phase protein array (RPPA) using 187 antibodies. Pie charts indicate the association of each RPPA cluster with intrinsic breast cancer subtypes. Subtype annotation (PAM50) was available for 633 of 747 tumors. Numbers below each cluster indicate the number of human breast cancer samples belonging to each cluster. (D) Heatmap of the expression levels (RPPA) of the 10 proteins that were also represented in the multiplexed IF assay (see B). Genomic alterations in PTEN and HER2 are indicated above the RPPA cluster assignment.

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