Temporal and spatial topography of cell proliferation in cancer

Giorgio Gaglia, Sheheryar Kabraji, Danae Rammos, Yang Dai, Ana Verma, Shu Wang, Caitlin E. Mills, Mirra Chung, Johann S. Bergholz, Shannon Coy, Jia-Ren Lin, Rinath Jeselsohn, Otto Metzger, Eric P. Winer, Deborah A. Dillon, Jean J. Zhao, Peter K. Sorger & Sandro Santagata

Nature Cell Biology. 2022 Mar; 24(3): 316-326. PMID: 35292783. PMCID: PMC8959396.

Proliferation is a fundamental trait of cancer cells but its properties and spatial organization in tumors are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and develop robust, multivariate, proliferation metrics. Across diverse cancers, the proliferative architecture is organized at two spatial scales: large domains, and local niches enriched for specific immune lineages. Some tumor cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as “cell cycle coherence”. By contrast, the cell cycles of other tumor cell populations are skewed toward specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive behavior. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

Publication | PubMed


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Data image
Temporal and spatial topography of cell proliferation in cancer

Data Overviews

The multiplexed T-CyCIF images can be viewed on Omero


Image analysis matlab codes can be found at www.github.com/santagatalab/manuscripts-codes-gaglia-kabraji-2021

Primary Data Access

Download the primary data

Download the primary data

The multiplexed single-cell spatially resolved data are shared in folders within the “files” section on Synapse (Synapse ID: syn22300771. Numerical data, contains the outputs of the image analysis pipeline and the matlab codes used to plot the figures in the paper. Please refer to the readme.txt file for description of the data.

Dataset/Folder Name Description
DATASET01_HER2_3x Figures: 1 (A-C,E), 2(A), 3(A-F), 4(B-G), ED*1 (B,D,G), ED2(C), ED3(A-D), ED5(F,I,K,L), ED7(A-F)
DATASET02_LungSCC Figures: 1(E), 2(D), ED1(A,B), ED2(A)
DATASET02_Ovarian Figures: 1(E), 2(D,H,I), ED1(A,B), ED2(B)
DATASET03_MCF10a Figures: 3(G-J), ED5(A,B,G,J)
DATASET04_GEMM_HER2dox Figures: 5(E-I), ED8(B,C)
DATASET05_TNBC_PreOnPost Figures: 2(E ), 6(B-G)
DATASET06_PantomicsTMA Figures; 1(F,G), 2(F,G), ED1(K)
DATASET07_HER2_ClinicalTrial Figures: 5(B,C)
DATASET08_ER_ClinicalTrial Figures: 2(E ), 6(H,I)
DATASET09_MultibreastTMA Figures; 2(B,D), 5(A), ED2(D-F), ED7(A)
DATASET10_ColonTMA Figures: 2(B,D), ED1(A,C), ED2(D-F)
DATASET11_GliomaTMA Figures: 2(B,D), 7(A-B), ED1(A,C), ED2(D-F), ED9(A-C)
DATASET12_MesotheliomaTMA Figures: 1(C), 2(B,D), 7(A-B), ED1(A,C, D, G), ED2(D-H), ED9(A-C)
DATASET14_PantomicsTMA_SerialSections Figures: 1(D), ED1(F)
DATASET15_MulticellLine_pCyCIF Figures: 3(K, L), ED6(B,D)
DATASET16_MCF7_Xenograft_TMA Figures: 3(M), ED6(E-G)
MultiLine GR metrics GR Metrics for the Multicell Line pCyCIF dataset
Spatial Correlation Whole Tissue Analysis Figures: 2(D)
Time Inference Comparison  
Functions Codes used for the ccD-CMD analysis, plus other helper functions
README Data file descriptions and usage notes