Flura-seq identifies organ-specific metabolic adaptations during early metastatic colonization
- Memorial Sloan-Kettering Cancer Center, United States
Abstract
Metastasis-initiating cells dynamically adapt to the distinct microenvironments of different organs, but these early adaptations are poorly understood due to the limited sensitivity of in situ transcriptomics. We developed fluorouracil-labeled RNA sequencing (Flura-seq) for in situ analysis with high sensitivity. Flura-seq utilizes cytosine deaminase (CD) to convert fluorocytosine to fluorouracil, metabolically labeling nascent RNA in rare cell populations in situ for purification and sequencing. Flura-seq revealed hundreds of unique, dynamic organ-specific gene signatures depending on the microenvironment in mouse xenograft breast cancer micrometastases. Specifically, the mitochondrial electron transport Complex I, oxidative stress and counteracting antioxidant programs were induced in pulmonary micrometastases, compared to mammary tumors or brain micrometastases. We confirmed lung metastasis-specific increase in oxidative stress and upregulation of antioxidants in clinical samples, thus validating Flura-seq's utility in identifying clinically actionable microenvironmental adaptations in early metastasis. The sensitivity, robustness and economy of Flura-seq are broadly applicable beyond cancer research.
Data availability
Sequencing data have been deposited in GEO under accession codes GSE93605 and GSE118937.
-
Organ-specific in situ transcriptomics of MDA231 cells identified by Flura-seqNCBI Gene Expression Omnibus, GSE118937.
-
Flura-seq of TGFB treated MDA231 cellsNCBI Gene Expression Omnibus, GSE93605.
-
Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancerNCBI Gene Expression Omnibus, GSE110590.
-
ubpopulations of MDA-MB-231 and Primary Breast CancersNCBI Gene Expression Omnibus, GSE2603.
-
Breast cancer relapse free survival and lung metastasis free survivalNCBI Gene Expression Omnibus, GSE5327.
-
Breast cancer relapse free survivalNCBI Gene Expression Omnibus, GSE2034.
-
Expression data from primary breast tumorsNCBI Gene Expression Omnibus, GSE12276.
Article and author information
Author details
Funding
National Institutes of Health (P01-CA094060)
- Joan Massagué
Damon Runyon Cancer Research Foundation (DR-12998)
- Harihar Basnet
Department of Defense (W81XWH-12-0074)
- Joan Massagué
National Institutes of Health (T32-CA009207)
- Karuna Ganesh
National Institutes of Health (T32-GM07739)
- Yun-Han Huang
National Institutes of Health (K08-CA230213)
- Karuna Ganesh
National Institutes of Health (F30-CA203238)
- Yun-Han Huang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Mouse experiments were performed following the protocols approved by the MSKCC Institutional Animal Care and Use Committee (IACUC) (#99-09-032).
Copyright
© 2019, Basnet et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,830
- views
-
- 827
- downloads
-
- 53
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Flura-seq identifies organ-specific metabolic adaptations during early metastatic colonization
eLife 8:e43627.
https://doi.org/10.7554/eLife.43627
Further reading
-
- Cancer Biology
- Evolutionary Biology
In growing cell populations such as tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.
-
- Cancer Biology
- Evolutionary Biology
In asexual populations that don’t undergo recombination, such as cancer, deleterious mutations are expected to accrue readily due to genome-wide linkage between mutations. Despite this mutational load of often thousands of deleterious mutations, many tumors thrive. How tumors survive the damaging consequences of this mutational load is not well understood. Here, we investigate the functional consequences of mutational load in 10,295 human tumors by quantifying their phenotypic response through changes in gene expression. Using a generalized linear mixed model (GLMM), we find that high mutational load tumors up-regulate proteostasis machinery related to the mitigation and prevention of protein misfolding. We replicate these expression responses in cancer cell lines and show that the viability in high mutational load cancer cells is strongly dependent on complexes that degrade and refold proteins. This indicates that the upregulation of proteostasis machinery is causally important for high mutational burden tumors and uncovers new therapeutic vulnerabilities.
