Tumor-Reprogrammed Stromal BCAT1 Fuels Branched Chain Ketoacid Dependency in Stromal-Rich PDAC Tumors

Zhu, Ziwen; Achreja, Abhinav; Meurs, Noah; Animasahun, Olamide; Owen, Sarah E; Mittal, Anjali; Parikh, Pooja; Lo, Ting-Wen; Franco-Barraza, Janusz; Shi, Jiaqi; Gunchick, Valerie; Sherman, Mara H; Cukierman, Edna; Pickering, Andrew M; Maitra, Anirban; Sahai, Vaibhav; Morgan, Meredith A.; Nagrath, Sunitha; Lawrence, Theodore S.; Nagrath, Deepak

doi:10.1038/s42255-020-0226-5

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Tumor-Reprogrammed Stromal BCAT1 Fuels Branched Chain Ketoacid Dependency in Stromal-Rich PDAC Tumors

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Author(s): Ziwen Zhu ¹ ^, ² ^, ³ , Abhinav Achreja ¹ ^, ² ^, ³ , Noah Meurs ¹ ^, ² ^, ³ , Olamide Animasahun ¹ ^, ³ ^, ⁴ , Sarah Owen ³ ^, ⁴ , Anjali Mittal ¹ ^, ³ ^, ⁴ , Pooja Parikh ¹ ^, ² ^, ³ , Ting-Wen Lo ³ ^, ⁴ , Janusz Franco-Barraza ⁵ , Jiaqi Shi ⁶ ^, ⁷ , Valerie Gunchick ¹¹ , Mara H. Sherman ⁸ , Edna Cukierman ⁵ , Andrew M. Pickering ⁹ , Anirban Maitra ¹⁰ , Vaibhav Sahai ¹¹ , Meredith A. Morgan ⁷ ^, ¹² , Sunitha Nagrath ³ ^, ⁴ ^, ⁷ , Theodore S. Lawrence ⁷ ^, ¹² , Deepak Nagrath ¹ ^, ² ^, ³ ^, ⁴ ^, ⁷ ^, ^*

Publication date (Electronic): 06 July 2020

Journal: Nature metabolism

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SUMMARY

Branched chain amino acids (BCAAs) supply both carbon and nitrogen in pancreatic cancers, and their increased levels have been associated with increased risk of pancreatic ductal adenocarcinomas (PDACs). It remains unclear, however, how stromal cells regulate BCAA metabolism in PDAC cells and how mutualistic determinants control BCAA metabolism in the tumor milieu. Here we show distinct catabolic, oxidative, and protein turnover fluxes between cancer-associated fibroblasts (CAFs) and cancer cells and a marked branched chain ketoacid (BCKA)-reliance in PDAC cells in stroma-rich tumors. We report that cancer-induced stromal reprogramming fuels this BCKA demand. The TGF-β/SMAD5 axis directly targets BCAT1 in CAFs and dictates internalization of the extracellular matrix from the tumor microenvironment to supply amino acid precursors for BCKA secretion by CAFs. The in vitro results were corroborated with human patient-derived circulating tumor cells (CTCs) and PDAC tissue slices. Our findings reveal therapeutically actionable targets in pancreatic stromal and cancer cells.

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Proteomics. Tissue-based map of the human proteome.

Mathias Uhlén, Linn Fagerberg, Björn Hallström … (2015)

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Genomic analyses identify molecular subtypes of pancreatic cancer.

Peter Bailey, David K Chang, Katia Nones … (2016)

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101736592

Journal ID (pubmed-jr-id): 48119

Journal ID (nlm-ta): Nat Metab

Journal ID (iso-abbrev): Nat Metab

Title: Nature metabolism

ISSN (Electronic): 2522-5812

Publication date Nihms-submitted: 31 May 2020

Publication date (Electronic): 06 July 2020

Publication date (Print): August 2020

Publication date PMC-release: 06 January 2021

Volume: 2

Issue: 8

Pages: 775-792

Affiliations

[1 ]Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, 48109, USA

[2 ]Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

[3 ]Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA

[4 ]Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

[5 ]Department of Cancer Biology, Fox Chase Cancer Center, Philadelphia, PA, 19111, USA

[6 ]Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA

[7 ]Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA

[8 ]Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, 97201, USA

[9 ]Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78245, USA

[10 ]Department of Translational Molecular Pathology and Sheikh Ahmed Center for Pancreatic Cancer Research, University of Texas M.D. Anderson Cancer Center, Houston, TX, 77030, USA

[11 ]Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA

[12 ]Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA

Author notes

[* ]To whom correspondence should be addressed: Deepak Nagrath, PhD, Department of Biomedical Engineering, NCRC Bldg 28, Room 3048W, University of Michigan, Ann Arbor, MI, 48109, dnagrath@ 123456umich.edu

AUTHOR CONTRIBUTIONS

Z.Z., A.A. and D.N. designed the experiments. Z.Z. performed most experiments. A.A. and O.A. developed and performed all of the bioinformatics analysis. S.O. and S.N. collected PDAC patient samples, isolated, characterized and developed CTC lines using microfluidics Labyrinth chip. A.A., O.A., N.M., and A.Mi developed and performed all metabolic and mass spectrometry assays. P.P. and T.W.L. assisted with assays. J.F.B. and E.C. provided CAFs and helped with stromal characterization, J.S. helped with patient tissue and IHC, V.G. and V.S. collected patient blood for CTC analysis, M.H.S. provided CAFs and helped with stromal characterization, A.M.P. helped with proteasomal analysis, A.Ma analyzed the data and helped in clinical correlations, M.A.M. and T.S.L. provided tissue slices and helped in designing various experiments. Z.Z., A.A. and D.N. wrote the manuscript with input from co-authors.

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Manuscript ID: NIHMS1599206

DOI: 10.1038/s42255-020-0226-5

PMC ID: 7438275

PubMed ID: 32694827

SO-VID: a118371e-7103-4cb3-83ee-db441c5488bd

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Tumor-Reprogrammed Stromal BCAT1 Fuels Branched Chain Ketoacid Dependency in Stromal-Rich PDAC Tumors

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SUMMARY

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Most cited references 56

Proteomics. Tissue-based map of the human proteome.

Integrating single-cell transcriptomic data across different conditions, technologies, and species

Genomic analyses identify molecular subtypes of pancreatic cancer.

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