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Systematic pan-cancer analysis of tumour purity

Author

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  • Dvir Aran

    (Institute for Computational Health Sciences, University of California
    Stanford University)

  • Marina Sirota

    (Institute for Computational Health Sciences, University of California)

  • Atul J. Butte

    (Institute for Computational Health Sciences, University of California)

Abstract

The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses.

Suggested Citation

  • Dvir Aran & Marina Sirota & Atul J. Butte, 2015. "Systematic pan-cancer analysis of tumour purity," Nature Communications, Nature, vol. 6(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9971
    DOI: 10.1038/ncomms9971
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    2. Xiaomei Li & Lin Liu & Gregory J Goodall & Andreas Schreiber & Taosheng Xu & Jiuyong Li & Thuc D Le, 2020. "A novel single-cell based method for breast cancer prognosis," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-20, August.
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    4. Hong-Tao Li & Liya Xu & Daniel J. Weisenberger & Meng Li & Wanding Zhou & Chen-Ching Peng & Kevin Stachelek & David Cobrinik & Gangning Liang & Jesse L. Berry, 2022. "Characterizing DNA methylation signatures of retinoblastoma using aqueous humor liquid biopsy," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Chunman Zuo & Yijian Zhang & Chen Cao & Jinwang Feng & Mingqi Jiao & Luonan Chen, 2022. "Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    6. María-Jesús Lobón-Iglesias & Mamy Andrianteranagna & Zhi-Yan Han & Céline Chauvin & Julien Masliah-Planchon & Valeria Manriquez & Arnault Tauziede-Espariat & Sandrina Turczynski & Rachida Bouarich-Bou, 2023. "Imaging and multi-omics datasets converge to define different neural progenitor origins for ATRT-SHH subgroups," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    7. Jian He & Rui Gao & Mei Meng & Miao Yu & Chengrong Liu & Jingquan Li & Yizhi Song & Hui Wang, 2021. "Lysophosphatidic Acid Receptor 6 (LPAR6) Is a Potential Biomarker Associated with Lung Adenocarcinoma," IJERPH, MDPI, vol. 18(21), pages 1-25, October.
    8. Christopher J. Hanley & Sara Waise & Matthew J. Ellis & Maria A. Lopez & Wai Y. Pun & Julian Taylor & Rachel Parker & Lucy M. Kimbley & Serena J. Chee & Emily C. Shaw & Jonathan West & Aiman Alzetani , 2023. "Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    9. Tianwei Yu, 2018. "A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-22, August.
    10. Philip East & Gavin P. Kelly & Dhruva Biswas & Michela Marani & David C. Hancock & Todd Creasy & Kris Sachsenmeier & Charles Swanton & Julian Downward & Sophie de Carné Trécesson, 2022. "RAS oncogenic activity predicts response to chemotherapy and outcome in lung adenocarcinoma," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    11. Miles C. Andrews & Junna Oba & Chang-Jiun Wu & Haifeng Zhu & Tatiana Karpinets & Caitlin A. Creasy & Marie-Andrée Forget & Xiaoxing Yu & Xingzhi Song & Xizeng Mao & A. Gordon Robertson & Gabriele Roma, 2022. "Multi-modal molecular programs regulate melanoma cell state," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    12. Rachel Marty Pyke & Dattatreya Mellacheruvu & Steven Dea & Charles W. Abbott & Lee McDaniel & Devayani P. Bhave & Simo V. Zhang & Eric Levy & Gabor Bartha & John West & Michael P. Snyder & Richard O. , 2022. "A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    13. Yin Li & Manling Jiang & Ling Aye & Li Luo & Yong Zhang & Fengkai Xu & Yongqi Wei & Dan Peng & Xiang He & Jie Gu & Xiaofang Yu & Guoping Li & Di Ge & Chunlai Lu, 2024. "UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    14. Khoa A. Tran & Venkateswar Addala & Rebecca L. Johnston & David Lovell & Andrew Bradley & Lambros T. Koufariotis & Scott Wood & Sunny Z. Wu & Daniel Roden & Ghamdan Al-Eryani & Alexander Swarbrick & E, 2023. "Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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