Yinyin Yuan

ICR official site


From 07/2012
Team leader, the Institute of Cancer Research, London, UK
Visiting fellow, Harvard Medical School, Boston, USA
10/2010 - 06/2012
Junior Research Fellow, Wolfson College, University of Cambridge, UK
04/2009 - 06/2012
Postdoc, Cambridge Research Institute, Cancer Research UK, Cambridge, UK
01/2005 - 03/2009
PhD, Department of Computer Science, University of Warwick, UK
10/2003 - 12/2004
MSc, Department of Computer Science, University of Warwick, UK
09/1999 - 07/2003
BSc, Department of Computer Science, University of Science and Technology of China (USTC), China

Selected Invited Talks

  1. NCRI, UK, Nov 2018
  2. Crick Symposium on Cancer Research, UK, Oct 2018
  3. German conference on bioinformatics, Austria, Sep 2018
  4. RE•WORK Deep Learning in Healthcare Summit, UK, Sep 2018
  5. CRUK all fellow meeting, UK, June 2018
  6. Evolutionary biology and ecology of cancer, UK, June 2018
  7. EMBL-EBI Industry Programme Workshop on Computational Immuno-oncology, UK, May 2018
  8. Tucson Symposium, Roche Diagnostics, US, Mar 2018
  9. CRUK Lung Cancer Centre of Excellence Conference, UK, Dec 2017
  10. NCRI, Cancer immunopathology Symposium, UK, Nov 2017
  11. RE•WORK Women in Artificial Intelligence in Healthcare, UK, Oct 2017
  12. British Machine Vision Association meeting, UK, Oct 2017
  13. Glioma Society, UK, Sep 2017
  14. CytoData, UK, June 2017
  15. Pathological Society of Great Britain, UK, June 2017
  16. Marshall Symposium on Cancer Evolution, UK, May 2017

Selected Publications

  1. Heindl A, Khan AM, Rodrigues DN, Eason K, Sadanandam A, Orbegoso C, Punta M, Sottoriva A, Lise S, Banerjee S, Yuan Y* (2018). Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity, Nature Communications, 9:3917.
  2. Narayanan PL*, Dodson A, Gusterson B, Dowsett M, and Yuan Y* (2018). DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images, Medical Imaging in Deep Learning 2018.
  3. Zormpas-Petridis K*, Failmezger H, Roxanis I, Blackledge M.D., Jamin Y, Yuan Y* (2018). Capturing global spatial context for accurate cell classification in skin cancer histology, Medical Image Computing and Computer Assisted Intervention (MICCAI) COMPAY workshop .
  4. Heindl A, Sestak I, Naidoo R, Cuzick J, Dowsett M, Yuan Y* (2018). Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer, JNCI: Journal of the National Cancer Institute 110(2), djx137.
  5. Barry P, et al (2018). The spatio-temporal evolution of lymph node spread in early breast cancer, Clinical Cancer Research, 10.1158/1078-0432.CCR-17-3374.
  6. Zhang AW, et al (2018). Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer. Cell, Volume 173, Issue 7, 1755 - 1769.e22.
  7. Booth TC, Larkin TJ, ,Yuan Y, Kettunen MI, Dawson SN, Scoffings D, Canuto HC, Vowler SL, Kirschenlohr H, Hobson MP, Markowetz F, Jefferies S, Brindle KM. (2017) Analysis of Heterogeneity in T2-Weighted MR Images Can Differentiate Pseudoprogression from Progression in Glioblastoma. PLoS ONE
  8. Yuan Y* (2016). Spatial Heterogeneity of the Tumor Microenvironment. Cold Spring Harb Perspect Med. 2016 Aug 1;6(8).
  9. Khan AM, Yuan Y* (2016). Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score. Scientific reports 6: 36231.
  10. Todd JR, Ryall KA, Vyse S, Wong JP, Natrajan RC, Yuan Y, Tan AC, Huang PH. (2016) Systematic analysis of tumour cell-extracellular matrix adhesion identifies independent prognostic factors in breast cancer. Oncotarget. Sep 27;7(39):62939-62953.
  11. Heindl A, Lan C, Rodrigues DN, Koelbel K, Yuan Y* (2016). Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer, Oncotarget Nov 1;7(44):71123-71135.
  12. Natrajan R, Sailem H, Mardakheh FM, Arias MG, Dowsett M, Bakal C, Yuan Y* (2016). Microenvironmental heterogeneity parallels breast cancer progression: A histology-genomics integration analysis, PLOS Medicine 16;13(2):e1001961.
  13. Locard-Paulet M, Lim L, Veluscek G, McMahon K, Sinclair J, Weverwijk A, Worboys JD, Yuan Y, Isacke CM, Jørgensen C* (2016). Phosphoproteomic analysis of tumor endothelial signaling identifies EPHA2 as a negative regulator of transendothelial migration, Science Signaling 9;9(414):ra15–ra15.
  14. Savage RS*, Yuan Y (2016). Predicting chemosensitivity and metastasis in breast cancer with omics/digital pathology data fusion, Royal Society Open Science 10;3(2).
  15. Mardakheh FK, Paul A, Kumper S, Sadok, A, Paterson, H, Mccarthy A, Yuan Y & Marshall CJ* (2015). Global Analysis of mRNA, Translation, and Protein Localization: Local Translation Is a Key Regulator of Cell Protrusions, Developmental Cell 35:3, 344-357.
  16. Hill DK, Kim E, Teruel J, Jamin Y, Widerøe M, Søgaard CD, Størkersen Ø, Rodrigues DN, Heindl A, Yuan Y, Bathen T, and Moestue S* (2015). Characterisation of Cancer Onset and Development in the TRAMP Model of Prostate Cancer using Diffusion-Weighted Magnetic Resonance Imaging, Journal of Magnetic Resonance Imaging DOI: 10.1002/jmri.25087.
  17. Nawaz S, Yuan Y*. Computational pathology: Exploring the spatial dimension of tumor ecology (2015). Special issue on “Tumor Microenvironment”, Cancer Letters doi: 10.1016/ j.canlet.2015.11.018.
  18. Lan C^, Heindl A^, Huang X, Xi S, Banerjee S, Liu J*, Yuan Y* (2015). Quantitative histology analysis of the ovarian tumor microenvironment, Scientific Reports 5:16317.
  19. Maley CC, Koelble K, Natrajan R, Aktipis A, Yuan Y* (2015). An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer, Breast Cancer Research 17(1):131.
  20. Heindl A, Nawaz S, Yuan Y* (2015). Mapping Spatial Heterogeneity of the Tumour Microenvironment: A New Era for Digital Pathology. Laboratory Investigation 95(4):377-84.
  21. Nawaz S, Heindl A, Koelble K, Yuan Y* (2015). Beyond immune density: critical role of spatial heterogeneity in oestrogen-receptor negative breast cancer, Modern Pathology doi:10.1038/ modpathol.2015.37.
  22. Jäger R, Migliorini G, Henrion M, Kandaswamy R, Speedy HE, Heindl A, Whiffin N, Carnicer MJ, Broome L, Dryden N, Nagano T, Schoenfelder S, Enge M, Yuan Y, Taipale J, Fraser P, Fletcher O & Houlston R* (2015). Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci, Nature Communications 6:6178.
  23. Yuan Y* (2015). Modelling the Spatial Heterogeneity and Molecular Correlates of Lymphocytic Infiltration in Triple-Negative Breast Cancer. Journal of the Royal Society Interface, 12 20141153.
  24. Moen Vollan HK, Vollan HKM, Rueda OM, Chin S-F, Curtis C, Turashvili G, Shah S, Lingjærde OC, Yuan Y, Ng CK, Dunning MJ, Dicks E, Provenzano E, Sammut S, McKinney S, Ellis IO, Pinder S, Purushotham A, Murphy LC, Kristensen VN, Group M, Brenton JD, Pharoah PDP, Børresen-Dale A-L, Aparicio S*, Caldas C* (2015). A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer, Molecular Oncology 9(1):115-27.
  25. Worboys J, Sinclair J, Yuan Y & Jorgensen C* (2014). Systematic empirical evaluation of proteotypic and quantotypic peptides for targeted analysis of the human kinome. Nature Methods 11: 1041-1044.
  26. Yuan Y*, Failmezger H, Rueda OM, Ali HR, Graf S, Chin S-FF, Schwarz RF, Curtis C, Dunning MJ, Bardwell H, Johnson N, Doyle S, Turashvili G, Provenzano E, Aparicio S, Caldas C, Markowetz F* (2012). Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Science Translational Medicine 4(157): 157ra143.
  27. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Graf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S, Langerod A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Borresen-Dale AL, Brenton JD, Tavare S, Caldas C*, Aparicio S*. (2012). The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486(7403): 346-352.
  28. Booth T, Larkin T, Yuan Y, Kettunen M, Markowetz F, Scoffings D, Jefferies S, Brindle KM* (2012). Minkowski functional image analysis allows pseudoprogression to be differentiated from progression in brain tumours. Neuro-Oncology 14: 35-35.
  29. Yuan Y*^, Savage RS^ & Markowetz F (2011). Patient-specific data fusion defines prognostic cancer subtypes. PLOS Computational Biology 7(10): e1002227.
  30. Yuan Y*, Rueda OM, Curtis C & Markowetz F* (2011). Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer. Bioinformatics 27(19): 2679-2685.
  31. Yuan Y*, Li CT & Windram O (2011). Directed partial correlation: Inferring large-scale gene regulatory network through induced topology disruptions. PLOS ONE 6(4): e16835.
  32. Yuan Y, Li CT* & Wilson R (2008). Partial mixture model for tight clustering of gene expression time-course. BMC Bioinformatics 9: 287.
  33. Li CT*, Yuan Y & Wilson R (2008). An unsupervised conditional random fields approach for clustering gene expression time series. Bioinformatics 24(21): 2467-2473.
  34. Yuan Y* & Li CT (2008). A Bayes random fields approach for integrative large-scale regulatory network analysis. Journal of Integrative Bioinformatics 5(2): 99.
  35. Li CT* & Yuan Y (2006). Digital watermarking scheme exploiting nondeterministic dependence for image authentication. Optical Engineering 45(12).
  36. Y. Yuan, C. Curtis, C. Caldas, F. Markowetz, "A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes," IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2010 Best Paper).
  37. Y. Yuan and C.-T. Li, "Inferring Causal Relations from Large-Scale Multivariate Time Series: A Fast Method for Gene Expression Data," IEEE Symposium on ComputationalIntelligence in Bioinformatics and Computational Biology (CIBCB), US, 2009.
  38. Y. Yuan and C.-T. Li, "Probabilistic Framework for Gene Expression Clustering Validation Based on Gene Ontology and Graph Theory," in Proc. of International Conference of Acoustics, Speech, and Signal Processing (ICASSP), pp. 625-628, Las Vegas, US, 2008.
  39. Y. Yuan and C.-T. Li, "Partial Mixture Model for Tight Clustering in Exploratory Gene Expression Analysis," in Proc. of International Symposium on BioInformatics and BioEngineering (BIBE), 1061-1065, Boston, US, 2007.
  40. Y. Yuan and C.-T. Li, "Unsupervised Clustering of Gene Expression Time Series with Conditional Random Fields," in Proc. of IEEE Workshop on Biomedical Applications for Digital Ecosystems (BADS), Cairns, Australia, 2007.
  41. Y. Yuan and C.-T. Li, "Fragile Watermarking Scheme Exploiting Non-deterministic Block-wise Dependency," in Proc. of International Conference on Pattern Recognition (ICPR), 4:23-26, Cambridge, UK, 2004.

* Corresponding author

^ Joint first author

Outside work

Outside work my passion is in travelling, sports, and music/gigs. My favourite sport is rock climbing (see my dedicated page), followed by hiking, skiing and diving (PADI advanced). I believe that climbing is one of the best sports for scientists, in which persistence, problem solving, and confidence are the key to succeed.

Team leader

Yinyin Yuan, Ph.D. in Computer Science
Division of Molecular Pathology
& Centre for Evolution and Cancer
& Centre for Molecular Pathology
The Institute of Cancer Research
Tel: +44-(0)20-8915-6632
Email: yinyin.yuan $a$

Selected Publications

Breast cancer nuclear integrity regulator (To be submitted)

Through bioinformatics integration of morphology and genomics in 1,000 primary breast tumours, we discovered genes acting as regulators of cancer nuclear morphology. Their function as regulators of nuclear integrity has been validated in vitro.

Microenvironmental heterogeneity parallels breast cancer progression: A histology-genomics integration analysis
Natrajan R, Sailem H, Mardakheh FM, Arias MG, Dowsett M, Bakal C, Yuan Y*
PLOS Medicine (2016) [Sweave; R package beta version]

We propose a clinically relevant role of tumour microenvironmental diversity for advanced breast tumors and highlight that ecological statistics can be translated into medical advances for identifying new biomarkers and for understanding the synergistic interplay between the microenvironment and cancer genomics.

Identification of quantotypic peptides

Nature Methods, 2014 [Sweave file]

We systematically identified prototypic peptides and empirically evaluated their quantotypic properties using mass spectrometry, which allowed us to identify quantotpyic peptides for 21% of the human kinome.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomics

Science Translational Medicine 2012 [PDF; Supplement; R package; Sweave; Data and code for Sweave; Lay-description ]

CRImage provides image analysis tools for automatic segmentation and classification of various cell types in pathological H&E images. We showed that quantitative cellular features can complement genomic and transcriptomic data to construct powerful prognosticators in two independent cohorts of 323 and 241 breast cancer patients.

The genomic and transcriptomic architecture of 2,000 breast tumours

Nature 2012 [PDF]

One of our major contributions towards cancer research is the computational discovery and validation of 10 novel subtypes, reported widely in the world, in a study of 2,000 breast tumours - the largest onco-genomic investigation of a single epithelial cancer type to date.

Integrative clustering with Dirichlet Process for subtype discovery

PLoS Comp Bio 2011 [PDF; Matlab code]

PSDF is a Bayesian non-parametric graphical model for patient-specific data fusion, which assigns subtypes by choosing patient-specific features from both genomic and transcriptomic data, and therefore informs both subtype classification and potential personalized treatment targets.

Dissecting regulatory heterogeneity in disease subtypes

Bioinformatics 2011 [PDF; R package]

DANCE is a penalized model for characterizing how DNA alterations lead to gene deregulation at the RNA level in a subtype-specific context, characterizing oncogene activation or tumour suppressor loss contributing to pathogenesis.

Sparse reconstruction of DNA-RNA interaction networks

IEEE/ACM Trans Comp Bio Bioinfo 2011 [PDF; R package]

lol was specifically designed for multicollinear predictor variables, and therefore is particularly suitable for association study to delineate the trans-regulation relations between DNA copy number aberrations and RNA expression levels.

Directed Partial Correlation for efficient large-scale gene network inference

PLoS ONE 2011 [PDF; R package; data]

DPC provides an efficient solution to large-scale transcriptional regulatory network inference. It combines the efficiency of partial correlation for setting up the network topology, and the concept of Granger causality to eliminate indirect regulations.