the Markowetz lab

Computational Oncology @ Cancer Research UK Cambridge Institute

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Publications

• = Review / Opinion
  Lab members underlined
  * = joint first authors
  ^ = joint senior authors Key publications are highlighted in yellow.

In the oven

• Integration across all scales: organ, tissue, genome Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer
  M. Crispin-Ortuzar, R. Woitek, E. Moore, M. Reinius, L. Beer, V. Bura, L. Rundo, C. McCague, S. Ursprung, L. Escudero Sanchez, P. Martin-Gonzalez, F. Mouliere, D. Chandrananda, J. Morris, T. Goranova, A.M. Piskorz, N. Singh, A Sahdev, R. Pintican, M. Zerunian, H. Addley, M. Jimenez-Linan, F. Markowetz^, E. Sala^, J.D. Brenton^
  medRxiv:2021.07.22.21260982
• Rascal to the rescue Absolute copy number fitting from shallow whole genome sequencing data
  C.M. Sauer, M.D. Eldridge, M. Vias, J.A. Hall, S. Boyle, G. Macintyre, T. Bradley, F. Markowetz, J.D. Brenton
  bioRxiv:2021.07.19.452658v1
• Step-by-step tutorial PathML: A unified framework for whole-slide image analysis with deep learning
  A.G. Berman, W.R. Orchard, M. Gehrung, F. Markowetz
  medRxiv:2021.07.07.21260138

2022

• A pan-cancer compendium of chromosomal instability
  Drews RM, Hernando B, Tarabichi M, Haase K, Lesluyes T, Smith PS, Morrill Gavarró L, Couturier DL, Liu L, Schneider M, Brenton JD, Van Loo P, Macintyre G^, Markowetz F^
  Nature. 2022 Jun;606(7916):976-983.
  PMID:35705807 | doi:10.1038/s41586-022-04789-9
   
• The Genomic Landscape of Early-Stage Ovarian High-Grade Serous Carcinoma
  Cheng Z, Mirza H, Ennis DP, Smith P, Morrill Gavarró L, Sokota C, Giannone G, Goranova T, Bradley T, Piskorz A, Lockley M; BriTROC-1 Investigators, Kaur B, Singh N, Tookman LA, Krell J, McDermott J, Macintyre G, Markowetz F, Brenton JD, McNeish IA
  Clin Cancer Res. 2022 Jul 1;28(13):2911-2922.
  PMID:35398881 | doi:10.1158/1078-0432.CCR-21-1643
• Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes
  Adam RS, Poel D, Ferreira Moreno L, Spronck JMA, de Back TR, Torang A, Gomez Barila PM, Ten Hoorn S, Markowetz F, Wang X, Verheul HMW, Buffart TE, Vermeulen L
  Mol Oncol 2022 Mar 17.
  PMID:35298091 | doi:10.1002/1878-0261.13210
• Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples
  Masqué-Soler N, Gehrung M, Kosmidou C, Li X, Diwan I, Rafferty C, Atabakhsh E, Markowetz F, Fitzgerald RC
  EBioMedicine. 2022 Feb;76:103814. Epub 2022 Jan 17.
  PMID:35051729 | doi:10.1016/j.ebiom.2022.103814
• Multi-omic machine learning predictor of breast cancer therapy response
  S-J. Sammut, M. Crispin-Ortuzar, S-F. Chin, E. Provenzano, H.A. Bardwell, W. Ma, W. Cope, A. Dariush, S-J. Dawson, J.E. Abraham, J. Dunn, L. Hiller, J. Thomas, D.A. Cameron, J.M.S. Bartlett, L. Hayward, P.D. Pharoah, F. Markowetz, O.M. Rueda, H.M. Earl, C. Caldas
  Nature. 2022 Jan;601(7894):623-629. Epub 2021 Dec 7.
  PMID:34875674 | doi:10.1038/s41586-021-04278-5
  

2021

• Reproducibility standards for machine learning in the life sciences
  B.J. Heil, M.M. Hoffman, F. Markowetz, S-I Lee, C.S. Greene, S.C. Hicks
  Nature Methods, 2021
  PMID:34462593 | doi:10.1038/s41592-021-01256-7
  
• . Predictive modelling of highly multiplexed tumour tissue images by graph neural networks
  P. Martin Gonzalez, M. Crispin Ortuzar, F. Markowetz
  MICCAI workshop 'Topological Data Analysis', accepted
  medRxiv 2021.07.28.21261179 | doi:10.1101/2021.07.28.21261179
• Artificial and natural intelligence - hand in hand Triage-driven diagnosis of Barrett esophagus for early detection of esophageal adenocarcinoma using deep learning
  M. Gehrung, M. Crispin Ortuzar, A. Berman, M. O'Donovan, R.C. Fitzgerald, F. Markowetz
  Nature Medicine, 2021 May;27(5):833-841
  PMID:33859411 | doi:10.1038/s41591-021-01287-9 | medRxiv:2020.07.16.20154732
• Automated FISH spot counting FrenchFISH: Poisson models for quantifying DNA copy-number from fluorescence in situ hybridisation of tissue sections
  G. Macintyre, A.M. Piskorz, A. Berman, E. Ross, D.B. Morse, K. Yuan, D. Ennis, J.A. Pike, T. Goranova, I. McNeish, J.D. Brenton, F. Markowetz
  JCO Clinical Cancer Informatics 2021 Feb;5:176-186.
  PMID:33570999 | bioRxiv:487926 | doi:10.1200/CCI.20.00075
• Another great PCAWG collaboration Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types
  S.C. Dentro*, I. Leshchiner*, K. Haase*, M. Tarabichi*, J. Wintersinger*, A.G. Deshwar*, K. Yu*, Y. Rubanova*, G. Macintyre*, I. Vazquez-Garcia, K. Kleinheinz, D.G. Livitz, S. Malikic, N. Donmez, S. Sengupta, J. Demeulemeester, P. Anur, C. Jolly, M. Cmero, D. Rosebrock, S. Schumacher, Y. Fan, M. Fittall, R.M. Drews, X. Yao, J. Lee, M. Schlesner, D.J. Adams, G. Getz, P.C. Boutros, M. Imielinski, R. Beroukhim, S.C. Sahinalp, Y. Ji, M. Peifer, I. Martincorena, F. Markowetz, V. Mustonen, Ke Yuan, M. Gerstung, P.T. Spellman, W. Wang, Q.D. Morris, D.C. Wedge, P. Van Loo, PCAWG Evolution and Heterogeneity Working Group, PCAWG network
  Cell, 2021 Apr 15;184(8):2239-2254.e39
  PMID:33831375 | bioRxiv:312041 | doi:10.1101/312041

2020

• How to biopsy radiological habitats Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer
  L. Beer*, P. Martin-Gonzalez*, M. Delgado-Ortet, M. Reinius, L. Rundo, R. Woitek, S. Ursprung, L. Escudero, H. Sahin, I-G. Funingana, J-E Ang, M. Jimenez-Linan, T. Lawton, G. Phadke, S. Davey, N.Q. Nguyen, F. Markowetz, J.D. Brenton, M. Crispin-Ortuzar, H. Addley, E. Sala
  Eur Radiol. 2020 Dec 14.
  PMID:33315123 | doi:10.1007/s00330-020-07560-8
• . Intestinal region-specific Wnt signalling profiles reveal interrelation between cell identity and oncogenic pathway activity in cancer development
  R.S. Adam, S.M van Neerven, C. Pleguezuelos-Manzano, S. Simmini, N. Léveillé, N.E de Groot, A.N. Holding, F. Markowetz, L. Vermeulen
  Cancer Cell Int. 2020 Dec 3;20(1):578.
  PMID:33292279 | doi:10.1186/s12935-020-01661-6
• How to use radiogenomics to generate virtual biopsies Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer
  P. Martin-Gonzalez, M. Crispin-Ortuzar L. Rundo, M. Delgado-Ortet, M. Reinius, L. Beer, R. Woitek, S. Ursprung, H. Addley, J. D. Brenton, F. Markowetz, E. Sala
  Insights into Imaging volume 11, Article number: 94 (2020)
  PMID:32804260 | doi:10.1186/s13244-020-00895-2
• automatically produce tumour-specific 3D-printed moulds Three-Dimensional Printed Molds for image-guided surgical biopsies: an open source computational platform
  M. Crispin-Ortuzar, M. Gehrung, S. Ursprung, A.B. Gill, A.Y. Warren, L. Beer, F.A. Gallagher, T.J Mitchell, I.A. Mendichovszky, A.N. Priest, G.D. Stewart, E. Sala, F. Markowetz
  JCO Clinical Cancer Informatics, 2020 :4, 736-748
  PMID:32804543 | doi:10.1200/CCI.20.00026 | bioRxiv:658831v2
• Ideal for multi-sample inference of tumor evolution Allele-specific multi-sample copy number segmentation
  E.M. Ross*, K. Haase*, P. Van Loo, F. Markowetz
  Bioinformatics, btaa538
  PMID:32449758 | doi:10.1093/bioinformatics/btaa538 | bioRxiv:166017
• . Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering
  L. Rundo, L. Beer, S. Ursprung, P. Martin Gonzalez, F. Markowetz, J.D. Brenton, M. Crispin Ortuzar, E. Sala, R. Woitek
  Computers in Biology and Medicine, Volume 120, May 2020, 103751
  PMID:32421652 | doi:10.1016/j.compbiomed.2020.103751
• We did the CN signatures Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy
  A. Jiménez-Sánchez*, P. Cybulska*, K. Lavigne*, S. Koplev, O. Cast, D-L Couturier, D. Memon, P. Selenica, I. Nikolovski, Y. Mazaheri, Y. Bykov, F.C. Geyer, G. Macintyre, L. Morrill Gavarró, R.M. Drews, M.B. Gill, A.D. Papanastasiou, R.E. Sosa, R.A. Soslow, T. Walther, R. Shen, D.S. Chi, K.J. Park, T. Hollmann, J.S. Reis-Filho, F. Markowetz, P. Beltrao, H.A. Vargas, D. Zamarin, J.D. Brenton, A. Snyder, B. Weigelt, E. Sala, M.L. Miller
  Nature Genetics, 52, pages582–593 (2020)
  PMID:32483290 | doi:10.1038/s41588-020-0630-5
• Analyzing subclonal structural variants SVclone: inferring structural variant cancer cell fraction
  M. Cmero, C.S. Ong, K. Yuan, J. Schröder, K. Mo, PCAWG Evolution and Heterogeneity Working Group, N.M. Corcoran, A.T. Papenfuss, C.M. Hovens, F. Markowetz, G. Macintyre
  Nature Comms. 11, Article number: 730 (2020)
  PMID:32024845 | bioRxiv:172486 | doi:10.1038/s41467-020-14351-8
• There we are, right in the middle of it! The evolutionary history of 2,658 cancers
  M. Gerstung*, C. Jolly*, I. Leshchiner*, SC. Dentro*, S. Gonzalez, T.J. Mitchell, Y. Rubanova, P. Anur, D. Rosebrock, K. Yu, M. Tarabichi, A. Deshwar, J. Wintersinger, K. Kleinheinz, I. Vázquez-García, K. Haase, S. Sengupta, G. Macintyre, S. Malikic, N. Donmez, D.G. Livitz, M. Cmero, J. Demeulemeester, S. Schumacher, Y. Fan, X. Yao, J. Lee, M. Schlesner, P.C. Boutros, D.D. Bowtell, H. Zhu, G. Getz, M. Imielinski, R. Beroukhim, S.C. Sahinalp, Y. Ji, M. Peifer, F. Markowetz, V. Mustonen, K. Yuan, W. Wang, Q.D. Morris, P.T. Spellman^, D.C. Wedge^, Peter Van Loo^, on behalf of the PCAWG Evolution and Heterogeneity Working Group and the PCAWG network.
  Nature 578, pages122–128 (2020)
  PMID:32025013 | bioRxiv:161562 | doi:10.1038/s41586-019-1907-7

2019

• CRISPR, single cells, machine learning - what more do you want? Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations
  A.N. Holding, H.V. Cook, F. Markowetz
  Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, Nov 2019
  PMID:31756390 | doi:10.1016/j.bbagrm.2019.194441
• ... Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging
  Li C, Wang S, Yan JL, Torheim T, Boonzaier NR, Sinha R, Matys T, Markowetz F, Price SJ
  J Neurosurg. 2019 Apr 26:1-8.
  PMID:31026822 | doi:10.3171/2018.12.JNS182926
• Master Regulator Analysis for ChIP-seq data VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer
  A.N. Holding*, F.M. Giorgi*, A. Donnelly, A.E Cullen, L.A. Selth, F. Markowetz
  Genome Biology, 2019 20:91
  PMID:31084623 | bioRxiv:266908 | doi:10.1186/s13059-019-1698-z
• ISMB 2019 Estimating the predictability of cancer evolution
  SR Hosseini, R Diaz-Uriarte, F Markowetz, N Beerenwinkel
  Bioinformatics, 35(14), July 2019, Pages i389–i397
  PMID:31510665 | doi:10.1093/bioinformatics/btz332
• Adaptive immune response co-evolves with metastatic genomes The genomic and immune landscapes of lethal metastatic breast cancer
  L De Mattos-Arruda*, S-J Sammut*, E.M. Ross, R. Bashford-Rogers, E. Greenstein, H. Markus, S. Morganella, Y. Teng, Y. Maruvka, B. Pereira, O.M. Rueda, S-F Chin, T. Contente-Cuomo, R. Mayor, A. Arias, H.R. Ali, W. Cope, D. Tiezzi, D. Reshef, N. Ciriaco, E. Martinez-Saez, V. Peg, S. Ramon y Cajal, J. Cortes, G. Vassiliou, G. Getz, S. Nik-Zainal, M. Murtaza, N. Friedman, F. Markowetz, J. Seoane^, Carlos Caldas^
  Cell Reports, 27(9), P2690-2708.e10, May 28, 2019
  PMID:31141692 | doi:10.1016/j.celrep.2019.04.098
• Immune differences even if there are no genomic differences Immuno-phenotypes of Pancreatic Ductal Adenocarcinoma: Metaanalysis of transcriptional subtypes
  I. de Santiago, C. Yau, M. Middleton, M. Dustin, F. Markowetz, S. Sivakumar
  International Journal of Cancer, 2019 Feb 5
  PMID:30720864 | doi:10.1002/ijc.32186 | bioRxiv:182261
• Habitats in the brain Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging: correlation with patient survival
  C. Li, J-L Yan, T. Torheim, M.A. McLean, N.R. Boonzaier, Y. Huang, B.R.J. Van Dijken, T. Matys, F. Markowetz, S.J. Price
  Radiotherapy and Oncology, 2019 May;134:17-24.
  PMID:31005212 | bioRxiv:180521 | doi:10.1101/180521
• Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals
  C. Li, S. Wang, P. Liu, T. Torheim, N.R. Boonzaier, B.R. van Dijken, C.B. Schönlieb, F. Markowetz, S.J. Price
  Neoplasia. 2019 Mar 31;21(5):442-449.
  PMID:30943446 | doi:10.1016/j.neo.2019.03.005
• Radiomics in brain cancer Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
  C. Li, S. Wang, A. Serra, T. Torheim, J-L. Yan, N.R. Boonzaier, Y. Huang, T. Matys, M.A. McLean, F. Markowetz, S.J. Price
  European Radiology, 2019 Feb 1
  PMID:30707277 | doi:10.1007/s00330-018-5984-z

2018

• Challenging the central dogma of ER biology Genome-wide Estrogen Receptor-alpha activation is sustained, not cyclical
  A.N. Holding, A.E. Cullen, F. Markowetz
  eLife, 2018;7:e40854
  PMID:30457555 | doi:10.7554/eLife.40854 | bioRxiv:398925
• We hate to contradict you, but ... Neutral tumor evolution?
  M. Tarabichi, I. Martincorena, M. Gerstung, F. Markowetz, PT Spellman, QD Morris, OC Lingjaerde, DC Wedge, P. Van Loo
  Nature Genetics, Nov 2018
  PMID:30374075 | bioRxiv:158006 | doi:10.1101/158006
• Heterogeneity in the brain Intratumoral heterogeneity of tumor infiltration of glioblastoma revealed by joint histogram analysis of diffusion tensor imaging
  C. Li, S. Wang, J-L Yan, R.J. Piper, H. Liu, T. Torheim, H. Kim, N.R. Boonzaier, R. Sinha, T. Matys, F. Markowetz, S.J. Price
  Neurosurgery, 2018 Sep 17 nyy388
  PMID:30239840 | doi:10.1093/neuros/nyy388 | bioRxiv:187450
• clinical impact of intra-tumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations Intra-tumor heterogeneity defines treatment-resistant HER2+ breast tumors.
  I.H. Rye*, A. Trinh*, A. Satersdal, D. Nebdal, OC. Lingjaerde, V. Almendro, K. Polyak, A-L. Borresen-Dale, A. Helland, F. Markowetz^, H. Russnes^
  Molecular Oncology, 2018 Aug 22
  PMID:30133130 | doi:10.1002/1878-0261.12375 | bioRxiv:297549
• A serendipitous metabolic target for glioblastoma The small molecule KHS101 induces bioenergetic dysfunction in glioblastoma cells through inhibition of mitochondrial HSPD1
  E.S. Polson, V.B. Kuchler, C. Abbosh, E.M. Ross, R.K. Mathew, H.A. Beard, B. Da Silva, A.N. Holding, S. Ballereau, E. Chuntharpursat-Bon, J. Williams, H.B.S. Griffiths, H Shao, A. Patel, A.J. Davies, A. Droop, P. Chumas, S.C. Short, M. Lorger, J.E. Gestwicki, L.D. Roberts, R.S. Bon, S.J. Allison, S. Zhu, F. Markowetz, H. Wurdak
  Science Translational Medicine, 2018 Aug 15;10(454). pii: eaar2718
  PMID:30111643 | doi:10.1126/scitranslmed.aar2718 | bioRxiv:205203
• Deconstructing the mutational forces shaping the complex genomes of ovarian cancers Copy-number signatures and mutational processes in ovarian carcinoma
  G. Macintyre*, T. Goranova*, D. De Silva, D. Ennis, A.M. Piskorz, M. Eldridge, D. Sie, L-A. Lewsley, A. Hanif, C. Wilson, S. Dowson, R.M. Glasspool, M. Lockley, E. Brockbank, A. Montes, A. Walther, S. Sundar, R. Edmondson, G.D. Hall, A. Clamp, C. Gourley, M. Hall, C. Fotopoulou, H. Gabra, J. Paul, A. Supernat, D. Millan, A. Hoyle, G. Bryson, C. Nourse, L. Mincarelli, L. Navarro Sanchez, B. Ylstra, M. Jimenez-Linan, L. Moore, O. Hofmann, F. Markowetz^, I.A. McNeish^, J.D. Brenton^
  Nature Genetics, 50, 1262–1270 (2018)
  PMID:30104763 | doi:10.1038/s41588-018-0179-8 | bioRxiv:174201
• Quantitative RIME A quantitative mass spectrometry-based approach to monitor the dynamics of endogenous chromatin-associated protein complexes
  E Papachristou, K Kishore, A Holding, K Harvey, T Roumeliotis, C Chilamakuri, S Omarjee, KM Chia, A Swarbrick, E Lim, F Markowetz, M Eldridge, R Siersbaek, C D'Santos, J Carroll
  Nature Communications, 2018 Jun 13;9(1):2311.
  PMID:29899353 | doi:10.1038/s41467-018-04619-5
• Peak height matters. Parallel factor ChIP provides essential internal control for quantitative differential ChIP-seq
  M.J. Guertin, A.E. Cullen, F. Markowetz, A.N. Holding
  Nucleic Acid Research, Volume 46, Issue 12, 6 July 2018, Pages e75
  PMID:29672735 | doi:10.1093/nar/gky252 | bioRxiv:182261
• Inferring a network from data on nodes, edges and paths Estimating cellular pathways from an ensemble of heterogeneous data sources
  A.M. Franks, F. Markowetz^, E.M. Airoldi^
  Annals of Applied Statistics, 2018, Vol. 12, No. 3, 1361-1384.
  doi:10.1214/16-AOAS915 | arXiv:1406.5799

2017

• Tissue heterogeneity predicts progression Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma.
  TC Booth, TJ Larkin, Y Yuan, MI Kettunen, SN Dawson, D Scoffings, HC Canuto, SL Vowler, H Kirschenlohr, MP Hobson, F Markowetz, S Jefferies, KM Brindle
  PLoS ONE, 2017 May 17;12(5):e0176528
  PMID:28520730 | doi:10.1371/journal.pone.0176528
• What's downstream of a genetic interaction? Inferring modulators of genetic interactions with epistatic Nested Effects Models
  M. Pirkl*, M. Diekmann*, M. van der Wees, H. Fröhlich, N. Beerenwinkel, F. Markowetz
  PLoS Comput Biol, 2017; 13(4):e1005496.
  PMID:28406896 | doi:10.1371/journal.pcbi.1005496
• My contribution to the 'Research Matters' collection All biology is computational biology
  F. Markowetz
  PLoS Biology, 2017 Mar 9;15(3):e2002050.
  PMID:28278152 | doi:10.1371/journal.pbio.2002050
• Rigorous removal of biases from ChIP-seq data BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes
  I. de Santiago*, W. Liu*, K. Yuan, M. O'Reilly, CSR Chilamakuri, BAJ Ponder, KB Meyer^, F. Markowetz^
  Genome Biology, 2017 Feb 24;18(1):39
  PMID:28235418 | doi:10.1186/s13059-017-1165-7 | bioRxiv:093393
• Biology and subtypes, we got it all. Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis
  S. Sivakumar*, I. de Santiago*, L. Chlon*, F. Markowetz
  PLOS Medicine, 2017 Jan 31;14(1):e1002223.
  PMID:28141826 | doi:10.1371/journal.pmed.1002223
• Diverse and surprising metastatic patterns and how to spot them How subclonal modelling is changing the metastatic paradigm
  G. Macintyre, P. Van Loo, N.M. Corcoran, D.C. Wedge, F. Markowetz^, C.M. Hovens^
  Clinical Cancer Research, 2017 Feb 1;23(3):630-635
  PMID:27864419 | doi:10.1158/1078-0432.CCR-16-0234
• IHC-based classifier to validate the prognostic and predictive value of molecular CRC subtyping Practical and robust identification of molecular subtypes in colorectal cancer by immunohistochemistry
  A. Trinh, K. Trumpi, F. De Sousa E Melo, X. Wang, J.H. de Jong, E. Fessler, P.J.K. Kuppen, M.S. Reimers, M. Swets, M. Koopman, I.D. Nagtegaal, M. Jansen, G.K.J. Hooijer, G.J.A. Offerhaus, O. Kranenburg, C.J. Punt, J.P. Medema, F. Markowetz, L. Vermeulen
  Clinical Cancer Research 2017 Jan 15;23(2):387-398
  PMID:27459899 | doi:10.1158/1078-0432.CCR-16-0680

2016

• Patterns of Immune Infiltration in Breast Cancer and their Clinical Implications: A Gene Expression-Based Retrospective Study
  R. Ali, L. Chlon, P. Pharoah, F. Markowetz, C. Caldas
  PLOS Medicine, 2016 Dec 13;13(12):e1002194
  PMID:27959923 | doi:10.1371/journal.pmed.1002194
• CLOE can capture both the acquisition and the loss of mutations, as well as episodes of convergent evolution A phylogenetic latent feature model for clonal deconvolution
  F. Marass, F. Mouliere, K. Yuan, N. Rosenfeld, F. Markowetz
  Annals of Applied Statistics, Volume 10, Number 4 (2016), 2377-2404.
  doi:10.1214/16-AOAS986 | arXiv:1604.01715
• News and Views A saltationist theory of cancer evolution
  F. Markowetz
  Nature Genetics, 48(10), 1102–1103 (2016)
  PMID:27681287 | doi:10.1038/ng.3687 | full text access
• Phylogenetic inference tailored to the size and noise of current single cell data OncoNEM: Inferring tumour evolution from single-cell sequencing data
  E.M. Ross and F. Markowetz
  Genome Biology 2016, 17:69
  PMID:27083415 | doi:10.1186/s13059-016-0929-9
• Accumulated metabolites of hydroxybutyric acid serve as diagnostic and prognostic biomarkers of high-grade serous ovarian carcinomas
  M. Hilvo, I. de Santiago, P. Gopalacharyulu, W.D. Schmitt, J. Budczies, M. Kuhberg, M. Dietel, T. Aittokallio, F. Markowetz, C. Denkert, J. Sehouli, C. Frezza, S. Darb-Esfahani, I. Braicu
  Cancer Research, 2016 Feb 15;76(4):796-804
  PMID:26685161 | doi:10.1158/0008-5472.CAN-15-2298
• How do germline and somatic genetic events combine to influence cancer development? Regulators of genetic risk of breast cancer identified by integrative network analysis
  M.A.A. Castro, I. de Santiago, T.M. Campbell, C. Vaughn, T.E. Hickey, E. Ross, W.D. Tilley, F. Markowetz, B.A.J. Ponder and K.B. Meyer
  Nature Genetics, 2016 Jan;48(1):12-21
  PMID:26618344 | doi:10.1038/ng.3458

2015

• Ask what reproducibility can do for you!
  • blog Five selfish reasons to work reproducibly
  F. Markowetz
  Genome Biology 2015, 16:274
  PMID:26646147 | doi:10.1186/s13059-015-0850-7
• My contribution to the 'About my lab' collection You are not working for me; I am working with you
  F. Markowetz
  PLoS Comput Biol 2015, 11(9): e1004387.
  PMID:26402330 | doi:10.1371/journal.pcbi.1004387
• Software: BitPhylogeny BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies
  K. Yuan*, T. Sakoparnig*, F. Markowetz^, N. Beerenwinkel^
  Genome Biology, 2015, 16:36
  PMID:25786108 | doi:10.1186/s13059-015-0592-6
• Heterogeneity goes translational!
  • PLoS Med Editorial
  • Press release
  Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction
  R.F. Schwarz, C.K.Y. Ng, S.L. Cooke, S. Newman, J. Temple, A.M. Piskorz, D. Gale, K. Sayal, M. Murtaza, P.J Baldwin, N. Rosenfeld, H.M. Earl, E. Sala, M. Jimenez-Linan, C.A. Parkinson, F. Markowetz^, J.D. Brenton^
  PLoS Medicine, 2015 Feb 24;12(2):e1001789.
  PMID:25710373 | doi:10.1371/journal.pmed.1001789
• The first book to comprehensively cover the field of systems genetics. Systems Genetics - linking genotypes and phenotypes
  Florian Markowetz and Michael Boutros (eds.)
  Cambridge University Press
  July 2015
  more info
   
  Including as chapters:
  • An Introduction to Systems Genetics. F Markowetz and M Boutros [ free PDF ]
  • Joining the dots - network analysis of gene perturbation data. X Wang, K Yuan, F Markowetz
• Everything you ever wanted to know about tumour evolution but were too afraid to ask. Cancer evolution: mathematical models and computational inference
  N. Beerenwinkel, R.F. Schwarz, M. Gerstung, F. Markowetz
  Systematic Biology, 2015 Jan;64(1):e1-25.
  PMID:25293804 | doi:10.1093/sysbio/syu081

2014

• 
  Images + genomics = awesome!
  • GB research highlight
  • Press release Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier
  F.C. Martins*, I. de Santiago*, A. Trinh*, J. Xian, A. Guo, K. Sayal, M. Jimenez-Linan, S. Deen, K. Driver, M. Mack, J. Aslop, P.D. Pharoah, F. Markowetz^, J.D. Brenton^
  Genome Biology, 2014, 15:526
  PMID:25608477 | doi:10.1186/s13059-014-0526-8
• Software: GoIFISH GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images
  A. Trinh, I.H. Rye, V. Almendro, A. Helland, H.G. Russnes^, F. Markowetz^
  Genome Biology, 2014, 15:442
  PMID:25168174 | doi:10.1186/s13059-014-0442-y
• Rigorous implementation of an intuitive measure of functional association.
  Software: SANTA SANTA: quantifying the functional content of molecular networks
  A. Cornish, F. Markowetz
  PLoS Comp Bio 2014, 10(9):e1003808.
  PMID:25210953 | doi:10.1371/journal.pcbi.1003808 | arXiv:1407.4658
• 
  Software: MEDICC Phylogenetic quantification of intra-tumour heterogeneity
  R.F. Schwarz, A. Trinh, B. Sipos, J.D. Brenton, N. Goldman, F. Markowetz
  PLoS Comp Bio 2014, 10(4): e1003535
  PMID:24743184 | doi:10.1371/journal.pcbi.1003535 | arXiv:1306.1685
• HM-NEM = HMM + NEM Reconstructing evolving signaling networks by Hidden Markov Nested Effects Models
  X. Wang, K. Yuan, C. Hellmayr, W. Liu, F. Markowetz
  Annals of Applied Statistics, Volume 8, Number 1 (2014), 1-647
  doi:10.1214/13-AOAS696 | PDF

2013

• Review article Dissecting cancer heterogeneity - an unsupervised classification approach
  X. Wang, F. Markowetz, F.D. Melo, J.P. Medema, L. Vermeulen
  The International Journal of Biochemistry & Cell Biology pii: S1357-2725(13)00282-3
  PMID:24004832 | doi:10.1016/j.biocel.2013.08.014
• Mechanisms behind FGFR2, the breast cancer GWAS top hit Master regulators of FGFR2 signalling and breast cancer risk
  M.N.C. Fletcher*, M.A.A. Castro*, X. Wang, I. de Santiago, M. O'Reilly, S-F. Chin, O.M. Rueda, C. Caldas, B.A.J. Ponder, F Markowetz^, K.B. Meyer^
  Nature Communications 4:2464 (2013)
  PMID:24043118 | doi:10.1038/ncomms3464
• 
   
  Data: DeSouza2013
   
  More here:
  • Nature Rev Cancer
  • Nature Rev Clin Onc
  • Nature Rev Gastro Poor prognosis colon cancer is defined by a distinct molecular subtype and develops from serrated precursor lesions
  F. De Sousa Mello*, X. Wang*, M. Jansen, E. Fessler, A. Trinh, L.P. de Rooij, J.H. de Jong, O.J. de Boer, R. van Leersum, M.F. Bijlsma, H Rodermond, M. van der Heijden, C.J. van Noesel, J.B. Tuynman, E. Dekker, F. Markowetz, J.P. Medema^, L. Vermeulen^
  Nature Medicine, 2013 May;19(5):614-8.
  PMID:23584090 | doi:10.1038/nm.3174

2012

• Software: CRImage
  Data: EGAS00000000098
  Reproducibility:
  • Sweave
  • Data and code in R
   
  More here:
  • Nature Outlook 2013
  • GenomeWeb
  • CRUK press release Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
  Y. Yuan, H. Failmezger, O.M. Rueda, H.R. Ali, S. Gräf, S-F. Chin, R.F. Schwarz, C Curtis, M.J. Dunning, H. Bardwell, N. Johnson, S. Doyle, G. Turashvili, E. Provenzano, S. Aparicio, C. Caldas, F. Markowetz
  Science Translational Medicine, 4, 157ra142 (2012)
  PMID:23100629 | doi:10.1126/scitranslmed.3004330
• Companion paper to Mulder et al, Nat Cell Bio 2012.
  Software: PANR Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations
  X. Wang, M.A. Castro, K.W. Mulder^, F. Markowetz^
  PLoS Comp Bio 8(6): e1002566
  PMID:22761558 | doi:10.1371/journal.pcbi.1002566
• see also Wang et al, PLoS Comp Bio 2012
  Data: Mulder2012
   
  More here
  • News and Views Diverse epigenetic strategies interact to control epidermal differentiation
  K.W. Mulder, X. Wang, C. Escriu, Y. Ito, R.F. Schwarz, J. Gillis, G. Sirokmany, G. Donati, S. Uribe-Lewis, P. Pavlidis, A. Murrell, F. Markowetz, F. Watt
  Nature Cell Biology 14(7), 753-763 (2012)
  PMID:22729083 | doi:10.1038/ncb2520
• 
  Network Data Integr- ation, Analysis, and Visualization
  Software: RedeR RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations
  M.A. Castro, X. Wang, M.N.C. Fletcher, K.B. Meyer, F. Markowetz
  Genome Biology 2012, 13:R29
  PMID:22531049 | doi:10.1186/gb-2012-13-4-r29
• Data: EGAS00000000083
   
  More here:
  • Nature Rev Cancer
  • Cancer Discovery
  • BBC News
  • ABC News @ youtube
  • CRUK video @ youtube The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
  C. Curtis*, S.P. Shah*, S.-F. Chin*, G. Turashvili*, O.M. Rueda, M.J. Dunning, D. Speed, A.G. Lynch, S. Samarajiwa, Y. Yuan, S. Gräf, G. Ha, G. Haffari, A. Bashashati, R. Russell, S. McKinney, METABRIC Group, A. Langerød, A. Green, E. Provenzano, G. Wishart, S. Pinder, P. Watson, F. Markowetz, L. Murphy, I. Ellis, A. Purushotham, A.-L. Børresen-Dale, J.D. Brenton, S. Tavaré, C. Caldas^ and S. Aparicio^
  Nature 486, 7403 (2012)
  PMID:22522925 | doi:10.1038/nature10983

2011

• For each patient we check concordance of signals in different data when assigning them to clusters.
  Software: PSDF Patient-specific data fusion defines prognostic cancer subtypes
  Y. Yuan*, R. S. Savage*, F. Markowetz
  PLoS Comp Bio 7(10): e1002227
  PMID:22028636 | doi:10.1371/journal.pcbi.1002227
• Software: DANCE Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer
  Y. Yuan, O. M. Rueda, C. Curtis, F. Markowetz
  Bioinformatics. 2011 Oct 1;27(19):2679-85.
  PMID:21804112 | doi:10.1186/10.1093/bioinformatics/btr450
• Differential C3NET reveals disease networks of direct physical interactions
  G. Altay, M. Asim, F. Markowetz, D. E. Neal
  BMC Bioinformatics 2011, 12:296
  PMID:21777411 | doi:10.1186/1471-2105-12-296
• Software: lol
   
  Conference version won best paper award at IEEE BIBM 2010 A sparse regulatory network of copy-number driven gene expression reveals putative breast cancer oncogenes
  Y. Yuan, C. Curtis, C. Caldas, F. Markowetz
  IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul 20
  PMID:21788678 | doi:10.1109/TCBB.2011.105 | arXiv:1010.1409
• Software: joda Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data
  E. Szczurek, F. Markowetz, Irit Gat-Viks, P. Biecek, J. Tiuryn, M. Vingron
  BMC Bioinformatics 2011, 12:249
  PMID:21693013 | doi:10.1186/1471-2105-12-249
• Software: HTSanalyzeR HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens
  X. Wang*, C. Terfve*, J.C. Rose, F. Markowetz
  Bioinformatics (2011) 27 (6): 879-880
  PMID:21258062 | doi:10.1093/bioinformatics/btr028

2010

• Machine Learning meets evolution: Finite state transducer beat comp- etitors in phylogenetic inference. Evolutionary distances in the twilight zone - a rational kernel approach
  R. F. Schwarz, W. Fletcher, F. Förster, B. Merget, M. Wolf, J. Schultz, F. Markowetz
  PLoS One, 2010 Dec 31;5(12):e15788
  PMID:21209825 | doi:10.1371/journal.pone.0015788 | arXiv:1011.5096
• How predictive is histone acetylation for gene expression over time? With a focus on key stem cell genes. Mapping dynamic histone acetylation patterns to gene expression in Nanog-depleted Murine embryonic stem cells
  F. Markowetz, K.W. Mulder, E.M. Airoldi, I.R. Lemischka, O.G. Troyanskaya
  PLoS Comp Bio, 2010 Dec 16;6(12):e1001034
  PMID:21187909 | doi:10.1371/journal.pcbi.1001034 | arXiv:1010.3268
• Constructing networks from single gene pert- urbations. Review based on ISMB 2009 and 2010 tutorials. How to understand the cell by breaking it: network analysis of gene perturbation screens
  F. Markowetz
  PLoS Comp Bio, 2010 Feb 26;6(2):e1000655.
  PMID:20195495 | doi:10.1371/journal.pcbi.1000655 | arXiv:0910.2938

2009

• 
  • News and Views: Kim and Orkin, Nat BT 2010
  • Editor's choice Sci. Sig. 2(98) 2009

  Systems-level dynamic analyses of fate change in murine embryonic stem cells
  R. Lu, F. Markowetz, R.D. Unwin, J.T. Leek, E.M. Airoldi, B.D. MacArthur, A. Lachmann, R. Rozov, A. Ma'ayan, L.A. Boyer, O.G. Troyanskaya, A.D. Wetton, I.R. Lemischka.
  Nature. 2009 Nov 19;462(7271):358-362
  PMID:19924215 | doi:10.1038/nature08575

2008

• The 'nem' R-package unites software from several labs Analyzing Gene Perturbation Screens With Nested Effects Models in R and Bioconductor
  H. Fröhlich, T. Beißbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, F. Markowetz
  Bioinformatics, 2008 Nov 1;24(21):2549-50.
  PMID:18718939 | doi:10.1093/bioinformatics/btn446
• An alternative and extended formulation of NEMs including feature selection Structure Learning in Nested Effects Models
  A. Tresch, F. Markowetz.
  Stat Appl in Gen and Mol Bio (SAGMB): Vol. 7: Iss. 1, Article 9, 2008.
  PMID:18312214 | doi:10.2202/1544-6115.1332 | arXiv:0710.4481
• Computational diagnostics with gene expression profiles
  C. Lottaz, D. Kostka, F. Markowetz, R. Spang.
  Methods Mol Biol. 2008;453:281-96.
  PMID:18712310 | doi:10.1007/978-1-60327-429-6

2007

• Divide-and-conquer approach to efficiently infer NEMs Nested Effects Models for High-Dimensional Phenotyping Screens
  F. Markowetz, D. Kostka, O.G. Troyanskaya, R. Spang.
  Bioinformatics 2007 23(13):i305-i312
  PMID:17646311 | doi:10.1093/bioinformatics/btm178
• 
  Comprehensive description of statistical network inference Inferring cellular networks - a review
  F. Markowetz, R. Spang.
  BMC Bioinformatics, 8(Suppl 6):S5, 2007
  PMID:17903286 | doi:10.1186/1471-2105-8-S6-S5
• Computational identification of cellular networks and pathways
  F. Markowetz, O.G. Troyanskaya.
  Molecular BioSystems, 3(7):478-482, 2007
  PMID:17579773 | doi:10.1039/b617014p

2006

• Computational Diagnostics
  R. Spang and F. Markowetz
  In: D. Ganten and K. Ruckpaul (eds.), Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine, Springer, 2006. ISBN: 3-540-44244-8
  book website

2005

• Nested Effects Models (NEMs) introduced Non-transcriptional Pathway Features Reconstructed from Secondary Effects of RNA Interference
  F. Markowetz, J. Bloch, R. Spang.
  Bioinformatics 2005 21: 4026-4032.
  PMID:16159925 | doi:10.1093/bioinformatics/bti662
• Extending statistical perturbation models from knock-outs to knock-downs Probabilistic Soft Interventions in Conditional Gaussian Networks
  F. Markowetz, S. Grossmann, R. Spang.
  Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), Barbados, 2005
  pdf
• Molecular Diagnosis: Classification, Model Selection, and Performance Evaluation
  F. Markowetz and R. Spang.
  Methods of Information in Medicine, 44(3): 438-43, 2005
  PMID:16113770 | pdf

2003

• Support Vector Machines for Protein Fold Class Prediction
  F. Markowetz, L. Edler, M. Vingron.
  Biometrical Journal 45 (2003) 3, 377-389
  doi:10.1002/bimj.200390019
• Acetylcysteine for Prevention of Contrast Nephropathy: Meta-analysis
  R. Birck, S. Krzossok, F. Markowetz, P. Schnülle, F. J. v. d. Woude and C. Braun.
  Lancet. 2003 Aug 23; 362(9384): 598-603.
  PMID:12944058 | doi:10.1016/S0140-6736(03)14189-X
• Evaluating the Effect of Perturbations in Reconstructing Network Topologies
  F. Markowetz and R. Spang.
  Proc. of the 3rd Int Workshop on Distributed Statistical Computing, Vienna, Austria, 2003
  pdf

2002

• Class Discovery in Gene Expression Data: Characterizing Splits by Support Vector Machines
  F. Markowetz and A. von Heydebreck.
  In Proc. of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., Springer 2002.
  pdf