Dr. Peter J. Schüffler

@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208},
}

@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22},
}

@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347},
}