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Horowitz, Peleg

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Horowitz

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Peleg

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Horowitz, Peleg

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2013 - 20152

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Author's Publications: search.filters.isAuthorOfPublication.fdf0b203-c11e-40d2-b9d4-64f6f7ec52de

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    Genomic sequencing of meningiomas identifies oncogenic SMO and AKT1 mutations
    (2013) Brastianos, Priscilla; Horowitz, Peleg; Santagata, Sandro; Jones, Robert T.; McKenna, Aaron; Getz, Gad; Ligon, Keith; Palescandolo, Emanuele; Van Hummelen, Paul; Ducar, Matthew D.; Raza, Alina; Sunkavalli, Ashwini; MacConaill, Laura E.; Stemmer-Rachamimov, Anat; Louis, David; Hahn, William; Dunn, Ian; Beroukhim, Rameen
    Meningiomas are the most common primary nervous system tumor. The tumor suppressor NF2 is disrupted in approximately half of meningiomas1 but the complete spectrum of genetic changes remains undefined. We performed whole-genome or whole-exome sequencing on 17 meningiomas and focused sequencing on an additional 48 tumors to identify and validate somatic genetic alterations. Most meningiomas exhibited simple genomes, with fewer mutations, rearrangements, and copy-number alterations than reported in other adult tumors. However, several meningiomas harbored more complex patterns of copy-number changes and rearrangements including one tumor with chromothripsis. We confirmed focal NF2 inactivation in 43% of tumors and found alterations in epigenetic modifiers among an additional 8% of tumors. A subset of meningiomas lacking NF2 alterations harbored recurrent oncogenic mutations in AKT1 (E17K) and SMO (W535L) and exhibited immunohistochemical evidence of activation of their pathways. These mutations were present in therapeutically challenging tumors of the skull base and higher grade. These results begin to define the spectrum of genetic alterations in meningiomas and identify potential therapeutic targets.
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    Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity
    (Nature Pub. Group, 2015) Majumder, Biswanath; Baraneedharan, Ulaganathan; Thiyagarajan, Saravanan; Radhakrishnan, Padhma; Narasimhan, Harikrishna; Dhandapani, Muthu; Brijwani, Nilesh; Pinto, Dency D.; Prasath, Arun; Shanthappa, Basavaraja U.; Thayakumar, Allen; Surendran, Rajagopalan; Babu, Govind K.; Shenoy, Ashok M.; Kuriakose, Moni A.; Bergthold, Guillaume; Horowitz, Peleg; Loda, Massimo; Beroukhim, Rameen; Agarwal, Shivani; Sengupta, Shiladitya; Sundaram, Mallikarjun; Majumder, Pradip K.
    Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.