50 research outputs found

    Multi-Target Prediction: A Unifying View on Problems and Methods

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    Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

    Abstract LB-A24: Molecular alteration of SMAD4 in hindgut-derived colorectal tumors identifies a distinct subset of patients and is associated with worse recurrence-free survival

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    Abstract This abstract has been withheld from publication due to its inclusion in the AACR-NCI-EORTC Molecular Targets Conference 2015 Official Press Program. It will be posted online at the time of its presentation in a press conference or in a session: 10:00 AM ET Friday, November 6. Citation Format: Jesse Joshua Smith, Lik Hang Lee, Xi Chen, Chao Wu, Raphael Pelossof, Garrett M. Nash, Larissa R. Temple, Jose G. Guillem, Martin R. Weiser, Philip B. Paty, Jinru Shia, Julio Garcia-Aguilar, Charles L. Sawyers. Molecular alteration of SMAD4 in hindgut-derived colorectal tumors identifies a distinct subset of patients and is associated with worse recurrence-free survival. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr LB-A24.</jats:p

    AXL Mediates Resistance to PI3Kα Inhibition by Activating the EGFR/PKC/mTOR Axis in Head and Neck and Esophageal Squamous Cell Carcinomas

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    Phosphoinositide-3-kinase (PI3K)-α inhibitors have shown clinical activity in squamous cell carcinomas (SCCs) of head and neck (H&amp;N) bearing PIK3CA mutations or amplification. Studying models of therapeutic resistance, we have observed that SCC cells that become refractory to PI3Kα inhibition maintain PI3K-independent activation of the mammalian target of rapamycin (mTOR). This persistent mTOR activation is mediated by the tyrosine kinase receptor AXL. AXL is overexpressed in resistant tumors from both laboratory models and patients treated with the PI3Kα inhibitor BYL719. AXL dimerizes with and phosphorylates epidermal growth factor receptor (EGFR), resulting in activation of phospholipase Cγ (PLCγ)-protein kinase C (PKC), which, in turn, activates mTOR. Combined treatment with PI3Kα and either EGFR, AXL, or PKC inhibitors reverts this resistance
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