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Integrated Molecular Profiling in Advanced Cancers Trial

ABSTRACT: Substantial progress has been made in the treatment of cancer through the use of targeted therapies, but what works for one patient might not work for another patient. Certain drugs are now being developed that target specific molecules in the body that are believed to be part of the disease. Biomarkers are specific characteristics of the cancer that may help provide prognostic information (i.e. how well patients will be regardless of the treatments given) or help predict sensitivity or resistance to a specific treatment. The study will collect archival tumor samples (previously collected biopsy or surgical tumor samples) to provide biomarker data about a patient’s cancer, in order to help their physicians to identify which clinical trials of molecularly targeted therapies may be most appropriate for the patient in the future.

DISEASE(S): Gastrointestinal Neoplasms,Genitourinary Cancer,Neoplasms, Unknown Primary,Melanoma,Colorectal Cancer,Advanced Cancer,Breast Cancer,Pancreatobiliary Gastrointestinal Cancer,Upper Aerodigestive Tract Cancer,Melanoma Cancers,Non-small Cell Lung Cancer,Rare Cancers,Colorectal Neoplasms,Carcinoma, Non-small-cell Lung,Gynecological Cancers,Unknown Primary Cancers,Urogenital Neoplasms

PROVIDER: 2118249 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Hass2017-PanRTK model for single cell line The model structure comprises heterodimerization and receptor trafficking as described in detail in the article below. For ligand input, set a respective event. The illustrated event sets the EGF concentration to 2.5 nMol in the model file. This model is described in the article: Predicting ligand-dependent tumors from multi-dimensional signaling features. Hass H, Masson K, Wohlgemuth S, Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J, MacBeath G, Schoeberl B, Raue A. NPJ Syst Biol Appl 2017; 3: 27 Abstract: Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo. This model is hosted on BioModels Database and identified by: MODEL1708210000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Integrated Molecular Profiling in Advanced Cancers Trial

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