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11 results on '"Morris, Connor J"'

1. Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma.

Author

Ellingson, Benjamin M, Hagiwara, Akifumi, Morris, Connor J, Cho, Nicholas S, Oshima, Sonoko, Sanvito, Francesco, Oughourlian, Talia C, Telesca, Donatello, Raymond, Catalina, Abrey, Lauren E, Garcia, Josep, Aftab, Dana T, Hessel, Colin, Rachmilewitz Minei, Tamar, Harats, Dror, Nathanson, David A, Wen, Patrick Y, and Cloughesy, Timothy F

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Rare Diseases, Cancer, Brain Cancer, Brain Disorders, Humans, Glioblastoma, Bevacizumab, Angiogenesis Inhibitors, Brain Neoplasms, Neoplasm Recurrence, Local, Irinotecan, Magnetic Resonance Imaging, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

PurposeAntiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy.Experimental designN = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival.ResultsOptimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth.ConclusionsRecurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

Published
2023

3. Supplementary Data 1 from Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma

Author

Ellingson, Benjamin M., primary, Hagiwara, Akifumi, primary, Morris, Connor J., primary, Cho, Nicholas S., primary, Oshima, Sonoko, primary, Sanvito, Francesco, primary, Oughourlian, Talia C., primary, Telesca, Donatello, primary, Raymond, Catalina, primary, Abrey, Lauren E., primary, Garcia, Josep, primary, Aftab, Dana T., primary, Hessel, Colin, primary, Rachmilewitz Minei, Tamar, primary, Harats, Dror, primary, Nathanson, David A., primary, Wen, Patrick Y., primary, and Cloughesy, Timothy F., primary

Published
2023
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4. Data from Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma

Author

Ellingson, Benjamin M., primary, Hagiwara, Akifumi, primary, Morris, Connor J., primary, Cho, Nicholas S., primary, Oshima, Sonoko, primary, Sanvito, Francesco, primary, Oughourlian, Talia C., primary, Telesca, Donatello, primary, Raymond, Catalina, primary, Abrey, Lauren E., primary, Garcia, Josep, primary, Aftab, Dana T., primary, Hessel, Colin, primary, Rachmilewitz Minei, Tamar, primary, Harats, Dror, primary, Nathanson, David A., primary, Wen, Patrick Y., primary, and Cloughesy, Timothy F., primary

Published
2023
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5. Supplemental Figure S1 from Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma

Author

Ellingson, Benjamin M., primary, Hagiwara, Akifumi, primary, Morris, Connor J., primary, Cho, Nicholas S., primary, Oshima, Sonoko, primary, Sanvito, Francesco, primary, Oughourlian, Talia C., primary, Telesca, Donatello, primary, Raymond, Catalina, primary, Abrey, Lauren E., primary, Garcia, Josep, primary, Aftab, Dana T., primary, Hessel, Colin, primary, Rachmilewitz Minei, Tamar, primary, Harats, Dror, primary, Nathanson, David A., primary, Wen, Patrick Y., primary, and Cloughesy, Timothy F., primary

Published
2023
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11. CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson’s Disease Associated Protein

Author

Li, Fengling, Ackloo, Suzanne, Arrowsmith, Cheryl H., Ban, Fuqiang, Barden, Christopher J., Beck, Hartmut, Beránek, Jan, Berenger, Francois, Bolotokova, Albina, Bret, Guillaume, Breznik, Marko, Carosati, Emanuele, Chau, Irene, Chen, Yu, Cherkasov, Artem, Corte, Dennis Della, Denzinger, Katrin, Dong, Aiping, Draga, Sorin, Dunn, Ian, Edfeldt, Kristina, Edwards, Aled, Eguida, Merveille, Eisenhuth, Paul, Friedrich, Lukas, Fuerll, Alexander, Gardiner, Spencer S, Gentile, Francesco, Ghiabi, Pegah, Gibson, Elisa, Glavatskikh, Marta, Gorgulla, Christoph, Guenther, Judith, Gunnarsson, Anders, Gusev, Filipp, Gutkin, Evgeny, Halabelian, Levon, Harding, Rachel J., Hillisch, Alexander, Hoffer, Laurent, Hogner, Anders, Houliston, Scott, Irwin, John J, Isayev, Olexandr, Ivanova, Aleksandra, Jacquemard, Celien, Jarrett, Austin J, Jensen, Jan H., Kireev, Dmitri, Kleber, Julian, Koby, S. Benjamin, Koes, David, Kumar, Ashutosh, Kurnikova, Maria G., Kutlushina, Alina, Lessel, Uta, Liessmann, Fabian, Liu, Sijie, Lu, Wei, Meiler, Jens, Mettu, Akhila, Minibaeva, Guzel, Moretti, Rocco, Morris, Connor J, Narangoda, Chamali, Noonan, Theresa, Obendorf, Leon, Pach, Szymon, Pandit, Amit, Perveen, Sumera, Poda, Gennady, Polishchuk, Pavel, Puls, Kristina, Pütter, Vera, Rognan, Didier, Roskams-Edris, Dylan, Schindler, Christina, Sindt, François, Spiwok, Vojtěch, Steinmann, Casper, Stevens, Rick L., Talagayev, Valerij, Tingey, Damon, Vu, Oanh, Walters, W. Patrick, Wang, Xiaowen, Wang, Zhenyu, Wolber, Gerhard, Wolf, Clemens Alexander, Wortmann, Lars, Zeng, Hong, Zepeda, Carlos A., Zhang, Kam Y. J., Zhang, Jixian, Zheng, Shuangjia, and Schapira, Matthieu

Abstract

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson’s disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KDlower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Published
2024
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