Araştırma Çıktıları
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Item Report of the third Asian Prostate Cancer study meeting(ELSEVIER INC, 2019-01-01) Lojanapiwat, Bannakij; Lee, Ji Youl; Gang, Zhu; Kim, Choung-Soo; Fai, Ng Chi; Hakim, Lukman; Umbas, Rainy; Ong, Teng Aik; Lim, Jasmine; Letran, Jason L.; Chiong, Edmund; Lee, Seung Hwan; Turkeri, Levent; Murphy, Declan G.; Moretti, Kim; Cooperberg, Matthew; Carlile, Robert; Hinotsu, Shiro; Hirao, Yoshihiko; Kitamura, Tadaichi; Horie, Shigeo; Onozawa, Mizuki; Kitagawa, Yasuhide; Namiki, Mikio; Fukagai, Takashi; Miyazaki, Jun; Akaza, HideyukiThe Asian Prostate Cancer (A-CaP) study is an Asia-wide initiative that was launched in December 2015 in Tokyo, Japan, with the objective of surveying information about patients who have received a histopathological diagnosis of prostate cancer (PCa) and are undergoing treatment and clarifying distribution of staging, the actual status of treatment choices, and treatment outcomes. The study aims to clarify the clinical situation for PCa in Asia and use the outcomes for the purposes of international comparison. Following the first meeting in Tokyo in December 2015, the second A-CaP meeting was held in Seoul, Korea, in September 2016. This, the third A-CaP meeting, was held on October 14, 2017, in Chiang Mai, Thailand, with the participation of members and collaborators from 12 countries and regions. In the meeting, participating countries and regions presented the current status of data collection, and the A-CaP office presented a preliminary analysis of the registered cases received from each country and region. Participants discussed ongoing challenges relating to data input and collection, institutional, and legislative issues that may present barriers to data sharing, and the outlook for further patient registrations through to the end of the registration period in December 2018. In addition to A-CaP-specific discussions, a series of special lectures were also delivered on the situation for health insurance in the United States, the correlation between insurance coverage and PCa outcomes, and the outlook for robotic surgery in the Asia-Pacific region. Members also confirmed the principles of authorship in collaborative studies, with a view to publishing original articles based on A-CaP data in the future. (C) 2018 Asian Pacific Prostate Society, Published by Elsevier Korea LLC.Item A framework for validating AI in precision medicine: considerations from the European ITFoC consortium(BMC, 2021-01-01) Tsopra, Rosy; Fernandez, Xose; Luchinat, Claudio; Alberghina, Lilia; Lehrach, Hans; Vanoni, Marco; Dreher, Felix; Sezerman, O. Ugur; Cuggia, Marc; de Tayrac, Marie; Miklasevics, Edvins; Itu, Lucian Mihai; Geanta, Marius; Ogilvie, Lesley; Godey, Florence; Boldisor, Cristian Nicolae; Campillo-Gimenez, Boris; Cioroboiu, Cosmina; Ciusdel, Costin Florian; Coman, Simona; Cubelos, Oliver Hijano; Itu, Alina; Lange, Bodo; Le Gallo, Matthieu; Lespagnol, Alexandra; Mauri, Giancarlo; Soykam, H. Okan; Rance, Bastien; Turano, Paola; Tenori, Leonardo; Vignoli, Alessia; Wierling, Christoph; Benhabiles, Nora; Burgun, AnitaBackground Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European ``ITFoC (Information Technology for the Future Of Cancer){''} consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the ``ITFoC Challenge{''}. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.Item Molecular modelling of the FOXO4-TP53 interaction to design senolytic peptides for the elimination of senescent cancer cells(ELSEVIER, 2021-01-01) Le, Hillary H.; Cinaroglu, Suleyman S.; Manalo, Elise C.; Ors, Aysegul; Gomes, Michelle M.; Sahbaz, Burcin Duan; Bonic, Karla; Marmolejo, Carlos A. Origel; Quentel, Arnaud; Plaut, Justin S.; Kawashima, Taryn E.; Ozdemir, E. Sila; Malhotra V, Sanjay; Ahiska, Yavuz; Sezerman, Ugur; Akcapinar, Gunseli Bayram; Saldivar, Joshua C.; Timucin, Emel; Fischer, Jared M.Background: Senescent cells accumulate in tissues over time as part of the natural ageing process and the removal of senescent cells has shown promise for alleviating many different age-related diseases in mice. Cancer is an age-associated disease and there are numerous mechanisms driving cellular senescence in cancer that can be detrimental to recovery. Thus, it would be beneficial to develop a senolytic that acts not only on ageing cells but also senescent cancer cells to prevent cancer recurrence or progression. Methods: We used molecular modelling to develop a series of rationally designed peptides to mimic and target FOXO4 disrupting the FOXO4-TP53 interaction and releasing TP53 to induce apoptosis. We then tested these peptides as senolytic agents for the elimination of senescent cells both in cell culture and in vivo. Findings: Here we show that these peptides can act as senolytics for eliminating senescent human cancer cells both in cell culture and in orthotopic mouse models. We then further characterized one peptide, ES2, showing that it disrupts FOXO4-TP53 foci, activates TP53 mediated apoptosis and preferentially binds FOXO4 compared to TP53. Next, we show that intratumoural delivery of ES2 plus a BRAF inhibitor results in a significant increase in apoptosis and a survival advantage in mouse models of melanoma. Finally, we show that repeated systemic delivery of ES2 to older mice results in reduced senescent cell numbers in the liver with minimal toxicity. Interpretation: Taken together, our results reveal that peptides can be generated to specifically target and eliminate FOXO4+ senescent cancer cells, which has implications for eradicating residual disease and as a combination therapy for frontline treatment of cancer. Funding: This work was supported by the Cancer Early Detection Advanced Research Center at Oregon Health \& Science University. (C) 2021 The Authors. Published by Elsevier B.V.Item The Importance of Nutrition Therapy as a Contributor to the Success of Cancer Treatment(KARE PUBL, 2020-01-01) Sonmez, OzlemCancer is a leading cause of morbidity and mortality in the world. Although developments in early cancer detection and treatments led to improved survival, malnutrition remains to be a significant problem that may affect the response to treatments. Cancer patients remain to be at high risk for malnutrition secondary to local and systemic/metabolic effects of tumors and cancer treatments. The mechanism of cancer cachexia is multifactorial and includes inadequate nutritional intake and systemic inflammation that leads to a metabolic imbalance. Weight loss and cachexia are of prognostic significance and are closely linked to the quality of life. Since early detection and intervention lead to improved outcomes, screening and monitoring nutritional status are critical components of cancer care leading to adequate nutritional therapies. This review article aims to provide an overview of the mechanisms of malnutrition and cancer related cachexia and recent guidelines with the current evidence on the importance of nutrition therapy for cancer patients.Item driveR: a novel method for prioritizing cancer driver genes using somatic genomics data(BMC, 2021-01-01) Ulgen, Ege; Sezerman, O. UgurBackground: Cancer develops due to ``driver{''} alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results: Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651-0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0-1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions: This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.