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Your search keyword '"Mateo Sokac"' showing total 3 results
Start Over Author "Mateo Sokac" Topic bladder cancer
3 results on '"Mateo Sokac"'

1. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer

Author

Roman Nawroth, Richard T. Bryan, Philippe Lamy, Tobias Maurer, Margaret A. Knowles, Gregers G. Hermann, Marc-Oliver Grimm, Ann Taber, Torben Steiniche, Jørgen Bjerggaard Jensen, David J. DeGraff, Francisco X. Real, Karin Mogensen, Joshua I. Warrick, Jay D. Raman, Anshita Goel, Ulrika Segersten, Karin Birkenkamp-Demtröder, Emil Christensen, Clarice S. Groeneveld, Xiaoqi Lin, Joshua J. Meeks, Brian J. Jordan, Núria Malats, Trine Strandgaard, Sia Viborg Lindskrog, Mateo Sokac, Frederik Prip, Gottfrid Sjödahl, Ellen C. Zwarthoff, Tatjana Simic, Iver Nordentoft, Marcus Horstmann, Lasse Maretty, Douglas G. Ward, Dejan Dragicevic, Veronika Weyerer, Carolyn D. Hurst, Aurélien de Reyniès, Kim E.M. van Kessel, Per-Uno Malmström, Astrid Christine Petersen, Arndt Hartmann, Danijel Sikic, Mattias Höglund, Lars Dyrskjøt, Nicolai Juul Birkbak, and Pathology

Subjects
Cancer microenvironment, 0301 basic medicine, Oncology, medicine.medical_specialty, Science, General Physics and Astronomy, Disease, Biology, Proteomics, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Internal medicine, Chromosome instability, Urologi och njurmedicin, Cancer genomics, medicine, Urology and Nephrology, Progression-free survival, Biomarker discovery, Cancer och onkologi, Multidisciplinary, Bladder cancer, General Chemistry, medicine.disease, Subtyping, Computational biology and bioinformatics, 030104 developmental biology, Cancer and Oncology, 030220 oncology & carcinogenesis
Abstract

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials., Multiple molecular profiling methods are required to study urothelial non-muscle-invasive bladder cancer (NMIBC) due to its heterogeneity. Here the authors integrate multi-omics data of 834 NMIBC patients, identifying a molecular subgroup associated with multiple alterations and worse outcomes.

Published
2021

2. An integrated multi-omics analysis identifies clinically relevant molecular subtypes of non-muscle-invasive bladder cancer

Author

Lars Dyrskjøt, Sia Viborg Lindskrog, Mattias Höglund, Emil Christensen, Margaret A. Knowles, Xiaoqi Lin, Mateo Sokac, Gregers G. Hermann, Danijel Sikic, Francisco X. Real, Jørgen Bjerggaard Jensen, Dejan Dragicevic, Núria Malats, Joshua I. Warrick, Anshita Goel, Ulrika Segersten, Marcus Horstmann, Gottfrid Sjödahl, Veronika Weyerer, Arndt Hartmann, Ellen C. Zwarthoff, Tatjana Simic, Carolyn D. Hurst, Douglas G. Ward, Iver Nordentoft, Ann Taber, Marc-Oliver Grimm, Aurélien de Reyniès, Roman Nawroth, Kim E.M. van Kessel, Per-Uno Malmström, Philippe Lamy, Karin Birkenkamp-Demtröder, Astrid Christine Petersen, Nicolai Juul Birkbak, Frederik Prip, Trine Strandgaard, Brian J. Jordan, Richard T. Bryan, Torben Steiniche, Karin Mogensen, Tobias Maurer, David J. DeGraff, Jay D. Raman, Clarice S. Groeneveld, Lasse Maretty, and Joshua J. Meeks

Subjects
Oncology, 0303 health sciences, medicine.medical_specialty, Bladder cancer, business.industry, Disease, Proteomics, medicine.disease, Subtyping, 3. Good health, Clinical trial, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Internal medicine, Chromosome instability, medicine, Biomarker discovery, business, 030304 developmental biology
Abstract

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we performed a large integrative multi-omics analysis of patients diagnosed with NMIBC (n=834). Transcriptomic analysis identified four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provided independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations were significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration was associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirmed the higher infiltration of class 2b tumors and demonstrated an association between higher immune cell infiltration and lower recurrence rates. Finally, a single-sample classification tool was built and the independent prognostic value of the transcriptomic classes was documented in 1306 validation samples. The classifier provides a framework for novel biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

Published
2020

3. Spatial transformation of multi-omics data unlocks novel insights into cancer biology

Author

Mateo Sokač, Asbjørn Kjær, Lars Dyrskjøt, Benjamin Haibe-Kains, Hugo JWL Aerts, and Nicolai J Birkbak

Subjects
deep learning, multi-omics, cancer, integrated gradients, immunotherapy, bladder cancer, Medicine, Science, Biology (General), QH301-705.5
Abstract

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

Published
2023
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