Εξειδίκευση τύπου :	Κεφάλαιο βιβλίου
Τίτλος:	Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges
Δημιουργός/Συγγραφέας:	Ouzounis, Sotirios Zojaji, Donya García-Mulero, Sandra Barreca, Marco Gandellini, Paolo [EL] Κατσίλα, Θεοδώρα[EN] Katsila, Theodora Sanz-Pamplona, Rebeca Callari, Maurizio
Επιμελητής έκδοσης:	Rani, Sweta Skalniak, Lukasz
Ημερομηνία:	2026
Γλώσσα:	Αγγλικά
ISBN:	978-1-0716-4733-2 978-1-0716-4734-9
ISSN:	1064-3745 1940-6029
DOI:	10.1007/978-1-0716-4734-9_16
Άλλο:	41028270
Περίληψη:	Tumors are complex ecosystems comprising diverse cell types actively participating to carcinogenesis, tumor progression, and treatment response. Understanding the tumor microenvironment (TME) dynamics has become of primary importance, especially with the increasing clinical implementation of immunotherapy. Low and high-throughput single cell and spatial technologies are providing high-resolution strategies for the study of the tumor ecosystem. However, their cost and complexity limit widespread use. Bulk transcriptomics has become a widely used strategy to obtain the expression profile of large cohorts of tumors or preclinical models. Several methods implementing a deconvolution analysis have been developed to estimate from bulk transcriptomics the prevalence of multiple cell types to reconstruct the tumor ecosystem composition.In this chapter, we introduce deconvolution analysis, the main steps, the recent advancements, and open challenges. Our emphasis lies on robust benchmarking methodologies, highlighting the importance of clear parameter definition and appropriate metric selection for reliable results across different software tools.Using CIBERSORTx and BayesPrism, we conduct a practical analysis on triple-negative breast cancer (TNBC) datasets from The Cancer Genome Atlas (TCGA) dataset. We illustrate the impact of various factors such as preprocessing methods, reference datasets, and software choice on deconvolution outcomes.Integrating insights from benchmarking analyses and real-world applications, we provide guidance to optimize and control for the quality of deconvolution analysis, weighting both its potential and limitations. Deconvolution analysis can contribute to unravelling the complexities of the tumor microenvironment, but further research is needed to enhance accuracy and reproducibility.
Τίτλος πηγής δημοσίευσης:	IMMUNO-model in Cancer. Methods in Molecular Biology
Τόμος/Κεφάλαιο:	2959
Σελίδες:	233 - 252
Σειρά δημοσίευσης:	Methods in Molecular Biology
Θεματική Κατηγορία:	[EL] Βιοπληροφορική[EN] Bioinformatics [EL] Νεοπλάσματα. Όγκοι. Ογκολογία (περ. Καρκίνος, κακινογόνες ουσίες)[EN] Neoplasms. Tumors. Oncology (Incl.cancer, carcinogens) [EL] Ανοσολογία[EN] Immunology [EL] Μοριακή Βιολογία[EN] Molecular Biology
Λέξεις-Κλειδιά:	Bulk Cancer Cell type Challenges Deconvolution Immune Microenvironment TME Transcriptomics
Χρηματοδότης:	Fondazione Michelangelo Breast Cancer Research Foundation ASPANOA Foundation Instituto de Salud Carlos III European Cooperation in Science and Technology
Αναγνωριστικό χρηματοδοτικού προγράμματος:	BCRF 21-181 BCRF 21-181 MCIN/AEI/10.13039/501100011033 CNS2022-136176 PI22/01938 CA21135
Κάτοχος πνευματικών δικαιωμάτων:	© The Author(s) 2026
Όροι και προϋποθέσεις δικαιωμάτων:	Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.1007/978-1-0716-4734-9_16
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο