8ec9dd63bcfb8ddd4db7155574493b46 303868.pdf 4673c1119ecb353d27aee9f5e1d9dd3857de4385 303868.pdf df8f3089c1c667c71d0ac1aa8d5632ff414e8c59e4880fcf42ecec9318bf748f 303868.pdf Title: Sorting Transcriptomics Immune Information from Tumor Molecular Features Allows Prediction of Response to Anti-PD1 Therapy in Patients with Advanced Melanoma Subject: Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy. 10pt Keywords: melanoma; immunotherapy; prediction of response; computational analysis; personalized medicine Author: Lucía Trilla-Fuertes, Angelo Gámez-Pozo, Guillermo Prado-Vázquez, Rocío López-Vacas, Andrea Zapater-Moros, Elena López-Camacho, María I. Lumbreras-Herrera, Virtudes Soriano, Fernando Garicano, Mª José Lecumberri, María Rodríguez de la Borbolla, Margarita Majem, Elisabeth Pérez-Ruiz, María González-Cao, Juana Oramas, Alejandra Magdaleno, Joaquín Fra, Alfonso Martín-Carnicero, Mónica Corral, Teresa Puértolas, Ricardo Ramos, Juan Ángel Fresno Vara and Enrique Espinosa Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Jan 4 08:58:45 2023 CET ModDate: Fri Jan 6 08:52:11 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 14 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2037299 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- EUNBMT+VnURWPalladioL Type 1 Custom yes yes no 10 0 WRKDYS+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 JCWYGJ+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 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PDFMetadata: Objects: 390 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Results Destination: section.2 Children: Item: Title: TCGA Cohort Destination: subsection.2.1 Item: Title: Functional Characterization of the TCGA Cohort Destination: subsection.2.2 Item: Title: Functional Differences According to Mutational Subtypes in the TCGA Cohort Destination: subsection.2.3 Item: Title: Biological Layer Analysis Destination: subsection.2.4 Children: Item: Title: Layer 1: Melanogenesis Destination: subsubsection.2.4.1 Item: Title: Layer 2: Immune Response Destination: subsubsection.2.4.2 Item: Title: Layer 3: Epidermis Development & Keratinization Destination: subsubsection.2.4.3 Item: Title: Layer 4: Extracellular Space & Membrane Destination: subsubsection.2.4.4 Item: Title: Layers 5, 6 and 7 Destination: subsubsection.2.4.5 Item: Title: Immune Classification Destination: subsection.2.5 Item: Title: Molecular Classification Destination: subsection.2.6 Item: Title: Cohort of Patients Treated with Anti-PD1 Therapy in the Spanish Melanoma Group (GEM) Destination: subsection.2.7 Item: Title: Sample Processing and RNA Capture Experiments Destination: subsection.2.8 Item: Title: Biological Layer Classification in GEM Cohort Destination: subsection.2.9 Item: Title: Relation to Survival of Molecular and Immune Classifications in GEM Cohort Destination: subsection.2.10 Item: Title: Discussion Destination: section.3 Item: Title: Materials and Methods Destination: section.4 Children: Item: Title: Preprocessing of TCGA Melanoma Data Destination: subsection.4.1 Item: Title: GEM Cohort of Advanced Melanoma Patients Treated with Anti-PD1 Inhibitors Destination: subsection.4.2 Item: Title: RNA Isolation Destination: subsection.4.3 Item: Title: RNA Capture and Sequencing Destination: subsection.4.4 Item: Title: Preprocessing of RNA Capture Data Destination: subsection.4.5 Item: Title: Network Construction and Functional Node Activities Destination: subsection.4.6 Item: Title: Biological Informative Layer Analyses Destination: subsection.4.7 Item: Title: Statistical Analyses Destination: subsection.4.8 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.5 Info: Title: Sorting Transcriptomics Immune Information from Tumor Molecular Features Allows Prediction of Response to Anti-PD1 Therapy in Patients with Advanced Melanoma Author: Lucía Trilla-Fuertes, Angelo Gámez-Pozo, Guillermo Prado-Vázquez, Rocío López-Vacas, Andrea Zapater-Moros, Elena López-Camacho, María I. Lumbreras-Herrera, Virtudes Soriano, Fernando Garicano, Mª José Lecumberri, María Rodríguez de la Borbolla, Margarita Majem, Elisabeth Pérez-Ruiz, María González-Cao, Juana Oramas, Alejandra Magdaleno, Joaquín Fra, Alfonso Martín-Carnicero, Mónica Corral, Teresa Puértolas, Ricardo Ramos, Juan Ángel Fresno Vara and Enrique Espinosa Subject: Immunotherapy based on anti-PD1 antibodies has improved the outcome of advanced melanoma. However, prediction of response to immunotherapy remains an unmet need in the field. Tumor PD-L1 expression, mutational burden, gene profiles and microbiome profiles have been proposed as potential markers but are not used in clinical practice. Probabilistic graphical models and classificatory algorithms were used to classify melanoma tumor samples from a TCGA cohort. A cohort of patients with advanced melanoma treated with PD-1 inhibitors was also analyzed. We established that gene expression data can be grouped in two different layers of information: immune and molecular. In the TCGA, the molecular classification provided information on processes such as epidermis development and keratinization, melanogenesis, and extracellular space and membrane. The immune layer classification was able to distinguish between responders and non-responders to immunotherapy in an independent series of patients with advanced melanoma treated with PD-1 inhibitors. We established that the immune information is independent than molecular features of the tumors in melanoma TCGA cohort, and an immune classification of these tumors was established. This immune classification was capable to determine what patients are going to respond to immunotherapy in a new cohort of patients with advanced melanoma treated with PD-1 inhibitors Therefore, this immune signature could be useful to the clinicians to identify those patients who will respond to immunotherapy. 10pt Keywords: melanoma; immunotherapy; prediction of response; computational analysis; personalized medicine Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Jan 04 08:58:45 CET 2023 ModDate: Fri Jan 06 08:52:11 CET 2023 ID: 0xe9b5dff6c58104bf94615c8b6b5ae484, 0xe9b5dff6c58104bf94615c8b6b5ae484 Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: JCWYGJ+URWPalladioL-Roma FontSubset: true FirstChar: 2 LastChar: 243 FontDescriptor: FontName: JCWYGJ+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: XDUNQU+URWPalladioL-Ital FontSubset: true FirstChar: 2 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