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Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data

BACKGROUND: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. METHODS: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE)...

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Autores principales: Sorokin, Maxim, Zolotovskaia, Marianna, Nikitin, Daniil, Suntsova, Maria, Poddubskaya, Elena, Glusker, Alexander, Garazha, Andrew, Moisseev, Alexey, Li, Xinmin, Sekacheva, Marina, Naskhletashvili, David, Seryakov, Alexander, Wang, Ye, Buzdin, Anton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9623986/
https://www.ncbi.nlm.nih.gov/pubmed/36316649
http://dx.doi.org/10.1186/s12885-022-10177-3
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author Sorokin, Maxim
Zolotovskaia, Marianna
Nikitin, Daniil
Suntsova, Maria
Poddubskaya, Elena
Glusker, Alexander
Garazha, Andrew
Moisseev, Alexey
Li, Xinmin
Sekacheva, Marina
Naskhletashvili, David
Seryakov, Alexander
Wang, Ye
Buzdin, Anton
author_facet Sorokin, Maxim
Zolotovskaia, Marianna
Nikitin, Daniil
Suntsova, Maria
Poddubskaya, Elena
Glusker, Alexander
Garazha, Andrew
Moisseev, Alexey
Li, Xinmin
Sekacheva, Marina
Naskhletashvili, David
Seryakov, Alexander
Wang, Ye
Buzdin, Anton
author_sort Sorokin, Maxim
collection PubMed
description BACKGROUND: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. METHODS: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. RESULTS: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. CONCLUSION: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10177-3.
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spelling pubmed-96239862022-11-02 Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data Sorokin, Maxim Zolotovskaia, Marianna Nikitin, Daniil Suntsova, Maria Poddubskaya, Elena Glusker, Alexander Garazha, Andrew Moisseev, Alexey Li, Xinmin Sekacheva, Marina Naskhletashvili, David Seryakov, Alexander Wang, Ye Buzdin, Anton BMC Cancer Research BACKGROUND: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. METHODS: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. RESULTS: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. CONCLUSION: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-10177-3. BioMed Central 2022-10-31 /pmc/articles/PMC9623986/ /pubmed/36316649 http://dx.doi.org/10.1186/s12885-022-10177-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sorokin, Maxim
Zolotovskaia, Marianna
Nikitin, Daniil
Suntsova, Maria
Poddubskaya, Elena
Glusker, Alexander
Garazha, Andrew
Moisseev, Alexey
Li, Xinmin
Sekacheva, Marina
Naskhletashvili, David
Seryakov, Alexander
Wang, Ye
Buzdin, Anton
Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title_full Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title_fullStr Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title_full_unstemmed Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title_short Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
title_sort personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of rna sequencing data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9623986/
https://www.ncbi.nlm.nih.gov/pubmed/36316649
http://dx.doi.org/10.1186/s12885-022-10177-3
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