Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines
IntroductionColorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1531175/full |
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| author | Alexandra Razumovskaya Mariia Silkina Mariia Silkina Mariia Silkina Andrey Poloznikov Timur Kulagin Maria Raigorodskaya Nina Gorban Anna Kudryavtseva Maria Fedorova Boris Alekseev Alexander Tonevitsky Alexander Tonevitsky Alexander Tonevitsky Sergey Nikulin Sergey Nikulin Sergey Nikulin |
| author_facet | Alexandra Razumovskaya Mariia Silkina Mariia Silkina Mariia Silkina Andrey Poloznikov Timur Kulagin Maria Raigorodskaya Nina Gorban Anna Kudryavtseva Maria Fedorova Boris Alekseev Alexander Tonevitsky Alexander Tonevitsky Alexander Tonevitsky Sergey Nikulin Sergey Nikulin Sergey Nikulin |
| author_sort | Alexandra Razumovskaya |
| collection | DOAJ |
| description | IntroductionColorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the treatment of metastatic colorectal cancer are 5-fluorouracil, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct a predictive gene expression-based classifier to separate responders and non-responders.MethodsIn this study, we analyzed gene expression profiles of seven patient-derived CRC organoids and performed correlation analyses between gene expression and IC50 values for the three standard-of-care chemotherapeutic drugs. We also included in the study publicly available datasets of colorectal cancer cell lines, thus combining two different in vitro models relevant to cancer research. Logistic regression was used to build gene expression-based classifiers for metastatic Stage IV and non-metastatic Stage II/III CRC patients. Prognostic performance was evaluated through Kaplan-Meier survival analysis and log-rank tests, while independent prognostic significance was assessed using multivariate Cox proportional hazards modeling.ResultsA small set of genes showed consistent correlation with resistance to chemotherapy across different datasets. While some genes were previously implicated in cancer prognosis and drug response, several were linked to drug resistance for the first time. The resulting gene expression signatures successfully stratified Stage II/III and Stage IV CRC patients, with potential clinical utility for improving treatment outcomes after further validation.DiscussionThis study highlights the advantages of integrating diverse experimental models, such as organoids and cell lines, to identify novel prognostic biomarkers and enhance the understanding of chemotherapy resistance in CRC. |
| format | Article |
| id | doaj-art-fd819aab6d2e4d46973d55581d741443 |
| institution | Kabale University |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-fd819aab6d2e4d46973d55581d7414432025-01-15T05:10:54ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-01-011210.3389/fmolb.2025.15311751531175Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell linesAlexandra Razumovskaya0Mariia Silkina1Mariia Silkina2Mariia Silkina3Andrey Poloznikov4Timur Kulagin5Maria Raigorodskaya6Nina Gorban7Anna Kudryavtseva8Maria Fedorova9Boris Alekseev10Alexander Tonevitsky11Alexander Tonevitsky12Alexander Tonevitsky13Sergey Nikulin14Sergey Nikulin15Sergey Nikulin16Faculty of Biology and Biotechnologies, National Research University Higher School of Economics, Moscow, RussiaFaculty of Biology and Biotechnologies, National Research University Higher School of Economics, Moscow, RussiaP. A. Hertsen Moscow Oncology Research Center, Branch of the National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, RussiaShemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, RussiaP. A. Hertsen Moscow Oncology Research Center, Branch of the National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, RussiaFaculty of Biology and Biotechnologies, National Research University Higher School of Economics, Moscow, RussiaP. A. Hertsen Moscow Oncology Research Center, Branch of the National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, RussiaCentral Clinical Hospital with Polyclinic, Administration of the President of the Russian Federation, Moscow, RussiaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, RussiaEngelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, RussiaP. A. Hertsen Moscow Oncology Research Center, Branch of the National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, RussiaFaculty of Biology and Biotechnologies, National Research University Higher School of Economics, Moscow, RussiaShemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, RussiaArt Photonics GmbH, Berlin, GermanyFaculty of Biology and Biotechnologies, National Research University Higher School of Economics, Moscow, RussiaP. A. Hertsen Moscow Oncology Research Center, Branch of the National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, RussiaDmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Ministry of Health of the Russian Federation, Moscow, RussiaIntroductionColorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the treatment of metastatic colorectal cancer are 5-fluorouracil, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct a predictive gene expression-based classifier to separate responders and non-responders.MethodsIn this study, we analyzed gene expression profiles of seven patient-derived CRC organoids and performed correlation analyses between gene expression and IC50 values for the three standard-of-care chemotherapeutic drugs. We also included in the study publicly available datasets of colorectal cancer cell lines, thus combining two different in vitro models relevant to cancer research. Logistic regression was used to build gene expression-based classifiers for metastatic Stage IV and non-metastatic Stage II/III CRC patients. Prognostic performance was evaluated through Kaplan-Meier survival analysis and log-rank tests, while independent prognostic significance was assessed using multivariate Cox proportional hazards modeling.ResultsA small set of genes showed consistent correlation with resistance to chemotherapy across different datasets. While some genes were previously implicated in cancer prognosis and drug response, several were linked to drug resistance for the first time. The resulting gene expression signatures successfully stratified Stage II/III and Stage IV CRC patients, with potential clinical utility for improving treatment outcomes after further validation.DiscussionThis study highlights the advantages of integrating diverse experimental models, such as organoids and cell lines, to identify novel prognostic biomarkers and enhance the understanding of chemotherapy resistance in CRC.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1531175/fullcolorectal cancerorganoidschemotherapydrug resistanceresponse predictiontranscriptomic gene signature |
| spellingShingle | Alexandra Razumovskaya Mariia Silkina Mariia Silkina Mariia Silkina Andrey Poloznikov Timur Kulagin Maria Raigorodskaya Nina Gorban Anna Kudryavtseva Maria Fedorova Boris Alekseev Alexander Tonevitsky Alexander Tonevitsky Alexander Tonevitsky Sergey Nikulin Sergey Nikulin Sergey Nikulin Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines Frontiers in Molecular Biosciences colorectal cancer organoids chemotherapy drug resistance response prediction transcriptomic gene signature |
| title | Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines |
| title_full | Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines |
| title_fullStr | Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines |
| title_full_unstemmed | Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines |
| title_short | Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines |
| title_sort | predicting patient outcomes with gene expression biomarkers from colorectal cancer organoids and cell lines |
| topic | colorectal cancer organoids chemotherapy drug resistance response prediction transcriptomic gene signature |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1531175/full |
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