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    Home > Active Ingredient News > Antitumor Therapy > Late night welfare: 8+ new ideas for drug resistance

    Late night welfare: 8+ new ideas for drug resistance

    • Last Update: 2021-08-08
    • Source: Internet
    • Author: User
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    Tumor microenvironment research has always been a hot spot in tumor research.
    Some studies have found that tumor microenvironment may affect the classification and drug sensitivity of colorectal cancer
    .

    Therefore, today the editor wants to share with you an article published in the journal npj Precision Oncology (IF: 8.
    25) in February this year that combined the analysis of tumor microenvironment and drug resistance.
    Finally, the author identified the prediction of colorectal Tumor microenvironment-specific gene expression characteristics of chemotherapy resistance in cancer patients
    .

    Tumor microenvironment plus drug resistance is another new idea for cancer analysis.
    Let's get it together! Shengxin people provide professional and reproducible life information analysis.
    Scan the code to understand A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients.
    Studies have found that Colorectal cancer (CRC) is a highly heterogeneous disease with unique molecular pathogenesis, histogenesis and drug sensitivity
    .

    In addition, studies have shown that the tumor microenvironment (TME) is closely related to the drug sensitivity and classification of colorectal cancer (CRC)
    .

    However, there are relatively few analyses using TME-specific gene signatures to identify CRC subtypes with unique clinical relevance
    .

    Therefore, the article shared by the editor today combines tumor microenvironment and drug resistance to classify and analyze colorectal cancer patients from multiple perspectives
    .

    2.
    Research data and methods 1.
    CRC data collection and candidate gene selection: The author collects public data sets from TCGA and GEO
    .

    Three data sets of GSE39395, GSE39396 and GSE62080 were used to construct SFM features
    .

    Five scRNA-seq data were used for SMF verification in the study, including CCR's GSE81861, HNSCC's GSE103322, melanoma's GSE72056, and BRCA's GSE75688
    .

    In addition, some data sets were used in the study to explore drug response, including combining GSE19860, GSE28702 and GSE69657 to analyze FOLFOX response; GSE104645 and GSE72970 to analyze FOLFOX or FOLFIRI response; and GSE5851 and PRJEB34338 to analyze the response of cetuximab
    .

    Next, three data sets of GSE39395, GSE39396 and GSE62080 were used for differential expression analysis to construct SFM features that can distinguish TME and are related to FOLFIRI sensitivity
    .

    In GSE39395 and GSE39396, FACS was used to separate cell subpopulations from 8 and 6 samples, respectively
    .

    The GSE62080 data set contains transcriptome data of 9 FOLFIRI responders and 12 non-responders
    .

    The author also performed differential expression analysis on the two cell populations of GSE39395 and GSE39396
    .

    GSE39396 also adopted a similar strategy
    .

    For GSE62080, directly perform differential expression analysis between FOLFIRI responders and non-responders
    .

    Finally, the intersection of the differentially expressed genes between these three data sets is further called the SFM gene feature
    .

    GSE39582 consists of 566 CRC samples as a discovery data set
    .

    In order to construct a large data set for verification, GSE14333, GSE17536, GSE17537, GSE33113, and GSE37892 are used as a unit, that is, 5 GEO batch data sets
    .

    In addition, the author directly downloaded 577 TCGA CRC gene expression profiles from CRCSC
    .

    This TCGA data was used as the second validation data set
    .

    Another 53 CRC samples came from Renji Hospital
    .

    The clinical data in the study are directly downloaded from the corresponding GEO website or obtained from supplementary materials in related literature
    .

    The clinical information of TCGA CRCs is downloaded from the database CRCSC, and the immune-related features are downloaded from a recent public study
    .

    2.
    Use K-means clustering algorithm to identify SFM subtypes: The author first uses K-means clustering algorithm to identify SFM subtypes on the GSE39582 data set on the basis of SFM
    .

    Among these clusters, k = 6 is the best
    .

    The authors then assessed the similarity and expression differences between SFM subtypes
    .

    In order to verify the robustness of SFM subtypes, the author further performed the same analysis in the verification data set
    .

    3.
    The function and pathway enrichment of SFM subtypes: In order to find abnormal signal pathways between SFM subtypes, the author analyzed the differential expression of each SFM subtype and other subtypes in the discovery cohort, and selected 2000 up-regulated ones.
    Down-regulated genes were further analyzed in each SFM subtype
    .

    About to apply these genes to ClueGO and CluePedia plug-ins
    .

    These two plug-ins can extract non-redundant biological information of a set of genes
    .

    The study uses Cytoscape to perform functional enrichment analysis on gene oncology (GO, BP, CC, MF, immune system processes) and KEGG
    .

    4.
    NTP implementation and feature application: Use GenePattern to perform NTP-based classification
    .

    NTP uses the nearest neighbor method to calculate the similarity between gene expression profiles and reference gene expression characteristics
    .

    Then random sub-sampling of the gene space is used to evaluate the zero distribution of the similarity coefficient
    .

    Finally, the similarity coefficient obtained from the given gene feature is compared with the zero distribution
    .

    Next, the author also assessed the association of SFM subtypes with a set of genetic characteristics
    .

    The gene signature list from previously published papers is: intestinal stem cell characteristics, colonic crypt characteristics, serrated CRC characteristics, EMT characteristics, FOLFIRI response characteristics, FOLFOX response characteristics and VEFG/EGFR inhibitor characteristics
    .

    5.
    Evaluation of cell infiltration: The study uses the CIBERSORT algorithm to estimate the infiltration of immune cells in CRC samples
    .

    In addition, the author also uses the microenvironmental cell population (MCP) counting algorithm to estimate the ratio of stromal cells to endothelial cells
    .

    6.
    Survival analysis: The Kaplan-Meier algorithm was used for survival analysis in the study
    .

    The log-rank test was used to calculate the P value of the difference between SFM subtypes
    .

    Cox proportional hazard regression constructs univariate and multivariate Cox models
    .

    7.
    Single-sample gene set enrichment analysis: In this study, the author used ssGSEA to evaluate the EGFR gene set activity of SFM subtypes in three data sets
    .

    The EGFR gene set consists of ligands or receptors related to the EGFR pathway
    .

    In addition, the authors conducted similar analyses on TGF-beta responses, exhausted T cells, thermal tumors, IPRES characteristics, and SFM gene characteristics in 4 single-cell data sets
    .

    8.
    Oncotype DX: The Oncotype DX colon cancer recurrence score based on 12 mRNA was established based on the transcriptome data of 1851 cases of stage II and stage III colon cancer
    .

    It has been considered as an independent prognostic factor of CRC
    .

    In order to confirm the prognostic value of the SFM subtype, the author proposed to link the SFM subtype Oncotype DX with DFS in univariate and multivariate Cox regression models
    .

    It was found that all cases confirmed the correlation between Oncotype DX and DFS
    .

    9.
    Using PathSeq algorithm for microbial detection: PathSeq algorithm can identify microbes based on human tissue RNA sequencing and WGS deep sequencing data
    .

    Using the PathSeq method, the author obtained the relative abundance of 1093 microorganisms in 429 CRC samples, of which 415 samples were annotated and analyzed by SFM subtypes
    .

    3.
    The main content and results of the research 1.
    The characteristics of chemotherapy resistance genes related to TME In the first part of the article, the author analyzed the characteristics of TME-related chemotherapy resistance genes
    .

    The study included the overall gene expression profile of 2269 patients with colorectal cancer
    .

    The authors first identified 896 probe sets involved in the FOLFIRI response, and then identified genes that are significantly differentially expressed in the TME component
    .

    After overlapping the differential probes, 317 probes were obtained, which corresponded to 250 unique genes (Figure 1a), and this gene feature was called "features related to FOLFIRI resistance and microenvironment" (SFM)
    .

    In order to confirm that SFM features can distinguish TME, the authors applied SFM features to 4 scRNA-seq data sets
    .

    The result of the t-SNE graph showed that in all 4 scRNA-seq data sets, SFM formed different clusters corresponding to different cell types, indicating that SFM has a universal ability to distinguish TME (Figure 1b-e)
    .

    In addition, the authors also found that malignant cells of different origins can also form different clusters, while non-malignant cells can cluster together regardless of the source, which indicates that the expression of SFM in normal cells does not have strong heterogeneity among patients
    .

    Next, the study evaluated the overlap of 9 published gene features with SFM, and found that some of them had a large overlap (Figure 1f)
    .

    However, the overlap between SFM and other gene features is very limited (Figure 1g)
    .

    Figure 1 Construction of SFM features and subtypes 2.
    K-means clustering of CRC subtypes.
    In this part, the author tested whether SFM can classify CRC subtypes and used the k-means clustering algorithm to find the data set (GSE39582) using SFM sort
    .

    Six subtypes have been identified, and they are called CRC SFM subtypes from SFM-A to SFM-F
    .

    And the robustness of SFM classification was verified in other cohorts
    .

    It can be observed that in the three large data sets, the proportions of each subtype are similar (Figure 1h)
    .

    3.
    The main characteristics of SFM subtypes Next, the author describes the main characteristics of SFM subtypes
    .

    Analysis found that SFM subtypes are related to unique clinicopathological, molecular and phenotypic characteristics, gene characteristics and specific enrichment of signal pathways.
    For example, it can be found that the proportion of stages II and III in each SFM category is higher than that of stages I and IV.
    (Figure 1i); SFM-E and SFM-F have a higher proportion of stage IV; TP53 has a higher mutation frequency in SFM-B and SFM-D, while KRAS mutations mostly occur in SFM-A
    .

    In terms of carcinogenic mutations, the authors further focused on 95 CRC driver mutations in the TCGA data set and found that the proportion of samples with more than 7 driver gene mutations in SFM-C and SFM-D was higher
    .

    There are significant differences in the mutation status of 53 genes in SFM subtypes
    .

    Next, the author uses the previously reported gene features to identify the cell and precursor origin of SFM subtypes based on the NTP algorithm
    .

    In addition, the authors also analyzed the abnormally regulated signaling pathways in each SFM subtype
    .

    Analyze 2000 up- and down-regulated genes of each SFM subtype
    .

    4.
    SFM subtype predicts chemotherapy response.
    In this part, the author analyzes the SFM subtype predicts chemotherapy response
    .

    Since the SFM feature is related to the FOLFIRI sensitivity from GSE62080, the author first used the SFM feature in the GSE62080 data set to perform K-means clustering to test whether the SFM subtype is related to the drug response
    .

    The results showed that 21 cases of GSE62080 can be divided into four SFM subtypes (Figure 2a)
    .

    Analysis found that SFM-E and SFM-F both responded to FOLFIRI
    .

    Both SFM-A and SFM-B are resistant to FOLFIRI
    .

    In order to comprehensively compare the differences in drug response between SFM subtypes, the authors used the NTP algorithm to apply previous drug gene characteristics to gene expression profiles, including FOLFIRI, FOLFOX, and vascular endothelial growth factor (VEGF) or epidermal growth factor receptor (EGFR) Inhibitor
    .

    It was found that there was a significant difference in drug sensitivity between SFM subtypes (Figure 2c)
    .

    In addition, the authors found that most of the SFM-A and SFM-B are significantly related to EGFR inhibitors (Figure 2c), and similar results can be observed with other drugs
    .

    Next, the authors included KRAS wild-type samples to further verify the sensitivity of cetuximab to SFM subtypes
    .

    The results again found that SFM-ABE can predict the response of cetuximab (Figure 2d)
    .

    In addition, some genes related to the activity of the EGFR pathway are thought to be related to the cetuximab response
    .

    Similarly, this specific gene set showed higher expression in the SFM-ABE subtype (Figure 2e, f)
    .

    Taken together, these findings indicate that regardless of the KRAS phenotype, SFM subtypes have predictive value for cetuximab response
    .

    Figure 2 SFM subtypes have obvious sensitivity to FOLFIRI and FOLFOX chemotherapy regimens and RGFR inhibitors.
    5.
    Different TMEs in SFM subtypes can distinguish TMEs due to the characteristics of SFM.
    This part of the author compares the TMEs between SFM subtypes
    .

    First, the author used the CIBERSORT method to study the cellular components between SFM subtypes
    .

    It was found that SFM subtypes showed different numbers of immune cells (Figure 3)
    .

    The study also found high expression of checkpoint biomarkers in SFM-CF, including CD274, PDCD1 and CTLA4 (Figure 31, n)
    .

    This indicates that SFM-CF has the effect of suppressing T cells
    .

    Both SFM-C and F are febrile tumors and respond to IFN-γ (Figure 3o, p)
    .

    SFM-F is rich in natural anti-PD1 resistance (IPRES) gene characteristics (Figure 3q, r), and shows a high IPRES score, which means that SFM-F has the characteristic of non-response to immunotherapy
    .

    The above results indicate that although SFM-C and -F have inhibitory effects on T cells, they have different responses to immunotherapy
    .

    This may be because SFM-C enriched MSI phenotype, immunosuppressive agent can block immunosuppression, while SFM-F enriched matrix/EMT phenotype can also cause immunosuppression, but immunosuppressive agent cannot reverse it
    .

    Figure 3 Immunity and TME-related differences between SFM subtypes 6.
    SFM subtypes are independent predictors of CRC.
    In this part, the authors examined the relationship between SFM subtypes and survival to test its prognostic value
    .

    The results showed that SFM subtypes were significantly related to DFS or OS (Figure 4a-d), and the prognosis of SFM-E and SFM-F was poor
    .

    The author compared SFM-E and SFM-F as a single high-risk group with the other 4 to confirm this.
    As expected, the two-classifier showed strong prognostic value
    .

    In addition, the author found that SFM subtypes have significant prognostic value in non-radio-chemotherapy patients based on the existing radio-chemotherapy information, but not in radio-chemotherapy patients
    .

    Next, the author used a binary classifier to perform a similar analysis, and found that the prognosis of the high-risk group of non-radio-chemotherapy patients was worse, while that of the radio-chemotherapy group did not
    .

    Since the Oncotype DX recurrence rate score is considered to be a prognostic classifier for colon cancer, the author evaluated its prognostic value in the joint data set, and then compared the proportion of SFM subtypes with Oncotype DX classifiers, and found that the Oncotype DX classifier Most of the identified high-risk and intermediate-risk cases can be classified as SFM high-risk group (Figure 4 g, h), which indicates that SFM subtypes have good prognostic value
    .

    In addition, in univariate Cox regression analysis, most classifiers have at least one subtype with a significant difference.
    However, when the multivariate Cox regression analysis is performed on each molecular subtype, it is adjusted according to age, gender, etc.
    , SFM There are still significant differences between CMS, CCS subtypes
    .

    The CCS3 subtype has the strongest prognostic value, followed by SFM-F and SFM-E
    .

    Figure 4 Survival differences of SFM subtypes 7.
    Different gut microbiome patterns among SFM subtypes.
    Finally, the author analyzed the different gut microbial patterns between SFM subtypes
    .

    The author implemented the PathSeq algorithm in the TCGA queue
    .

    Among the 415 CRC subtype-annotated cases, the relative abundance values ​​of 1093 microorganisms at the species level were obtained, and it was found that almost every SFM subtype has a different bacterial community (Figure 5)
    .

    Figure 5 Heat map of the relative abundance of SFM subtypes of dominant bacteria.
    The main content of this article has been introduced.
    In summary, in the analysis, the author combined multiple public platform data to construct a new CRC classifier and divide it into 6 Each molecular subtype, this SFM feature can distinguish TME and is related to drug response
    .

    In addition, this genetic feature can resolve the heterogeneity of CRC, and these SFM subtypes help to improve the precision treatment of CRC
    .

    In general, the research analyzes colorectal cancer samples from multiple platforms and focuses on the tumor microenvironment and drug resistance characteristics for pure bioinformatics analysis.
    This research perspective combines tumor microenvironment and drug resistance.
    For the analysis methods involved, friends can learn for reference
    .

    TME+ drug resistance ideas have been out scan code to snap up
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