Portal for identifying genetic and pharmacologic dependencies and biomarkers that predicts them by providing access to datasets, visualizations, and analysis tools that are being used by Cancer Dependency Map Project at Broad Institute. Project to systematically identify genes and small molecule dependencies and to determine markers that predict sensitivity. All data generated by DepMap Project are available to public under CC BY 4.0 license on quarterly basis and pre-publication.

This DepMap release contains data from CRISPR knockout screens from project Achilles, as well as genomic characterization data from the CCLE project.For more information, please see README.txt.

Information about the dataset files:

1) pancan_rnaseq_freeze.tsv.gz: Publicly available gene expression data for the TCGA Pan-cancer dataset. File: PanCanAtlas EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/3586c0da-64d0-4b74-a449-5ff4d9136611] [https://doi.org/10.1016/j.celrep.2018.03.046]

2) pancan_mutation_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset. File: mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

3) pancan_GISTIC_threshold.tsv.gz: Publicly available Gene- level copy number information of the TCGA Pan-cancer dataset. This file is processed using script process_copynumber.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. The files copy_number_loss_status.tsv.gz and copy_number_gain_status.tsv.gz generated from this data are used as inputs in our Galaxy pipeline. [https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443] [https://doi.org/10.1016/j.celrep.2018.03.046]

4) mutation_burden_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/][http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

5) sample_freeze.tsv or sample_freeze_version4_modify.tsv: The file lists the frozen samples as determined by TCGA PanCancer Atlas consortium along with raw RNAseq and mutation data. These were previously determined and included for all downstream analysis All other datasets were processed and subset according to the frozen samples.[https://github.com/greenelab/pancancer/]

6) vogelstein_cancergenes.tsv: compendium of OG and TSG used for the analysis. [https://github.com/greenelab/pancancer/]

7) CCLE_DepMap_18Q1_maf_20180207.txt.gz Publicly available Mutational data for CCLE cell lines from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2FCCLE_DepMap_18Q1_maf_20180207.txt]

8) ccle_rnaseq_genes_rpkm_20180929.gct.gz: Publicly available Expression data for 1019 cell lines (RPKM) from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RNAseq_genes_rpkm_20180929.gct.gz]

9) CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct: Publicly available merged Mutational and copy number alterations that include gene amplifications and deletions for the CCLE cell lines. This data is represented in the binary format and provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://data.broadinstitute.org/ccle_legacy_data/binary_calls_for_copy_number_and_mutation_data/CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct]

10) GDSC_cell_lines_EXP_CCLE_names.csv.gz Publicly available RMA normalized expression data for Genomics of Drug Sensitivity in Cancer(GDSC) cell-lines. File gdsc_cell_line_RMA_proc_basalExp.csv was downloaded. This data was subsetted to 389 cell lines that are common among CCLE and GDSC. All the GDSC cell line names were replaced with CCLE cell line names for further processing. [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip]

11) GDSC_CCLE_common_mut_cnv_binary.csv.gz: A subset of merged Mutational and copy number alterations that include gene amplifications and deletions for common cell lines between GDSC and CCLE. This file is generated using CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct and a list of common cell lines.

12) gdsc1_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC1 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC1_fitted_dose_response_15Oct19.xlsx]

13) gdsc2_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC2 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_fitted_dose_response_15Oct19.xlsx]

14) compounds.csv: list of pharmacological compounds tested for our analysis

15) tcga_dictonary.tsv: list of cancer types used in the analysis.

16) seg_based_scores.tsv: Measurement of total copy number burden, Percent of genome altered by copy number alterations. This file was used as part of the Pancancer analysis by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/]

DEPMAP measured gene effect from DEPMAP portal

The dataset is from DepMap. The MAF file contains information on all the somatic point mutations and indel in the DepMap cell lines. The calls are an ensemble of calls from MuTect1, MuTect2, and Strelka. A description of the various columns is in the DepMap Release README file.

Cancer cell line genetic dependencies estimated using the DEMETER2 model. DEMETER2 is applied to three large-scale RNAi screening datasets: the Broad Institute Project Achilles, Novartis Project DRIVE, and the Marcotte et al. breast cell line dataset. The model is also applied to generate a combined dataset of gene dependencies covering a total of 712 unique cancer cell lines. For more information visit https://depmap.org/R2-D2/. Visit the Cancer Dependency Map portal at https://depmap.org to explore related datasets. Email questions to depmap@broadinstitute.org This dataset includes gene dependencies estimated using the DEMETER2 model, the raw input datasets used to fit the models, as well as associated metadata. See Readme file for more details about the dataset contents and version history.-------------------------------------------------------------------Version history: (see README for more details)-------------------------------------------------------------------v1: Initial data releasev2: - Removed small number of non-human genes (e.g. GFP, RFP) from shRNA-to-gene mapping - Updated cell line names to be consistent with DepMap names, according to the following map (old -> new):v3: Added estimated seed effect matricesv4: Added RNAseq and mutation data files used in analysis for manuscriptv5: Fixed minor bug with Marcotte LFC data that caused hairpins targeting multiple genes to appear multiple times in the LFC matrix. This created bias in the seed effect estimates for those hairpins, causing very minor differences to the resulting model parameters.v6: Added tables with shRNA quality metrics for Achilles and DRIVE data

This benchmark data was train and evaluate the models presented in the paper: A. Partin and P. Vasanthakumari et al. "Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis"

The benchmark data for Cross-Study Analysis (CSA) include four kinds of data, which are cell line response data, cell line multi-omics data, drug feature data, and data partitions. The figure below illustrates the curation, processing, and assembly of benchmark data, and a unified schema for data curation. Cell line response data were extracted from five sources, including the Cancer Cell Line Encyclopedia (CCLE), the Cancer Therapeutics Response Portal version 2 (CTRPv2), the Genomics of Drug Sensitivity in Cancer version 1 (GDSC1), the Genomics of Drug Sensitivity in Cancer version 2 (GDSC2), and the Genentech Cell Line Screening Initiative (GCSI). These are five large-scale cell line drug screening studies. We extracted their multi-dose viability data and used a unified dose response fitting pipeline to calculate multiple dose-independent response metrics as shown in the figure below, such as the area under the dose response curve (AUC) and the half-maximal inhibitory concentration (IC50). The multi-omics data of cell lines were extracted from the the Dependency Map (DepMap) portal of CCLE, including gene expressions, DNA mutations, DNA methylation, gene copy numbers, protein expressions measured by reverse phase protein array (RPPA), and miRNA expressions. Data preprocessing was performed, such as descritizing gene copy numbers and mapping between different gene identifier systems. Drug information was retrived from PubChem. Based on the drug SMILES (Simplified Molecular Input Line Entry Specification) strings, we calculated their molecular fingerprints and descriptors using the Mordred and RDKit Python packages. Data partition files were generated using the IMPROVE benchmark data preparation pipeline. They indicate, for each modeling analysis run, which samples should be included in the training, validation, and testing sets, for building and evaluating the drug response prediction (DRP) models. The Table below shows the numbers of cell lines, drugs, and experiments in each dataset. Across the five datasets, there are 785 unique cell lines and 749 unique drugs. All cell lines have gene expression, mutation, DNA methylation, and copy number data available. 760 of the cell lines have RPPA protein expressions, and 781 of them have miRNA expressions.

Further description is provided here: https://jdacs4c-improve.github.io/docs/content/app_drp_benchmark.html

Chromatin Profiles of Human Lung Cancer from the Depmap Portal (depmap.org). This dataset contains to chromatin profiles of Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC). This set comprise 112 NSCLC and 48 SCLC cell lines, with a total of 160 features (chromatin modifications).

Disclaimer

The CCLE data were generated and shared by the Broad Institute of Harvard and MIT as part of the Cancer Cell Line Encyclopedia project. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets.

Data Usage Policy

CCLE publishes its data under the Terms and Conditions linked here. The DepMap data, including the CCLE data, are provided under Creative Commons Attribution 4.0 license.

Contact depmap@broadinstitute.org for more information

Please cite the following when using these data

Cancer Cell Line Encyclopedia Consortium, and Genomics of Drug Sensitivity in Cancer Consortium. 2015. Pharmacogenomic Agreement between Two Cancer Cell Line Data Sets. Nature 528 (7580):84–87. https://doi.org/10.1038/nature15736.

Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, William R. Sellers, Robert Schlegel, Levi A. Garraway, et. al. 2012. The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature 483 (7391):603–7. https://doi.org/10.1038/nature11003.

For omics data:

Mahmoud Ghandi, Franklin W. Huang, Judit Jané-Valbuena, Gregory V. Kryukov, ... Todd R. Golub, Levi A. Garraway & William R. Sellers. 2019. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

For metabolomics:

Haoxin Li, Shaoyang Ning, Mahmoud Ghandi, Gregory V. Kryukov, Shuba Gopal, ... Levi A. Garraway & William R. Sellers. The landscape of cancer cell line metabolism. Nature Medicine 25, 850-860 (2019).

Metabolomic Profiles of Human Lung Cancer from the Depmap Portal (depmap.org). This dataset contains to metabolite expression profiles of Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC). This set comprise 120 NSCLC and 50 SCLC cell lines, with a total of 225 features (metabolite expression).

The transcription factor TEAD, together with its coactivator YAP/TAZ, is a key transcriptional modulator of the Hippo pathway. Activation of TEAD transcription by YAP has been implicated in a number of malignancies, and this complex represents a promising target for drug discovery. Here, we employed covalent fragment screening approach followed by structure-based design to develop an irreversible TEAD inhibitor MYF-03-69. Using a range of in vitro and cell-based assays we demonstrated that through a covalent binding with TEAD palmitate pocket, MYF-03-69 disrupts YAP-TEAD association, suppresses TEAD transcriptional activity and inhibits cell growth of Hippo signaling defective malignant pleural mesothelioma (MPM). Further, a cell viability screening with a panel of 903 cancer cell lines indicated a high correlation between TEAD-YAP dependency and the sensitivity to MYF-03-69. To validate MYF-03-69 as potent and selective pan-TEAD inhibitor, we interrogated the proteome-wide selectivity profile of MYF-03-69 on cysteine labeling using a streamlined cysteine activity-based protein profiling (SLC-ABPP) approach and generated the spreadsheet "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach". We employed the cysteine reactive desthiobiotin iodoacetamide (DBIA) probe which was reported to map more than 8,000 cysteines and performed a competition study on NCI-H226 cells pretreated with 0.5, 2, 10 or 25 µM of MYF-03-69 for 3 hours in triplicate. The cysteines that were conjugated >50% (competition ratio CR>2) compared to DMSO control were analyzed and assigned to the protein targets. In the DMSO control group, although DBIA mapped 12,498 cysteines in total, the TEAD PBP cysteines were not detected. This might be due to low TEAD1-4 protein abundance and/or inability of the PBP cysteines to be labeled given that they are mostly modified by palmitate under physiological conditions. Among 12,498 mapped cysteines, only 7 cysteines were significantly labeled (i.e. exhibited >50% conjugation or CR>2) by 25 µM of MYF-03-69, and all of these sites exhibited dose-dependent engagement. To study the whole transcriptome perturbation by TEAD inhibitor MYF-03-69, mRNA sequencing was performed in NCI-H226 cells that were treated with 0.1 μM, 0.5 μM, and 2 μM of MYF-03-69 and generated the spreadsheet "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments". The genes that were differentially expressed with statistical significance (Fold change > 1.5 and adjusted p value < 0.05) are listed in this dataset. To investigate whether TEAD inhibition by MYF-03-69 was selectively lethal to YAP/TEAD-dependent cancers, PRISM screening across a broad panel of cell lineages were performed and generated the spreadsheet "Supplementary_Dataset_3". 903 cancer cells were treated with TEAD inhibitor MYF-03-69 for 5 days. The viability values were measured at 8-point dose manner (3-fold dilution from 10 μM) and fitted a dose-response curve for each cell line. Area under the curve (AUC) was calculated as a measurement of compound effect on cell viability. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were listed in the spreadsheet and used to estimate gene-dependency. The CERES Score of most dependent TEAD isoform was used to represent TEAD dependency. With PRISM screen dataset of TEAD inhibitor MYF-03-69, we investigated whether TEAD inhibition recapulates genetically knockout outcome of YAP or TEADs and generated the spreadsheet "Supplementary_Dataset_4". Correlation analysis between compound PRISM sensitivity (log2.AUC of each cell line) and dependency of certain gene (CRISPR knockout score for each cell line, from DepMap Public 20Q4 Achilles_gene_effect.csv dataset) across the PRISM cell line panel. The Pearson correlation coefficients and associated p-values were computed. Positive correlations correspond to dependency correlating with increased sensitivity. The q-values (a corrected significance value accounting for false discovery rate) are computed from p-values using the Benjamini Hochberg algorithm. Associations with q-values above 0.1 are filtered out. This correlation analysis reveals that the dependency scores of TEAD1 and YAP1 according to genomic knockout dataset (DepMap portal) provided the highest correlation with the compound PRISM sensitivity profile. This is followed by TP53BP2, a gene that is also involved in Hippo pathway as activator of TAZ. Methods For "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach", the date was collected on NCI-H226 cells using the same methods reported in reference paper Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries | Nature Biotechnology. (Kuljanin, M.; Mitchell, D. C.; Schweppe, D. K.; Gikandi, A. S.; Nusinow, D. P.; Bulloch, N. J.; Vinogradova, E. V.; Wilson, D. L.; Kool, E. T.; Mancias, J. D.; Cravatt, B. F.; Gygi, S. P., Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nature Biotechnology 2021, 39, 630-641) The competition ratio CR was calculated as descibed in the above reference paper. For "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments", the date was collected on NCI-H226 cells treated with MYF-03-69 at indicated concentrations for 6 hours (n=3). The RNA was extracted using RNeasy plus mini kit (Qiagen, cat no.74134) according to the manufacturer instructions. Then libraries were prepared using Roche Kapa mRNA HyperPrep strand specific sample preparation kits from 200 ng of purified total RNA according to the manufacturer’s protocol on a Beckman Coulter Biomek i7. The finished dsDNA libraries were quantified by Qubit fluorometer and Agilent TapeStation 4200. Uniquely dual indexed libraries were pooled in an equimolar ratio and shallowly sequenced on an Illumina MiSeq to further evaluate library quality and pool balance. The final pool was sequenced on an Illumina NovaSeq 6000 targeting 40 million 100bp read pairs per library at the Dana-Farber Cancer Institute Molecular Biology Core Facilities. Sequenced reads were aligned to the UCSC hg19 reference genome assembly and gene counts were quantified using STAR (v2.7.3a). Differential gene expression testing was performed by DESeq2 (v1.22.1). RNAseq analysis was performed using the VIPER snakemake pipeline. KEGG pathway enrichment analysis was performed through metascape webportal. For "Supplementary_Dataset_3", the date was collected using the methods reported in reference paper Discovering the anticancer potential of non-oncology drugs by systematic viability profiling | Nature Cancer. Briefly, up to 931 barcoded cell lines in pools of 20-25 were thawed and plated into 384-well plates (1250 cells/well for adherent cell pools, 2000 cells/well for suspension or mixed suspension/adherent cell pools) containing compound (top concentration: 10 µM, 8-point, threefold dilution). All conditions were tested in triplicate. Cells were lysed after 5 days of treatment and mRNA based Luminex detection of barcode abundance from lysates was carried out as in the reference paper above. Luminex median fluorescence intensity (MFI) data was input to a standardized R pipeline (https://github.com/broadinstitute/prism_data_processing) to generate viability estimates relative to vehicle treatment for each cell line and treatment condition, and to fit dose-response curves from viability data. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were downloaded from DepMap portal (DepMap Data Downloads) and listed with the viability data. For "Supplementary_Dataset_4", the data was correlation analysis results of "Supplementary_Dataset_3", which was performed in the R pipeline mentioned above (https://github.com/broadinstitute/prism_data_processing).

(B) Analyzis of the depmap portal (www.depmap.org) reveals MYCN-amplified SK-N-DZ as hypersensitive cell lines to the GPX4 inhibitors RSL3 and ML210.. List of tagged entities: SK-N-DZ (cellosaurus:CVCL_1701), , cell viability assay (bao:BAO_0003009)

Chronos dependency score of 17081 genes from Depmap project (https://depmap.org/portal/download/all/, Public 22Q1) for 24 human liver cancer cell lines of CCLE.

TP53 status and pathogenicity for each model utilized for this study. Mutation status was derived from DepMap (https://depmap.org/portal/) for cell lines or provided by the PDX CRO.

Mutational activation of the KRAS oncogene is a major genetic driver of pancreatic ductal adenocarcinoma (PDAC) growth. KRAS-dependent PDAC growth is mediated primarily through persistent activation of the RAF-MEK-ERK mitogen-activated protein kinase (MAPK) cascade, one of the most extensively studied cancer signaling networks. While substrates of RAF and MEK kinases are highly restricted, ERK1/2 has been attributed to over 1,000 substrates. In this study, we used the highly selective ERK1/2 inhibitor, SCH772984, and proteomic and phosphoproteomic analyses to extend the repertoire of ERK-dependent phosphosites and phosphoproteins in PDAC. We validated the specificity of SCH772984 in our cell lines using multiplexed inhibitor beads coupled with mass spectrometry (MIB/MS). We then performed phosphoproteomics and global proteomics in a panel of PDAC cell lines and identified 5,117 ERK-dependent phosphosites on 2,252 proteins, of which 88% and 67%, respectively, were not previously associated with ERK. We then utilized our recently annotated serine/threonine kinome motif database to dissect the phosphoproteome and reveal an expansive ERK-regulated kinase network. We found that ERK- and immediate downstream kinase RSK-substrate motifs predominated after one hour of ERK inhibition, whereas cell cycle regulatory cyclin-dependent kinase motifs predominated by 24 h, reflecting a highly dynamic ERK-dependent phosphoproteome. We find compensatory activation of HIPK, CLK, PKN, PAK, and DYRK family kinases. Finally, using the genome-wide CRISPR-Cas9 dataset in the Cancer Dependency Map portal (DepMap), we determined that approximately 18% of ERK dependent phosphoproteins are essential for pancreatic cancer growth, and these are enriched in nuclear proteins. Together, our findings provide a system-wide profile of the mechanistic basis for ERK-driven pancreatic cancer growth.

The relationship between cancer cell line viability and NPM1 dependency, determined from genome-wide CRISPR-Cas9 screens (https://depmap.org/portal/interactive). A score of zero indicates that a gene is not essential. Common essential genes have a median score of -1. A lower score indicates that a gene is more likely to be dependent in a given cell line.

The relationship between NPM1 dependency and cell line metastatic propensity, determined from genome-wide CRISPR-Cas9 screens (https://depmap.org/portal/interactive). A score of zero indicates that a gene is not essential. Common essential genes have a median score of -1. A lower score indicates that a gene is more likely to be dependent in a given cell line.

BackgroundFerroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes.MethodsRNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets.ResultsSeven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways.ConclusionIn summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.

Ulceration and immune status are independent prognostic factors for survival in melanoma patients. Herein univariate Cox regression analysis revealed 53 ulcer-immunity-related DEGs. We performed consensus clustering to divide The Cancer Genome Atlas (TCGA) cohort (n = 467) into three subtypes with different prognosis and biological functions, followed by validation in three merged Gene Expression Omnibus (GEO) cohorts (n = 399). Multiomics approach was used to assess differences among the subtypes. Cluster 3 showed relatively lesser amplification and expression of immune checkpoint genes. Moreover, Cluster 3 lacked immune-related pathways and immune cell infiltration, and had higher proportion of non-responders to immunotherapy. We also constructed a prognostic model based on ulceration and immune related genes in melanoma. EIF3B was a hub gene in the intersection between genes specific to Cluster 3 and those pivotal for melanoma growth (DepMap, https://depmap.org/portal/download/). High EIF3B expression in TCGA and GEO datasets was related to worst prognosis. In vitro models revealed that EIF3B knockdown inhibited melanoma cell migration and invasion, and decreased TGF-β1 level in supernatant compared with si-NC cells. EIF3B expression was negatively correlated with immune-related signaling pathways, immune cell gene signatures, and immune checkpoint gene expression. Moreover, its low expression could predict partial response to anti-PD-1 immunotherapy. To summarize, we established a prognostic model for melanoma and identified the role of EIF3B in melanoma progression and immunotherapy resistance development.