List of genes for high and low P-FN groups.

6basalBC_bams.tar.gz.splita6basalBC_bams.tar.gz, split, part a (of 16 parts, a-p, ~1GB/each). Archive contains targeted region BED file and sequencing data BAM files for 6 basal-like breast cancer cell lines (HCC38, HCC1143, HCC1187, HCC1395, HCC1954, HCC1937; each suffixed with "T") and their corresponding normal DNA sample (each suffixed with "BL"), using hg19 coordinates.6basalBC_bams.tar.gz.splitb6basalBC_bams.tar.gz, split, part b.6basalBC_bams.tar.gz.splitc6basalBC_bams.tar.gz, split, part c.6basalBC_bams.tar.gz.splitd6basalBC_bams.tar.gz, split, part d.6basalBC_bams.tar.gz.splite6basalBC_bams.tar.gz, split, part e.6basalBC_bams.tar.gz.splitf6basalBC_bams.tar.gz, split, part f.6basalBC_bams.tar.gz.splitg6basalBC_bams.tar.gz, split, part g.6basalBC_bams.tar.gz.splith6basalBC_bams.tar.gz, split, part h.6basalBC_bams.tar.gz.spliti6basalBC_bams.tar.gz, split, part i.6basalBC_bams.tar.gz.splitj6basalBC_bams.tar.gz, split, part j.6basalBC_bams.tar.gz.splitk6basalBC_bams.tar.gz, split, p...

Human colorectal cancer (CRC) cell lines are a used widely-used to model system for investigation investigate of tumour biology, experimental therapytherapeutic and biomarkers discovery. However, to what extent these established CRC cell lines represent and maintain the genetic diversity of primary cancers is uncertain. In this study, we analyzed 70 CRC cell lines were analysed for mutations using whole exome sequencing and DNA copy-number using by whole-exome sequencing and SNP microarray profilings, respectively. Presence of gGene expression was defined using RNA-Seq. Data from cellCell line datas were was compared to those that published from for primary CRCs published by in The the Cancer Genome Atlas Network. Notably, we found that The spectrum of exome mutations and DNA copy-number aberrations spectra in 70 CRC cell lines closely resembled those seen inat of primary colorectal tumours. Similarities included the presence of at least two hypermutation phenotypes, as defined by signatures of for defective DNA mismatch repair and DNA polymerase ε (POLE) proof-reading deficiency, and along with concordant mutation profiles in the broadly altered WNT, MAPK, PI3K, TGFβ and p53 pathways. In additionFurther, we documented mutations were enriched in genes involved in chromatin remodelling (ARID1A, CHD6, SRCAP) and histone methylation or acetylation (ASH1L, EP300, EP400, MLL2, MLL3, PRDM2, TRRAP). Chromosomal instability was prevalent in non-hypermutated cases, with similar patterns of whole, partial and focal chromosomal aberrations and overlapping significant minimal regions ofchromosomal gains and losses. While paired cell lines derived from the same tumour were found to exhibited considerable mutation and DNA copy-number differences, in silico simulations suggest that these differenceslargely mainly reflected a pre-existing heterogeneity in the tumour cells heterogeneity. In conclusion, our results establish that human CRC lines are representative of the main subtypes of primary tumours at the genomic level, further validating underscoring their utility as tools for to investigating investigate CRC biology and drug responses. Overall design: 69 colorectal cancer cell lines were analysed for DNA copy number profiles.

Variant calling data from whole-genome sequencing of the Jurkat cell line. The data set includes output files from four variant calling tools (jurkat_raw_variant_caller_output.tar.gz), final variant calls after filtering and merging the calls from the separate tools (jurkat_final_variant_calls.tar.gz), and files with variant effect information (jurkat_variant_effects.tar.gz).

Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. Real-time PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P=0.006). We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers. Gene expression data: Samples GSM388181-GSM388198. The experiment utilized six breast cancer cell lines: MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D, and three non-malignant cell lines: HMECs (non-malignant human mammary epithelial cells), IMR90 (normal lung fibroblasts) and WS1 (normal skin fibroblasts). All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. Both malignant and non-malignant cell lines were treated with the translation inhibitor emetine dihydrochloride hydrate. For each cell line, parallel cell cultures were grown in 175 cm2 flasks until 70-80 % confluence. Half of the subconfluent cultures were treated with 100 μg ml-1 of emetine dihydrochloride hydrate while the other half were left as untreated controls. Genome-wide copy number data: Samples GSM388211-GSM388216. The experiment utilized six breast cancer cell lines; MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D. All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. All samples were hybridized once on 44k Agilent Human Genome CGH microarrays according to manufacturers instructions. Genomic DNA pooled from healthy female donors was used as a reference in all hybridizations. DNA from cell line samples were labeled with Cy5 and DNA from reference were labeled with Cy3.

Intratumoral heterogeneity (ITH) refers to the presence of distinct tumor cell populations. It provides vital information for the clinical prognosis, drug responsiveness, and personalized treatment of cancer patients. As genomic ITH in various cancers affects the expression patterns of genes, the expression profile could be utilized for determining ITH level. Herein, we present a novel approach to directly detect high ITH defined as a larger number of subclones from the gene expression pattern through machine learning approaches. We examined associations between gene expression profile and ITH of 12 cancer types from The Cancer Genome Atlas (TCGA) database. Using stomach adenocarcinoma (STAD) showing high association, we evaluated the performance of our method in predicting ITH by employing three machine learning algorithms using gene expression profile data. We classified tumors into high and low heterogeneity groups using the learning model through the selection of LASSO feature. The result showed that support vector machines (SVMs) outperformed other algorithms (AUC = 0.84 in SVMs and 0.82 in Naïve Bayes) and we were able to improve predictive power by using both combined data from mutation and expression. Furthermore, we evaluated the prediction ability of each model using simulation data generated by mixing cell lines of the Cancer Cell Line Encyclopedia (CCLE), and obtained consistent results with using real dataset. Our approach could be utilized for discriminating tumors with heterogeneous cell populations to characterize ITH.

Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer. This research was concluded that DMKN is a trigger of epithelial-mesenchymal transition-driven melanoma.

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1708 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response. Overall design: lncRNA expression profiling of 60 cancer cell lines

This dataset contains the results of Avana library CRISPR-Cas9 genome-scale knockout (prefixed with Achilles) as well as mutation, copy number and gene expression data (prefixed with CCLE) for cancer cell lines as part of the Broad Institute’s Cancer Dependency Map project. We have repackaged our fileset to include all quarterly-updating datasets produced by DepMap.The Avana CRISPR-Cas9 genome-scale knockout data has expanded to include 625 cell lines, the RNAseq data includes 1,210 cell lines, and the copy number data includes 1,657 cell lines. Please see the README files for details regarding data processing pipeline procedures updates.As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis for unrestricted use. For the latest datasets available, further analyses, and to subscribe to our mailing list visit https://depmap.org.Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984. Additional Achilles processing information is published here https://www.biorxiv.org/content/10.1101/720243v1.full. Some cell lines were process using copy number data based on the Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk.cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines. A detailed description of the pipelines and tool versions for CCLE expression can be found here: https://github.com/broadinstitute/gtex-pipeline/blob/v9/TOPMed_RNAseq_pipeline.md.version 2: uploaded a new version of CCLE_gene_cn.csv to correctly reflect released cell lines.version 3: uploaded a new version of CCLE_gene_cn.csv that has the log2 transform correctly applied to it and to removed duplicate cell lines.

## GDSC dataset

**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.

**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.

**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.

**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.

**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.

## TCGA dataset

**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.

**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.

**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.

**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.

## PMID17185464 (Bortezomib) dataset

**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.

**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).

Protein-Protein, Genetic, and Chemical Interactions for He N (2005):Regulation of lung cancer cell growth and invasiveness by beta-TRCP. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Beta-transducin-repeat-containing protein (beta-TRCP) serves as a substrate-recognition subunit of Skp1/Cullin/F-box (SCF)(beta-TRCP) E3 ligases, involved in regulation of several important signaling molecules. SCF(beta-TRCP) E3 ligases play a critical role in cell mitosis as well as in various signaling pathways. Here, we provide evidence to support that beta-TRCP negatively regulates cell growth and motility of lung cancer cells. With specific antibodies, we detect loss of beta-TRCP1 protein in several lung cancer cell lines. One cell line contains an inactivated mutation of the beta-TRCP1 gene. Loss of beta-TRCP1 protein is also found in subsets of lung cancer specimens. We observe that retrovirus-mediated stable expression of beta-TRCP1 in beta-TRCP1 negative cells inhibits cell growth in soft-agar and tumor formation in nude mice. Furthermore, expression of beta-TRCP1 alters cell motility, as indicated by morphological changes and a reduced level of active matrix metalloproteinase (MMP)11. Conversely, inactivation of beta-TRCP1 by specific siRNA accelerates cell invasion. Of the 10 known substrates of SCF(beta-TRCP) E3 ligases, the protein level of cell division cycle 25 (CDC25)A is clearly affected in these lung cancer cells. Cells treated with CDC25A inhibitors become less invasive. Thus, loss of beta-TRCP1 may promote both growth and cell motility of lung cancer cells, possibly through regulation of CDC25A and the MMP11 level.

Protein-Protein, Genetic, and Chemical Interactions for Liu XH (2005):Cyclooxygenase-2 suppresses hypoxia-induced apoptosis via a combination of direct and indirect inhibition of p53 activity in a human prostate cancer cell line. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Although p53-inactivating mutations have been described in the majority of human cancers, their role in prostate cancer is controversial as mutations are uncommon, particularly in early lesions. p53 is activated by hypoxia and other stressors and is primarily regulated by the Mdm2 protein. Cyclooxygenase (COX)-2, an inducible enzyme that catalyzes the conversion of arachidonic acid to prostaglandins and other eicosanoids, is also induced by hypoxia. COX-2 and resultant prostaglandins increase tumor cell proliferation, resistance to apoptosis, and angiogenesis. Previous reports indicate a complex, reciprocal relationship between p53 and COX-2. To elucidate the effects of COX-2 on p53 in response to hypoxia, we transfected the COX-2 gene into the p53-positive, COX-2-negative MDA-PCa-2b human prostate cancer cell line. The expression of functional p53 and Mdm2 was compared in COX-2+ versus COX-2- cells under normoxic and hypoxic conditions. Our results demonstrated that hypoxia increases both COX-2 protein levels and p53 transcriptional activity in these cells. Forced expression of COX-2 increased tumor cell viability and decreased apoptosis in response to hypoxia. COX-2+ cells had increased Mdm2 phosphorylation in either normoxic or hypoxic conditions. Overexpression of COX-2 abrogated hypoxia-induced p53 phosphorylation and promoted the binding of p53 to Mdm2 protein in hypoxic cells. In addition, COX-2-expressing cells exhibited decreased hypoxia-induced nuclear accumulation of p53 protein. Finally, forced expression of COX-2 suppressed both basal and hypoxia-induced p53 transcriptional activity, and this effect was mimicked by the addition of PGE2 to wild-type cells. These results demonstrated a role for COX-2 in the suppression of hypoxia-induced p53 activity via both direct effects and indirect modulation of Mdm2 activity. These data imply that COX-2-positive prostate cancer cells can have impaired p53 function even in the presence of wild-type p53 and that p53 activity can be restored in these cells via inhibition of COX-2 activity.

Bulk ATAC-seq in human hTERT-RPE-1 cell line with modification via - F6 clone with mutation at position -69 (G>A) upstream and -71 (C>T) upstream from the start codon of TMEM16 gene

Database that allows for the exploration of cancer on somatic missense mutations from the Cancer Genome Atlas and Cancer Cell Line Encyclopedia. The site maps proteins and mutations using 3D models and is an interface to the algorithms e-Driver and e-Drug allowing for the prediction of novel cancer drivers or drug biomarkers.

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.

Included files:

Master Model: the model includes detailed regulation of the major players involved in the crosstalks between Notch and p53 pathways

Modular Model: the model is a reduction of the master model. To reduce the master model, we lumped together some entities that belonged to a module.

Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense-mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen revealed disruption of kinase SMG1’s phosphorylation of UPF1 as a potent disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro. Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which..., Whole transcriptome RNA-seq LS180 or NCI-H358 cells were run in biological duplicate, treated with DMSO or 5µM LY3023414. For RNA extraction, cells were pelleted, frozen in liquid nitrogen, and stored at -80oC until RNA extraction. RNA extraction was performed using a Qiagen AllPrep DNA/RNA Mini Kit (Qiagen, Maryland, USA, Cat# 80204) per manufacturer’s instruction with cell homogenization and lysis in RLT buffer with a QIAshredder (Qiagen, Maryland, USA, Cat# 79656). RNA quality control using Agilent Tapestation 2200 (Agilent, California, USA, Cat# G2964AA) and the Agilent RNA ScreenTape (Agilent, California, USA, Cat# 5067- 5576) with Agilent RNA ScreenTape Sample Buffer and Ladder (Agilent, California, USA, Cat# 5067- 5577, Cat# 5067- 5578) per manufacturer’s instruction. Library prep using Illumina RNA library prep kit (Illumina, California, USA, Cat #RS-122-2001) and sequenced on an Illumina HiSeq 4000 150 cycle paired-end using manufacturer’s instructions. The data provided here i..., , # RNA-seq data for LS180 and NCI-H358 Cancer cell lines

https://doi.org/10.5061/dryad.69p8cz9dj

Description of the data and file structure

Data is RNA-seq fastq files from paired end 150 cycle NGS on an Illumina Hiseq 4000. There are two cell lines represented, LS180 and NCI-H358. Each were treated with either DMSO or the drug LY3023414. Both cell lines were treated, library prepared, and sequenced as biological replicates in duplicate, as indicated by a -1 and -2 in their file names. Both index and read files are included in this data set.

Files and variables

File: 9-H358-LY-1_S9_L002_I1_001.fastq.gz

Description:Â NCI-H358 cell line treated with LY3023414 biological replicate 1

File: 10-H358-LY-2_S10_L002_I1_001.fastq.gz

Description:Â NCI-H358 cell line treated with LY3023414 biological replicate 2

File: 11-H358-DMSO-1_S11_L002_I1_001.fastq.gz

Description:Â NCI-H358 cell line treated with DM...

The functional consequences of missense variants in disease genes are difficult to predict. We assessed if gene expression profiles could distinguish between BRCA1 or BRCA2 pathogenic truncating and missense mutation carriers and familial breast cancer cases whose disease was not attributable to BRCA1 or BRCA2 mutations (BRCAX cases). 72 cell lines from affected women in high-risk breast ovarian families were assayed after exposure to ionising irradiation, including 23 BRCA1 carriers, 22 BRCA2 carriers, and 27 BRCAX individuals. A subset of 10 BRCAX individuals carried rare BRCA1/2 sequence variants considered to be of low clinical significance (LCS). BRCA1 and BRCA2 mutation carriers had similar expression profiles, with some subclustering of missense mutation carriers. The majority of BRCAX individuals formed a distinct cluster, but BRCAX individuals with LCS variants had expression profiles similar to BRCA1/2 mutation carriers. Gaussian Process Classifier predicted BRCA1, BRCA2 and BRCAX status, with a maximum of 62% accuracy, and prediction accuracy decreased with inclusion of BRCAX samples carrying an LCS variant, and inclusion of pathogenic missense carriers. Similarly, prediction of mutation status with gene lists derived using Support Vector Machines was good for BRCAX samples without an LCS variant (82-94%), poor for BRCAX with an LCS (40-50%), and improved for pathogenic BRCA1/2 mutation carriers when the gene list used for prediction was appropriate to mutation effect being tested (71-100%). This study indicates that mutation effect, and presence of rare variants possibly associated with a low risk of cancer, must be considered in the development of array-based assays of variant pathogenicity.

Site for collection and distribution of clinical data related to genetic analysis of drug abuse phenotypes. Anonymous data on family structure, age, sex, clinical status, and diagnosis, DNA samples and cell line cultures, and data derived from genotyping and other genetic analyses of these clinical data and biomaterials, are distributed to qualified researchers studying genetics of mental disorders and other complex diseases at recognized biomedical research facilities. Phenotypic and Genetic data will be made available to general public on release dates through distribution mechanisms specified on website.

Non-small-cell lung cancer harboring epidermal growth factor receptor (EGFR) mutations attains a meaningful response to EGFR-tyrosine kinase inhibitors (TKIs). However, acquired resistance to EGFR-TKIs could affect long-term outcome in almost all patients. To identify the potential mechanisms of resistance, we established cell lines resistant to EGFR-TKIs from the human lung cancer cell lines PC9 and11-18, which harbored activating EGFR mutations. One erlotinib-resistant cell line from PC9 and two erlotinib-resistant cell lines and two gefitinib-resistant cell lines from 11-18 were independently established. Almost complete loss of mutant delE746-A750 EGFR gene was observed in the erlotinib-resistant cells isolated from PC9, and partial loss of the mutant L858R EGFR gene copy was specifically observed in the erlotinib- and gefitinib-resistant cells from 11-18. However, constitutive activation of EGFR downstream signaling, PI3K/Akt, was observed even after loss of the mutated EGFR gene in all resistant cell lines even in the presence of the drug. In the erlotinib-resistant cells from PC9, constitutive PI3K/Akt activation was effectively inhibited by lapatinib (a dual TKI of EGFR and HER2) or BIBW2992 (pan-TKI of EGFR family proteins). Furthermore, erlotinib with either HER2 or HER3 knockdown by their cognate siRNAs also inhibited PI3K/Akt activation. Transfection of activating mutant EGFR complementary DNA restored drug sensitivity in the erlotinib-resistant cell line. Our study indicates that loss of addiction to mutant EGFR resulted in gain of addiction to both HER2/HER3 and PI3K/Akt signaling to acquire EGFR-TKI resistance.

Molecular characteristics of all analysed samples in the comparison to the population data and CCLE database data.