Colorectal cancer (CRC) cell lines are widely used pre-clinical model systems. Comprehensive insights into their molecular characteristics may improve model selection for biomedical studies.n = 612 genes) to detect single nucleotide variants and insertions/deletions; (ii) high resolution DNA copy number profiling; (iii) gene expression profiling at exon resolution; (iv) small RNA expression profiling by deep sequencing; and (v) protein expression analysis (n = 297 proteins) by reverse phase protein microarrays.We have performed DNA, RNA and protein profiling of 34 cell lines, including (i) targeted deep sequencing  contains supplementary material, which is available to authorized users. Colorectal cancers (CRC) are molecularly heterogeneous and can be divided into clinically relevant subtypes associated with patient prognosis and treatment response. At the DNA level, this includes the genomic instability phenotypes microsatellite instability (MSI) and chromosomal instability (CIN), as well as the epigenomic CpG island methylator phenotype (CIMP). About 15% of primary CRCs have MSI, while the rest are microsatellite stable (MSS), most of which have the CIN phenotype. MSI tumors have errors in the mismatch repair machinery and display numerous single nucleotide variants (SNVs) and insertions/deletions (indels) . CIMP tuAt the transcriptional level, several classification schemes have identified biologically distinct subtypes of CRCs –7. The rCMS classification is heavily influenced by the tumor microenvironment, as demonstrated by strong expression of mesenchymal marker genes in the stroma of tumors of the stem-like/mesenchymal subtype CMS4 , 10. HowHere we present a resource of information for 34 CRC cell lines by multi-level data integration, including targeted deep sequencing, DNA copy numbers, gene expression, microRNA (miRNA) expression and protein expression. We describe consistent gene/pathway regulation across data types and associate this with known CRC subtypes. Each data set and data combination are presented in accessible tables and figures, emphasizing specific alterations of biological or clinical interest for further experimental studies.Thirty-four CRC cell lines purchased from cell line repositories or kindly provided by collaborators . One μg of DNA in low-EDTA TE-buffer was prepared according to the Affymetrix SNP 6.0 Cytogenetics Copy Number Assay User Guide and hybridized to Affymetrix Genome-Wide SNP 6.0 microarrays according to the Affymetrix Genome-Wide Human SNP Nsp/Sty User Guide. Resulting raw data were within recommended QC thresholds . Signal extraction and pre-processing of raw data was performed as previously described , using tThe percentage of the genome affected by copy number aberrations (CNAs) was defined as the percentage of bases with aberrant copy number out of the total number of bases with a copy number value available.To detect potential CNA targets, The GISTIC algorithm v2.0.22  was run Microarray gene expression analyses were performed using Affymetrix HTA 2.0 Transcriptome Arrays , according to the manufacturer’s instructions. The data was normalized and summarized at the gene level using the Guanine Cytosine Count Correction and Signal Space Transformation algorithms with Robust Multi-array Average (SST-RMA) implemented in the Affymetrix Expression Console Software . The HTA-2_0.na35.2.hg19.transcript.csv annotation file identified 67,528 annotated genes (transcript clusters). The data was filtered to exclude non-coding RNA probes, and genes annotated by multiple probesets were filtered to retain one probeset per gene by prioritizing annotation databases: RefSeq, ENSEMBL, other databases. The filtered dataset contained data for 18,740 probesets.Principal component analysis (PCA) was performed including only the 1000 genes with the largest cross-sample variation. PC1 had a bimodal density distribution and samples with PC1 score larger than the between-peaks minima were defined as “high”. Gene set tests were performed using camera , 30. SinThe cell lines have been classified according to CMS based on the nearest predicted subtype, using an adapted classifier independent of gene expression signaling from the tumor microenvironment .The gene expression data has been submitted to the NCBI’s Gene Expression Omnibus with accession number GSE97023.Small RNA sequencing libraries were prepared using a recently published low-bias protocol  and resu2 values describing the relative protein abundance in each sample were used for downstream analyses. Differential expression analysis of RPPA data was performed with R package limma [Reverse Phase Protein lysate Array (RPPA) analysis with 297 antibodies targeting 235/62 proteins/phospho-proteins  were handled as follows: Genes consistently gained or lost  were assigned to the correct category accordingly. Genes that differed in copy number category  was assigned the median PCF value. Only genes represented in all samples were included. The mRNA expression was defined to be associated with copy number in cis if Wilcoxon testing determined (i) the mRNA expression to be significantly different in samples with gain versus samples with neutral copy number or loss, or (ii) the mRNA expression was significantly different in samples with loss versus samples with neutral copy number or gain. We corrected for multiple testing by false discovery rate (FDR) using the p.adjust function in the R stat package.We explored the influence of n = 5120 genes for in cis analyses. Only unique MSS cell lines (n = 18) and genes with aberrant copy number > 2 cell lines were investigated . GO enrichment analysis of significant in cis genes was done with the PANTHER overrepresentation test with the GO consortium online tool [in cis analyses were investigated for overlaps with the MSigDB version 5.2 [To limit false positives, genes within the lower quartile of mean gene expression were excluded and only genes with IQR > 0.7 were retained, ine tool . Signifision 5.2 , 36.Associations between CNA and gene expression were additionally assessed by gene-wise Spearman correlations of copy number- and expression values across samples, and genes with correlations above 0.7 were considered to show an association.in-cis expression in unique CIN cell lines. We applied a cutoff of 4 times gain/loss threshold (0.15/−0.15) to nominate potentially high amplitude CNAs, and gene expression values outside 3 times the standard deviation from sample mean expression across all genes were considered outliers and hence interesting. We looked for concurrent CNAs and gene expression events by retrieving genes for which the minimum/maximum CNA value and gene expression value belonged to the same cell line.To identify potential CNA drivers, we looked for outliers in CNA estimates corresponding to high or low POLE mutated HCC2998 [A panel of 34 CRC cell lines was analyzed at the DNA, RNA, and protein level  non-synonymous variants (SNVs or indels) in the 612 sequenced genes, significantly more than the 18 (range 4–26) found in MSS cell lines . This corresponds to 82 (range 63–212) and 12 (range 3–17) non-synonymous variants per million coding basepairs sequenced, respectively. For reference, The Cancer Genome Atlas reported approximately 1–300 somatic mutations per million basepairs for primary CRCs [POLD1 R689W mutation  compared to MSS cell lines , reflecting the high prevalence of MSI in the CMS1 subtype, and CMS2 cell lines had more CNAs compared to CMS1/3/4, although not statistically significant .We confirmed an inverse relationship between the number of SNVs/indels and DNA copy number aberrations , reflecting the type of genomic instability  harbored few DNA copy-number aberrations, two broad gains and four focal losses were observed with frequencies higher than 40%  had 24 separate regions affected in more than 40% of the cell lines , GPHN (loss 14q) and SMAD4 (loss 18q), as well as genes located in known fragile genomic areas, like FHIT (3p), WWOX (16q) and MACROD2 (20p).Potential target genes of CNAs were identified in 7 and 23 focal areas of gain and loss respectively  compared to the colon-like MSS cell lines  and vice versa. This included higher frequency of chromosomes 8 and 13 gain and loss of focal regions on 3p, 4q, 14q, 17p, 20p and 22q in colon-like cell lines and gain of 5q and 22q in undifferentiated cell lines . This grouping was significantly associated with the CMS groups (CMS2/3 versus CMS1/4), but less so with MSI-status . The finding was corroborated by morphological appearances; for example the undifferentiated cell lines LoVo and RKO appeared more mesenchymal, while colon-like IS3 and WiDr formed large epithelial-like sheets in culture  protein used as a blood-based biomarker for monitoring CRC patients, was more than 100-fold higher in colon-like cell lines. In undifferentiated cell lines, TGFB1 and TGFB2 were 3- and 7-fold higher, respectively. The five transcription factors with the most significant upregulation in colon-like cell lines were MYB, MECOM, ETS2, HNF4A and CDX1  and EGFR (n = 2). A complete overview of SNVs/indels, CNAs and the combination of these events in individual cell lines is shown in Fig. n = 83 genes in the Cancer Gene Census represented in the targeted sequencing data).In some cell lines, a simultaneous SNV/indel and CNA in the same gene was observed, including gains and SNVs in the oncogenes in cis association between copy number state and gene expression  . Biological processes enriched among lost in cis genes were mitotic cell cycle process, protein transport and intracellular transport  out of 1148 genes with copy number gain had a significant in cis copy number and gene expression regulation, 56 were differentially expressed between colon-like and undifferentiated cell lines, including the transcription factors ELF1 and KLF5 and the lysosomal marker LAMP1 , MYC (SW480), PPFIBP1 (IS1), and RAD21 (HT29) , MYC SW80, PPFIB), MYC SW80, PPFIBTo assess the correspondence between mRNA and protein-level expression (RPPA data) we calculated the Pearson’s correlations for each gene-protein pair among the cell lines Fig. . ExcludiPTEN in KM12 and Co115, with concomitant hyper-phosphorylation of the AKT protein at residue T308.To facilitate selection of cell lines as appropriate research models, we performed gene expression enrichment analysis of eight gene sets representing important pathways or processes in CRC, including ERK/MAPK, PI3K/AKT, EGFR, TGFß and WNT signaling, in addition to signatures of epithelial to mesenchymal transition, citric acid cycle activation and the gastro-intestinal signature Fig. . StrikinCRC cell lines have previously been shown to recapitulate the mutational and transcriptional heterogeneity of primary tumors , 14, 45.POLD1 R689W mutation [POLE mutated cell lines allowed for detection of expected mutation signatures. In the cell lines with a lower mutation load, broader sequencing coverage (whole exome or genome sequencing) would be more appropriate for accurate analyses of mutation processes. Unexpectedly, we find no evidence of MSI in CL-40, which has previously been reported as an MSI cell line [Consistent with known characteristics of the MSI and CIN phenotypes, we observed inverse levels of SNVs/indels and CNAs. All MSI cell lines had a mismatch repair deficiency-associated mutation signature, however, DLD-1 and HCT15 additionally had a high contribution from C > A variants in CpCpT trinucleotides and a low indel burden, a phenotype recently found to be the caused by the combination of MSI and a mutation . Althougell line . This cecolon-like and undifferentiated. Colon-like cell lines were either CMS2 or CMS3, expressed higher levels of gastro-intestinal marker genes, including key transcription factors such as HNF4A and MYB. HNF4A has been nominated as a candidate driver for the 20q13.12 focal amplification peak suggesting a possible causal relationship between overexpression and expression subtype [Two distinct subgroups of CRC cell lines were evident at the mRNA, miRNA and protein expression levels. Based on gene set associations we termed these groups  subtype , 46. Col subtype , indepen subtype  was amon subtype .TGFβ induced genes including TGFβ1/2 cytokines. Recently it was shown that TGFβ signaling in cancer associated fibroblasts (CAFs) promotes tumor initiating capacity of CRC cells, and that CRC organoids with high TGFβ expression has a high metastatic potential [All CMS4 and most CMS1 models were classified as undifferentiated, consistent with primary tumors where CMS1 and CMS4 display a more stromal and undifferentiated signature . As a grotential . As suchThe notion that poorly differentiated tumors have inferior prognosis is not new –50 and tKLF5 (higher expressed in colon-like cell lines). Additionally, we identified high level amplifications with concurrent high gene expression in individual cell lines, including ERBB2 in Colo678, also corroborated by high protein expression. The use of HER2-inhibitors together with the kinase inhibitor lapatinib was recently described as a treatment option in HER2 amplified, KRAS wild-type metastatic CRC in a phase 2 trial [ADAM10 expression, suggested to be involved in acquired resistance to HER2-inhibition in breast cancer models. [CCNE1 (cyclin E1), which also had concurrent high protein expression levels. V9P represents a model for overexpression of cyclin E1, commonly observed in many cancers and which has been linked to chromosome instability [Recurrently amplified chromosomal regions may harbor oncogenes that become overexpressed from the increase in gene-dosage . About ¼ 2 trial . We also models. , and Coltability , 55.By integration of DNA, RNA and protein data, we show that CRC cell lines represent consistent molecular subgroups defined by genomic instability phenotypes at the DNA level (sequence aberrations and CNAs) and differentiation at the expression level . The data are made available per cell line in summary illustrations and detailed tables, and is a resource to select relevant models for further studies of cancer-cell intrinsic differences among CMS groups, functional biological mechanisms of CRC as well as pharmacogenomics.Additional file 1: Tables S1-S14.Supplementary tables. (XLSX 4615 kb)Additional file 2: Figure S1.a SNVs and indel counts in 34 cell lines. MSI cell lines generally displayed numerous SNVs/indels, in contrast to MSS cell lines, although DLD-1/HCT15 were less typical with a lower indel burden compared to remaining MSIs. b The percentage of the genome with aberrant CNA reflects MSI status rather than CMS subtype. The figure includes 29 unique MSI/MSS cell lines. c CMS frequency of CNAs. Vertical axis indicates frequency, horizontal axes shows chromosomes 1–22, separated by vertical lines . The most common gains in CMS2 (5 or more out of 9 CMS2 MSI/MSS cell lines) were found on 3q, 8q, 13q, 17q, 20p and 20q, while regions of loss were frequent on 1p, 3p, 4q, 6p, 6q, 8p, 16p, 16q, 17p, 18 p, 18q, 20p and 22q. In CMS4 the most common gains (4 or more out of 7 CMS4 MSI/MSS cell lines) were found on 3q, 5p, 5q, 7p, 7q, 12p, 20p, 20q and 22q, while losses were frequent on 3p, 4p, 4q, 6q, 15q, 17p, 18q and 22q. The plots for CMS2 and CMS4 are placed together for easier visual comparison. A frequency plot for CMS3 was included, but the low sample number limits interpretations of frequent alterations in this group. d Differential frequencies of CNAs in undifferentiated versus colon-like cell lines. The vertical axis indicates the frequency difference between undifferentiated – colon-like cell lines (i.e. the frequency in undifferentiated cell lines minus the frequency of aberration in colon-like cell lines). The horizontal axis indicates chromosomes 1–22 . Yellow areas represent regions with higher frequencies of CNAs in colon-like cell lines, purple areas represent regions with higher frequencies of CNAs in undifferentiated cell lines. CMS: consensus molecular subtype, CNA: copy number aberration, MSI: microsatellite instable, MSS: microsatellite stable, SNV: single nucleotide variant. (PDF 830 kb)DNA level aberrations. Additional file 3: Figure S2.a PCA plots show the spontaneous split between the two subgroups in all three datasets . b Volcano plots show differentially expressed genes in undifferentiated (blue) versus colon-like (yellow) cell lines on the mRNA, miRNA and protein levels. Horizontal dashed lines mark the highest p-value that produces an adjusted p-value of <0.01. Vertical dashed lines mark log2 fold-change . The top five differentially expressed mRNA/miRNA/proteins in terms of log2 fold-change within these thresholds are indicated by names, and the rest by filled circles. PCA: principal component analysis, PC1: principal component 1, PC2: principal component 2. (PDF 1095 kb)Expression differences between colon-like and undifferentiated cell lines. Additional file 4: Figure S3.a A total of 280 genes were nominated, figure shows 15 nominated genes overlapping with the COSMIC Cancer Gene Census. Vertical axis shows gene expression values  and horizontal axis shows copy number aberration values . The analysis pinpointed CNA values that were substantially higher in one or a few cell lines compared to remaining cell lines and hence 14 of the nominated genes were high amplification events, while one gene (SS18) was nominated on basis of all samples having loss except V9P. b Ten outlier genes had available associated protein data and for most genes the increase in gene expression was consistent on the protein level. Vertical axis shows relative protein expression , horizontal axis shows gene expression values . (PDF 1233 kb)Outlier analysis reveals high level amplification events associated with marked mRNA expression changes, pointing to potential driver genes in individual cell lines.


ERCC5, TP53, PTH1, CLTCL1, NOTCH2, MAP2K3, CDK11A/B, USP6, ADCH5, BCLAF1, CDKN2A, FANCD2, HRAS, and NOTCH1 were the most frequently altered genes. Amplifications of MYC, PLAG1, NCOA2, RUNX1T1, COX6C, and EGFR and losses of FBXW7, TET2, XPC, and FANCE were frequently observed in cell lines. The exomic variations between cell lines and their corresponding cancer tissues were highly consistent. Genetic variations were mainly involved in the MAPK, Jak-STAT, TGF-beta, Notch, and apoptosis signaling pathways. Conclusively, these panel of PSCC cell lines established in our laboratory harbor some common or specific biological characteristics and genomic variations, and they may serve as optimal models to investigate the molecular mechanisms underlying the progression, metastasis, relapses, and treatment resistance of PSCC and to develop effective treatment strategy.Cell line models are essential tools to study the molecular mechanisms underlying tumor initiation and progression. There are limited treatment options for penile squamous cell carcinoma (PSCC), accounting for 1–2% of male tumors in developing countries, and limited progress in preclinical research in PSCC due to lacking available models with identified genomic characteristics. Here, biological and molecular characteristics and whole-genomic alterations were analyzed in a panel of PSCC cell lines newly established in our laboratory. These cell lines were all human papillomavirus (HPV)-negative, epithelial-like, immortalized, and tumorigenic in nude mice, whereas they displayed different proliferation, migration and invasion capacities in vitro, and tumorigenic ability in nude mice. They were all cisplatin sensitive, anti-EGFR therapy resistant, and androgen irresponsive. Whole-genomic sequecing analysis revealed that transition mutations (C:G>T:A and T:A>C:G) were the most common substitution types in these cell lines, whereas  Risk factors for PSCC include poor hygiene, phimosis, smoking, lack of circumcision, and human papillomavirus (HPV) infection1. The 5-year overall survival in patients with metastatic inguinal lymph nodes (LNs) is approximately 50% and with pelvic LN metastasis is ~0%, compared with >90% in patients without LN involvement6. Due to the limited basic and clinical studies, treatment options are very deficient for PSCC patients, especially those with advanced disease.Penile squamous cell carcinoma (PSCC) is a rare malignancy in developed countries with an incidence of (0.3–1)/100,000. However, PSCC constitutes up to 1–2% of male cancer in the developing world9. However, the biological functions of these genetic aberrations have not been clarified by in vitro and animal experiments, due to fewer available PSCC cell lines with various genetic backgrounds. Japanese researchers established three penile cancer cell lines in the last century12, and then a pair of cell lines Ki-PeCa-P1/L1 from a primary lesion and its corresponding metastatic LN13 and a cell line from human verrucous penile carcinoma14 were established by German and Brazilian researchers, respectively, in 2012 and 2016. Even so, few findings about molecular mechanisms underlying PeSCC oncogenesis based on these cell lines were reported in subsequent years.Previous studies have revealed the mutational landscapes of PSCC using next-generation sequencing, which contributes greatly to our understanding of the tumorigenesis and development of penile cancer15, and compared the biological and genetic characteristics of these five cell lines; some common or specific genomic variations were found, which will contribute to clarifying molecular mechanisms of PSCC tumorigenesis and searching for potential therapeutic targets of PSCC in further studies.To investigate molecular mechanisms underlying PSCC oncogenesis, we established four other new PSCC cell lines after development of an HPV-negative PSCC cell line with a TP53 mutation in 2016All cell lines were derived from Chinese PSCC patients, whose detailed clinicopathological characteristics are presented in Additional Table In vitro culture showed that all PSCC cell lines grew well as a well-attached monolayer, displayed a distinct epithelial appearance (with a cobblestone pattern), and exhibited typical characteristics of malignant change: large nucleus, scarce cytoplasm, prominent nucleoli, irregular size and shape Fig. . Flow cy16; however, HPV DNA could not be detected in all cell lines and their corresponding tumor tissues   supplemented with 10% fetal bovine serum (FBS). The growth curves and doubling times of the cell lines are presented in Fig. p < 0.01 or 0.001), whereas Penl1, Penl2, and LM156 that were from metastatic LNs had stronger invasive ability, especially Penl2 . Although the invasion rate of 149RM was lower than three cell lines from metastatic LNs, 149RM had stronger invasion activity than 149RCa , which may be partially because 149RCa and 149RM are derived from the same patient’s local recurrent lesion and nearby scrotal invasion lesion, respectively.Among these five cell lines, 149RM had the highest migration potential  (0.85–2.21 μg/mL) , and androgens did not affect the growth of these cells  Fig. .Fig. 4ThEGFR) by WB assay and found that EGFR highly overexpressed in PSCC cell lines Penl1, Penl2, 149RCa, and 149RM, whereas there was a moderate EGFR expression in LM156 cells   Fig. , whereasmL) Fig. .Whole-genome sequencing (WGS) analyses revealed that these cell lines exhibited a similar mutational spectrum, containing 1229–1290 single-nucleotide polymorphisms (SNPs) per megabyte compared to the National Center for Biotechnology Information (NCBI) human reference genome (hg19), and transition mutations (C:G>T:A and T:A>C:G) were the most frequent substitution types Fig. . The sinTP53, PTH1, ERCC5, CLTCL1, USP6, NOTCH2, ADCH5, MAP2K3, CDK11A/B, and BCLAF1 (5/5), CDKN2A and FANCD2 (4/5), NOTCH1, HRAS, KDR, and CIITA (3/5) are the most frequently altered genes, most of which accord with previous reports9. Among them, 23 were tested by Sanger sequencing, and 22 were validated, except for NOTCH2: exon2: c.C238T: p.N46S in cell line 149RM  requires further investigations. Additionally, we detected several deleterious or possibly damaging variants in PSCC cell lines, which were not indexed in the COSMIC database , PLAG1, NCOA2, RUNX1T1, COX6C, and CHCHD7 (4/5), HOXA13 (3/5), EGFR, HOXA9, HOXA11, ETV1 and PIK3CA (2/5), and losses of tumor suppressors FBXW7, TET2, XPC, and FANCE (2/5) were frequently observed in these cell lines. Surprisingly, we also found that there were partial losses of certain oncogenes  and amplifications of certain tumor suppressors  in these cell lines. However, due to the complexity of molecular interaction in cancer, the roles that these changes play in penile carcinoma need a further investigation. CNV profiles of critical functional genes in these cell lines are shown as an integrative heatmap in Fig. CNV analyses showed that five cell lines presented different amplification/deletion profiles, the whole-genomic CNVs are shown in Fig. We combined SNVs, InDels, and CNVs to perform KEGG pathway analysis, and determined that genomic alterations in these cell lines were mainly involed in the MAPK, Jak-STAT, TGF-beta, Notch, and apoptosis signaling pathways. Main aberrant pathways and involved gene variations were showed in Table The distribution and number of SVs are exhibited in Fig. 20. Various tumors, even same tumor, have been confirmed as being driven from different molecular mechanisms19, so it is necessary to obtain cell line models with identified genetic characteristics for the PSCC study. Although a few PSCC cell lines were developed several years ago14, little molecular mechanisms underlying PSCC initiation and progression have been clarified using these cell line models.Cell line models are essential tools to study the molecular mechanisms underlying the tumor progression, metastasis, relapses, and treatment resistance15 and four additional PSCC cell lines: Penl2, 149RCa, 149RM, and LM156, well characterized them in genomic alterations by WGS assay. All cell lines consist of pure, immortalized, and epithelial-like cells without HPV infection and harbor some common or specific biological characteristics and genomic variations. These cell lines present different growth, migratory, invasive, and tumorigenic abilities. Among them, 149RM has the highest mobility but the lower invasion ability, as well as the shortest doubling time, which may contribute to the rapid wound healing. Three cell lines from metastatic LNs, especially Penl2, has stronger invasion potential, thus these cell lines would be better cell models for the investigation of mechanisms underlying LN metastasis in penile cancer. Although 149RM from a nearby scrotal invasion lesion was expected to be more aggressive, it has a lower invasion rate. We supposed that this scrotal lesion was originated by direct contact of primary tumor, but not by invasion of tumor cells. Intriguingly, 149RM had a similar doubling time as 149RCa, whereas the 149RM xenograft tumors were smaller than 149RCa. We presumed that the microenvironment in vivo play more important role in xenografts’ growth, the underlying mechanisms need to be further explored.We have successfully established a panel of PSCC cell lines including Penl121. Interestingly, this mutation pattern differed from that in other solid tumor22. This PSCC-specific mutation signature and the etiology of penile cancer need further investigation. Mutation or loss of CDKN2A gene, TP53 mutation, RAS mutation, amplifications of MYC and EGFR genes have been reported in many human malignancies26, including penile carcinoma27, and the clinical data imply that the loss-of-function mutations of tumor suppressor genes and activating variations of oncogenes play a critical role in the carcinogenesis and invasion of PSCC28. We found that all five cell lines harbor CDKN2A deletion/mutation, MYC amplification, and TP53 mutations, which strongly support the above findings and hypothesis. These findings are also in accordance with Ali et al.’s comprehensive genomic profiling analysis, which involved 3769 exons of 236 cancer-related genes plus 47 introns from 19 genes frequently rearranged in cancer7.The WGS data of cell lines revealed that C:G>T:A and T:A>C:G transitions were the most frequent substitution modes in PSCC cells, which were consistent with previous genomic profiling analysis in penile cancer29. Marchi et al. found that AR was one of top ten driver candidates in penile cancer by a multidimensional integrative analysis30. However, compared to normal glans tissues, AR expression in penile cancer tissue was greatly downregulated by increased microRNAs30 or promoter hypermethylation31, which is in accord with our result that the five cell lines established in our laboratory expressed very low levels of AR. It seems that androgen stimulation plays a negative role in penile cancer. Additionally, we found that penile cancer cells did not response to androgen stimulation in vitro. So we are unable to identify whether AR plays a driver candidate role or a passenger role, which needs to be validated in future functional studies.PSCC is a male-specific malignancy, which may be similar to prostate cancer that correlates with androgen stimulation and AR expression32. Notably, ERCC5 mutations in all of five PSCC cell line, the mutations of FANCD2 in four of five PSCC cell lines, ATM and MSH6 mutations and BRCA2 loss in Penl2, and loss or stop-gain mutations of FBXW7 in Penl1, 149RM, and 149RCA were found, and these genes are all important DNA damage repair-related genes35, which may well account for the cisplatin sensitivity of these cell lines. Therefore, the status of DNA damage repair-related genes may be a promising marker to identify PSCC patients who can benefit from DNA-damaging chemotherapy.Our further investigations revealed that all five PSCC cell lines were sensitive to cisplatin, a DNA-damaging anti-cancer drug used in the clinicEGFR is frequently overexpressed in PSCC, and anti-EGFR therapy may be a potential therapeutic option for PSCC patients36. We detected EGFR amplification in two cell lines, and EGFR overexpression in most of these cell lines, but they were all resistant to EGFR inhibitors. Activating HRAS and PI3KCA mutations were found to correlate with resistance to anti-EGFR therapy38, so we presume that HRAS G13V/G13R mutations in 149RCa, 149RM, and LM156 cells and PI3KCA amplification in Penl2 and 149RM cells may relate with their resistance to anti-EGFR therapy, which need to be verify in further investigations. Thus, these PSCC cell lines may be helpful models for further investigation of the mechanisms underlying anti-EGFR resistance and strategies overcoming this resistance in PSCC.Previous studies have shown that In summary, we established a panel of cell lines derived from PSCC patients with different clinicopathological characteristics, and these cell lines display diverse biological characteristics. WGS analysis revealed that these cell lines harbor a genomic alteration spectrum similar to that in PSCC patients reported by previous studies, and they display their specific gene alterations and amplification/loss spectrums, suggesting that this panel of PSCC cell lines might be suitable model systems for PSCC research. The comprehensive biological and genetic information should aid researchers in the selection of representative penile cancer cell lines for investigating molecular mechanisms and screening potential therapeutics.15. To remove cancer-associated fibroblasts, cells were briefly digested with 0.25% trypsin/ethylenediaminetetraacetic acid (Invitrogen) for 4–5 times as previously reported39. Cells were cultured in DMEM supplemented with 10% FBS  at 37 °C with humidified air and 5% CO2, subcultured at a ratio of 1:3 to 1:4 and cryopreserved in complete growth medium supplemented with 10% (v/v) dimethyl sulfoxide at 1 × 106 cells/mL in liquid nitrogen. All PSCC cell lines were continuously passaged every 3 days for over 50 passages, without any exogenous transfection or stimulation. Cell lines at passage 15–25 were used for further analyses. Cell morphology of cell lines was examined and photographed under a phase-contrast microscope.Fresh tumor specimens were obtained from patients in the Department of Urology, Sun Yat-Sen University Cancer Center (SYSUCC). Four new cell lines Penl2, 149RCa, 149RM, and LM156 were established using the same procedure as Penl1, developed previously in our laboratory® 488-conjugated anti-Pan-CK antibody or corresponding mouse (DA1E) mAb IgG XP® isotype control  for 30 min at room temperature, followed by a flow cytometry analysis. The results were analyzed with Kaluza software (Beckman Coulter Inc.). The percentage of cells with Pan-CK positive represented the purity of cell lines15. The experiments were independently performed for three times, and representative images were shown.Cells were harvested by trypsinization, fixed with BD Cytofix/Cytoperm solution (BD Bioscience) and stained with Alexa Fluor41. Briefly, protein samples from cell lysates were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes. After blocking for non-specific binding, the blots were inoculated with primary antibodies against EGFR , Vimentin, Pan-CK, AR and glyceraldehyde phosphate dehydrogenase (GAPDH) , followed by reaction with corresponding HRP-conjugated secondary antibodies. Bands were visualized via Chemidoc Touch (Bio-Rad).WB assay was performed as previously described42. Mycoplasma contamination using the PCR Mycoplasma Test Kit  according to the manufacturer’s instruction39.Genome DNA was extracted from cell lines and corresponding tumor tissues, and subjected to analyze the infection of HPV and mycoplasma and the STR profiles. HPV infection was analyzed in MyGenostics Inc.  by quantitative real-time PCR assays with specific primers and probes  and then processing with the ABI3730xl Genetic Analyzer. The STR profile of each cell line was compared with those from the ATCC, DSMZ, or JRCB databases for reference matching.The STR profile analysis was performed by PCR amplification3–1 × 104 cells were seeded in 24-well plates in triplicate. Cell were harvested and counted after typan blue staining44 using a Beckman Z2 Coulter® Particle Count and Size Analyzer (Beckman Coulter). Cell growth curve was analyzed in an exponential growth phase using Graphpad 5.0 software to obtain cell doubling time.For cell proliferation assay, 5 × 1050 values were calculated using Graphpad Prism 5.Cell viability assay was performed as described previously. Briefly, 5000 cells/well were seeded in 96-well plates for 24 h, and then exposed to androgen , cisplatin , cetuximab, erlotinib, or afatinib . Cell viability was measured by cell counting kit (CCK)-8  assay after 48 h. IC45. Briefly, cells were seeded in 6-well plates and grown to confluence overnight. Cells were slightly scratched with a 200 μL-tip and then washed twice with phosphate buffered saline. Fresh serum-free medium were then added and wound healing was monitored after 12 h of incubation.The migration ability of cell lines was assessed using a monolayer wound healing assay47. Briefly, 2 × 105 cells in 200 μL serum-free or complete medium were seeded into the Boyden chamber with Matrigel . The chambers were put in 24-well plates with medium with 10% FBS to incubate for 24 h. The cells attached to the undersurface of filter membrane were fixed and stained in 0.1% crystal violet. The invaded cells were counted in five random fields (100×) under a microscope.Cell invasion was tested using a Transwell assay6 cells (passage 20–25) suspended in 100 μL 20% Matrigel  were injected subcutaneously into the right flanks of 6-week-old female and male nude mice (10 mice per cell line). After 3 weeks, mice were sacrificed and tumors were removed for H&E staining and pathology examination.To confirm the tumorigenicity of these cell lines, 5 × 10WGS analysis for PSCC cell lines (90 Gb PF data for each cell line), WES analysis was performed for cell line-corresponding cancer tissues  were performed by Wuxi Apptec  using the Illumina HiSeq X sequencing system. The raw data of WGS/WES in this study were deposited at the NCBI (SRA accession number: SRP117294). High-quality reads were aligned against the NCBI human reference genome (hg19) using Burrows-Wheeler Aligner (v0.5.9) with default parameters, followed by passing a hard filter  Splicing site, frameshift insertion/deletion, stop-gain or stop-loss mutations that are excluded in SNP142common database; (2) Non-synonymous mutations that are excluded in SNP142common database, which functional impacts were “deleterious (D)” predicted by SIFT or “probably damaging/possibly damaging (P/D)” predicted by PolyPhen2; (3) Non-frameshift insertion/deletion mutations that are included in COSMIC  70 database and excluded in SNP142common database; (4) SNVs and InDels included in SNP142common database and COSMIC70 database, which functional impacts were “D” predicted by SIFT or “P/D” predicted by PolyPhen2.48, SPSS 20.0, and GraphPad 5.0 software programs were used for statistical analyses in this study. One-way ANOVA was utilized to compare the differences among the five cell lines while Student’s t-test was used to compared the difference between two groups. p < 0.05 was considered as significantly different.All of in vitro experiments were performed in triplicate and repeated three times and animal experiments were repeated twice. Heatmap Illustrator (HemI 1.0)Additional Fig S1-S3Additional Table S1Additional Table S2Additional Table S3Additional Table S4Additional Table S5Additional Table S6Additional Table S7Additional Table S8Additional Table S9Additional Table S10Additional Table S11Additional Table S12Additional Table S13Additional Table S14Additional Table S15Additional Table S16Additional Methods


We have developed a novel analysis method that can interrogate the authenticity of biological samples used for generation of transcriptome profiles in public data repositories. The method uses RNA sequencing information to reveal mutations in expressed transcripts and subsequently confirms the identity of analysed cells by comparison with publicly available cell-specific mutational profiles. Cell lines constitute key model systems widely used within cancer research, but their identity needs to be confirmed in order to minimise the influence of cell contaminations and genetic drift on the analysis. Using both public and novel data, we demonstrate the use of RNA-sequencing data analysis for cell line authentication by examining the validity of COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO colorectal cancer cell lines. We successfully authenticate the studied cell lines and validate previous reports indicating that DLD1 and HCT15 are synonymous. We also show that the analysed HKE3 cells harbour an unexpected KRAS-G13D mutation and confirm that this cell line is a genuine KRAS dosage mutant, rather than a true isogenic derivative of HCT116 expressing only the wild type KRAS. This authentication method could be used to revisit the numerous cell line based RNA sequencing experiments available in public data repositories, analyse new experiments where whole genome sequencing is not available, as well as facilitate comparisons of data from different experiments, platforms and laboratories. Cell lines offer an unlimited source of biological material and represent homogeneous cell type populations, which facilitates both experimental procedures and interpretation of results in comparison to the analysis of tissues and organs. They are also easy to use since well-developed protocols are available for culturing, genetic manipulation, molecular analysis and other assay-based experiments. Cell lines offers a cost-effective source of materials that bypasses ethical concerns raised by the use of other biological material like human or animal tissues. Using cell lines to model human biology, test efficacy of therapies and produce therapeutic proteins is common practice in research, yet it is widely acknowledged that contamination of said cell lines is a prevalent problem. [The prevalence of using human cell lines as problem. , 2 Mycopproblem.  Common cproblem.  Cross-spproblem. et al. has emphasised the need for new and robust methods for cell line authentication. [The awareness of pitfalls related to cell line authenticity has increased rapidly since 2007.  The analication. With the advent of high-throughput sequencing technologies such as RNA sequencing (RNA-seq), new venues for exploiting large amounts of transcript data have become available.  RNA-seq Catalogue of somatic mutations in cancer (COSMIC) [In the present study, we present a pipeline utilising RNA-seq data analysis in combination with already existing mutational profiles for human cell line authentication. We show that comparing RNA-seq data from several colorectal cancer cell lines  to databases such as the fastq-dump from the SRA toolkit. [Seven colorectal cancer cell lines, COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO (with two different datasets for HCT116), were analysed in the study. HCT116a, HKE3 and RKO were analysed using data obtained from in-house culturing and sequencing. The data for COLO205, HCT116b, HCT15 and HT29 was downloaded from the Gene Expression Omnibus (GEO) database  as SRA ftoolkit.  The DLD1toolkit. RNeasy Plus Mini Kit (Qiagen) as per the manufacturer’s instructions with three replicates each for HCT116/RKO and four replicates for HKE3. Cells were lysed directly in the dish using 600 μL buffer RLT Plus supplemented with β-mercaptoethanol. The extracted RNA was stored in −80°C prior to sequencing.The HCT116a and RKO cell lines were cultivated in standard McCoy medium with 10% Foetal Bovine Serum and were split 1:5 every third or fourth day (Sigma Aldrich). The HKE3 cell line was similarly cultivated in standard DMEM media with 10% FBS. RNA extractions were performed at 80% confluence with the TruSeq mRNA kit with poly-A selection (200 ng RNA per sample); all samples had a RIN value of 10 as measured with the Agilent 2100 BioAnalyzer. Clustering was performed using the cBot cluster-generation system. Sequencing was performed on a HiSeq2500 instrument with a 2x101 bp setup in HighOutput mode (HiSeq Control Software 2.0.12.0/RTA 1.17.21.3) for HCT116 and RKO, and with a 2x126 bp setup in RapidHighOutput mode for HKE3 (HiSeq Control Software 2.2.38/RTA 1.18.61). Conversion of obtained bcl files to FASTQ was performed using bcl2FASTQ (v1.8.3) and the Sanger / phred33 / Illumina 1.8+ quality scale from Illumina’s CASAVA software suite.RNA-seq library preparation was performed with Illumina’s GRCh37 reference was performed using the STAR (v2.5.1b) two-pass method, where splice-junctions found in a first alignment step is used in the second. This two-pass method increases the quality of the alignment as it pertains to downstream variant calling  compared to single-pass methods. [GATK Best Practice workflow  [HaplotypeCaller. In order to increase the coverage of the data, confident homozygous reference calls were also included. The resulting variants were filtered to have at least a Fisher Strand value above 30 and a Quality by Depth value greater than two. Clusters of at least three SNVs within a 35 base pair window were also filtered, followed by annotation with SnpSift and SnpEff (v4.2). [Python and R scripts, for which detailed code is available in the supplementary information. COSMIC data from the GRCh37 assembly [featureCounts (v1.4.5). [DESeq2 (v1.6.3) [Kallisto (v0.42.1). [The RNA-seq read alignment to the methods.  RNA-seq id=3891)  Briefly, (v4.2). , 23 Downv1.4.5).  Differen(v1.6.3) . Analysi0.42.1).  The raw Dynazyme II DNA Polymerase (Thermo Scientific) as per the manufacturer’s instructions in reaction volumes of 50 μL with 100 ng of DNA template and 1 U of Dynazyme. cDNA synthesis was performed using the SuperScript III First Strand Synthesis System (Thermo Scientific) with oligo(dT) as per the manufacturer’s instructions. Custom DNA primers were purchased from Integrated DNA Technologies and Sanger sequencing was performed by Eurofin Genomics. Primer sequences are listed in All PCR reactions were performed using αE. coli transformed with a GST-RAF-RBD construct were grown overnight at 37°C in 20 ml of LB medium containing ampicillin. The pre-culture was subsequently added to 500 ml of LB medium and grown until the OD600 was 0.6–0.8. GST-RBD expression was induced by adding IPTG at a final concentration of 1 mM, followed by incubation for 1–2 h at 37°C. The bacteria were collected by centrifugation and the pellets were frozen down at −80°C. Frozen pellets were resuspended in 10 ml of PBS (ThermoFisher Scientific) containing 1% NP40  and leupeptin , followed by sonication using a Syclon Ultrasonic Homogenizer at 8–10% power with repeated 6 s on / 1 s off cycles for a total of 5 min. Following centrifugation at 4000 rpm for 30 min (4°C), the supernatant was collected and incubated at 4°C with rotation and 500 μL of Glutathione sepharose 4b beads  for 3 h. The samples were centrifuged at 4000 rpm for 1 min, followed by discarding of the supernatants. Beads were resuspended in 1 ml of PBS containing 1% NP40 and aliquoted into two 1.5 ml eppendorf tubes. Beads were then subjected to 5 wash cycles: ice cold PBS containing 1% NP40 (twice), ice cold PBS (twice), and a final wash in ice cold magnesium lysis buffer . The washing supernatant was discarded, beads were resuspended in MLB buffer and stored at 4°C with protease inhibitors. Bead concentration was determined using a BSA dilution curve as a standard, running 10 μL of beads in a polyacrylamide gel followed by Coomasie staining to visualise the proteins.DHet al. [et al. [Ultimate Ultra 3000 chromatography system coupled to a Q-Exactive mass spectrometer . The sample set was analysed as four biological replicates and two technical replicates: 5 μl of tryptic digest was loaded on a homemade column , packed with UChrom C18 (1.8 μm) reverse phase media (nanoLCMS Solutions LCC) and then separated by a reverse phase gradient at a constant flow rate of 250 nl/min with an increasing linear gradient (2–35%) of buffer B . The mass spectrometer was operated in positive ion mode with 2000 V potential applied to the column. Spectra were acquired through a top 12 data-dependent tandem MS analysis mode in the range of 350–1600 m/z, selecting the 12 most intense ions for collision and acquisition. The raw data was analysed using MaxQuant (v1.5.0.12) with the following parameters: enzyme: trypsin; fixed modifications: carbamylation of cysteines; variable modifications: oxidation (M), N-terminal acetylation and Gly to Asp; variable number of miscleavages: two. Data was searched against a human Fasta database, with a false discovery rate cutoff of 1% (forward and reverse) at both peptide and protein level. Protein abundance was calculated using an LFQ-algorithm. For Western blotting experiments the GST-RBD pulldowns were immunoblotted with an antibody against KRAS (BD Transduction Labs).HCT116 and HKE3 cells were grown on 100 mm tissue culture plates to 80% confluence. Plates were washed twice in HBS buffer and then lysed in modified MLB buffer . Lysates were put on ice for 10 min with strong vortexing every 2–3 minutes, followed by centrifugation at 12000 rpm. Total protein content was measured by BCA assay (Pierce), a BSA dilution curve (measured in triplicate) and with two measurements for each sample. Cleared lysates were incubated with 15 μL of GST-RBD beads for 45 min in a rocker at 4°C, followed by washing once in full MLB buffer and four times with MLB buffer without detergent. An on-bead digestion protocol was applied as described by Turriziani et al.  followed [et al.  The trypet al. were able to interrogate the origin of several widely used lung cancer cell lines and identify HeLa contaminations. [et al. has previously shown that variants from RNA-seq data cover 40% of those found from whole genome sequencing, and up to 81% when filtering for expressed genes. [Cell line contamination is a ubiquitous problem and can occur in several different ways. Mycoplasma contaminations has previously been investigated using RNA-seq data by identifying reads that map to the mycoplasma genome.  In additnations.  Cirulli d genes.  The aim d genes.  COSMIC cSTAR aligner. [Genome Analysis Toolkit (GATK). [SnpSift/SnpEff, [The first step of the pipeline is the alignment of the raw sequencing reads using the aligner.  The alig (GATK).  GATK has (GATK).  The resu/SnpEff, , 23 yielTo generate a SNV profile, the identified SNVs were filtered to only include those containing the highest impact variants for each transcript, as these are most likely to affect the biology of the cell lines. In order to assure a high statistical confidence of the generated SNV profiles, the variants failed by the GATK filters for strand bias and quality score are also removed, as are those with a total allelic depth less than 10.  The genoWe have chosen to test the authentication pipeline on both novel and publicly available RNA-seq data from several cell lines, namely COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO. These are all widely used model systems for colorectal cancer and cover a broad range of cancer subtypes. COLO205 is related to several other COLO cell lines derived from the same adenocarcinoma patient.  The DLD1An overview of the sequencing parameters can be seen in concordance as the proportion of RNA-seq SNVs matching those in the COSMIC SNV profiles . As can be seen in We compared RNA-seq SNV profiles from the eight cell lines with the COSMIC data on cell line-specific SNV profiles. While the COSMIC database lacks an entry for the DLD1 cell line, it is possible to validate its relationship with HCT15 by comparing the DLD1 data to the HCT15 COSMIC SNV profile. HKE3 similarly lacks a COSMIC profile, but its origin makes comparisons with the HCT116 profile possible. The data used in this study includes cell lines that have between 241 and 7649 unique SNVs in the COSMIC database, covering a large range of mutations. We define coverage as the proportion of COSMIC SNVs that are found by the RNA-seq analysis (filtered RNA-seq SNVs ÷ unique COSMIC SNVs). The average coverage of the analysed cell lines is 39.8% . These results were validated by PCR amplification and Sanger sequencing of KRAS exon 2 using RNA from both HCT116 and HKE3  shows that the KRAS gene is less expressed in HKE3 than in HCT116. An isoform expression analysis using Kallisto  show thaAs the fold change of KRAS-G13D between HCT116 and HKE3 is below the common threshold of 2 for biological significance, we sought to validate this down-regulation on the protein level. Quantification of KRAS-G13D protein levels was performed using a Ras Binding Domain (RBD) pulldown assay followed by Mass Spectrometry (MS), which show a clear difference in the levels of G13D expression between HCT116 and HKE3 . WesternCell line authentication is an important and non-trivial problem in biological research. While mycoplasma infections and inter-species contaminations do happen and can affect the outcome and interpretation of experimental results, the more common problem is that of cross-contamination with other human cell lines. There are numerous problems associated not only with the technical aspects of cell line authentication, but also in defining standards and best practice procedures, implementation by researchers world-wide as well as curation of data repositories storing experimental results performed with non-authenticated cells.  An increet al. proposed a threshold of 90% concordance for their 48-locus SNP array with a coverage of at least 85%. Even the cell line with the fewest COSMIC SNVs in our data  pass this threshold, demonstrating the strength and validity of our strategy. The average number of SNVs for the cell lines presented here is 1303, while the average number of unique SNVs in the COSMIC database as a whole is 666. These numbers attest to the power of using high-through sequencing data for cell line authentication. Cirulli et al. found that approximately 40% of genomic SNVs were captured by RNA-seq, making our average coverage of 39.8% in line with previous data. [We have shown that our pipeline is robust to different sequencing parameters, including single or paired-end reads, varying read lengths as well as for different sequencing depths. The lowest concordance for any of the eight cell lines examined herein was 96.2%, while going as high as 99.3%. Yu us data.  These reAs sequencing depth increases, so does the number of SNVs, but even experiments with relatively few reads can be used successfully for cell line authentication with our pipeline. The cell line with the fewest reads  has approximately 72 000 filtered SNVs in total and a coverage of 29.3%, similar to those cell lines with around 90 million reads. The majority of the experiments with a shorter read length  also has fewer SNVs in total (approximately 38 000 each) and a lower coverage compared to those with longer read lengths. The exception is HCT15, which has a coverage of 43.9%. This is most likely due to the large number of COSMIC SNVs available for HCT15 (7649 unique SNVs), compared to those available for COLO205 and HT29 . Another study analysed 81 million reads, which resulted in approximately 40 000 variants that passed their quality controls, leading to 501 SNVs per million reads. [The analysis of the data subsets show that the coverage decreases with lowered sequencing depth, while maintaining a high concordance. This shows that the coverage is affected by both the sequencing parameters and the number of variants in the COSMIC database. The robustness of the pipeline is further demonstrated by the fact that this variable coverage is not concomitant with lowered concordance. However, if there are too few SNVs found the authenticity will be less clear-cut, regardless of concordance. While the characterisation of the SNVs show that the impacts are relatively similar, there is a higher proportion of SNVs in the MODIFIER category for the mismatched SNVs than for the matching ones. Both the HIGH and MODERATE categories, however, had a smaller proportion in the mismatched SNVs.de novo mutation may have been introduced during culturing of HKE3, or that a chromosomal re-arrangement has taken place. However, defining the exact mechanism by which HKE3 expresses the KRAS-G13D mutation is beyond the scope of this study. While this was not the intended effect of the disruption cassette, the resulting cell line is still of great interest, as it can provide novel insights into the function of KRAS-G13D dosage and its effect on cellular signalling.One of the strengths of using RNA-seq data for cell line authentication compared to existing methods is that the sequence information itself allows for more in-depth analysis. The results from our pipeline indicated that one of the mutations in HKE3 didn’t correspond to what was known about the cell line, warranting a more thorough investigation. HKE3 was derived from HCT116 using a disruption cassette, which was intended to replace the original KRAS-G13D mutation in HCT116 with a non-expressed KRAS-G12C mutation. The results show that while the cassette has successfully been incorporated in the genome of HKE3, it does not entirely disrupt the expression of either KRAS allele. Data from RNA-seq, mass spectrometry as well as PCRs and Sanger sequencing on both the DNA and RNA level from two separate labs show that the HKE3 cells investigated here harbour a KRAS-G13D dosage effect, rather than a full knockout. The deviation from a 1:0:1 (wt:G13D:G12C) ratio of the distribution of aligned reads to 1:1:1 indicates that the cassette may have been incorporated elsewhere on the genome. As HCT116 has a higher rate of proliferation than HKE3, it is unlikely that the reported results are due to a HCT116 contamination. In the case of a contamination, HCT116 would overtake the HKE3 population, and no KRAS-G12C mutation would be seen. Another possible explanation is that a i.e. they are derived from the same patient. One out of 48 loci in the SNP panel differ between the cell lines: the mutation rs2355988, for which the genotype of HCT15 is G/C, while that of DLD1 is C/C. While this particular mutation cannot be seen in the RNA-seq data, the concordance of DLD1 with the COSMIC HCT15 profile is 98.7%. The existing conclusion that DLD1 and HCT15 are synonymous cell lines is thus confirmed and demonstrates how established results can be re-evaluated by our authentication pipeline. Contaminations of HeLa cells were also ruled out for all of the studied cell lines. While outside the scope of this article, the general applicability of the method makes it possible to check for contaminations of any other cell line for which there exists a SNV profile, in both current and future databases.The cell lines DLD1 and HCT15 have previously been shown to be related. According to the SNP profiling from Yu and colleagues, these are classified as synonymous cell lines, As RNA-seq SNV data contains both global and more in-depth data than previously established authentication methods such as STR profiling and SNV genotyping panels, our pipeline could potentially replace said methods when RNA-seq data is available. Its strengths lie in the fact that many more variants are discovered and thus give a more complete picture of the cell lines analysed, even for those cases where relatively few variants are listed in the COSMIC database. While such a replacement has theoretical merits, the cost of RNA-seq is not yet low enough to warrant spending the money purely for cell line authentication. However, studies aiming to use RNA-seq data for gene expression analyses can benefit from performing cell line authentication using our pipeline on the same data. There are great opportunities to retroactively authenticate and validate the numerous already published RNA-seq datasets in the literature using the presented methodology. There also lies considerable potential in comparing transcriptome-scale SNV profiles, especially for those cell lines where no COSMIC profile exist. Creating such profiles would allow for authentication of any cell line for which RNA-seq is (or becomes) available. Both existing and future RNA-seq data from cell line experiments could be used to create such profiles using our pipeline, fully utilising the strength and scope of the sequencing technology.We have created a pipeline for cell line authentication using RNA-seq data, shown its robustness to several sequencing parameters, used it to validate already existing conclusions regarding cell line authenticity as well as shown how such data can be utilised for detailed analysis of cell lines with known mutations. Our pipeline was used to confirm that the cell lines DLD1 and HCT15 are synonymous, and we have also shown that the HKE3 cell line, derived from HCT116, harbours a KRAS-G13D dosage effect rather than a full knockout. This information can be used to conduct new experiments to gain insight into the effect of KRAS-G13D on the cell and its affected pathways. The inherent power of high-throughput sequencing technologies gives our pipeline the potential to replace already existing authentication methods, but may also be used to develop novel transcriptome-scale SNV profiles for authentication based on already existing cell line RNA-seq data.S1 FigA) Number of SNVs as dependent on subsampling proportion; (B) concordance of filtered SNVs as dependent on subsampling proportion.The effect of subsampling single replicates (corresponding to approximately 30 million reads) to a lower read depth on cell line authentication statistics; COLO205 (blue), HCT116a (orange), HCT116b (light blue), HCT15 (yellow), HT29 (green), RKO (red). The concordance for all subsampling proportions for all cell lines remains above 90% except in two cases: the 2% and 1% subsamples of COLO205, where the number of filtered SNVs were zero. ((EPS)Click here for additional data file.S2 FigHeatmap showing the coverage of RNA-seq SNVs compared to those in COSMIC profiles (colour gradient and second percentage in boxes) and the concordance of these SNVs (first percentage in boxes).(EPS)Click here for additional data file.S3 FigCharacterisation of the RNA-seq matching (black) and mismatched (grey) SNV impacts for individual cell lines compared to COSMIC profiles. Counts for each category is shown above each bar.(EPS)Click here for additional data file.S4 Fig(A) PCR amplification and Sanger sequencing of KRAS exon 2 (KRASF1 + KRASR2) of RNA from (1) HCT116 and (2) HKE3. Sanger sequencing of KRAS-G13D in (B) HCT116 and (C) HKE3.(EPS)Click here for additional data file.S5 Fig(1–4): HCT116 Neo, HCT116 TK, HKE3 Neo, HKE3 TK (DNA); (5–8): same as 1–4, but with cDNA. Ladder from below: 250, 500, 750 and 1000 bp.PCR amplification of the disruption cassette in HCT116 and HKE3 using NeoF1, NeoR1, TkF1 and TkR1 primers. (EPS)Click here for additional data file.S6 Fig(1) HCT116 and (2) HKE3 using PKF1 and PKR1 primers according to Shirasawa et al. [PCR amplification of the non-disrupted  and disrupted  KRAS locus cassette in a et al. .(EPS)Click here for additional data file.S7 Fig(1) wild type (KRASFwt + KRASR3) or (2) TK (KRASFTk + KRASR3) transcripts in HKE3 RNA. Ladder from below: 250, 500, 750 and 1000 bp.PCR amplification of either (EPS)Click here for additional data file.S1 FileList of primer sequences used in the study.(TXT)Click here for additional data file.S2 FileThe R code used to create the figures and tables in the study.(PDF)Click here for additional data file.


However, there is limited understanding of the metabolic response to NAD depletion across cancer cell lines and whether all cell lines respond in a uniform manner. To explore this we selected two non-small cell lung carcinoma cell lines that are sensitive to the NAMPT inhibitor GNE-617 , one that shows intermediate sensitivity (NCI-H441), and one that is insensitive (LC-KJ). Even though NAD was reduced in all cell lines there was surprising heterogeneity in their metabolic response. Both sensitive cell lines reduced glycolysis and levels of di- and tri-nucleotides and modestly increased oxidative phosphorylation, but they differed in their ability to combat oxidative stress. H1334 cells activated the stress kinase AMPK, whereas A549 cells were unable to activate AMPK as they contain a mutation in LKB1, which prevents activation of AMPK. However, A549 cells increased utilization of the Pentose Phosphate pathway (PPP) and had lower reactive oxygen species (ROS) levels than H1334 cells, indicating that A549 cells are better able to modulate an increase in oxidative stress. Inherent resistance of LC-KJ cells is associated with higher baseline levels of NADPH and a delayed reduction of NAD upon NAMPT inhibition. Our data reveals that cell lines show heterogeneous response to NAD depletion and that the underlying molecular and genetic framework in cells can influence the metabolic response to NAMPT inhibition.Nicotinamide adenine dinucleotide (NAD) is a cofactor involved in a wide range of cellular metabolic processes and is a key metabolite required for tumor growth. NAMPT, nicotinamide phosphoribosyltransferase, which converts nicotinamide (NAM) to nicotinamide mononucleotide (NMN), the immediate precursor of NAD, is an attractive therapeutic target as inhibition of NAMPT reduces cellular NAD levels and inhibits tumor growth  Appropriate regulation of cellular metabolism is critical to sustain cell proliferation and involves a tremendous complexity that includes cross-talk across a variety of metabolic pathways . In partin vitro and in vivo.NAD and its derivatives  serve as cofactors in multiple metabolic pathways regulating the redox status of cells (NAD/NADH ratio), signal transduction (e.g. cofactor for SIRT) and for DNA repair (e.g. cofactor for PARP1). NAD is Depletion of NAD in cells has been shown to block glycolysis, increase utilization of the pentose phosphate pathway (PPP) and increase glutaminogenesis , 11. HowCell lines were obtained from American Type Culture Collection (ATCC), expanded, and stored at early passage in a central cell bank at Genentech. Short tandem repeat (STR) profiles were determined for each line using the Promega PowerPlex 16 System. STR profiling was performed once and compared with external STR profiles of cell lines (when available) to determine cell line ancestry. SNP profiles were performed each time new stocks were expanded for cryopreservation. Cell line identity was verified by high-throughput SNP profiling using Fluidigm multiplexed assays. SNPs were selected based on minor allele frequency and presence on commercial genotyping platforms. SNP profiles were compared with SNP calls from available internal and external data (when available) to determine or confirm ancestry. In cases where data were unavailable or cell line ancestry was questionable, DNA or cell lines were repurchased to perform profiling to confirm cell line ancestry.During the experiments, cells were maintained in RPMI with 10% FBS and 2mM Glutamine. All cell lines were maintained below a passage number of 20. The small molecule inhibitor, GNE-617, was synthesized in-house, 2010, 2One potential consequence of a block in glycolysis is for cells to shunt excess glycolytic metabolites into the PPP . Cells u13C-6]-glucose labeling experiment was performed to directly examine carbon flow through the PPP pathway in each cell line. Baseline levels of pentose-5-phosphate (P-5-P) were low in both H441 and A549 cells , the enzyme responsible for the conversion of glucose-6-phosphate to 6-phosphoguconate in the PPP, was depleted by siRNA in A549, H1334 and H441 cells . Levels A function of the P-5-P produced by the PPP is to serve as a precursor for the generation of purines and pyrimidines. Both sensitive cell lines, A549 and H1344, increased levels of IMP and UMP  and theiEven though we did not observe an increase in carbon flow through the PPP in H1334 or H441 cells , it was Baseline NAD levels in H1334, A549 and LC-KJ cell lines were very similar, while H441 cells had a ~2-fold higher baseline level  consisteThe ratio of reduced to oxidized glutathione (GSH:GSSG) is another indicator of oxidative stress, as cells accumulate GSSG in response to elevated levels of free radicals 21]. Mor. Mor21].To examine levels of oxidative stress in cells, ROS levels in each cell line were examined at 24, 48 and 72 hours following NAMPT inhibition . ROS levA key metabolic sensor in cells is the enzyme AMP-kinase, which is activated in response to an increase in the AMP/ATP ratio . In A54913C-6]-glucose and levels of metabolites associated with OXPHOS were examined , one cell line that showed intermediate sensitivity (H441) and one cell line that was inherently insensitive in short term viability assays (LC-KJ). It was previously reported that depletion of NAD reduced glycolytic flux , 12, 20 A consequence of a block in glycolysis is that cells can divert carbon flow into the PPP, which generates R-5-P for the biosynthesis of purines and pyrimidines as well as NADPH for the synthesis of fatty acids, cholesterol and reduced glutathione . NADPH iSurprisingly, H441 cells did not display an increase in ROS levels, even though there was a large decrease in NAD, NADH, NADP and NADPH levels . One posCell lines that were sensitive to NAMPT inhibition also had a significant reduction in di- and tri-phosphorylated nucleotides, and this was associated with a corresponding increase in levels of mono-phosphorylated nucleotides . One impIn contrast to GNE-617 sensitive cell lines, minimal metabolic effects were noted in LC-KJ cells, which were the most refractory to NAD depletion. There are a number of reasons that may explain differential sensitivities across these four cell lines to NAMPT inhibition. Both of the two most sensitive cell lines (H1334 and A549) have a similar doubling time, similar levels of NAMPT protein, and also similar NAD baseline levels . HoweverWhile NAMPT levels correlate with sensitivity to NAMPT inhibition, our data suggests that predicting sensitivity is more complicated. For example, LC-KJ cells have similar NAMPT levels as the sensitive cell lines A549 and H1334, yet they are inherently more resistant to NAMPT inhibition. We hypothesize that the underlying metabolic rate of a cell, or tumor, may be an important contributor to sensitivity, as a cell line with a slow doubling time may have a low metabolic turnover and thus be more resistant to NAD depletion. Additionally, our data demonstrates that the underlying profile of each cell line can modulate the metabolic response to NAMPT inhibition. This raises the possibility that inhibitors targeting different metabolic pathways may be effectively combined with a NAMPT inhibitor in some cell lines; however, these combinations may have to be tailored to match the underlying genetic and metabolic dependency of a particular cell. Nevertheless, our data suggests that novel combinations may be used to maximize the benefit of a NAMPT inhibitor.This study revealed striking differences in the response of different cell lines to NAD depletion. While the two sensitive cell lines showed defects in glycolysis, a decrease in di-and tri-phosphorylated nucleotides, and a modest increase in OXPHOS, they coped with increased metabolic stress differently. H1334 cells activated AMPK whereas A549 cells, which have a mutation in LKB1, were unable to activate AMPK. However, A549 cells dramatically increased carbon flux through the PPP, unlike H1334 cells, and contain a mutation in KEAP1 that results in constitutive activity of NRF2. Thus, even though these cell lines are similarly sensitive to NAMP inhibition, their metabolic response can be governed by their underlying genetic profile and metabolic dependencies. In conclusion, our data reveal that depletion of a single key central metabolite such as NAD can have wide effects on overall cellular metabolism and that each cell line has a unique way of dealing with metabolic stress that can be dictated by its genetic profile.S1 FigA) Western blot analysis of NAMPT protein levels across a panel of NSCLC cell lines. B) Shown is the IC50 value for GNE-617 for each of the 4 cell lines used in this study. Cells were incubated with a dose response of GNE-617 for 4 days .(TIF)Click here for additional data file.S2 FigA) Larger image of western blot shown in B) Larger image of western blot shown in C) Larger image of western blots shown in D) Larger images of gels shown in (TIF)Click here for additional data file.S1 Table2 fold change for each metabolite relative to the level determined in control (DMSO) treated cells.The raw data for each metabolite at each time-point is shown (n = 5), along with the average data and the log-(XLSX)Click here for additional data file.


Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage. Hu et al. map the impact of knockdown of 625 cancer and DNA repair genes on the cellular response to every class of chemotherapy. This map can be used to predict drug responses and identify synergistic drug combinations, and it reveals two factors, ARID1A and GPBP1, whose loss contributes to PARP inhibitor resistance. BRCA1, BRCA2, and ERCC1 are more responsive to certain chemotherapies  caused by BRCA1/2 mutation that is necessary to repair DNA lesions incurred by PARP inhibition -approved chemotherapies. We demonstrate that the map recovers many known modulators of chemo-sensitivity and is able to link therapies with common mechanisms of action. We show that the map is a predictive tool to computationally infer cancer cell line drug sensitivity and design drug combinations with targeted inhibitors of ATR and APEX1. We also identify ARID1A and GPBP1 as factors whose loss contributes to PARP inhibitor and platinum resistance, a finding that is supported by data from HGSOC patients. This chemical-genetic interaction map can be used to identify factors that dictate responses to chemotherapy and aid in the translation from tumor genomics to therapeutics.S)  of 10%, 5%, and <1% to cutoffs of S = ±3, ±4, and ±5, respectively  studies as well as the METABRIC breast cancer cohort, covering over 3,000 samples to identify a set of over 200 breast and 170 ovarian cancer genes whose deletion occurred with high frequency in these studies  Cancer . We alsoectively . Altoget 10% FDR . These i 10% FDR .S = −4.4; BRCA2 S = −5.6). This finding also extended to members of the BRCA pathway, SHFM1 (S = −2.9) and PALB2 (S = −4.9), which mediate HR as previously reported   . Synthet = −5.1) . Using t = −5.1) . Illustr = −5.1) . This wa = −5.1) . We concWhile broad classes of chemotherapeutics target various aspects of DNA processing and repair, their exact mechanisms of action are often unclear . TherefoBased on the similarity of profiles between related drugs, we next sought to combine genetic interactions based on drug class to identify a consensus chemical-genetic interaction map. In this consensus map, a connection between a gene and a compound category reflects a concordance of response across multiple related drugs and compared against a randomly permuted background. At an FDR of 0.1% we identified 125 connections between genes and different drug classes . While cThe ability of the chemical-genetic interaction map to identify causal genetic relationships also raises the possibility that a quantitative map could complement pharmacogenomics efforts using large panels of cancer cell lines . While pS = 6.5), and in cell lines, EIF4A1 expression is positively correlated with methotrexate sensitivity across 645 cell lines , consistent with the network prediction. Alternatively, SNX24 knockdown was synthetic lethal with paclitaxel (S = −6.8), and SNX24 expression was negatively correlated with drug sensitivity . Thus, computational analysis of chemical-genetic interaction maps can be used to complement cancer cell line screens and may be able to produce biomarkers that bridge correlation with causation.The significance and quantitative nature of the overlap between our map and expression-based correlates of drug sensitivity found in cancer cell lines prompted us to explore whether this map could be used to systematically predict cancer cell line sensitivity in an unsupervised fashion. For each drug, we used the relative expression of each of the genes in its network to derive a drug response prediction for every cell line . We evaluated this method using a sliding cutoff to define the specific network for each drug and found that more stringent networks provided increased power to predict drug sensitivity, with nearly 60%–70% of drugs predicted accurately at a cutoff between 5 and 6 . At a cuS = −7.7) and BMN673  (S = −3.9) (There has been considerable interest in the development of targeted therapies that inhibit DNA-repair machinery to be used in combination with agents that generate specific types of DNA damage . We obse = −5.0) , with a  = −5.0)   . Analysi = −5.0) . We also = −3.9) . We test = −3.9) . CRT0044 = −3.9)  and show = −3.9) . TherefoS score < −4 had a 40% chance of being scored as a synthetic lethal in half the cell lines tested. We note that no interaction was scored as synthetic lethal in all cell lines. Since two of the tested lines were BRCA1 mutant , a likely reason for this is that factors whose loss leads to PARP sensitivity in HR-competent MCF10A cells may not be relevant in BRCA1 mutant cells that are already HR deficient. Therefore, these data suggest that interaction strength in MCF10A cells can be used to predict genetic interactions in other cell lines, highlighting the quantitative nature of this map.We next evaluated the map as a systematic resource to predict molecules involved in DNA repair and associated mechanisms of resistance to chemotherapy. We focused on PARP inhibitors olaparib, veliparib, rucaparib, and BMN673, as well as cisplatin, since this class was highly covered in the map, has similar mechanisms of action, and generates DNA damage that depends on repair via HR . As cont−/− MCF10A cells, which were resistant to BMN673 in comparison to parental MCF10A cells  for these 10 ARID1A mutant cases was significantly lower than for the rest of this cohort  analysis of control and GPBP1-knockdown MCF10A cells treated with or without BMN673. qRT-PCR of GPBP1-knockdown cells confirmed 90% knockdown at the mRNA level in this experiment . In respBRCA1 mutant cancer cell lines, suggesting that GPBP1 loss may bypass the requirement of BRCA1 for HR  Dashboard (https://ctd2-dashboard.nci.nih.gov/).We present a quantitative map to link the efficacy of chemotherapeutics to tumor genetics that can serve as a platform for the functional and therapeutic translation of tumor genomes. In contrast to most standard genetic screens, this approach provides a quantitative readout that approximates genetic interaction strength and allows for the comparison of responses across many drugs. This map is able to recapture many known synthetic lethal interactions, and future work may expand on this map beyond the set of genes screened here, as well as using complementary technologies such as CRISPR/Cas9 to evaluate the impact of gene knockout versus knockdown as well as gene activation. To aid in integration of these data with ongoing efforts to systematize cancer-related screens, data from this network have been deposited into the Cancer Target Discovery and Development  and maintained in the MCF10A media described earlier. Rucaparib was provided by Clovis Oncology; other drugs were purchased from Selleckchem.MCF10A cells were maintained in DMEM/F12 medium supplemented with 5% horse serum, epidermal growth factor (20 ng/mL), insulin (10 μg/mL), hydrocortisone (0.5 mg/mL), cholera toxin (100 ng/mL), and penicillin/streptomycin. All cell lines were obtained from the ATCC and cultured according to listed protocols, except for ARID1A50) concentration or DMSO for 72 hr, after which cells were stained with Hoescht 33342 and counted using a Thermo CellInsight high-content microscope.MCF10A cells were reverse transfected in 384-well plates  using 5 ng of esiRNA (Sigma) with RNAiMax (0.05 μL per well) as a transfection reagent in quadruplicate. Cells were transfected for 24 hr, and then the entire plate was treated with one drug at a half maximal inhibitory concentration  or negative (gene loss drives sensitivity to drug) values. FDR was calculated based on the percentage of negative-control knockdowns (GFP) whose score exceeded a given threshold. The described protocol is available in MATLAB, and code and raw data to recreate the dataset are available at https://github.com/BandyopadhyayLab/.After drug or DMSO treatment, each plate was median centered to 2,000 cells per well to normalize relative proliferation rates. Plates had a minimum internal correlation across the 4 replicate wells of 0.7. Each well in the drug-treated plate was then compared to the same well in the DMSO-treated plate. We observed an overall linear relationship between drug and DMSO plates, indicating that most esiRNAs have no effect on drug sensitivity. Next, the set of 4 normalized replicate values in the DMSO plate was compared to the same in the drug plate, and both the fold change in cell number and the p value of significance of this difference in medians were calculated using a modified t test. The 1234567


MDR1/ABCB1) and BCRP (ABCG2) are known ATP-binding cassette (ABC) drug transporters that mediating MDR. ABCB5 is a close relative to ABCB1, which also mediates MDR. Constitutive activation of the EGF receptor is tightly linked to the development of chemotherapeutic resistance. BetA inhibited P-gp, BCRP, ABCB5 and mutation activated EGFR overexpressing cells with similar efficacy as their drug-sensitive parental counterparts. Furthermore, the mRNA expressions of ABCB1, BCRP, ABCB5 and EGFR were not related to the 50% inhibition concentrations (IC50) for BetA in a panel of 60 cell lines of the National Cancer Institute (NCI), USA. In addition to well-established MDR mechanisms, we attempted to identify other molecular mechanisms that play a role in mediating BetA's cytotoxic activity. For this reason, we performed COMPARE and hierarchical cluster analyses of the transcriptome-wide microarray-based mRNA expression of the NCI cell lines panel. Various genes significantly correlating to BetA's activity were involved in different biological processes, e.g., cell cycle regulation, microtubule formation, signal transduction, transcriptional regulation, chromatin remodeling, cell adhesion, tumor suppression, ubiquitination and proteasome degradation. Immunoblotting and in silico analyses revealed that the inhibition of AMFR activity might be one of the mechanisms for BetA to overcome MDR phenotypes. In conclusion, BetA may have therapeutic potential for the treatment of refractory tumors.Betulinic acid (BetA) is a naturally occurring pentacyclic triterpene isolated from the outer bark of white-barked birch trees and many other medicinal plants. Here, we studied betulinic acid's cytotoxic activity against drug-resistant tumor cell lines. P-glycoprotein ( Vitex negundo  is a lupane-type triterpenoid firstly identified and isolated in the 18th century by Johann Tobias Lowitz from the outer bark of white-barked birch trees  are classified into either host factors, in which decreased absorption and distribution with increased metabolism and excretion take place for certain drugs, or cancer cell genetic alterations, in which alternative pathways to evade apoptosis and triggered cell death are activated. Therefore, the design of MDR reversing agents should take into account the factors mentioned above to improve the cancer therapeutic approaches.in vitro and metastasis in vivo  is an ubiquitin E3-ligase cell surface glycoprotein, also known as GP78, that is known to play a role in metastasis, tumor progression and recurrence. AMFR regulates cell motility signaling In the present work, BetA's anticancer activity against drug-resistant tumor cell lines was studied, in which either MDR-conferring ABC-transporters  or mutation-activated EGFR were overexpressed. Furthermore, we investigated the transcriptome-wide mRNA expression profiles of tumor 60 cell lines by COMPARE and hierarchical cluster analyses, to determine multiple other unknown molecular determinants that affect the response of tumor cells to BetA. Using gene-hunting approach, one of the sensitivity determinants genes, AMFR, was selected as a target candidate for BetA. We present novel evidence via molecular docking and immunoblotting analysis that inhibition of AMFR activity might be one of the mechanisms for BetA to overcome MDR phenotypes.Drug sensitive CCRF-CEM and multidrug-resistant P-glycoprotein (P-gp)-overexpressing CEM/ADR5000 leukemic cells were cultured in RPMI-1640 medium supplemented with 10% FBS and 1% penicillin/streptomycin . Doxorubicin  was added to maintain overexpression of P-gp  in resistant cells  reduction assay was performed to assess cytotoxicity of BetA in a concentration range of 1010IC50 values) deposited at the NCI website (http://dtp.nci.nih.gov) to identify candidate genes mediate sensitivity and resistance, respectively, to BetA. This gene-hunting approach is based on Pearson's rank correlation test. To obtain COMPARE rankings, a scale index of correlation coefficients  has been generated.We performed COMPARE analysis for a transcriptome-wide search for correlations between gene expressions and BetA response (loghttps://discover.nci.nih.gov/cimminer/), we performed agglomerative hierarchical cluster analysis (WARD method) to cluster the mRNA expression of genes identified by COMPARE analysis and a heatmap was prepared accordingly.Using the CIMMINER program  were treated with varying concentrations of BetA, harvested after 24 h, and washed with PBS. Using M-PER™ mammalian protein extraction buffer (Thermo Fisher Scientific), the entire proteins were extracted from the cell lysates. Then, sodium dodecyl sulfate polyacrylamide gel electrophoresis was performed to isolate the proteins, the proteins were then transferred to polyvinylidene fluoride membranes (Ruti®-PVDF)  for western blotting. Five percent of bovine serum albumin was used to block the membranes and then the membranes were incubated with specific primary antibodies against AMFR   and β-actin  . The blots were probed with horseradish peroxidase-linked IgG secondary antibodies  for 2 h at room temperature. Finally, LuminataTM Classico Western HRP substrate  was added for 5 min in the dark. Alpha Innotech FluorChem Q system  was used for documentation and band analysis.Both sensitive and resistant breast cancer cell lines (10Table 3). Using AutodockTools-1.5.6rc3, the proteins' PDB files were converted to PDBQT format and set as the macromolecule, upon which docking is to be performed. 2D structures of the BetA was constructed and later converted to 3D structures using Corina Online Demo. A grid box was allocated to define docking spaces upon the macromolecule. Energies for each atom type in the ligand were calculated at each grid point using Autogrid 4.2. These calculated energies were later used to predict binding affinity of BetA. Docking was carried out using Autodock 4.2 with 250 runs and 2.5 million evaluations for each cycle via the Lamarckian algorithm. Lowest binding energies were retrieved from the correspondent dlg file and amino acids were analyzed by Autodock Tools. Images were created using Visual Molecular Dynamics VMD.Molecular docking was done in an approach previously reported by us -overexpressing CEM/ADR5000 cells and drug-sensitive parental CCRF-CEM cells using a resazurin assay. No cross-resistance of the CEM/ADR5000 cells was observed  with the log10IC50 values of the NCI cell lines were correlated with standard anticancer drugs . Clusters 5, 6, and 7 contained in its majority of cell lines sensitive to BetA, whereas clusters 1, 2, 3, and 4 contained in its majority resistant ones.The mRNA expression values of all NCI cell lines for the genes listed in Table 10IC50 values for BetA in sensitive NCI cell lines was observed. This prompted us to investigate whether the expression of AMFR in our MDR cells model will be affected when treated with BetA or not. Therefore, we performed western blot analysis after treating breast cancer cell lines with varying concentration of BetA. The results showed that BetA was able to inhibit expression of AMFR in a dose dependent manner . In addition to its ligand AMF. As shown in Table In this study, we investigated the cytotoxic activity of BetA against multidrug-resistant cancer cells and determined the molecular mechanisms associated with either sensitivity or resistance of cancer cells toward BetA. Therefore, we treated cancer cell lines expressing different MDR conferring genes  with BetA. To determine the molecular mechanisms, we mined the NCI's Developmental Therapeutics Program database for the documented screening of BetA with a panel of 60 cancer cell lines. The rationale of this approach was to define genes, whose expressions correlated to the pattern of cellular responsiveness to BetA. The genes assessed by this approach belonged to diverse classes, e.g. oncogenes, tumor suppressor genes, drug resistance mediating transporters, heat shock proteins, telomerase, cytokine receptors, molecules of the cell cycle and apoptotic pathways, DNA repair enzymes, components of cytoskeleton, intracellular signaling molecules, and enzymes of metabolism  transporters  family activates signaling pathways regulating cellular proliferation, angiogenesis initiation, apoptosis inhibition and survival, which subsequently results in increasing tumor masses and chemotherapy refractoriness. In glioblastoma tumors, the common EGFR mutation is in frame deletion of exon 2–7 resulting in ligand-binding domain deletion of the EGFR , signal transduction and transcriptional regulation , protein synthesis , chromatin remodeling , cell adhesion , tumor suppression , ubiquitination and proteasome degradation . All of these mechanisms foster tumor progression.Since the established MDR mechanisms investigated in this study were not involved in resistance toward BetA, we performed a microarray-based transcriptome wide screening of genes using COMPARE analysis, whose mRNA expression correlated with the login vitro and in vivo. The AMF–paclitaxel conjugate inhibited colon and breast cancer cells proliferation more effectively than free paclitaxel. Intra-tumoral injection of the AMF–paclitaxel conjugate also induced more effective tumor regression and increased survival in K1735M1 and B16-F1/paclitaxel resistant mouse melanoma models , inhibitors of CDKs and tumor suppressors . Many tumor cells overexpress CDKs increasing their proliferative capacity. CDK-cyclin complexes tightly monitor the four sequential phases of cell cycle, namely G1, S, G2, and M phase, and dysregulation in any phase results in uncontrolled cell growth  did not hamper BetA activity in tumor cells. Furthermore, microarray-based expression profiling of 60 NCI cell lines revealed that BetA exerts cytotoxic activity toward cancer cells by multiple mechanisms rather than by a single one. Summing up all findings in this study, we can postulate that BetA is a promising candidate with multiple modes of action to treat refractory tumors. Further investigations are needed to assure its efficacy and safety in vitro experiments and wrote the manuscript; NM: performed COMPARE and cluster analysis; YS: provided ABCB5 transfected cell lines; HA-A: designed the bioassays and edited the manuscript; TE: supervised the work, provided the facilities for the study and edited the manuscript. All authors read the manuscript and approved the final version.MS: carried out the HA-A was employed by Steigerwald, Darmstadt, Germany. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.