Abstract The epithelial–mesenchymal transition (EMT) and its reversal, mesenchymal–epithelial transition (MET), are fundamental processes involved in tumor cell invasion and metastasis. SEMA3F is a secreted semaphorin and tumor suppressor downregulated by TGF-β1 and ZEB1-induced EMT. Here, we report that neuropilin (NRP)-2, the high-affinity receptor for SEMA3F and a coreceptor for certain growth factors, is upregulated during TGF-β1–driven EMT in lung cancer cells. Mechanistically, NRP2 upregulation was TβRI dependent and SMAD independent, occurring mainly at a posttranscriptional level involving increased association of mRNA with polyribosomes. Extracellular signal—regulated kinase (ERK) and AKT inhibition blocked NRP2 upregulation, whereas RNA interference-mediated attenuation of ZEB1 reduced steady-state NRP2 levels. In addition, NRP2 attenuation inhibited TGF-β1–driven morphologic transformation, migration/invasion, ERK activation, growth suppression, and changes in gene expression. In a mouse xenograft model of lung cancer, NRP2 attenuation also inhibited locally invasive features of the tumor and reversed TGF-β1–mediated growth inhibition. In support of these results, human lung cancer specimens with the highest NRP2 expression were predominantly E-cadherin negative. Furthermore, the presence of NRP2 staining strengthened the association of E-cadherin loss with high-grade tumors. Together, our results demonstrate that NRP2 contributes significantly to TGF-β1–induced EMT in lung cancer. Cancer Res; 73(23); 7111–21. ©2013 AACR.

CCR Translation for This Article from Development of an Integrated Genomic Classifier for a Novel Agent in Colorectal Cancer: Approach to Individualized Therapy in Early Development

Kinase inhibitor sensitivity of the CUTC cell lines.

PDF - 1164KB, Supplemental Table 1: Inhibition of Wee1 inhibits proliferation of AML cells independent of known molecular abnormalities. Supplemental Figure 1: AML cells are sensitive to MK1775 independent of p53 functionality. Supplemental Figure 2: MK1775 sensitizes AML cells to cytarabine independent of p53 function. Supplemental Figure 3: MK1775 sensitizes AML cells to cytarabine independent of p53 function. Supplemental Figure 4: MK1775 inhibits phosphorylation of CDK1, but does not cause widespread unscheduled mitosis. Supplemental Figure 5: Treatment with MK1775 does not sensitize AML cells to doxorubicin. Supplemental Figure 6: Treatment of AML cells with cytarabine and MK1775 is synergistic independent of p53 function in an isogenic model. Supplemental Figure 7: Treatment of lung cancer cells with antimetabolites and MK1775 is synergistic. Supplemental Figure 8: MK1775 and cytarabine is more effective than cytarabine alone in vivo.

Supplementary Methods, Supplementary Figure and Table Legends

PDF file - 97K, shRNA target sequences (S1); Primers used for quantitative RT-PCR (S2).

PDF file - 1864K, NRP2 is stably up-regulated by TGFβ in lung cancer cells without increasing the halflife of mRNA or protein (S1); NRP2 up-regulation is blocked by inhibition of TβRI, AKT, ERK and ZEB1, but not by expression of inhibitory SMAD7 (S2); NRP2 knockdown blunts TGFβ-mediated transcriptional responses, ERK and AKT phosphorylation but does not influence SMAD activation (S3); NRP2 knockdown blocks cell scattering induced by TGFβ (S4); Immunohistochemical detection of NRP2 and cytokeratin in TGFβ exposed xenograft tumors with NRP2 or control knockdowns (S5); E-cadherin and NRP2 immunostaining of samples from a tumor microarray (S6); Predicted miRNA target sites in the 3' UTR of NRP2 and up-regulated NRP2 protein levels in hnRNP E1 knockdown cells (S7).

Supplementary Figure S1. Copy number alteration profile for TERT locus in cell lines harboring homozygous TERT promoter mutations. Copy number changes are expressed as shades of red (gain) or blue (loss). Names of cell lines and log-ratio (lr) values are shown. Supplementary Figure S2. Genome-wide copy number alteration profile of PTC-derived cell lines (top) vs. ATC-derived cell lines (bottom). Chromosome numbers are shown on the top panel. Copy number changes are expressed as shades of red (gain) or blue (loss). Supplementary Figure S3. Representation of the chromosomal location for the 16 recurrent copy number alterations identified by GISTIC in 58 thyroid cancer cell lines. Supplementary Figure S4. Hierarchical clustering of gene expression profiles of thyroid cancer cell lines. Spearman correlation distance metric and Ward agglomeration method were used. Colors identify cell lines originating from tumors of different histologic subtypes. Font color indicates the histological type of the primary tumor from which these cell lines were generated, as follows: red= anaplastic; purple= poorly differentiated; green=follicular; blue= papillary thyroid cancer. Colored dots represent driver alteration: blue= BRAFV600E; orange= N-/H-/KRAS; purple= CCDC6-RET; green= NF1; yellow= MKRN1-BRAF; gray= FGFR2-OGDH; black= unknown. Supplementary Figure S5. Expression of individual genes in the thyroid differentiation signature. Supplementary Figure S6. Correlation of BRS calculated using our algorithm vs. the algorithm published by TCGA. The color of the circles indicates tumor genotype: blue- BRAFV600E, red- RAS gene mutation, green - neither BRAFV600E nor RAS mutation, orange - both BRAFV600E and RAS gene mutations present. Tumors with low BRS are BRAFV600E-like; tumors with high BRS are RAS-like. Supplementary Figure S7. Representative pictures of BAM files from MSK-IMPACT sequencing of CUTC5 specimens for three mutations: A. BRAF p.V600E; B. TP53 C135W; and C. ARID1A E1108*. Each panel shows the sequencing reads for the cell line (top) and primary tumor (bottom). The reference allele and protein translation is shown in the bottommost area, whereas the alternative allele is highlighted in the sequencing reads within each panel. Percentages on the left represent the frequency of the alternative allele in each of the samples tested. All three mutations are detected at low frequencies in the original pleural effusion sample, from which CUTC5 cell line was established. Supplementary Figure S8. Evolution of allelic frequencies from thyroid primary tumors to cell lines for A. TERT promoter; and B. TP53. Graphic representation of the alternative allele frequencies ("Alt Allele Freq"). Cell line names are color-coded on the right side of each graph. All TERT promoter mutations are the canonical c.-124C>T or c.-146C>T, whereas TP53 mutations are indicated. Supplementary Figure S9. Genome-wide copy number alteration profile of primary tumor/cell line and/or patient-derived xenograft (PDX) paired samples from 11 patients. Chromosome numbers are shown on the top panel. Copy number changes are expressed as shades of red (gain) or blue (loss). Colored dots indicate specimen types: cell line (green), primary tumor (orange) or PDX (purple). Cell line names are provided. Supplementary Figure S10. Global MSK-IMPACT copy number profile of THJ560 paired samples. Sample-centered representation of the copy number alterations, expressed as log2 ratios, for primary tumor (top panel), cell line (middle panel) and patient-derived xenograft (bottom panel). Table inserts show the top deletions, corresponding to the CDKN2A/CDKN2B locus for the cell line and xenograft, which are absent in the primary tumor.

Electropherograms of the CUTC5, CUTC48, CUTC60 and CUTC61 cell line, PDX, and patient samples.

Supplementary Table S1. Cell line authentication by Short Tandem Repeat Profiling (STR) Supplementary Table S2. Full list of genetic variants identified by MSK-IMPACT sequencing Supplementary Table S3. Details for identified high-confidence gene rearrangements resulting in in-frame fusion proteins Supplementary Table S4. Recurrent copy number alterations in thyroid cancer cell lines. Supplementary Table S5. Genes located within the recurrent copy number alteration regions. Supplementary Table S6. Differentially expressed genes, which are located within CNA regions. "Matching_CNA_type" column indicates whether the direction of the expression change corresponds to the CNA type (133 out of 134, overexpression for gene amplification and underexpression for gene deletions). Supplementary Table S7. Thyroid cancer cell lines gene expression data. Normalized gene expression values for all genes in the 44 cell lines assessed by Affymetrix expression microarray in this study ("CU" prefix, University of Colorado). Supplementary Table S8. Genes with differential expression in PTC vs. ATC cell lines. List of differentially expressed genes between PTC-derived vs. ATC-derived cell lines (limma, adjusted p-value<0.05). Supplementary Table S9. Genes with differential expression in PTC vs. ATC tumors. List of top 1000 differentially expressed genes when comparing PTC vs. ATC tumors from other studies (19,20), assessed with the same platform used for cell line transcriptomic profiling. Supplementary Table S10. Thyroid differentiation score (TDS) in thyroid cancer cell lines and tissues. TDS values are calculated for 44 cell lines from this study ("CU" prefix, University of Colorado) and thyroid tissues from other studies, assessed with the same platform: 9 PTC-normal pairs ("He", (19)) 17 PDTCs and 20 ATCs ("Landa", (20)). Supplementary Table S11. Association of TDS16 and TDS13 with clinical and histopathologic characteristics of PTC tumors from the TCGA. P-values calculated with Kruskal-Wallis rank sum test are shown. Supplementary Table S12. BRAFV600E-RAS scores (BRS) and the sensitivity of thyroid cancer cell lines to trametinib and PD-0325901. Drug sensitivity is expressed as a relative area under the dose response curve.

Biopreservation and BiobankingVol. 19, No. 5 EditorialThe Continuing Saga of Cell Line MisidentificationJim Vaught and Christopher T. KorchJim VaughtEditor-in-Chief, Biopreservation and Biobanking.Search for more papers by this author and Christopher T. KorchAddress correspondence to: Christopher T. Korch, PhD, Assistant Clinical Professor, Mailstop 8117, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, 12801 East 17th Avenue, Aurora, Colorado 80045, USA E-mail Address: christopher.korch@cuanschutz.eduDivision of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.Search for more papers by this authorPublished Online:18 Oct 2021https://doi.org/10.1089/bio.2021.29094.jjvAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"The Continuing Saga of Cell Line Misidentification." Biopreservation and Biobanking, 19(5), pp. 357–358FiguresReferencesRelatedDetailsCited byQuality Control of Cell Lines Using DNA as Target16 February 2022 | DNA, Vol. 2, No. 1 Volume 19Issue 5Oct 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Jim Vaught and Christopher T. Korch.The Continuing Saga of Cell Line Misidentification.Biopreservation and Biobanking.Oct 2021.357-358.http://doi.org/10.1089/bio.2021.29094.jjvPublished in Volume: 19 Issue 5: October 18, 2021Online Ahead of Print:October 11, 2021PDF download

Cell lines are essential models for biomedical research. However, they have a common and important problem that needs to be addressed. Cell lines can be misidentified, meaning that they no longer correspond to the donor from whom the cells were first obtained. This problem may arise due to cross-contamination: the accidental introduction of cells from another culture. The contaminant, which is often a rapidly dividing cell line, will overgrow and replace the original culture. The end result is a false cell line, also known as a misidentified or imposter cell line. False cell lines may come from an entirely different species, tissue, or cell type than the original donor. If undetected, false cell lines produce unreliable and irreproducible results that pollute the biomedical literature and threaten the development of reliable drug discovery and meaningful patient treatments.The goal of this study was to ascertain how widespread this problem is and how it affects the literature, as well as to estimate how much funding has been used to produce pools of scientific literature of questionable value. We focus on HEp-2 [HeLa] and Intestine 407 [HeLa], two false cell lines that are widely used in the scientific literature but were shown to be cross-contaminated in 1967. These two cell lines have been used in 8497 and 1397 published articles and extensively described as laryngeal cancer and normal intestine, respectively, rather than their true identity: the cervical cancer cell line HeLa. Discussed are tools, approaches, and resources that can address this issue-both retrospectively and prospectively.

Hybrid (both intra-species and inter-species) cell lines arise through intentional or nonintentional fusion of somatic cells having different origins. Hybrid cell lines can pose a problem for authentication testing to confirm cell line identity, since the results obtained may not conform to the results expected for the two parental cell types. Thus, depending on the identity testing methodology, a hybrid cell may display characteristics of one of the parental cell type or of both. In some instances, the hybrid cell line may display characteristics that are different from those displayed by either parental cell type; these differences may not necessarily indicate cellular cross-contamination. Testing should be performed as soon as possible after an intended fusion has occurred, so that a baseline reference profile is available for later comparison. In this article, we describe the various approaches that have been used for identifying hybrid cell lines and the results that might be expected when using various technologies for this purpose.

Abstract Cancer cell lines are critical models to study tumor progression and response to therapy. In 2008, we showed that approximately 50% of thyroid cancer cell lines were redundant or not of thyroid cancer origin. We therefore generated new authenticated thyroid cancer cell lines and patient-derived xenograft (PDX) models using in vitro and feeder cell approaches, and characterized these models in vitro and in vivo. We developed four thyroid cancer cell lines, two derived from 2 different patients with papillary thyroid cancer (PTC) pleural effusions, CUTC5, and CUTC48; one derived from a patient with anaplastic thyroid cancer (ATC), CUTC60; and one derived from a patient with follicular thyroid cancer (FTC), CUTC61. One PDX model (CUTC60-PDX) was also developed. Short tandem repeat (STR) genotyping showed that each cell line and PDX is unique and match the original patient tissue. The CUTC5 and CUTC60 cells harbor the BRAF (V600E) mutation, the CUTC48 cell line expresses the RET/PTC1 rearrangement, and the CUTC61 cells have the HRAS (Q61R) mutation. Moderate to high levels of PAX8 and variable levels of NKX2-1 were detected in each cell line and PDX. The CUTC5 and CUTC60 cell lines form tumors in orthotopic and flank xenograft mouse models. Implications: We have developed the second RET/PTC1-expressing PTC-derived cell line in existence, which is a major advance in studying RET signaling. We have further linked all cell lines to the originating patients, providing a set of novel, authenticated thyroid cancer cell lines and PDX models to study advanced thyroid cancer.

Abstract Purpose: Thyroid cancer cell lines are valuable models but have been neglected in pancancer genomic studies. Moreover, their misidentification has been a significant problem. We aim to provide a validated dataset for thyroid cancer researchers. Experimental Design: We performed next-generation sequencing (NGS) and analyzed the transcriptome of 60 authenticated thyroid cell lines and compared our findings with the known genomic defects in human thyroid cancers. Results: Unsupervised transcriptomic analysis showed that 94% of thyroid cell lines clustered distinctly from other lineages. Thyroid cancer cell line mutations recapitulate those found in primary tumors (e.g., BRAF, RAS, or gene fusions). Mutations in the TERT promoter (83%) and TP53 (71%) were highly prevalent. There were frequent alterations in PTEN, PIK3CA, and of members of the SWI/SNF chromatin remodeling complex, mismatch repair, cell-cycle checkpoint, and histone methyl- and acetyltransferase functional groups. Copy number alterations (CNA) were more prevalent in cell lines derived from advanced versus differentiated cancers, as reported in primary tumors, although the precise CNAs were only partially recapitulated. Transcriptomic analysis showed that all cell lines were profoundly dedifferentiated, regardless of their derivation, making them good models for advanced disease. However, they maintained the BRAFV600E versus RAS-dependent consequences on MAPK transcriptional output, which correlated with differential sensitivity to MEK inhibitors. Paired primary tumor-cell line samples showed high concordance of mutations. Complete loss of p53 function in TP53 heterozygous tumors was the most prominent event selected during in vitro immortalization. Conclusions: This cell line resource will help inform future preclinical studies exploring tumor-specific dependencies.

Research in toxicology relies on in vitro models such as cell lines. These living models are prone to change and may be described in publications with insufficient information or quality control testing. This article sets out recommendations to improve the reliability of cell-based research.

A variety of analytical approaches have indicated that melanoma cell line UCLA‐SO‐M14 (M14) and breast carcinoma cell line MDA‐MB‐435 originate from a common donor. This indicates that at some point in the past, one of these cell lines became misidentified, meaning that it ceased to correspond to the reported donor and instead became falsely identified (through cross‐contamination or other means) as a cell line from a different donor. Initial studies concluded that MDA‐MB‐435 was the misidentified cell line and M14 was the authentic cell line, although contradictory evidence has been published, resulting in further confusion. To address this question, we obtained early samples of the melanoma cell line (M14), a lymphoblastoid cell line from the same donor (ML14), and donor serum preserved at the originator's institution. M14 samples were cryopreserved in December 1975, before MDA‐MB‐435 cells were established in culture. Through a series of molecular characterizations, including short tandem repeat (STR) profiling and cytogenetic analysis, we demonstrated that later samples of M14 and MDA‐MB‐435 correspond to samples of M14 frozen in 1975, to the lymphoblastoid cell line ML14, and to the melanoma donor's STR profile, sex and blood type. This work demonstrates conclusively that M14 is the authentic cell line and MDA‐MB‐435 is misidentified. With clear provenance information and authentication testing of early samples, it is possible to resolve debates regarding the origins of problematic cell lines that are widely used in cancer research.