scholarly journals Matching cell lines with cancer type and subtype of origin via mutational, epigenomic and transcriptomic patterns

2019 ◽  
Author(s):  
Marina Salvadores ◽  
Francisco Fuster-Tormo ◽  
Fran Supek

AbstractCell lines are commonly used as cancer models. Because the tissue and/or cell type of origin provide important context for understanding mechanisms of cancer, we systematically examined whether cell lines exhibit features matching the cancer type that supposedly originated them. To this end, we aligned the mRNA expression and DNA methylation data between ∼9,000 solid tumors and ∼600 cell lines to remove the global differences stemming from growth in cell culture. Next, we created classification models for cancer type and subtype using tumor data, and applied them to cell line data. Overall, the transcriptomic and epigenomic classifiers consistently identified 35 cell lines which better matched a different tissue or cell type than the one the cell line was originally annotated with; we recommend caution in using these cell lines in experimental work. Six cell lines were identified as originating from the skin, of which five were further corroborated by the presence of a UV-like mutational signature in their genome, strongly suggesting mislabelling. Overall, genomic evidence additionally supports that 22 (3.6% of all considered) cell lines may be mislabelled because we predict they originate from a different tissue/cell type. Finally, we cataloged 366 cell lines in which both transcriptomic and epigenomic profiles strongly resemble the tumor type of origin, designating them as ‘golden set’ cell lines. We suggest these cell lines are better suited for experimental work that depends on tissue identity and propose tentative assignments to cancer subtypes. Finally, we show that accounting for the uncertain tissue-of-origin labels can change the interpretation of drug sensitivity and CRISPR genetic screening data. In particular, in brain, lung and pancreatic cancer cell lines, many novel determinants of drug sensitivity or resistance emerged by focussing on the cell lines that are best matched to the cancer type of interest.

2020 ◽  
Vol 6 (27) ◽  
pp. eaba1862
Author(s):  
Marina Salvadores ◽  
Francisco Fuster-Tormo ◽  
Fran Supek

Cell lines are commonly used as cancer models. The tissue of origin provides context for understanding biological mechanisms and predicting therapy response. We therefore systematically examined whether cancer cell lines exhibit features matching the presumed cancer type of origin. Gene expression and DNA methylation classifiers trained on ~9000 tumors identified 35 (of 614 examined) cell lines that better matched a different tissue or cell type than the one originally assigned. Mutational patterns further supported most reassignments. For instance, cell lines identified as originating from the skin often exhibited a UV mutational signature. We cataloged 366 “golden set” cell lines in which transcriptomic and epigenomic profiles strongly resemble the cancer type of origin, further proposing their assignments to subtypes. Accounting for the uncertain tissue of origin in cell line panels can change the interpretation of drug screening and genetic screening data, revealing previously unknown genomic determinants of sensitivity or resistance.


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.


2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e14544-e14544
Author(s):  
Eva Budinska ◽  
Jenny Wilding ◽  
Vlad Calin Popovici ◽  
Edoardo Missiaglia ◽  
Arnaud Roth ◽  
...  

e14544 Background: We identified CRC gene expression subtypes (ASCO 2012, #3511), which associate with established parameters of outcome as well as relevant biological motifs. We now substantiate their biological and potentially clinical significance by linking them with cell line data and drug sensitivity, primarily attempting to identify models for the poor prognosis subtypes Mesenchymal and CIMP-H like (characterized by EMT/stroma and immune-associated gene modules, respectively). Methods: We analyzed gene expression profiles of 35 publicly available cell lines with sensitivity data for 82 drug compounds, and our 94 cell lines with data on sensitivity for 7 compounds and colony morphology. As in vitro, stromal and immune-associated genes loose their relevance, we trained a new classifier based on genes expressed in both systems, which identifies the subtypes in both tissue and cell cultures. Cell line subtypes were validated by comparing their enrichment for molecular markers with that of our CRC subtypes. Drug sensitivity was assessed by linking original subtypes with 92 drug response signatures (MsigDB) via gene set enrichment analysis, and by screening drug sensitivity of cell line panels against our subtypes (Kruskal-Wallis test). Results: Of the cell lines 70% could be assigned to a subtype with a probability as high as 0.95. The cell line subtypes were significantly associated with their KRAS, BRAF and MSI status and corresponded to our CRC subtypes. Interestingly, the cell lines which in matrigel created a network of undifferentiated cells were assigned to the Mesenchymal subtype. Drug response studies revealed potential sensitivity of subtypes to multiple compounds, in addition to what could be predicted based on their mutational profile (e.g. sensitivity of the CIMP-H subtype to Dasatinib, p<0.01). Conclusions: Our data support the biological and potentially clinical significance of the CRC subtypes in their association with cell line models, including results of drug sensitivity analysis. Our subtypes might not only have prognostic value but might also be predictive for response to drugs. Subtyping cell lines further substantiates their significance as relevant model for functional studies.


1990 ◽  
Vol 10 (7) ◽  
pp. 3334-3342 ◽  
Author(s):  
D Nitsch ◽  
A F Stewart ◽  
M Boshart ◽  
R Mestril ◽  
F Weih ◽  
...  

The relationship between DNase I-hypersensitive sites (HSs) and transcriptional enhancers of the rat tyrosine aminotransferase (TAT) gene was examined by comparing HSs in and around the TAT gene with the activity of the corresponding DNA sequences in transient transfection assays. In this manner, we identified two HSs as liver-specific enhancers. Of three hepatoma cell lines examined, only one sustained TAT mRNA levels comparable to those of liver. In this cell line, both enhancers were strongly active, and strong hypersensitivity in chromatin over the enhancers was evident. The other two hepatoma cell lines had reduced levels of TAT mRNA and no or altered hypersensitivity over either the enhancers or the promoter. One of these lines carried a negative regulator of the TAT gene, the tissue specific extinguisher Tse-1. This cell line exhibited all HSs characteristic of the strongly active gene except at the promoter; however, one enhancer was inactive even though hypersensitive in chromatin. In a TAT-nonexpressing cell line, inactivity of both enhancers correlated with absence of the respective HSs. We conclude that although hypersensitivity in chromatin necessarily accompanies cell-type-specific enhancer activity, the occurrence of cell-type-specific HSs does not imply that the underlying sequences harbor enhancers active in transient transfection assays.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 633
Author(s):  
Hanwen Zhu ◽  
Boting Ning

Background: MicroRNAs are essential gene expression regulators and play important roles in various biological processes, such as cancer. They have shown great translational promise as either diagnostic biomarkers or therapeutic targets. While the similarities between transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia have been thoroughly studied before, less is known on the microRNA side. This project aims to provide critical biological knowledge on the extent of consensus microRNA expression and regulation between cell line models and primary human tumors.  Method: First, we examined the similarity of miRNA expression profiles between CCLE cell lines and TCGA tumor samples for each cancer type. Next, we compared the expression of miRNAs associating the hallmarks of cancer pathways. Finally, we constructed miRNA-mRNA regulatory network for each cancer type and evaluated whether the regulatory role of each miRNA is conserved between cell lines and tumor samples.   Results: Our results indicate that, similar to gene expression, how well cancer cell line microRNA expression would capture the transcriptomic profile of human cancer tissues is greatly affected by the tumor type and purity. The cell-type composition for a cancer type also affects how accurately cancer cell lines could reflect the miRNA expression in tumor tissues. Furthermore, through network analysis, we show that certain microRNAs, not all, regulate the same set of target genes in both the cell line and human cancer tissues.  Conclusions: Through systematically comparing the miRNA expression profile and the regulatory network, our study highlights the biological differences between cell line and tumor samples and provides resources for future miRNA and cancer studies.


2021 ◽  
Author(s):  
Sara Pidò ◽  
Carolina Testa ◽  
Pietro Pinoli

AbstractLarge annotated cell line collections have been proven to enable the prediction of drug response in the preclinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from CCLE, containing the connections among cell lines and drugs by means of their IC50 values. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15064-e15064
Author(s):  
Aditya Kulkarni ◽  
Diana Restifo ◽  
Igor A. Astsaturov ◽  
Umesh Kathad ◽  
Joseph McDermott ◽  
...  

e15064 Background: The clinical success of PARP inhibitors (PARPi) in homologous recombination (HR) deficient (HRD+) solid tumors has broadened the scope of identifying additional agents and vulnerabilities in cancers with DNA repair deficiencies. However, with more than 40% of BRCA1/2-deficient patients failing to respond to PARPi or acquiring resistance with prolonged PARPi administration, newer agents are also needed. LP-184, an acylfulvene, is a prodrug activated by PTGR1. Threshold expression levels of PTGR1 are higher in several tumors, providing a window of specificity for its cytotoxic action. DNA damage inflicted by acylfulvene (AF) agents is reliant upon HR pathway genes including BRCA1 for correction and removal. We hypothesized that tumors with high PTGR1 expression and HR deficiency will therefore be uniquely targeted and demonstrate synthetic lethality when exposed to LP-184. Methods: We evaluated ex vivo antitumor activity of LP-184 in selected PDX models representing lung, pancreatic and prostate cancers with high PTGR1 and known HR defects. Dissociated tumor fragments were treated with LP-184 across a concentration range of 5 nM to 36 uM for 5 days. Cell viability was quantified by CellTiter Glo. LP-184 IC50s were compared with PARPi efficacy. We further confirmed the dependency of PTGR1 in HR deficient tumor cells by comparing LP-184 sensitivity in the BRCA2 deficient cell line CAPAN-1 and the ATM mutant cell line PANC03.27, with and without PTGR1 suppression following an engineered CRISPR knockout of PTGR1. We also analyzed TCGA data to estimate the percentage of tumors with elevated PTGR1 and co-occurring damaging mutations in a panel of 60 HR genes. Results: The mean LP-184 IC50 across 15 HRD+ PDX models tested was 288 nM (range 31 - 2900 nM). LP-184 turned out to be 6 - 340X more potent ex vivo than the PARPi Olaparib in these models. 9 of 15 models were associated with no clinical response to or initial response followed by progression on approved standard of care (SOC) agents. 6 of 15 models showed < 10% tumor growth inhibition in vivo with SOC treatment. Regardless of cancer type, models with high-impact, loss-of-function mutations in ATM, ATR and BRCA1 showed exquisite sensitivity to LP-184 (mean IC50 ̃ 60 nM). CRISPRi-mediated stable suppression of PTGR1 in the pancreatic cancer cell lines CAPAN-1 and PANC03.27 entirely abrogated LP-184 sensitivity relative to isogenic parental cell lines. 17.6% of lung adenocarcinomas (n = 517), 4.5% of pancreatic adenocarcinomas (n = 179) and 9.6% of prostate adenocarcinomas (n = 498) displayed elevated PTGR1 along with damaging HR related mutations, and are likely to be responsive to LP-184 based on analysis of TCGA data. Conclusions: LP-184 is broadly effective in HRD+ tumors that may be less responsive to SOC including PARPi and could be useful clinically in a subset of tumors with high PTGR1 and HR defects.


Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.


2018 ◽  
Author(s):  
Leopoldo Gómez-Caudillo ◽  
Ángel G. Martínez-Batallar ◽  
Ariadna J. Ortega-Lozano ◽  
Diana L. Fernández-Coto ◽  
Haydee Rosas-Vargas ◽  
...  

AbstractGlioblastoma Multiforme is a cancer type with an important mitochondrial component. Here was used mitochondrial proteome Random Sampling in 2D gels from T98G (oxidative metabolism) and U87MG (glycolytic metabolism) cell lines to obtain and analyze representative spots (regardless of their intensity, size, or difference in abundance between cell lines) by Principal Component Analysis for protein identification. Identified proteins were ordered into specific Protein-Protein Interaction networks, to each cell line, showing mitochondrial processes related to metabolic change, invasion, and metastasis; and other nonmitochondrial processes such as DNA translation, chaperone response, and autophagy in gliomas. T98G and U87MG cell lines were used as glioblastoma transition model; representative proteomic signatures, with the most important biological processes in each cell line, were defined. This pipeline analysis describes the metabolic status of each line and defines clear mitochondria performance differences for distinct glioblastoma stages, introducing a new useful strategy for the understanding of glioblastoma carcinogenesis formation.Biological significanceThis study defines the mitochondria as an organelle that follows and senses the carcinogenesis process by an original proteomic approach, a random sampling in 2DE gels to obtain a representative spots sample and analyzing their relative abundance by Principal Components Analysis; to faithfully describe glioblastoma cells biology.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4142-4142
Author(s):  
David Kotlyar ◽  
Constantinos Petrovas ◽  
Arik Cooper ◽  
David Ambrozak ◽  
Christina Annunziata ◽  
...  

Abstract Background: Reactivation of HIV from latently infected T cells with oncologic agents (such as vorinostat) has been proposed as a means to potentially reduce or eliminate the HIV reservoir. A CDK9 inhibitor used in hematologic malignancies, flavopiridol, has previously been shown to selectively kill HIV infected cell lines and in addition, CDK9 inhibition was shown to synergize with TRAIL in killing cervical cancer and non-small cell lung cancer lines. Aims: Here we aim to show that, in in vitro assays, that the CDK9 inhibitors, Flavopiridol and Dinaciclib, can selectively kill HIV infected cell lines, and primary infected CD4+ T cells. We also wish to explore potential synergy with TRAIL to kill cervical cancer lines, HIV infected cell lines, and also selectively kill CD4+ T cells infected with HIV in vitro. Methods: For cell line experiments, the CEM leukemia cell line, and the ACH2 cell line, (CEM cells with 2 integrated copies of HIV) were used. For one experiment, cells were incubated with either Flavopiridol (75nM) [F] or Dinaciclib (10 nM) [D] for 14 hr. Separately, the duration of drug exposure was extended to 54 hrs, with 1.4 million cells/mL for each cell line. Cells had drug alone, drug with TRAIL (1 hr after drug) at a concentration of 10 ng/mL or TRAIL alone. For cervical cancer line experiments, HeLa cells were used (HEK cells were control). Cells were incubated with Dinaciclib (12.5 nM) [D] for 24 hrs. TRAIL was added one hr after D was applied. For primary CD4+ T cell infections, PBMCs were obtained from healthy donors, CD4s were isolated, and infected with a GFP containing virus (MOI ~0.1). Cells which expressed GFP actively produced virus, while cells without GFP expression did not. GFP+, GFP- and mock infected CD4+ T cells were exposed to D for 20 hrs. In another experiment cells were also exposed to D and TRAIL 1 hr after D was applied. Results: The ACH2 line had a survival (assessed as VIVID and Annexin negative cells) of 93% on avg and the CEM line 90% with no drug after 14 hr of culture. With Dinaciclib [D], viability decreased to 58% in ACH2 cells, but remained at 80% in CEM cells (p<0.05). Flavopiridol [F] did not cause significant decline in viability. After 54 hrs of exposure, D, and F+ TRAIL showed marked declines in cell number (see Table 1). Surprisingly, the CEM line had marked declines with TRAIL alone, but the ACH2 line was resistant to TRAIL alone. HeLa cells showed marked decrease in viability (via XTT assay) with synergy between D and TRAIL. With 24 hrs of D (12.5 nM) exposure HeLa viability decreased with an OD from 1.65 to 1.19 (28% decrease). With D and TRAIL (10 ng/mL), viability decreased with an OD from 1.65 to 0.67 (59% decrease, p<0.05). HEK cells showed no significant decrease. GFP+ infected T cells showed a decrease in viability from 66% to 32.2% after 20 hrs of Dinaciclib exposure (8 nM), see Fig. 1, error bars shown. GFP- T cells showed no significant difference in viability (90.2 to 82.2%) and MOCK infected T cells showed no significant difference in viability (87.3 to 78.3%). In 2 donors, TRAIL was added to Dinaciclib which resulted in further decreases in viability (mean 72.7% viable to 43% with D and to 33.6% with D and TRAIL) Conclusions: CDK9 inhibition shows promise as a strategy for both cervical cancer treatment and HIV reservoir modulation. Combining CDK9 inhibition with TRAIL also shows potential. Interestingly, it is also possible that HIV integration may play a novel role in TRAIL resistance. Further work is needed to further elucidate the details of the prospective death pathways involved. Figure 1 Viability of Infected CD4+ T cells - 20 hr exposure Figure 1. Viability of Infected CD4+ T cells - 20 hr exposure Figure 2 Figure 2. Abstract 4142. Table 1 54 Hour Experiment – Cell lines Cell Type Starting Concentration Final Concentration Cell Type Starting Concentration Final Concentration ACH2 alone 1.4 million 4.05 million CEM alone 1.4 million 3.37 million ACH2 + Dinaciclib (10 nM) 1.4 million 780,000 CEM + Dinaciclib (10 nM) 1.4 million 2.2 million ACH2 + Flavopiridol (75 nM) 1.4 million 2.8 million CEM + Flavopiridol (75 nM) 1.4 million 2.36 million ACH2+ TRAIL (10 ng/mL) 1.4 million 3.71 million CEM + TRAIL (10 ng/mL) 1.4 million 730,000 ACH2+ Flavopiridol (75 nM) + TRAIL (10 ng/mL) 1.4 million 940,000 CEM+Flavopiridol (75 nM) + TRAIL (10 ng/mL) 1.4 million 1.79 million Disclosures No relevant conflicts of interest to declare.


Sign in / Sign up

Export Citation Format

Share Document