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

2020 ◽  
Vol 6 (27) ◽  
pp. eaba1862
Author(s):  
Marina Salvadores ◽  
Francisco Fuster-Tormo ◽  
Fran Supek
Keyword(s):  
Cell Lines ◽  
Genetic Screening ◽  
Therapy Response ◽  
Cancer Type ◽  
Cell Type ◽  
Biological Mechanisms ◽  
Mutational Signature ◽  
Cancer Models ◽  
Tissue Of Origin ◽  
The One

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.

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
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Experimental Work ◽  
Drug Sensitivity ◽  
Tissue Cell ◽  
Cancer Type ◽  
Tumor Type ◽  
Cell Type ◽  
Cancer Subtypes ◽  
The One

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.

scholarly journals Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

2021 ◽  
Vol 17 (2) ◽  
pp. e1008720
Author(s):  
John P. Lloyd ◽  
Matthew B. Soellner ◽  
Sofia D. Merajver ◽  
Jun Z. Li
Keyword(s):  
Sample Size ◽  
Cell Lines ◽  
Drug Response ◽  
Joint Analysis ◽  
Cancer Type ◽  
Cancer Prediction ◽  
Cancer Models ◽  
Cancer Types ◽  
Tissue Differences ◽  
Pan Cancer

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

scholarly journals Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

2019 ◽  
Author(s):  
John P. Lloyd ◽  
Matthew Soellner ◽  
Sofia D. Merajver ◽  
Jun Z. Li
Keyword(s):  
Sample Size ◽  
Cell Lines ◽  
Drug Response ◽  
Joint Analysis ◽  
Cancer Type ◽  
Cancer Prediction ◽  
Cancer Models ◽  
Cancer Types ◽  
Tissue Differences ◽  
Pan Cancer

ABSTRACTIncreased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines with MEK inhibitor (MEKi) response and RNA, SNP, and CNV data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ=0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ=0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as we estimate that exclusion of between-tissue signals leads to a 22% decrease in performance metrics. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that the higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

Small Molecular Leads Differentially Active Against HER2 Positive and Triple Negative Breast Cancer Cell Lines

2019 ◽  
Vol 15 (7) ◽  
pp. 738-742 ◽  
Author(s):  
Adnan Badran ◽  
Atia-tul-Wahab ◽  
Sharmeen Fayyaz ◽  
Elias Baydoun ◽  
Muhammad Iqbal Choudhary
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Triple Negative ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Cancer Type

Background:Breast cancer is the most prevalent cancer type in women globally. It is characterized by distinct subtypes depending on different gene expression patterns. Oncogene HER2 is expressed on the surface of cell and is responsible for cell growth regulation. Increase in HER2 receptor protein due to gene amplification, results in aggressive growth, and high metastasis in cancer cells.Methods:The current study evaluates and compares the anti-breast cancer effect of commercially available compounds against HER2 overexpressing BT-474, and triple negative MDA-MB-231 breast cancer cell lines.Results:Preliminary in vitro cell viability assays on these cell lines identified 6 lead molecules active against breast cancer. Convallatoxin (4), a steroidal lactone glycoside, showed the most potent activity with IC50 values of 0.63 ± 0.56, and 0.69 ± 0.59 µM against BT-474 and MDA-MB-231, respectively, whereas 4-[4-(Trifluoromethyl)-phenoxy] phenol (3) a phenol derivative, and Reserpine (5) an indole alkaloid selectively inhibited the growth of BT-474, and MDA-MB-231 breast cancer cells, respectively.Conclusion:These results exhibited the potential of small molecules in the treatment of HER2 amplified and triple negative breast cancers in vitro.

Cytometric Profiling of CD133+ Cells in Human Colon Carcinoma Cell Lines Identifies a Common core Phenotype and Cell Type-specific Mosaics

2013 ◽  
Vol 28 (3) ◽  
pp. 267-273 ◽  
Author(s):  
Marica Gemei ◽  
Rosa Di Noto ◽  
Peppino Mirabelli ◽  
Luigi Del Vecchio
Keyword(s):  
Colorectal Cancer ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Colon Carcinoma ◽  
Cell Lines ◽  
Carcinoma Cell ◽  
Cell Type ◽  
Carcinoma Cell Lines ◽  
Cell Type Specific

In colorectal cancer, CD133+ cells from fresh biopsies proved to be more tumorigenic than their CD133– counterparts. Nevertheless, the function of CD133 protein in tumorigenic cells seems only marginal. Moreover, CD133 expression alone is insufficient to isolate true cancer stem cells, since only 1 out of 262 CD133+ cells actually displays stem-cell capacity. Thus, new markers for colorectal cancer stem cells are needed. Here, we show the extensive characterization of CD133+ cells in 5 different colon carcinoma continuous cell lines (HT29, HCT116, Caco2, GEO and LS174T), each representing a different maturation level of colorectal cancer cells. Markers associated with stemness, tumorigenesis and metastatic potential were selected. We identified 6 molecules consistently present on CD133+ cells: CD9, CD29, CD49b, CD59, CD151, and CD326. By contrast, CD24, CD26, CD54, CD66c, CD81, CD90, CD99, CD112, CD164, CD166, and CD200 showed a discontinuous behavior, which led us to identify cell type-specific surface antigen mosaics. Finally, some antigens, e.g. CD227, indicated the possibility of classifying the CD133+ cells into 2 subsets likely exhibiting specific features. This study reports, for the first time, an extended characterization of the CD133+ cells in colon carcinoma cell lines and provides a “dictionary” of antigens to be used in colorectal cancer research.

Analysis of the effect of DEK overexpression on the survival and proliferation of bone marrow stromal cells

2019 ◽  
Vol 44 (4) ◽  
pp. 510-516
Author(s):  
Türkan Çakar ◽  
Ayten Kandilci
Keyword(s):  
Cell Lines ◽  
Stromal Cells ◽  
Cell Sorting ◽  
Bone Marrow Stromal Cells ◽  
Nuclear Protein ◽  
Cell Type ◽  
Facs Analysis ◽  
Periodic Growth ◽  
Stromal Cell Line ◽  
Rpmi 8226

Abstract Objective DEK is ubiquitously expressed and encodes a nuclear protein, which is also released from some cells. Overexpression of DEK suppresses proliferation of some blood cell progenitors whereas it increases proliferation of epithelial tumors. We showed that DEK is overexpressed in BM cells of 12% of multiple myeloma (MM) patients. Here, we aimed to test if DEK overexpression effects the proliferation and viability of BM stromal cells or MM cells co-cultured with DEK-overexpressing stromal cells, mimicking the BM microenvironment. Methods DEK is stably overexpressed in the BM stromal cell line HS27A. Periodic growth curve and fluorescent activated cell sorting (FACS) analysis was performed to determine the effect of DEK overexpression on HS27A cells and MM cell lines (RPMI-8226 and U266) that are co-cultured with these HS27A cells. Results We showed that, on the contrary to blood progenitors or ephitelial cells, DEK overexpression doesn’t alter the viability or proliferation of the HS27A cells, or the MM cell lines which are co-cultured with DEK-overexpressing HS27A cells. Conclusions Our results suggest that effect of DEK overexpression on the proliferation is cell type and context dependent and increased DEK expression is tolerable by the stromal cells and the co-cultured MM cell lines without effecting proliferation and viability.

scholarly journals MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data

2019 ◽  
Author(s):  
Elmer A. Fernández ◽  
Yamil D. Mahmoud ◽  
Florencia Veigas ◽  
Darío Rocha ◽  
Mónica Balzarini ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Therapy Response ◽  
Cell Types ◽  
Gene Signature ◽  
Response To Therapy ◽  
Support Vector ◽  
Data Sets ◽  
Cell Type ◽  
Before And After

AbstractRNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

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