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

Salvadores, Marina; Fuster‐Tormo, Francisco; Supek, Fran

doi:10.1101/809400

Cited by 5 publications

(4 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Choosing the right cell line for specific experiments is key to getting the most reliable results. Therefore, a clear understanding of the context and properties of the selected cell line is critical for exploring biological mechanisms and predicting therapy response (19,20). Our previous study found that different porcine cell lines reveal differential susceptibility to SVV (13).…”

Section: Introductionmentioning

confidence: 99%

The Insufficient Activation of RIG-I–Like Signaling Pathway Contributes to Highly Efficient Replication of Porcine Picornaviruses in IBRS-2 Cells

Zhang

Yang

et al. 2021

Molecular & Cellular Proteomics

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

The Insufficient Activation of RIG-I–Like Signaling Pathway Contributes to Highly Efficient Replication of Porcine Picornaviruses in IBRS-2 Cells

Zhang

Yang

et al. 2021

Molecular & Cellular Proteomics

View full text Add to dashboard Cite

show abstract

“…Notably, Yu et al compared the transcriptomes of CCLs to The Cancer Genome Atlas (TCGA) by correlation analysis, resulting in a panel of CCLs recommended as most representative of 22 tumor types 15 . Most recently, Najgebauer et al 16 and Salvadores et al 17 have developed methods to assess CCLs using molecular traits such as copy number alterations (CNA), somatic mutations, DNA methylation and transcriptomics. While all of these studies have provided valuable information, they leave two major challenges unmet.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the transcriptional fidelity of cancer models

Peng

Gleyzer

Tai

et al. 2020

Preprint

View full text Add to dashboard Cite

33 34 35 * These authors made equal contributions. 36 37 38 Correspondence to: patrick.cahan@jhmi.edu 39 40 Article type: Analysis 41 42 Website: http://www.cahanlab.org/resources/cancerCellNet_web 43 44 Code: https://github.com/pcahan1/cancerCellNet 45 46 47 48 49 2 ABSTRACT 50 51Cancer researchers use cell lines, patient derived xenografts, and genetically engineered mice 52 as models to investigate tumor biology and to identify therapies. The generalizability and power 53 of a model derives from the fidelity with which it represents the tumor type of investigation, 54 however, the extent to which this is true is often unclear. The preponderance of models and the 55 ability to readily generate new ones has created a demand for tools that can measure the extent 56 and ways in which cancer models resemble or diverge from native tumors. Here, we present a 57 computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally 58 occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied 59 this tool to 657 cancer cell lines, 415 patient derived xenografts, and 26 distinct genetically 60 engineered mouse models, documenting the most faithful models, identifying cancers 61 underserved by adequate models, and finding models with annotations that do not match their 62 classification. By comparing models across modalities, we find that genetically engineered mice 63 have higher transcriptional fidelity than patient derived xenografts and cell lines in four out of 64 five tumor types. We have made CancerCellNet available as freely downloadable software and 65 as a web application that can be applied to new cancer models. 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81Models are widely used to investigate cancer biology and to identify potential therapeutics. 82Popular modeling modalities are cancer cell lines (CCLs) 1 , genetically engineered mouse 83 models (GEMMs) 2 , and patient derived xenografts (PDXs) 3 .These classes of models differ in the 84 types of questions that they are designed to address. CCLs are often used to address cell 85 intrinsic mechanistic questions 4 , GEMMs to chart progression of molecularly defined-disease 5 , 86 and PDXs to explore patient-specific response to therapy in a physiologically relevant context 6 . 87Models also differ in the extent to which the they represent specific aspects of a cancer type 7 . 88Even with this intra-and inter-class model variation, all models should represent the tumor type 89 or subtype under investigation, and not another type of tumor, and not a non-cancerous tissue. 90Therefore, cancer-models should be selected not only based on the specific biological question 91 but also based on the similarity of the model to the cancer type under investigation 8,9 . 92 Various methods have been proposed to determine the similarity of cancer models to 93 their intended subjects. Domcke et al devised a 'suitability score' as a metric of the molecular 94 similarity of CCLs to high grade serous ovarian carcinom...

show abstract

“…The majority of cell lines (64%) are assigned to clusters containing their specific cancer type though there is significant variation across cancer types with a median of 78% cell lines of a given cancer type showing concordance with the cancer type of its assigned cluster (Figure 3C). Prominent examples of divergence from expected cancer types include the cell lines FU97, NCI-H226, SW-1710 and JHH1, which have previously been associated with STAD, LUSC, BLCA and LIHC assigned as LIHC, MESO, KIRC/KIRP and UCEC respectively, an observation supported by other comprehensive approaches 60 . The cancer type with the most divergent assignments from expectation is LUAD.…”

Section: Resultsmentioning

confidence: 69%

Discovery of novel therapeutic targets in cancer using patient-specific gene regulatory networks

Forbes

Cohen

et al. 2022

Preprint

View full text Add to dashboard Cite

Most cancer types lack effective targeted therapeutic options and in cancers where first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of ATAC-seq and RNA-seq on patient biopsies in a high-throughput manner. Here we present a computational approach that leverages these datasets to identify novel drug targets based on tumor lineage. We constructed patient-specific gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained using three-dimensional genomic data for enhancer to promoter contacts. Next, we identify the key transcription factors (TFs) in these networks, which are used to identify therapeutic vulnerabilities either by direct targeting of TFs or proteins that they co-operate with. We validate four novel candidates identified for neuroendocrine, liver and renal cancers, which have a dismal prognosis with current therapeutic options. We present a novel approach to use the increasing amounts of functional genomics data from patient biospecimens for identification of novel drug targets.

show abstract

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

Abstract: 10

Cited by 5 publications

References 61 publications

The Insufficient Activation of RIG-I–Like Signaling Pathway Contributes to Highly Efficient Replication of Porcine Picornaviruses in IBRS-2 Cells

The Insufficient Activation of RIG-I–Like Signaling Pathway Contributes to Highly Efficient Replication of Porcine Picornaviruses in IBRS-2 Cells

Evaluating the transcriptional fidelity of cancer models

Discovery of novel therapeutic targets in cancer using patient-specific gene regulatory networks

Contact Info

Product

Resources

About