Dataset Information

A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles.

ABSTRACT: Patient-derived cell lines are often used in pre-clinical cancer research, but some cell lines are too different from tumors to be good models. Comparison of genomic and expression profiles can guide the choice of pre-clinical models, but typically not all features are equally relevant. We present TumorComparer, a computational method for comparing cellular profiles with higher weights on functional features of interest. In this pan-cancer application, we compare ∼600 cell lines and ∼8,000 tumor samples of 24 cancer types, using weights to emphasize known oncogenic alterations. We characterize the similarity of cell lines and tumors within and across cancers by using multiple datum types and rank cell lines by their inferred quality as representative models. Beyond the assessment of cell lines, the weighted similarity approach is adaptable to patient stratification in clinical trials and personalized medicine.

SUBMITTER: Sinha R

PROVIDER: S-EPMC9017219 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles.

Sinha Rileen R Luna Augustin A Schultz Nikolaus N Sander Chris C

Cell reports methods 20210621 2

Patient-derived cell lines are often used in pre-clinical cancer research, but some cell lines are too different from tumors to be good models. Comparison of genomic and expression profiles can guide the choice of pre-clinical models, but typically not all features are equally relevant. We present TumorComparer, a computational method for comparing cellular profiles with higher weights on functional features of interest. In this pan-cancer application, we compare ∼600 cell lines and ∼8,000 tumor ...[more]

PMID: 35475239

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Project description:BackgroundA substantial volume of RNA sequencing data were generated from cancer cell lines. However, it requires specific bioinformatics skills to compare gene expression levels across cell lines. This has hindered non-bioinformaticians from fully utilizing these valuable datasets in their research. To bridge this gap, we established a curated Pan-cancer Cell Line Transcriptome Atlas (PCTA) dataset. This resource aims to provide a user-friendly platform, allowing researchers without extensive bioinformatics expertise to access and leverage the wealth of information within the dataset for their studies. Importantly, PCTA stands out by offering sufficient sample numbers per cell line in comparison to other pan-cancer datasets.MethodsCell lines' meta data and RNA sequencing data were retrieved from the Cancer Cell Line Encyclopedia (CCLE), SRA and ARCHS4 databases. Utilizing the programming language R, we conducted data retrieval, normalization, and visualization. Only expression data for protein-coding genes and long-non-coding RNAs (LncRNAs) were considered in this study, streamlining the focus to enhance the precision and relevance of the analysis.ResultsThe resulting PCTA dataset encompasses the expression matrix of 24,965 genes, featuring data from 84,385 samples derived from 5,677 studies. This comprehensive compilation spans 535 cell lines, representing a spectrum of 114 cancer types originating from 30 diverse tissue types. On UMAP plots, cell lines originating from the same type of tissue tend to cluster together, illustrating the dataset's ability to capture biological relationships. To unravel molecular signatures, marker genes were identified for each cancer type. Additionally, an interactive and user-friendly web application (https://pcatools.shinyapps.io/PCTA_app/ ) was developed for researchers to explore the PCTA dataset. This platform allows users to examine the expression pattern of their genes of interest across a diverse array of samples. Data are visualized as violin-, box-, and point- plots, enhancing the interpretability of the findings.ConclusionThe PCTA stands as a comprehensive resource, offering insights into gene expression patterns across diverse cancer cell lines and providing a valuable tool to explore molecular signatures and potential therapeutic targets in cancer research.

Project description:Tumor mass dormancy is the key intermediate step between immune surveillance and cancer progression, yet due to its transitory nature it has been difficult to capture and characterize. Little is understood of its prevalence across cancer types and of the mutational background that may favor such a state. While this balance is finely tuned internally by the equilibrium between cell proliferation and cell death, the main external factors contributing to tumor mass dormancy are immunological and angiogenic. To understand the genomic and cellular context in which tumor mass dormancy may develop, we comprehensively profiled signals of immune and angiogenic dormancy in 9,631 cancers from the Cancer Genome Atlas and linked them to tumor mutagenesis. We find evidence for immunological and angiogenic dormancy-like signals in 16.5% of bulk sequenced tumors, with a frequency of up to 33% in certain tissues. Mutations in the CASP8 and HRAS oncogenes were positively selected in dormant tumors, suggesting an evolutionary pressure for controlling cell growth/apoptosis signals. By surveying the mutational damage patterns left in the genome by known cancer risk factors, we found that aging-induced mutations were relatively depleted in these tumors, while patterns of smoking and defective base excision repair were linked with increased tumor mass dormancy. Furthermore, we identified a link between APOBEC mutagenesis and dormancy, which comes in conjunction with immune exhaustion and may partly depend on the expression of the angiogenesis regulator PLG as well as interferon and chemokine signals. Tumor mass dormancy also appeared to be impaired in hypoxic conditions in the majority of cancers. The microenvironment of dormant cancers was enriched in cytotoxic and regulatory T cells, as expected, but also in macrophages and showed a reduction in inflammatory Th17 signals. Finally, tumor mass dormancy was linked with improved patient survival outcomes. Our analysis sheds light onto the complex interplay between dormancy, exhaustion, APOBEC activity and hypoxia, and sets directions for future mechanistic explorations.

Project description:BackgroundThe human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.ResultsIn this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.ConclusionsOur algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Dataset Information

A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles.

Publications

A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles.

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