Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/028159

Comparing cancer cell lines and tumor samples by genomic profiles

Rileen Sinha·Nikolaus Schultz·Chris Sander
View original source ↗Export metadata ↓

Abstract

Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method - TumorComparer - to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest ( weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.

Explore related subjects

Cancer BiologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rileen Sinha, Nikolaus Schultz, Chris Sander. 2015-10-02. Comparing cancer cell lines and tumor samples by genomic profiles. https://doi.org/10.1101/028159

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Stepwise Evolution and Epistatic Interaction of Driver Mutations from Endometrial Hyperplasia to Carcinoma

To characterize early oncogenesis, pathologically identified pre-cancerous tissue can be analyzed for the presence of cancer drivers. Here, we argue that in such studies, analyses of the driver status of variants, of the association between step-specific prevalence and progression through tumorigenesis, and of driver co-occurrence and mutual exclusivity should be accompanied by estimates of inherent mutation rate of variants and presented within an evolutionary framework of selective epistasis. To illustrate this point, we examine the transition of endometrial tissue from atypical hyperplasia to carcinoma. We apply a step-specific analysis, demonstrating that the strength of selection on somatic driver mutations promoting cell division and survival differs between hyperplasia to carcinoma. We demonstrate that mutations of PTEN, which are highly prevalent in carcinomas and have been argued to exert substantial driver effects, exhibit an even larger effect of increasing cellular division and survival within developing hyperplasias. A determination of cooccurrence or mutual exclusivity may be a product of genes sharing or differing in underlying sources of mutation, as opposed to a product of biological interaction and selection. By accounting for tumor-specific mutational processes that influence co-occurrence, we calculate epistatic selective intensities between pairs of drivers. Mutations of KRAS and FGFR2 are often mutually exclusive and were indeed found to exhibit significant antagonistic selective epistasis. However, mutations of PIK3CA and PIK3R1, which also have been identified as showing mutual exclusivity, do not demonstrate significant antagonistic selective epistasis. Thus, evidence of mutually exclusivity is insufficient to determine epistasis. Accordingly, the application of quantitative approaches that distinctly analyze mutation and selection on cancer variants has the potential to substantially illuminate the trajectory of tumorigenesis and cancer progression.

Cancer Biology↗
bioRxiv2026

Comprehensive transcriptome data of melittin- and un-treated murine hepatoma Hepa 1-6 cells

Melittin, the principal bioactive peptide of bee venom, exerts potent antitumor activity against hepatocellular carcinoma (HCC). However, the comprehensive transcriptomic alterations it elicits in hepatoma cells remain poorly characterized. Here, we present an integrated transcriptome dataset from melittin- and un-treated murine Hepa 1-6 hepatoma cells, encompassing messenger RNA (mRNA) and microRNA (miRNA) expression profiles. Cells were exposed to 4 g/mL melittin in serum-free DMEM for 20 min, and total RNA was subjected to ribosomal RNA-depleted strand-specific RNA sequencing on an Illumina NovaSeq6000 platform (paired-end 150 bp) and small RNA sequencing on an Illumina HiSeq2500 platform (single-end 50 bp). Raw data were processed using Cutadapt to remove adapters and low-quality reads, yielding clean datasets with Q20 [≥] 99.85%, Q30 [≥] 98.48%, and valid data ratios exceeding 85%. All raw and processed sequencing data are publicly available. This transcriptomic resource provides a valuable resource and basis for elucidating the regulatory networks underlying melittin-induced anti-hepatoma effects. DatasetThe dataset can be accessed through the National Genomics Data Center, China National Center website by searching with the BioProject accession number PRJCA065485 Reviewers may use this link for anonymous access during the review process. Direct URL to data: Genome Sequence Archive-CNCB-NGDC. Dataset LicenseCC BY 4.0

Cancer Biology↗
bioRxiv2026

LDB1-dependent enhancer connectivity defines T-cell leukemia identities and masks metabolic vulnerabilities

Spatial enhancer connectivity is fundamental to proper gene regulation. Enhancer dysregulation has emerged as a hallmark of cancers, including T-cell acute lymphoblastic leukemias (T-ALL). T-ALL are aggressive malignancies characterized by marked transcriptional heterogeneity driven by distinct stages of developmental arrest and diverse noncoding alterations. How these cancers co-opt nuclear architecture to rewire enhancer connectivity remains poorly understood. Here, we report that the LDB1 chromatin architectural complex is an essential mediator of enhancer-oncogene looping that sustains oncogenic transcriptional programs across multiple T-ALL subtypes. Integrating bulk and single-cell transcriptomic data from patients with T-ALL and healthy hematopoietic controls, we show that the LDB1-dependent regulatory circuitry defines the molecular identities of distinct T-ALL subtypes while restricting plasticity toward alternative cell states. LDB1 loss dismantles chromatin looping among cell state-defining enhancers liberating them to form promiscuous interactions with nearby genes. This enhancer rewiring stimulates expression of key metabolic genes, creating a mevalonate pathway dependency exploitable with statin treatment. Our study establishes LDB1 as a central executor of T-ALL regulatory circuitry and more broadly illustrates chromatin rewiring as a source of targetable dependencies in cancer.

Cancer Biology↗