Karyotypic divergence confounds cellular phenotypes in large pharmacogenomic studies

ABSTRACTBackgroundSomatic copy-number alterations that affect large genomic regions are a major source of genomic diversity in cancer and can impact cellular phenotypes. Clonal heterogeneity within cancer cell lines can affect phenotypic presentation, including drug response.MethodsWe aggregated and analyzed SNP and copy number profiles from six pharmacogenomic datasets encompassing 1,691 cell lines screened for 13 molecules. To look for sources of genotype and karyotype discordances, we compared SNP genotypes and segmental copy-ratios across 5 kb genomic bins. To assess the impact of genomic discordances on pharmacogenomic studies, we assessed gene expression and drug sensitivity data for compared discordant and concordant lines.ResultsWe found 6/1,378 (0.4%) cell lines profiled in two studies to be discordant in both genotypic and karyotypic identity, 51 (3.7%) discordant in genotype, 97 (7.0%) discordant in karyotype, and 125 (9.1%) potential misidentifications. We highlight cell lines REH, NCI-H23 and PSN1 as having drug response discordances that may hinge on divergent copy-number qConclusionsOur study highlights the low level of misidentification as evidence of effective cell line authentication standards in recent pharmacogenomic studies. However, the proclivity of cell lines to acquire somatic copy-number variants can alter the cellular phenotype, resulting in a biological and predictable effects on drug sensitivity. These findings highlight the need for verification of cell line copy number profiles to inform interpretation of drug sensitivity data in biomedical studies.

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Connecting gene expression subtypes of colorectal cancer (CRC) with cell lines and drug resistance.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e14544 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e14544-e14544

Author(s):

Eva Budinska ◽

Jenny Wilding ◽

Vlad Calin Popovici ◽

Edoardo Missiaglia ◽

Arnaud Roth ◽

...

Keyword(s):

Gene Expression ◽

Cell Line ◽

Clinical Significance ◽

Cell Lines ◽

Drug Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis

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.

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Revealing Transcriptome Deregulation upon Genomic Complexity in Multiple Myeloma

Blood ◽

10.1182/blood-2020-136211 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 3-4

Author(s):

Matteo Claudio Da Via' ◽

Bachisio Ziccheddu ◽

Matteo Dugo ◽

Marta Lionetti ◽

Katia Todoerti ◽

...

Keyword(s):

Multiple Myeloma ◽

Cell Lines ◽

In Silico ◽

Copy Number ◽

Drug Sensitivity ◽

Genetic Alterations ◽

Prognostic Impact ◽

Immunoglobulin Gene ◽

Driver Genes ◽

The Impact

Introduction Multiple Myeloma (MM) is characterized by hyperdiploidy (HD) or immunoglobulin gene (IgH) rearrangements as initiating events. Clonal heterogeneity is a hallmark of its biology as highlighted by Next Generation Sequencing. In this context, data on the impact of peculiar mutations, copy number aberrations (CNAs), and chromosomal rearrangements (CRs) at the transcriptomic level are still scanty. In this study, we aimed to dissect the transcriptional deregulation promoted by the most recurrent genomic drivers. Based on this geno-trascriptomic link, we also aimed to identify biomarkers that could suggest personalized treatments. Methods We analyzed 517 newly diagnosed patients from the IA12 release of the CoMMpass study, focusing on mutations in MM driver genes, structural variants, copy number segments and raw transcript counts. RNAseq data was processed with the VOOM/LIMMA pipeline. To perform an in-silico drug sensitivity screen, we anchored cell lines to patients samples using the Celligner algorithm and interrogated the DepMap dataset. Results We first analyzed the global impact of genetic aberrations on the transcriptome. Chr(1q)amp/gain, followed by IgH translocations and HD showed the highest number of deregulated transcripts. Individual mutations had much less impact, with the exception of NRAS and chr(13q) genes (DIS3, TGDS, RB1). Next, we investigated differential influence between hotspots (HS) vs nonHS mutations within driver genes. KRAS and NRAS, showed little changes between nonHS and wild type (WT), as the transcriptome was mostly impacted by HS mutations. IRF4 K123 showed a specific transcriptional profile, while nonHS mutations still carried functional relevance although on different genes. For BRAF, the kinase dead D594 mutation surprisingly impacted the most in comparison to V600 and WT cases. Next, we explored the effect of bi-allelic genetic events with known prognostic impact. TP53 double-hits were associated with an upregulation of PHF19, a MM poor prognostic marker, and downregulation of SLAMF7, a new immunotherapy target. CYLD and TRAF3 double-hits correlated with NF-κB pathway activation, and the former showed a significant BCL2 upregulation. Bi-allelic events on chr13 exhibited gene-specific consequences: DIS3 inactivation deregulated mostly lncRNAs, while TGDS impacted on genes involved in cell-cycle regulation. Regarding chromosomal gains, only chr(1q)amp (> 3 copies) showed a gene dosage effect with upregulation of the potential therapeutic targets MCL1 and SLAMF7. Given that the BCL2 axis was perturbated by several genetic alterations, we systematically compared the expression levels of BCL2, NOXA, MCL1 and BCL2L1 in CYLD inactivated, t(11;14) and chr(1q)amp patients. BCL2 levels were higher in the CYLD group, which parallels with the overexpression of the anti-apoptotic gene BCL2L1. NOXA, which promotes MCL1 degradation, was significantly upregulated in t(11;14). Chr(1q)amp patients showed a concomitant MCL1 overexpression and NOXA downregulation. To correlate these results to drug sensitivity, we performed an in-silico screen. We first selected MM and lymphoma cell lines from the DepMap dataset based on a gene expression profile that was most similar to the MM samples, then analyzed candidate drugs. The SKMM2 MM cell line, harboring t(11;14), del(CYLD) e NOXAamp was highly sensitive to Venetoclax. The same was true for the lymphoma ones RI1 and OCI-LY3, both harboring NOXAamp, but negative for t(11;14). On the contrary, the U266 and MOLP8 both with t(11;14) carrying a MCL1amp due to a chr(1q)amp were fully resistant. Of note, these latter resulted sensitive to the pan-BCL2 axis inhibitor Sabutoclax. Conclusions Our study suggests a link between the genomic architecture and transcriptome in MM, where CNAs and CRs had a stronger impact on expression than gene mutations. However, given that not all mutations are identical, HS ones need further testing as they may represent a future treatment target. Moreover, the mutational status is crucial since, while mono-allelic events are often of little transcriptional value, compound heterozygosity carries a huge influence on transcriptomic which provides biological basis for the observed prognostic impact of "double-hit" MM. Finally, we suggest that a comprehensive profiling of the BCL2 pathway may identify biomarkers of sensitivity to BCL2 inhibitors in addition to the t(11;14). Disclosures D'Agostino: GSK: Membership on an entity's Board of Directors or advisory committees. Corradini:Celgene: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Sanofi: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Incyte: Consultancy; Daiichi Sankyo: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other; BMS: Other; F. Hoffman-La Roche Ltd: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Novartis: Consultancy, Honoraria, Other: Travel and accommodations paid by for; Servier: Consultancy, Honoraria; Kite: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Other: Travel and accommodations paid by for; KiowaKirin: Consultancy, Honoraria. Bolli:Celgene: Honoraria; Janssen: Honoraria.

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A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity

10.1101/2021.12.03.471100 ◽

2021 ◽

Author(s):

Sara Pidò ◽

Carolina Testa ◽

Pietro Pinoli

Keyword(s):

Cell Line ◽

Cell Lines ◽

Drug Response ◽

Missing Values ◽

Drug Sensitivity ◽

Factorization Method ◽

Data Types ◽

Drug Profile ◽

The Matrix ◽

Ic50 Values

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.

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Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720021500359 ◽

2021 ◽

Author(s):

Akram Emdadi ◽

Changiz Eslahchi

Keyword(s):

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Matrix Factorization ◽

Drug Response ◽

Drug Sensitivity ◽

Cancer Cell Lines ◽

Tissue Type ◽

Factorization Approach ◽

Clinical Drug

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.

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A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

10.1101/2020.06.18.158907 ◽

2020 ◽

Author(s):

Evanthia Koukouli ◽

Dennis Wang ◽

Frank Dondelinger ◽

Juhyun Park

Keyword(s):

Gene Expression ◽

Regression Model ◽

Cell Line ◽

Cell Lines ◽

Dose Response ◽

Drug Response ◽

Drug Sensitivity ◽

Cancer Drug ◽

Functional Regression ◽

Response Curves

AbstractCancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalised regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumourgenesis and DNA damage response.Author SummaryTumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularisation and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

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Revisiting inconsistency in large pharmacogenomic studies

10.1101/026153 ◽

2015 ◽

Cited By ~ 5

Author(s):

Zhaleh Safikhani ◽

Mark Freeman ◽

Petr Smirnov ◽

Nehme El-Hachem ◽

Adrian She ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Dose Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Dose ◽

Response Curves ◽

Sensitivity Data ◽

Pharmacogenomic Studies

Background: In 2012, two large pharmacogenomic studies, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were published, each reported gene expression data and measures of drug response for a large number of drugs and hundreds of cell lines. In 2013, we published a comparative analysis that reported gene expression profiles for the 471 cell lines profiled in both studies and dose response measurements for the 15 drugs characterized in the common cell lines by both studies. While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. Our paper was widely discussed and we received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis: that drugs with different response characteristics should have been treated differently, that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines to accurately assess differences in the studies, that we missed some biomarkers that are consistent between studies, and that the software analysis tools we provided with our analysis should have been easier to run, particularly as the GDSC and CCLE released additional data. Methods: For each drug, we used published sensitivity data from the GDSC and CCLE to separately estimate drug dose-response curves. We then used two statistics, the area between drug dose-response curves (ABC) and the Matthews correlation coefficient (MCC), to robustly estimate the consistency of continuous and discrete drug sensitivity measures, respectively. We also used recently released RNA-seq data together with previously published gene expression microarray data to assess inter-platform reproducibility of cell line gene expression profiles. Results: This re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs -- 17-AAG and PD-0332901 -- and three targeted drugs -- PLX4720, nilotinib and crizotinib -- with moderate to good consistency in drug sensitivity data between GDSC and CCLE. Not enough sensitive cell lines were screened in both studies to robustly assess consistency for three other targeted drugs, PHA-665752, erlotinib, and sorafenib. Concurring with our published results, we found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Further, to discover "consistency" between studies required the use of multiple statistics and the selection of specific measures on a case-by-case basis. Conclusion: Our results reaffirm our initial findings of an inconsistency in drug sensitivity measures for eight of fifteen drugs screened both in GDSC and CCLE, irrespective of which statistical metric was used to assess correlation. Taken together, our findings suggest that the phenotypic data on drug response in the GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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PharmacoDB: an integrative database for miningin vitrodrug screening studies

10.1101/195149 ◽

2017 ◽

Author(s):

Petr Smirnov ◽

Victor Kofia ◽

Alexander Maru ◽

Mark Freeman ◽

Chantal Ho ◽

...

Keyword(s):

Cell Line ◽

Cell Lines ◽

Large Scale ◽

Drug Response ◽

Chemical Compounds ◽

Integrative Analysis ◽

Multiple Drug ◽

List Type ◽

Pharmacogenomic Studies

ABSTRACTRecent pharmacogenomic studies profiled large panels of cancer cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbation, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging this valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in thein vitropharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated pharmacogenomic datasets that are otherwise disparate and difficult to integrate.Key pointsCuration of cell line and drug identifiers in the largest pharmacogenomic studies published to dateUniform processing of drug sensitivity data to reduce heterogeneity across studiesMultiple drug response summary metrics enabling visual comparison and integrative analysis

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Predicting drug sensitivity of cancer cells based on DNA methylation levels

PLoS ONE ◽

10.1371/journal.pone.0238757 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0238757

Author(s):

Sofia P. Miranda ◽

Fernanda A. Baião ◽

Julia L. Fleck ◽

Stephen R. Piccolo

Keyword(s):

Dna Methylation ◽

Drug Development ◽

Cell Line ◽

Cell Lines ◽

Drug Response ◽

Drug Sensitivity ◽

Classification Algorithms ◽

Low Grade ◽

Regression Algorithms ◽

Treatment Responses

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.

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Consistency of in vitro drug sensitivities within pharmacological classes

Journal of Undergraduate Life Sciences ◽

10.33137/juls.v15i1.37046 ◽

2021 ◽

Vol 15 (1) ◽

pp. 12

Author(s):

Casey Hon ◽

Sisira Nair ◽

Petr Smirnov ◽

Hossein Sharifi-Noghabi ◽

Nikta Feizi ◽

...

Keyword(s):

Cell Lines ◽

Drug Sensitivity ◽

Cancer Therapeutics ◽

The Other ◽

Comparative Analyses ◽

Potential Sources ◽

The Common ◽

Pharmacogenomic Studies

Multiple comparative analyses between the common drugs and cell lines of the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP) have previously shown low consistency between the in vitro phenotypic measures of a drug in one study with the other. While several potential sources of inconsistency have been tested, the similar targets of tested compounds has yet to be tested as a contributing factor of discrepancy. This analysis includes two methods of reclassifying drugs into classes based on their targets to identify the truer set of consistent cell lines, showing an increased correlation between the two pharmacogenomic studies.

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A Novel Analysis Method for Evaluating the Interplay of Oxygen and Ionizing Radiation at the Gene Level

Frontiers in Genetics ◽

10.3389/fgene.2021.597635 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jeannette Jansen ◽

Patricia Vieten ◽

Francesca Pagliari ◽

Rachel Hanley ◽

Maria Grazia Marafioti ◽

...

Keyword(s):

Cell Cycle ◽

Cell Line ◽

Cell Lines ◽

Radiation Treatment ◽

Hierarchical Cluster ◽

Specific Gene ◽

Analysis Method ◽

Whole Genome Analysis ◽

Gene Level ◽

The Impact

Whilst the impact of hypoxia and ionizing radiations on gene expression is well-understood, the interplay of these two effects is not. To better investigate this aspect at the gene level human bladder, brain, lung and prostate cancer cell lines were irradiated with photons (6 Gy, 6 MV LINAC) in hypoxic and normoxic conditions and prepared for the whole genome analysis at 72 h post-irradiation. The analysis was performed on the obtained 20,000 genes per cell line using PCA and hierarchical cluster algorithms to extract the most dominant genes altered by radiation and hypoxia. With the help of the introduced novel radiation-in-hypoxia and oxygen-impact profiles, it was possible to overcome cell line specific gene regulation patterns. Based on that, 37 genes were found to be consistently regulated over all studied cell lines. All DNA-repair related genes were down-regulated after irradiation, independently of the oxygen state. Cell cycle-dependent genes showed up-regulation consistent with an observed change in cell population in the S and G2/M phases of the cell cycle after irradiation. Genes behaving oppositely in their regulation behavior when changing the oxygen concentration and being irradiated, were immunoresponse and inflammation related genes. The novel analysis method, and by consequence, the results presented here have shown how it is important to consider the two effects together (oxygen and radiation) when analyzing gene response upon cancer radiation treatment. This approach might help to unrevel new gene patterns responsible for cancer radioresistance in patients.

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