Investigation of radiosensitivity gene signatures in cancer cell lines

MiR-223-3p in Cancer Development and Cancer Drug Resistance: Same Coin, Different Faces

MicroRNAs (miRNAs) are mighty post-transcriptional regulators in cell physiology and pathophysiology. In this review, we focus on the role of miR-223-3p (henceforth miR-223) in various cancer types. MiR-223 has established roles in hematopoiesis, inflammation, and most cancers, where it can act as either an oncogenic or oncosuppressive miRNA, depending on specific molecular landscapes. MiR-223 has also been linked to either the sensitivity or resistance of cancer cells to treatments in a context-dependent way. Through this detailed review, we highlight that for some cancers (i.e., breast, non-small cell lung carcinoma, and glioblastoma), the oncosuppressive role of miR-223 is consistently reported in the literature, while for others (i.e., colorectal, ovarian, and pancreatic cancers, and acute lymphocytic leukemia), an oncogenic role prevails. In prostate cancer and other hematological malignancies, although an oncosuppressive role is frequently described, there is less of a consensus. Intriguingly, NLRP3 and FBXW7 are consistently identified as miR-223 targets when the miRNA acts as an oncosuppressor or an oncogene, respectively, in different cancers. Our review also describes that miR-223 was increased in biological fluids or their extracellular vesicles in most of the cancers analyzed, as compared to healthy or lower-risk conditions, confirming the potential application of this miRNA as a diagnostic and prognostic biomarker in the clinic.

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation

Introduction

The search for biomarkers to predict radiosensitivity is important not only to individualize radiotherapy of cancer patients but also to forecast radiation exposure risks. The aim of this study was to devise a machine-learning method to stratify radiosensitivity and to investigate its association with genome-wide copy number variations (CNVs) as markers of sensitivity to ionizing radiation.

Methods

We used the Affymetrix CytoScan HD microarrays to survey common CNVs in 129 fibroblast cell strains. Radiosensitivity was measured by the surviving fraction at 2 Gy (SF2). We applied a dynamic programming (DP) algorithm to create a piecewise (segmented) multivariate linear regression model predicting SF2 and to identify SF2 segment-related distinctive CNVs.

Results

SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. The weighted mean relative errors (MRE) decreased with increasing the segments' number. The borders of the utmost segments have stabilized after partitioning SF2 into 5 subranges.

Discussion

The 5-segment model associated C-3SFBP marker with the most-RS and C-7IUVU marker with the most-RR cell strains. Both markers were mapped to gene regions (MCC and SLC1A6, respectively). In addition, C-3SFBP marker is also located in enhancer and multiple binding motifs. Moreover, for most CNVs significantly correlated with SF2, the radiosensitivity increased with the copy-number decrease.In conclusion, the DP-based piecewise multivariate linear regression method helps narrow the set of CNV markers from the whole radiosensitivity range to the smaller intervals of interest. Notably, SF2 partitioning not only improves the SF2 estimation but also provides distinctive markers. Ultimately, segment-related markers can be used, potentially with tissues' specific factors or other clinical data, to identify radiotherapy patients who are most RS and require reduced doses to avoid complications and the most RR eligible for dose escalation to improve outcomes.

Ablative radiotherapy improves survival but does not cure autochthonous mouse models of prostate and colorectal cancer

BACKGROUND

Genetically engineered mouse models (GEMMs) of cancer are powerful tools to study mechanisms of disease progression and therapy response, yet little is known about how these models respond to multimodality therapy used in patients. Radiation therapy (RT) is frequently used to treat localized cancers with curative intent, delay progression of oligometastases, and palliate symptoms of metastatic disease.

METHODS

Here we report the development, testing, and validation of a platform to immobilize and target tumors in mice with stereotactic ablative RT (SART). Xenograft and autochthonous tumor models were treated with hypofractionated ablative doses of radiotherapy.

RESULTS

We demonstrate that hypofractionated regimens used in clinical practice can be effectively delivered in mouse models. SART alters tumor stroma and the immune environment, improves survival in GEMMs of primary prostate and colorectal cancer, and synergizes with androgen deprivation in prostate cancer. Complete pathologic responses were achieved in xenograft models, but not in GEMMs.

CONCLUSIONS

While SART is capable of fully ablating xenografts, it is unable to completely eradicate disease in GEMMs, arguing that resistance to potentially curative therapy can be modeled in GEMMs.

Validation of In Vitro Trained Transcriptomic Radiosensitivity Signatures in Clinical Cohorts

Transcriptomic personalisation of radiation therapy has gained considerable interest in recent years. However, independent model testing on in vitro data has shown poor performance. In this work, we assess the reproducibility in clinical applications of radiosensitivity signatures. Agreement between radiosensitivity predictions from published signatures using different microarray normalization methods was assessed. Control signatures developed from resampled in vitro data were benchmarked in clinical cohorts. Survival analysis was performed using each gene in the clinical transcriptomic data, and gene set enrichment analysis was used to determine pathways related to model performance in predicting survival and recurrence. The normalisation approach impacted calculated radiosensitivity index (RSI) values. Indeed, the limits of agreement exceeded 20% with different normalisation approaches. No published signature significantly improved on the resampled controls for prediction of clinical outcomes. Functional annotation of gene models suggested that many overlapping biological processes are associated with cancer outcomes in RT treated and non-RT treated patients, including proliferation and immune responses. In summary, different normalisation methods should not be used interchangeably. The utility of published signatures remains unclear given the large proportion of genes relating to cancer outcome. Biological processes influencing outcome overlapped for patients treated with or without radiation suggest that existing signatures may lack specificity.

Systematic review of comparative transcriptomic studies of cellular resistance to genotoxic stress

The development of resistance by tumor cells to various types of therapy is a significant problem that decreases the effectiveness of oncology treatments. For more than two decades, comparative transcriptomic studies of tumor cells with different sensitivities to ionizing radiation and chemotherapeutic agents have been conducted in order to identify the causes and mechanisms underlying this phenomenon. However, the results of such studies have little in common and often contradict each other. We have assumed that a systematic analysis of a large number of such studies will provide new knowledge about the mechanisms of development of therapeutic resistance in tumor cells. Our comparison of 123 differentially expressed gene (DEG) lists published in 98 papers suggests a very low degree of consistency between the study results. Grouping the data by type of genotoxic agent and tumor type did not increase the similarity. The most frequently overexpressed genes were found to be those encoding the transport protein ABCB1 and the antiviral defense protein IFITM1. We put forward a hypothesis that the role played by the overexpression of the latter in the development of resistance may be associated not only with the stimulation of proliferation, but also with the limitation of exosomal communication and, as a result, with a decrease in the bystander effect. Among down regulated DEGs, BNIP3 was observed most frequently. The expression of BNIP3, together with BNIP3L, is often suppressed in cells resistant to non-platinum genotoxic chemotherapeutic agents, whereas it is increased in cells resistant to ionizing radiation. These observations are likely to be mediated by the binary effects of these gene products on survival, and regulation of apoptosis and autophagy. The combined data also show that even such obvious mechanisms as inhibition of apoptosis and increase of proliferation are not universal but show multidirectional changes.

Characterization of Intrinsic Radiation Sensitivity in a Diverse Panel of Normal, Cancerous and CRISPR-Modified Cell Lines

Intrinsic radiosensitivity is a major determinant of radiation response. Despite the extensive amount of radiobiological data available, variability among different studies makes it very difficult to produce high-quality radiosensitivity biomarkers or predictive models. Here, we characterize a panel of 27 human cell lines, including those derived from lung cancer, prostate cancer, and normal tissues. In addition, we used CRISPR-Cas9 to generate a panel of lines with known DNA repair defects. These cells were characterised by measuring a range of biological features, including the induction and repair of DNA double-strand breaks (DSBs), cell cycle distribution, ploidy, and clonogenic survival following X-ray irradiation. These results offer a robust dataset without inter-experimental variabilities for model development. In addition, we used these results to explore correlations between potential determinants of radiosensitivity. There was a wide variation in the intrinsic radiosensitivity of cell lines, with cell line Mean Inactivation Doses (MID) ranging from 1.3 to 3.4 Gy for cell lines, and as low as 0.65 Gy in Lig4^-/- cells. Similar substantial variability was seen in the other parameters, including baseline DNA damage, plating efficiency, and ploidy. In the CRISPR-modified cell lines, residual DSBs were good predictors of cell survival (R² = 0.78, p = 0.009), as were induced levels of DSBs (R² = 0.61, p = 0.01). However, amongst the normal and cancerous cells, none of the measured parameters correlated strongly with MID (R² < 0.45), and the only metrics with statistically significant associations are plating efficiency (R² = 0.31, p = 0.01) and percentage of cell in S phase (R² = 0.37, p = 0.005). While these data provide a valuable dataset for the modelling of radiobiological responses, the differences in the predictive power of residual DSBs between CRISPR-modified and other subgroups suggest that genetic alterations in other pathways, such as proliferation and metabolism, may have a greater impact on cellular radiation response. These pathways are often neglected in response modelling and should be considered in the future.

Targeted radionuclide therapy directed to the tumor phenotypes: A dosimetric approach using MC simulations

BACKGROUND

In Targeted Radionuclide Therapy (TRT), the continuous technological effort in imaging tumor phenotypes (i.e. sub-volumes with different phenotypic characteristics) and in precise radiopharmaceutical tumor-targeting, is allowing for a better dosimetric optimization at the tumor phenotype level. The aim of this study was to evaluate the dosimetric efficiency (considering strategic absorbed dose delivery to the phenotypes) of personalized TRT directed to the tumor phenotypes.

METHODS

The dosimetric assessment was performed using a four-phenotype realistic tumor model implemented within the ICRP reference voxel phantom and simulations using the state-of-the-art Monte Carlo program PENELOPE. The dose assessment was performed for five radionuclides commonly used in therapy and/or diagnostic procedures: ¹²⁵I, ^99mTc, ¹⁷⁷Lu, ¹⁶¹Tb and ⁶⁷Ga. Two irradiation scenarios were considered: (i) the Whole Tumor Treatment Planning Scenario (WTTPS), i.e. the four phenotypes irradiated with the same radionuclide; (ii) the Phenotype Treatment Planning Scenario (PTPS), i.e. each phenotype irradiated by a single radionuclide. The optimal radionuclide configurations were studied considering the maximization of the absorbed dose delivered to the tumor and the minimization of dose to healthy tissues.

RESULTS

In WTTPS, ¹²⁵I outperforms the other radionuclides in terms of the ratio of the maximum absorbed dose delivered to the tumor and the minimum absorbed dose delivered to healthy tissues. In the PTPS, the use of ¹⁶¹Tb in combination with the other radionuclides maximizes the absorbed dose in the tumor tissues while simultaneously minimizing dose to healthy tissue, compared to the WTTPS. In agreement with recent pre-clinical studies, our computational results confirm and indicate the beneficial additive dosimetric effects of Auger and conversion electrons of ¹⁶¹Tb with respect to ¹⁷⁷Lu, when considering the same cumulated activity for both. Interestingly, in considering a realistic tumor model, the better dosimetric performances of ¹⁶¹Tb were confirmed also for tumor volumes ranging from 1.98 cm³ to 33.32 cm³.

CONCLUSIONS

Dose assessment in realistic non-homogeneous tumor models could provide more insights with respect to consider only homogenous water-spheres tumor models and should be taken into account in dosimetry-based TRT planning studies.

A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma

Background and Purpose

Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso).

Methods

In this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients.

Results

We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates.

Conclusions

We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Purpose

We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.

Methods and Materials

Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.

Results

In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.

Conclusions

The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity

Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R² for the published models on test data was very poor at 0.01 (range: −0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: −0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.

Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy

Purpose

To explore the association of genes in “PD-L1 expression and PD-1 check point pathway in cancer” to radiotherapy survival benefit.

Methods and materials

Gene expression data and clinical information of cancers were downloaded from TCGA. Radiotherapy survival benefit was defined based on interaction model. Fast backward multivariate Cox regression was performed using stacking multiple interpolation data to identify radio-sensitive (RS) genes.

Results

Among the 73 genes in PD-L1/PD-1 pathway, we identified 24 RS genes in BRCA data set, 25 RS genes in STAD data set and 20 RS genes in HNSC data set, with some crossover genes. Theoretically, there are two types of RS genes. The expression level of Type I RS genes did not affect patients' overall survival (OS), but when receiving radiotherapy, patients with different expression level of Type I RS genes had varied survival benefit. Oppositely, Type II RS genes affected patients' OS. And when receiving radiotherapy, those with lower OS could benefit a lot. Type II RS genes in BRCA had strong positive correlation and closely biological interactions. When performing cluster analysis using these related Type II RS genes, patients could be divided into RS group and non-RS group in BRCA and METABRIC data sets.

Conclusions

Our study explored potential radio-sensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway and revealed a strong association between this pathway and radiotherapy survival benefit. New types of RS genes could be identified based on expanded definition to RS genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01951-x.

A Mechanistic DNA Repair and Survival Model (Medras): Applications to Intrinsic Radiosensitivity, Relative Biological Effectiveness and Dose-Rate

Variations in the intrinsic radiosensitivity of different cells to ionizing radiation is now widely believed to be a significant driver in differences in response to radiotherapy. While the mechanisms of radiosensitivity have been extensively studied in the laboratory, there are a lack of models which integrate this knowledge into a predictive framework. This paper presents an overview of the Medras model, which has been developed to provide a mechanistic framework in which different radiation responses can be modelled and individual responses predicted. This model simulates the repair of radiation-induced DNA damage, incorporating the overall kinetics of repair and its fidelity, to predict a range of biological endpoints including residual DNA damage, mutation, chromosome aberration, and cell death. Validation of this model against a range of exposure types is presented, including considerations of varying radiation qualities and dose-rates. This approach has the potential to inform new tools to deliver mechanistic predictions of radiation sensitivity, and support future developments in treatment personalization.

Metabolic reprograming of antioxidant defense: a precision medicine perspective for radiotherapy of lung cancer?

Radiotherapy plays a key role in the management of lung cancer patients in curative and palliative settings. Traditionally, radiotherapy was either given alone or in combination with surgery, classical cytotoxic chemotherapy, or both. Technical and physical innovations achieved during the last two decades have helped to enhance the accuracy of radiotherapy dose delivery and have facilitated geometric radiotherapy individualization. Furthermore, multimodal combinations with molecularly tailored drugs or immunotherapy yielded promising survival benefits in selected patients. Yet high locoregional failure rates and frequent development of metastases still limit the patient outcome. One major obstacle to successful treatment is the high molecular heterogeneity observed in lung cancer. So far, clinical radiotherapy does not routinely use the knowledge on molecular subtypes with regard to therapy individualization and predictive biomarkers are missing. Herein, altered cancer metabolism has attracted novel attention during recent years as it promotes tumor growth and progression as well as resistance to anticancer therapies. The present perspective will exemplarily highlight how clinically relevant molecular subtypes defined by co-occurring somatic mutations in KRAS-driven lung cancer impact the metabolic phenotype of cancer cells, how the metabolic phenotype supports intrinsic radioresistance by the improved antioxidant defense, and also discuss potential subtype-specific actionable metabolic vulnerabilities. Understanding metabolic phenotypes of radioresistance and metabolic bottlenecks of cancer cells undergoing radiotherapy in a cancer-specific context will offer largely unexploited future avenues for biological individualization and optimization of radiotherapy. Transcriptional profiles will provide additional benefit in defining metabolic phenotypes associated with radioresistance, particularly in cases, where such dependencies cannot be identified by specific somatic mutations.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

Long non-coding RNAs (lncRNAs) are involved in diverse biological processes, including DNA damage repair, and are of interest as potential biomarkers of radiosensitivity. We investigated whether lncRNA radiosensitivity signatures could be derived for use in cancer patients treated with radiotherapy. Signature development involved radiosensitivity measurements for cell lines and primary tumor samples, and patient outcome after radiotherapy. A 10-lncRNA signature trained on radiosensitivity measurements in bladder cell lines showed a trend towards independent validation. In multivariable analyses, patients with tumors classified as radioresistant by the lncRNA signature had poorer local relapse-free survival (P = 0.065) in 151 patients with muscle-invasive bladder cancer who underwent radiotherapy. An mRNA-based radiosensitivity index signature performed similarly to the lncRNA bladder signature for local relapse-free survival (P = 0.055). Pathway analysis showed the lncRNA signature associated with molecular processes involved in radiation responses. Knockdown of one of the lncRNAs in the signature showed a modest increase in radiosensitivity in one cell line. An alternative approach involved training on primary cervical tumor radiosensitivity or local control after radiotherapy. Both approaches failed to generate a cervix lncRNA radiosensitivity signature, which was attributed to the age of samples in our cohorts. Our work highlights challenges in validating lncRNA signatures as biomarkers in archival tissue from radiotherapy cohorts, but supports continued investigation of lncRNAs for a role in radiosensitivity.

Differential miRNAs expression pattern of irradiated breast cancer cell lines is correlated with radiation sensitivity

Radiotherapy is a fundamental step in the treatment of breast cancer patients. The treatment efficiency is however reduced by the possible onset of radiation resistance. In order to develop the effective treatment approach, it is important to understand molecular basis of radiosensitivity in breast cancer. The purpose of the present study was to investigate different radiation response of breast cancer cell lines, and find out if this response may be related to change in the microRNAs expression profile. MDA-MB-231 and T47D cells were subjected to different doses of radiation, then MTT and clonogenic assays were performed to assess radiation sensitivity. Cytofluorometric and western blot analysis were performed to gain insight into cell cycle distribution and protein expression. MicroRNA sequencing and bioinformatics prediction methods were used to identify the difference in microRNAs expression between two breast cancer cells and the related genes and pathways. T47D cells were more sensitive to radiation respect to MDA-MB-231 cells as demonstrated by a remarkable G2 cell cycle arrest followed by a greater reduction in cell viability and colony forming ability. Accordingly, T47D cells showed higher increase in the phosphorylation of ATM, TP53 and CDK1 (markers of radiation response) and faster and more pronounced increase in RAD51 and γH2AX expression (markers of DNA damage), when compared to MDA-MB-231 cells. The two cell lines had different microRNAs expression profiles with a confirmed significant differential expression of miR-16-5p, which targets cell cycle related genes and predicts longer overall survival of breast cancer patients, as determined by bioinformatics analysis. These results suggest a possible role for miR-16-5p as radiation sensitizing microRNA and as prognostic/predictive biomarker in breast cancer.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Cytotoxicity, dose-enhancement and radiosensitization of glioblastoma cells with rare earth nanoparticles

Glioblastoma is a heterogeneous disease with multiple genotypic origins. Despite treatment protocols such as surgery, radiotherapy and chemotherapy, the prognosis for patients remains poor. This study investigates the cytotoxic and radiation dose-enhancing and radiosensitizing ability of five rare earth oxide nanoparticles, in two different immortalized mammalian cell lines; U-87 MG and Mo59K. Significant cytotoxicity was observed in U-87 MG cells when exposed to Nd2O3 and La2O3. Autophagy was also detected in cells after incubation with Nd2O3. Radiosensitization was observed in U-87 MG when incubated with Gd2O3, CeO2-Gd and Nd2O3:Si. Importantly, these elements did not cause any intrinsic toxicity in the absence of irradiation and so could be considered biocompatible. The Gd2O3 and CeO2-Gd nanoparticles were also seen to generate ROS in U-87 MG cells after irradiation. Furthermore, the Mo59K and U-87 MG cells responded very differently to exposure to the rare earth nanoparticles. This may indicate the importance of the genotype of cells in the successful use of rare earth oxides for treatment.

Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy

Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.

Toward a New Framework for Clinical Radiation Biology

Radiation biology has entered the era of precision oncology, and this article reviews time-tested factors that determine the effects of fractionated radiation therapy in a wide variety of tumor types and normal tissues: the association of tumor control with radiation dose, the importance of fractionation and overall treatment time, and the role of tumor hypoxia. Therapeutic gain can only be achieved if the increased tumor toxicity produced by biological treatment modifications is balanced against injury to early-responding and late-responding normal tissues. Developments in precision oncology and immuno-oncology will allow an emphasis on treatment individualization and predictive biomarker development.

Mechanistic Modelling of Radiation Responses

Radiobiological modelling has been a key part of radiation biology and therapy for many decades, and many aspects of clinical practice are guided by tools such as the linear-quadratic model. However, most of the models in regular clinical use are abstract and empirical, and do not provide significant scope for mechanistic interpretation or making predictions in novel cell lines or therapies. In this review, we will discuss the key areas of ongoing mechanistic research in radiation biology, including physical, chemical, and biological steps, and review a range of mechanistic modelling approaches which are being applied in each area, highlighting the possible opportunities and challenges presented by these techniques.

Developing Predictive or Prognostic Biomarkers for Charged Particle Radiotherapy

The response to radiotherapy can vary greatly among individuals, even though advances in technology allow for the highly localized placement of therapeutic doses of radiation to a tumor. This variability in patient response to radiation is biologically driven, but the individuality of tumor and healthy tissue biology are not used to create individual treatment plans. Biomarkers of radiosensitivity, whether intrinsic or from hypoxia, would move radiation oncology from precision medicine to precise, personalized medicine. Charged particle radiotherapy allows for even greater dose conformity, but the biological advantages of charged particle radiotherapy have not yet been cultivated. The development of biomarkers that would drive biologically based clinical trials, identify patients for whom charged particles are most appropriate, or aid in particle-selection strategies could be envisioned with appropriate biomarkers. Initially, biomarkers for low-linear energy transfer (LET) radiation responses should be tested against charged particles. Biomarkers of tumor radioresistance to low-LET radiations could be used to identify patients for whom the enhanced relative biological effectiveness (RBE) of charged particles would be more effective compared with low-LET radiations and those for whom specific DNA-repair inhibitors, in combination with charged particles, may also be appropriate. Furthermore, heavy charged particles can overcome the radioresistance of hypoxic tumors when used at the appropriate LET. Biomarkers for hypoxia could identify hypoxic tumors and, in combination with imaging, define hypoxic regions of a tumor for specific ion selection. Moreover, because of the enhanced RBE for charged particles, the risk for adverse healthy tissue effects may be greater, even though charged particles have greater tumor conformality. There are many validated healthy-tissue biomarkers available to test against charged particle exposures. Lastly, newer biological techniques, as well as newer bioinformatic and computational methods, are rapidly changing the landscape for biomarker identification, validation, and clinical trial design.

Inhibition of PI3K signalling increases the efficiency of radiotherapy in glioblastoma cells

Glioblastoma, the most common primary brain tumour, is also considered one of the most lethal cancers per se. It is highly refractory to therapeutic intervention, as highlighted by the mean patient survival of only 15 months, despite an aggressive treatment approach, consisting of maximal safe surgical resection, followed by radio- and chemotherapy. Radiotherapy, in particular, can have effects on the surviving fractions of tumour cells, which are considered adverse to the desired clinical outcome: It can induce increased cellular proliferation, as well as enhanced invasion. In this study, we established that differentiated glioblastoma cells alter their DNA repair response following repeated exposure to radiation and, therefore, high single-dose irradiation (SD-IR) is not a good surrogate marker for fractionated dose irradiation (FD-IR), as used in clinical practice. Integrating irradiation into a combination therapy approach, we then investigated whether the pharmacological inhibition of PI3K signalling, the most abundantly activated survival cascade in glioblastoma, enhances the efficacy of radiotherapy. Of note, treatment with GDC-0941, which blocks PI3K-mediated signalling, did not enhance cell death upon irradiation, but both treatment modalities functioned synergistically to reduce the total cell number. Furthermore, GDC-0941 not only prevented the radiation-induced increase in the motility of the differentiated cells, but further reduced their speed below that of untreated cells. Therefore, combining radiotherapy with the pharmacological inhibition of PI3K signalling is a potentially promising approach for the treatment of glioblastoma, as it can reduce the unwanted effects on the surviving fraction of tumour cells.

Differential miRNA expression profiling reveals miR-205-3p to be a potential radiosensitizer for low- dose ionizing radiation in DLD-1 cells

Enhanced radiosensitivity at low doses of ionizing radiation (IR) (0.2 to 0.6 Gy) has been reported in several cell lines. This phenomenon, known as low doses hyper-radiosensitivity (LDHRS), appears as an opportunity to decrease toxicity of radiotherapy and to enhance the effects of chemotherapy. However, the effect of low single doses IR on cell death is subtle and the mechanism underlying LDHRS has not been clearly explained, limiting the utility of LDHRS for clinical applications. To understand the mechanisms responsible for cell death induced by low-dose IR, LDHRS was evaluated in DLD-1 human colorectal cancer cells and the expression of 80 microRNAs (miRNAs) was assessed by qPCR array. Our results show that DLD-1 cells display an early DNA damage response and apoptotic cell death when exposed to 0.6 Gy. miRNA expression profiling identified 3 over-expressed (miR-205-3p, miR-1 and miR-133b) and 2 down-regulated miRNAs (miR-122-5p, and miR-134-5p) upon exposure to 0.6 Gy. This miRNA profile differed from the one in cells exposed to high-dose IR (12 Gy), supporting a distinct low-dose radiation-induced cell death mechanism. Expression of a mimetic miR-205-3p, the most overexpressed miRNA in cells exposed to 0.6 Gy, induced apoptotic cell death and, more importantly, increased LDHRS in DLD-1 cells. Thus, we propose miR-205-3p as a potential radiosensitizer to low-dose IR.

Applying Precision Oncology Principles in Radiation Oncology

Radiation therapy is a critical component in the curative management of many solid tumor types, and advances in radiation delivery techniques during the past decade have led to improved disease control and quality of life for patients. During the same period, remarkable advances have also been made in understanding the genomic landscape of tumors; however, treatment decisions in radiation oncology continue to depend primarily on clinical and histopathologic characteristics rather than on the genetic features of the tumor or the patient. With the development of novel genomic techniques and their increasing use in clinical practice, radiation oncology is uniquely positioned to leverage these advances to identify novel biomarkers that could inform radiation dose, field, and the use of concurrent systemic agents. Here, we summarize efforts to use genomic techniques to guide radiation decisions, and we highlight some of the current opportunities and challenges that exist in attempting to apply precision oncology principles in radiation oncology.

Development of a radiosensitivity gene signature for patients with soft tissue sarcoma

Adjuvant radiotherapy is an important clinical treatment option for the majority of sarcomas. The motivation of current study is to identify a gene signature and to predict radiosensitive patients who are most likely to benefit from radiotherapy. Using the public available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure for identifying a gene signature and predicting radiosensitive patients through. The result showed that the predicted radiosensitive patients who received radiotherapy had a significantly better survival with a reduced rate of new tumor event and disease progression. Strata analysis showed that the predicted radiosensitive patients had significantly better survival under radiotherapy independent of histologic types. A hierarchical cluster analysis was used to validate the gene signature, and the results showed the predicted sensitivity for each patient well matched the results from cluster analysis. Together, we demonstrate a radiosensitive molecular signature that can be potentially used for identifying radiosensitive patients with sarcoma.

Overcoming adaptive resistance in mucoepidermoid carcinoma through inhibition of the IKK-β/IκBα/NFκB axis

Patients with mucoepidermoid carcinoma (MEC) experience low survival rates and high morbidity following treatment, yet the intrinsic resistance of MEC cells to ionizing radiation (IR) and the mechanisms underlying acquired resistance remain unexplored. Herein, we demonstrated that low doses of IR intrinsically activated NFκB in resistant MEC cell lines. Moreover, resistance was significantly enhanced in IR-sensitive cell lines when NFκB pathway was stimulated. Pharmacological inhibition of the IKK-β/IκBα/NFκB axis, using a single dose of FDA-approved Emetine, led to a striking sensitization of MEC cells to IR and a reduction in cancer stem cells. We achieved a major step towards better understanding the basic mechanisms involved in IR-adaptive resistance in MEC cell lines and how to efficiently overcome this critical problem.

Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature

Soft tissue sarcomas are rare and aggressive tumors arising from connective tissues. Adjuvant radiotherapy is a commonly used treatment approach for the majority of sarcomas. We attempted to identify a gene signature that can predict radiosensitive patients who are most likely to have a better treatment response from radiotherapy, compared with disease progression. Using the publicly available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure to identify a predictive gene signature for radiosensitivity. The results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved treatment response. We further provide supportive evidence to validate our sensitivity prediction. Results showed that the predicted radiosensitive patients who received radiotherapy had significantly improved survival than patients who did not. ROC analysis showed that the developed gene signature had a powerful prediction on treatment response. We further found that predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of new tumor events. Finally, we validated our gene signature using a hierarchical cluster analysis, and found that the predicted sensitivities were well-matched with results from the cluster analysis. These results are consistent with our expectation, suggesting that the identified gene signature and radiosensitivity prediction are effective. The genes involved in the signature may provide a molecular basis for prognostic studies and radiotherapy target discovery.

A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation

Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity (R² = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter (R² = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE (R² = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type.

A 13-gene expression-based radioresistance score highlights the heterogeneity in the response to radiation therapy across HPV-negative HNSCC molecular subtypes

BACKGROUND

Radiotherapy for head and neck squamous cell carcinomas (HNSCC) is associated with a substantial morbidity and inconsistent efficacy. Human papillomavirus (HPV)-positive status is recognized as a marker of increased radiosensitivity. Our goal was to identify molecular markers associated with benefit to radiotherapy in patients with HPV-negative disease.

METHODS

Gene expression profiles from public repositories were downloaded for data mining. Training sets included 421 HPV-negative HNSCC tumors from The Cancer Genome Atlas (TCGA) and 32 HNSCC cell lines with available radiosensitivity data (GSE79368). A radioresistance (RadR) score was computed using the single sample Gene Set Enrichment Analysis tool. The validation sets included two panels of cell lines (NCI-60 and GSE21644) and HPV-negative HNSCC tumor datasets, including 44 (GSE6631), 82 (GSE39366), and 179 (GSE65858) patients, respectively. We finally performed an integrated analysis of the RadR score with known recurrent genomic alterations in HNSCC, patterns of protein expression, biological hallmarks, and patterns of drug sensitivity using TCGA and the E-MTAB-3610 dataset (659 pancancer cell lines, 140 drugs).

RESULTS

We identified 13 genes differentially expressed between tumor and normal head and neck mucosa that were associated with radioresistance in vitro and in patients. The 13-gene expression-based RadR score was associated with recurrence in patients treated with surgery and adjuvant radiotherapy but not with surgery alone. It was significantly different among different molecular subtypes of HPV-negative HNSCC and was significantly lower in the "atypical" molecular subtype. An integrated analysis of RadR score with genomic alterations, protein expression, biological hallmarks and patterns of drug sensitivity showed a significant association with CCND1 amplification, fibronectin expression, seven hallmarks (including epithelial-to-mesenchymal transition and unfolded protein response), and increased sensitivity to elesclomol, an HSP90 inhibitor.

CONCLUSIONS

Our study highlights the clinical relevance of the molecular classification of HNSCC and the RadR score to refine radiation strategies in HPV-negative disease.

The promising impact of molecular profiling on treatment strategies in oral cancers

Oral squamous cell carcinoma (OSCC) is a major cause of cancer-associated morbidity and mortality. Although OSCC may develop from easily accessible oral preneoplastic lesions (OPLs), no intervention has been reported so far that reduces the rate of malignant transformation. A comprehensive molecular characterization of oral carcinogenesis may help refining treatment strategies both in patients with OPLs and OSCC. Herein, we review main molecular alterations occurring at different steps during oral carcinogenesis and show how molecularly-based medicine and surgery may impact the outcome of OSCC in the future.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

Intrinsic Radiosensitivity and Cellular Characterization of 27 Canine Cancer Cell Lines

Canine cancer cell lines have progressively been developed, but are still underused resources for radiation biology research. Measurement of the cellular intrinsic radiosensitivity is important because understanding the difference may provide a framework for further elucidating profiles for prediction of radiation therapy response. Our studies have focused on characterizing diverse canine cancer cell lines in vitro and understanding parameters that might contribute to intrinsic radiosensitivity. First, intrinsic radiosensitivity of 27 canine cancer cell lines derived from ten tumor types was determined using a clonogenic assay. The 27 cell lines had varying radiosensitivities regardless tumor type (survival fraction at 2 Gy, SF2 = 0.19-0.93). In order to understand parameters that might contribute to intrinsic radiosensitivity, we evaluated the relationships of cellular radiosensitivity with basic cellular characteristics of the cell lines. There was no significant correlation of SF2 with S-phase fraction, doubling time, chromosome number, ploidy, or number of metacentric chromosomes, while there was a statistically significant correlation between SF2 and plating efficiency. Next, we selected the five most radiosensitive cell lines as the radiosensitive group and the five most radioresistant cell lines as the radioresistant group. Then, we evaluated known parameters for cell killing by ionizing radiation, including radiation-induced DNA double strand break (DSB) repair and apoptosis, in the radiosensitive group as compared to the radioresistant group. High levels of residual γ-H2AX foci at the sites of DSBs were present in the four out of the five radiosensitive canine cancer cell lines. Our studies suggested that substantial differences in intrinsic radiosensitivity exist in canine cancer cell lines, and radiation-induced DSB repair was related to radiosensitivity, which is consistent with previous human studies. These data may assist further investigations focusing on the detection of DSB for predicting individual response to radiation therapy for dogs, regardless of tumor type.

Targeting the DNA Damage Response in Cancer

An underlying hallmark of cancers is their genomic instability, which is associated with a greater propensity to accumulate DNA damage. Historical treatment of cancer by radiotherapy and DNA-damaging chemotherapy is based on this principle, yet it is accompanied by significant collateral damage to normal tissue and unwanted side effects. Targeted therapy based on inhibiting the DNA damage response (DDR) in cancers offers the potential for a greater therapeutic window by tailoring treatment to patients with tumors lacking specific DDR functions. The recent approval of olaparib (Lynparza), the poly (ADP-ribose) polymerase (PARP) inhibitor for treating tumors harboring BRCA1 or BRCA2 mutations, represents the first medicine based on this principle, exploiting an underlying cause of tumor formation that also represents an Achilles' heel. This review highlights the different concepts behind targeting DDR in cancer and how this can provide significant opportunities for DDR-based therapies in the future.

A radiosensitivity gene signature in predicting glioma prognostic via EMT pathway

A 31-gene signature derived by integrating four different microarray experiments, has been found to have a potential for predicting radiosensitivity of cancer cells, but it was seldom validated in clinical cancer samples. We proposed that the gene signature may serve as a predictive biomarker for estimating the overall survival of radiation-treated patients. The significance of gene signature was tested in two previously published datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Altas (TCGA), respectively. In GEO data set, patients predicted to be radiosensitive(RS) had an improved overall survival when compared with radioresistant(RR) patients in either radiotherapy(RT)-treated or non radiotherapy(RT)-treated subgroups(p<0.0001 in the RT-treated group). Multivariate Cox regression analysis showed that the gene signature is the strongest predictor(p=0.0093) in the RT-treated subgroup of patients. However, it does not remain significant (p=0.7668) in non radiotherapy-treated group when adjusting for age and Karnofsky performance score (KPS) as covariates. Similarly, in the TCGA data set, radiotherapy-treated glioblastoma multiforme(GBM) patients assigned to RS group had an improved overall survival compared with RR group(p<0.0001). Geneset enrichment analysis(GSEA) analysis revealed that enrichment of epithelial mesenchymal transition(EMT) pathway was observed with radioresistant phenotype. These results suggest that the signature is a predictive biomarker for radiation-treated glioma patients' prognostic.

PRRT: Defining the paradigm shift to achieve standardization and individualization

Peptide receptor radionuclide therapy is a treatment for inoperable or metastatic neuroendocrine tumors. A key issue is the need to standardize the treatment and develop randomized controlled trials. Standardization would help define the characteristics of response, including progression-free survival; provide homogeneous phase II and III studies; delineate the position of peptide receptor radionuclide therapy in the therapeutic algorithm for neuroendocrine tumors; and establish the basis for approval by the regulatory authorities. Standardization of treatments is the starting point to redefine the treatment paradigm from a one-size-fits-all to a personalized treatment. To delineate the treatment paradigm, treatments should be optimized for efficacy and minimization of long-term toxicity, through dosimetry, and adapted to each individual, including relevant patient characteristics. Although differences in therapy outcomes may be explained by the specific absorbed dose (or biologically effective dose), they may also be related to discrete tumor- and patient-specific features. In this respect, a particular area of investigation is the assessment of genetic elements regulating tumor cell proliferation, especially those involved in the response to cytotoxic therapies.