Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

Gene Expression Profiling Identifies Akt as a Target for Radiosensitization in Gastric Cancer Cells

Background

Despite the important role of radiotherapy in cancer treatment, a subset of patients responds poorly to treatment majorly due to radioresistance. Particularly the role of radiotherapy has not been established in gastric cancer (GC). Herein, we aimed to identify a radiosensitivity gene signature and to discover relevant targets to enhance radiosensitivity in GC cells.

Methods

An oligonucleotide microarray (containing 22,740 probes) was performed in 12 GC cell lines prior to radiation. A clonogenic assay was performed to evaluate the survival fraction at 2 Gy (SF2) as a surrogate marker for radiosensitivity. Genes differentially expressed (fold change > 6, q-value < 0.025) were identified between radiosensitive and radioresistant cell lines, and quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was performed for validation. Gene set and pathway analyses were performed using Ingenuity Pathway Analysis (IPA).

Results

Radiosensitive (SF2 < 0.4) and radioresistant cell lines (SF2 ≥ 0.6) exhibited a marked difference in gene expression. We identified 68 genes that are differentially expressed between radiosensitive and radioresistant cell lines. The identified genes showed interactions via AKT, HIF1A, TGFB1, and TP53, and their functions were associated with the genetic networks associated with cellular growth and proliferation, cellular movement, and cell cycle. The Akt signaling pathway exhibited the highest association with radiosensitivity. Combinatorial treatment with MK-2206, an allosteric Akt inhibitor, and radiotherapy significantly increased cell death compared with radiotherapy alone in two radioresistant cell lines (YCC-2 and YCC-16).

Conclusion

We identified a GC-specific radiosensitivity gene signature and suggest that the Akt signaling pathway could serve as a therapeutic target for GC radiosensitization.

Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy

Peptide receptor radionuclide therapy (PRRT) is a type of radiotherapy that targets peptide receptors and is typically used for neuroendocrine tumours (NETs). Some of the key challenges in its use are the prediction of efficacy and toxicity, patient selection, and response optimisation. In this Review, we assess current knowledge on the molecular profile of NETs and the strategies and tools used to predict, monitor, and assess the toxicity of PRRT. The few mutations in tumour genes that can be evaluated (eg, ATM and DAXX) are limited to pancreatic NETs and are most likely not informative. Assays that are transcriptomic or based on genes are effective in the prediction of radiotherapy response in other cancers. A blood-based assay for eight genes (the PRRT prediction quotient [PPQ]) has an overall accuracy of 95% for predicting responses to PRRT in NETs. No molecular markers exist that can predict the toxicity of PRRT. Candidate molecular targets include seven single nucleotide polymorphisms (SNPs) that are susceptible to radiation. Transcriptomic evaluations of blood and a combination of gene expression and specific SNPs, assessed by machine learning with algorithms that are tumour-specific, might yield molecular tools to enhance the efficacy and safety of PRRT.

A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer

Treatment personalisation remains an unmet need in oropharynx cancer (OPC). We aimed to determine whether gene expression signatures improved upon clinico-pathological predictors of outcome in OPC. The clinico-pathological predictors, AJCC version 7 (AJCC 7), AJCC 8, and a clinical algorithm, were assessed in 4 public series of OPC (n = 235). Literature review identified 16 mRNA gene expression signatures of radiosensitivity, HPV status, tumour hypoxia, and microsatellite instability. We quality tested signatures using a novel sigQC methodology, and added signatures to clinico-pathological variables as predictors of survival, in univariate and multivariate analyses. AJCC 7 Stage was not predictive of recurrence-free survival (RFS) or overall survival (OS). AJCC 8 significantly predicted RFS and OS. Gene signature quality was highly variable. Among HPV-positive cases, signatures for radiosensitivity, hypoxia, and microsatellite instability revealed significant underlying inter-tumour biological heterogeneity, but did not show prognostic significance when adjusted for clinical covariates. Surprisingly, among HPV-negative cases, a gene signature for HPV status was predictive of survival, even after adjustment for clinical covariates. Across the whole series, several gene signatures representing HPV and microsatellite instability remained significant in multivariate analysis. However, quality control and independent validation remain to be performed to add prognostic information above recently improved clinico-pathological variables.

A Radiosensitivity Gene Signature and XPO1 Predict Clinical Outcomes for Glioma Patients

Objective: Glioma is the most common and fatal primary brain tumor that has a high risk of recurrence in adults. Identification of predictive biomarkers is necessary to optimize therapeutic strategies. This study investigated the predictive efficacy of a previously identified radiosensitivity signature as well as Exportin 1 (XPO1) expression levels.

Methods: A total of 1,552 patients diagnosed with glioma were analyzed using the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. The radiosensitive and radioresistant groups were identified based on a radiosensitivity signature. Patients were also stratified into XPO1-high and XPO1-low groups based on XPO1 mRNA expression levels. Overall survival rates were compared across patient groups. Differential gene expression was detected and analyzed through pathway enrichment and Gene Set Enrichment Analysis (GSEA). To predict 1-, 3-, and 5-years survival rates for glioma patients, a nomogram was established combining the radiosensitivity gene signature, XPO1 status, and clinical characteristics. An artificial intelligence clustering system and a survival prediction system of glioma were developed to predict individual risk.

Results: This proposed classification based on a radiosensitivity gene signature and XPO1 expression levels provides an independent prognostic factor for glioma. The RR-XPO1-high group shows a poor prognosis and may benefit most from radiotherapy-combined anti-XPO1 treatment. The nomogram based on the radiosensitivity gene signature, XPO1 expression, and clinical characteristics performs more optimally compared to the WHO classification and IDH status in predicting survival rates for glioma patients. The online clustering and prediction systems make it accessible to predict risk and optimize treatment for a special patient. The cell cycle, p53, and focal adhesion pathways are associated with more invasive glioma cases.

Conclusion: Combining the radiosensitivity signature and XPO1 expression is a favorable approach to predict outcomes as well as determine optimal therapeutic strategies for glioma patients.

Circle RNA circABCB10 Modulates PFN2 to Promote Breast Cancer Progression, as Well as Aggravate Radioresistance Through Facilitating Glycolytic Metabolism Via miR-223-3p

Background: Breast cancer (BC) is a common tumor in women worldwide, and irradiation (IR) resistance is a major obstacle for BC therapy. Circle RNAs (circRNAs) were identified as implicated in the progression of cancer and IR resistance. However, the role of circABCB10 in BC progression and IR resistance is not well defined. Materials and Methods: The levels of circABCB10, miR-223-3p, and profilin-2 (PFN2) were detected by quantitative real-time polymerase chain reaction. The cell viability and survival rate were monitored by MTT assay. The glucose consumption, lactic acid production, LDH-A activity, and ATP production were evaluated to measure glycolysis. The protein levels of hypoxia inducible factor-1α (HIF-1α), hexokinase 2 (HK2), lactate dehydrogenase A chain (LDH-A), and PFN2 were estimated by Western blot assay. The colony formation rate was tested by colony formation assay. Dual-luciferase reporter assay was constructed to validate the interaction between miR-223-3p and circABCB10 or PFN2. The mice xenograft assay was performed to further verify the effects of circABCB10 on BC progression in vivo. Results: CircABCB10 and PFN2 were elevated, while miR-223-3p was reduced in BC tissues and cells. CircABCB10 sponged miR-223-3p, and PFN2 was a target of miR-223-3p in BC cells. CircABCB10 silencing inhibited cell proliferation, glycolysis, colony formation, and decreased IR resistance in BC cells by modulating miR-223-3p. Meanwhile, circABCB10 depletion restrained xenograft tumor growth in vivo. Also, miR-223-3p overexpression refrained cell proliferation, glycolysis, and colony formation while improving IR sensitivity in BC cells by regulating PFN2. Besides, circABCB10 knockdown declined PFN2 in BC cells via miR-223-3p. The glycolysis inhibitor 2-deoxy-D-glucose enhanced IR sensitivity in BC cells via circABCB10. Conclusion: CircABCB10 knockdown contributed to irradiation sensitivity by negatively regulating glycolysis via the miR-223-3p/PFN axis in breast cancer.

A three-lncRNA signature predicts clinical outcomes in low-grade glioma patients after radiotherapy

Although radiation therapy (RT) plays a critical role in the treatment of low-grade glioma (LGG), many patients suffer from adverse effects without experiencing survival benefits. In various carcinomas, long non-coding RNAs (lncRNAs) contribute to pathogenic processes, including tumorigenesis, metastasis, chemoresistance, and radioresistance. Currently, the role of lncRNAs in the radiosensitivity of LGG is largely unknown. Here, we downloaded clinical data for 167 LGG patients from The Cancer Genome Atlas database and divided them between radiosensitive and radioresistant groups based on their clinical outcomes after receiving radiotherapy. We identified 37 lncRNAs that were differentially expressed (DElncRNAs) between the groups. Functional enrichment analysis revealed that their potential target mRNAs were mainly enriched in the PI3K-Akt and MAPK signaling pathways and in DNA damage response. Kaplan-Meier survival analysis revealed that increased expression of six lncRNAs was significantly associated with radiosensitivity. We then developed a risk signature based on three of the DElncRNAs that served as an independent biomarker for predicting LGG patient outcomes after radiotherapy. In vitro experiments further validated the biological function of these lncRNAs on low-grade glioma radiation response.

Impact of accumulated alterations in driver and passenger genes on response to radiation therapy

OBJECTIVE

Although various single genetic factors have been shown to affect radiosensitivity, high-throughput DNA sequencing analyses have revealed complex genomic landscapes in many cancer types. The aim of this study is to elucidate the association between accumulated alterations in driver and passenger genes and radiation therapy response.

METHODS

We used 59 human solid cancer cell lines derived from 11 organ sites. Radiation-induced cell death was measured using a standard colony-forming assay delivered as a single dose ranging from 0 to 12 Gy. Comprehensive genomic data for the cell lines were acquired from the Catalogue Of Somatic Mutations In Cancer v. 80. Random forest classifiers were constructed to predict radioresistant phenotypes using genomic features. The Cancer Genome Atlas data sets were used to evaluate the clinical impact of the genomic feature following radiotherapy.

RESULTS

The 59 cancer cell lines harbored either nucleotide variations or copy number variations in a median of 157 genes per cell. Radiosensitivity of the cancer cells was correlated with neither the number of driver gene mutations nor the number of passenger gene mutations. However, the proportion of driver gene alterations to total gene alterations in gene sets selected from the Kyoto Encyclopedia Genes and Genomes predicted radioresistant cells with sensitivity of 85% and specificity of 73%. High probability of radioresistance predicted by the model was associated with worse overall survival following definitive radiotherapy in patients of The Cancer Genome Atlas data sets.

CONCLUSION

Cellular radiosensitivity was associated with the proportion of driver to total gene alterations in the selected oncogenic pathways, which may be a biomarker candidate for response to radiation therapy.

ADVANCES IN KNOWLEDGE

These findings suggest that accumulated alterations in not only driver genes but also passenger genes affect radiosensitivity.

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.

A Radiosensitivity Gene Signature and PD-L1 Status Predict Clinical Outcome of Patients with Glioblastoma Multiforme in The Cancer Genome Atlas Dataset

PURPOSE

Combination of radiotherapy and immune checkpoint blockade such as programmed death- 1 (PD-1) or programmed death-ligand 1 (PD-L1) blockade is being actively tested in clinical trial. We aimed to identify a subset of patients that could potentially benefit from this strategy using The Cancer Genome Atlas (TCGA) dataset for glioblastoma (GBM).

MATERIALS AND METHODS

A total of 399 cases were clustered into radiosensitive versus radioresistant (RR) groups based on a radiosensitivity gene signature and were also stratified as PD-L1 high versus PD-L1 low groups by expression of CD274 mRNA. Differential and integrated analyses with expression and methylation data were performed. CIBERSORT was used to enumerate the immune repertoire that resulted from transcriptome profiles.

RESULTS

We identified a subset of GBM, PD-L1-high-RR group which showed worse survival compared to others. In PD-L1-high-RR, differentially expressed genes (DEG) were highly enriched for immune response and mapped into activation of phosphoinositide 3-kinase-AKT and mitogen-activated protein kinase (MAPK) signaling pathways. Integration of DEG and differentially methylated region identified that the kinase MAP3K8-involved in T-cell receptor signaling was upregulated and BAI1, a factor which inhibits angiogenesis, was silenced. CIBERSORT showed that a higher infiltration of the immune repertoire, which included M2 macrophages and regulatory T cells.

CONCLUSION

Taken together, PD-L1-high-RR group could potentially benefit from radiotherapy combined with PD-1/PD-L1 blockade and angiogenesis inhibition.

Tumor immune microenvironment in cancer patients with leukocytosis

Tumor-related leukocytosis (TRL) is correlated with poor survival in various types of cancers, but the microenvironment of TRL-associated human tumors has not been fully elucidated. Here, we aimed to characterize the immune microenvironment of cancer patients with TRL. The transcriptional signatures of tumor tissues obtained from cervical cancer patients with (TRL^pos) and without TRL (TRL^neg) were compared. As a surrogate for TRL diagnosis, a leukocytosis signature (LS) score was derived using genes differentially expressed between TRL^pos and TRL^neg tumors. The immunological profiles of patients in the TCGA database with high (LS^high) or low LS scores were compared. TRL^pos tumors were transcriptionally distinct from TRL^neg tumors, exhibiting up-regulation of radioresistance and down-regulation of adaptive immune response-related genes. In the TCGA cervical cancer cohort (n = 303), patients with high LS had inferior survival rates compared to those with low LS (P = 0.023). LS^high tumors were enriched in radioresistance, wound healing, and myeloid-derived suppressor cell (MDSC) signatures and had a higher infiltration of M2 macrophages and a lower infiltration of M1 macrophages and lymphocytes. LS^high tumors also expressed higher levels of CXCR2 chemokines, CSF2, and CSF3. In the pan-cancer cohort (n = 9984), LS^high tumors also exhibited poor survival, signatures of a suppressive immune microenvironment, and higher expression of CXCR2 chemokines. Our data provide evidence for a suppressive immune microenvironment in patients with TRL and suggest promising targets, such as the CXCR2 axis, for its therapeutic intervention.

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT-sensitive and chemotherapy-sensitive), RSCR (RT-sensitive and chemotherapy-resistant), RRCS (RT-resistant and chemotherapy-sensitive) and RRCR (RT-resistant and chemotherapy-resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

ATM Expression Is Elevated in Established Radiation-Resistant Breast Cancer Cells and Improves DNA Repair Efficiency

Repair of damaged DNA induced by radiation plays an important role in the development of radioresistance, which greatly restricts patients' benefit from radiotherapy. However, the relation between radioresistance development and DNA double-strand break repair pathways (mainly non-homologous end joining and homologous recombination) and how these pathways contribute to radioresistance are unclear. Here, we established a radioresistant breast cancer cell line by repeated ionizing radiation and studied the alteration in DNA repair capacity. Compared with parental sham-treated cells, radioresistant breast cancer cells present elevated radioresistance, enhanced malignancy, increased expression of Ataxia-telangiectasia mutated (ATM), and increased DNA damage repair efficiency, as reflected by accelerated γ-H2AX kinetic. These defects can be reversed by ATM inhibition or ATM knockdown, indicating a potential link between ATM, DNA repair pathway and radiosensitivity. We propose that cancer cells develop elevated radioresistance through enhanced DNA damage repair efficiency mediated by increased ATM expression. Our work might provide a new evidence supporting the potential of ATM as a potential target of cancer therapy.

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.

Robustness of Clonogenic Assays as a Biomarker for Cancer Cell Radiosensitivity

Photon radiation therapy is a major curative treatment for cancer. However, the lack of robust predictive biomarkers for radiosensitivity precludes personalized radiation therapy. Clonogenic assays are the gold standard method for measuring the radiosensitivity of cancer cells. Although a large number of publications describe the use of clonogenic assays to measure cancer cell radiosensitivity, the robustness of results from different studies is unclear. To address this, we conducted a comprehensive detailed literature search of 256 common cancer cell lines and identified the eight cell lines most-frequently examined for photon sensitivity using clonogenic assays. Survival endpoints and experimental parameters from all 620 relevant experiments were compiled and analyzed. We found that the coefficients of variation for SF₂ (surviving fraction after 2 Gy irradiation) and for D₁₀ (dose that yields a surviving fraction of 10%) were below 30% for all cell lines, indicating that SF₂ and D₁₀ have acceptable inter-assay precision. These data support further analysis of published data on clonogenic assays using SF₂ and D₁₀ as survival endpoints, which facilitates robust identification of biological profiles representative of cancer cell sensitivity to photons.

A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening

Accurate patient-derived models of cancer are needed for profiling the disease and for testing therapeutics. These models must not only be accurate, but also suitable for high-throughput screening and analysis. Here we compare two derivative cancer models, microtumors and spheroids, to the gold standard model of patient-derived orthotopic xenografts (PDX) in glioblastoma multiforme (GBM). To compare these models, we constructed a custom NanoString panel of 350 genes relevant to GBM biology. This custom assay includes 16 GBM-specific gene signatures including a novel GBM subtyping signature. We profiled 11 GBM-PDX with matched orthotopic cells, derived microtumors, and derived spheroids using the custom NanoString assay. In parallel, these derivative models underwent drug sensitivity screening. We found that expression of certain genes were dependent on the cancer model while others were model-independent. These model-independent genes can be used in profiling tumor-specific biology and in gauging therapeutic response. It remains to be seen whether or not cancer model-specific genes may be directly or indirectly, through changes to tumor microenvironment, manipulated to improve the concordance of in vitro derivative models with in vivo models yielding better prediction of therapeutic response.

Genomic stratification based on radiosensitivity and PD-L1 for tailoring therapeutic strategies in cervical cancer

Aim: We performed an immunogenomic analysis to help identify a model for the cervical cancer therapeutic module. Patients & methods: Patients were divided into groups according to radiosensitivity and PD-L1 expression status. Results: The PD-L1-low-radioresistant (RR) group indicated worse overall survival under radiotherapy. In addition, the PD-L1-low-RR group showed the lowest immunophenoscore and predicted poor response to anti-PD-1 treatment. Differentially expressed genes associated with the PD-L1-low-RR group were found to play a role in the immune response pathways. FGF19 and PLA2G5 were validated to be upregulated in the PD-L1-low-RR group in the Gene Expression Omnibus database, which predicted the overall survival in the The Cancer Genome Atlas. Conclusion: It will be important to test radiosensitivity and PD-L1 stratification to predict the response to strategies in cervical cancer patients.

Integrating radiosensitive genes improves prediction of radiosensitivity or radioresistance in patients with oesophageal cancer

Oesophageal cancer is a serious disease worldwide. In China, the incidence of esophageal cancer was reported to be ~478,000 in 2015. In the same year, the incidence of esophageal cancer in the United States was ~16,910. Radiotherapy serves as an important tool in the treatment of oesophageal cancer, and although radiation therapy has progressed over time, the prognosis of the majority of patients with oesophageal cancer remains poor. Additionally, the sensitivity of patients with oesophageal cancer to radiotherapy and chemotherapy is not yet clear. Although there are a number of studies on the radiosensitivity of oesophageal cancer cell lines, the vastly different results from different cell lines make them unreliable to use as a guide in clinical practice. Therefore, a common radiosensitive gene signature may provide more reliable results, and using different combinations of common gene signatures to predict the outcome of patients with oesophageal cancer may generate a unique gene signature in oesophageal cancer. In the present study, the radiosensitive index and prognostic index were calculated to predict clinical outcomes. The prognostic index of a 41-gene signature combination is the largest combination of gene signatures used for classifying oesophageal cancer patients into radiosensitive (RS) and radioresistance (RR) groups, to the best of our knowledge, and this gene signature was more effective in patients classified as having Stage III oesophageal cancer. Furthermore, four genes (carbonyl reductase 1, serine/threonine kinase PAK2, ras-related protein Rab 13 and twinfilin-1) may be sufficient to classify patients into either RS or RR. Subsequent to gene enrichment analysis, the cell communication pathway was significantly different between RS and RR groups in oesophageal cancer. These results may provide useful insights in improving radiotherapy strategies in clinical decisions.

PD-1 and PD-L1 Expression Predicts Radiosensitivity and Clinical Outcomes in Head and Neck Cancer and is Associated with HPV Infection

Objectives: PD-1 and PD-L1 overexpression in malignant tumors in response to radiotherapy is correlated with a poor prognosis. Human papilloma virus (HPV) infection impacts intrinsic radiosensitivity of head and neck cancers (HNCs). Herein, this study aims to determine PD-1/PD-L1 expression differences in tumors with different HPV statuses and their prognostic value in patients with different radiosensitivity gene signatures to define the characteristics of patients who will benefit from radiotherapy combined with anti-PD-1/PD-L1 therapy. Material and methods: According to the identified gene signature related to radiosensitivity, 517 patients from the TCGA HNSCC cohort were selected and divided into the radioresistant (RR) group and radiosensitive (RS) group using a K-mean clustering algorithm. All data analyses were conducted using SPSS and GraphPad Prism. Results: PD-L1 expression is upregulated in tumor tissue (unpaired t test, P=0.0363; paired t test, P=0.0584) compared with normal tissue. PD-L1 was positively correlated with PD-1 expression (P<0.0001). The HPV/p16-positive group was significantly high PD-1 expression (P<0.0001). PD-L1 expression (P=0.0005) and PD-1 expression (P<0.0001) were significantly increased in the RS group compared with that in the RR group. In the patients who were treated with radiotherapy, the PD-1-high group was associated with better recurrence-free survival (RFS) (HR, 0.4892; 95% CI, 0.2357-1.015; P=0.023). Within the RR group, high PD-L1 expression was associated with reduced overall survival (OS) (HR, 2.196; 95% CI, 1.081-4.46; P=0.0108) compared with low PD-L1 expression. In the RR group, HPV/p16-negative patients with high PD-L1 expression exhibited reduced OS (HPV: HR, 2.334; 95% CI, 0.7828-6.961; P=0.0313; p16: HR,2.486; 95% CI, 0.8559-7.219; P=0.0192) compared with that of patients with low PD-L1 expression. In the PD-L1-high group, RR patients exhibited reduced OS (HR, 0.4858; 95% CI, 0.2136-1.105; P=0.0189) and RFS (HR, 0.4371; 95% CI, 0.1421-1.345; P=0.0231) compared with that of RS patients. Conclusion: Our findings demonstrated that high PD-1/PD-L1 expression was strongly related to radiosensitivity, and high PD-1 expression was significantly associated with HPV/p16-positive HNCs. Patients in the radioresistant group and patients in the HPV/p16-negative group with a radioresistant gene signature could benefit from the combination of radiotherapy and anti-PD-1/PD-L1 therapy.

Peroxiredoxins in Cancer and Response to Radiation Therapies

Peroxiredoxins have a long-established cellular function as regulators of redox metabolism by catalyzing the reduction of peroxides (e.g., H₂O₂, lipid peroxides) with high catalytic efficiency. This activity is also critical to the initiation and relay of both phosphorylation and redox signaling in a broad range of pathophysiological contexts. Under normal physiological conditions, peroxiredoxins protect normal cells from oxidative damage that could promote oncogenesis (e.g., environmental stressors). In cancer, higher expression level of peroxiredoxins has been associated with both tumor growth and resistance to radiation therapies. However, this relationship between the expression of peroxiredoxins and the response to radiation is not evident from an analysis of data in The Cancer Genome Atlas (TCGA) or NCI60 panel of cancer cell lines. The focus of this review is to summarize the current experimental knowledge implicating this class of proteins in cancer, and to provide a perspective on the value of targeting peroxiredoxins in the management of cancer. Potential biases in the analysis of the TCGA data with respect to radiation resistance are also highlighted.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends to be more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients.

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.

Diverse Long RNAs Are Differentially Sorted into Extracellular Vesicles Secreted by Colorectal Cancer Cells

The regulation and functional roles of secreted coding and long noncoding RNAs (lncRNAs; >200 nt) are largely unknown. We previously showed that mutant KRAS colorectal cancer (CRC) cells release extracellular vesicles (EVs) containing distinct proteomes, microRNAs (miRNAs), and circular RNAs. Here, we comprehensively identify diverse classes of CRC extracellular long RNAs secreted in EVs and demonstrate differential export of specific RNAs. Distinct noncoding RNAs, including antisense transcripts and transcripts derived from pseudogenes, are enriched in EVs compared to cellular profiles. We detected strong enrichment of Rab13 in mutant KRAS EVs and demonstrate functional delivery of Rab13 mRNA to recipient cells. To assay functional transfer of lncRNAs, we implemented a CRISPR/Cas9-based RNA-tracking system to monitor delivery to recipient cells. We show that gRNAs containing export signals from secreted RNAs can be transferred from donor to recipient cells. Our data support the existence of cellular mechanisms to selectively export diverse classes of RNA.

Emerging biomarkers for the combination of radiotherapy and immune checkpoint blockers

Over the past few years, multiple immune checkpoint blockers (ICBs) have achieved unprecedented clinical success and have been approved by regulatory agencies for the treatment of an increasing number of malignancies. However, only a limited fraction of patients responds to ICBs employed as a standalone intervention, calling for the development of combinatorial regimens. Radiation therapy (RT) stands out as a very promising candidate for this purpose. Indeed, RT mediates antineoplastic effects not only by cytotoxic and cytostatic mechanisms, but also by modulating immunological functions, both locally (within the irradiated field) and systemically. As combinatorial regimens involving RT and ICBs are being developed and clinically tested at an accelerating pace, it is paramount to identify biomarkers that reliably predict the likelihood of individual patients to respond. Here, we discuss emerging biomarkers that may potentially predict the response of cancer patients to RT plus ICBs.

A radiosensitivity gene signature and PD-L1 predict the clinical outcomes of patients with lower grade glioma in TCGA

PURPOSE

Identifying predictive factors for the clinical outcome of patients with lower grade gliomas following radiotherapy could help optimize patient treatments. Here, we investigate the predictive efficacy of both a previously identified "31-gene signature" and programmed death ligand-1 (PD-L1) expression.

MATERIAL AND METHODS

We identified 511 patients with lower grade glioma (Grade 2 and 3) in The Cancer Genome Atlas dataset and divided them into two clusters: radiosensitive (RS) and radioresistant (RR). Patients were also classified as PD-L1-high or PD-L1-low based on CD274 mRNA expression. Five-year survival rates were compared across patient groups, and differentially expressed genes were identified via a gene enrichment analysis.

RESULTS

Among 511 patients with lower grade glioma in The Cancer Genome Atlas dataset, we identified a group that was characterized by radioresistant and high PD-L1 (the PD-L1-high-RR group). Multivariate Cox models demonstrated that the membership in the PD-L1-high-RR can predict overall survival regarding to RT. Differentially expressed genes associated with the PD-L1-high-RR group were found to play a role in the immune response, including the T-cell receptor signaling pathway.

CONCLUSION

We tested the predictive value of a "31-gene signature" and PD-L1 expression status in a dataset of patients with lower grade glioma. Our results suggest that the patient population classified as the PD-L1-high-RR may benefit most from radiotherapy combined with anti-PD-1/PD-L1 treatment. Prospective clinical trial is necessary to validate the findings in a homogenous treated patient cohort.

Induction of immunoglobulin transcription factor 2 and resistance to MEK inhibitor in melanoma cells

Primary or acquired resistance to MEK inhibitors has been a barrier to successful treatment with MEK inhibitors in many tumors. In this study, we analyzed genome-wide gene expression profiling data from 6 sensitive and 6 resistant cell lines to identify candidate genes whose expression changes are associated with responses to a MEK inhibitor, selumetinib (AZD6244). Of 62 identified differentially expressed genes, we selected Immunoglobulin Transcription Factor 2, also known as transcription factor 4 as a potential drug resistance marker for further analysis. This was because the ITF-2 expression increase in resistant cell lines was relatively high and a previous study has suggested that ITF-2 functions as an oncogene in human colon cancers. We also established an AZD6244 resistant cell line (M14/AZD-3) from an AZD6244 sensitive M14 cell line. The expression of the ITF-2 was elevated both in primary AZD6244 resistant cell line, LOX-IMVI and acquired resistant cell line, M14/AZD-3. Targeted silencing of ITF-2 by siRNA significantly enhanced susceptibility to AZD6244 in resistant cells. Wnt/β-catenin pathway was activated through direct interaction of p-ERK and GSK3β. Our results suggest that up-regulation of the ITF-2 gene expression is associated with cellular resistance to MEK inhibitors, and activation of Wnt signaling pathway through interaction of p-ERK and GSK3β seems to be a mechanism for increase of ITF-2.

Inter-assay precision of clonogenic assays for radiosensitivity in cancer cell line A549

Clonogenic assays are the gold standard for determining radiosensitivity, which governs tumor response to radiation therapy. Although multiple studies of clonogenic assays on cancer cell lines have been published, the robustness of this technique has not been examined by comparative analysis of data from different studies. To address this issue, we investigated the inter-assay precision of clonogenic assays by analyzing in-house and published data on A549, a cell line frequently studied in this context. The coefficients of variation for SF2, the surviving fraction after 2 Gy irradiation, and D10, the radiation dose that reduces survival to 10%, were below 30% for both in-house data obtained from 20 independent experiments performed under consistent experimental settings (i.e., radiation type, dose rate, and timing of cell seeding) and data collected from 192 publications using diverse experimental settings. Multivariate analyses of the published data revealed that timing of cell seeding significantly affected SF2. These data indicate that SF2 and D10 of clonogenic assay have acceptable inter-assay precision, and that timing of cell seeding influences the inter-assay precision of SF2. These results provide a rationale for combined analysis of published clonogenic assay data, which may help to discover robust biological properties associated with tumor radiosensitivity.

Niclosamide sensitizes triple-negative breast cancer cells to ionizing radiation in association with the inhibition of Wnt/β-catenin signaling

Triple-negative breast cancer (TNBC) is one of the most difficult breast cancers to treat because there is no targeted treatment, and conventional cytotoxic chemotherapy followed by adjuvant radiation therapy is the standard of care for patients with TNBC. We herein reported that ionizing radiation (IR) induced Wnt3a, LRP6 and β-catenin expression and consequently activated Wnt/β-catenin signaling in TNBC MDA-MB-231, MDA-MB-468 and Hs578T cells. Moreover, depletion of β-catenin by shRNA sensitized TNBC cells to IR, whereas treatment of Wnt3a protein or overexpression of β-catenin resulted in radioresistance of TNBC cells. Niclosamide, a potent inhibitor of Wnt/β-catenin signaling, not only inhibited constitutive Wnt/β-catenin signaling, but also blocked IR-induced Wnt/β-catenin signaling in TNBC cells. In addition, niclosamide sensitized TNBC cells to IR, prevented Wnt3a-induced radioresistance, and overcame β-catenin-induced radioresistance in TNBC cells. Importantly, animals treated with the combination of niclosamide and γ-ray local tumor irradiation had significant inhibition of MDA-MB-231 tumor growth compared with treated with local tumor irradiation alone. These findings indicate that Wnt/β-catenin signaling pathway plays an important role in the development of radioresistance of TNBC cells, and that niclosamide had significant radiosensitizing effects by inhibiting Wnt/β-catenin signaling in TNBC cells. Our study also provides rationale for further preclinical and clinical evaluation of niclosamide in TNBC management.

Development of a prediction model for radiosensitivity using the expression values of genes and long non-coding RNAs

Radiotherapy has become a popular and standard approach for treating cancer patients because it greatly improves patient survival. However, some of the patients receiving radiotherapy suffer from adverse effects and do not obtain survival benefits. This may be attributed to the fact that most radiation treatment plans are designed based on cancer type, without consideration of each individual's radiosensitivity. A model for predicting radiosensitivity would help to address this issue. In this study, the expression levels of both genes and long non-coding RNAs (lncRNAs) were used to build such a prediction model. Analysis of variance and Tukey's honest significant difference tests (P < 0.001) were utilized in immortalized B cells (GSE26835) to identify differentially expressed genes and lncRNAs after irradiation. A total of 41 genes and lncRNAs associated with radiation exposure were revealed by a network analysis algorithm. To develop a predictive model for radiosensitivity, the expression profiles of NCI-60 cell lines along, with their radiation parameters, were analyzed. A genetic algorithm was proposed to identify 20 predictors, and the support vector machine algorithm was used to evaluate their prediction performance. The model was applied to 2 datasets of glioblastoma, The Cancer Genome Atlas and GSE16011, and significantly better survival was observed in patients with greater predicted radiosensitivity.

Molecular Signatures of Radiation Response in Breast Cancer: Towards Personalized Decision-Making in Radiation Treatment

Recent advances in gene expression profiling have allowed for a more sophisticated understanding of the biology of breast cancers. These advances led to the development of molecular signatures that now allow clinicians to more individually tailor recommendations regarding the utility and necessity of systemic therapies for women with breast cancer. Indeed, these molecularly based tests have been incorporated into national and international best practice guidelines and are now part of routine practice. Similar, though slower, progress is being made in the development of molecular signatures predictive of radiation response and necessity for women with breast cancer. This article will discuss the history of radiation response signature development, the current state of these signatures under ongoing clinical development, the barriers to their clinical adoption, and upcoming changes and opportunities that may allow for the personalized radiation treatment recommendations enabled by the development of these signatures.

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.

Prognostic value of integrin variants and expression in post-operative patients with HBV-related hepatocellular carcinoma

Integrins are a large family of cell surface receptors that bind extracellular matrix proteins and participate in cancer progression. However, the prognostic value of integrin family genes in post-operative patients with HBV-related hepatocellular carcinoma (HCC) remains unknown. In this study, we investigated 18 single nucleotide polymorphisms (SNPs) in integrin family genes and found that the AG/GG genotypes at rs988574 in ITGA1 predicted a better prognosis compared to carriers of the AA genotype (P = 0.025, HR = 0.69, 95%CI = 0.50–0.96). Moreover, rs988574 genotype combined with serum level of AFP had a better prognostic value in HBV-related HCC patients (P = 0.026, HR = 1.75, 95% CI = 1.07–2.85). Furthermore, we compared the expression of 24 integrin family genes in HBV-related HCC tissues and adjacent normal tissues. Survival analysis demonstrated that expression of three of the family members, ITGA5, ITGB5 and ITGA2B, were significantly associated with the overall survival (OS) or relapse-free survival (RFS) of HBV-related HCC patients. Additionally, patients with lower expression of both ITGA5 and ITGB5 had the best OS and RFS (P = 0.017 and P = 0.002, respectively). Our study demonstrated that rs988574 of ITGA1 and the expression of ITGA5, ITGB5 and ITGA2B are potential independent prognostic bio-markers and therapeutic targets for HBV-related HCC patients and may be useful for the diagnosis of HBV-related HCC.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

A radiosensitivity gene signature and PD-L1 status predict clinical outcome of patients with invasive breast carcinoma in The Cancer Genome Atlas (TCGA) dataset

BACKGROUND AND PURPOSE

We investigated the link between the radiosensitivity gene signature and programmed cell death ligand 1 (PD-L1) status and clinical outcome in order to identify a group of patients that would possibly receive clinical benefit of radiotherapy (RT) combined with anti-PD1/PD-L1 therapy.

MATERIAL AND METHODS

We validated the identified gene signature related to radiosensitivity and analyzed the PD-L1 status of invasive breast cancer in The Cancer Genome Atlas (TCGA) dataset. To validate the gene signature, 1045 patients were selected and divided into two clusters using a consensus clustering algorithm based on their radiosensitive (RS) or radioresistant (RR) designation according to their prognosis. Patients were also stratified as PD-L1-high or PD-L1-low based on the median value of CD274 mRNA expression level as surrogates of PD-L1.

RESULTS

Patents assigned to the RS group had decreased risk of recurrence-free survival (RFS) rate than patients in the RR group by univariate analysis (HR 0.45, 95% CI 0.25-0.81, p=0.008) only when treated with RT. The RS group was independently associated with the PD-L1-high group, and CD274 mRNA expression was significantly higher in the RS group (p<0.001) than the RR group. In the PD-L1-high group, the RS group was associated with better RFS compared to the RR group (HR 0.37, 95% CI 0.16-0.87, p=0.022) in multivariate analysis. The level of PD-L1 expression may represent the immunogenicity of tumors, and thus, we speculated that the PD-L1-high group had more immunogenic tumors, which could be more sensitive to radiation-induced immunologic cell death.

CONCLUSION

We first evaluated the predictive value of the radiosensitivity gene signature and described a relationship with this radiosensitivity gene signature and PD-L1. The radiosensitivity gene signature and PD-L1 status were important factors for prediction of the clinical outcome of RT in patients with invasive breast cancer and may be used for selecting patients who will benefit from RT combined with anti-PD1/PDL1 therapy.

In vitro effects of tetraiodothyroacetic acid combined with X-irradiation on basal cell carcinoma cells

We investigated radiosensitization in an untreated basal cell carcinoma (TE.354.T) cell line and post-pretreatment with tetraiodothyroacetic acid (tetrac) X 1 h at 37°C, 0.2 and 2.0 µM tetrac. Radioresistant TE.354.T cells were grown in modified medium containing fibroblast growth factor-2, stem cell factor-1 and a reduced calcium level. We also added reproductively inactivated (30 Gy) “feeder cells” to the medium. The in vitro doubling time was 34.1 h, and the colony forming efficiency was 5.09 percent. These results were therefore suitable for clonogenic radiation survival assessment. The 250 kVp X-ray survival curve of control TE.354.T cells showed linear-quadratic survival parameters of α_X-ray = 0.201 Gy⁻¹ and β_X-ray = 0.125 Gy⁻². Tetrac concentrations of either 0.2 or 2.0 µM produced α_X-ray and β_X-ray parameters of 2.010 and 0.282 Gy⁻¹ and 2.050 and 0.837 Gy⁻², respectively. The surviving fraction at 2 Gy (SF₂) for control cells was 0.581, while values for 0.2 and 2.0 µM tetrac were 0.281 and 0.024. The SF₂ data show that tetrac concentrations of 0.2 and 2.0 µM sensitize otherwise radioresistant TE.354.T cells by factors of 2.1 and 24.0, respectively. Thus, radioresistant basal cell carcinoma cells may be radiosensitized pharmacologically by exposure to tetrac.

Inhibition of radiation-induced glioblastoma invasion by genetic and pharmacological targeting of MDA-9/Syntenin

Glioblastoma multiforme (GBM) is an intractable tumor despite therapeutic advances, principally because of its invasive properties. Radiation is a staple in therapeutic regimens, although cells surviving radiation can become more aggressive and invasive. Subtraction hybridization identified melanoma differentiation-associated gene 9 [MDA-9/Syntenin; syndecan-binding protein (SDCBP)] as a differentially regulated gene associated with aggressive cancer phenotypes in melanoma. MDA-9/Syntenin, a highly conserved double-PDZ domain-containing scaffolding protein, is robustly expressed in human-derived GBM cell lines and patient samples, with expression increasing with tumor grade and correlating with shorter survival times and poorer response to radiotherapy. Knockdown of MDA-9/Syntenin sensitizes GBM cells to radiation, reducing postradiation invasion gains. Radiation induces Src and EGFRvIII signaling, which is abrogated through MDA-9/Syntenin down-regulation. A specific inhibitor of MDA-9/Syntenin activity, PDZ1i (113B7), identified through NMR-guided fragment-based drug design, inhibited MDA-9/Syntenin binding to EGFRvIII, which increased following radiation. Both genetic (shmda-9) and pharmacological (PDZ1i) targeting of MDA-9/Syntenin reduced invasion gains in GBM cells following radiation. Although not affecting normal astrocyte survival when combined with radiation, PDZ1i radiosensitized GBM cells. PDZ1i inhibited crucial GBM signaling involving FAK and mutant EGFR, EGFRvIII, and abrogated gains in secreted proteases, MMP-2 and MMP-9, following radiation. In an in vivo glioma model, PDZ1i resulted in smaller, less invasive tumors and enhanced survival. When combined with radiation, survival gains exceeded radiotherapy alone. MDA-9/Syntenin (SDCBP) provides a direct target for therapy of aggressive cancers such as GBM, and defined small-molecule inhibitors such as PDZ1i hold promise to advance targeted brain cancer therapy.

Drosophila Wnt and STAT Define Apoptosis-Resistant Epithelial Cells for Tissue Regeneration after Irradiation

Drosophila melanogaster larvae irradiated with doses of ionizing radiation (IR) that kill about half of the cells in larval imaginal discs still develop into viable adults. How surviving cells compensate for IR-induced cell death to produce organs of normal size and appearance remains an active area of investigation. We have identified a subpopulation of cells within the continuous epithelium of Drosophila larval wing discs that shows intrinsic resistance to IR- and drug-induced apoptosis. These cells reside in domains of high Wingless (Wg, Drosophila Wnt-1) and STAT92E (sole Drosophila signal transducer and activator of transcription [STAT] homolog) activity and would normally form the hinge in the adult fly. Resistance to IR-induced apoptosis requires STAT and Wg and is mediated by transcriptional repression of the pro-apoptotic gene reaper. Lineage tracing experiments show that, following irradiation, apoptosis-resistant cells lose their identity and translocate to areas of the wing disc that suffered abundant cell death. Our findings provide a new paradigm for regeneration in which it is unnecessary to invoke special damage-resistant cell types such as stem cells. Instead, differences in gene expression within a population of genetically identical epithelial cells can create a subpopulation with greater resistance, which, following damage, survive, alter their fate, and help regenerate the tissue.

After widespread radiation damage in the developing fruit fly, organs are formed by regeneration from an apoptosis-resistant subpopulation of cells marked by high levels of Wingless and STAT.

Like other insects, Drosophila larvae have epithelial structures called imaginal discs that will give rise to most of the external adult structures, such as wings, limbs, or antennae; these organ precursors are formed by a single layer of epithelial cells that folds into a sac. Imaginal discs manage to regenerate efficiently if they are damaged. Previous studies have shown that dying cells produce signals that activate cell proliferation of some of their neighbors, allowing them to regenerate the disc and thereby enabling the flies to develop into normal adults. But a dedicated stem cell population that contributes to regeneration, if any, remained to be identified. Here, we report the identification of a subpopulation of cells in wing imaginal discs that is more resistant to the cytotoxic effects of radiation and drugs. We show that the protection of these cells depends on two signaling pathways—Wingless and STAT—that are conserved in humans. Following tissue damage by radiation, we observe that protected cells change their location and their identity, allowing them to fill in for dead cells in other parts of the same organ precursor. In sum, this work identified ways in which a subset of cells in Drosophila imaginal wing discs is preserved through radiation exposure so that they could participate in regeneration of the organ after radiation damage. We also discuss how this situation may resemble human cancers.

Cisplatin suppresses the growth and proliferation of breast and cervical cancer cell lines by inhibiting integrin β5-mediated glycolysis

Cancer cells harbor lower energy consumption after rounds of anticancer drugs, but the underlying mechanism remains unclear. In this study, we investigated metabolic alterations in cancer cells exposed to cisplatin. The present study exhibited cisplatin, known as a chemotherapeutic agent interacting with DNA, also acted as an anti-metabolic agent. We found that glycolysis levels of breast and cervical cancer cells were reduced after cisplatin treatment, resulting in cells growth and proliferation inhibition. We demonstrated that cisplatin suppressed glycolysis-related proteins expression, including glucose transporter 1 (GLUT1), glucose transporter 4 (GLUT4) and lactate dehydrogenase B (LDHB), through down-regulating integrin β5 (ITGB5)/focal adhesion kinase (FAK) signaling pathway. ITGB5 overexpression rescued cisplatin-induced inhibition of cancer cell glycolysis, growth and proliferation. Conclusively, we reveal a novel insight into cisplatin-induced anticancer mechanism, suggesting alternative strategies to the current therapeutic approaches of targeting ITGB5, as well as of a combination of cisplatin with glucose up-regulation chemotherapeutic agents to enhance anticancer effect.

Regulation of Cancer Cell Behavior by the Small GTPase Rab13

The members of the Rab family of GTPases are master regulators of cellular membrane trafficking. With ∼70 members in humans, Rabs have been implicated in all steps of membrane trafficking ranging from vesicle formation and transport to vesicle docking/tethering and fusion. Vesicle trafficking controls the localization and levels of a myriad of proteins, thus regulating cellular functions including proliferation, metabolism, cell-cell adhesion, and cell migration. It is therefore not surprising that impairment of Rab pathways is associated with diseases including cancer. In this review, we highlight evidence supporting the role of Rab13 as a potent driver of cancer progression.

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates.

TRPM7 maintains progenitor-like features of neuroblastoma cells: implications for metastasis formation

Neuroblastoma is an embryonal tumor derived from poorly differentiated neural crest cells. Current research is aimed at identifying the molecular mechanisms that maintain the progenitor state of neuroblastoma cells and to develop novel therapeutic strategies that induce neuroblastoma cell differentiation. Mechanisms controlling neural crest development are typically dysregulated during neuroblastoma progression, and provide an appealing starting point for drug target discovery.

Transcriptional programs involved in neural crest development act as a context dependent gene regulatory network. In addition to BMP, Wnt and Notch signaling, activation of developmental gene expression programs depends on the physical characteristics of the tissue microenvironment. TRPM7, a mechanically regulated TRP channel with kinase activity, was previously found essential for embryogenesis and the maintenance of undifferentiated neural crest progenitors. Hence, we hypothesized that TRPM7 may preserve progenitor-like, metastatic features of neuroblastoma cells.

Using multiple neuroblastoma cell models, we demonstrate that TRPM7 expression closely associates with the migratory and metastatic properties of neuroblastoma cells in vitro and in vivo. Moreover, microarray-based expression profiling on control and TRPM7 shRNA transduced neuroblastoma cells indicates that TRPM7 controls a developmental transcriptional program involving the transcription factor SNAI2. Overall, our data indicate that TRPM7 contributes to neuroblastoma progression by maintaining progenitor-like features.

Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy

Radiotherapy is an essential component of treatment for more than half of newly diagnosed cancer patients. The response to radiotherapy varies widely between individuals and although advances in technology have allowed the adaptation of radiotherapy fields to tumour anatomy, it is still not possible to tailor radiotherapy based on tumour biology. A biomarker of intrinsic radiosensitivity would be extremely valuable for individual dosing, aiding decision making between radical treatment options and avoiding toxicity of neoadjuvant or adjuvant radiotherapy in those unlikely to benefit. This systematic review summarises the current evidence for biomarkers under investigation as predictors of radiotherapy benefit. Only 10 biomarkers were identified as having been evaluated for their radiotherapy-specific predictive value in over 100 patients in a clinical setting, highlighting that despite a rich literature there were few high-quality studies for inclusion. The most extensively studied radiotherapy predictive biomarkers were the radiosensitivity index and MRE11; however, neither has been evaluated in a randomised controlled trial. Although these biomarkers show promise, there is not enough evidence to justify their use in routine practice. Further validation is needed before biomarkers can fulfil their potential and predict treatment outcomes for large numbers of patients.

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.

Identification of common gene networks responsive to radiotherapy in human cancer cells

Identification of the genes that are differentially expressed between radiosensitive and radioresistant cancers by global gene analysis may help to elucidate the mechanisms underlying tumor radioresistance and improve the efficacy of radiotherapy. An integrated analysis was conducted using publicly available GEO datasets to detect differentially expressed genes (DEGs) between cancer cells exhibiting radioresistance and cancer cells exhibiting radiosensitivity. Gene Ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) networks analysis were also performed. Five GEO datasets including 16 samples of radiosensitive cancers and radioresistant cancers were obtained. A total of 688 DEGs across these studies were identified, of which 374 were upregulated and 314 were downregulated in radioresistant cancer cell. The most significantly enriched GO terms were regulation of transcription, DNA-dependent (GO: 0006355, P=7.00E-09) for biological processes, while those for molecular functions was protein binding (GO: 0005515, P=1.01E-28), and those for cellular component was cytoplasm (GO: 0005737, P=2.81E-26). The most significantly enriched pathway in our KEGG analysis was Pathways in cancer (P=4.20E-07). PPI network analysis showed that IFIH1 (Degree=33) was selected as the most significant hub protein. This integrated analysis may help to predict responses to radiotherapy and may also provide insights into the development of individualized therapies and novel therapeutic targets.