Predicting response to radiotherapy: Evolutions and revolutions

BAG1, MGMT, FOXO1, and DNAJA1 as potential drug targets for radiosensitizing cancer cell lines

BACKGROUND AND PURPOSE

Activation of some signaling pathways can promote cell survival and have a negative impact on tumor response to radiotherapy. Here, the role of differences in expression levels of genes related to the poly(ADP-ribose) polymerase-1 (PARP-1), heat shock protein 90 (Hsp90), B-cell lymphoma 2 (Bcl-2), and phosphoinositide 3-kinase (PI3K) pathways in the survival or death of cells following X-ray exposure was investigated.

METHODS

Eight human cell cultures (MCF-7 and MDA-MB-231: breast cancers; MCF-12A: apparently normal breast; A549: lung cancer; L132: normal lung; G28, G44 and G112: glial cancers) were irradiated with X-rays. The colony-forming and real-time PCR based on a custom human pathway RT² Profiler PCR Array assays were used to evaluate cell survival and gene expression, respectively.

RESULTS

The surviving fractions at 2 Gy for the cell lines, in order of increasing radioresistance, were found to be as follows: MCF-7 (0.200 ± 0.011), G44 (0.277 ± 0.065), L132 (0.367 ± 0.023), MDA-MB-231 (0.391 ± 0.057), G112 (0.397 ± 0.113), A549 (0.490 ± 0.048), MCF-12A (0.526 ± 0.004), and G28 (0.633 ± 0.094). The rank order of radioresistance at 6 Gy was: MCF-7 < L132 < G44 < MDA-MB-231 < A549 < G28 < G112 < MCF-12A. PCR array data analysis revealed that several genes were differentially expressed between irradiated and unirradiated cell cultures. The following genes, with fold changes: BCL2A1 (21.91), TP53 (8743.75), RAD51 (11.66), FOX1 (65.86), TCP1 (141.32), DNAJB1 (3283.64), RAD51 (51.52), and HSPE1 (12887.29) were highly overexpressed, and BAX (-127.21), FOX1 (-81.79), PDPK1 (-1241.78), BRCA1 (-8.70), MLH1 (-12143.95), BCL2 (-18.69), CCND1 (-46475.98), and GJA1 (-2832.70) were highly underexpressed in the MDA-MB-231, MCF-7, MCF-12A, A549, L132, G28, G44, and G112 cell lines, respectively. The radioresistance in the malignant A549 and G28 cells was linked to upregulation in the apoptotic, DNA repair, PI3K, and Hsp90 pathway genes BAG1, MGMT, FOXO1, and DNAJA1, respectively, and inhibition of these genes resulted in significant radiosensitization.

CONCLUSIONS

Targeting BAG1, MGMT, FOXO1, and DNAJA1 with specific inhibitors might effectively sensitize radioresistant tumors to radiotherapy.

Clinical Radiobiology for Radiation Oncology

This chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.

Correlation between tumor voxel dose response matrix and tumor biomarker profile in patients with head and neck squamous cell carcinoma

BACKGROUND

We have developed a novel imaging analysis procedure that is highly predictive of local failure after chemoradiation in head and neck cancer. In this study we investigated whether any pretreatment biomarkers correlated with key imaging parameters.

METHODS

Pretreatment biopsy material was available for 28 patients entered into an institutional trial of adaptive radiotherapy in which FDG-PET images were collected weekly during treatment. The biopsies were immunohistochemically stained for CD44, EGFR, GLUT1, ALDH1, Ki-67 and p53 and quantified using image analysis. Expression levels were correlated with previously derived imaging parameters, the pretreatment SUV_max and the dose response matrix (DRM).

RESULTS

The different parameters of the SUV_max and DRM did not correlate with each other. We observed a positive and highly significant (p = 0.0088) correlation between CD44 expression and volume of tumor with a DRM greater than 0.8. We found no correlation between any DRM parameter and GLUT1, p53, Ki-67 and EGFR or ALDH1. GLUT1 expression did correlate with the maximum SUV₀ and the volume of tumor with an SUV₀ greater than 20.

CONCLUSIONS

The pretreatment SUV_max and DRM are independent imaging parameters that combine to predict local recurrence. The significant correlation between CD44 expression, a known cancer stem cell (CSC) marker, and volume of tumor with a DRM greater than 0.8 is consistent with concept that specific foci of cells are responsible for tumor recurrence and that CSCs may be randomly distributed in tumors in specific niches. Dose painting these small areas may lead to improved tumor control.

Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis

BACKGROUND

Lower-grade glioma (LGG) is a type of central nervous system tumor that includes WHO grade II and grade III gliomas. Despite developments in medical science and technology and the availability of several treatment options, the management of LGG warrants further research. Surgical treatment for LGG treatment poses a challenge owing to its often inaccessible locations in the brain. Although radiation therapy (RT) is the most important approach in this condition and offers more advantages compared to surgery and chemotherapy, it is associated with certain limitations. Responses can vary from individual to individual based on genetic differences. The relationship between non-coding RNA and the response to radiation therapy, especially at the molecular level, is still undefined.

METHODS

In this study, using The Cancer Genome Atlas dataset and bioinformatics, the gene co-expression network that is involved in the response to radiation therapy in lower-grade gliomas was determined, and the ceRNA network of radiotherapy response was constructed based on three databases of RNA interaction. Next, survival analysis was performed for hub genes in the co-expression network, and the high-efficiency biomarkers that could predict the prognosis of patients with LGG undergoing radiotherapy was identified.

RESULTS

We found that some modules in the co-expression network were related to the radiotherapy responses in patients with LGG. Based on the genes in those modules and the three databases, we constructed a ceRNA network for the regulation of radiotherapy responses in LGG. We identified the hub genes and found that the long non-coding RNA, DRAIC, is a potential molecular biomarker to predict the prognosis of radiotherapy in LGG.

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.

Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation.

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.

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.

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.

Cofilin-1 and phosphoglycerate kinase 1 as promising indicators for glioma radiosensibility and prognosis

Glioma is a primary malignancy in central nervous system. Radiotherapy has been used as one of the standard treatments for glioma for decades. Since radioresistance can reduce the curative efficacy of radiotherapy in glioma, investigating the cause of radioresistance and predicting the tumour radiosensibility appeared particularly important. We previously reported that CFL1 and PGK1 are over-expressed in radioresistant U251 glioma cells. In this study, the level of CFL1 and PGK1 of 113 glioma tissues were measured by ELISA method. The relevance of the expression of these two proteins to radiosensibility was analyzed by mean test and multivariate logistic regression. The survival analysis was carried out in 85 irradiated patients and 105 followed-up patients respectively. The relationship between protein expression and clinical parameters was explored in overall 113 patients, and the correlation between CFL1 and PGK1 were determined as well. Our results showed that the expression of CFL1 and PGK1 were significantly higher (P < 0.001) in radioresistant patients than others. The multivariate Logistic regression demonstrated that the expression of CFL1 (p < 0.001) and PGK1 (p < 0.001) were associated with radioresistance in glioma. The multivariate Cox regression in overall survival suggested that CFL1 level or PGK1 level could be the independent prognosis factor for poor prognosis in 113 glioma patients. In addition, CFL1 expression was positively correlated with PGK1 expression in glioma. The results suggested that as promising indicators, CFL1 and PGK1 could be used to evaluate glioma radiosensibility and prognosis. These two proteins could also be the potential therapeutic targets of glioma.

Dynamic In Vivo Profiling of DNA Damage and Repair after Radiotherapy Using Canine Patients as a Model

Time resolved data of DNA damage and repair after radiotherapy elucidates the relation between damage, repair, and cell survival. While well characterized in vitro, little is known about the time-course of DNA damage response in tumors sampled from individual patients. Kinetics of DNA damage after radiotherapy was assessed in eight dogs using repeated in vivo samples of tumor and co-irradiated normal tissue analyzed with comet assay and phosphorylated H2AX (γH2AX) immunohistochemistry. In vivo results were then compared (in silico) with a dynamic mathematical model for DNA damage formation and repair. Maximum %DNA in tail was observed at 15–60 min after irradiation, with a rapid decrease. Time-courses of γH2AX-foci paralleled these findings with a small time delay and were not influenced by covariates. The evolutionary parameter search based on %DNA in tail revealed a good fit of the DNA repair model to in vivo data for pooled sarcoma time-courses, but fits for individual sarcoma time-courses suffer from the heterogeneous nature of the in vivo data. It was possible to follow dynamics of comet tail intensity and γH2AX-foci during a course of radiation using a minimally invasive approach. DNA repair can be quantitatively investigated as time-courses of individual patients by integrating this resulting data into a dynamic mathematical model.

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.

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.

A Raman spectroscopic study of cell response to clinical doses of ionizing radiation

The drive toward personalized radiation therapy (RT) has created significant interest in determining patient-specific tumor and normal tissue responses to radiation. Raman spectroscopy (RS) is a non-invasive and label-free technique that can detect radiation response through assessment of radiation-induced biochemical changes in tumor cells. In the current study, single-cell RS identified specific radiation-induced responses in four human epithelial tumor cell lines: lung (H460), breast (MCF-7, MDA-MB-231), and prostate (LNCaP), following exposure to clinical doses of radiation (2-10 Gy). At low radiation doses (2 Gy), H460 and MCF-7 cell lines showed an increase in glycogen-related spectral features, and the LNCaP cell line showed a membrane phospholipid-related radiation response. In these cell lines, only spectral information from populations receiving 10 Gy or less was required to identify radiation-related features using principal component analysis (PCA). In contrast, the MDA-MB-231 cell line showed a significant increase in protein relative to nucleic acid and lipid spectral features at doses of 6 Gy or higher, and high-dose information (30, 50 Gy) was required for PCA to identify this biological response. The biochemical nature of the radiation-related changes occurring in cells exposed to clinical doses was found to segregate by status of p53 and radiation sensitivity. Furthermore, the utility of RS to identify a biological response in human tumor cells exposed to therapeutic doses of radiation was found to be governed by the extent of the biochemical changes induced by a radiation response and is therefore cell line specific. The results of this study demonstrate the utility and effectiveness of single-cell RS to identify and measure biological responses in tumor cells exposed to standard radiotherapy doses.

Direct and bystander radiation effects: a biophysical model and clinical perspectives

In planning treatment for each new patient, radiation oncologists pay attention to the aspects that they control. Thus their attention is usually focused on volume and dose. The dilemma for the physician is how to protract the treatment in a way that maximizes control of the tumor and minimizes normal tissue injury. The initial radiation-induced damage to DNA may be a biological indicator of the quantity of energy transferred to the DNA. However, until now the biophysical models proposed cannot explain either the early or the late adverse effects of radiation, and a more general theory appears to be required. The bystander component of tumor cell death after radiotherapy measured in many experimental works highlights the importance of confirming these observations in a clinical situation.

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

Background

In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy.

Results

Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway.

Conclusions

Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.

Comparison of RBE values of high-LET α-particles for the induction of DNA-DSBs, chromosome aberrations and cell reproductive death

Background

Various types of radiation effects in mammalian cells have been studied with the aim to predict the radiosensitivity of tumours and normal tissues, e.g. DNA double strand breaks (DSB), chromosome aberrations and cell reproductive inactivation. However, variation in correlations with clinical results has reduced general application. An additional type of information is required for the increasing application of high-LET radiation in cancer therapy: the Relative Biological Effectiveness (RBE) for effects in tumours and normal tissues. Relevant information on RBE values might be derived from studies on cells in culture.

Methods

To evaluate relationships between DNA-DSB, chromosome aberrations and the clinically most relevant effect of cell reproductive death, for ionizing radiations of different LET, dose-effect relationships were determined for the induction of these effects in cultured SW-1573 cells irradiated with gamma-rays from a Cs-137 source or with α-particles from an Am-241 source. RBE values were derived for these effects. Ionizing radiation induced foci (IRIF) of DNA repair related proteins, indicative of DSB, were assessed by counting gamma-H2AX foci. Chromosome aberration frequencies were determined by scoring fragments and translocations using premature chromosome condensation. Cell survival was measured by colony formation assay. Analysis of dose-effect relations was based on the linear-quadratic model.

Results

Our results show that, although both investigated radiation types induce similar numbers of IRIF per absorbed dose, only a small fraction of the DSB induced by the low-LET gamma-rays result in chromosome rearrangements and cell reproductive death, while this fraction is considerably enhanced for the high-LET alpha-radiation. Calculated RBE values derived for the linear components of dose-effect relations for gamma-H2AX foci, cell reproductive death, chromosome fragments and colour junctions are 1.0 ± 0.3, 14.7 ± 5.1, 15.3 ± 5.9 and 13.3 ± 6.0 respectively.

Conclusions

These results indicate that RBE values for IRIF (DNA-DSB) induction provide little valid information on other biologically-relevant end points in cells exposed to high-LET radiations. Furthermore, the RBE values for the induction of the two types of chromosome aberrations are similar to those established for cell reproductive death. This suggests that assays of these aberrations might yield relevant information on the biological effectiveness in high-LET radiotherapy.

Cancer stem cells at the crossroads of current cancer therapy failures--radiation oncology perspective

Despite continuous improvements in cancer management, locoregional recurrence or metastatic spread still occurs in a high proportion of patients after radiotherapy or combined treatments. One underlying reason might be a low efficacy of current treatments on eradication of cancer stem cells (CSCs). It has been recognised for a long time, that only the small subpopulation of CSCs can cause recurrences and that all CSCs need to be killed for permanent tumour cure. However, only recently novel technologies have allowed to enrich CSCs and to investigate their biology. An emerging experimental and clinical database provides first hints that cell populations accumulated by putative stem cell markers or tumours that highly express such markers may be more radioresistant than their marker-negative counterparts. Other data support a higher tolerance of CSCs to hypoxia and preferential location in specific microenvironmental niches. However, conflicting data, methodological problems of the assays and a generally small database on only few tumour types necessitate further large and well-designed prospective experimental and clinical investigations that specifically address this question to corroborate this hypothesis. If such investigations confirm biological differences between CSCs and non-CSCs, this would imply that novel treatment strategies need to be tested specifically for their effect on CSCs. Another implication is that also biomarkers for prediction of local tumour control after radiotherapy or combined treatments need to reflect the behaviour of CSCs and not of the bulk of all cancer cells. This review discusses the importance of CSCs for treatment failure and challenges occurring from the CSC concept for cancer diagnosis, treatment and prediction of outcome. It is concluded that CSC-based endpoints and biomarkers are eventually expected to considerably improve tumour cure rates in the clinics through individualised tailoring of treatment.