The independence of intrinsic radiosensitivity as a prognostic factor for patient response to radiotherapy of carcinoma of the cervix

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

A hypoxia biomarker does not predict benefit from giving chemotherapy with radiotherapy in the BC2001 randomised controlled trial

BACKGROUND

BC2001 showed combining chemotherapy (5-FU + mitomycin-C) with radiotherapy improves loco-regional disease-free survival in patients with muscle-invasive bladder cancer (MIBC). We previously showed a 24-gene hypoxia-associated signature predicted benefit from hypoxia-modifying radiosensitisation in BCON and hypothesised that only patients with low hypoxia scores (HSs) would benefit from chemotherapy in BC2001. BC2001 allowed conventional (64Gy/32 fractions) or hypofractionated (55Gy/20 fractions) radiotherapy. An exploratory analysis tested an additional hypothesis that hypofractionation reduces reoxygenation and would be detrimental for patients with hypoxic tumours.

METHODS

RNA was extracted from pre-treatment biopsies (298 BC2001 patients), transcriptomic data generated (Affymetrix Clariom-S arrays), HSs calculated (median expression of 24-signature genes) and patients stratified as hypoxia-high or -low (cut-off: cohort median).

PRIMARY ENDPOINT

invasive loco-regional control (ILRC); secondary overall survival.

FINDINGS

Hypoxia affected overall survival (HR = 1.30; 95% CI 0.99-1.70; p = 0.062): more uncertainty for ILRC (HR = 1.29; 95% CI 0.82-2.03; p = 0.264). Benefit from chemotherapy was similar for patients with high or low HSs, with no interaction between HS and treatment arm. High HS associated with poor ILRC following hypofractionated (n = 90, HR 1.69; 95% CI 0.99-2.89 p = 0.057) but not conventional (n = 207, HR 0.70; 95% CI 0.28-1.80, p = 0.461) radiotherapy. The finding was confirmed in an independent cohort (BCON) where hypoxia associated with a poor prognosis for patients receiving hypofractionated (n = 51; HR 14.2; 95% CI 1.7-119; p = 0.015) but not conventional (n = 24, HR 1.04; 95% CI 0.07-15.5, p = 0.978) radiotherapy.

INTERPRETATION

Tumour hypoxia status does not affect benefit from BC2001 chemotherapy. Hypoxia appears to affect fractionation sensitivity. Use of HSs to personalise treatment needs testing in a biomarker-stratified trial.

FUNDING

Cancer Research UK, NIHR, MRC.

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AIMS

To explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed 'radiosensitivity index' (RSI), to assess its suitability as an input into dose-adjustment algorithms.

MATERIALS AND METHODS

The original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.

RESULTS

Preclinically, RSI showed a negative correlation (Spearman's rho = -0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97-1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).

CONCLUSIONS

These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.

Towards Data Driven RT Prescription: Integrating Genomics into RT Clinical Practice

The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.

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

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

High-Complexity cellular barcoding and clonal tracing reveals stochastic and deterministic parameters of radiation resistance

It is elusive whether clonal selection of tumor cells in response to ionizing radiation (IR) is a deterministic or stochastic process. With high resolution clonal barcoding and tracking of over 400 000 HNSCC patient-derived tumor cells the clonal dynamics of tumor cells in response to IR was analyzed. Fractionated IR induced a strong selective pressure for clonal reduction which significantly exceeded uniform clonal survival probabilities indicative for a strong clone-to-clone difference within tumor cell lines. IR induced clonal reduction affected the majority of tumor cells ranging between 96% and 75% and correlated to the degree of radiation sensitivity. Survival to IR is driven by a deterministic clonal selection of a smaller population which commonly survives radiation, while increased clonogenic capacity is a result of clonal competition of cells which have been selected stochastically. A 2-fold increase in radiation resistance results in a 4-fold (P < .05) higher deterministic clonal selection showing that the ratio of these parameters is amenable to radiation sensitivity which correlates to prognostic biomarkers of HNSCC. Evidence for the existence of a rare subpopulation with an intrinsically radiation resistant phenotype commonly surviving IR was found at a frequency of 0.6% to 3.3% (P < .001, FDR 3%). With cellular barcoding we introduce a novel functional heterogeneity associated qualitative readout for tracking dynamics of clonogenic survival in response to radiation. This enables the quantification of intrinsically radiation resistant tumor cells from patient samples and reveals the contribution of stochastic and deterministic clonal selection processes in response to IR.

Development and validation of genomic predictors of radiation sensitivity using preclinical data

Background

Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited.

Methods

We proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework.

Results

Through the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response.

Conclusion

To summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy.

Cell lines of the same anatomic site and histologic type show large variability in intrinsic radiosensitivity and relative biological effectiveness to protons and carbon ions

PURPOSE

To show that intrinsic radiosensitivity varies greatly for protons and carbon (C) ions in addition to photons, and that DNA repair capacity remains important in governing this variability.

METHODS

We measured or obtained from the literature clonogenic survival data for a number of human cancer cell lines exposed to photons, protons (9.9 keV/μm), and C-ions (13.3-77.1 keV/μm). We characterized their intrinsic radiosensitivity by the dose for 10% or 50% survival (D_10% or D_50% ), and quantified the variability at each radiation quality by the coefficient of variation (COV) in D_10% and D_50% . We also treated cells with DNA repair inhibitors prior to irradiation to assess how DNA repair capacity affects their variability.

RESULTS

We found no statistically significant differences in the COVs of D_10% or D_50% between any of the radiation qualities investigated. The same was true regardless of whether the cells were treated with DNA repair inhibitors, or whether they were stratified into histologic subsets. Even within histologic subsets, we found remarkable differences in radiosensitivity for high LET C-ions that were often greater than the variations in RBE, with brain cancer cells varying in D_10% (D_50% ) up to 100% (131%) for 77.1 keV/μm C-ions, and non-small cell lung cancer and pancreatic cancer cell lines varying up to 55% (76%) and 51% (78%), respectively, for 60.5 keV/μm C-ions. The cell lines with modulated DNA repair capacity had greater variability in intrinsic radiosensitivity across all radiation qualities.

CONCLUSIONS

Even for cell lines of the same histologic type, there are remarkable variations in intrinsic radiosensitivity, and these variations do not differ significantly between photon, proton or C-ion radiation. The importance of DNA repair capacity in governing the variability in intrinsic radiosensitivity is not significantly diminished for higher LET radiation.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

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

Comparable radiation response of ex vivo and in vivo irradiated tumor samples determined by residual γH2AX

PURPOSE

a) To investigate if an ex vivo cultured and irradiated tumor biopsy reflects and predicts the radiation response of the corresponding in vivo irradiated tumor measured with the DNA double strand break marker γH2AX foci.

MATERIALS AND METHODS

Five human head and neck squamous cell carcinoma (hHNSCC) xenograft models were used. Fine needle biopsies were taken from anesthetized tumor-bearing NMRI nude mice prior to in vivo single dose irradiation (0, 2, 4, or 8 Gy) under ambient blood flow. Biopsies were ex vivo reoxygenated and irradiated with equivalent doses. Tumors and biopsies were fixed 24 h post irradiation, and γH2AX foci were assessed in oxygenated tumor regions.

RESULTS

Linear regression analysis showed comparable slopes of the residual γH2AX foci dose-response curves in four out of five hHNSCC models when in vivo and ex vivo cohorts were compared. The slopes from ex vivo biopsies and in vivo tumors could classify the respective tumor model as sensitive or resistant according to the intrinsic radiation sensitivity (TCD₅₀).

CONCLUSION

The ability of ex vivo irradiated tumor biopsies to reflect and predict the intrinsic radiation response of in vivo tumors increases the translational potential of the ex vivo γH2AX foci assay as a diagnostic tool for clinical practice.

Mechanistic Modelling of Radiation Responses

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

Applying Precision Oncology Principles in Radiation Oncology

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

Hypoxia gene expression signatures as predictive biomarkers for personalising radiotherapy

Hypoxia is a generic micro-environmental factor of solid tumours. High levels of hypoxia lead to resistance to radiotherapy, which can be targeted by adding hypoxia-modifying therapy to improve clinical outcomes. Not all patients benefit from hypoxia-modifying therapy, and there is a need for biomarkers to enable progression to biologically personalised radiotherapy. Gene expression signatures are a relatively new category of biomarkers that can reflect tumour hypoxia. This article reviews the published hypoxia gene signatures, summarising their development and validation. The challenges of gene signature derivation and development, and advantages and disadvantages in comparison with other hypoxia biomarkers are also discussed. Current evidence supports investment in gene signatures as a promising hypoxia biomarker approach for clinical utility.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

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.

Radiosensitization by gold nanoparticles: Will they ever make it to the clinic?

The utilization of gold nanoparticles (AuNPs) as radiosensitizers has shown great promise in pre-clinical research. In the current review, the physical, chemical, and biological pathways via which AuNPs enhance the effects of radiation are presented and discussed. In particular, the impact of AuNPs on the 5 Rs in radiobiology, namely repair, reoxygenation, redistribution, repopulation, and intrinsic radiosensitivity, which determine the extent of radiation enhancement effects are elucidated. Key findings from previous studies are outlined. In addition, crucial parameters including the physicochemical properties of AuNPs, route of administration, dosing schedule of AuNPs and irradiation, as well as type of radiation therapy, are highlighted; the optimal selection and combination of these parameters enable the achievement of a greater therapeutic window for AuNP sensitized radiotherapy. Future directions are put forward as a means to provide guidelines for successful translation of AuNPs to clinical applications as radiosensitizers.

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.

Precision medicine for early breast cancer radiotherapy: Opening up new horizons?

So far most efforts put forth to test the value of predictive and prognostic tools in the field of breast radiotherapy remained globally disappointing, or at least below the convincing levels reached for systemic therapy. Nevertheless the addition of predictive tools to the clinical armament tends to prevail over the use of the sole prognostic factors, also in radiotherapy. A number of predictive assays, clinically validated or not, have recently elicited significant associations between molecular profiles and tumor biological aggressiveness and/or radiosensitivity levels. Will it take a long time for these radiation-specific assays to provide added value to the - already crowded - constellation of predictive tools in the breast cancer? On the one hand, optimizing radiotherapy through the integration of precision medicine into the breast cancer management still remains a challenging issue. On the other hand, recent advances in predictive assays aimed at distinguishing patients with a more radioresistant tumor that necessitates radiation dose escalation or a switch to therapeutic approaches other than radiotherapy, plea in favor of an increasing role, in a near future, for radiation-specific molecular signatures. Streamlining predictive assays platforms via concerted actions should imperatively be given high priority, also in terms of health economics.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

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.

The contribution of women to radiobiology: Marie Curie and beyond

Marie Sklodowska-Curie, an extraordinary woman, a Polish scientist who lived and worked in France, led to the development of nuclear energy and the treatment of cancer. She was the laureate of two Nobel Prizes, the first woman in Europe who obtained the degree of Doctor of Science and opened the way for women to enter fields which had been previously reserved for men only. As a result of her determination and her love of freedom, she has become an icon for many female scientists active in radiation sciences. They are successors of Maria Curie and without the results of their work, improvement in radiation oncology will not be possible. Many of them shared some elements of Maria Curie's biography, like high ethical and moral standards, passionate dedication to work, strong family values, and scientific collaboration with their husbands. The significance of Tikvah Alper, Alma Howard, Shirley Hornsey, Juliana Denekamp, Helen Evans, Eleanor Blakely, Elizabeth L. Travis, Fiona Stewart, Andree Dutreix, Catharine West, Peggy Olive, Ingela Turesson, Penny Jeggo, Irena Szumiel, Eleonor Blakely, Sara Rockwell and Carmel Mothersill contribution to radiation oncology is presented. All the above mentioned ladies made significant contribution to the development of radiotherapy (RT) and more efficient cancer treatment. Due to their studies, new schedules of RT and new types of ionizing radiation have been applied, lowering the incidence of normal tissue toxicity. Their achievements herald a future of personalized medicine.

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.

Technical innovation in adjuvant radiotherapy: Evolution and evaluation of new treatments for today and tomorrow

Recent innovations in breast cancer radiotherapy include intensity modulated radiotherapy, brachytherapy and intraoperative radiotherapy and current trials are seeking to evaluate their value in optimizing local control while maintaining cosmetic effects. Future clinical dividends in local control and survival may come from the identification of molecular signatures of breast cancer radiosensitivity, the development of predictive signatures and identification of immunohistochemical markers of risk of local recurrence. The importance of tumour heterogeneity is being increasingly recognized as an important factor in determining radiotherapy response and an improved understanding of the biology of the tumour microenvironment may identify targets that allow enhanced radiosensitisation or reversal of radioresistance when inhibited. This review describes recent developments in these areas.

Residual γH2AX foci after ex vivo irradiation of patient samples with known tumour-type specific differences in radio-responsiveness

PURPOSE

To apply our previously published residual ex vivo γH2AX foci method to patient-derived tumour specimens covering a spectrum of tumour-types with known differences in radiation response. In addition, the data were used to simulate different experimental scenarios to simplify the method.

MATERIALS AND METHODS

Evaluation of residual γH2AX foci in well-oxygenated tumour areas of ex vivo irradiated patient-derived tumour specimens with graded single doses was performed. Immediately after surgical resection, the samples were cultivated for 24h in culture medium prior to irradiation and fixed 24h post-irradiation for γH2AX foci evaluation. Specimens from a total of 25 patients (including 7 previously published) with 10 different tumour types were included.

RESULTS

Linear dose response of residual γH2AX foci was observed in all specimens with highly variable slopes among different tumour types ranging from 0.69 (95% CI: 1.14-0.24) to 3.26 (95% CI: 4.13-2.62) for chondrosarcomas (radioresistant) and classical seminomas (radiosensitive) respectively. Simulations suggest that omitting dose levels might simplify the assay without compromising robustness.

CONCLUSION

Here we confirm clinical feasibility of the assay. The slopes of the residual foci number are well in line with the expected differences in radio-responsiveness of different tumour types implying that intrinsic radiation sensitivity contributes to tumour radiation response. Thus, this assay has a promising potential for individualized radiation therapy and prospective validation is warranted.

Adapting a drug screening platform to discover associations of molecular targeted radiosensitizers with genomic biomarkers

Large collections of annotated cancer cell lines are powerful tools for precisely matching targeted drugs with genomic alterations that can be tested as biomarkers in the clinic. Whether these screening platforms, which utilize short-term cell survival to assess drug responses, can be applied to precision radiation medicine is not established. To this end, 32 cancer cell lines were screened using 18 targeted therapeutic agents with known or putative radiosensitizing properties (227 combinations). The cell number remaining after drug exposure with or without radiation was assessed by nonclonogenic assays. We derived short-term radiosensitization factors (SRF2Gy) and calculated clonogenic survival assay-based dose enhancement factors (DEFSF0.1). Radiosensitization was characterized by SRF2Gy values of mostly ∼1.05 to 1.2 and significantly correlated with drug-induced changes in apoptosis and senescence frequencies. SRF2Gy was significantly correlated with DEFSF0.1, with a respective sensitivity and specificity of 91.7% and 81.5% for a 3-day endpoint, and 82.8% and 84.2% for a robotic 5-day assay. KRAS mutations (codons 12/13) were found to be a biomarker of radiosensitization by midostaurin in lung cancer, which was pronounced under conditions that enriched for stem cell-like cells. In conclusion, although short-term proliferation/survival assays cannot replace the gold-standard clonogenic survival assay for measuring cellular radiosensitivity, they capture with high accuracy the relative change in radiosensitivity that is caused by a radiosensitzing targeted agent.

IMPLICATIONS

This study supports a paradigm shift regarding the utility of short-term assays for precision radiation medicine, which should facilitate the identification of genomic biomarkers to guide the testing of novel drug/radiation combinations.

Abrogation of radioresistance in glioblastoma stem-like cells by inhibition of ATM kinase

Resistance to radiotherapy in glioblastoma (GBM) is an important clinical problem and several authors have attributed this to a subpopulation of GBM cancer stem cells (CSCs) which may be responsible for tumour recurrence following treatment. It is hypothesised that GBM CSCs exhibit upregulated DNA damage responses and are resistant to radiation but the current literature is conflicting. We investigated radioresistance of primary GBM cells grown in stem cell conditions (CSC) compared to paired differentiated tumour cell populations and explored the radiosensitising effects of the ATM inhibitor KU-55933. We report that GBM CSCs are radioresistant compared to paired differentiated tumour cells as measured by clonogenic assay. GBM CSC's display upregulated phosphorylated DNA damage response proteins and enhanced activation of the G2/M checkpoint following irradiation and repair DNA double strand breaks (DSBs) more efficiently than their differentiated tumour cell counterparts following radiation. Inhibition of ATM kinase by KU-55933 produced potent radiosensitisation of GBM CSCs (sensitiser enhancement ratios 2.6-3.5) and effectively abrogated the enhanced DSB repair proficiency observed in GBM CSCs at 24 h post irradiation. G2/M checkpoint activation was reduced but not abolished by KU-55933 in GBM CSCs. ATM kinase inhibition overcomes radioresistance of GBM CSCs and, in combination with conventional therapy, has potential to improve outcomes for patients with GBM.

Radiation sensitivity assay with a panel of patient-derived spheroids of small cell carcinoma of the cervix

Small cell carcinoma of the uterine cervix (SCCC) is a rare cancer with a poor prognosis for which no standard treatment exists. Here, we successfully established panels of patient-derived spheroid cultures from six SCCC patient samples by cancer tissue-originated spheroids (CTOS) method. To assess the intrinsic radiosensitivity and mechanism of radioresistance in individual SCCC patients, we further developed an in vitro sensitivity assay for radiation. Radiation sensitivity in the CTOS assay varied among individual cases and was consistent with in vivo radiation sensitivity using CTOS-derived xenograft tumors in the examined cases. Furthermore, by comparing gene expression in CTOSs with different radiosensitivity, we found that expression of hypoxia-inducible factor-1α (HIF-1α) target genes was upregulated in resistant CTOSs. HIF-1α protein levels increased several hours after irradiation. In a radioresistant CTOS, an inhibitor of heat shock protein 90 (HSP90) suppressed radiation-induced HIF-1α expression. Suppression of HIF-1α by small hairpin RNA significantly enhanced the effect of radiation, at least in part by promoting radiation-induced apoptosis. HSP90 inhibitor also increased radiation sensitivity. Our results indicate that radiation-induced HIF-1α upregulation was one mechanism of radioresistance in a radioresistant SCCC CTOS. Accumulating CTOS lines may provide a good platform to study characters of rare cancers like SCCC.

Current issues in locally advanced colorectal cancer treated by preoperative chemoradiotherapy

In patients with locally advanced rectal cancer, preoperative chemoradiotherapy has proven to significantly improve local control and cause lower treatment-related toxicity compared with postoperative adjuvant treatment. Preoperative chemoradiotherapy followed by total mesorectal excision or tumor specific mesorectal excision has evolved as the standard treatment for locally advanced rectal cancer. The paradigm shift from postoperative to preoperative therapy has raised a series of concerns however that have practical clinical implications. These include the method used to predict patients who will show good response, sphincter preservation, the application of conservative management such as local excision or “wait-and-watch” in patients obtaining a good response following preoperative chemoradiotherapy, and the role of adjuvant chemotherapy. This review addresses these current issues in patients with locally advanced rectal cancer treated by preoperative chemoradiotherapy.

Investigation of radiosensitivity gene signatures in cancer cell lines

Intrinsic radiosensitivity is an important factor underlying radiotherapy response, but there is no method for its routine assessment in human tumours. Gene signatures are currently being derived and some were previously generated by expression profiling the NCI-60 cell line panel. It was hypothesised that focusing on more homogeneous tumour types would be a better approach. Two cell line cohorts were used derived from cervix [n = 16] and head and neck [n = 11] cancers. Radiosensitivity was measured as surviving fraction following irradiation with 2 Gy (SF2) by clonogenic assay. Differential gene expression between radiosensitive and radioresistant cell lines (SF2 median) was investigated using Affymetrix GeneChip Exon 1.0ST (cervix) or U133A Plus2 (head and neck) arrays. There were differences within cell line cohorts relating to tissue of origin reflected by expression of the stratified epithelial marker p63. Of 138 genes identified as being associated with SF2, only 2 (1.4%) were congruent between the cervix and head and neck carcinoma cell lines (MGST1 and TFPI), and these did not partition the published NCI-60 cell lines based on SF2. There was variable success in applying three published radiosensitivity signatures to our cohorts. One gene signature, originally trained on the NCI-60 cell lines, did partially separate sensitive and resistant cell lines in all three cell line datasets. The findings do not confirm our hypothesis but suggest that a common transcriptional signature can reflect the radiosensitivity of tumours of heterogeneous origins.

Pre-treatment haemoglobin and peripheral blood lymphocyte count as independent predictors of outcome in carcinoma of cervix

AIMS

To evaluate pre-treatment haemoglobin and peripheral blood lymphocyte (PBL) counts as predictors of treatment outcome in cervix carcinoma treated with radical chemoradiation.

MATERIALS AND METHODS

Pre-treatment PBL counts and haemoglobin concentrations were retrieved from full blood count examinations from 111 patients who received concurrent chemoradiotherapy. Overall survival and relapse-free survival were obtained using the Kaplan-Meier method by ranking the data by median haemoglobin and PBL, singly and then in association. Their independence and significance as predictors of outcome were analysed using the Cox proportional hazard model.

RESULTS

Survival rates were significantly higher in patients whose haemoglobin level or PBL counts were at or above the corresponding median value. At 5 years, rates of overall survival were 77% versus 41% (P = 0.0003) and 75% versus 42% (P = 0.002), when dichotomised around median haemoglobin and PBL, respectively. In multivariate and univariate analyses, both PBL and haemoglobin were independent and significant predictors for risk of death and relapse. Their predictive power was dramatically enhanced when the data were stratified into four groups by associating patients with haemoglobin ≥ median or < median with those whose PBL was ≥ or < median.

CONCLUSION

Baseline PBL and haemoglobin seem to be strong, independent predictors of treatment outcome in carcinoma of the cervix, particularly if patient response is ranked using the predictors simultaneously. The hypothesis needs to be tested and, if confirmed, the markers should be used in combination to identify those at greater risk of failure who may benefit from additional therapy, with further validation in prospective trials offering treatment modification.

Poor prognosis associated with human papillomavirus α7 genotypes in cervical carcinoma cannot be explained by intrinsic radiosensitivity

PURPOSE

To investigate the relationship between human papillomavirus (HPV) genotype and outcome after radiation therapy and intrinsic radiosensitivity.

METHODS AND MATERIALS

HPV genotyping was performed on cervix biopsies by polymerase chain reaction using SPF-10 broad-spectrum primers, followed by deoxyribonucleic acid enzyme immunoassay and genotyping by reverse hybridization line probe assay (LiPA25) (version 1) (n=202). PapilloCheck and quantitative reverse transcription-polymerase chain reaction were used to genotype cervix cancer cell lines (n=16). Local progression-free survival after radiation therapy alone was assessed using log-rank and Cox proportionate hazard analyses. Intrinsic radiosensitivity was measured as surviving fraction at 2 Gy (SF2) using clonogenic assays.

RESULTS

Of the 202 tumors, 107 (53.0%) were positive for HPV16, 29 (14.4%) for HPV18, 9 (4.5%) for HPV45, 23 (11.4%) for other HPV genotypes, and 22 (10.9%) were negative; 11 (5.5%) contained multiple genotypes, and 1 tumor was HPV X (0.5%). In 148 patients with outcome data, those with HPVα9-positive tumors had better local progression-free survival compared with α7 patients in univariate (P<.004) and multivariate (hazard ratio 1.54, 95% confidence interval 1.11-1.76, P=.021) analyses. There was no difference in the median SF2 of α9 and α7 cervical tumors (n=63). In the cell lines, 9 were α7 and 4 α9 positive and 3 negative. There was no difference in SF2 between α9 and α7 cell lines (n=14).

CONCLUSION

The reduced radioresponsiveness of α7 cervical tumors is not related to intrinsic radiosensitivity.

Predictive value of modelled tumour control probability based on individual measurements of in vitro radiosensitivity and potential doubling time

OBJECTIVE

The aim of this study was to compare patient-specific radiobiological parameters with population averages in predicting the clinical outcome after radiotherapy (RT) using a tumour control probability (TCP) model based on the biological effective dose (BED).

METHODS

A previously published study of 46 head and neck carcinomas with individually identified radiobiological parameters, radiosensitivity and potential doubling time (Tpot), and known tumour size was investigated. These patients had all been treated with external beam RT, and the majority had also received brachytherapy. The TCP for each individual based on the BED using patient-specific radiobiological parameters was compared with the TCP based on the BED using average radiobiological parameters (α=0.3 Gy(-1), Tpot=3 days).

RESULTS

43 patients remained in the final analysis. There was only a weak trend for increasing local tumour control with increasing BED in both groups. However, when the TCP was calculated, the use of patient-specific parameters was better for identifying local control correctly. The sensitivity and specificity for tumour-specific parameters were 63% and 80%, respectively. The corresponding values for population-based averages were 0% and 91%, respectively. The positive predictive value was 92% when tumour-specific parameters were used compared with 0% for population-based averages. A receiver operating characteristic curve confirmed the superiority of patient-specific parameters over population averages in predicting local control.

CONCLUSION

Individual radiobiological parameters are better than population-derived averages when used in a mathematical model to predict TCP after curative RT in head and neck carcinomas.

ADVANCES IN KNOWLEDGE

TCP based on individual radiobiological parameters is better than TCP based on population-based averages for identifying local control correctly.

Differences in Phosphorylated Histone H2AX Foci Formation and Removal of Cells Exposed to Low and High Linear Energy Transfer Radiation

The use of particle ion beams in cancer radiotherapy has a long history. Today, beams of protons or heavy ions, predominantly carbon ions, can be accelerated to precisely calculated energies which can be accurately targeted to tumors. This particle therapy works by damaging the DNA of tissue cells, ultimately causing their death. Among the different types of DNA lesions, the formation of DNA double strand breaks is considered to be the most relevant of deleterious damages of ionizing radiation in cells. It is well-known that the extremely large localized energy deposition can lead to complex types of DNA double strand breaks. These effects can lead to cell death, mutations, genomic instability, or carcinogenesis. Complex double strand breaks can increase the probability of mis-rejoining by NHEJ. As a consequence differences in the repair kinetics following high and low LET irradiation qualities are attributed mainly to quantitative differences in their contributions of the fast and slow repair component. In general, there is a higher contribution of the slow component of DNA double strand repair after exposure to high LET radiation, which is thought to reflect the increased amount of complex DNA double strand breaks. These can be accurately measured by the γ-H2AX assay, because the number of phosphorylated H2AX foci correlates well with the number of double strand breaks induced by low or / and high LET radiation.

Predicting outcomes in radiation oncology--multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Clinically relevant biomarkers in targeted radiotherapy

Three classic parameters have been recognized as predictors or biomarkers of radiation response: intrinsic radiosensitivity, degree of hypoxia and repopulation capacity of clonogenic cells during a course of fractionated radiation therapy. Although good functional assays exist to measure these tumor parameters, and their use has led to the understanding of factors affecting outcome after radiotherapy, their application in clinical practice is hampered by technical difficulties, the length of time needed to obtain results and the lack of prospective randomized clinical trials. Recently, with the progress in molecular biology, genome-wide screening methods have been used to look for genetic signatures that can distinguish between good and bad outcome after radiotherapy. One of the most promising candidates is the epidermal growth factor receptor which is overexpressed or mutated in a variety of malignancies, such lung and head and neck cancer. Inhibition of this receptor has led to radio-sensitization with the prolongation of median survival in several cancers. Since there is significant variability in the response of patients with the same disease to radiotherapy, it would be very valuable to be able to predict which patients would benefit from a molecularly targeted therapy administered with concomitant radiation in order to increase the response rate (and cure rate) of those patients with radioresistant tumors. Optimally, this assay should be able to provide results in an efficient and reproducible manner and detect tumor genetic mutations that would provide specificity to the intervention. One approach currently in clinical practice to overcome intrinsic radioresistance and repopulation is stereotactic body radiotherapy coupled with image-guided radiation, a highly precise and powerful form of radiation, allowing radiation oncologist to treat tumors with more aggressive biological doses of radiation without causing serious normal tissues injury.

Enhanced stability of microRNA expression facilitates classification of FFPE tumour samples exhibiting near total mRNA degradation

BACKGROUND

As degradation of formalin-fixed paraffin-embedded (FFPE) samples limits the ability to profile mRNA expression, we explored factors predicting the success of mRNA expression profiling of FFPE material and investigated an approach to overcome the limitation.

METHODS

Bladder (n=140, stored 3-8 years) and cervix (n=160, stored 8-23 years) carcinoma FFPE samples were hybridised to Affymetrix Exon 1.0ST arrays. Percentage detection above background (%DABG) measured technical success. Biological signal was assessed by distinguishing cervix squamous cell carcinoma (SCC) and adenocarcinoma (AC) using a gene signature. As miR-205 had been identified as a marker of SCC, precursor mir-205 was measured by Exon array and mature miR-205 by qRT-PCR. Genome-wide microRNA (miRNA) expression (Affymetrix miRNA v2.0 arrays) was compared in eight newer FFPE samples with biological signal and eight older samples without.

RESULTS

RNA quality controls (QCs) (e.g., RNA integrity (RIN) number) failed to predict profiling success, but sample age correlated with %DABG in bladder (R=-0.30, P<0.01) and cervix (R=-0.69, P<0.01). Biological signal was lost in older samples and neither a signature nor precursor mir-205 separated samples by histology. miR-205 qRT-PCR discriminated SCC from AC, validated by miRNA profiling (26-fold higher in SCC; P=1.10 × 10(-5)). Genome-wide miRNA (R=0.95) and small nucleolar RNA (R=0.97) expression correlated well in the eight newer vs older FFPE samples and better than mRNA expression (R=0.72).

CONCLUSION

Sample age is the best predictor of successful mRNA profiling of FFPE material, and miRNA profiling overcomes the limitation of age and copes well with older samples.

A molecular assay of tumor radiosensitivity: a roadmap towards biology-based personalized radiation therapy

The last two decades have seen technological developments that have led to more accurate delivery of radiation therapy (RT), which has resulted in clinical gains in many solid tumors. However, a fundamental question and perhaps the next major hurdle is whether biological strategies can be developed to further enhance the effectiveness and efficiency of RT. This article addresses the development of a novel genomics-based molecular assay to predict tumor radiosensitivity, and proposes that this assay may prove pivotal in the development of biologically guided RT.

Biological predictors of cervical cancer response to radiation therapy

The addition of cisplatin-based chemotherapy to standard radiation therapy reduces the risk of recurrence and disease-related death rates from locally advanced cervical cancers by as much as 50%. However, the absolute gains are relatively small for patients with early tumors, many of whom would have been cured with radiation alone, and recurrence rates are still high for patients who have very large or advanced-stage tumors. As a result, there is a pressing need for more accurate predictors of radiocurability. A variety of types of biomarkers have been shown to correlate with cervical cancer response to radiation therapy. These include traditional clinical and morphologic predictors, non-molecular biomarkers, including hypoxia and fluorodeoxyglucose-positron emission tomography (FDG-PET) avidity, as well as molecular biomarkers, which include single-gene markers or array-based multigene predictors. Multi-gene predictors of response remain immature in cervical cancer, but studies thus far have paved the way for future studies to validate these findings. Methods will need to be standardized and markers will need to be validated on homogeneous patient populations and treatment approaches before they can become useful tools for clinical decision making. In addition, new biomarkers will be of major value only if they add to the predictive value of traditional clinical and morphologic predictors. Ultimately, the most useful biomarkers will identify patients who will benefit from specific molecularly targeted agents in addition to radiation therapy or perhaps identify patient who are at low risk for recurrence, for whom the dose of radiation or chemotherapy can be reduced.

Biochemical signatures of in vitro radiation response in human lung, breast and prostate tumour cells observed with Raman spectroscopy

This work applies noninvasive single-cell Raman spectroscopy (RS) and principal component analysis (PCA) to analyze and correlate radiation-induced biochemical changes in a panel of human tumour cell lines that vary by tissue of origin, p53 status and intrinsic radiosensitivity. Six human tumour cell lines, derived from prostate (DU145, PC3 and LNCaP), breast (MDA-MB-231 and MCF7) and lung (H460), were irradiated in vitro with single fractions (15, 30 or 50 Gy) of 6 MV photons. Remaining live cells were harvested for RS analysis at 0, 24, 48 and 72 h post-irradiation, along with unirradiated controls. Single-cell Raman spectra were acquired from 20 cells per sample utilizing a 785 nm excitation laser. All spectra (200 per cell line) were individually post-processed using established methods and the total data set for each cell line was analyzed with PCA using standard algorithms. One radiation-induced PCA component was detected for each cell line by identification of statistically significant changes in the PCA score distributions for irradiated samples, as compared to unirradiated samples, in the first 24-72 h post-irradiation. These RS response signatures arise from radiation-induced changes in cellular concentrations of aromatic amino acids, conformational protein structures and certain nucleic acid and lipid functional groups. Correlation analysis between the radiation-induced PCA components separates the cell lines into three distinct RS response categories: R1 (H460 and MCF7), R2 (MDA-MB-231 and PC3) and R3 (DU145 and LNCaP). These RS categories partially segregate according to radiosensitivity, as the R1 and R2 cell lines are radioresistant (SF(2) > 0.6) and the R3 cell lines are radiosensitive (SF(2) < 0.5). The R1 and R2 cell lines further segregate according to p53 gene status, corroborated by cell cycle analysis post-irradiation. Potential radiation-induced biochemical response mechanisms underlying our RS observations are proposed, such as (1) the regulated synthesis and degradation of structured proteins and (2) the expression of anti-apoptosis factors or other survival signals. This study demonstrates the utility of RS for noninvasive radiobiological analysis of tumour cell radiation response, and indicates the potential for future RS studies designed to investigate, monitor or predict radiation response.

Real-time cell-impedance sensing assay as an alternative to clonogenic assay in evaluating cancer radiotherapy

Intrinsic radiosensitivity of normal and tumour tissues has been shown to be an independent prognostic factor for patients' response to radiotherapy. This study compares the real-time cell-impedance sensing (RT-CES) assay with the conventional clonogenic assay in terms of in-vitro radiosensitivity. One objective in this study was to predict in-vivo response to gold nanoparticle (GNP) treatment on the basis of in-vitro RT-CES testing results. Four adenocarcinoma cancer cell lines were tested using both the RT-CES and the clonogenic assays. Cell-survival curves were plotted, and the mean SF2 values obtained by these two different assay methods were compared using ANOVA. Radiation sensitivities obtained in-vitro were then compared with the in-vivo results. On the basis of the measurement of cell colonies, the RT-CES assay has similar radiosensitivity to the clonogenic assay, but significantly shortens the testing time from 14-21 days to only 72 h. Intrinsic GNP enhanced radiation sensitivity using tumour volume (mm(3)) in vivo is comparable with that using RT-CES cell survival assay in vitro. Furthermore, the RT-CES system provides real-time information regarding the state of cell radiosensitivity that may give useful information towards personalizing radiotherapy. The RT-CES assay enables more reliable and time-efficient results in the evaluation of radiosensitivity.

Molecular biology: the key to personalised treatment in radiation oncology?

We know considerably more about what makes cells and tissues resistant or sensitive to radiation than we did 20 years ago. Novel techniques in molecular biology have made a major contribution to our understanding at the level of signalling pathways. Before the "New Biology" era, radioresponsiveness was defined in terms of physiological parameters designated as the five Rs. These are: repair, repopulation, reassortment, reoxygenation and radiosensitivity. Of these, only the role of hypoxia proved to be a robust predictive and prognostic marker, but radiotherapy regimens were nonetheless modified in terms of dose per fraction, fraction size and overall time, in ways that persist in clinical practice today. The first molecular techniques were applied to radiobiology about two decades ago and soon revealed the existence of genes/proteins that respond to and influence the cellular outcome of irradiation. The subsequent development of screening techniques using microarray technology has since revealed that a very large number of genes fall into this category. We can now obtain an adequately robust molecular signature, predicting for a radioresponsive phenotype using gene expression and proteomic approaches. In parallel with these developments, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) can now detect specific biological molecules such as haemoglobin and glucose, so revealing a 3D map of tumour blood flow and metabolism. The key to personalised radiotherapy will be to extend this capability to the proteins of the molecular signature that determine radiosensitivity.

A small interfering RNA screen of genes involved in DNA repair identifies tumor-specific radiosensitization by POLQ knockdown

The effectiveness of radiotherapy treatment could be significantly improved if tumor cells could be rendered more sensitive to ionizing radiation (IR) without altering the sensitivity of normal tissues. However, many of the key therapeutically exploitable mechanisms that determine intrinsic tumor radiosensitivity are largely unknown. We have conducted a small interfering RNA (siRNA) screen of 200 genes involved in DNA damage repair aimed at identifying genes whose knockdown increased tumor radiosensitivity. Parallel siRNA screens were conducted in irradiated and unirradiated tumor cells (SQ20B) and irradiated normal tissue cells (MRC5). Using gammaH2AX foci at 24 hours after IR, we identified several genes, such as BRCA2, Lig IV, and XRCC5, whose knockdown is known to cause increased cell radiosensitivity, thereby validating the primary screening end point. In addition, we identified POLQ (DNA polymerase ) as a potential tumor-specific target. Subsequent investigations showed that POLQ knockdown resulted in radiosensitization of a panel of tumor cell lines from different primary sites while having little or no effect on normal tissue cell lines. These findings raise the possibility that POLQ inhibition might be used clinically to cause tumor-specific radiosensitization.