From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities

[Predictive assays for responses of tumors and normal tissues in radiation oncology]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the target volume. Tumor sensitivity to radiotherapy may be particularly inconstant depending on location, histology, somatic genetic parameters and the capacity of the immune system to infiltrate the tumor. In addition, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better predict the tumor response but also the radiation-induced late effects.

Quantitative proteomic analysis reveals AK2 as potential biomarker for late normal tissue radiotoxicity

Background

Biomarkers for predicting late normal tissue toxicity to radiotherapy are necessary to personalize treatments and to optimize clinical benefit. Many radiogenomic studies have been published on this topic. Conversely, proteomics approaches are not much developed, despite their advantages.

Methods

We used the isobaric tags for relative and absolute quantitation (iTRAQ) proteomic approach to analyze differences in protein expression levels in ex-vivo irradiated (8 Gy) T lymphocytes from patients with grade ≥ 2 radiation-induced breast fibrosis (grade ≥ 2 bf+) and patients with grade < 2 bf + after curative intent radiotherapy. Patients were selected from two prospective clinical trials (COHORT and PHRC 2005) and were used as discovery and confirmation cohorts.

Results

Among the 1979 quantified proteins, 23 fulfilled our stringent biological criteria. Immunoblotting analysis of four of these candidate proteins (adenylate kinase 2, AK2; annexin A1; heat shock cognate 71 kDa protein; and isocitrate dehydrogenase 2) confirmed AK2 overexpression in 8 Gy-irradiated T lymphocytes from patients with grade ≥ 2 bf + compared with patients with grade < 2 bf+. As these candidate proteins are involved in oxidative stress regulation, we also evaluated radiation-induced reactive oxygen species (ROS) production in peripheral blood mononuclear cells from patients with grade ≥ 2 bf + and grade < 2 bf+. Total ROS level, and especially superoxide anion level, increased upon ex-vivo 8 Gy-irradiation in all patients. Analysis of NADPH oxidases (NOXs), a major source of superoxide ion in the cell, showed a significant increase of NOX4 mRNA and protein levels after irradiation in both patient groups. Conversely, only NOX4 mRNA level was significantly different between groups (grade ≥ 2 bf + and grade < 2 bf+).

Conclusion

These findings identify AK2 as a potential radiosensitivity candidate biomarker. Overall, our proteomic approach highlights the important role of oxidative stress in late radiation-induced toxicity, and paves the way for additional studies on NOXs and superoxide ion metabolism.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1351-8) contains supplementary material, which is available to authorized users.

Radiation-Induced Chromosomal Aberrations and Immunotherapy: Micronuclei, Cytosolic DNA, and Interferon-Production Pathway

Radiation-induced chromosomal aberrations represent an early marker of late effects, including cell killing and transformation. The measurement of cytogenetic damage in tissues, generally in blood lymphocytes, from patients treated with radiotherapy has been studied for many years to predict individual sensitivity and late morbidity. Acentric fragments are lost during mitosis and create micronuclei (MN), which are well correlated to cell killing. Immunotherapy is rapidly becoming a most promising new strategy for metastatic tumors, and combination with radiotherapy is explored in several pre-clinical studies and clinical trials. Recent evidence has shown that the presence of cytosolic DNA activates immune response via the cyclic GMP-AMP synthase/stimulator of interferon genes pathway, which induces type I interferon transcription. Cytosolic DNA can be found after exposure to ionizing radiation either as MN or as small fragments leaking through nuclear envelope ruptures. The study of the dependence of cytosolic DNA and MN on dose and radiation quality can guide the optimal combination of radiotherapy and immunotherapy. The role of densely ionizing charged particles is under active investigation to define their impact on the activation of the interferon pathway.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Fluorescent imaging of cancerous tissues for targeted surgery

To maximize tumor excision and minimize collateral damage are the primary goals of cancer surgery. Emerging molecular imaging techniques have made "image-guided surgery" developed into "molecular imaging-guided surgery", which is termed as "targeted surgery" in this review. Consequently, the precision of surgery can be advanced from tissue-scale to molecule-scale, enabling "targeted surgery" to be a component of "targeted therapy". Evidence from numerous experimental and clinical studies has demonstrated significant benefits of fluorescent imaging in targeted surgery with preoperative molecular diagnostic screening. Fluorescent imaging can help to improve intraoperative staging and enable more radical cytoreduction, detect obscure tumor lesions in special organs, highlight tumor margins, better map lymph node metastases, and identify important normal structures intraoperatively. Though limited tissue penetration of fluorescent imaging and tumor heterogeneity are two major hurdles for current targeted surgery, multimodality imaging and multiplex imaging may provide potential solutions to overcome these issues, respectively. Moreover, though many fluorescent imaging techniques and probes have been investigated, targeted surgery remains at a proof-of-principle stage. The impact of fluorescent imaging on cancer surgery will likely be realized through persistent interdisciplinary amalgamation of research in diverse fields.

A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making

BACKGROUND

Decision Support Systems, based on statistical prediction models, have the potential to change the way medicine is being practiced, but their application is currently hampered by the astonishing lack of impact studies. Showing the theoretical benefit of using these models could stimulate conductance of such studies. In addition, it would pave the way for developing more advanced models, based on genomics, proteomics and imaging information, to further improve the performance of the models.

PURPOSE

In this prospective single-center study, previously developed and validated statistical models were used to predict the two-year survival (2yrS), dyspnea (DPN), and dysphagia (DPH) outcomes for lung cancer patients treated with chemo radiation. These predictions were compared to probabilities provided by doctors and guideline-based recommendations currently used. We hypothesized that model predictions would significantly outperform predictions from doctors.

MATERIALS AND METHODS

Experienced radiation oncologists (ROs) predicted all outcomes at two timepoints: (1) after the first consultation of the patient, and (2) after the radiation treatment plan was made. Differences in the performances of doctors and models were assessed using Area Under the Curve (AUC) analysis.

RESULTS

A total number of 155 patients were included. At timepoint #1 the differences in AUCs between the ROs and the models were 0.15, 0.17, and 0.20 (for 2yrS, DPN, and DPH, respectively), with p-values of 0.02, 0.07, and 0.03. Comparable differences at timepoint #2 were not statistically significant due to the limited number of patients. Comparison to guideline-based recommendations also favored the models.

CONCLUSION

The models substantially outperformed ROs' predictions and guideline-based recommendations currently used in clinical practice. Identification of risk groups on the basis of the models facilitates individualized treatment, and should be further investigated in clinical impact studies.

The role of imaging in radiation therapy planning: past, present, and future

The use of ionizing radiation for cancer treatment has undergone extraordinary development during the past hundred years. The advancement of medical imaging has been critical in helping to achieve this change. The invention of computed tomography (CT) was pivotal in the development of treatment planning. Despite some disadvantages, CT remains the only three-dimensional imaging modality used for dose calculation. Newer image modalities, such as magnetic resonance (MR) imaging and positron emission tomography (PET), are also used secondarily in the treatment-planning process. MR, with its better tissue contrast and resolution than those of CT, improves tumor definition compared with CT planning alone. PET also provides metabolic information to supplement the CT and MR anatomical information. With emerging molecular imaging techniques, the ability to visualize and characterize tumors with regard to their metabolic profile, active pathways, and genetic markers, both across different tumors and within individual, heterogeneous tumors, will inform clinicians regarding the treatment options most likely to benefit a patient and to detect at the earliest time possible if and where a chosen therapy is working. In the post-human-genome era, multimodality scanners such as PET/CT and PET/MR will provide optimal tumor targeting information.

[Intrinsic radiosensitivity: predictive assays that will change daily practice]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the targeted volume. Nevertheless, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better understand the mechanisms of radiation-induced late effects. We also present the possibilities of clinical use of predictive assays in the close future.

Prediction of normal tissue toxicity as part of the individualized treatment with radiotherapy in oncology patients

Normal tissue toxicity caused by radiotherapy conditions the success of the treatment and the quality of life of patients. Radiotherapy is combined with surgery in both the preoperative or postoperative setting for the treatment of most localized solid tumour types. Furthermore, radical radiotherapy is an alternative to surgery in several tumour locations. The possibility of predicting such radiation-induced toxicity would make possible a better treatment schedule for the individual patient. Radiation-induced toxicity is, at least in part, genetically determined. From decades, several predictive tests have been proposed to know the individual sensitivity of patients to the radiotherapy schedules. Among them, initial DNA damage, radiation-induced apoptosis, gene expression profiles, and gene polymorphisms have been proposed. We report here an overview of the main studies regarding to this field. Radiation-induced apoptosis in peripheral blood lymphocytes seem to be the most promising assay tested in prospective clinical trials, although they have to be validated in large clinical studies. Other promising assays, as those related with single nucleotide polymorphisms, need to be validated as well.

Radiation-related changes in serum proteome profiles detected by mass spectrometry in blood of patients treated with radiotherapy due to larynx cancer

The study aimed to detect features of human serum proteome that were associated with exposure to ionizing radiation. The analyzed group consisted of 46 patients treated with radical radiotherapy for larynx cancer; patients were irradiated with total doses in a range from 51 to 72 Gy. Three consecutive blood samples were collected from each patient: before the start, 2 weeks after the start, and 4-6 weeks after the end of radiotherapy. The low-molecular-weight fraction of the serum proteome (2,000-13,000 Da) was analyzed by the MALDI-ToF mass spectrometry. Proteome profiles of serum samples collected before the start of radiotherapy and during the early stage of the treatment were similar. In marked contrast, mass profiles of serum samples collected several weeks after the end of the treatment revealed clear changes. We found that 41 out of 312 registered peptide ions changed their abundance significantly when serum samples collected after the final irradiation were compared with samples collected at the two earlier time points. We also found that abundances of certain serum peptides were associated with total doses of radiation received by patients. The results of this pilot study indicate that features of serum proteome analyzed by mass spectrometry have potential applicability as a retrospective marker of exposure to ionizing radiation.

DNA repair targeting and radiotherapy: a focus on the therapeutic ratio

Radiotherapy (RT) results in the production of a variety of ionizing radiation-induced lesion in DNA. Specific pathways of DNA repair are required to repair the variety of lesions, which include DNA single-strand breaks (SSBs), DNA double-strand breaks (DSBs), DNA base alterations, and DNA-DNA or DNA-protein cross-links. Nonrepaired DNA damage can lead to normal and tumor cell death via apoptosis, mitotic catastrophe, autophagy, or terminal growth arrest senescence. Targeting the sensing and repair of DNA damage is an exciting concept. This must be combined with precision RT to limit the volume of irradiated normal tissue, including the use of image-guided radiotherapy (IGRT) and brachytherapy. The therapeutic ratio of combined targeting of DNA combined with RT could also be preserved using biological approaches and includes the following: (1) the documentation of relative defects in DNA damage sensing and repair in malignant cells; (2) the preferential use of certain DNA repair pathways (eg, base excision repair or homologous recombination) in malignant tissues compare with normal tissues; (3) the targeting of repair defects in chronically hypoxic cells; and (4) optimal scheduling of a DNA repair inhibitor in the neoadjuvant, concurrent, or adjuvant combined treatment settings. In this review, we discuss the general rationale and the optimal timing and duration of DNA repair inhibition during fractionated RT with the emphasis on preserving the therapeutic ratio of cancer treatment.

Normal Tissue Complication Probability (NTCP) modeling of late rectal bleeding following external beam radiotherapy for prostate cancer: A Test of the QUANTEC-recommended NTCP model

PURPOSE/BACKGROUND

Validating a predictive model for late rectal bleeding following external beam treatment for prostate cancer would enable safer treatments or dose escalation. We tested the normal tissue complication probability (NTCP) model recommended in the recent QUANTEC review (quantitative analysis of normal tissue effects in the clinic).

MATERIAL AND METHODS

One hundred and sixty one prostate cancer patients were treated with 3D conformal radiotherapy for prostate cancer at the British Columbia Cancer Agency in a prospective protocol. The total prescription dose for all patients was 74 Gy, delivered in 2 Gy/fraction. 159 3D treatment planning datasets were available for analysis. Rectal dose volume histograms were extracted and fitted to a Lyman-Kutcher-Burman NTCP model.

RESULTS

Late rectal bleeding (>grade 2) was observed in 12/159 patients (7.5%). Multivariate logistic regression with dose-volume parameters (V50, V60, V70, etc.) was non-significant. Among clinical variables, only age was significant on a Kaplan-Meier log-rank test (p=0.007, with an optimal cut point of 77 years). Best-fit Lyman-Kutcher-Burman model parameters (with 95% confidence intervals) were: n = 0.068 (0.01, +infinity); m =0.14 (0.0, 0.86); and TD50 = 81 (27, 136) Gy. The peak values fall within the 95% QUANTEC confidence intervals. On this dataset, both models had only modest ability to predict complications: the best-fit model had a Spearman's rank correlation coefficient of rs = 0.099 (p = 0.11) and area under the receiver operating characteristic curve (AUC) of 0.62; the QUANTEC model had rs=0.096 (p= 0.11) and a corresponding AUC of 0.61. Although the QUANTEC model consistently predicted higher NTCP values, it could not be rejected according to the χ(2) test (p = 0.44).

CONCLUSIONS

Observed complications, and best-fit parameter estimates, were consistent with the QUANTEC-preferred NTCP model. However, predictive power was low, at least partly because the rectal dose distribution characteristics do not vary greatly within this patient cohort.

Accurate accumulation of dose for improved understanding of radiation effects in normal tissue

The actual distribution of radiation dose accumulated in normal tissues over the complete course of radiation therapy is, in general, poorly quantified. Differences in the patient anatomy between planning and treatment can occur gradually (e.g., tumor regression, resolution of edema) or relatively rapidly (e.g., bladder filling, breathing motion) and these undermine the accuracy of the planned dose distribution. Current efforts to maximize the therapeutic ratio require models that relate the true accumulated dose to clinical outcome. The needed accuracy can only be achieved through the development of robust methods that track the accumulation of dose within the various tissues in the body. Specific needs include the development of segmentation methods, tissue-mapping algorithms, uncertainty estimation, optimal schedules for image-based monitoring, and the development of informatics tools to support subsequent analysis. These developments will not only improve radiation outcomes modeling but will address the technical demands of the adaptive radiotherapy paradigm. The next 5 years need to see academia and industry bring these tools into the hands of the clinician and the clinical scientist.

Biomarkers and surrogate endpoints for normal-tissue effects of radiation therapy: the importance of dose-volume effects

Biomarkers are of interest for predicting or monitoring normal tissue toxicity of radiation therapy. Advances in molecular radiobiology provide novel leads in the search for normal tissue biomarkers with sufficient sensitivity and specificity to become clinically useful. This article reviews examples of studies of biomarkers as predictive markers, as response markers, or as surrogate endpoints for radiation side effects. Single nucleotide polymorphisms are briefly discussed in the context of candidate gene and genomewide association studies. The importance of adjusting for radiation dose distribution in normal tissue biomarker studies is underlined. Finally, research priorities in this field are identified and discussed.

Endogenous and radiation-induced expression of gammaH2AX in biopsies from patients treated for carcinoma of the uterine cervix

BACKGROUND AND PURPOSE

The possibility of using gammaH2AX foci as a marker of DNA damage and as a potential predictor of tumour response to treatment was examined using biopsies from 3 sets of patients with advanced carcinoma of the cervix. The relation between endogenous gammaH2AX expression and hypoxia was also examined.

MATERIALS AND METHODS

Set 1 consisted of 26 biopsies that included pre-treatment and 24h post-radiation treatment samples. Pre-treatment biopsies from 12 patients in Set 2 were used to develop image analysis software while pre-treatment biopsies from 33 patients in Set 3 were examined for the relation between staining for the hypoxia marker pimonidazole and endogenous gammaH2AX expression. Formalin-fixed paraffin-embedded sections were analyzed after antigen retrieval and fluorescence antibody labeling for the hypoxia markers CAIX or pimonidazole in combination with gammaH2AX staining.

RESULTS

Before treatment, 24+/-19% of cells contained gammaH2AX foci, with most positive cells containing fewer than 5 foci per nucleus. Twenty-four hours after exposure to the first fraction of 1.8-2.5Gy, 38+/-19% contained foci. CAIX positive cells were 1.4 times more likely to exhibit endogenous gammaH2AX foci, and pimonidazole-positive cells were 2.8 times more likely to contain gammaH2AX foci. For 18 patients for whom both pre-treatment and 24h post-irradiation biopsies were available, local control was unrelated to the fraction of cells that retained gammaH2AX foci. However, 24h after irradiation, tumours that had received 2.5Gy showed a significantly higher fraction of cells with residual gammaH2AX foci than tumours given 1.8Gy.

CONCLUSIONS

Endogenous gammaH2AX foci are enriched in hypoxic tumour regions. Small differences in delivered dose can produce quantifiable differences in residual DNA damage that can overshadow inter-tumour differences in response.

Bioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data

Radiotherapy outcomes are determined by complex interactions between physical and biological factors, reflecting both treatment conditions and underlying genetics. Recent advances in radiotherapy and biotechnology provide new opportunities and challenges for predicting radiation-induced toxicities, particularly radiation pneumonitis (RP), in lung cancer patients. In this work, we utilize datamining methods based on machine learning to build a predictive model of lung injury by retrospective analysis of treatment planning archives. In addition, biomarkers for this model are extracted from a prospective clinical trial that collects blood serum samples at multiple time points. We utilize a 3-way proteomics methodology to screen for differentially expressed proteins that are related to RP. Our preliminary results demonstrate that kernel methods can capture nonlinear dose-volume interactions, but fail to address missing biological factors. Our proteomics strategy yielded promising protein candidates, but their role in RP as well as their interactions with dose-volume metrics remain to be determined.

[Virtual microscopy in systems pathology]

Genomics and proteomics have evolved towards systems biology. The general goal here is the construction of complex, functional models of biological systems on the basis of molecular networks. Such models enable improved quality in interpretation and evaluation of quantitative measurements and afford a substantially deeper functional understanding. Systems pathology differs from systems biology by attaching the same importance to spatial modelling of tissue alterations as to gene regulatory modelling. In this way, systems pathology is able to deploy disease models for improved diagnosis, prognosis and therapy. In the present work a generic process for systems pathology is created, integrating gene regulatory and morphological models towards molecular disease models. For this purpose, fluorescent virtual microscopy will be essential as it delivers morphological and molecular tissue data with high spatial resolution and high throughput. Using epidermal differentiation as an example, it is shown how - using virtual microscopy - the spatiotemporal expression of biomarkers can be modelled by reconstructing protein networks from fluorescent tissue sections.