Personalized radiation therapy and biomarker-driven treatment strategies: a systematic review

Can we use a simple blood test to reduce unnecessary adverse effects from radiotherapy by timely identification of radiotherapy-resistant rectal cancers? MeD-Seq rectal study protocol

BACKGROUND

Chemoradiation therapy (CRT) followed by surgery is currently the standard of care to treat patients with locally advanced rectal cancer (LARC). CRT reduces local recurrences, but is associated with significant damage to the surrounding healthy tissue that can severely impact patients quality of life. Additionally, a proportion of patients (hardly) benefit from CRT. We aim to develop a diagnostic innovation, using DNA-methylation, which can enable a more selective and thereby more effective use of the available therapies for rectal cancer patients.

METHODS

MeD-Seq Rectal is a prospective single centre, observational study. 75 patients diagnosed with rectal cancer and will receive CRT as neoadjuvant treatment are will be included. DNA-methylation profiling will be performed on liquid biopsies to predict pathological response to CRT.

DISCUSSION

To data no clinical or image-based features were found that predict response to CRT. we hypothesize that DNA methylation patterns in liquid biopsies may provide a promising and patient-friendly strategy to predict CRT resistance upfront.

TRIAL REGISTRATION

This trial is registered at ClinicalTrials.gov (NCT06035471).

Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation

Introduction

Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents.

Areas Covered

Established clinically relevant biomarkers are used in surveillance, as prognostic indicators, and sometimes for treatment planning; however, the inability to intercept early recurrence or predict upfront resistance to treatment remains a significant challenge that limits the selection of patients for adjuvant therapy. We discuss attempts at intercepting early failure. We examine biomarkers that have made it into the clinic where they are used for treatment monitoring and management alteration, and novel biomarkers that lead the field with targeted adjuvant therapy seeking to harness these.

Expert Opinion

Given the growth of data correlating interventions with omic analysis toward identifying biomarkers of radiation resistance, more robust markers of recurrence that link to biology will increasingly be leveraged toward targeted adjuvant therapy to make a successful transition to the clinic in the coming years.

Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer

Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

A novel TMTP1-modified theranostic nanoplatform for targeted in vivo NIR-II fluorescence imaging-guided chemotherapy for cervical cancer

Near-infrared II (NIR-II, 900-1700 nm) fluorescence bioimaging with advantages of good biosafety, excellent spatial resolution, high sensitivity, and contrast has attracted great attention in biomedical research fields. However, most of the nanoprobes used for NIR-II fluorescence imaging have poor tumor-targeting ability and therapeutic efficiency. To overcome these limitations, a novel NIR-II-emissive theranostic nanoplatform for fluorescence imaging and treatment of cervical cancer was designed and prepared. The NIR-II-emissive dye IR-783 and chemotherapy drug doxorubicin (DOX) were encapsulated into liposomes, and the tumor-targeting peptide TMTP1 (a polypeptide with a sequence of cyclic ASN Val Val Arg Gln Cys) was conjugated to the surface of the liposomes to form IR-783-DOX-TMTP1 nanoparticles (NPs) via self-assembly methods. The IR-783-DOX-TMTP1 NPs showed strong NIR-II emission, excellent biocompatibility and a long lifetime in vivo. Furthermore, high-definition NIR-II fluorescence microscopy images of ear blood vessels and intratumoral blood vessels were obtained from IR-783-DOX-TMTP1 NP-stained mice with high spatial resolution under 808 nm laser excitation. Moreover, IR-783-DOX-TMTP1 NPs showed strong tumor-targeting ability and highly efficient chemotherapeutic characteristics towards cervical tumors. The novel targeting and NIR-II-emissive IR-783-DOX-TMTP1 NPs have great potential in diagnosis and therapy for cervical cancer.

A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response

Recent studies have shown that a tumor's biological response to radiation varies over time and has a dynamic nature. Dynamic biological features of tumor cells underscore the importance of using fractionation and adapting the treatment plan to tumor volume changes in radiation therapy treatment. Adaptive radiation therapy (ART) is an iterative process to adjust the dose of radiation in response to potential changes during the treatment. One of the key challenges in ART is how to determine the optimal timing of adaptations corresponding to tumor response to radiation. This paper aims to develop an automated treatment planning framework incorporating the biological uncertainties to find the optimal adaptation points to achieve a more effective treatment plan. First, a dynamic tumor-response model is proposed to predict weekly tumor volume regression during the period of radiation therapy treatment based on biological factors. Second, a Reinforcement Learning (RL) framework is developed to find the optimal adaptation points for ART considering the uncertainty in biological factors with the goal of achieving maximum final tumor control while minimizing or maintaining the toxicity level of the organs at risk (OARs) per the decision-maker's preference. Third, a beamlet intensity optimization model is solved using the predicted tumor volume at each adaptation point. The performance of the proposed RT treatment planning framework is tested using a clinical non-small cell lung cancer (NSCLC) case. The results are compared with the conventional fractionation schedule (i.e., equal dose fractionation) as a reference plan. The results show that the proposed approach performed well in achieving a robust optimal ART treatment plan under high uncertainty in the biological parameters. The ART plan outperformed the reference plan by increasing the mean biological effective dose (BED) value of the tumor by 2.01%, while maintaining the OAR BED within +0.5% and reducing the variability, in terms of the interquartile range (IQR) of tumor BED, by 25%.

Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma

Lung cancer is by far the leading cause of cancer death among both men and women. Radiation therapy is one of the main approaches to lung cancer treatment, and its planning is crucial for the therapy outcome. However, the current practice that uniformly delivers the dose does not take into account the patient-specific tumour features that may affect treatment success. Since radiation therapy is by its very nature a sequential procedure, Deep Reinforcement Learning (DRL) is a well-suited methodology to overcome this limitation. In this respect, in this work we present a DRL controller optimizing the daily dose fraction delivered to the patient on the basis of CT scans collected over time during the therapy, offering a personalized treatment not only for volume adaptation, as currently intended, but also for daily fractionation. Furthermore, this contribution introduces a virtual radiotherapy environment based on a set of ordinary differential equations modelling the tissue radiosensitivity by combining both the effect of the radiotherapy treatment and cell growth. Their parameters are estimated from CT scans routinely collected using the Particle Swarm Optimization algorithm. This permits the DRL to learn the optimal behaviour through an iterative trial and error process with the environment. We performed several experiments considering three rewards functions modelling treatment strategies with different tissue aggressiveness and two exploration strategies for the exploration-exploitation dilemma. The results show that our DRL approach can adapt to radiation therapy treatment, optimizing its behaviour according to the different reward functions and outperforming the current clinical practice.

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

PURPOSE

Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care.

METHODS

A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020.

RESULTS

We identified several studies at each point of patient's care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.

CONCLUSION

Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.

Liquid biopsy in NSCLC: a new challenge in radiation therapy

Lung cancer is the most common cancer and the leading cause of cancer mortality worldwide. To date, tissue biopsy has been the gold standard for the diagnosis and the identification of specific molecular mutations, to guide choice of therapy. However, this procedure has several limitations. Liquid biopsy could represent a solution to the intrinsic limits of traditional biopsy. It can detect cancer markers such as circulating tumor DNA or RNA (ctDNA, ctRNA), and circulating tumor cells, in plasma, serum or other biological fluids. This procedure is minimally invasive, reproducible and can be used repeatedly. The main clinical applications of liquid biopsy in non-small cell lung cancer (NSCLC) patients are the early diagnosis, stratification of the risk of relapse, identification of mutations to guide application of targeted therapy and the evaluation of the minimum residual disease. In this review, the current role of liquid biopsy and associated markers in the management of NSCLC patients was analyzed, with emphasis on ctDNA and CTCs, and radiotherapy.

The immunohistochemical and histologic effects of contrast medium on uterus, fallopian tubes and ovaries, given during hysterosalpingography: rat study

Objective:

Previous studies have shown that damage occurs to internal genital tract during hysterosalpingography (HSG). The aim was to show that endometrial and tubal epithelium underwent free radical damage during HSG in an animal model.

Material and Methods:

Forty rats were evaluated in five different groups. Two groups received ionizing radiation (15-20 miliRad three times) only. Two further groups received ionizing radiation in combination with iohexol (1-2 mL). The remaining group served as control. Groups were evaluated after seven and forty-two days. Inflammation and cellular changes were evaluated histopathologically. Cellular activity of antioxidant enzymes was assessed immunohistochemically.

Results:

Inflammation, and cellular changes were detected at certain rates in all groups (p<0.001). Glutathione reductase, catalase, superoxide dismutase, glutathione S-transferase activities were found to be increased after the HSG (p<0.001).

Conclusion:

It is obvious that the cell suffers acute and chronic damage during HSG due to both radioactivity and chemicals. Although there is a lot of research done before, there is no definitive method yet to protect against the harmful effects of iodinated contrast agents and ionizing radiation. So, new methods need to be explored to protect cells and tissues from reactive oxygen radical damage caused by HSG.

Methodological Development of Combination Drug and Radiotherapy in Basic and Clinical Research

Newer technical improvements in radiation oncology have been rapidly implemented in recent decades, allowing an improved therapeutic ratio. The development of strategies using local and systemic treatments concurrently, mainly targeted therapies, has however plateaued. Targeted molecular compounds and immunotherapy are increasingly being incorporated as the new standard of care for a wide array of cancers. A better understanding of possible prior methodology issues is therefore required and should be integrated into upcoming early clinical trials including individualized radiotherapy-drug combinations. The outcome of clinical trials is influenced by the validity of the preclinical proofs of concept, the impact on normal tissue, the robustness of biomarkers and the quality of the delivery of radiation. Herein, key methodological aspects are discussed with the aim of optimizing the design and implementation of future precision drug-radiotherapy trials.

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

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

TMTP1-modified Indocyanine Green-loaded Polymeric Micelles for Targeted Imaging of Cervical Cancer and Metastasis Sentinel Lymph Node in vivo

Metastasis is one of the most threatening aspects of cervical cancer. We developed a method to intraoperatively map the primary tumor, metastasis and metastatic sentinel lymph nodes (SLNs), providing real-time intraoperative guidance in cervical cancer.

Methods: TMTP1, a tumor metastasis targeting peptide, was employed to modify the indocyanine green (ICG)-loaded poly (ethylene glycol)- poly (lactic-co-glycolic acid) (PEG-PLGA) micelles. The cervical cancer subcutaneous tumor model and lung metastasis model were established to determine the active targeting of ICG-loaded TMTP1-PEG-PLGA micelles (ITM) for the primary tumor and occult metastasis of cervical cancer. Human cervical cancer HeLa cells engineered by firefly luciferase were injected into the right hocks of BALB/c nude mice to develop the SLN metastasis model. The ITM and control ICG-loaded PEG-PLGA micelles (IM) were injected into the right hind footpads in the SLN metastasis model, and the migration and retention of micelles were recorded under near-infrared fluorescence. K14-HPV16 transgenic mice were also used to detect the image capability of ITM to target cancerous lesions.

Results: ITM could actively target imaging of the primary tumor and cervical cancer metastasis. ITM quickly diffused from the injection site to SLNs along lymphatic capillaries and remained in the SLNs for 12 h. Moreover, ITM specifically accumulated in the tumor metastatic SLNs (T-SLNs), which could be successfully distinguished from normal SLNs (N-SLNs).

Conclusion: ITM could achieve active targeting of the primary tumor, metastasis and T-SLNs, providing precise and real-time intraoperative guidance for cervical cancer.

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.

Practice-changing radiation therapy trials for the treatment of cancer: where are we 150 years after the birth of Marie Curie?

As we mark 150 years since the birth of Marie Curie, we reflect on the global advances made in radiation oncology and the current status of radiation therapy (RT) research. Large-scale international RT clinical trials have been fundamental in driving evidence-based change and have served to improve cancer management and to reduce side effects. Radiation therapy trials have also improved practice by increasing quality assurance and consistency in treatment protocols across multiple centres. This review summarises some of the key RT practice-changing clinical trials over the last two decades, in four common cancer sites for which RT is a crucial component of curative treatment: breast, lung, urological and lower gastro-intestinal cancer. We highlight the global inequality in access to RT, and the work of international organisations, such as the International Atomic Energy Agency (IAEA), the European SocieTy for Radiotherapy and Oncology (ESTRO), and the United Kingdom National Cancer Research Institute Clinical and Translational Radiotherapy Research Working Group (CTRad), that aim to improve access to RT and facilitate radiation research. We discuss some emerging RT technologies including proton beam therapy and magnetic resonance linear accelerators and predict likely future directions in clinical RT research.

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology

Purpose

Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant amount of patients with a high level of clinical details, multicenter studies are necessary. A challenge in creating high quality Big Data studies involving several treatment centers is the lack of semantic interoperability between data sources. We present the ontology we developed to address this issue.

Methods

Radiation Oncology anatomical and target volumes were categorized in anatomical and treatment planning classes. International delineation guidelines specific to radiation oncology were used for lymph nodes areas and target volumes. Hierarchical classes were created to generate The Radiation Oncology Structures (ROS) Ontology. The ROS was then applied to the data from our institution.

Results

Four hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (.owl) format and made available online on Bioportal and GitHub under an Apache 2.0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our “record-and-verify” system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW).

Conclusion

In this study we describe a new ontology, specific to radiation oncology, that reports all anatomical and treatment planning structures that can be delineated. This ontology will be used to integrate dosimetric data in the Assistance Publique—Hôpitaux de Paris CDW that stores data from 6.5 million patients (as of February 2017).

Radioresistance of the breast tumor is highly correlated to its level of cancer stem cell and its clinical implication for breast irradiation

BACKGROUND AND PURPOSE

Growing evidence suggested the coexistence of cancer stem cells (CSCs) within solid tumors. We aimed to study radiosensitivity parameters for the CSCs and differentiated tumor cells (TCs) and the correlation of the fractions of CSCs to the overall tumor radioresistance.

MATERIAL AND METHODS

Surviving fractions of breast cancer cell lines were analyzed using a dual-compartment Linear-quadratic model with independent fitting parameters: radiosensitive α_TC, β_TC, α_CSC, β_CSC, and fraction of CSCs f. The overall tumor radio-resistance, the biological effective doses and tumor control probability were estimated as a function of CSC fraction for different fractionation regimens. The pooled clinical outcome data were fitted to the single- and dual-compartment linear-quadric models.

RESULTS

CSCs were more radioresistant characterized by smaller α compared to TCs: α_TC=0.1±0.2, α_CSC=0.04±0.07 for MCF-7 (f=0.1%), α_TC=0.08±0.25, α_CSC=0.04±0.18 for SUM159PT (f=2.46%). Higher f values were correlated with increasing radioresistance in cell lines. Analysis of clinical outcome data is in accordance of a dual-compartment CSC model prediction. Higher percentage of BCSCs resulted in more overall tumor radioresistance and less biological effectiveness.

CONCLUSIONS

Percentage of CSCs strongly correlated to overall tumor radioresistance. This observation suggested potential individualized radiotherapy to account for heterogeneous population of CSCs and their distinct radiosensitivity for breast cancer.

Repurposing DNA repair factors to eradicate tumor cells upon radiotherapy

Cancer is the leading cause of death worldwide. Almost 50% of all cancer patients undergo radiation therapy (RT) during treatment, with varying success. The main goal of RT is to kill tumor cells by damaging their DNA irreversibly while sparing the surrounding normal tissue. The outcome of RT is often determined by how tumors recognize and repair their damaged DNA. A growing body of evidence suggests that tumors often show abnormal expression of DNA double-strand break (DSB) repair genes that are absent from normal cells. Defects in a specific DNA repair pathway make tumor cells overly dependent on alternative or backup pathways to repair their damaged DNA. These tumor cell-specific abnormalities in the DNA damage response (DDR) machinery can potentially be used as biomarkers for treatment outcomes or as targets for sensitization to ionizing radiation (IR). An improved understanding of genetic or epigenetic alterations in the DNA repair pathways specific to cancer cells has paved the way for new treatments that combine pharmacological exploitation of tumor-specific molecular vulnerabilities with IR. Inhibiting DNA repair pathways has the potential to greatly enhance the therapeutic ratio of RT. In this review, we will discuss DNA repair pathways in active cells and how these pathways are deregulated in tumors. We will also describe the impact of targeting cancer-specific aberrations in the DDR as a treatment strategy to improve the efficacy of RT. Finally, we will address the current roadblocks and future prospects of these approaches.

The role of Next-Generation Sequencing in tumoral radiosensitivity prediction

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Next-Generation Sequencing cost has significantly decreased recently.

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NGS is used to comprehensively assess tumoral radiosensitivity.

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Personalized and dose-adapted radiotherapy could be achieved through the use of these technologies.

Technological advances have led to more precise radiation delivery, which has resulted in significant clinical gains. A better understanding of tumoral radiosensitivity is still needed to develop strategies and further personalize radiation treatments. Next-Generation Sequencing (NGS) and system biology have significantly transformed the field of oncology in the last two decades, but have only a few clinical applications in radiation oncology. This review describes the technical aspects and evolutions of NGS and discusses the latest clinical applications of genomics to predict tumoral radiosensitivity.

Irradiation enhances susceptibility of tumor cells to the antitumor effects of TNF-α activated adipose derived mesenchymal stem cells in breast cancer model

Gene modified or cytokine activated mesenchymal stem cells (MSCs) have been used as a treatment in various types of cancer. Moreover, irradiation is usually applied as either a standard primary or adjuvant therapy. Here, we showed that the expression of TNF related apoptosis-inducing ligand (TRAIL) and Dickouf-3 (Dkk-3), the promising anticancer proteins, increased in murine adipose-derived mesenchymal stromal cells (AD-MSCs) following activation with TNF-α, resulting in the induction of apoptosis in cancer cells. Also, anticancer effects of TNF-α activated AD-MSCs were intensified with irradiation. In vivo results showed that TNF-α preactivated AD-MSCs combined with irradiation decreased tumor size and increased survival rate in tumor bearing mice. On the other hands, both TNF-α preactivated AD-MSCs with or without irradiation prevented metastasis in ling and liver, and increased apoptosis in tumor mass. Finally, flowcytometry assay demonstrated that naïve AD-MSCs combined with irradiation but not TNF-α activated MSCs with irradiation increased Treg population in lymph node and spleen. Altogether, obtained results suggest that TNF-α activated MSCs combined with irradiation therapy can serve as new strategy in breast cancer therapy.

Big Data and machine learning in radiation oncology: State of the art and future prospects

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed.

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.

Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel

Biomarkers are important for early detection of cancer, prognosis, response prediction, and detection of residual or relapsing disease. Special attention has been given to diagnostic markers for prostate cancer since it is thought that early detection and surgery might reduce prostate cancer-specific mortality. The use of prostate-specific antigen, PSA (KLK3), has been debated on the base of cohort studies that show that its use in preventive screenings only marginally influences mortality from prostate cancer. Many groups have identified alternative or additional markers, among which PCA3, in order to detect early prostate cancer through screening, to distinguish potentially lethal from indolent prostate cancers, and to guide the treatment decision. The large number of markers proposed has led us to the present study in which we analyze these indicators for their diagnostic and prognostic potential using publicly available genomic data. We identified 380 markers from literature analysis on 20,000 articles on prostate cancer markers. The most interesting ones appeared to be claudin 3 (CLDN3) and alpha-methysacyl-CoA racemase highly expressed in prostate cancer and filamin C (FLNC) and keratin 5 with highest expression in normal prostate tissue. None of the markers proposed can compete with PSA for tissue specificity. The indicators proposed generally show a great variability of expression in normal and tumor tissue or are expressed at similar levels in other tissues. Those proposed as prognostic markers distinguish cases with marginally different risk of progression and appear to have a clinically limited use. We used data sets sampling 152 prostate tissues, data sets with 281 prostate cancers analyzed by microarray analysis and a study of integrated genomics on 218 cases to develop a multigene score. A multivariate model that combines several indicators increases the discrimination power but does not add impressively to the information obtained from Gleason scoring. This analysis of 10 years of marker research suggests that diagnostic and prognostic testing is more difficult in prostate cancer than in other neoplasms and that we must continue to search for better candidates.

Molecular imaging biomarkers of resistance to radiation therapy for spontaneous nasal tumors in canines

PURPOSE

Imaging biomarkers of resistance to radiation therapy can inform and guide treatment management. Most studies have so far focused on assessing a single imaging biomarker. The goal of this study was to explore a number of different molecular imaging biomarkers as surrogates of resistance to radiation therapy.

METHODS AND MATERIALS

Twenty-two canine patients with spontaneous sinonasal tumors were treated with accelerated hypofractionated radiation therapy, receiving either 10 fractions of 4.2 Gy each or 10 fractions of 5.0 Gy each to the gross tumor volume. Patients underwent fluorodeoxyglucose (FDG)-, fluorothymidine (FLT)-, and Cu(II)-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM)-labeled positron emission tomography/computed tomography (PET/CT) imaging before therapy and FLT and Cu-ATSM PET/CT imaging during therapy. In addition to conventional maximum and mean standardized uptake values (SUV(max); SUV(mean)) measurements, imaging metrics providing response and spatiotemporal information were extracted for each patient. Progression-free survival was assessed according to response evaluation criteria in solid tumor. The prognostic value of each imaging biomarker was evaluated using univariable Cox proportional hazards regression. Multivariable analysis was also performed but was restricted to 2 predictor variables due to the limited number of patients. The best bivariable model was selected according to pseudo-R(2).

RESULTS

The following variables were significantly associated with poor clinical outcome following radiation therapy according to univariable analysis: tumor volume (P=.011), midtreatment FLT SUV(mean) (P=.018), and midtreatment FLT SUV(max) (P=.006). Large decreases in FLT SUV(mean) from pretreatment to midtreatment were associated with worse clinical outcome (P=.013). In the bivariable model, the best 2-variable combination for predicting poor outcome was high midtreatment FLT SUV(max) (P=.022) in combination with large FLT response from pretreatment to midtreatment (P=.041).

CONCLUSIONS

In addition to tumor volume, pronounced tumor proliferative response quantified using FLT PET, especially when associated with high residual FLT PET at midtreatment, is a negative prognostic biomarker of outcome in canine tumors following radiation therapy. Neither FDG PET nor Cu-ATSM PET were predictive of outcome.

Protection and measurement in radiation therapy

In radiation therapy, unlike most other applications involving radiation, the intention is to deliver high doses of radiation to diseased tissue, constrained by the effects of radiation to healthy tissue. With regard to patient exposure, the radiation protection framework of justification, optimization, and limitation is a direct part of the prescription process of radiation therapy. Staff and public exposures are typically far below occupational maximum permissible exposures. However, a number of other issues arise in radiation therapy that fall into the category of radiation protection. After an historical review, this paper discusses several contemporary and emerging concerns within radiation therapy, including fetal dose, secondary malignancies, and dose to implantable devices, all of which involve accurate dose assessment outside the intended treatment volume. Other concerns include quality and safety, molecularly based disease assessment and treatment, and other novel treatment strategies. The paper ends with a discussion of the interplay between best practices and regulatory oversight.

DOC-2/DAB2 interacting protein status in high-risk prostate cancer correlates with outcome for patients treated with radiation therapy

PURPOSE

This pilot study investigates the role of DOC-2/DAB2 Interacting Protein (DAB2IP) and enhancer of zeste homolog 2 (EZH2) as prognostic biomarkers in high-risk prostate cancer patients receiving definitive radiation therapy.

METHODS AND MATERIALS

Immunohistochemistry was performed and scored by an expert genitourinary pathologist. Clinical endpoints evaluated were freedom from biochemical failure (FFBF), castration resistance-free survival (CRFS), and distant metastasis-free survival (DMFS). Log-rank test and Cox regression were used to determine significance of biomarker levels with clinical outcome.

RESULTS

Fifty-four patients with high-risk prostate cancer (stage ≥ T3a, or Gleason score ≥ 8, or prostate-specific antigen level ≥ 20 ng/mL) treated with radiation therapy from 2005 to 2012 at our institution were evaluated. Nearly all patients expressed EZH2 (98%), whereas 28% of patients revealed DAB2IP reduction and 72% retained DAB2IP. Median follow-up was 34.0 months for DAB2IP-reduced patients, 29.9 months for DAB2IP-retained patients, and 32.6 months in the EZH2 study. Reduction in DAB2IP portended worse outcome compared with DAB2IP-retained patients, including FFBF (4-year: 37% vs 89%, P=.04), CRFS (4-year: 50% vs 90%, P=.02), and DMFS (4-year: 36% vs 97%, P=.05). Stratified EZH2 expression trended toward significance for worse FFBF and CRFS (P=.07). Patients with reduced DAB2IP or highest-intensity EZH2 expression exhibited worse FFBF (4-year: 32% vs 95%, P=.02), CRFS (4-year: 28% vs 100%, P<.01), and DMFS (4-year: 39% vs 100%, P=.04) compared with the control group.

CONCLUSION

Loss of DAB2IP is a potent biomarker that portends worse outcome despite definitive radiation therapy for patients with high-risk prostate cancer. Enhancer of zeste homolog 2 is expressed in most high-risk tumors and is a less potent discriminator of outcome in this study. The DAB2IP status in combination with degree of EZH2 expression may be useful for determining patients with worse outcome within the high-risk prostate cancer population.

Relationship between radiation-induced apoptosis of T lymphocytes and chronic toxicity in patients with prostate cancer treated by radiation therapy: a prospective study

PURPOSE

To assess the correlation of radiation-induced apoptosis in vitro of CD4 and CD8 T lymphocytes with late toxicity of prostate cancer patients treated with radiation therapy.

METHODS AND MATERIALS

214 patients were prospectively included in the study. Peripheral blood was drawn from patients before treatment and irradiated with 8 Gy. The percentage of CD4+ and CD8+ T lymphocytes that underwent radiation-induced apoptosis was assessed by flow cytometry. Toxicity and mortality were correlated in 198 cases with pretreatment apoptosis and clinical and biological variables by use of a Cox proportional hazards model.

RESULTS

The mean percentage of CD4+ and CD8+ T lymphocyte radiation-induced apoptosis was 28.58% (±14.23) and 50.76% (±18.9), respectively. Genitourinary (GU) toxicity was experienced by 39.9% of patients, while gastrointestinal (GI) toxicity was experienced by 19.7%. The probability of development of GU toxicity was nearly doubled (hazard ratio [HR] 1.99, P=.014) in those patients in whom the percentage of in vitro radiation-induced apoptosis of CD4+ T-lymphocytes was ≤28.58%. It was also almost double in patients who received doses ≥50 Gy in 65% of the bladder volume (V65 ≥50) (HR 1.92, P=.048). No correlation was found between GI toxicity and any of the variables studied. The probability of death during follow-up, after adjustment for different variables, was 2.7 times higher in patients with a percentage of CD8+ T lymphocyte apoptosis ≤50.76% (P=.022).

CONCLUSIONS

In conclusion, our study shows, in the largest prospective cohort of prostate cancer patients undergoing radiation therapy, that in vitro radiation-induced apoptosis of CD4+ T lymphocytes assessed before radiation therapy was associated with the probability of developing chronic GU toxicity. In addition, the radiation dose received in the urinary bladder (V65 ≥50) affected the occurrence of GU toxicity. Finally, we also demonstrate that radiation-induced apoptosis of CD8+ T lymphocytes was associated with overall survival, although larger series are needed to confirm this finding.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.