Radiogenomics: using genetics to identify cancer patients at risk for development of adverse effects following radiotherapy

Acceptability and usefulness of the EORTC 'Write In three Symptoms/Problems' (WISP): a brief open-ended instrument for symptom assessment in cancer patients

BACKGROUND

The use of open-ended questions supplementing static questionnaires with closed questions may facilitate the recognition of symptoms and toxicities. The open-ended 'Write In three Symptoms/Problems (WISP)' instrument permits patients to report additional symptoms/problems not covered by selected EORTC questionnaires. We evaluated the acceptability and usefulness of WISP with cancer patients receiving active and palliative care/treatment in Austria, Chile, France, Jordan, the Netherlands, Norway, Spain and the United Kingdom.

METHODS

We conducted a literature search on validated instruments for cancer patients including open-ended questions and analyzing their responses. WISP was translated into eight languages and pilot tested. WISP translations were pre-tested together with EORTC QLQ-C30, QLQ-C15-PAL and relevant modules, followed by patient interviews to evaluate their understanding about WISP. Proportions were used to summarize patient responses obtained from interviews and WISP.

RESULTS

From the seven instruments identified in the literature, only the free text collected from the PRO-CTAE has been analyzed previously. In our study, 161 cancer patients participated in the pre-testing and interviews (50% in active treatment). Qualitative interviews showed high acceptability of WISP. Among the 295 symptoms/problems reported using WISP, skin problems, sore mouth and bleeding were more prevalent in patients in active treatment, whereas numbness/tingling, dry mouth and existential problems were more prevalent in patients in palliative care/treatment.

CONCLUSIONS

The EORTC WISP instrument was found to be acceptable and useful for symptom assessment in cancer patients. WISP improves the identification of symptoms/problems not assessed by cancer-generic questionnaires and therefore, we recommend its use alongside the EORTC questionnaires.

A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma

BACKGROUND AND PURPOSE

Radiation-induced brain injury (RBI) is a severe radiotoxicity for nasopharyngeal carcinoma (NPC) patients, greatly affecting their long-term life quality and survival. We aim to establish a comprehensive predictive model including clinical factors and newly developed genetic variants to improve the precision of RBI risk stratification.

MATERIALS AND METHODS

By performing a large registry-based retrospective study with magnetic resonance imaging follow-up on RBI development, we conducted a genome-wide association study and developed a polygenic risk score (PRS) for RBI in 1189 NPC patients who underwent intensity-modulated radiotherapy. We proposed a tolerance dose scheme for temporal lobe radiation based on the risk predicted by PRS. Additionally, we established a nomogram by combining PRS and clinical factors for RBI risk prediction.

RESULTS

The 38-SNP PRS could effectively identify high-risk individuals of RBI (P = 1.42 × 10-34). Based on genetic risk calculation, the recommended tolerance doses of temporal lobes should be 57.6 Gy for individuals in the top 10 % PRS subgroup and 68.1 Gy for individuals in the bottom 50 % PRS. Notably, individuals with high genetic risk (PRS > P50) and receiving high radiation dose in the temporal lobes (D0.5CC > 65 Gy) had an approximate 50-fold risk over individuals with low PRS and receiving low radiation dose (HR = 50.09, 95 %CI = 24.27-103.35), showing an additive joint effect (Pinteraction < 0.001). By combining PRS with clinical factors including age, tumor stage, and radiation dose of temporal lobes, the predictive accuracy was significantly improved with C-index increased from 0.78 to 0.85 (P = 1.63 × 10-2).

CONCLUSIONS

The PRS, together with clinical factors, could improve RBI risk stratification and implies personalized radiotherapy.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Genome-wide association study of treatment-related toxicity two years following radiotherapy for breast cancer

BACKGROUND AND PURPOSE

Up to a quarter of breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity. If patients at high risk of adverse effects could be identified at diagnosis, their treatment could be tailored accordingly. This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity two years following whole breast radiotherapy.

MATERIALS AND METHODS

A genome-wide association study (GWAS) was performed in 1,640 breast cancer patients with complete SNP, clinical, treatment and toxicity data, recruited across 18 European and US centres into the prospective REQUITE cohort study. Toxicity data (CTCAE v4.0) were collected at baseline, end of radiotherapy, and annual follow-up. A total of 7,097,340 SNPs were tested for association with the residuals of toxicity endpoints, adjusted for clinical, treatment co-variates and population substructure.

RESULTS

Quantile-quantile plots showed more associations with toxicity above the p < 5 × 10-5 level than expected by chance. Eight SNPs reached genome-wide significance. Nipple retraction grade ≥ 2 was associated with the rs188287402 variant (p = 2.80 × 10-8), breast oedema grade ≥ 2 with rs12657177 (p = 1.12 × 10-10), rs75912034 (p = 1.12 × 10-10), rs145328458 (p = 1.06 × 10-9) and rs61966612 (p = 1.23 × 10-9), induration grade ≥ 2 with rs77311050 (p = 2.54 × 10-8) and rs34063419 (p = 1.21 × 10-8), and arm lymphoedema grade ≥ 1 with rs643644 (p = 3.54 × 10-8). Heritability estimates across significant endpoints ranged from 25% to 39%. Our study did not replicate previously reported SNPs associated with breast radiation toxicity at the pre-specified significance level.

CONCLUSIONS

This GWAS for long-term breast radiation toxicity provides further evidence for significant association of common SNPs with distinct toxicity endpoints.

Association between single nucleotide polymorphism of DNA damage repair related genes and radiosensitivity in healthy individuals

Radiosensitivity in humans can influence radiation-induced normal tissue toxicity. As radiosensitivity has a genetic predisposition, we aimed to investigate the possible association between four single nucleotide polymorphism (SNP) sites and the radiosensitivity in healthy people. We genotyped four selected SNPs: TRIP12 (rs13018957), UIMC1 (rs1700490) and POLN (rs2022302), and analyzed the association between SNP and the radiosensitivity in healthy people. We distinguished radiosensitivity by chromosome aberration analysis in healthy individuals. Healthy donors were classified into three groups based on chromosomal aberrations: resistant, normal and sensitive. Using the normal group as a reference, the genotypes CT and CC of rs13018957 (CT: OR = 26.13; CC: OR = 15.97), AA of rs1700490 (OR = 32.22) and AG of rs2022302 (OR = 13.98) were risk factors for radiosensitivity. The outcomes of the present study suggest that four SNPs are associated with radiosensitivity. This study lends insights to the underlying mechanisms of radiosensitivity and improves our ability to identify radiosensitive individuals.

Radiogenomics: a key component of precision cancer medicine

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Clinical Pharmacology of Radiotheranostics in Oncology

The combined use of diagnostic and therapeutic radioligands with the same molecular target, also known as theranostics, enables accurate patient selection, targeted therapy, and prediction of treatment response. Radioiodine, bone-seeking radioligands and norepinephrine analogs have been used for many years for diagnostic imaging and radioligand therapy of thyroid carcinoma, bone metastases, pheochromocytoma, paraganglioma, and neuroblastoma, respectively. In recent years, radiolabeled somatostatin analogs and prostate-specific membrane antigen ligands have shown clinical efficacy in the treatment of neuroendocrine tumors and prostate cancer, respectively. Several candidate compounds are targeting novel theranostic targets such as fibroblast activation protein, C-X-C chemokine receptor 4, and gastrin-releasing peptide receptor. In addition, several strategies to improve efficacy of radioligand therapy are being evaluated, including dosimetry-based dose optimization, multireceptor targeting, upregulation of target receptors, radiosensitization, pharmacogenomics, and radiation genomics. Design and evaluation of novel radioligands and optimization of dose and dose schedules, within the complex context of individualized multimodal cancer treatment, requires a multidisciplinary approach that includes clinical pharmacology. Significant increases in the use of these radiopharmaceuticals in routine oncological practice can be expected, which will have major impact on patient care as well as (radio)pharmacy utilization.

A two-stage genome-wide association study identifies novel germline genetic variations in CACNA2D3 associated with radiotherapy response in nasopharyngeal carcinoma

BACKGROUND

Radiotherapy (RT) is the standard treatment for nasopharyngeal carcinoma (NPC). However, due to individual differences in radiosensitivity, biomarkers are needed to tailored radiotherapy to cancer patients. However, comprehensive genome-wide radiogenomic studies on them are still lacking. The aim of this study was to identify genetic variants associated with radiotherapy response in patients with NPC.

METHODS

This was a large‑scale genome-wide association analysis (GWAS) including a total of 981 patients. 319 individuals in the discovery stage were genotyped for 688,783 SNPs using whole genome-wide screening microarray. Significant loci were further genotyped using MassARRAY system and TaqMan SNP assays in the validation stages of 847 patients. This study used logistic regression analysis and multiple bioinformatics tools such as PLINK, LocusZoom, LDBlockShow, GTEx, Pancan-meQTL and FUMA to examine genetic variants associated with radiotherapy efficacy in NPC.

RESULTS

After genome-wide level analysis, 19 SNPs entered the validation stage (P < 1 × 10^- 6), and rs11130424 ultimately showed statistical significance among these SNPs. The efficacy was better in minor allele carriers of rs11130424 than in major allele carriers. Further stratified analysis showed that the association existed in patients in the EBV-positive, smoking, and late-stage (III and IV) subgroups and in patients who underwent both concurrent chemoradiotherapy and induction/adjuvant chemotherapy.

CONCLUSION

Our study showed that rs11130424 in the CACNA2D3 gene was associated with sensitivity to radiotherapy in NPC patients.

TRIAL REGISTRATION NUMBER

Effect of genetic polymorphism on nasopharyngeal carcinoma chemoradiotherapy reaction, ChiCTR-OPC-14005257, Registered 18 September 2014, http://www.chictr.org.cn/showproj.aspx?proj=9546 .

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

A two-stage genome-wide association study to identify novel genetic loci associated with acute radiotherapy toxicity in nasopharyngeal carcinoma

Background

Genetic variants associated with acute side effects of radiotherapy in nasopharyngeal carcinoma (NPC) remain largely unknown.

Methods

We performed a two-stage genome-wide association analysis including a total of 1084 patients, where 319 individuals in the discovery stage were genotyped for 688,783 SNPs using whole genome-wide screening microarray. Significant variants were then validated in an independent cohort of 765 patients using the MassARRAY system. Gene mapping, linkage disequilibrium, genome-wide association analysis, and polygenic risk score were conducted or calculated using FUMA, LDBlockShow, PLINK, and PRSice software programs, respectively.

Results

Five SNPs (rs6711678, rs4848597, rs4848598, rs2091255, and rs584547) showed statistical significance after validation. Radiotherapy toxicity was more serious in mutant minor allele carriers of all five SNPs. Stratified analysis further indicated that rs6711678, rs4848597, rs4848598, and rs2091255 correlated with skin toxicity in patients of EBV positive, late stage (III and IV), receiving both concurrent chemoradiotherapy and induction/adjuvant chemotherapy, and with OR values ranging from 1.92 to 2.66. For rs584547, high occurrence of dysphagia was found in A allele carriers in both the discovery (P = 1.27 × 10^− 6, OR = 1.55) and validation (P = 0.002, OR = 4.20) cohorts. Furthermore, prediction models integrating both genetic and clinical factors for skin reaction and dysphagia were established. The area under curve (AUC) value of receiver operating characteristic (ROC) curves were 0.657 (skin reaction) and 0.788 (dysphagia).

Conclusions

Rs6711678, rs4848597, rs4848598, and rs2091255 on chromosome 2q14.2 and rs584547 were found to be novel risk loci for skin toxicity and dysphagia in NPC patients receiving radiotherapy.

Trial registration

Chinese Clinical Trial Register (registration number: ChiCTR-OPC-14005257 and CTXY-140007-2).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01631-8.

Assessment of gene expressions from squamous cell carcinoma of the head and neck to predict radiochemotherapy-related xerostomia and dysphagia

PURPOSE

We tested the hypothesis that gene expressions from biopsies of locally advanced head and neck squamous cell carcinoma (HNSCC) patients can supplement dose-volume parameters to predict dysphagia and xerostomia following primary radiochemotherapy (RCTx).

MATERIAL AND METHODS

A panel of 178 genes previously related to radiochemosensitivity of HNSCC was considered for nanoString analysis based on tumour biopsies of 90 patients with locally advanced HNSCC treated by primary RCTx. Dose-volume parameters were extracted from the parotid, submandibular glands, oral cavity, larynx, buccal mucosa, and lips. Normal tissue complication probability (NTCP) models were developed for acute, late, and for the improvement of xerostomia grade ≥2 and dysphagia grade ≥3 using a cross-validation-based least absolute shrinkage and selection operator (LASSO) approach combined with stepwise logistic regression for feature selection. The final signatures were included in a logistic regression model with optimism correction. Performance was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

NTCP models for acute and late xerostomia and the improvement of dysphagia resulted in optimism-corrected AUC values of 0.84, 0.76, and 0.70, respectively. The minimum dose to the contralateral parotid was selected for both acute and late xerostomia and the minimum dose to the larynx was selected for dysphagia improvement. For the xerostomia endpoints, the following gene expressions were selected: RPA2 (cellular response to DNA damage), TCF3 (salivary gland cells development), GBE1 (glycogen storage and regulation), and MAPK3 (regulation of cellular processes). No gene expression features were selected for the prediction of dysphagia.

CONCLUSION

This hypothesis-generating study showed the potential of improving NTCP models using gene expression data for HNSCC patients. The presented models require independent validation before potential application in clinical practice.

Redox signaling at the crossroads of human health and disease

Redox biology is at the core of life sciences, accompanied by the close correlation of redox processes with biological activities. Redox homeostasis is a prerequisite for human health, in which the physiological levels of nonradical reactive oxygen species (ROS) function as the primary second messengers to modulate physiological redox signaling by orchestrating multiple redox sensors. However, excessive ROS accumulation, termed oxidative stress (OS), leads to biomolecule damage and subsequent occurrence of various diseases such as type 2 diabetes, atherosclerosis, and cancer. Herein, starting with the evolution of redox biology, we reveal the roles of ROS as multifaceted physiological modulators to mediate redox signaling and sustain redox homeostasis. In addition, we also emphasize the detailed OS mechanisms involved in the initiation and development of several important diseases. ROS as a double‐edged sword in disease progression suggest two different therapeutic strategies to treat redox‐relevant diseases, in which targeting ROS sources and redox‐related effectors to manipulate redox homeostasis will largely promote precision medicine. Therefore, a comprehensive understanding of the redox signaling networks under physiological and pathological conditions will facilitate the development of redox medicine and benefit patients with redox‐relevant diseases.

Clinical factors affecting the determination of radiotherapy-induced skin toxicity in breast cancer

Purpose

Radiotherapy is essential for the treatment of breast cancer (BC). However, adverse effects may occur in healthy tissue, during treatment and even after several months. Although it is known that this clinical radiosensitivity is multifactorial, the factors involved are unknown yet. In this study, we evaluated the effect of these factors on the development of radiodermatitis in patients undergoing radiotherapy.

Materials and Methods

Demographic and lifestyle data collected during face-to-face interviews of 122 BC patients and data from clinical records were investigated. Most patients underwent conventional three-dimensional radiotherapy treatment. A total dose of 50 Gy was administered (2 Gy/day), followed by a boost in a tumor bed with a total dose of 18 Gy (2 Gy/day). Radiotoxicity was evaluated weekly using the Radiation Therapy Oncology Group classification system (range, 0 to 4, according to the severity).

Results

In the present study, 75.4% of patients presented acute skin toxic effects with different degrees of severity. In 25% of cases, these effects manifested at the end of the fourth week at a cumulative dose of 40 Gy. The association of grade ≥2 acute skin reactions with body mass index (BMI) and breast size and between grade 3–4 and age was positive compared with controls. However, the role of the other factors could not be confirmed.

Conclusion

Analysis of the factors related to individual radiosensitivity suggests that age, BMI and breast size play an important role in the development of acute skin toxicity during treatment. Particular attention to patients who present these characteristics would help to control treatment effectiveness and therefore optimize their quality of life.

Genetic predictors of radiation-induced morbidity in prostate cancer patients

Cancer survivors often face adverse effects of treatment, which have a significant impact on morbidity and mortality. Normal-tissue side effects following radiotherapy (RT), as one of therapeutic modalities, are common and may seriously affect quality of life which is especially important in long-term prostate cancer (PC) survivors. Upgrading in our knowledge in radiation biology have led to the better understanding that genetics plays a significant role in determining a patient's predisposition to developing late RT toxicity, leading to the new field of research called "radiogenomics". With the evolution of DNA sequencing technologies and genomic analysis, radiogenomics made an appearance as a state-of-the-art science in the field of personalized medicine with the goal of detection the genetic determinants RT adverse reactions. A single-nucleotide polymorphism (SNPs) - based assay could be used to predict the risk of RT side effects along with clinical features and treatment factors. Several SNPs have been identified that are associated with late radiation-induced morbidity in PC patients. Most importantly, these SNPs make up genes expressed in the tissues that are likely at the root of these symptoms, including the bladder, rectum, and small intestine, which are most exposed in PC RT. Furthermore, genome-wide association studies are likely to lead to an increasing number of genetic polymorphisms that can predict long-term RT complications. Finally, radiogenomics attempts to predict which PC patients will show radiosensitivity, so that radiation oncologists, as well as surgeons, can change treatment accordingly in order to reduce side effects or increase the RT effectiveness.

Risk Factors Related to Acute Radiation Dermatitis in Breast Cancer Patients After Radiotherapy: A Systematic Review and Meta-Analysis

Background

Acute radiation dermatitis (ARD) is the most common acute response after adjuvant radiotherapy in breast cancer patients and negatively affects patients’ quality of life. Some studies have reported several risk factors that can predict breast cancer patients who are at a high risk of ARD. This study aimed to identify patient- and treatment-related risk factors associated with ARD.

Methods

PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, and WanFang literature databases were searched for studies exploring the risk factors in breast cancer patients. The pooled effect sizes, relative risks (RRs), and 95% CIs were calculated using the random-effects model. Potential heterogeneity and sensitivity analyses by study design, ARD evaluation scale, and regions were also performed.

Results

A total of 38 studies composed of 15,623 breast cancer patients were included in the analysis. Of the seven available patient-related risk factors, four factors were significantly associated with ARD: body mass index (BMI) ≥25 kg/m² (RR = 1.11, 95% CI = 1.06–1.16, I² = 57.1%), large breast volume (RR = 1.02, 95% CI = 1.01–1.03, I² = 93.2%), smoking habits (RR = 1.70, 95% CI = 1.24–2.34, I² = 50.7%), and diabetes (RR = 2.24, 95% CI = 1.53–3.27, I² = 0%). Of the seven treatment-related risk factors, we found that hypofractionated radiotherapy reduced the risk of ARD in patients with breast cancer compared with that in conventional fractionated radiotherapy (RR = 0.28, 95% CI = 0.19–0.43, I² = 84.5%). Sequential boost and bolus use was significantly associated with ARD (boost, RR = 1.91, 95% CI = 1.34–2.72, I² = 92.5%; bolus, RR = 1.94, 95% CI = 1.82–4.76, I² = 23.8%). However, chemotherapy regimen (RR = 1.17, 95% CI = 0.95–1.45, I² = 57.2%), hormone therapy (RR = 1.35, 95% CI = 0.94–1.93, I² = 77.1%), trastuzumab therapy (RR = 1.56, 95% CI = 0.18–1.76, I² = 91.9%), and nodal irradiation (RR = 1.57, 95% CI = 0.98–2.53, I² = 72.5%) were not correlated with ARD. Sensitivity analysis results showed that BMI was consistently associated with ARD, while smoking, breast volume, and boost administration were associated with ARD depending on study design, country of study, and toxicity evaluation scale used. Hypofractionation was consistently shown as protective. The differences between study design, toxicity evaluation scale, and regions might explain a little of the sources of heterogeneity.

Conclusion

The results of this systematic review and meta-analysis indicated that BMI ≥ 25 kg/m² was a significant predictor of ARD and that hypofractionation was consistently protective. Depending on country of study, study design, and toxicity scale used, breast volume, smoking habit, diabetes, and sequential boost and bolus use were also predictive of ARD.

Clinical and Preclinical Outcomes of Combining Targeted Therapy With Radiotherapy

In the era of precision medicine, radiation medicine is currently focused on the precise delivery of highly conformal radiation treatments. However, the tremendous developments in targeted therapy are yet to fulfill their full promise and arguably have the potential to dramatically enhance the radiation therapeutic ratio. The increased ability to molecularly profile tumors both at diagnosis and at relapse and the co-incident progress in the field of radiogenomics could potentially pave the way for a more personalized approach to radiation treatment in contrast to the current ‘‘one size fits all’’ paradigm. Few clinical trials to date have shown an improved clinical outcome when combining targeted agents with radiation therapy, however, most have failed to show benefit, which is arguably due to limited preclinical data. Several key molecular pathways could theoretically enhance therapeutic effect of radiation when rationally targeted either by directly enhancing tumor cell kill or indirectly through the abscopal effect of radiation when combined with novel immunotherapies. The timing of combining molecular targeted therapy with radiation is also important to determine and could greatly affect the outcome depending on which pathway is being inhibited.

Correlation between the secondary structure and surface activity of β-sheet forming cationic amphiphilic peptides and their anticancer activity

Cancer is one of the main causes of death worldwide. The current cancer treatment strategies often lack selectivity for cancer cells resulting in dose-limiting adverse effects and reduced quality of life. Recently, anticancer peptides (ACPs) have emerged as an alternative treatment with higher selectivity, less adverse effects, and lower propensity for drug resistance. However, most of the current studies on the ACPs are focused on α-helical ACPs and there is lack of systematic studies on β-sheet forming ACPs. Herein we report the development of a new series of rationally designed short cationic amphiphilic β-sheet forming ACPs and their structure activity relationship. The peptides had the general formula (XY₁XY₂)₃, with X representing hydrophobic amino acids (isoleucine (I) or leucine (L)), Y₁ and Y₂ representing cationic amino acids (arginine (R) or lysine (K)). The cytotoxicity of the designed ACPs in HCT 116 colorectal cancer, HeLa cervical cancer and human dermal fibroblast (HDF) cells was assessed by MTT test. The physicochemical properties of the peptides were characterized by various techniques including RP-HPLC, LC-MS, and Circular Dichroism (CD) spectroscopy. The surface activity of the peptides at the air-water interface and their interaction with the lipid monolayers as models for cell membranes were studied by Langmuir trough. The peptides consisting of I with R and K had selective anticancer activity while the combination of L and R diminished the anticancer activity of the peptides but rendered them more toxic to HDFs. The anticancer activity of the peptides was directed by their surface activity (amphiphilicity) and their secondary structure in hydrophobic surfaces including cancer cell membranes. The selectivity of the peptides for cancer cells was a result of their higher penetration into cancer cell membranes compared to normal cell membranes. The peptides exerted their anticancer activity by disrupting the mitochondrial membranes and eventually apoptosis. The results presented in this study provide an insight into the structure-activity relationship of this class of ACPs which can be employed as guidance to design new ACPs with improved anticancer activity and lower toxicity against normal cells.

Evaluation of the action of LPSF/GQ-16 on cytokines and PPAR-γ gene expression after in vitro irradiation of peripheral blood mononuclear cells

PURPOSE

Patients submitted to radiotherapy (RT) may present in their healthy tissues surrounding the treated tumor, some typical acute inflammatory reactions induced by ionizing radiation (IR). The manifestation of inflammatory processes is a result of exacerbation of the immune system, as a response to radiation exposure, and this can be a limiting factor for RT protocols. To counteract this, some thiazolidinediones, such as LPSF/GQ-16, may be useful for modulating the patient's radioinduced inflammatory response in normal tissues. In this context, the present work aims to evaluate the activity of LPSF/GQ-16 on the levels of cytokines and the expression of the gene PPARγ in mononuclear cells irradiated in vitro, to analyze the immunomodulatory activity of the molecule and its action on radiomitigation.

MATERIALS AND METHODS

For this, blood samples from eight donors were collected and irradiated with 2 Gy, then the PBMC (peripheral blood mononuclear cells) were cultured and treated with LPSF/GQ-16. The levels of cytokines TNF-α, IFN-γ, IL-2 and IL-4 were quantified by CBA, while the genes of TNF-α, IFN-γ and PPARγ were analyzed by RT-PCR.

RESULTS

LPSF/GQ-16 significantly reduced the expression of proinflammatory cytokines (IFN-γ and TNF-α) in irradiated and nonirradiated groups. There was no significant reduction of anti-inflammatory cytokines (IL-2 and IL-4) by LPSF/GQ-16. The mRNA expression of PPAR-γ, IFN-γ and TNF-α in the presence of LPSF/GQ-16 was higher in the nonirradiated sample.

CONCLUSION

LPSF/GQ-16 showed effective activity after irradiation, with an important immunomodulatory activity in irradiated PBMCs.

Rationally designed short cationic α-helical peptides with selective anticancer activity

HYPOTHESIS

Naturally derived or synthetic anticancer peptides (ACPs) have emerged as a new generation of anticancer agents with higher selectivity for cancer cells and less propensity for drug resistance. Despite the structural diversity of ACPs, α-helix is the most common secondary structure among them. Herein we report the development of a new library of short cationic amphiphilic α-helical ACPs with selective cytotoxicity against colorectal and cervical cancer.

EXPERIMENTS

The peptides had a general formula C(XXYY)₃ with C representing amino acid cysteine (providing a -SH group for molecular conjugation), X representing hydrophobic amino acids (isoleucine (I) or leucine (L)), and Y representing cationic amino acids (arginine (R) or lysine (K)). Two variants of the peptides were synthesized by adding additional Isoleucine residues to the C-terminal and replacing the N-terminal cysteine with LC-propargylglycine (LC-G) to investigate the effect of N-terminal and C-terminal variation on the anticancer activity. The structure and physicochemical properties of the peptides were determined by RP-HPLC, LC-MS and CD spectroscopy. The cytotoxicity of the peptides in different cell lines was assessed by MTT test, cell proliferation assay and mitochondrial damage assay. The mechanism of cell selectivity of the peptides was investigated by studying their interfacial behaviour at the air/water and lipid/water interface using Langmuir trough.

FINDINGS

The peptides consisting of K residues in their hydrophilic domains exhibited more selective anticancer activity whereas the peptides containing R exhibited strong toxicity in normal cells. The anticancer activity of the peptides was a function of their helical content and their hydrophobicity. Therefore, the addition of two I residues at C-terminal enhanced the anticancer activity of the peptides by increasing their hydrophobicity and their helical content. These two variants also exhibited strong anticancer activity against colorectal cancer multicellular tumour spheroids (MCTS). The higher toxicity of the peptides in cancer cells compared to normal cells was the result of higher penetration into the negatively charged cancer cell membranes, leading to higher cellular uptake, and their cytotoxic effect was mainly exerted by damaging the mitochondrial membranes leading to apoptosis. The results from this study provide a basis for rational design of new α-helical ACPs with enhanced anticancer activity and selectivity.

Cytotoxicity, in vivo toxicity, and chemical composition of the hexane extract of Plectranthus amboinicus (Lour.) Spreng

Cancer is a universal health issue, and many anticancer therapeutic drugs have been isolated from natural products. This study analyzed the cytotoxic and apoptotic activity of Plectranthus amboinicus leaf hexane (PALH) extract in MDA-MB-231 (median inhibitory concentration [IC₅₀] = 39.26 μg/mL) and MCF7 (IC₅₀ = 89.05 μg/mL) breast cancer cell lines. Cells appeared rounded and shrunken, indicating morphological changes due to apoptosis induction. The primary constituent of PALH was phenol, 5-methyl-2-(1-methylethyl) (44%). PALH extract treatment increased the percentage of late apoptotic cells in the MDA-MB231 cell line (58% ± 1.5% at 200 μg/mL) compared to the control group, as evidenced by the activated caspase-3 and caspase-7 identified and captured by fluorescence microscopy. The relative migration rate in MDA-MB-231 cells treated with 10 μg/mL of PALH extract for 48 h was significantly lower compared to the control group. Analysis of acute (2000 mg/kg/BW) and subacute (250 and 500 mg/kg/BW) toxicity of PALH extract in mice showed no mortality or adverse effects in the kidney and liver histology compared to the control group. PALH extract can be considered nontoxic as it does not cause any adverse changes and so can be proposed as a potential breast anticancer agent.

Brain Damage and Patterns of Neurovascular Disorder after Ionizing Irradiation. Complications in Radiotherapy and Radiation Combined Injury

Exposure to ionizing radiation, mechanical trauma, toxic chemicals or infections, or combinations thereof (i.e., combined injury) can induce organic injury to brain tissues, the structural disarrangement of interactive networks of neurovascular and glial cells, as well as on arrays of the paracrine and systemic destruction. This leads to subsequent decline in cognitive capacity and decompensation of mental health. There is an ongoing need for improvement in mitigating and treating radiation- or combined injury-induced brain injury. Cranial irradiation per se can cause a multifactorial encephalopathy that occurs in a radiation dose- and time-dependent manner due to differences in radiosensitivity among the various constituents of brain parenchyma and vasculature. Of particular concern are the radiosensitivity and inflammation susceptibility of: 1. the neurogenic and oligodendrogenic niches in the subependymal and hippocampal domains; and 2. the microvascular endothelium. Thus, cranial or total-body irradiation can cause a plethora of biochemical and cellular disorders in brain tissues, including: 1. decline in neurogenesis and oligodendrogenesis; 2. impairment of the blood-brain barrier; and 3. ablation of vascular capillary. These changes, along with cerebrovascular inflammation, underlie different stages of encephalopathy, from the early protracted stage to the late delayed stage. It is evident that ionizing radiation combined with other traumatic insults such as penetrating wound, burn, blast, systemic infection and chemotherapy, among others, can exacerbate the radiation sequelae (and vice versa) with increasing severity of neurogenic and microvascular patterns of radiation brain damage.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

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.

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.

Thymol induces mitochondrial pathway-mediated apoptosis via ROS generation, macromolecular damage and SOD diminution in A549 cells

BACKGROUND

Thymol is a monoterpene phenol found in thyme species plants. The present study was carried out to investigate the effect of thymol and its molecular mechanism on non-small lung cancer (A549) cells.

METHODS

The cytotoxic effect of thymol on A549 cells was assessed via MTT assay. ROS production, macromolecular damage, apoptosis were determined using DCF-DA, PI, AO/EtBr stains, respectively. ROS-dependent effect of thymol was confirmed using NAC. The expression of caspase-9, Bcl-2, Bax and cell cycle profile was analyzed via western blot and FACS, respectively.

RESULTS

The antiproliferative effect of thymol on A549 cells was found to be both dose and time dependent with IC₅₀ values of 112 μg/ml (745 μM) at 24 h. Thymol treatment favored apoptotic cell death and caused G0/G1 cell cycle arrest. It mediated cellular and nuclear morphological changes, phosphatidylserine translocation, and mitochondrial membrane depolarization. Additionally, upregulation of Bax, downregulation of Bcl-2, and apoptotic fragmented DNA were also observed. Thymol induced ROS by reducing the SOD level which was confirmed via in vitro and in silico analysis. Furthermore, the levels of lipid peroxides and protein carbonyl content were elevated in thymol-treated groups. Notably, N-acetyl cysteine pretreatment reversed the efficacy of thymol on A549 cells. Moreover, thymol-treated human PBMC cells did not show any significant cytotoxicity.

CONCLUSION

Overall, our results confirmed that thymol can act as a safe and potent therapeutic agent to treat NSCLC.

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

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

Pathogenic Germline Mutations in DNA Repair Genes in Combination With Cancer Treatment Exposures and Risk of Subsequent Neoplasms Among Long-Term Survivors of Childhood Cancer

PURPOSE

To investigate cancer treatment plus pathogenic germline mutations (PGMs) in DNA repair genes (DRGs) for identification of childhood cancer survivors at increased risk of subsequent neoplasms (SNs).

METHODS

Whole-genome sequencing was performed on blood-derived DNA from survivors in the St Jude Lifetime Cohort. PGMs were evaluated in 127 genes from 6 major DNA repair pathways. Cumulative doses of chemotherapy and body region-specific radiotherapy (RT) were abstracted from medical records. Relative rates (RRs) and 95% CIs of SNs by mutation status were estimated using multivariable piecewise exponential models.

RESULTS

Of 4,402 survivors, 495 (11.2%) developed 1,269 SNs. We identified 538 PGMs in 98 DRGs (POLG, MUTYH, ERCC2, and BRCA2, among others) in 508 (11.5%) survivors. Mutations in homologous recombination (HR) genes were significantly associated with an increased rate of subsequent female breast cancer (RR, 3.7; 95% CI, 1.8 to 7.7), especially among survivors with chest RT ≥ 20 Gy (RR, 4.4; 95% CI, 1.6 to 12.4), or with a cumulative dose of anthracyclines in the second or third tertile (RR, 4.4; 95% CI, 1.7 to 11.4). Mutations in HR genes were also associated with an increased rate of subsequent sarcoma among those who received alkylating agent doses in the third tertile (RR, 14.9; 95% CI, 4.0 to 38.0). Mutations in nucleotide excision repair genes were associated with subsequent thyroid cancer for those treated with neck RT ≥ 30 Gy (RR, 12.9; 95% CI, 1.6 to 46.6) with marginal statistical significance.

CONCLUSION

Our study provides novel insights regarding the contribution of genetics, in combination with known treatment-related risks, for the development of SNs. These findings have the potential to facilitate identification of high-risk survivors who may benefit from genetic counseling and/or testing of DRGs, which may further inform personalized cancer surveillance and prevention strategies.

Genome-wide association study identifies genetic susceptibility loci and pathways of radiation-induced acute oral mucositis

Background

Radiation-induced oral mucositis (OM) is one of the most common acute complications for head and neck cancer. Severe OM is associated with radiation treatment breaks, which harms successful tumor management. Radiogenomics studies have indicated that genetic variants are associated with adverse effects of radiotherapy.

Methods

A large-scale genome-wide scan was performed in 1467 nasopharyngeal carcinoma patients, including 753 treated with 2D-CRT from Genetic Architecture of the Radiotherapy Toxicity and Prognosis (GARTP) cohort and 714 treated with IMRT (192 from the GARTP and 522 newly recruited). Subgroup analysis by radiotherapy technique was further performed in the top associations. We also performed physical and regulatory mapping of the risk loci and gene set enrichment analysis of the candidate target genes.

Results

We identified 50 associated genomic loci and 64 genes via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping and gene-based analysis, and 36 of these loci were replicated in subgroup analysis. Interestingly, one of the top loci located in TNKS, a gene relevant to radiation toxicity, was associated with increased OM risk with OR = 3.72 of the lead SNP rs117157809 (95% CI 2.10–6.57; P = 6.33 × 10⁻⁶). Gene set analyses showed that the 64 candidate target genes were enriched in the biological processes of regulating telomere capping and maintenance and telomerase activity (Top P = 7.73 × 10⁻⁷).

Conclusions

These results enhance the biological understanding of radiotherapy toxicity. The association signals enriched in telomere function regulation implicate the potential underlying mechanism and warrant further functional investigation and potential individual radiotherapy applications.

A review of original articles published in the emerging field of radiomics

PURPOSE

To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018.

METHODS

We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name. Qualitative and quantitative analyses were performed for the manually extracted data for identification and visualization of the trends in radiomics research.

RESULTS

The annual growth rate in the number of published papers was 177.82% (p < 0.001). The characteristics and trends of research hotspots in the field of radiomics were clarified and visualized in this study. It was found that the field of radiomics is at a more mature stage for lung, breast, and prostate cancers than for other sites. Radiomics studies primarily focused on radiological characterization (215) and monitoring (182). Logistic regression and LASSO were the two most commonly used techniques for feature selection. Non-clinical researchers without a medical background dominated radiomics studies (70.52%), the vast majority of which only highlighted positive results (97.80%) while downplaying negative findings.

CONCLUSIONS

The reporting of quantifiable knowledge about the characteristics and trajectories of radiomics can inform researchers about the gaps in the field of radiomics and guide its future direction.

Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.

Developing more sensitive genomic approaches to detect radioresponse in precision radiation oncology: From tissue DNA analysis to circulating tumor DNA

Despite the common application and considerable efforts to achieve precision radiotherapy (RT) in several types of cancer, RT has not yet entered the era of precision medicine; the ability to predict radiosensitivity and treatment responses in tumors and normal tissues is lacking. Therefore, development of genome-based methods for individual prognosis in radiation oncology is urgently required. Traditional DNA sequencing requires tissue samples collected during invasive operations; therefore, repeated tests are nearly impossible. Intra- and inter-tumoral heterogeneity may undermine the predictive power of a single assay from tumor samples. In contrast, analysis of circulating tumor DNA (ctDNA) allows for non-invasive and near real-time sampling of tumors. By investigating the genetic composition of tumors and monitoring dynamic changes during treatment, ctDNA analysis may potentially be clinically valuable in prediction of treatment responses prior to RT, surveillance of responses during RT, and evaluation of residual disease following RT. As a biomarker for RT response, ctDNA profiling may guide personalized treatments. In this review, we will discuss approaches of tissue DNA sequencing and ctDNA detection and summarize their clinical applications in both traditional RT and in combination with immunotherapy.

The Implications of Genetic Testing on Radiation Therapy Decisions: A Guide for Radiation Oncologists

The advent of affordable and rapid next-generation DNA sequencing technology, along with the US Supreme Court ruling invalidating gene patents, has led to a deluge of germline and tumor genetic variant tests that are being rapidly incorporated into clinical cancer decision-making. A major concern for clinicians is whether the presence of germline mutations may increase the risk of radiation toxicity or secondary malignancies. Because scarce clinical data exist to inform decisions at this time, the American Society for Radiation Oncology convened a group of radiation science experts and clinicians to summarize potential issues, review relevant data, and provide guidance for adult patients and their care teams regarding the impact, if any, that genetic testing should have on radiation therapy recommendations. During the American Society for Radiation Oncology workshop, several main points emerged, which are discussed in this manuscript: (1) variants of uncertain significance should be considered nondeleterious until functional genomic data emerge to demonstrate otherwise; (2) possession of germline alterations in a single copy of a gene critical for radiation damage responses does not necessarily equate to increased risk of radiation-induced toxicity; (3) deleterious ataxia-telangiesctasia gene mutations may modestly increase second cancer risk after radiation therapy, and thus follow-up for these patients after indicated radiation therapy should include second cancer screening; (4) conveying to patients the difference between relative and absolute risk is critical to decision-making; and (5) more work is needed to assess the impact of tumor somatic alterations on the probability of response to radiation therapy and the potential for individualization of radiation doses. Data on radiosensitivity related to specific genetic mutations is also briefly discussed.

Genome-Wide Association Study of Susceptibility Loci for Radiation-Induced Brain Injury

BACKGROUND

Radiation-induced brain injury is a nonnegligible issue in the management of cancer patients treated by partial or whole brain irradiation. In particular, temporal lobe injury (TLI), a deleterious late complication in nasopharyngeal carcinoma, greatly affects the long-term life quality of these patients. Although genome-wide association studies (GWASs) have successfully identified single nucleotide polymorphisms (SNPs) associated with radiation toxicity, genetic variants contributing to the radiation-induced brain injury have not yet been assessed.

METHODS

We recruited and performed follow-up for a prospective observational cohort, Genetic Architecture of Radiotherapy Toxicity and Prognosis, using magnetic resonance imaging for TLI diagnosis. We conducted genome-wide association analysis in 1082 patients and validated the top associations in two independent cohorts of 1119 and 741 patients, respectively. All statistical tests were two-sided.

RESULTS

We identified a promoter variant rs17111237 (A > G, minor allele frequency [MAF] = 0.14) in CEP128 associated with TLI risk (hazard ratio = 1.45, 95% confidence interval = 1.26 to 1.66, Pcombined=3.18 × 10-7) which is in moderate linkage disequilibrium (LD) with rs162171 (MAF = 0.18, R2 = 0.69), the top signal in CEP128 (hazard ratio = 1.46, 95% confidence interval = 1.29-1.66, Pcombined= 6.17 × 10-9). Combining the clinical variables with the top SNP, we divided the patients into different subgroups with varying risk with 5-year TLI-free rates ranging from 33.7% to 95.5%. CEP128, a key component of mother centriole, tightly interacts with multiple radiation-resistant genes and plays an important role in maintaining the functional cilia, which otherwise will lead to a malfunction of the neural network. We found that A > G alteration at rs17111237 impaired the promoter activity of CEP128 and knockdown of CEP128 decreased the clonogenic cell survival of U87 cells under radiation. Noteworthy, 12.7% (27/212) of the GWAS-based associated genes (P < .001) were enriched in the neurogenesis pathway.

CONCLUSIONS

This three-stage study is the first GWAS of radiation-induced brain injury that implicates the genetic susceptibility gene CEP128 involved in TLI development and provides the novel insight into the underlying mechanisms of radiation-induced brain injury.

Precision Radiotherapy and Radiation Risk Assessment: How Do We Overcome Radiogenomic Diversity?

Precision medicine is a rapidly developing area that aims to deliver targeted therapies based on individual patient characteristics. However, current radiation treatment is not yet personalized; consequently, there is a critical need for specific patient characteristics of both tumor and normal tissues to be fully incorporated into dose prescription. Furthermore, current risk assessment following environmental, occupational, or accidental exposures to radiation is based on population effects, and does not account for individual diversity underpinning radiosensitivity. The lack of personalized approaches in both radiotherapy and radiation risk assessment resulted in the current situation where a population-based model, effective dose, is being used. In this review article, to stimulate scientific discussion for precision medicine in both radiotherapy and radiation risk assessment, we propose a novel radiological concept and metric - the personalized dose and the personalized risk index - that incorporate individual physiological, lifestyle-related and genomic variations and radiosensitivity, outlining the potential clinical application for precision medicine. We also review on recent progress in both genomics and biobanking research, which is promising for providing novel insights into individual radiosensitivity, and for creating a novel conceptual framework of precision radiotherapy and radiation risk assessment.

Association of GSTP1 and ERCC1 polymorphisms with toxicity in locally advanced head and neck cancer platinum-based chemoradiotherapy treatment

BACKGROUND

Platinum-based chemoradiotherapy (CRT) is the standard treatment for locally advanced head and neck squamous-cell carcinomas (HNSCC), and most patients experience serious toxicities. The aim of this study was to investigate the association between candidate genes involved in radiation/platinum pathways and acute toxicity of CRT to determine the predictive value of these polymorphisms for toxicity.

METHODS

Thirty-six selected single nucleotide polymorphisms (SNPs) in 29 genes were genotyped in 110 patients treated with cisplatin-based CRT. DNA was obtained from blood samples, and SNP analysis was performed using a MassARRAY iPLEX Gold (Sequenom) method.

RESULTS

Patients with ERCC1 rs11615-C allele (P = .0066), ERCC1 rs735482-C allele (P = .0204), and ERCC4 rs1799801-C allele (P = .0286) had lower risk of grade 2-3 hematologic toxicity. In addition, the presence of G allele of GSTP1 was associated with a significantly lower risk of severe dysphagia (P = .0004).

CONCLUSION

Polymorphisms in ERCC1 and GSTP1 may act as prognostic factors of acute toxicity during treatment with CRT in HNSCC patients.

Novel insights into the role of aptamers in the fight against cancer

PURPOSE

Aptamers are a class of single-stranded nucleic acid (DNA or RNA) oligonucleotides that are screened in vitro by a technique called systematic evolution of ligands by exponential enrichment (SELEX). They have stable three-dimensional structures that can bind to various targets with high affinity and specificity. Due to distinct properties such as easy synthesis, high stability, small size, low toxicity and immunogenicity, they have been largely studied as anticancer agents/tools. Consequently, aptamers are starting to play important roles in disease prevention, diagnosis and therapy. This review focuses on studies that evaluated the effect of aptamers on various aspects of cancer therapy. It also provides novel and unique insights into the role of aptamers on the fight against cancer.

METHODS

We reviewed literatures about the role of aptamers against cancer from PUBMED databases in this article.

RESULTS

Here, we summarized the role of aptamers on the fight against cancer in a unique point of view. Meanwhile, we presented novel ideas such as aptamer-pool-drug conjugates for the treatment of refractory cancers.

CONCLUSIONS

Aptamers and antibodies should form a "coalition" against cancers to maximize their advantages and minimize disadvantages.

Discovery of Metabolic Biomarkers Predicting Radiation Therapy Late Effects in Prostate Cancer Patients

Patients presenting with prostate cancers undergo clinical staging evaluations to determine the extent of disease to guide therapeutic recommendations. Management options may include watchful waiting, surgery, or radiation therapy. Thus, initial risk stratification of prostate cancer patients is important for achieving optimal therapeutic results or cancer cure and preservation of quality of life. Predictive biomarkers for risks of complications or late effects of treatment are needed to inform clinical decisions for treatment selection. Here, we analyzed pre-treatment plasma metabolites in a cohort of prostate cancer patients (N = 99) treated with Stereotactic Body Radiation Therapy (SBRT) at Medstar-Georgetown University Hospital in a longitudinal, quality-of-life study to determine if individuals experiencing radiation toxicities can be identified by a molecular profile in plasma prior to treatment. We used a multiple reaction mass spectrometry-based molecular phenotyping of clinically annotated plasma samples in a retrospective outcome analysis to identify candidate biomarker panels correlating with adverse clinical outcomes following radiation therapy. We describe the discovery of candidate biomarkers, based on small molecule metabolite panels, showing high correlations (AUCs ≥ 95%) with radiation toxicities, suitable for validation studies in an expanded cohort of patients.

A review of radiation genomics: integrating patient radiation response with genomics for personalised and targeted radiation therapy

Background

The success of radiation therapy for cancer patients is dependent on the ability to deliver a total tumouricidal radiation dose capable of eradicating all cancer cells within the clinical target volume, however, the radiation dose tolerance of the surrounding healthy tissues becomes the main dose-limiting factor. The normal tissue adverse effects following radiotherapy are common and significantly impact the quality of life of patients. The likelihood of developing these adverse effects following radiotherapy cannot be predicted based only on the radiation treatment parameters. However, there is evidence to suggest that some common genetic variants are associated with radiotherapy response and the risk of developing adverse effects. Radiation genomics is a field that has evolved in recent years investigating the association between patient genomic data and the response to radiation therapy. This field aims to identify genetic markers that are linked to individual radiosensitivity with the potential to predict the risk of developing adverse effects due to radiotherapy using patient genomic information. It also aims to determine the relative radioresponse of patients using their genetic information for the potential prediction of patient radiation treatment response.

Methods and materials

This paper reports on a review of recent studies in the field of radiation genomics investigating the association between genomic data and patients response to radiation therapy, including the investigation of the role of genetic variants on an individual’s predisposition to enhanced radiotherapy radiosensitivity or radioresponse.

Conclusion

The potential for early prediction of treatment response and patient outcome is critical in cancer patients to make decisions regarding continuation, escalation, discontinuation, and/or change in treatment options to maximise patient survival while minimising adverse effects and maintaining patients’ quality of life.

Treatment data and technical process challenges for practical big data efforts in radiation oncology

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

Association Between Inflammatory Biomarker C-Reactive Protein and Radiotherapy-Induced Early Adverse Skin Reactions in a Multiracial/Ethnic Breast Cancer Population

Purpose This study examined an inflammatory biomarker, high-sensitivity C-reactive protein (hsCRP), in radiotherapy (RT)-induced early adverse skin reactions or toxicities in breast cancer. Patients and Methods Between 2011 and 2013, 1,000 patients with breast cancer who underwent RT were evaluated prospectively for skin toxicities through the National Cancer Institute-funded Wake Forest University Community Clinical Oncology Program Research Base. Pre- and post-RT plasma hsCRP levels and Oncology Nursing Society skin toxicity criteria (0 to 6) were used to assess RT-induced skin toxicities. Multivariable logistic regression analyses were applied to ascertain the associations between hsCRP and RT-induced skin toxicities after adjusting for potential confounders. Results The study comprised 623 white, 280 African American, 64 Asian/Pacific Islander, and 33 other race patients; 24% of the patients were Hispanic, and 47% were obese. Approximately 42% and 15% of patients developed RT-induced grade 3+ and 4+ skin toxicities, respectively. The hsCRP levels differed significantly by race and body mass index but not by ethnicity. In multivariable analysis, grade 4+ skin toxicity was significantly associated with obesity (odds ratio [OR], 2.17; 95% CI, 1.41 to 3.34], post-RT hsCRP ≥ 4.11 mg/L (OR, 1.61; 95% CI, 1.07 to 2.44), and both factors combined (OR, 3.65; 95% CI, 2.18 to 6.14). Above-median post-RT hsCRP (OR, 1.93; 95% CI, 1.03 to 3.63), and change in hsCRP (OR, 2.80; 95% CI, 1.42 to 5.54) were significantly associated with grade 4+ skin toxicity in nonobese patients. Conclusion This large prospective study is the first to our knowledge of hsCRP as an inflammatory biomarker in RT-induced skin toxicities in breast cancer. We demonstrate that nonobese patients with elevated RT-related change in hsCRP levels have a significantly increased risk of grade 4+ skin toxicity. The outcomes may help to predict RT responses and guide decision making.

Data Analysis in Radiotherapy Treatments

Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.

Single nucleotide polymorphisms in ATM, TNF-α and IL6 genes and risk of radiotoxicity in breast cancer patients

Although oncological therapies have improved in the last decades, breast cancer (BC) remains a serious health problem worldwide. Radiotherapy (RT) is one of the most frequently used treatments for cancer aimed at eliminating tumor cells. However, it can also alter the surrounding normal tissue, especially the skin, and patient reactions may vary as a result of extrinsic and intrinsic factors. We evaluated the association of gene polymorphisms ATM Asp1853Asn, IL-6 G-174C and TNF-α G-308A involved in central phenotype pathways and development of individual radiosensitivity in BC patients with an exacerbated response to RT. Although univariate analysis results did not show a significant association with this trait, the interaction analysis between polymorphisms showed an increased risk of patients presenting wild-type TNF-α G-308A genotype and mutant IL-6 G-174C genotype, and heterozygous TNF-α G-308A genotype and heterozygous IL-6G-174C genotype. On the other hand, our results showed that breast size and patient age influenced the determination of RT-associated effects. Considering that the trait is multifactorial, other significant elements for the determination of individual radiosensitivity should be considered, together with the establishment of specific polymorphic variants.

New physical approaches to treat cancer stem cells: a review

Cancer stem cells (CSCs) have been identified as the main center of tumor therapeutic resistance. They are highly resistant against current cancer therapy approaches particularly radiation therapy (RT). Recently, a wide spectrum of physical methods has been proposed to treat CSCs, including high energetic particles, hyperthermia (HT), nanoparticles (NPs) and combination of these approaches. In this review article, the importance and benefits of the physical CSCs therapy methods such as nanomaterial-based heat treatments and particle therapy will be highlighted.

Older Age, Early Symptoms and Physical Function are Associated with the Severity of Late Symptom Clusters for Men Undergoing Radiotherapy for Prostate Cancer

AIMS

To identify symptom clusters and predisposing factors associated with long-term symptoms and health-related quality of life after radiotherapy in men with prostate cancer.

MATERIALS AND METHODS

Patient-reported outcomes (PROs) data from the Medical Research Council RT01 radiotherapy with neoadjuvant androgen deprivation therapy trial of 843 patients were used. PROs were collected over 5 years with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI) and the 36 item Short-Form Health Survey (SF-36). Symptom clusters were explored using hierarchical cluster analysis. The association of treatment dose, baseline patient characteristics and early symptom clusters with the change in severity of PROs over 3 years was investigated with multivariate linear mixed effects models.

RESULTS

Seven symptom clusters of three or more symptoms were identified. The clusters were stable over time. The longitudinal profiles of symptom clusters showed the onset of acute symptoms during treatment for all symptom clusters and significant recovery by 6 months. Some clusters, such as physical health and sexual function, were adversely affected more than others by androgen deprivation therapy, and were less likely to return to pretreatment levels over time. Older age was significantly associated with decreased long-term physical function, physical health and sexual function (P < 0.001). Both baseline and acute symptom clusters were significant antecedents for impaired function and health-related quality of life at 3 years.

CONCLUSIONS

Men with poorer physical function and health before or during treatment were more likely to report poorer PROs at year 3. Early assessment using PROs and lifestyle interventions should be used to identify those with higher needs and provide targeted rehabilitation and symptom management.

Precision Oncology and Genomically Guided Radiation Therapy: A Report From the American Society for Radiation Oncology/American Association of Physicists in Medicine/National Cancer Institute Precision Medicine Conference

PURPOSE

To summarize important talking points from a 2016 symposium focusing on real-world challenges to advancing precision medicine in radiation oncology, and to help radiation oncologists navigate the practical challenges of precision, radiation oncology.

METHODS AND MATERIALS

The American Society for Radiation Oncology, American Association of Physicists in Medicine, and National Cancer Institute cosponsored a meeting on precision medicine in radiation oncology. In June 2016 numerous scientists, clinicians, and physicists convened at the National Institutes of Health to discuss challenges and future directions toward personalized radiation therapy. Various breakout sessions were held to discuss particular components and approaches to the implementation of personalized radiation oncology. This article summarizes the genomically guided radiation therapy breakout session.

RESULTS

A summary of existing genomic data enabling personalized radiation therapy, ongoing clinical trials, current challenges, and future directions was collected. The group attempted to provide both a current overview of data that radiation oncologists could use to personalize therapy, along with data that are anticipated in the coming years. It seems apparent from the provided review that a considerable opportunity exists to truly bring genomically guided radiation therapy into clinical reality.

CONCLUSIONS

Genomically guided radiation therapy is a necessity that must be embraced in the coming years. Incorporating these data into treatment recommendations will provide radiation oncologists with a substantial opportunity to improve outcomes for numerous cancer patients. More research focused on this topic is needed to bring genomic signatures into routine standard of care.

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.