Functional promoter variant rs2868371 of HSPB1 is associated with risk of radiation pneumonitis after chemoradiation for non-small cell lung cancer

Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis

OBJECTIVE

Radiation pneumonitis (RP) is the major dose-limiting toxicity of thoracic radiotherapy. This study aimed to developed a dual-omics (single nucleotide polymorphisms, SNP and dosiomics) prediction model for symptomatic RP.

MATERIALS AND METHODS

The potential SNPs, which are of significant difference between the RP grade ≥ 3 group and the RP grade ≤ 1 group, were selected from the whole exome sequencing SNPs using the Fisher's exact test. Patients with lung cancer who received thoracic radiotherapy at our institution from 2009 to 2016 were enrolled for SNP selection and model construction. The factorization machine (FM) method was used to model the SNP epistasis effect, and to construct the RP prediction model (SNP-FM). The dosiomics features were extracted, and further selected using the minimum redundancy maximum relevance (mRMR) method. The selected dosiomics features were added to the SNP-FM model to construct the dual-omics model.

RESULTS

For SNP screening, peripheral blood samples of 28 patients with RP grade ≥ 3 and the matched 28 patients with RP grade ≤ 1 were sequenced. 81 SNPs were of significant difference (P < 0.015) and considered as potential SNPs. In addition, 21 radiation toxicity related SNPs were also included. For model construction, 400 eligible patients (including 108 RP grade ≥ 2) were enrolled. Single SNP showed no strong correlation with RP. On the other hand, the SNP-SNP interaction (epistasis effect) of 19 SNPs were modeled by the FM method, and achieved an area under the curve (AUC) of 0.76 in the testing group. In addition, 4 dosiomics features were selected and added to the model, and increased the AUC to 0.81.

CONCLUSIONS

A novel dual-omics model by synergizing the SNP epistasis effect with dosiomics features was developed. The enhanced the RP prediction suggested its promising clinical utility in identifying the patients with severe RP during thoracic radiotherapy.

Emerging Roles of Circulating Tumor DNA for Increased Precision and Personalization in Radiation Oncology

Recent breakthroughs in circulating tumor DNA (ctDNA) technologies present a compelling opportunity to combine this emerging liquid biopsy approach with the field of radiogenomics, the study of how tumor genomics correlate with radiotherapy response and radiotoxicity. Canonically, ctDNA levels reflect metastatic tumor burden, although newer ultrasensitive technologies can be used after curative-intent radiotherapy of localized disease to assess ctDNA for minimal residual disease (MRD) detection or for post-treatment surveillance. Furthermore, several studies have demonstrated the potential utility of ctDNA analysis across various cancer types managed with radiotherapy or chemoradiotherapy, including sarcoma and cancers of the head and neck, lung, colon, rectum, bladder, and prostate . Additionally, because peripheral blood mononuclear cells are routinely collected alongside ctDNA to filter out mutations associated with clonal hematopoiesis, these cells are also available for single nucleotide polymorphism analysis and could potentially be used to detect patients at high risk for radiotoxicity. Lastly, future ctDNA assays will be utilized to better assess locoregional MRD in order to more precisely guide adjuvant radiotherapy after surgery in cases of localized disease, and guide ablative radiotherapy in cases of oligometastatic disease.

Radiogenomics in lung cancer: Where are we?

Huge technological and biomedical advances have improved the survival and quality of life of lung cancer patients treated with radiotherapy. However, during treatment planning, a probability that the patient will experience adverse effects is assumed. Radiotoxicity is a complex entity that is largely dose-dependent but also has important intrinsic factors. One of the most studied is the genetic variants that may be associated with susceptibility to the development of adverse effects of radiotherapy. This review aims to present the current status of radiogenomics in lung cancer, integrating results obtained in association studies of SNPs (single nucleotide polymorphisms) related to radiotherapy toxicities. We conclude that despite numerous publications in this field, methodologies and endpoints vary greatly, making comparisons between studies difficult. Analyzing SNPs from the candidate gene approach, together with the study in cohorts limited by the sample size, has complicated the possibility of having validated results. All this delays the incorporation of genetic biomarkers in predictive models for clinical application. Thus, from all analysed SNPs, only 12 have great potential as esophagitis genetic risk factors and deserve further exploration. This review highlights the efforts that have been made to date in the radiogenomic study of radiotoxicity in lung cancer.

The Normal, the Radiosensitive, and the Ataxic in the Era of Precision Radiotherapy: A Narrative Review

(1) Background: radiotherapy is a cornerstone of cancer treatment. When delivering a tumoricidal dose, the risk of severe late toxicities is usually kept below 5% using dose-volume constraints. However, individual radiation sensitivity (iRS) is responsible (with other technical factors) for unexpected toxicities after exposure to a dose that induces no toxicity in the general population. Diagnosing iRS before radiotherapy could avoid unnecessary toxicities in patients with a grossly normal phenotype. Thus, we reviewed iRS diagnostic data and their impact on decision-making processes and the RT workflow; (2) Methods: following a description of radiation toxicities, we conducted a critical review of the current state of the knowledge on individual determinants of cellular/tissue radiation; (3) Results: tremendous advances in technology now allow minimally-invasive genomic, epigenetic and functional testing and a better understanding of iRS. Ongoing large translational studies implement various tests and enriched NTCP models designed to improve the prediction of toxicities. iRS testing could better support informed radiotherapy decisions for individuals with a normal phenotype who experience unusual toxicities. Ethics of medical decisions with an accurate prediction of personalized radiotherapy's risk/benefits and its health economics impact are at stake; (4) Conclusions: iRS testing represents a critical unmet need to design personalized radiotherapy protocols relying on extended NTCP models integrating iRS.

Treatment-related pulmonary adverse events induced by chemoradiation and Durvalumab affect survival in locally advanced non-small cell lung cancer

PURPOSE

We compared treatment-related pulmonary adverse events (TRPAE), progression-free survival (PFS), and overall survival (OS) among locally advanced non-small cell lung cancer (NSCLC) patients who received concurrent chemoradiotherapy (CRT) versus CRT followed by immune check point inhibitor (ICI) immunotherapy (CRTI).

MATERIALS AND METHODS

TRPAE was defined as any pulmonary events as defined in CTCAE v.5 occurring within 12 months after completion of radiotherapy. Outcomes were compared between CRT and CTRI by Cox proportional hazard regression and Kaplan-Meier analyses. We also assessed if TRPAE-induced discontinuation of ICI affected survival.

RESULTS

We analyzed 326 patients treated between July 2010 and November 2019; 195 patients received CRT and 131 received CRTI. The incidences of severe grade ≥ 3 TRPAE were similar between the two groups, however, symptomatic TRPAE was almost doubled in CRTI group (65.7 % CTRI vs 35.9 % CRT, P < 0.0001). The rates of 4-year OS and PFS were 54.5 % vs 36.7 % (P = 0.0003) and 43.8 % vs 35.8 % (P = 0.038) in CRT + Durvalumab and CRT group, respectively. Receipt of ICI Durvalumab was associated with better 4-year OS (HR 0.53, 95 % CI 0.36-0.78, P = 0.001) and PFS (HR 0.55, 95 % CI 0.38-0.80, P = 0.002). Patients who discontinued ICI because of TRPAE had worse 4-year OS (P = 0.001) and higher rates of distant metastasis (P = 0.003) than those who completed planned ICI after developing TRPAE.

CONCLUSION

CRT followed by adjuvant ICI led to improved 4-year OS and PFS consistent with published data. CRTI was associated with higher incidence of grade ≥ 2 TRPAE in both high and low mean lung dose groups without significant difference in grade ≥ 3 TRPAE. Discontinuation of ICI due to TRPAE was associated with poorer OS and distant disease control than completing ICI as planned after developing TRPAE.

Improving Patients' Life Quality after Radiotherapy Treatment by Predicting Late Toxicities

Personalized treatment and precision medicine have become the new standard of care in oncology and radiotherapy. Because treatment outcomes have considerably improved over the last few years, permanent side-effects are becoming an increasingly significant issue for cancer survivors. Five to ten percent of patients will develop severe late toxicity after radiotherapy. Identifying these patients before treatment start would allow for treatment adaptation to minimize definitive side effects that could impair their long-term quality of life. Over the last decades, several tests and biomarkers have been developed to identify these patients. However, out of these, only the Radiation-Induced Lymphocyte Apoptosis (RILA) assay has been prospectively validated in multi-center cohorts. This test, based on a simple blood draught, has been shown to be correlated with late radiation-induced toxicity in breast, prostate, cervical and head and neck cancer. It could therefore greatly improve decision making in precision radiation oncology. This literature review summarizes the development and bases of this assay, as well as its clinical results and compares its results to the other available assays.

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

PURPOSE

Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.

METHODS

A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.

RESULTS

Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings.

CONCLUSION

A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.

Dosimetric Factors and Radiomics Features Within Different Regions of Interest in Planning CT Images for Improving the Prediction of Radiation Pneumonitis

PURPOSE

This study aimed to establish machine learning models using dosimetric factors and radiomics features within 5 regions of interest (ROIs) in treatment planning computed tomography images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥2).

METHODS AND MATERIALS

This study retrospectively collected data on 79 patients with lung cancer (25 RP ≥2) who underwent chemoradiotherapy between 2015 and 2018. We defined 5 ROIs in planning computed tomography images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV and TL-PTV and the mean dose within the other ROIs. A total of 1924 radiomics features were extracted from all 5 ROIs. We selected the best predictors for classifying 2 groups of patients using a sequential backward elimination support vector machine model. A permutation test was used to assess its statistical significance (P < .05).

RESULTS

The best predictors for symptomatic RP were the combination of 11 radiomics features, 5 dosimetric factors, age, and T stage, achieving an area under the curve (AUC) of 0.94 (95% confidence interval [CI], 0.85-1) (accuracy, 90%; sensitivity, 80% [95% CI, 44%-96%]; specificity, 95% [95% CI, 73%-100%]; P = 8 × 10-4). The clinical characteristics, dosimetric factors, and their combination showed limited predictive power (accuracy, 63.3%, 70%, and 70%; AUC [95% CI]: 0.73 [0.54-0.92], 0.53 [0.31-0.75], and 0.72 [0.51-0.92], respectively). The radiomics features of PTV-GTV and TL-PTV outperformed those of the other ROIs (accuracy, 76.7% and 76.7%; AUC [95% CI]: 0.82 [0.65-0.99] and 0.80 [0.59-1], respectively).

CONCLUSIONS

Combining dosimetric factors and radiomics features within different ROIs can improve the prediction of symptomatic RP. Our results can help physicians adjust the radiation dose distribution of the dose-sensitive lungs and target volumes based on personalized RP estimates.

Radiation-Induced Lung Injury-Current Perspectives and Management

Radiotherapy plays an important role in the treatment of localized primary malignancies involving the chest wall or intrathoracic malignancies. Secondary effects of radiotherapy on the lung result in radiation-induced lung disease. The phases of lung injury from radiation range from acute pneumonitis to chronic pulmonary fibrosis. Radiation pneumonitis is a clinical diagnosis based on the history of radiation, imaging findings, and the presence of classic symptoms after exclusion of infection, pulmonary embolism, heart failure, drug-induced pneumonitis, and progression of the primary tumor. Computed tomography (CT) is the preferred imaging modality as it provides a better picture of parenchymal changes. Lung biopsy is rarely required for the diagnosis. Treatment is necessary only for symptomatic patients. Mild symptoms can be treated with inhaled steroids while subacute to moderate symptoms with impaired lung function require oral corticosteroids. Patients who do not tolerate or are refractory to steroids can be considered for treatment with immunosuppressive agents such as azathioprine and cyclosporine. Improvements in radiation technique, as well as early diagnosis and appropriate treatment with high-dose steroids, will lead to lower rates of pneumonitis and an overall good prognosis.

Low dose ionizing radiation effects on the immune system

Ionizing radiation interacts with the immune system in many ways with a multiplicity that mirrors the complexity of the immune system itself: namely the need to maintain a delicate balance between different compartments, cells and soluble factors that work collectively to protect, maintain, and restore tissue function in the face of severe challenges including radiation damage. The cytotoxic effects of high dose radiation are less relevant after low dose exposure, where subtle quantitative and functional effects predominate that may go unnoticed until late after exposure or after a second challenge reveals or exacerbates the effects. For example, low doses may permanently alter immune fitness and therefore accelerate immune senescence and pave the way for a wide spectrum of possible pathophysiological events, including early-onset of age-related degenerative disorders and cancer. By contrast, the so called low dose radiation therapy displays beneficial, anti-inflammatory and pain relieving properties in chronic inflammatory and degenerative diseases. In this review, epidemiological, clinical and experimental data regarding the effects of low-dose radiation on the homeostasis and functional integrity of immune cells will be discussed, as will be the role of immune-mediated mechanisms in the systemic manifestation of localized exposures such as inflammatory reactions. The central conclusion is that ionizing radiation fundamentally and durably reshapes the immune system. Further, the importance of discovery of immunological pathways for modifying radiation resilience amongst other research directions in this field is implied.

Validation of Polymorphisms Associated with the Risk of Radiation-Induced Oesophagitis in an Independent Cohort of Non-Small-Cell Lung Cancer Patients

Simple Summary

Genetic variants identified in association with radiation therapy side effects in non-small-cell lung cancer patients require an independent validation. Therefore, the aim of our study was to replicate, in an independent cohort, the analyses of previously published studies associating single-nucleotide polymorphisms with radiation-induced oesophagitis. Following the original models, 2 of the 18 variants associated with radiation-induced oesophagitis in non-small-cell lung cancer patients were confirmed. Furthermore, we meta-analysed our cohort together with those of the reference studies. Twelve variants located in genes of inflammation and DNA double-strand break repair pathways remained associated with oesophagitis. These variants could be included in models for clinical prediction of radiation-induced oesophagitis to evaluate their performance.

Abstract

Several studies have identified single-nucleotide polymorphisms (SNPs) associated with adverse effects in non-small-cell lung cancer (NSCLC) patients treated with radiation therapy. Here, using an independent cohort, we aimed to validate the reported associations. We selected 23 SNPs in 17 genes previously associated with radiation-induced oesophagitis for validation in a cohort of 178 Spanish NSCLC patients. Of them, 18 SNPs were finally analysed, following the methods described in the original published studies. Two SNPs replicated their association with radiation-induced oesophagitis (rs7165790 located in the BLM gene: odds ratio (OR) = 0.16, 95% CI = 0.04–0.65, p-value = 0.010; rs4772468 at FGF14: OR = 4.36, 95% CI = 1.15–16.46, p-value = 0.029). The SNP rs2868371 at HSPB1 was also validated but displayed an opposite effect to the formerly described (OR = 3.72; 95% CI = 1.49–9.25; p-value = 0.004). Additionally, we tested a meta-analytic approach including our results and the previous datasets reported in the referenced publications. Twelve SNPs (including the two previously validated) retained their statistically significant association with radiation-induced oesophagitis. This study strengthens the role of inflammation and DNA double-strand break repair pathways in the risk prediction of developing radiation-induced oesophagitis in NSCLC patients. The validated variants are good candidates to be evaluated in risk prediction models for patient stratification based on their radiation susceptibility.

Radiation-induced lung injury: current evidence

Chemo-radiotherapy and systemic therapies have proven satisfactory outcomes as standard treatments for various thoracic malignancies; however, adverse pulmonary effects, like pneumonitis, can be life-threatening. Pneumonitis is caused by direct cytotoxic effect, oxidative stress, and immune-mediated injury. Radiotherapy Induced Lung Injury (RILI) encompasses two phases: an early phase known as Radiation Pneumonitis (RP), characterized by acute lung tissue inflammation as a result of exposure to radiation; and a late phase called Radiation Fibrosis (RF), a clinical syndrome that results from chronic pulmonary tissue damage. Currently, diagnoses are made by exclusion using clinical assessment and radiological findings. Pulmonary function tests have constituted a significant step in evaluating lung function status during radiotherapy and useful predictive tools to avoid complications or limit toxicity. Systemic corticosteroids are widely used to treat pneumonitis complications, but its use must be standardized, and consider in the prophylaxis setting given the fatal outcome of this adverse event. This review aims to discuss the clinicopathological features of pneumonitis and provide practical clinical recommendations for prevention, diagnosis, and management.

Genomics models in radiotherapy: From mechanistic to machine learning

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

Validation study of the association between genetic variant of IL4 and severe radiation pneumonitis in lung cancer patients treated with radiation therapy

BACKGROUND AND PURPOSE

Recent researches demonstrated that single nucleotide polymorphisms (SNPs) of genes involving inflammation, DNA repair, etc. were associated with risk of radiation pneumonitis (RP). However, these studies were single-centered, from single ethnic origin, without validation from independent cohort studies from other populations. In order to identify clinical valuable SNPs for RP, in this study we selected 19 RP-related SNPs candidates previously published before 2016 for validation in our cohort.

MATERIAL AND METHODS

359 lung cancer patients with radiotherapy were included in our prospective study (NCT02490319). Peripheral blood samples from these patients were genotyped by MassArray and Sanger Sequence method. Multivariate Cox hazard and other analyses were applied to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) of all factors possibly related to the risk of RP.

RESULTS

Patients with elder age, MLD ≥15 Gy, V₂₀ ≥24% had higher risk of RP ≥grade 3 compared with their counterparts (HR = 2.020, 95% CI: 1.045-3.906, P = 0.037; HR = 2.502, 95% CI: 1.346-4.652, P = 0.004; HR = 2.256, 95% CI: 1.191-4.272, P = 0.013, respectively). Moreover, patients receiving IMRT were associated with decreased incidence of RP (HR = 0.520, 95% CI: 0.280-0.963, P = 0.037). Importantly, CT + TT genotype of IL4: rs2243250 was strongly related to decreased risk of RP ≥grade 3 (HR = 0.195, 95% CI: 0.090-0.424, P = 0.000037, P_c = 0.0006).

CONCLUSION

IL4: rs2243250 was validated to be significantly related to RP of grade ≥3 in our cohort. Our results further emphasized the prevalence and clinical value of IL4: rs2243250 on RP, and may thus be one of the important predictors of severe RP before radiotherapy.

Targeted next-generation DNA sequencing identifies Notch signaling pathway mutation as a predictor of radiation response

Purpose: Identifying the association between somatic mutations and the radiation response of tumor is essential for understanding the mechanisms and practicing personalized radiotherapy. The present study aimed to discover specific genes or pathways that are associated with radiation response using targeted next-generation DNA sequencing.Material and methods: Fifty-five patients with various solid tumors whose specimen were sequenced using institutional panel which includes 148 cancer-related genes and received radiotherapy for a measurable tumor were analyzed. Patients with irradiated tumors in complete or partial remission for more than 6 months were defined as responders. Association between mutations including pathogenic single nucleotide variants and insertions/deletions in the 148 genes and 39 molecular pathways and radiation response was investigated.Results: Analyzing 17 responders and 38 non-responders, biologically effective dose (BED), but not concurrent chemotherapy, was associated with radiation response. No single gene correlated with radiation response. Mutations in Notch signaling pathway were associated with radiosensitivity after correction for multiple comparison (adjusted p = .094). When BED and Notch signaling pathway mutation were tested with logistic regression, both variables were associated with radiation response.Conclusions: Our results suggest that somatic mutations in Notch signaling pathway may be related to sensitivity to radiation, although these results should be validated in a larger and more homogeneous cohort.

Radiation-Induced Lung Injury: Assessment and Management

Radiation-induced lung injury (RILI) encompasses any lung toxicity induced by radiation therapy (RT) and manifests acutely as radiation pneumonitis and chronically as radiation pulmonary fibrosis. Because most patients with thoracic and breast malignancies are expected to undergo RT in their lifetime, many with curative intent, the population at risk is significant. Furthermore, indications for thoracic RT are expanding given the advent of stereotactic body radiation therapy (SBRT) or stereotactic ablative radiotherapy (SABR) for early-stage lung cancer in nonsurgical candidates as well as oligometastatic pulmonary disease from any solid tumor. Fortunately, the incidence of serious pulmonary complications from RT has decreased secondary to advances in radiation delivery techniques. Understanding the temporal relationship between RT and injury as well as the patient, disease, and radiation factors that help distinguish RILI from other etiologies is necessary to prevent misdiagnosis. Although treatment of acute pneumonitis is dependent on clinical severity and typically responds completely to corticosteroids, accurately diagnosing and identifying patients who may progress to fibrosis is challenging. Current research advances include high-precision radiation techniques, an improved understanding of the molecular basis of RILI, the development of small and large animal models, and the identification of candidate drugs for prevention and treatment.

Genomics of radiation sensitivity in squamous cell carcinomas

Radiotherapy is an important modality in the management of squamous cell cancers with 50% of patients receiving radiotherapy at some point. Despite technological advances, the risk of severe toxicity in a proportion of radiosensitive patients limits radiation doses that can be safely prescribed affecting the potential for cure. While comorbidities, lifestyle and treatment factors can influence interindividual variations, genetic factors are thought to play a major role, accounting for approximately 80% of the variance observed. Over the last decade, substantial progress has been made in the field of radiogenomics, with compelling associations for SNPs identified in genes involved in DNA-damage response, cell-cycle control, apoptosis, antioxidant defenses and cytokine production. Future research efforts should be collaborative, focused on validating and broadening their clinical applicability. Numerous obstacles exist to the clinical application of this knowledge, which need to be overcome before personalized radiation therapy becomes a routine component of oncologic care.

The single nucleotide variant rs2868371 associates with the risk of mortality in non-small cell lung cancer patients: A multicenter prospective validation

BACKGROUND AND PURPOSE

Definitive radiation therapy (RT) with or without chemotherapy has become the standard treatment for non-metastatic unresectable non-small cell lung cancer (NSCLC). However, treatment outcomes can differ substantially and patients' genetic background could play a crucial role. Potential associations between single-nucleotide polymorphisms (SNP) in Heat shock protein beta-1 (HSPB1) and survival have been reported in prior single-institution retrospective reports.

MATERIALS AND METHODS

The current assay aims to validate such connection in a prospective multicenter study in a European cohort including 181 NSCLC patients. Median follow-up time for all patients was 13 months (range, 3-57 months).

RESULTS

The results obtained show an association between the rs2868371 GG genotype and better overall survival (HR: 0.35; 95%CI: 0.13-0.96; p = 0.042) in multivariate analysis. Two-year overall survival rate was 72% for patients carrying the rs2868371 GG genotype versus 36% for those patients harboring the rs2868371 CC/CG genotypes (p = 0.013). Additionally, the rs2868371 GG genotype was found to be associated with better disease-free survival in the multivariate analysis (HR: 0.36; 95%CI: 0.13-0.99; p = 0.048). In silico analysis of the potential functional SNP suggested significant difference in the affinity of the Glucocorticoid Receptor binding site between alternative allelic variants, confirmed by chromatin immunoprecipitation analysis displaying stronger affinity for the risk allele (C). Furthermore, our findings indicate that the rs2868371 influences (mRNA) HSPB1 expression, offering insight into the regulation of HSPB1 transcription.

CONCLUSION

The functional HSPB1 rs2868371 promoter variant may affect lung cancer survival by regulation of HSPB1 expression levels through glucocorticoid receptor interaction.

Common Inflammation-Related Candidate Gene Variants and Acute Kidney Injury in 2647 Critically Ill Finnish Patients

Acute kidney injury (AKI) is a syndrome with high incidence among the critically ill. Because the clinical variables and currently used biomarkers have failed to predict the individual susceptibility to AKI, candidate gene variants for the trait have been studied. Studies about genetic predisposition to AKI have been mainly underpowered and of moderate quality. We report the association study of 27 genetic variants in a cohort of Finnish critically ill patients, focusing on the replication of associations detected with variants in genes related to inflammation, cell survival, or circulation. In this prospective, observational Finnish Acute Kidney Injury (FINNAKI) study, 2647 patients without chronic kidney disease were genotyped. We defined AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We compared severe AKI (Stages 2 and 3, n = 625) to controls (Stage 0, n = 1582). For genotyping we used iPLEX^TM Assay (Agena Bioscience). We performed the association analyses with PLINK software, using an additive genetic model in logistic regression. Despite the numerous, although contradictory, studies about association between polymorphisms rs1800629 in TNFA and rs1800896 in IL10 and AKI, we found no association (odds ratios 1.06 (95% CI 0.89–1.28, p = 0.51) and 0.92 (95% CI 0.80–1.05, p = 0.20), respectively). Adjusting for confounders did not change the results. To conclude, we could not confirm the associations reported in previous studies in a cohort of critically ill patients.

Role of Germline Genetics in Identifying Survivors at Risk for Adverse Effects of Cancer Treatment

The growing population of cancer survivors often faces adverse effects of treatment, which have a substantial impact on morbidity and mortality. Although certain adverse effects are thought to have a significant heritable component, much work remains to be done to understand the role of germline genetic factors in the development of treatment-related toxicities. In this article, we review current understanding of genetic susceptibility to a range of adverse outcomes among cancer survivors (e.g., fibrosis, urinary and rectal toxicities, ototoxicity, chemotherapy-induced peripheral neuropathy, subsequent malignancies). Most previous research has been narrowly focused, investigating variation in candidate genes and pathways such as drug metabolism, DNA damage and repair, and inflammation. Few of the findings from these earlier candidate gene studies have been replicated in independent populations. Advances in understanding of the genome, improvements in technology, and reduction in laboratory costs have led to recent genome-wide studies, which agnostically interrogate common and/or rare variants across the entire genome. Larger cohorts of patients with homogeneous treatment exposures and systematic ascertainment of well-defined outcomes as well as replication in independent study populations are essential aspects of the study design and are increasingly leading to the discovery of variants associated with each of the adverse outcomes considered in this review. In the long-term, validated germline genetic associations hold tremendous promise for more precisely identifying patients at highest risk for developing adverse treatment effects, with implications for frontline therapy decision-making, personalization of long-term follow-up guidelines, and potential identification of targets for prevention or treatment of the toxicity.

Radiation biology and oncology in the genomic era

Radiobiology research is building the foundation for applying genomics in precision radiation oncology. Advances in high-throughput approaches will underpin increased understanding of radiosensitivity and the development of future predictive assays for clinical application. There is an established contribution of genetics as a risk factor for radiotherapy side effects. An individual's radiosensitivity is an inherited polygenic trait with an architecture that includes rare mutations in a few genes that confer large effects and common variants in many genes with small effects. Current thinking is that some will be tissue specific, and future tests will be tailored to the normal tissues at risk. The relationship between normal and tumor cell radiosensitivity is poorly understood. Data are emerging suggesting interplay between germline genetic variation and epigenetic modification with growing evidence that changes in DNA methylation regulate the radiosensitivity of cancer cells and histone acetyltransferase inhibitors have radiosensitizing effects. Changes in histone methylation can also impair DNA damage response signaling and alter radiosensitivity. An important effort to advance radiobiology in the genomic era was establishment of the Radiogenomics Consortium to enable the creation of the large radiotherapy cohorts required to exploit advances in genomics. To address challenges in harmonizing data from multiple cohorts, the consortium established the REQUITE project to collect standardized data and genotyping for ~5,000 patients. The collection of detailed dosimetric data is important to produce validated multivariable models. Continued efforts will identify new genes that impact on radiosensitivity to generate new knowledge on toxicity pathogenesis and tests to incorporate into the clinical decision-making process.

Late respiratory effects of cancer treatment

PURPOSE OF REVIEW

The aim of this article is to examine significant advances in our understanding of the late respiratory effects of cancer treatment, including surgery, radiotherapy, chemotherapy, biological therapies and haematopoietic stem cell transplant, and to provide a framework for assessing such patients.

RECENT FINDINGS

Oncology therapies have advanced considerably over recent years but pulmonary toxicity remains a concern. Advances have been made in our understanding of the risk factors, including genetic ones that lead to toxicity from radiotherapy and chemotherapy and risk stratification models are being developed to aid treatment planning. Targeted biological treatments are continuously being developed and consequently the Pneumotox database of pulmonary toxicity continues to be an essential resource. Early detection of bronchiolitis obliterans in haematopoietic stem cell transplant patients has been found to be critical, with some positive results from intervention trials.

SUMMARY

Pulmonary toxicity is a common unwanted consequence of life enhancing or saving cancer treatments which remain difficult to treat. Developments in these fields are mainly in the areas of prevention, early detection and monitoring of unwanted side effects. We discuss some of these developments within this review.

Association of DNA repair gene polymorphisms with the risk of radiation pneumonitis in lung cancer patients

A total of 149 lung cancer patients were recruited to receive intensity modulated radiation therapy (IMRT). The association of developing radiation pneumonitis (RP) with genetic polymorphism was evaluated. The risks of four polymorphic sites in three DNA repair related genes (ERCC1, rs116615:T354C and rs3212986:C1516A; ERCC2, rs13181:A2251C; XRCC1, rs25487:A1196G) for developing grade ≥ 2 RP were assessed respectively. It was observed that ERCC1 T354C SNP had a significant effect on the development of grade ≥ 2 RP (CT/TT vs. CC, adjusted HR = 0.517, 95% CI, 0.285-0.939; adjusted P = 0.030). It is the first time demonstrating that CT/TT genotype of ERCC1 354 was significantly associated with lower RP risk after radio therapy.

Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy

Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.

Radiogenomics: Identification of Genomic Predictors for Radiation Toxicity

The overall goal of radiogenomics is the identification of genomic markers that are predictive for the development of adverse effects resulting from cancer treatment with radiation. The principal rationale for a focus on toxicity in radiogenomics is that for many patients treated with radiation, especially individuals diagnosed with early-stage cancers, the survival rates are high, and therefore a substantial number of people will live for a significant period of time beyond treatment. However, many of these patients could suffer from debilitating complications resulting from radiotherapy. Work in radiogenomics has greatly benefited from creation of the Radiogenomics Consortium (RGC) that includes investigators at multiple institutions located in a variety of countries. The common goal of the RGC membership is to share biospecimens and data so as to achieve large-scale studies with increased statistical power to enable identification of relevant genomic markers. A major aim of research in radiogenomics is the development of a predictive instrument to enable identification of people who are at greatest risk for adverse effects resulting from cancer treatment using radiation. It is anticipated that creation of a predictive assay characterized by a high level of sensitivity and specificity will improve precision radiotherapy and assist patients and their physicians to select the optimal treatment for each individual.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era

The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost-utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.

Meta-analysis of Genome Wide Association Studies Identifies Genetic Markers of Late Toxicity Following Radiotherapy for Prostate Cancer

Nearly 50% of cancer patients undergo radiotherapy. Late radiotherapy toxicity affects quality-of-life in long-term cancer survivors and risk of side-effects in a minority limits doses prescribed to the majority of patients. Development of a test predicting risk of toxicity could benefit many cancer patients. We aimed to meta-analyze individual level data from four genome-wide association studies from prostate cancer radiotherapy cohorts including 1564 men to identify genetic markers of toxicity. Prospectively assessed two-year toxicity endpoints (urinary frequency, decreased urine stream, rectal bleeding, overall toxicity) and single nucleotide polymorphism (SNP) associations were tested using multivariable regression, adjusting for clinical and patient-related risk factors. A fixed-effects meta-analysis identified two SNPs: rs17599026 on 5q31.2 with urinary frequency (odds ratio [OR] 3.12, 95% confidence interval [CI] 2.08-4.69, p-value 4.16×10(-8)) and rs7720298 on 5p15.2 with decreased urine stream (OR 2.71, 95% CI 1.90-3.86, p-value=3.21×10(-8)). These SNPs lie within genes that are expressed in tissues adversely affected by pelvic radiotherapy including bladder, kidney, rectum and small intestine. The results show that heterogeneous radiotherapy cohorts can be combined to identify new moderate-penetrance genetic variants associated with radiotherapy toxicity. The work provides a basis for larger collaborative efforts to identify enough variants for a future test involving polygenic risk profiling.

HSPB1 polymorphisms might be associated with radiation-induced damage risk in lung cancer patients treated with radiotherapy

Several studies investigating the association between heat shock protein beta-1 (HSPB1) polymorphisms and radiation-induced damage in lung cancer patients administrated with radiotherapy have derived conflicting results. This meta-analysis aimed to assess the association between the HSPB1 genes' (rs2868370 and rs2868371) polymorphisms and the risk of radiation-induced damage in lung cancer patients. After an electronic literature search, four articles including six studies were found to be eligible for this meta-analysis. No association was observed between rs2868370 genotypes and radiation-induced damage risk. However, rs2868371 showed a statistically increased risk of radiation-induced damage under CC vs. CG/GG model (OR = 1.59, 95 % CI = 1.10-2.29). Subgroup analysis by ethnicity showed that the genotypes of rs2868371 were also associated with a significantly increased risk of radiation-induced damage in CC vs. CG/GG model (OR = 1.86, 95 % CI = 1.21-2.83) among mixed ethnicities which are mainly comprised of white people. When the data was stratified by organ-damaged, a significant association was only observed in the esophagus group (OR = 2.94, 95 % CI = 1.35-6.37, for CC vs. CG/GG model). In conclusion, the present study demonstrated that the rs2868371 genotypes of HSPB1 might be associated with radiation-induced esophagus damage risk, especially in Caucasians but not in the Asian population.

Impact of single-nucleotide polymorphisms on radiation pneumonitis in cancer patients

Radiation pneumonitis (RP) is one of the most important dose-limiting toxicities in the radiotherapy of thoracic tumors, which reduces the rate of local tumor control and overall survival and severely affects the patients' quality of life. Single-nucleotide polymorphisms (SNPs) have recently attracted increasing attention as biomarkers for predicting the development of RP. SNPs in inflammation-related, DNA repair-related, stress response-related and angiogenesis-related genes were proved to be associated with RP, with different underlying mechanisms. Radiogenomics focuses on the differences in radiosensitivity caused by gene sequence variation, which may prove helpful in investigating the abovementioned associations. In this review, we aimed to investigate the associations between RP and SNPs reported in recent studies and highlight the main content and prospects of radiogenomics.

iTRAQ-Based Quantitative Proteomic Analysis of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a common disease in the southern provinces of China with a poor prognosis. To better understand the pathogenesis of NPC and identify proteins involved in NPC carcinogenesis, we applied iTRAQ coupled with two-dimensional LC-MS/MS to compare the proteome profiles of NPC tissues and the adjacent non-tumor tissues. We identified 54 proteins with differential expression in NPC and the adjacent non-tumor tissues. The differentially expressed proteins were further determined by RT-PCR and Western blot analysis. In addition, the up-regulation of HSPB1, NPM1 and NCL were determined by immunohistochemistry using tissue microarray. Functionally, we found that siRNA mediated knockdown of NPM1 inhibited the migration and invasion of human NPC CNE1 cell line. In summary, this is the first study on proteome analysis of NPC tissues using an iTRAQ method, and we identified many new differentially expressed proteins which are potential targets for the diagnosis and therapy of NPC.

The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism-Based Models: A Step Toward Prevention

Radiotherapy is a mainstay of cancer treatment, used in either a curative or palliative manner to treat approximately 50% of patients with cancer. Normal tissue toxicity limits the doses used in standard radiation therapy protocols and impedes improvements in radiotherapy efficacy. Damage to surrounding normal tissues can produce reactions ranging from bothersome symptoms that negatively affect quality of life to severe life-threatening complications. Improved ways of predicting, before treatment, the risk for development of normal tissue toxicity may allow for more personalized treatment and reduce the incidence and severity of late effects. There is increasing recognition that the cause of normal tissue toxicity is multifactorial and includes genetic factors in addition to radiation dose and volume of exposure, underlying comorbidities, age, concomitant chemotherapy or hormonal therapy, and use of other medications. An understanding of the specific genetic risk factors for normal tissue response to radiation has the potential to enhance our ability to predict adverse outcomes at the treatment-planning stage. Therefore, the field of radiogenomics has focused upon the identification of genetic variants associated with normal tissue toxicity resulting from radiotherapy. Innovative analytic methods are being applied to the discovery of risk variants and development of integrative predictive models that build on traditional normal tissue complication probability models by incorporating genetic information. Results from initial studies provide promising evidence that genetic-based risk models could play an important role in the implementation of precision medicine for radiation oncology through enhancing the ability to predict normal tissue reactions and thereby improve cancer treatment.

Associations of LIG4 and HSPB1 genetic polymorphisms with risk of radiation-induced lung injury in lung cancer patients treated with radiotherapy

Objective. This study aims to explore the correlations of genetic polymorphisms in LIG4 and HSPB1 genes with the radiation-induced lung injury (RILI), especially radiation pneumonitis (RP), in lung cancer patients. Methods. A total of 160 lung cancer patients, who were diagnosed with inoperable lung cancer and received radiotherapy, were included in the present study from September 2009 to December 2011. TaqMan Real-Time PCR (RT-PCR) was used to verify the SNPs of LIG4 and HSPB1 genes. Chi-square criterion was used to compare the differences in demographic characteristics, exposure to risk factors, and SNPs genotypes. Crude odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated by logistic regression analysis. All statistical analyses were conducted in SPSS 18.0. Results. A total of 32 (20.0%) lung cancer patients had RP after receiving radiotherapy. Of the 32 cases, 4 cases were of grade 2, 24 cases were of grade 3, and 4 cases were of grade 4. However, our results indicated that the general condition and treatment of all patients had no significant difference with RP risk (P > 0.05). Meanwhile, our results revealed that there was no significant association between the frequencies of LIG4 rs1805388 and HSPB1 rs2868371 genotype distribution and the risk of RP (P > 0.05). Conclusion. In conclusion, we demonstrated that the genetic polymorphisms in LIG4 rs1805388 and HSPB1 rs2868371 were not obviously correlated with the risk of RP and RILI of lung cancer.

Predictive SNPs for radiation-induced damage in lung cancer patients with radiotherapy: a potential strategy to individualize treatment

In the treatment of lung cancer, radiotherapy has become one of the most important therapies, despite its sometimes unpredictable side effects. As such, identifying lung cancer patients who are at high risk of developing severe radiation-induced damage (mainly radiation pneumonitis and radiation-induced esophageal toxicity) and applying effect intervention or monitoring techniques are important. Although human diversity to a certain amount is explained by clinical and dosimetric factors, the presence of specific genetic determinants also influences the occurrence of radiation-induced damage. Here we summarize the data on mechanisms of radiation pneumonitis and radiation-induced esophageal toxicity supporting the involvement of variances of genes in the evolution of radiation-induced damage. Furthermore, the available evidence from current clinical studies of genetic polymorphisms for the prediction of radiation pneumonitis and radiation-induced esophageal toxicity is discussed. Eventually, this may help to truly individualize radiotherapy, using a personal genetic profile of the most relevant genes for each lung cancer patient.

Nondosimetric risk factors for radiation-induced lung toxicity

The decision to administer a radical course of radiotherapy (RT) is largely influenced by the dose-volume metrics of the treatment plan, but what are the patient-related and other factors that may independently increase the risk of radiation lung toxicity? Poor pulmonary function has been regarded as a risk factor and a relative contraindication for patients undergoing radical RT, but recent evidence suggests that patients with poor spirometry results may tolerate conventional or high-dose RT as well as, if not better than, patients with normal function. However, caution may need to be exercised in patients with underlying interstitial pulmonary fibrosis. Furthermore, there is emerging evidence of molecular markers of increased risk of toxicity. This review discusses patient-related risk factors other than dosimetry for radiation lung toxicity.

Radiogenomics: the search for genetic predictors of radiotherapy response

'Radiogenomics' is the study of genetic variation associated with response to radiotherapy. Radiogenomics aims to uncover the genes and biologic pathways responsible for radiotherapy toxicity that could be targeted with radioprotective agents and; identify genetic markers that can be used in risk prediction models in the clinic. The long-term goal of the field is to develop single nucleotide polymorphism-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. The field has evolved over the last two decades in parallel with advances in genomics, moving from narrowly focused candidate gene studies to large, collaborative genome-wide association studies. Several confirmed genetic variants have been identified and the field is making progress toward clinical translation.

Recent advances in radiotherapy for thoracic tumours

Radiation Oncology technology has continued to advance at a rapid rate and is bringing significant benefits to patients. This review outlines some of the advances in technology and radiotherapy treatment of thoracic cancers including brachytherapy, stereotactic radiotherapy, tomotherapy and intensity modulated radiotherapy. The importance of functional imaging with PET and management of movement are highlighted. Most of the discussion relates to non-small cell lung cancer but management of mesothelioma and small cell lung cancer are also covered. This technology has substantial benefits to patients in terms of decreasing toxicity both in the short and longer term.

Quantification of radiation-induced lung damage with CT scans: the possible benefit for radiogenomics

BACKGROUND

Radiation-induced lung damage (RILD) is an important problem. Although physical parameters such as the mean lung dose are used in clinical practice, they are not suited for individualised radiotherapy. Objective, quantitative measurements of RILD on a continuous instead of on an ordinal, semi-quantitative, semi-subjective scale, are needed.

METHODS

Hounsfield unit (HU) changes before versus three months post-radiotherapy were correlated per voxel with the radiotherapy dose in 95 lung cancer patients. Deformable registration was used to register pre- and post-CT scans and the density increase was quantified for various dose bins. The dose-response curve for increased HU was quantified using the slope of a linear regression (HU/Gy). The end-point for the toxicity analysis was dyspnoea ≥ grade 2.

RESULTS

Radiation dose was linearly correlated with the change in HU (mean R(2) = 0.74 ± 0.28). No differences in HU/Gy between groups treated with stereotactic radiotherapy, conventional radiotherapy alone, sequential or concurrent chemo- radiotherapy were observed. In the whole patient group, 33/95 (34.7%) had dyspnoea ≥ G2. Of the 48 patients with a HU/Gy below the median, 16 (33.3%) developed dyspnoea ≥ G2, while in the 47 patients with a HU/Gy above the median, 17 (36.1%) had dyspnoea ≥ G2 (not significant). Individual patients showed a nearly 21-fold difference in radiosensitivity, with HU/Gy ranging from 0 to 10 HU/Gy.

CONCLUSIONS

HU changes identify objectively the whole range of individual radiosensitivity on a continuous, quantitative scale. CT density changes may allow more robust and accurate radiogenomics studies.