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.
Normal-tissue adverse effects following radiotherapy are common and significantly affect quality of life. These effects cannot be accounted for by dosimetric, treatment, or demographic factors alone, ...and evidence suggests that common genetic variants are associated with radiotherapy adverse effects. The field of radiogenomics has evolved to identify such genetic risk factors. Radiogenomics has two goals: (i) to develop an assay to predict which patients with cancer are most likely to develop radiation injuries resulting from radiotherapy, and (ii) to obtain information about the molecular pathways responsible for radiation-induced normal-tissue toxicities. This review summarizes the history of the field and current research.
A single-nucleotide polymorphism–based predictive assay could be used, along with clinical and treatment factors, to estimate the risk that a patient with cancer will develop adverse effects from radiotherapy. Such an assay could be used to personalize therapy and improve quality of life for patients with cancer.
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.
To quantify the risk of second primary breast cancer in the contralateral breast (CB) after radiotherapy (RT) for first breast cancer.
The study population included participants in the Women's ...Environmental, Cancer, and Radiation Epidemiology study: 708 cases (women with asynchronous bilateral breast cancer) and 1399 controls (women with unilateral breast cancer) counter-matched on radiation treatment. Participants were <55 years of age at first breast cancer. Absorbed doses to quadrants of the CB were estimated. Rate ratios (RR) and 95% confidence intervals (CI) were calculated using multivariable-adjusted conditional logistic regression models.
Across all patients, the mean radiation dose to the specific quadrant of the CB tumor was 1.1 Gy. Women <40 years of age who received >1.0 Gy of absorbed dose to the specific quadrant of the CB had a 2.5-fold greater risk for CB cancer than unexposed women (RR = 2.5, 95% CI 1.4-4.5). No excess risk was observed in women >40 years of age. Women <40 years of age with follow-up periods >5 years had a RR of 3.0 (95% CI 1.1-8.1), and the dose response was significant (excess RR per Gy of 1.0, 95% CI 0.1-3.0).
Women <40 years of age who received a radiation dose >1.0 Gy to the CB had an elevated, long-term risk of developing a second primary CB cancer. The risk is inversely related to age at exposure and is dose dependent.
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.
There is increasing evidence supporting the role of genetic variants in the development of radiation-induced toxicity. However, previous candidate gene association studies failed to elucidate the ...common genetic variation underlying this phenotype, which could emerge years after the completion of treatment. We performed a genome-wide association study on a Spanish cohort of 741 individuals with prostate cancer treated with external beam radiotherapy (EBRT). The replication cohorts consisted of 633 cases from the UK and 368 cases from North America. One locus comprising TANC1 (lowest unadjusted P value for overall late toxicity=6.85×10(-9), odds ratio (OR)=6.61, 95% confidence interval (CI)=2.23-19.63) was replicated in the second stage (lowest unadjusted P value for overall late toxicity=2.08×10(-4), OR=6.17, 95% CI=2.25-16.95; Pcombined=4.16×10(-10)). The inclusion of the third cohort gave unadjusted Pcombined=4.64×10(-11). These results, together with the role of TANC1 in regenerating damaged muscle, suggest that the TANC1 locus influences the development of late radiation-induced damage.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract Background and purpose This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity 2 years after radiotherapy. Materials and methods A genome ...wide association study was performed in 1850 patients from the RAPPER study: 1217 received adjuvant breast radiotherapy and 633 had radical prostate radiotherapy. Genotype associations with both overall and individual endpoints of toxicity were tested via univariable and multivariable regression. Replication of potentially associated SNPs was carried out in three independent patient cohorts who had radiotherapy for prostate (516 RADIOGEN and 862 Gene-PARE) or breast (355 LeND) cancer. Results Quantile–quantile plots show more associations at the P < 5 × 10−7 level than expected by chance (164 vs. 9 for the prostate cases and 29 vs. 4 for breast cases), providing evidence that common genetic variants are associated with risk of toxicity. Strongest associations were for individual endpoints rather than an overall measure of toxicity in all patients. However, in general, significant associations were not validated at a nominal 0.05 level in the replication cohorts. Conclusions This largest GWAS to date provides evidence of true association between common genetic variants and toxicity. Associations with toxicity appeared to be tumour site-specific. Future GWAS require higher statistical power, in particular in the validation stage, to test clinically relevant effect sizes of SNP associations with individual endpoints, but the required sample sizes are achievable.
•A machine learning-based genetic model was developed to predict the risk of radiation-induced hematuria using genome-wide single nucleotide polymorphisms (SNPs) data.•The predictive model ...distinguished the high-risk group from the low-risk group in the validation data with the odds ratio of 2.87.•Post-hoc bioinformatics analyses identified key biological correlates that have been previously reported to be associated with the bladder and urinary tract.
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.
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.
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.
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.
Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each ...patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.
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