Radiogenomics – current status, challenges and future directions

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.

Personalising Prostate Radiotherapy in the Era of Precision Medicine: A Review

Prostate cancer continues to be the most commonly diagnosed cancer among Canadian men. The introduction of routine screening and advanced treatment options have allowed for a decrease in prostate cancer-related mortality, but outcomes following treatment continue to vary widely. In addition, the overtreatment of indolent prostate cancers causes unnecessary treatment toxicities and burdens health care systems. Accurate identification of patients who should undergo aggressive treatment, and those which should be managed more conservatively, needs to be implemented. More tumour and patient information is needed to stratify patients into low-, intermediate-, and high-risk groups to guide treatment options. This paper reviews the current literature on personalised prostate cancer management, including targeting tumour hypoxia, genomic and radiomic prognosticators, and radiobiological tumour targeting. A review of the current applications and future directions for the use of big data in radiation therapy is also presented. Prostate cancer management has a lot to gain from the implementation of personalised medicine into practice. Using specific tumour and patient characteristics to personalise prostate radiotherapy in the era of precision medicine will improve survival, decrease unnecessary toxicities, and minimise the heterogeneity of outcomes following treatment.

Current status and future challenges for molecular imaging

Molecular imaging (MI), used in its wider sense of biology at the molecular level, is a field that lies at the intersection of molecular biology and traditional medical imaging. As advances in medicine have exponentially expanded over the last few decades, so has our need to better understand the fundamental behaviour of living organisms in a non-invasive and timely manner. This commentary draws from topics the authors addressed in their presentations at the 2017 Royal Society Meeting 'Challenges for chemistry in molecular imaging', as well as a discussion of where MI is today and where it is heading in the future.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

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.

A score combining baseline neutrophilia and primary tumor SUVpeak measured from FDG PET is associated with outcome in locally advanced cervical cancer

PURPOSE

We investigated whether a score combining baseline neutrophilia and a PET biomarker could predict outcome in patients with locally advanced cervical cancer (LACC).

METHODS

Patients homogeneously treated with definitive chemoradiation plus image-guided adaptive brachytherapy (IGABT) between 2006 and 2013 were analyzed retrospectively. We divided patients into two groups depending on the PET device used: a training set (TS) and a validation set (VS). Primary tumors were semi-automatically delineated on PET images, and 11 radiomics features were calculated (LIFEx software). A PET radiomic index was selected using the time-dependent area under the curve (td-AUC) for 3-year local control (LC). We defined the neutrophil SUV grade (NSG = 0, 1 or 2) score as the number of risk factors among (i) neutrophilia (neutrophil count >7 G/L) and (ii) high risk defined from the PET radiomic index. The NSG prognostic value was evaluated for LC and overall survival (OS).

RESULTS

Data from 108 patients were analyzed. Estimated 3-year LC was 72% in the TS (n = 69) and 65% in the VS (n = 39). In the TS, SUV_peak was selected as the most LC-predictive biomarker (td-AUC = 0.75), and was independent from neutrophilia (p = 0.119). Neutrophilia (HR = 2.6), high-risk SUV_peak (SUV_peak > 10, HR = 4.4) and NSG = 2 (HR = 9.2) were associated with low probability of LC in TS. In multivariate analysis, NSG = 2 was independently associated with low probability of LC (HR = 7.5, p < 0.001) and OS (HR = 5.8, p = 0.001) in the TS. Results obtained in the VS (HR = 5.2 for OS and 3.5 for LC, p < 0.02) were promising.

CONCLUSION

This innovative scoring approach combining baseline neutrophilia and a PET biomarker provides an independent prognostic factor to consider for further clinical investigations.

Radiogenomics: towards a personalized radiation oncology

PURPOSE OF REVIEW

This article evaluates the field of radiogenomics within recent developments in genomics and radiation biology.

RECENT FINDINGS

Many pediatric cancer survivors have undergone treatment with radiation, putting them at risk for long-term side-effects associated with this therapy, especially cardiac disease and secondary malignancies. Advancements in our understanding of radiation biology have led to the understanding that genetics plays a major role in determining a patient's susceptibility to developing long-term side-effects, leading to the field of 'radiogenomics'. Although initial candidate gene studies did not demonstrate replicable genetic variants that affected radiosensitivity, genome-wide association studies have recently begun to identify genes that may help explain some of the observed variation in radiosensitivity. As genomic sciences continues to progress and whole genome studies become more accessible, our understanding of the genes responsible for radiosensitivity will continue to progress.

SUMMARY

The field of radiogenomics continues to evolve with the availability and improved cost of genomic technologies allowing the study of an increasing fraction of the human genome. Studies into genetic factors influencing individual radiosensitivity will increase our understanding of radiobiology and improve our ability to counsel patients on the adverse effects they will likely experience.

Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

The optimal design and patient selection for interventional trials in radiogenomics seem trivial at first sight. However, radiogenomics do not give binary information like in e.g. targetable mutation biomarkers. Here, the risk to develop severe side effects is continuous, with increasing incidences of side effects with higher doses and/or volumes. In addition, a multi-SNP assay will produce a predicted probability of developing side effects and will require one or more cut-off thresholds for classifying risk into discrete categories. A classical biomarker trial design is therefore not optimal, whereas a risk factor stratification approach is more appropriate. Patient selection is crucial and this should be based on the dose-response relations for a specific endpoint. Alternatives to standard treatment should be available and this should take into account the preferences of patients. This will be discussed in detail.

Individual patient data meta-analysis shows a significant association between the ATM rs1801516 SNP and toxicity after radiotherapy in 5456 breast and prostate cancer patients

PURPOSE

Several small studies have indicated that the ATM rs1801516 SNP is associated with risk of normal tissue toxicity after radiotherapy. However, the findings have not been consistent. In order to test this SNP in a well-powered study, an individual patient data meta-analysis was carried out by the International Radiogenomics Consortium.

MATERIALS AND METHODS

The analysis included 5456 patients from 17 different cohorts. 2759 patients were given radiotherapy for breast cancer and 2697 for prostate cancer. Eight toxicity scores (overall toxicity, acute toxicity, late toxicity, acute skin toxicity, acute rectal toxicity, telangiectasia, fibrosis and late rectal toxicity) were analyzed. Adjustments were made for treatment and patient related factors with potential impact on the risk of toxicity.

RESULTS

For all endpoints except late rectal toxicity, a significantly increased risk of toxicity was found for carriers of the minor (Asn) allele with odds ratios of approximately 1.5 for acute toxicity and 1.2 for late toxicity. The results were consistent with a co-dominant pattern of inheritance.

CONCLUSION

This study convincingly showed a significant association between the ATM rs1801516 Asn allele and increased risk of radiation-induced normal tissue toxicity.