Utilizing the genomically adjusted radiation dose (GARD) to personalize adjuvant radiotherapy in triple negative breast cancer management

Estimating Impacts of p16 Status on Tumor Radiosensitivity in Head and Neck Cancer using Predictive Models

The intrinsic radiosensitivity index (RSI) and genomic-adjusted radiation dose (GARD) were reported to be able to predict the surviving fraction at 2 Gy and therapeutic effect when delivering actual treatment doses using the gene expression profiles of clinical cases. Given the impact of p16 status, a surrogate marker of the human papillomavirus (HPV) infection, on radiosensitivity, we attempted to apply the RSI and GARD to estimate p16-associated radiosensitivity in head and neck squamous cell carcinoma (HNSC). For this purpose, The Cancer Genome Atlas (TCGA) dataset was employed. In the GARD calculation, we assumed that p16-positive patients received 60 Gy in 30 fractions, while p16-negative patients received 70 Gy in 35 fractions. p16 positivity was associated with favorable characteristics compared to negative patients. The RSI and GARD analyses demonstrated increased radiosensitivity and high therapeutic effect in p16-positive patients, compared to p16-negative patients. Additionally, tumor microenvironmental conditions predicted by other models were also significantly affected by p16 status. Collectively, the models used in this study could be a promising tool for estimating p16-associated radiosensitivity in HNSC.

NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy

This position paper, led by the NRG Oncology Particle Therapy Work Group, focuses on the concept of relative biologic effect (RBE) in clinical proton therapy (PT), with the goal of providing recommendations for the next-generation clinical trials with PT on the best practice of investigating and using RBE, which could deviate from the current standard proton RBE value of 1.1 relative to photons. In part 1, current clinical utilization and practice are reviewed, giving the context and history of RBE. Evidence for variation in RBE is presented along with the concept of linear energy transfer (LET). The intertwined nature of tumor radiobiology, normal tissue constraints, and treatment planning with LET and RBE considerations is then reviewed. Part 2 summarizes current and past clinical data and then suggests the next steps to explore and employ tools for improved dynamic models for RBE. In part 3, approaches and methods for the next generation of prospective clinical trials are explored, with the goal of optimizing RBE to be both more reflective of clinical reality and also deployable in trials to allow clinical validation and interpatient comparisons. These concepts provide the foundation for personalized biologic treatments reviewed in part 4. Finally, we conclude with a summary including short- and long-term scientific focus points for clinical PT. The practicalities and capacity to use RBE in treatment planning are reviewed and considered with more biological data in hand. The intermediate step of LET optimization is summarized and proposed as a potential bridge to the ultimate goal of case-specific RBE planning that can be achieved as a hypothesis-generating tool in near-term proton trials.

Combinatorial Approaches for Chemotherapies and Targeted Therapies With Radiation: United Efforts to Innovate in Patient Care

Combinatorial therapies consisting of radiation therapy (RT) with systemic therapies, particularly chemotherapy and targeted therapies, have moved the needle to augment disease control across nearly all disease sites for locally advanced disease. Evaluating these important combinations to incorporate more potent therapies with RT will aid our understanding of toxicity and efficacy for patients. This article discusses multiple disease sites and includes a compilation of contributions from expert Red Journal editors from each disease site. Leveraging improved systemic control with novel agents, we must continue efforts to study novel treatment combinations with RT.

Moving the Needle Forward in Genomically-Guided Precision Radiation Treatment

Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient's tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.

Individualising radiation therapy decisions in breast cancer patients based on tumour infiltrating lymphocytes and genomic biomarkers

Radiation therapy (RT) has long been fundamental for the curative treatment of breast cancer. While substantial progress has been made in the anatomical and technological precision of RT delivery, and some approaches to de-escalate or omit RT based on clinicopathologic features have been successful, there remain substantial opportunities to refine individualised RT based on tumour biology. A major area of clinical and research interest is to ascertain the individualised risk of loco-regional recurrence to direct treatment decisions regarding escalation and de-escalation of RT. Patient-tailored treatment with RT is considerably lagging behind compared with the massive progress made in the field of personalised medicine that currently mainly applies to decisions on the use of systemic therapy or targeted agents. Herein we review select literature surrounding the use of tumour genomic biomarkers and biomarkers of the immune system, including tumour infiltrating lymphocytes (TILs), within the management of breast cancer, specifically as they relate to progress in moving toward analytically validated and clinically tested biomarkers utilized in RT.

A clinicogenomic model including GARD predicts outcome for radiation treated patients with HPV+ oropharyngeal squamous cell carcinoma

Background

Treatment decision-making in oropharyngeal squamous cell carcinoma (OPSCC) includes clinical stage, HPV status, and smoking history. Despite improvements in staging with separation of HPV-positive and -negative OPSCC in AJCC 8th edition (AJCC8), patients are largely treated with a uniform approach, with recent efforts focused on de-intensification in low-risk patients. We have previously shown, in a pooled analysis, that the genomic adjusted radiation dose (GARD) is predictive of radiation treatment benefit and can be used to guide RT dose selection. We hypothesize that GARD can be used to predict overall survival (OS) in HPV-positive OPSCC patients treated with radiotherapy (RT).

Methods

Gene expression profiles (Affymetrix Clariom D) were analyzed for 234 formalin-fixed paraffin-embedded samples from HPV-positive OPSCC patients within an international, multi-institutional, prospective/retrospective observational study including patients with AJCC 7th edition stage III-IVb. GARD, a measure of the treatment effect of RT, was calculated for each patient as previously described. In total, 191 patients received primary RT definitive treatment (chemoradiation or RT alone, and 43 patients received post-operative RT. Two RT dose fractionations were utilized for primary RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (range 50.88-74) for primary RT definitive cases and 66 Gy (range 44-70) for post-operative RT cases. The median follow up was 46.2 months (95% CI, 33.5-63.1). Cox proportional hazards analyses were performed with GARD as both a continuous and dichotomous variable and time-dependent ROC analyses compared the performance of GARD with the NRG clinical nomogram for overall survival.

Results

Despite uniform radiation dose utilization, GARD showed significant heterogeneity (range 30-110), reflecting the underlying genomic differences in the cohort. On multivariable analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.951 (0.911, 0.993), p = 0.023) compared to AJCC8 (HR = 1.999 (0.791, 5.047)), p = 0.143). ROC analysis for GARD at 36 months yielded an AUC of 80.6 (69.4, 91.9) compared with an AUC of 73.6 (55.4, 91.7) for the NRG clinical nomogram. GARD≥64.2 was associated with improved OS (HR = 0.280 (0.100, 0.781), p = 0.015). In a virtual trial, GARD predicts that uniform RT dose de-escalation results in overall inferior OS but proposes two separate genomic strategies where selective RT dose de-escalation in GARD-selected populations results in clinical equipoise.

Conclusions

In this multi-institutional cohort of patients with HPV-positive OPSCC, GARD predicts OS as a continuous variable, outperforms the NRG nomogram and provides a novel genomic strategy to modern clinical trial design. We propose that GARD, which provides the first opportunity for genomic guided personalization of radiation dose, should be incorporated in the diagnostic workup of HPV-positive OPSCC patients.

Towards Data Driven RT Prescription: Integrating Genomics into RT Clinical Practice

The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.

Advances in Radiotherapy for Breast Cancer

Breast cancer is the most prevalent cancer in women, and the second leading cause of cancer death in women in the United States. Radiation therapy is an important component in the multimodal management of breast cancer, including early stage and locally advanced breast cancers, as well as metastatic cases. Breast cancer radiation therapy has seen significant advancements over the past 20 years. This article discusses the latest advances in the radiotherapeutic management of breast cancer, especially focusing on the technological advances in radiation treatment planning and techniques that have exploited the understanding of radiation biology.

High versus standard radiation dose of definitive concurrent chemoradiotherapy for esophageal cancer: A systematic review and meta-analysis of randomized clinical trials

OBJECTIVES

Compare the efficacy and safety of high vs standard radiation dose of definitive concurrent chemoradiotherapy (dCCRT) for esophageal cancer (EC).

METHODS AND MATERIALS

This meta-analysis is registered in PROSPERO, and it was followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Eligible randomized clinical trials (RCTs) comparing high dose (HD;≥59.4 Gy/1.8 Gy) and standard doses (SD; 50 Gy/2Gy or 50.4 Gy/1.8 Gy) were identified on electronic databases. STATA16.0 was used for statistical analysis. A meta-analysis was performed to compare treatment effect and toxicity.

RESULTS

Four articles with a total of 1014 patients were finally included. The results showed that the two groups had similar 1-, 2-, and 3-year OS rates (RR = 1.08, 95 % CI = 0.90-1.30, P = 0.395; RR = 1.07, 95 % CI = 0.95-1.20, P = 0.272; RR = 1.06, 95 % CI = 0.97-1.17, P = 0.184; respectively) and 2-, and 3-year locoregional progression-free survival (LRPFS) (RR = 0.95, 95 % CI = 0.81-1.10, P = 0.478; RR = 0.97, 95 % CI = 0.85-1.11, P = 0.674; respectively). The HD-RT group had higher grade ≥ 3 treatment-related toxicities (OR = 1.35, 95 % CI = 1.03-1.77, P = 0.029) and treatment-related deaths rates (OR = 1.85, 95 % CI = 1.04-3.28, P = 0.036) compared with the SD-RT group. Results of subgroup analysis also indicated that HD could not bring benefit compared to SD, even with modern radiotherapy techniques.

CONCLUSION

SD-RT had similar treatment effect but lower Grade ≥ 3 treatment-related toxicities rates compared with the HD-RT. Therefore, SD (50 Gy/2Gy or 50.4 Gy/1.8 Gy) should be considered as the recommended dose in dCCRT for EC. Further RCTs are needed to verify our conclusions.

Radiosensitivity is associated with antitumor immunity in estrogen receptor-negative breast cancer

PURPOSE

This study evaluated radiosensitivity and the tumor microenvironment (TME) to identify characteristics of breast cancer patients who would benefit most from radiation therapy.

METHODS

We analyzed 1903 records from the Molecular Taxonomy of Breast Cancer International Consortium cohort using the radiosensitivity index and gene expression deconvolution algorithms, CIBERSORT and xCell, that estimates the TME composition of tumor samples. In this study, patients were stratified according to TME and radiosensitivity. We performed integrative analyses of clinical and immuno-genomic data to characterize molecular features associated with radiosensitivity.

RESULTS

Radiosensitivity was significantly associated with activation of antitumor immunity. In contrast, radioresistance was associated with a reactive stromal microenvironment. The immuno-genomic analysis revealed that estrogen receptor (ER) pathway activity was correlated with suppression of antitumor immunity. In ER-negative disease, the best prognosis was shown in the immune-high and radiosensitive group patients, and the lowest was in the immune-low and radioresistant group patients. In ER-positive disease, immune signature and radiosensitivity had no prognostic significance.

CONCLUSION

Taken together, these results suggest that tumor radiosensitivity is associated with activation of antitumor immunity and a better prognosis, particularly in patients with ER-negative breast cancer.

Predicting tumour radiosensitivity to deliver precision radiotherapy

Owing to advances in radiotherapy, the physical properties of radiation can be optimized to enable individualized treatment; however, optimization is rarely based on biological properties and, therefore, treatments are generally planned with the assumption that all tumours respond similarly to radiation. Radiation affects multiple cellular pathways, including DNA damage, hypoxia, proliferation, stem cell phenotype and immune response. In this Review, we summarize the effect of these pathways on tumour responses to radiotherapy and the current state of research on genomic classifiers designed to exploit these variations to inform treatment decisions. We also discuss whether advances in genomics have generated evidence that could be practice changing and whether advances in genomics are now ready to be used to guide the delivery of radiotherapy alone or in combination.

Patterns of regional recurrence according to molecular subtype in patients with pN2 breast cancer treated with limited field regional irradiation

OBJECTIVE

There is little evidence regarding the radiotherapy modification based on molecular subtypes in breast cancer. This study aimed to identify the risk and patterns of regional recurrence according to molecular subtype in patients with pN2 breast cancer.

METHODS

We identified 454 patients who underwent radical surgery for breast cancer with 4-9 axillary lymph node metastases. All patients underwent axillary lymph node dissection, adjuvant chemotherapy and limited-field regional nodal irradiation. The rates and patterns of regional recurrence were compared between the following three subgroups: luminal type (estrogen receptor- and/or progesterone receptor-positive), HER2-type (estrogen receptor- and progesterone receptor-negative and HER2-positive) and triple-negative type (estrogen receptor-, progesterone receptor- and HER2-negative).

RESULTS

Regional recurrence occurred in 18/454 patients (4%). The risk of regional recurrence was higher in the triple-negative (hazard ratio 7.641) and HER2-type (hazard ratio 4.032) subtypes than in the luminal subtype. The predominant pattern of regional recurrence was inside the radiotherapy field in triple-negative breast cancer and outside the radiotherapy field in HER2-type and luminal-type cancers.

CONCLUSIONS

In patients with pN2 breast cancer, the risk of regional recurrence was higher in the triple-negative and HER2-type than in the luminal type. In-field recurrence was predominant in triple-negative cancer, while out-field recurrence was frequent in luminal and HER2-type breast cancers.

Personalized medicine in radiation oncology and radiation sensitivity index: Pathbreaking genomic way to define the role of radiation in cancer management

ABSTRACTS

The technological developments associated with the branch of Radiation Oncology have been directed towards precise delivery of the dose, leading to improved survival in various solid malignancies. Radiation therapy as a treatment modality, is an integral component of more than half of the diagnosed malignancies. In spite of the role of adaptive radiation therapy evolving over the last decade, the fundamental question remains as to the difference in radiation response between individuals. Recently, the role of the radiosensitivity index has emerged, which has shown immense potential in the development of biologically driven tumor radiation therapy. The role of these novel methods of genome-based molecular assays needs to be explored to help in decision-making between radical treatment options for various malignancies and reduce the associated toxicity burden. In this article, we explore the current evidence available for various malignancy sites and provide a comprehensive review of the predictive values of various molecular markers available and their impact on the radiosensitivity index.

Breast Cancer in Geriatric Patients: Current Landscape and Future Prospects

Breast cancer is the most common cancer diagnosed among women worldwide and more than half are diagnosed above the age of 60 years. Life expectancy is increasing and the number of breast cancer cases diagnosed among older women are expected to increase. Undertreatment, mostly due to unjustifiable fears of advanced-age and associated comorbidities, is commonly practiced in this group of patients who are under-represented in clinical trials and their management is not properly addressed in clinical practice guidelines. With modern surgery and anesthesia, breast surgeries are considered safe and is usually associated with very low complication rates, regardless of extent of surgery. However, oncoplastic surgery and management of the axilla can be tailored based on patients'- and disease-related factors. Most of chemotherapeutic agents, along with targeted therapy and anti-Human epidermal growth factor receptor-2 (HER2) drugs can be safely given for older patients, however, dose adjustment and close monitoring of potential adverse events might be needed. The recently introduced cyclin-D kinase (CDK) 4/6-inhibitors in combination with aromatase inhibitors (AI) or fulvestrant, which changed the landscape of breast cancer therapy, are both safe and effective in older patients and had substituted more aggressive and potentially toxic interventions. Despite its proven efficacy, adjusting or even omitting adjuvant radiation therapy, at least in low-risk older patients, is safe and frequently practiced. In this paper, we review existing data related to breast cancer management among older patients across the continuum; from resection of the primary tumor through adjuvant chemotherapy, radiation and endocrine therapy up to the management of recurrent and advanced-stage disease.

Integrating Radiosensitivity Gene Signature Improves Glioma Outcome and Radiotherapy Response Prediction

Response to radiotherapy (RT) in gliomas varies widely between patients. It is necessary to identify glioma-associated radiosensitivity gene signatures for clinically stratifying patients who will benefit from adjuvant radiotherapy after glioma surgery. Methods: Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) glioma patient datasets were used to validate the predictive potential of two published biomarkers, the radiosensitivity index (RSI) and 31-gene signature (31-GS). To adjust these markers for the characteristics of glioma, we integrated four new glioma-associated radiosensitivity predictive indexes based on RSI and 31-GS by the Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. A receiver operating characteristic (ROC) curve, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to compare the radiosensitivity predictive ability of these six gene signatures. Subgroup analysis was used to evaluate the discriminative capacity of those gene signatures in identifying radiosensitive patients, and a nomogram was built to improve the histological grading system. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore related biological processes. Results: We validated and compared the predictive potential of two published predictive indexes. The AUC area of 31-GS was higher than that of RSI. Based on the RSI and 31-GS, we integrated four new glioma-associated radiosensitivity predictive indexes—PI10, PI12, PI31 and PI41. Among them, a 12-gene radiosensitivity predictive index (PI12) showed the most promising predictive performance and discriminative capacity. Examination of a nomogram created from clinical features and PI12 revealed that its predictive capacity was superior to the traditional WHO classification system. (C-index: 0.842 vs. 0.787, p ≤ 2.2 × 10⁻¹⁶) The GO analysis and GSEA showed that tumors with a high PI12 score correlated with various aspects of the malignancy of glioma. Conclusions: The glioma-associated radiosensitivity gene signature PI12 is a promising radiosensitivity predictive biomarker for guiding effective personalized radiotherapy for gliomas.

Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model

The use of a 10 gene transcriptional signature as part of the GARD model has been shown to be predictive of radiotherapy benefit for a range of cancers, with the potential to determine an optimal overall dose per patient. We used publicly available RNA-seq transcriptomics data from a luminal B breast cancer patient and from 14 prostate cancer patients to explore the radiosensitivity indices (RSI) and so GARD estimates of both tumour and proximal normal biopsies from each individual. Clear differences of clinical relevance in derived radiobiological properties between tumour and proximal normal tissues were evident for the breast cancer patient, whilst such differences across the prostate cancer cohort were more equivocal. Using the prostate cancer cohort's median tumour predicted GARD value as a threshold for high therapeutic effect for radiotherapy, we found evidence that a higher overall prescribed dose than the widely used 72 Gy/36fx could benefit half of these patients. This exploratory study demonstrates the potential combining the GARD model with sequencing based transcriptomics could have in informing personalised radiotherapeutic practise for both breast and prostate cancer patients.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

How Low Can You Go? The Radiobiology of Hypofractionation

Hypofractionated radical radiotherapy is now an accepted standard of care for tumour sites such as prostate and breast cancer. Much research effort is being directed towards more profoundly hypofractionated (ultrahypofractionated) schedules, with some reaching UK standard of care (e.g. adjuvant breast). Hypofractionation exerts varying influences on each of the major clinical end points of radiotherapy studies: acute toxicity, late toxicity and local control. This review will discuss these effects from the viewpoint of the traditional 5 Rs of radiobiology, before considering non-canonical radiobiological effects that may be relevant to ultrahypofractionated radiotherapy. The principles outlined here may assist the reader in their interpretation of the wealth of clinical data presented in the tumour site-specific articles in this special issue.

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.

Genomically Guided Breast Radiation Therapy: A Review of the Current Data and Future Directions

Purpose

To highlight the current evidence and the limitations in data to support a personalized approach in breast oncology radiation therapy management and define steps needed for clinical implementation.

Methods and Materials

A critical review of the current literature on the use of genomics in breast radiation therapy was undertaken by a group of breast radiation oncologists to discuss current data, future directions, and challenges.

Results

A summary of the existing data, ongoing clinical trials, and future directions is provided. The authors note many groups have developed radiation-specific genomic assays, which demonstrate promise in prediction of local control and benefit from radiation therapy; however, prospective validation of their utility is needed. Limitations continue to exist in our understanding of tumor biology and how it can be integrated into clinical practice.

Conclusions

Given the relative ubiquity of breast radiation therapy, the variety of dose and fractionation approaches, and the current data to support a personalized approach, it is our belief that the delivery of breast radiation therapy is uniquely poised for a genomically personalized radiation therapy approach. Prospective clinical trials implementing genomic signatures are needed at this time to advance the field.

Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis

BACKGROUND

Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm.

METHODS

We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic.

FINDINGS

Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97-0·99]; p=0·0017) and overall survival (0·97 [0·95-0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97-1·01; p=0·53) for time to first recurrence and 1·00 (0·96-1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97-1·03; p=1·00) for time to first recurrence and 1·00 (0·98-1·02; p=0·87) for overall survival.

INTERPRETATION

The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose.

FUNDING

None. VIDEO ABSTRACT.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

Long non-coding RNAs (lncRNAs) are involved in diverse biological processes, including DNA damage repair, and are of interest as potential biomarkers of radiosensitivity. We investigated whether lncRNA radiosensitivity signatures could be derived for use in cancer patients treated with radiotherapy. Signature development involved radiosensitivity measurements for cell lines and primary tumor samples, and patient outcome after radiotherapy. A 10-lncRNA signature trained on radiosensitivity measurements in bladder cell lines showed a trend towards independent validation. In multivariable analyses, patients with tumors classified as radioresistant by the lncRNA signature had poorer local relapse-free survival (P = 0.065) in 151 patients with muscle-invasive bladder cancer who underwent radiotherapy. An mRNA-based radiosensitivity index signature performed similarly to the lncRNA bladder signature for local relapse-free survival (P = 0.055). Pathway analysis showed the lncRNA signature associated with molecular processes involved in radiation responses. Knockdown of one of the lncRNAs in the signature showed a modest increase in radiosensitivity in one cell line. An alternative approach involved training on primary cervical tumor radiosensitivity or local control after radiotherapy. Both approaches failed to generate a cervix lncRNA radiosensitivity signature, which was attributed to the age of samples in our cohorts. Our work highlights challenges in validating lncRNA signatures as biomarkers in archival tissue from radiotherapy cohorts, but supports continued investigation of lncRNAs for a role in radiosensitivity.

Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls. Personalized Interventions and Breast Cancer

Breast cancer, the most frequent malignancy in women worldwide, is a heterogeneous group of diseases, characterized by distinct molecular aberrations. In precision medicine, radiation oncology for breast cancer aims at tailoring treatment according to tumor biology and each patient’s clinical features and genetics. Although systemic therapies are personalized according to molecular sub-type [i.e. endocrine therapy for receptor-positive disease and anti-human epidermal growth factor receptor 2 (HER2) therapy for HER2-positive disease] and multi-gene assays, personalized radiation therapy has yet to be adopted in the clinical setting. Currently, attempts are being made to identify prognostic and/or predictive factors, biomarkers, signatures that could lead to personalized treatment in order to select appropriate patients who might, or might not, benefit from radiation therapy or whose radiation therapy might be escalated or de-escalated in dosages and volumes. This overview focuses on what has been achieved to date in personalized post-operative radiation therapy and individual patient radiosensitivity assessments by means of tumor sub-types and genetics.

Breast cancer radiotherapy: What physicians need to know in the era of the precision medicine

Breast cancer is the most common cancer in women worldwide and encompasses a broad spectrum of diseases in one with significant epidemiological, clinical, and biological heterogeneity, which determines a different natural history and prognostic profile. Although classical tumour staging (TNM) still provides valuable information, the current reality is that the clinicians must consider other biological and molecular factors that directly influence treatment decision-making. The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. This knowledge has brought with it a major impact regarding surgical and systemic management and has been practice-changing, but it has also created significant uncertainties regarding how best integrate the radiotherapy treatment into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualised treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. The aim of the present review is to clarify the meaning of this term and to critically evaluate its role and impact on contemporary breast cancer radiotherapy.

Local Treatment of Triple-Negative Breast Cancer

ABSTRACT

Triple-negative breast cancer, compared with other molecular subtypes, poses particular challenges for optimizing the timing and the extent of locoregional treatments. In the past, the combination of increased rates of both locoregional and distant recurrences led to a preference of radical surgery and extensive radiation therapy; however, since the introduction of more effective chemotherapy, a sharp de-escalation in the extent of locoregional treatments followed. Current evidence confirms that less aggressive surgery in combination with tailored radiation therapy offers improved oncological outcomes combined with better quality of life. However, further research is required to optimize locoregional treatments, considering the significant heterogeneity in biological behavior and tumor response to systemic treatments.

Insights into the theranostic value of precision medicine on advanced radiotherapy to breast cancer

Breast cancer is the most common cancer in women worldwide. "Breast cancer" encompasses a broad spectrum of diseases (i.e., subtypes) with significant epidemiological, clinical, and biological heterogeneity. Each of these subtypes has a different natural history and prognostic profile. Although tumour staging (TNM classification) still provides valuable information in the overall management of breast cancer, the current reality is that clinicians must consider other biological and molecular factors that directly influence treatment decision-making, including extent of surgery, indication for chemotherapy, hormonal therapy, and even radiotherapy (and treatment volumes). The management of breast cancer has changed radically in the last 15 years due to significant advances in our understanding of these tumours. While these changes have been extremely positive in terms of surgical and systemic management, they have also created significant uncertainties concerning integration of local and locoregional radiotherapy into the therapeutic scheme. In parallel, radiotherapy itself has also experienced major advances. Beyond the evident technological advances, new radiobiological concepts have emerged, and genomic data and other patient-specific factors must now be integrated into individualized treatment approaches. In this context, "precision medicine" seeks to provide an answer to these open questions and uncertainties. Although precision medicine has been much discussed in the last five years or so, the concept remains somewhat ambiguous, and it often appear to be used as a "catch-all" term. The present review aims to clarify the meaning of this term and, more importantly, to critically evaluate the role and impact of precision medicine on breast cancer radiotherapy. Finally, we will discuss the current and future of precision medicine in radiotherapy.

Radiotherapy for unresectable locally advanced non-small cell lung cancer: a narrative review of the current landscape and future prospects in the era of immunotherapy

Significant recent advances have occurred in the use of radiation therapy for locally advanced non-small cell lung cancer (LA-NSCLC). In fact, the past few decades have seen both therapeutic gains and setbacks in the evolution of radiotherapy for LA-NSCLC. The PACIFIC trial has heralded a new era of immunotherapy and has raised important questions for future study, such as the future directions of radiation therapy for LA-NSCLC in the era of immunotherapy. Modern radiotherapy techniques such as three-dimensional (3D) conformal radiotherapy and intensity-modulated radiotherapy (IMRT) provide opportunities for improved target conformity and reduced normal-tissue exposure. However, the low-dose radiation volume brought by IMRT and its effects on the immune system deserve particular attention when combing radiotherapy and immunotherapy. Particle radiotherapy offers dosimetric advantages and exhibits great immunoregulatory potential. With the ongoing improvement in particle radiotherapy techniques and knowledge, the combination of immunotherapy and particle radiotherapy has tremendous potential to improve treatment outcomes. Of particular importance are questions on the optimal radiation schedule in the settings of radio-immunotherapy. Strategies for the reduction of the irradiated field such as involved-field irradiation (IFI) and omission of clinical target volume (CTV) hold promise for better preservation of immune function while not compromising locoregional and distant control. In addition, different dose-fractionation regimens can have diverse effects on the immune system. Thus, prospective trials are urgently needed to establish the optimal dose fractionation regimen. Moreover, personalized radiotherapy which allows the tailoring of radiation dose to each individual's genetic background and immune state is of critical importance in maximizing the benefit of radiation to patients with LA-NSCLC.

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT-sensitive and chemotherapy-sensitive), RSCR (RT-sensitive and chemotherapy-resistant), RRCS (RT-resistant and chemotherapy-sensitive) and RRCR (RT-resistant and chemotherapy-resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

Mathematical oncology and it's application in non melanoma skin cancer - A primer for radiation oncology professionals

Cancers of the skin (the majority of which are basal and squamous cell skin carcinomas, but also include the rarer Merkel cell carcinoma) are overwhelmingly the most common of all types of cancer. Most of these are treated surgically, with radiation reserved for those patients with high risk features or anatomical locations less suitable for surgery. Given the high incidence of both basal and squamous cell carcinomas, as well as the relatively poor outcome for Merkel cell carcinoma, it is useful to investigate the role of other disciplines regarding their diagnosis, staging and treatment. Mathematical modelling is one such area of investigation. The use of mathematical modelling is a relatively recent addition to the armamentarium of cancer treatment. It has long been recognised that tumour growth and treatment response is a complex, non-linear biological phenomenon with many mechanisms yet to be understood. Despite decades of research, including clinical, population and basic science approaches, we continue to be challenged by the complexity, heterogeneity and adaptability of tumours, both in individual patients in the oncology clinic and across wider patient populations. Prospective clinical trials predominantly focus on average outcome, with little understanding as to why individual patients may or may not respond. The use of mathematical models may lead to a greater understanding of tumour initiation, growth dynamics and treatment response.