Needs and Challenges for Radiation Oncology in the Era of Precision Medicine

dbCRAF: a curated knowledgebase for regulation of radiation response in human cancer

Radiation therapy (RT) is one of the primary treatment modalities of cancer, with 40-60% of cancer patients benefiting from RT during their treatment course. The intrinsic radiosensitivity or acquired radioresistance of tumor cells would affect the response to RT and clinical outcomes in patients. Thus, mining the regulatory mechanisms in tumor radiosensitivity or radioresistance that have been verified by biological experiments and computational analysis methods will enhance the overall understanding of RT. Here, we describe a comprehensive database dbCRAF (http://dbCRAF.xialab.info/) to document and annotate the factors (1,677 genes, 49 proteins and 612 radiosensitizers) linked with radiation response, including radiosensitivity, radioresistance in cancer cells and prognosis in cancer patients receiving RT. On the one hand, dbCRAF enables researchers to directly access knowledge for regulation of radiation response in human cancer buried in the vast literature. On the other hand, dbCRAF provides four flexible modules to analyze and visualize the functional relationship between these factors and clinical outcome, KEGG pathway and target genes. In conclusion, dbCRAF serves as a valuable resource for elucidating the regulatory mechanisms of radiation response in human cancers as well as for the improvement of RT options.

The State of Preclinical Modeling for Early Phase Cancer Trials Using Molecularly Targeted Agents with Radiation

Preclinical studies inform and guide the development of novel treatment combination strategies that bridge the laboratory with the clinic. We aimed to evaluate approaches cancer researchers used to justify advancing new combinations of molecularly targeted agents and radiation treatment into early-phase human clinical trials. Unsolicited early phase clinical trial proposals submitted to the National Cancer Institute's Cancer Therapy Evaluation Program between January 2016 and July 2020 were curated to quantify key characteristics and proportion of preclinical data provided by trialists seeking to conduct molecularly targeted agent-radiation combination studies in cancer patients. These data elucidate the current landscape for how the rationale for a molecularly targeted agent-radiation combination therapy is supported by preclinical research and illustrate unique challenges faced in translation at the intersection of precision medicine and radiation oncology.

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.

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.

Local Disease-Free Survival Rate (LSR) Application to Personalize Radiation Therapy Treatments in Breast Cancer Models

Cancer heterogeneity represents the main issue for defining an effective treatment in clinical practice, and the scientific community is progressively moving towards the development of more personalized therapeutic regimens. Radiotherapy (RT) remains a fundamental therapeutic treatment used for many neoplastic diseases, including breast cancer (BC), where high variability at the clinical and molecular level is known. The aim of this work is to apply the generalized linear quadratic (LQ) model to customize the radiant treatment plan for BC, by extracting some characteristic parameters of intrinsic radiosensitivity that are not generic, but may be exclusive for each cell type. We tested the validity of the generalized LQ model and analyzed the local disease-free survival rate (LSR) for breast RT treatment by using four BC cell cultures (both primary and immortalized), irradiated with clinical X-ray beams. BC cells were chosen on the basis of their receptor profiles, in order to simulate a differential response to RT between triple negative breast and luminal adenocarcinomas. The MCF10A breast epithelial cell line was utilized as a healthy control. We show that an RT plan setup based only on α and β values could be limiting and misleading. Indeed, two other parameters, the doubling time and the clonogens number, are important to finely predict the tumor response to treatment. Our findings could be tested at a preclinical level to confirm their application as a variant of the classical LQ model, to create a more personalized approach for RT planning.

Harnessing data science to advance radiation oncology

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.

Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers

Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets.

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

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