A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation

Optimizing fractionation schedules for de-escalation radiotherapy in head and neck cancers using deep reinforcement learning

PURPOSE

Patients with locally-advanced head and neck squamous cell carcinomas (HNSCCs), particularly those related to human papillomavirus (HPV), often achieve good locoregional control (LRC), yet they suffer significant toxicities from standard chemoradiotherapy. This study aims to optimize the daily dose fractionation based on individual responses to radiotherapy (RT), minimizing toxicity while maintaining a low risk of LRC failure.

METHOD

A virtual environment was developed to simulate tumor dynamics under RT for optimizing dose schedules. Patients predicted to maintain LRC were selected for de-escalation experiments. The proliferation saturation index (PSI) and linear-quadratic model were used to predict responses. A deep reinforcement learning (DRL) agent optimized fractionation schemes by interacting with the simulation environment, aiming to reduce the OAR's biologically effective dose (BED) while preserving LRC. The impact of model uncertainty was analyzed and a support vector machine (SVM) model was used to segment parameter space and identify patients more robust to noise.

RESULTS

Personalized de-escalation plans were compared to conventional RT in a cohort of 5000 virtual patients. Personalized fractionation reduced the tumor dose and OAR's BED by 29%, with an average OAR BED reduction of 5.61 ± 2.96 Gy. Prognostic outcomes were nearly identical, with 99.80% of patients in the low-risk LRC failure group. Model uncertainty impacted dosimetric indicators and prognosis, but the high-BED benefit group showed greater robustness to noise. SVM decision boundaries defined parameters range for patient selection.

CONCLUSION

Optimizing fractionated doses based on patient responses minimizes toxicity while maintaining LRC in HNSCCs. Stratifying patients can mitigate model uncertainty and reduce treatment risks.

Extracellular matrix stiffness in endometrial cancer: driving progression and modulating treatment sensitivity via the ROCK1/YAP1 axis

Endometrial cancer (EC) is among the most prevalent gynecological malignancies, with advanced or recurrent cases posing significant treatment challenges due to limited responses to conventional therapies. Growing evidence highlights the critical role of extracellular matrix (ECM) stiffness in driving tumor progression by shaping the tumor microenvironment. In this study, we demonstrate that ECM stiffness is significantly higher in EC tissues compared to normal endometrium, correlating with elevated expression of ROCK1, a mechanosensitive kinase. Using atomic force microscopy (AFM), we quantified ECM stiffness, while polyacrylamide gels with varying stiffness were employed to mimic ECM conditions in vitro. Bioinformatics analyses, immunofluorescence, Western blotting, and co-immunoprecipitation experiments revealed that ROCK1 modulates the phosphorylation of YAP1, promoting its nuclear localization and transcriptional activity, thereby driving aggressive tumor behaviors, including enhanced proliferation, migration, invasion, and reduced apoptosis. Pharmacological inhibition of ROCK1 with Y-27632 mitigated these effects, suppressing tumor growth, restoring apoptosis, and inducing cell cycle arrest. Treatment with Y-27632 improved sensitivity to chemotherapy and radiotherapy, and significantly enhanced macrophage-mediated phagocytosis, thereby boosting anti-tumor immune responses. In hormone-resistant EC cells, ROCK1 inhibition restored sensitivity to progesterone therapy. Notably, in vivo experiments in a xenograft mouse model confirmed the therapeutic potential of Y-27632, as combination therapy with progesterone showed superior tumor-suppressive effects compared to monotherapy. These findings underscore the dual role of ECM stiffness and ROCK1 in driving tumor progression and influencing treatment outcomes. By elucidating the relationship between ECM stiffness, ROCK1/YAP1 signaling, and treatment sensitivity, this study highlights the potential of targeting the ROCK1/YAP1 axis as a therapeutic strategy. ROCK1 serves as both a biomarker for prognosis and a target for improving personalized treatment approaches, offering new avenues to enhance clinical outcomes for EC patients.

Genetic predisposition in sarcomas: clinical implications and management

Recent studies indicate up to 20% of sarcomas may be associated with predisposition genes, and this number will probably increase as genetic testing becomes more available. Evidence on the management of patients with sarcoma and genetic predisposition remains, however, scarce. This review compiles available research on genetic predisposition syndromes associated with sarcoma and sarcoma treatment within such syndromes, addressing key gaps in knowledge. We explore the current evidence on how genetic predisposition may influence treatment decisions and clinical management, focusing on surgery, radiotherapy, systemic treatment, and surveillance. Evidence-based recommendations are currently not available for most syndromes, and we have therefore included pragmatic advice for clinicians. Unanswered questions and unmet needs are also identified, underscoring the importance of multidisciplinary input from specialists such as geneticists, radiologists, surgeons and oncologists. The review stresses the need for future research to improve clinical outcomes for patients with sarcoma and genetic predisposition.

Funding

No funding has been provided for this work.

Gene Signatures and Oncology Treatment Implications

Gene expression signatures (GES) are a powerful tool in oncology used for classification, prognostication, and therapeutic response prediction of malignancies. In this article, we review the disease site guidelines by the National Comprehensive Cancer Network that use GES for treatment planning and clinical use. We identified 4 cancer types for which treatment decisions are frequently influenced by GES. Future developments in the field of GES are likely to include expanded data sources to personalize radiation therapy dosing and predict response to immunotherapy. Ongoing challenges in GES may be addressed to ensure that all patients with cancer benefit from precision oncology.

Differentiation Stage Predicts Radiosensitivity in Mesenchymal-Like Pancreatic Cancer

PURPOSE

To derive a genomic classifier to predict radiosensitivity of pancreatic cancer cell lines and patients with pancreatic cancer to allow genomic-guided radiation therapy.

METHODS AND MATERIALS

We collected a comprehensive data set of full clonogenic cell survival curves of 45 pancreatic cancer cell lines irradiated with clinical photon and proton beams. We derived classifiers based on data from human embryonic and fetal pancreas single-cell RNA-sequencing to distinguish between epithelial and mesenchymal cells and to predict pancreas cell-line differentiation stage. Independent testing was done with an embryonic mouse pancreas single-cell RNA-sequencing data set. We then used bulk RNA-seq profiles from the Cancer Cell Line Encyclopedia to classify our pancreatic cancer cell lines using our epithelial-mesenchymal and differentiation stage classifiers. We then correlated the differentiation stage classifier with the radiosensitivity of the pancreatic cancer cell lines as well as with pancreatic cancer patient data from The Cancer Genome Atlas.

RESULTS

We found wide variability in radiosensitivity to both photons and protons among pancreatic cancer cell lines. We showed that the differentiation stage is predictive of radiosensitivity of mesenchymal pancreatic cancer cell lines but not epithelial pancreatic cancer cell lines. We found that chromatin compaction is associated with the differentiation stage and showed that the less differentiated mesenchymal pancreatic cancer cell lines tend to be radioresistant and with more compact chromatin than the radiosensitive differentiated cell lines. Patients with more differentiated tumors exhibit better overall survival.

CONCLUSIONS

We found that mesenchymal-like undifferentiated pancreatic cancer cell lines are more radioresistant than mesenchymal-like differentiated ones and that patients with pancreatic cancer with mesenchymal-like undifferentiated tumors treated with radiation therapy tend to have lower overall survival compared with patients with mesenchymal-like differentiated tumors. We show that it is feasibility to use the differentiation stage of mesenchymal pancreatic cancer cells to predict tumor specific radiosensitivity.

Developing and validating a prognostic disulfidptosis-related signature for glioblastoma: predicting radioresistance and synergestic effect with immunotherapy

BACKGROUND

Programmed cell death (PCD) modulated radioresistance is one of the predominant causes of treatment failure in glioblastoma (GBM). Disulfidptosis, a newly discovered form of PCD, plays a crucial role in GBM progression. However, the association among disulfidptosis, radiosensitivity and radiotherapy (RT) in GBM remain unclear.

METHODS

We systematically analyzed disulfidptosis-related genes in 1075 GBM patients and constructed a disulfidptosis-related gene signature (DRS). Correlations among the DRS, patient prognosis and immune microenvironment were fully explored. The effects of DRS and EFEMP2 on radiotherapy efficacy were investigated via single cell sequencing analysis and validated via in vitro and in vivo experiments.

RESULTS

The DRS was identified as a robust and independent prognostic biomarker for GBM by multivariate Cox regression analysis, receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in multiple cohorts. High DRS is characterized by radioresistance, and EFEMP2 was proven to be the key gene involved in this process by single cell sequencing analysis, CCK-8 assay and a clonogenic survival assay. In high-DRS patients, the cancer-immunity cycle is attenuated because the antitumor cytotoxicity of CD8+ T cells is inhibited by immune checkpoints. Preclinically, the overexpression of EFEMP2 induced radioresistance and enhancing the efficacy of programmed cell death ligand-1 (PD-L1) blockade in GL261-bearing mice. The combination of irradiation and anti-PD-L1 therapy had a synergistic effect on GBM murine models in which EFEMP2 was overexpressed.

CONCLUSION

Our study bioinformatically and experimentally reveals the molecular landscape of disulfidptosis in GBM, develops a predictive signature for predicting prognosis as well as radioresistance, and provides a synergistic treatment that combines radiotherapy with immunotherapy for radioresistant GBM patients with high DRS or EFEMP2 expression.

Establishing a Deep Learning Model That Integrates Pretreatment and Midtreatment Computed Tomography to Predict Treatment Response in Non-Small Cell Lung Cancer

PURPOSE

Patients with identical stages or similar tumor volumes can vary significantly in their responses to radiation therapy (RT) due to individual characteristics, making personalized RT for non-small cell lung cancer (NSCLC) challenging. This study aimed to develop a deep learning model by integrating pretreatment and midtreatment computed tomography (CT) to predict the treatment response in NSCLC patients.

METHODS AND MATERIALS

We retrospectively collected data from 168 NSCLC patients across 3 hospitals. Data from Shanghai General Hospital (SGH, 35 patients) and Shanxi Cancer Hospital (SCH, 93 patients) were used for model training and internal validation, while data from Linfen Central Hospital (LCH, 40 patients) were used for external validation. Deep learning, radiomics, and clinical features were extracted to establish a varying time interval long short-term memory network for response prediction. Furthermore, we derived a model-deduced personalize dose escalation (DE) for patients predicted to have suboptimal gross tumor volume regression. The area under the receiver operating characteristic curve (AUC) and predicted absolute error were used to evaluate the predictive Response Evaluation Criteria in Solid Tumors classification and the proportion of gross tumor volume residual. DE was calculated as the biological equivalent dose using an /α/β ratio of 10 Gy.

RESULTS

The model using only pretreatment CT achieved the highest AUC of 0.762 and 0.687 in internal and external validation respectively, whereas the model integrating both pretreatment and midtreatment CT achieved AUC of 0.869 and 0.798, with predicted absolute error of 0.137 and 0.185, respectively. We performed personalized DE for 29 patients. Their original biological equivalent dose was approximately 72 Gy, within the range of 71.6 Gy to 75 Gy. DE ranged from 77.7 to 120 Gy for 29 patients, with 17 patients exceeding 100 Gy and 8 patients reaching the model's preset upper limit of 120 Gy.

CONCLUSIONS

Combining pretreatment and midtreatment CT enhances prediction performance for RT response and offers a promising approach for personalized DE in NSCLC.

A systematic review of stereotactic radiosurgery for metastatic spinal sarcomas

PURPOSE

Sarcomas metastasizing to the spine are a rare entity. Ideally an En-bloc resection is necessary to achieve durable local control (LC) rates. However, anatomical constraints often limit the degree of tumor resection. Because of this, other therapeutic modalities either replacing or as an adjuvant to resection are necessary. Stereotactic radiosurgery (SRS) is a reasonable candidate therapy.

METHODS

We conducted a systematic review of the literature using the following databases: PubMed, Science Direct, and Cochrane library. We used a combination of the following terms connected by boolean operators: "Metastatic Sarcoma, Sarcoma of the Spine, Spine Sarcoma, Metastasis, stereotactic radiosurgery, SRS." All retrospective and prospective cohorts, as well as randomized control trials reporting on patients with histopathologically confirmed metastatic sarcomas of the bony elements of the vertebrae, thecal sac, cord, or associated soft tissues of the spine were included. We excluded animal studies, case reports, case series, patients < 18 (pediatric cohorts), review articles and meta-analyses. No date filters were applied to our search.

RESULTS

Our final analysis included 5 studies ranging from 2009 to 2024 reporting on 260 patients and 371 associated lesions. Leiomyosarcoma was the most frequently reported histologic subtype (60%). Most lesions were localized to the thoracic spine (48.6%). 75% of studies reported a median dose < 30 Gy, and achieved biologically equivalent doses (BEDs) ranging from < 50-100. Pooled 1-year median survival was 64.5% (IQR: 61.8-75.10). Pooled 1-year median LC was 86% (IQR: 79.4-88.5). Three of five studies (60%) for OS and 4/5 (80%) for LC had data availability suitable for meta-analysis. The 1-year OS and LC rates proportions across these studies were 67% (proportion = 0.67, 95% CI: 0.57-0.75, p = 0.07, I2 = 63%), and 84% (proportion = 0.84, 95% CI: 0.78-0.89, p = 0.10, I2 = 52%) respectively. Median follow up across all studies was 18 months (IQR:12.7-31.3).

CONCLUSIONS

SRS is a reasonable alternative therapy in either the up front, salvage or adjuvant setting which can facilitate durable LC.

Hypoxia-Associated Gene Signatures Are Not Prognostic in High-Risk Localized Prostate Cancers Undergoing Androgen Deprivation Therapy With Radiation Therapy

PURPOSE

Men with high-risk prostate cancer (PCa) are treated with androgen deprivation therapy (ADT) and radiation therapy, but the disease reoccurs in 30% of patients. Biochemical recurrence of PCa after treatment is influenced by tumor hypoxia. Tumors with high levels of hypoxia are aggressive, resistant to treatment, and have increased metastatic capacity. Gene expression signatures derived from diagnostic biopsies can predict tumor hypoxia and radiosensitivity, but none are in routine clinical use, due to concerns about the applicability of these biomarkers to new patient cohorts. There has been no or limited testing in cohorts of high-risk PCa.

METHODS AND MATERIALS

We generated transcriptomic data for cohorts of patients with high-risk PCa. Patients were treated with ADT followed by external beam radiation therapy with or without a brachytherapy boost. Biomarkers curated from the literature were calculated from pretreatment biopsy gene expression data. The primary endpoint for survival analyses was biochemical recurrence-free survival and the secondary endpoints were distant metastasis-free survival and overall survival.

RESULTS

The performance of the selected biomarkers was poor, with none achieving prognostic significance for biochemical recurrence-free survival or distant metastasis-free survival in any cohort. The brachytherapy boost cohort received shorter durations of ADT than the conventionally fractionated or hypofractionated cohorts (Wilcoxon rank sum test, P = 2.1 × 10-18 and 2.3 × 10-10, respectively) and had increased risk of distant metastasis (log-rank test, P = 8 × 10-4). There were no consistent relationships between biomarker score and outcome for any of the endpoints.

CONCLUSIONS

Hypoxia and radiosensitivity biomarkers were not prognostic in patients with high-risk PCa treated with ADT plus radiation therapy. We speculate that the lack of prognostic capability could be caused by the variable hypoxia-modifying effects of the ADT that these high-risk patients received before and during definitive treatment with radiation therapy. A deeper understanding of biomarker construction, performance, and inter-cohort transferability in relation to patient characteristics, sample handling, and treatment modalities is required before hypoxia biomarkers can be recommended for routine clinical use in the pretreatment setting.

Exploring treatment complexity in maxillary ameloblastoma: A case study on the efficacy of radiotherapy with transcriptomic and literature insights

In this report, we describe the case of a patient initially experiencing symptoms including nasal obstruction and rhinorrhoea, leading to surgeries and a diagnosis of ameloblastoma. Despite complete surgical resections, MRI follow-ups indicated tumour regrowth, prompting a shift to radiotherapy. The treatment plan involved a high-dose (60Gy in 30 fractions of 2Gy) volumetric modulated arc therapy. During treatment, the patient experienced minimal side effects and showed significant clinical improvement, including tumour size reduction and resolution of facial paralysis. Follow-up MRI confirmed shrinkage of the tumour, with some persistent symptoms like diplopia. To better understand the radiosensitivity of ameloblastomas, transcriptomic analysis highlighted the potential of a radiosensitivity index to predict treatment response, indicating ameloblastomas exhibit higher radiosensitivity compared to odontogenic keratocysts. Finally, literature analysis showed a small number of cases with wide range of radiation dose. In conclusion, the case illustrates the complex diagnostic journey of ameloblastomas and underscores the efficacy of radiotherapy for inoperable cases, and the importance of predictive biomarkers to enhance treatment outcomes.

Identification and validation of soft tissue sarcoma-specific transcriptomic model for predicting radioresistance

PURPOSE

We aimed to identify the transcriptomic signatures of soft tissue sarcoma (STS) related to radioresistance and establish a model to predict radioresistance.

MATERIALS AND METHODS

Nine STS cell lines were cultured. Adenosine triphosphate-based viability was determined 5 days after irradiation with 8 Gy of X-rays in a single fraction. Radiosensitive and radioresistant groups were stratified according to the survival rates. Whole transcriptomic sequencing analysis was performed and differentially expressed genes (DEGs) were identified between the radiosensitive and radioresistant groups. For model generation, a cohort of 59 patients with sarcomas from The Cancer Genome Atlas (TCGA) was used. DEGs of the responder and non-responder groups according to the radiotherapy-best response were identified. The overlapping DEGs between those from TCGA data and the STS cell line were subjected to linear regression to develop a formula, namely the STS-specific radioresistance index (STS-RRI), and its performance was compared with that of the previously established radiosensitivity index (RSI).

RESULTS

We selected thirteen overlapping DEGs and established STS-RRI using seven of them: STS-RRI = 1.5185 × MYO16-0.01575 × MYH11 + 3.900375 × KCTD16 + 0.105375 × SYNPO2-0.777375 × MYPN-0.849875 × PCSK6-0.700125 × LTK + 39.4635. Delong's test revealed that the STS-RRI performed better at stratifying responder and non-responder in TCGA cohort than the RSI (p = .002). The progression-free survival curves of the TCGA cohort were significantly discriminated by STS-RRI (p = .013) but not by RSI (p = .241).

CONCLUSION

We developed the STS-RRI to predict the radioresistance of patients with STS in the TCGA dataset, showing a higher performance than RSI.

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.

Bladder Cancer Treatments in the Age of Personalized Medicine: A Comprehensive Review of Potential Radiosensitivity Biomarkers

Bladder cancer is one of the most frequently diagnosed cancers in men. While cystectomy remains the primary treatment, advances in radiotherapy and chemotherapy have highlighted the value of bladder-preserving strategies, which can also enhance patients' quality of life. Despise these advances, around 20% of patients may still require salvage cystectomy due to tumor radioresistance. This underscores the need to develop radiosensitivity predictive assays. Radiotherapy acts by inducing DNA damage, primarily through DNA double-strand breaks, which can significantly affect treatment outcomes if left unrepaired. In addition to activating DNA repair pathways, the response to radiation also involves the tumor microenvironment, cell death pathways, immune responses and different types of cell death and proliferation receptors. In recent years, personalized medicine, which tailors treatments to individual patients, has gained increasing attention in cancer care. The development of chemo- and radiosensitivity predictive assays has become a key focus of cancer research. Despite the potential impact of such assays on bladder cancer treatment, there is still no reliable test that can help clinicians and informs patients in choosing the best treatment. This review aims to highlight studies that attempted to characterize bladder cancer radiosensitivity and to discuss the potential biomarkers that could be used to develop bladder cancer radiosensitivity predictive assays.

Personalized Brachytherapy: Applications and Future Directions

Brachytherapy offers a highly conformal and adaptive approach to radiation therapy for various oncologic conditions. This review explores the rationale, applications, technological advances, and future directions of personalized brachytherapy. Integration of advanced imaging techniques, 3D-printed applicators, and artificial intelligence are rapidly enhancing brachytherapy delivery and efficiency, while genomic tests and molecular biomarkers are refining patient and dose selection. Emerging research on combining brachytherapy with immunotherapy offers unique synergistic potential, and technologies such as intensity-modulated and shielded brachytherapy applicators present novel opportunities to further optimize dose distributions. Despite these promising advances, the field faces challenges including a need to train more practitioners and develop new approaches to treating a broader range of malignancies. As personalized medicine evolves, brachytherapy's ability to deliver highly targeted, individualized treatments positions it as a critical component in future cancer care.

Changes in Daily Apparent Diffusion Coefficient on Fully Quantitative Magnetic Resonance Imaging Correlate With Established Genomic Pathways of Radiation Sensitivity and Reveal Novel Biologic Associations

PURPOSE

Changes in quantitative magnetic resonance imaging (qMRI) are frequently observed during chemotherapy or radiation therapy (RT). It is hypothesized that qMRI features are reflective of underlying tissue responses. It's unknown what underlying genomic characteristics underly qMRI changes. We hypothesized that qMRI changes may correlate with DNA damage response (DDR) capacity within human tumors. Therefore, we designed the current study to correlate qMRI changes from daily RT treatment with underlying tumor transcriptomic profiles.

METHODS AND MATERIALS

Study participants were prospectively enrolled (National Clinical Trial 03500081). RNA expression levels for 757 genes from pretreatment biopsies were obtained using a custom panel that included signatures of radiation sensitivity and DDR. Daily qMRI data were obtained from a 1.5 Tesla MR linear accelerator. Using these images, d-slow, d-star, perfusion, and apparent diffusion coefficient-mean values in tumors were plotted per-fraction, over time, and associated with genomic pathways.

RESULTS

A total of 1022 qMRIs were obtained from 39 patients and both genomic data and qMRI data from 27 total patients. For 20 of those patients, we also generated normal tissue transcriptomic data. Radio sensitivity index values most closely associated with tissue of origin. Multiple genomic pathways including DNA repair, peroxisome, late estrogen receptor responses, KRAS signaling, and UV response were significantly associated with qMRI feature changes (P < .001).

CONCLUSIONS

Genomic pathway associations across metabolic, RT sensitivity, and DDR pathways indicate common tumor biology that may correlate with qMRI changes during a course of treatment. Such data provide hypothesis-generating novel mechanistic insight into the biologic meaning of qMRI changes during treatment and enable optimal selection of imaging biomarkers for biologically MR-guided RT.

Identifying hub genes for chemo-radiotherapy sensitivity in cervical cancer: a bi-dataset in silico analysis

PURPOSE

To identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer.

METHODS

The gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database.

RESULTS

A total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value < 0.05). A nomogram and calibration curve were generated using the six hub genes to predict prognosis with high accuracy. A regulatory network comprising TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and several competitive endogenous RNA (ceRNA) networks comprising mRNAs, miRNAs, and lncRNAs were constructed. Data from HPA indicated that the protein expression of the six hub genes differed significantly between patients with CESC and healthy individuals.

CONCLUSION

Upregulation of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 is associated with radiotherapy sensitivity and is involved in various cellular processes in CESC. These six genes may serve as biomarkers for predicting the radiotherapy response and prognosis in patients with CESC.

KEAP1-mutant atypical meningioma: illustrative case

BACKGROUND

While genetic testing of tumors is commonly used to inform the selection of systemic therapies, there is limited evidence for the application of radiotherapy for brain cancer. Recent studies have shown that Kelch-like ECH-associated protein 1 (KEAP1), a key regulator of cellular responses to oxidative and electrophilic stress, is associated with radioresistance in multiple cancer types. Several studies have reported the clinical significance of KEAP1 mutation in brain metastasis; however, the effect of KEAP1 mutations on radioresponse in meningioma has never been reported.

OBSERVATIONS

The authors present the case of a 40-year-old female with a KEAP1 mutation-positive atypical meningioma that was initially treated with resection followed by intensity-modulated radiation therapy (IMRT). Recurrence was observed at 15 months, requiring reoperation and adjuvant stereotactic radiosurgery (SRS). An excellent treatment response was observed at 7 months post-SRS with an improvement in reported symptoms, although bevacizumab was required for the resolution of radiation necrosis observed 2 months post-SRS.

LESSONS

To the authors' knowledge, this is the first report of KEAP1-mutant meningioma, including its clinical course after comprehensive management. Notably, treatment included multimodal radiotherapy with IMRT followed by SRS. SRS led to an excellent treatment response at the 7-month follow-up. However, radiation necrosis developed after both radiotherapy treatments, suggesting that radiological modification can be beneficial in patients with KEAP1 mutations. https://thejns.org/doi/10.3171/CASE24387.

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

Particle Beam Radiobiology Status and Challenges: A PTCOG Radiobiology Subcommittee Report

Particle therapy (PT) represents a significant advancement in cancer treatment, precisely targeting tumor cells while sparing surrounding healthy tissues thanks to the unique depth-dose profiles of the charged particles. Furthermore, their linear energy transfer and relative biological effectiveness enhance their capability to treat radioresistant tumors, including hypoxic ones. Over the years, extensive research has paved the way for PT's clinical application, and current efforts aim to refine its efficacy and precision, minimizing the toxicities. In this regard, radiobiology research is evolving toward integrating biotechnology to advance drug discovery and radiation therapy optimization. This shift from basic radiobiology to understanding the molecular mechanisms of PT aims to expand the therapeutic window through innovative dose delivery regimens and combined therapy approaches. This review, written by over 30 contributors from various countries, provides a comprehensive look at key research areas and new developments in PT radiobiology, emphasizing the innovations and techniques transforming the field, ranging from the radiobiology of new irradiation modalities to multimodal radiation therapy and modeling efforts. We highlight both advancements and knowledge gaps, with the aim of improving the understanding and application of PT in oncology.

Stromal cell-expressed malignant gene patterns contribute to the progression of squamous cell carcinomas across different sites

Background

Squamous cell carcinomas (SCCs) across different anatomical locations possess common molecular features. Recent studies showed that stromal cells may contribute to tumor progression and metastasis of SCCs. Limited by current sequencing technology and analysis methods, it has been difficult to combine stroma expression profiles with a large number of clinical information.

Methods

With the help of transfer learning on the cell line, single-cell, and bulk tumor sequencing data, we identified and validated 2 malignant gene patterns (V1 and V5) expressed by stromal cells of SCCs from head and neck (HNSCC), lung (LUSC), cervix (CESC), esophagus, and breast.

Results

Pattern V5 reflected a novel malignant feature that explained the mixed signals of HNSCC molecular subtypes. Higher expression of pattern V5 was related to shorter PFI with gender and cancer-type specificity. The other stromal gene pattern V1 was associated with poor PFI in patients after surgery in all the three squamous cancer types (HNSCC p = 0.0055, LUSC p = 0.0292, CESC p = 0.0451). Cancer-associated fibroblasts could induce HNSCC cancer cells to express pattern V1. Adjuvant radiotherapy may weaken the effect of high V1 on recurrence and metastasis, depending on the tumor radiosensitivity.

Conclusion

Considering the prognostic value of stromal gene patterns and its universality, we suggest that the genetic subtype classification of SCCs may be improved to a new system that integrates both malignant and non-malignant components.

Using the genomic adjusted radiation dose (GARD) to personalize the radiation dose in nasopharyngeal cancer

BACKGROUND

Locally advanced nasopharyngeal cancer (NPC) patients undergoing radiotherapy are at risk of treatment failure, particularly locoregional recurrence. To optimize the individual radiation dose, we hypothesize that the genomic adjusted radiation dose (GARD) can be used to correlate with locoregional control.

METHODS

A total of 92 patients with American Joint Committee on Cancer / International Union Against Cancer stage III to stage IVB recruited in a randomized phase III trial were assessed (NPC-0501) (NCT00379262). Patients were treated with concurrent chemo-radiotherapy plus (neo) adjuvant chemotherapy. The primary endpoint is locoregional failure free rate (LRFFR).

RESULTS

Despite the homogenous physical radiation dose prescribed (Median: 70 Gy, range 66-76 Gy), there was a wide range of GARD values (median: 50.7, range 31.1-67.8) in this cohort. In multivariable analysis, a GARD threshold (GARDT) of 45 was independently associated with LRFFR (p = 0.008). By evaluating the physical dose required to achieve the GARDT (RxRSI), three distinct clinical subgroups were identified: (1) radiosensitive tumors that RxRSI at dose < 66 Gy (N = 59, 64.1 %) (b) moderately radiosensitive tumors that RxRSI dose within the current standard of care range (66-74 Gy) (N = 20, 21.7 %), (c) radioresistant tumors that need a significant dose escalation above the current standard of care (>74 Gy) (N = 13, 14.1 %).

CONCLUSION

GARD is independently associated with locoregional control in radiotherapy-treated NPC patients from a Phase 3 clinical trial. GARD may be a potential framework to personalize radiotherapy dose for NPC patients.

Advances and residual knowledge gaps in the neck management of head and neck squamous cell carcinoma patients with advanced nodal disease undergoing definitive (chemo)radiotherapy for their primary

PURPOSE

Substantial changes have been made in the neck management of patients with head and neck squamous cell carcinomas (HNSCC) in the past century. These have been fostered by changes in cancer epidemiology and technological progress in imaging, surgery, or radiotherapy, as well as disruptive concepts in oncology. We aimed to review changes in nodal management, with a focus on HNSCC patients with nodal involvement (cN+) undergoing (chemo)radiotherapy.

METHODS

A narrative review was conducted to review current advances and address knowledge gaps in the multidisciplinary management of the cN+ neck in the context of (chemo)radiotherapy.

RESULTS

Metastatic neck nodes are associated with poorer prognosis and poorer response to radiotherapy, and have therefore been systematically treated by surgery. Radical neck dissection (ND) has gradually evolved toward more personalized and less morbid approaches, i.e., from functional to selective ND. Omission of ND has been made feasible by use of positron-emission tomography/computed tomography to monitor the radiation response in cN+ patients. Human papillomavirus-driven oropharyngeal cancers and their cystic nodes have shown dramatically better prognosis than tobacco-related cancers, justifying a specific prognostic classification (AJCC) creation. Finally, considering the role of lymph nodes in anti-tumor immunity, de-escalation of ND and prophylactic nodal irradiation in combination are intense areas of investigation. However, the management of bulky cN3 disease remains an issue, as aggressive multidisciplinary strategies or innovative combined treatments have not yet significantly improved their prognosis.

CONCLUSION

Personalized neck management is an increasingly important aspect of the overall therapeutic strategies in cN+ HNSCC.

Androgen Deprivation Therapy and Outcomes After Radiation Therapy in Black Patients With Prostate Cancer

Importance

Prostate cancer in Black men compared with White men may be more sensitive to radiation therapy resulting in better outcomes in equal-access settings. The outcomes of androgen-deprivation therapy (ADT) vs radiation therapy itself remains uncharacterized.

Objectives

To quantify any outcome modification by receipt of ADT on the association between Black race and prostate cancer outcomes following radiation therapy.

Design, Setting, and Participants

This was a retrospective, nationwide cohort study of Black and White patients treated in the US Veterans Healthcare system between 2000 and 2020 receiving definitive radiation for localized prostate cancer. Data were analyzed from January 2000 to December 2020.

Exposure

Patient self-identified race and use of ADT defined as any gonadotrophin-releasing hormone agonist or antagonist prescription within 6 months of radiation.

Main Outcomes and Measures

Biochemical recurrence (BCR) from time of completion of radiation therapy (prostate-specific antigen nadir plus 2 ng/mL) and development of metastatic disease or prostate cancer mortality (PCSM) from time of recurrence.

Results

A total of 26 542 patients (8716 Black men with median [IQR] age of 64 [59-69] years and 17 826 White men with median [IQR] age of 67 [62-72] years) received definitive radiation therapy for nonmetastatic prostate cancer and had complete staging and follow-up data. A total of 5144 patients experienced BCR (3384 White and 1760 Black patients). The cumulative incidence of BCR at 10 years was not significantly different between Black and White men (1602 [22.14%] vs 3099 [20.13%], respectively) with multivariable hazard ratio (HR) of 1.03 (95% CI, 0.97-1.09; P = .33). In men receiving ADT, Black men had an HR for BCR of 0.90 (95% CI, 0.82-0.99; P = .03) compared with White men, and in men not receiving ADT, Black men had an HR of 1.13 (95% CI, 1.05-1.22; P = .002). Black race was associated with a decreased risk of developing metastatic disease (HR, 0.90; 95% CI, 0.82-0.98; P = .02) or PCSM (subdistribution HR, 0.72; 95% CI, 0.63-0.82; P < .001) from time of biochemical recurrence.

Conclusions and Relevance

Black patients treated with radiation appear to specifically benefit from the addition of ADT with regard to biochemical control. Additionally, BCR in Black men results in a lower rate of metastatic disease and death from prostate cancer. Future analyses of radiosensitivity in Black men should evaluate for the possibility of outcome modification by ADT.

Development and validation of a radiosensitivity model to evaluate radiotherapy benefits in pan-cancer

Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

Unraveling the Myth of Radiation Resistance in Soft Tissue Sarcomas

There is a misconception that sarcomas are resistant to radiotherapy. This manuscript summarizes available (pre-) clinical data on the radiosensitivity of soft tissue sarcomas. Currently, clinical practice guidelines suggest irradiating sarcomas in 1.8-2 Gy once daily fractions. Careful observation of myxoid liposarcomas patients during preoperative radiotherapy led to the discovery of this subtype's remarkable radiosensitivity. It resulted subsequently in an international prospective clinical trial demonstrating the safety of a reduced total dose, yet still delivered with conventional 1.8-2 Gy fractions. In several areas of oncology, especially for tumors of epithelial origin where radiotherapy plays a curative role, the concurrent application of systemic compounds aiming for radiosensitization has been incorporated into routine clinical practice. This approach has also been investigated in sarcomas and is summarized in this manuscript. Observing relatively low α/β ratios after preclinical cellular investigations, investigators have explored hypofractionation with daily doses ranging from 2.85-8.0 Gy per day in prospective clinical studies, and the data are presented. Finally, we summarize work with mouse models and genomic investigations to predict observed responses to radiotherapy in sarcoma patients. Taken together, these data indicate that sarcomas are not resistant to radiation therapy.

Personalized Radiation Therapy for Breast Cancer

Breast cancer is diagnosed in nearly 3 million people worldwide. Radiation therapy is an integral component of disease management for patients with breast cancer, and is used after breast-conserving surgery or a mastectomy to reduce the risk of a local recurrence. The following review describes the methods used to personalize radiation therapy by optimizing patient selection, using advanced treatment techniques to lessen the radiation dose to normal organs, and using hypofractionation in order to shorten the duration of radiation treatment.

A multicenter high-quality data registry for advanced proton therapy approaches: the POWER registry

BACKGROUND

Paucity and low evidence-level data on proton therapy (PT) represent one of the main issues for the establishment of solid indications in the PT setting. Aim of the present registry, the POWER registry, is to provide a tool for systematic, prospective, harmonized, and multidimensional high-quality data collection to promote knowledge in the field of PT with a particular focus on the use of hypofractionation.

METHODS

All patients with any type of oncologic disease (benign and malignant disease) eligible for PT at the European Institute of Oncology (IEO), Milan, Italy, will be included in the present registry. Three levels of data collection will be implemented: Level (1) clinical research (patients outcome and toxicity, quality of life, and cost/effectiveness analysis); Level (2) radiological and radiobiological research (radiomic and dosiomic analysis, as well as biological modeling); Level (3) biological and translational research (biological biomarkers and genomic data analysis). Endpoints and outcome measures of hypofractionation schedules will be evaluated in terms of either Treatment Efficacy (tumor response rate, time to progression/percentages of survivors/median survival, clinical, biological, and radiological biomarkers changes, identified as surrogate endpoints of cancer survival/response to treatment) and Toxicity. The study protocol has been approved by the IEO Ethical Committee (IEO 1885). Other than patients treated at IEO, additional PT facilities (equipped with Proteus®ONE or Proteus®PLUS technologies by IBA, Ion Beam Applications, Louvain-la-Neuve, Belgium) are planned to join the registry data collection. Moreover, the registry will be also fully integrated into international PT data collection networks.

A hypoxia biomarker does not predict benefit from giving chemotherapy with radiotherapy in the BC2001 randomised controlled trial

BACKGROUND

BC2001 showed combining chemotherapy (5-FU + mitomycin-C) with radiotherapy improves loco-regional disease-free survival in patients with muscle-invasive bladder cancer (MIBC). We previously showed a 24-gene hypoxia-associated signature predicted benefit from hypoxia-modifying radiosensitisation in BCON and hypothesised that only patients with low hypoxia scores (HSs) would benefit from chemotherapy in BC2001. BC2001 allowed conventional (64Gy/32 fractions) or hypofractionated (55Gy/20 fractions) radiotherapy. An exploratory analysis tested an additional hypothesis that hypofractionation reduces reoxygenation and would be detrimental for patients with hypoxic tumours.

METHODS

RNA was extracted from pre-treatment biopsies (298 BC2001 patients), transcriptomic data generated (Affymetrix Clariom-S arrays), HSs calculated (median expression of 24-signature genes) and patients stratified as hypoxia-high or -low (cut-off: cohort median).

PRIMARY ENDPOINT

invasive loco-regional control (ILRC); secondary overall survival.

FINDINGS

Hypoxia affected overall survival (HR = 1.30; 95% CI 0.99-1.70; p = 0.062): more uncertainty for ILRC (HR = 1.29; 95% CI 0.82-2.03; p = 0.264). Benefit from chemotherapy was similar for patients with high or low HSs, with no interaction between HS and treatment arm. High HS associated with poor ILRC following hypofractionated (n = 90, HR 1.69; 95% CI 0.99-2.89 p = 0.057) but not conventional (n = 207, HR 0.70; 95% CI 0.28-1.80, p = 0.461) radiotherapy. The finding was confirmed in an independent cohort (BCON) where hypoxia associated with a poor prognosis for patients receiving hypofractionated (n = 51; HR 14.2; 95% CI 1.7-119; p = 0.015) but not conventional (n = 24, HR 1.04; 95% CI 0.07-15.5, p = 0.978) radiotherapy.

INTERPRETATION

Tumour hypoxia status does not affect benefit from BC2001 chemotherapy. Hypoxia appears to affect fractionation sensitivity. Use of HSs to personalise treatment needs testing in a biomarker-stratified trial.

FUNDING

Cancer Research UK, NIHR, MRC.

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma

Background

Radiotherapy is an effective treatment for head and neck squamous cell carcinoma (HNSCC), however how to predict the prognosis is not clear.

Methods

Here we collected 262 radiosensitivity-associated genes, screened and constructed a prognostic nine-gene risk model through univariate COX, lasso regression, stepwise regression and multivariate COX analysis for transcriptome and clinical information of HNSCC patients obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases.

Results

The reliability and robustness of the risk model were verified by receiver operating characteristic (ROC) curves, risk maps, and Kaplan-Meier (KM) curves analysis. Differences in immune cell infiltration and immune-related pathway enrichment between high-risk and low-risk subgroups were determined by multiple immune infiltration analyses. Meanwhile, the mutation map and the responses to immunotherapy were also differentiated by the prognostic nine-gene signature associated with radiosensitivity. These nine genes expression in HNSCC was verified in the Human Protein Atlas (HPA) database. After that, these nine genes expression was verified to be related to radiation resistance through in-vitro cell experiments.

Conclusions

All results showed that the nine-gene signature associated with radiosensitivity is a potential prognostic indicator for HNSCC patients after radiotherapy and provides potential gene targets for enhancing the efficacy of radiotherapy.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control

Introduction

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.

Methods

In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.

Results

Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.

Discussion

Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

A genomic score to predict local control among patients with brain metastases managed with radiation

BACKGROUND

Clinical predictors of local recurrence following radiation among patients with brain metastases (BrM) provide limited explanatory power. We developed a DNA-based signature of radiotherapeutic efficacy among patients with BrM to better characterize recurrence risk.

METHODS

We identified 570 patients with 1487 BrM managed with whole-brain (WBRT) or stereotactic radiation therapy at Brigham and Women's Hospital/Dana-Farber Cancer Institute (2013-2020) for whom next-generation sequencing panel data (OncoPanel) were available. Fine/Gray's competing risks regression was utilized to compare local recurrence on a per-metastasis level among patients with versus without somatic alterations of likely biological significance across 84 genes. Genes with a q-value ≤ 0.10 were utilized to develop a "Brain-Radiation Prediction Score" ("Brain-RPS").

RESULTS

Genomic alterations in 11 (ATM, MYCL, PALB2, FAS, PRDM1, PAX5, CDKN1B, EZH2, NBN, DIS3, and MDM4) and 2 genes (FBXW7 and AURKA) were associated with decreased or increased risk of local recurrence, respectively (q-value ≤ 0.10). Weighted scores corresponding to the strength of association with local failure for each gene were summed to calculate a patient-level RPS. On multivariable Fine/Gray's competing risks regression, RPS [1.66 (1.44-1.91, P < .001)], metastasis-associated edema [1.60 (1.16-2.21), P = .004], baseline size [1.02 (1.01-1.03), P < .001] and receipt of WBRT without local therapy [4.04 (2.49-6.58), P < .001] were independent predictors of local failure.

CONCLUSIONS

We developed a genomic score to quantify local recurrence risk following brain-directed radiation. To the best of our knowledge, this represents the first study to systematically correlate DNA-based alterations with radiotherapeutic outcomes in BrM. If validated, Brain-RPS has potential to facilitate clinical trials aimed at genome-based personalization of radiation in BrM.

Can Ki-67 predict radiotherapy response in neuroendocrine tumors? Retrospective analysis of a monocentric series of patients

BACKGROUND

The impact of radiotherapy (RT) in neuroendocrine neoplasms is still unknown, and outcomes could be improved by a better insight in RT response predictors. This retrospective analysis investigates the potential correlation between Ki-67 and RT response to evaluate its role as biological marker of radiosensitivity.

MATERIAL AND METHODS

Data from patients treated at an Italian NET-referral center between 2015 and 2020 were retrieved. Inclusion criteria included: histologically-proven diagnosis of NEN, Ki-67 status, indication (symptomatic and/or ablative) and at least one post-RT radiological assessment.

RESULTS

Forty-two patients and 63 different treatment lines were included. Primary tumors presented Ki-67 values < 3% in 21% of cases, between 3 and 20% in 45% and >20% in the remaining 33%. Almost all patients were metastatic at the time of RT, which was performed with symptomatic purpose in 43% of cases. At a median time of three months, a complete response on the target lesion was observed in nine cases (14%), a partial response in 17 (27%), stability in 23 (37%) and local progression in 14 (22%). With median FU of 22.8 months, OS does not show statistically significant differences among three Ki-67 groups. Considering all lines of therapy, the relationship between ORR and Ki-67, did not show statistically significant differences, even following adjustments for drug types and delivered RT doses.

CONCLUSION

No association between Ki67 and local tumor response to RT could be observed in the present cohort, regardless of whether the evaluation was performed on a categorical or continuous scale.

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.

Treatment outcomes of stereotactic body radiation therapy for primary and metastatic sarcoma of the spine

PURPOSE

This study evaluated the treatment outcomes of spine stereotactic body radiation therapy (SBRT) in sarcoma patients.

MATERIALS AND METHODS

A total of 44 sarcoma patients and 75 spinal lesions (6 primary tumors, 69 metastatic tumors) treated with SBRT were retrospectively reviewed between 2006 and 2017. The median radiation dose was 33 Gy (range, 18-45 Gy) in 3 fractions (range, 1-5) prescribed to the 75% isodose line.

RESULTS

The median follow-up duration was 18.2 months. The 1-year local control was 76.4%, and patients treated with single vertebral body were identified as a favorable prognostic factor on multivariate analyses. Progression-free survival at 1 year was 31.9%, with the interval between initial diagnosis and SBRT and extent of disease at the time of treatment being significant prognostic factors. The 1-year overall survival was 80.5%, and PTV and visceral metastases were independently associated with inferior overall survival.

CONCLUSION

SBRT for spinal sarcoma is effective in achieving local control, particularly when treating a single vertebral level with a limited extent of disease involvement, resulting in an excellent control rate. The extent of disease at the time of SBRT is significantly correlated with survival outcomes and should be considered when treating spine sarcoma.

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.

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AIMS

To explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed 'radiosensitivity index' (RSI), to assess its suitability as an input into dose-adjustment algorithms.

MATERIALS AND METHODS

The original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.

RESULTS

Preclinically, RSI showed a negative correlation (Spearman's rho = -0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97-1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).

CONCLUSIONS

These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.

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.

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.

Novel insights into tumorigenesis and prognosis of endometrial cancer through systematic investigation and validation on mitophagy-related signature

In-depth studies on the pathogenesis of endometrial cancer (EC) are critical because of the increasing global incidence of EC. Mitophagy, a mitochondrial quality control process, plays an important role in carcinogenesis and tumor progression. This study aimed to develop a novel mitophagy-based signature to predict the tumorigenesis and prognosis of EC. Data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, and 29 mitophagy-related genes were downloaded from the Pathway Unification Database. EC patients were classified into two risk groups based on the two-key- gene signature, TOMM40 and MFN1, which were constructed using Cox regression analysis. A better prognosis was noted in the low-risk group. The model was validated for four aspects: clinical features, mutation status, clinical therapeutic response, and immune cell infiltration status. Moreover, according to the contribution to the risk model, TOMM40 was selected for further in vitro experiments. The silencing of TOMM40 inhibited mitochondrial degradation; suppressed cell proliferation; induced cell apoptosis and G1 phase cell cycle arrest; inhibited migration, invasion, and epithelial-mesenchymal transition; and suppressed cell stemness. In conclusion, the mitophagy-related risk score provides a novel perspective for survival and drug selection during the individual treatment of EC patients. TOMM40 serves as an oncogene in EC and promotes tumor progression via a mitophagy-related pathway. Thus, TOMM40 is a potential therapeutic target in EC.

Machine Learning & Molecular Radiation Tumor Biomarkers

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.

Improving the efficacy of combined radiotherapy and immunotherapy: focusing on the effects of radiosensitivity

Cancer treatment is gradually entering an era of precision, with multitude studies in gene testing and immunotherapy. Tumor cells can be recognized and eliminated by the immune system through the expression of tumor-associated antigens, but when the cancer escapes or otherwise suppresses immunity, the balance between cancer cell proliferation and immune-induced cancer cell killing may be interrupted, resulting in tumor proliferation and progression. There has been significant attention to combining conventional cancer therapies (i.e., radiotherapy) with immunotherapy as opposed to treatment alone. The combination of radio-immunotherapy has been demonstrated in both basic research and clinical trials to provide more effective anti-tumor responses. However, the absolute benefits of radio-immunotherapy are dependent on individual characteristics and not all patients can benefit from radio-immunotherapy. At present, there are numerous articles about exploring the optimal models for combination radio-immunotherapy, but the factors affecting the efficacy of the combination, especially with regard to radiosensitivity remain inconclusive. Radiosensitivity is a measure of the response of cells, tissues, or individuals to ionizing radiation, and various studies have shown that the radiosensitivity index (RSI) will be a potential biomarker for predicting the efficacy of combination radio-immunotherapy. The purpose of this review is to focus on the factors that influence and predict the radiosensitivity of tumor cells, and to evaluate the impact and predictive significance of radiosensitivity on the efficacy of radio-immunotherapy combination.

The Coming of Age of Breast Radiotherapy

Exactly 50 years ago, the investigators of the National Surgical Adjuvant Breast and Bowel Project began to design the B-06 trial [...].

The role of the genomic mutation signature and tumor mutation burden on relapse risk prediction in head and neck squamous cell carcinoma after concurrent chemoradiotherapy

Personalized genetic profiling has focused on improving treatment efficacy and predicting risk stratification by identifying mutated genes and selecting targeted agents according to genetic testing. Therefore, we evaluated the role of genetic profiling and tumor mutation burden (TMB) using next-generation sequencing in patients with head and neck squamous cell carcinoma (HNSC). The relapse mutation signature (RMS) and chromatin remodeling mutation signature (CRMS) were explored to predict the risk of relapse in patients with HNSC treated with concurrent chemoradiotherapy (CCRT) with platinum-based chemotherapy. Patients in the high RMS and CRMS groups showed significantly shorter relapse-free survival than those in the low RMS and CRMS groups, respectively (p < 0.001 and p = 0.006). Multivariate Cox regression analysis showed that extranodal extension, CCRT response, and three somatic mutation profiles (TMB, RMS, and CRMS) were independent risk predictors for HNSC relapse. The predictive nomogram showed satisfactory performance in predicting relapse-free survival in patients with HNSC treated with CCRT.

Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature

Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.

Radiation induces IRAK1 expression to promote radioresistance by suppressing autophagic cell death via decreasing the ubiquitination of PRDX1 in glioma cells

Radiotherapy is the standard adjuvant treatment for glioma patients; however, the efficacy is limited by radioresistance. The function of Interleukin-1 receptor associated kinase 1 (IRAK1) in tumorigenesis and radioresistance remains to be elucidated. IRAK1 expression and its correlation with prognosis were analyzed in glioma tissues. We found that glioma patients with overexpressed IRAK1 show a poor prognosis. Notably, ionizing radiation (IR) remarkably induces IRAK1 expression, which was decreased by STING antagonist H-151 treatment. JASPAR prediction, ChIP assays, and dual luciferase reporter assays indicated that transcription factor FOXA2, suppressed by STING inhibition, directly binds to the IRAK1 promoter region and activates its transcription. IRAK1 knockdown inhibits malignancy and enhances the radiosensitivity of glioma in vitro and in vivo. To explore the potential IRAK1 interacting targets mediating the radioresistance of glioma cells, IP/Co-IP, LC-MS/MS, GST pull-down, and ubiquitination analyses were conducted. Mechanistically, IRAK1 bound to PRDX1, a major member of antioxidant enzymes, and further prevents ubiquitination and degradation of PRDX1 mediated by E3 ubiquitin ligase HECTD3; Both the DOC and HECT domains of HECTD3 directly interacted with PRDX1 protein. Overexpression of PRDX1 reverses the radiotherapy sensitization effect of IRAK1 depletion by diminishing autophagic cell death. These results suggest the IRAK1-PRDX1 axis provides a potential therapeutic target for glioma patients.

Evolution-Informed Strategies for Combating Drug Resistance in Cancer

The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.