The Radiosensitivity Index (RSI) Gene Signature Identifies Distinct Tumor Immune Microenvironment Characteristics Associated with Susceptibility to Radiotherapy

A Meta-Analysis and Review of Radiation Dose Escalation in Definitive Radiation Therapy between Squamous Cell Carcinoma and Adenocarcinoma of Esophageal Cancer

Esophageal cancer, ranked as the eighth most prevalent cancer globally, is characterized by a low survival rate and poor prognosis. Concurrent chemoradiation therapy (CCRT) is the standard therapy in the non-surgical treatment of localized carcinoma of the esophagus. Nevertheless, the radiation doses employed in CCRT remain notably lower compared to the curative definite chemoradiation therapy utilized in the management of other carcinomas. In order to increase the local control rates and enhance the treatment outcomes, several clinical trials have used high-dose radiation to analyze the effect of dose escalation. Despite the integration of technically advanced RT schemes such as intensity-modulated radiation therapy (IMRT), the results of these trials have failed to demonstrate a significant improvement in overall survival or local progression-free survival. In this review, we investigated previous clinical trials to determine the ineffectiveness of radiation dose escalation in the context of CCRT for esophageal cancer. We aim to clarify the factors contributing to the limited efficacy of escalated radiation doses in improving patient outcomes. Furthermore, we delve into recent research endeavors, exploring prospective radiation dose modifications being altered based on the histological characteristics of the carcinoma. The exploration of these recent studies not only sheds light on potential refinements to the existing treatment protocols but also seeks to identify novel approaches that may pave the way for more efficacious and personalized therapeutic strategies for esophageal cancer management.

A light-controlled single-atom nanozyme hydrogels for glutathione depletion mediated low-dose radiotherapy

Due to the unique ability to mimic natural enzymes, single-atom nanoenzymes (SAE) have garnered significant attention and research in tumor therapy. However, their efficacy often faces challenges in terms of drug delivery methods, and the research regarding their applications in radiotherapy is scarce. Herein, we introduce a light-controlled SAE hydrogel platform (SH) for glutathione-depletion-mediated low-dose radiotherapy. The SH incorporates a Cu single-atom enzyme (CuSA), and upon irradiation with 1064 nm near-infrared light, the CuSA can convert light energy into heat, which in turn degrades the hydrogel, enabling the release of CuSA into tumor cells or tissues. The diffused CuSA not only can facilitate the conversion of H2O2into hydroxyl radicals (•OH), but also can effectively depletes cellular glutathione. This leads to increased sensitivity of tumor cells to radiotherapy, resulting in enhanced cytotoxicity even at low doses. The animal study results further confirmed the good tumor-killing efficacy of this SH system. To the best of our knowledge, this stands as the pioneering report on leveraging a single-atom enzyme for GSH depletion-mediated low-dose radiotherapy.

Classification of Brainstem Gliomas Based on Tumor Microenvironment Status

The inter-tumor heterogeneity of the tumor microenvironment (TME) and how it correlates with clinical profiles and biological characteristics in brainstem gliomas (BSGs) remain unknown, dampening the development of novel therapeutics against BSGs. The TME status was determined with a list of pan-cancer conserved gene expression signatures using a single-sample gene set enrichment analysis (ssGSEA) and was subsequently clustered via consensus clustering. BSGs exhibited a high inter-tumor TME heterogeneity and were classified into four clusters: "immune-enriched, fibrotic", "immune-enriched, non-fibrotic", "fibrotic", and "depleted". The "fibrotic" cluster had a higher proportion of diffuse intrinsic pontine gliomas (p = 0.041), and "PA-like" tumors were more likely to be "immune-enriched, fibrotic" (p = 0.044). The four TME clusters exhibited distinct overall survival (p < 0.001) and independently impacted BSG outcomes. A four-gene panel as well as a radiomics approach were constructed to identify the TME clusters and achieved high accuracy for determining the classification. Together, BSGs exhibited high inter-tumor heterogeneity in the TME and were classified into four clusters with distinct clinical outcomes and tumor biological properties. The TME classification was accurately identified using a four-gene panel that can potentially be examined with the immunohistochemical method and a non-invasive radiomics method, facilitating its clinical application.

Validation of In Vitro Trained Transcriptomic Radiosensitivity Signatures in Clinical Cohorts

Transcriptomic personalisation of radiation therapy has gained considerable interest in recent years. However, independent model testing on in vitro data has shown poor performance. In this work, we assess the reproducibility in clinical applications of radiosensitivity signatures. Agreement between radiosensitivity predictions from published signatures using different microarray normalization methods was assessed. Control signatures developed from resampled in vitro data were benchmarked in clinical cohorts. Survival analysis was performed using each gene in the clinical transcriptomic data, and gene set enrichment analysis was used to determine pathways related to model performance in predicting survival and recurrence. The normalisation approach impacted calculated radiosensitivity index (RSI) values. Indeed, the limits of agreement exceeded 20% with different normalisation approaches. No published signature significantly improved on the resampled controls for prediction of clinical outcomes. Functional annotation of gene models suggested that many overlapping biological processes are associated with cancer outcomes in RT treated and non-RT treated patients, including proliferation and immune responses. In summary, different normalisation methods should not be used interchangeably. The utility of published signatures remains unclear given the large proportion of genes relating to cancer outcome. Biological processes influencing outcome overlapped for patients treated with or without radiation suggest that existing signatures may lack specificity.

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.

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.

Prediction of radiosensitivity and radiocurability using a novel supervised artificial neural network

BACKGROUND

Radiotherapy has been widely used to treat various cancers, but its efficacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity. However, there is still a lack of emerging powerful models, artificial neural networks (ANN), in the practice of gene-based radiosensitivity prediction. In addition, ANN may overfit and learn biologically irrelevant features.

METHODS

We developed a novel ANN with Selective Connection based on Gene Patterns (namely ANN-SCGP) to predict radiosensitivity and radiocurability. We creatively used gene patterns (gene similarity or gene interaction information) to control the "on-off" of the first layer of weights, enabling the low-dimensional features to learn the gene pattern information. ANN-SCGP was trained and tested in 82 cell lines and 1,101 patients from the 11 pan-cancer cohorts.

RESULTS

For survival fraction at 2 Gy, the root mean squared errors (RMSE) of prediction in ANN-SCGP was the smallest among all algorithms (mean RMSE: 0.1587-0.1654). For radiocurability, ANN-SCGP achieved the first and second largest C-index in the 12/20 and 4/20 tests, respectively. The low dimensional output of ANN-SCGP reproduced the patterns of gene similarity. Moreover, the pan-cancer analysis indicated that immune signals and DNA damage responses were associated with radiocurability.

CONCLUSIONS

As a model including gene pattern information, ANN-SCGP had superior prediction abilities than traditional models. Our work provided novel insights into radiosensitivity and radiocurability.

Current and future approaches in the surgical management of T3b/T4 primary and locoregionally advanced melanoma

Currently accepted principles of surgical management-margin width, use of sentinel node biopsy, performance of radical node dissections for node-positive cases-and some aspects of postoperative management (use of radiation for desmoplastic melanoma primaries and for clinically node-positive disease) will change in the future with the potential widespread adoption of adjuvant and neoadjuvant therapies.

Radioimmunotherapy in HPV-Associated Head and Neck Squamous Cell Carcinoma

HPV-associated head and neck squamous cell carcinoma (HNSCC) is a cancer entity with unique biological and clinical characteristics that requires more personalized treatment strategies. As the backbone of conventional therapeutics, radiation is now harnessed to synergize with immunotherapy in multiple malignancies. Accumulating preclinical and clinical data have suggested the potential of radioimmunotherapy in eliciting local and systemic anti-tumor response via direct killing of tumor cells and immunogenic cell death. However, this effect remains uncertain in HPV-associated HNSCC. Owing to its intrinsic radiosensitivity and distinct tumor microenvironment, HPV-associated HNSCC may represent a good candidate for radioimmunotherapy. In this review, we provide a detailed illustration of the biology, the genomic features, and immune landscapes of HPV-associated HNSCC that support the synergism between radiation and immune agents. The interaction between radiotherapy and immunotherapy is described. We also highlight the present evidence as well as ongoing trials using different combination strategies in the recurrent/metastatic or definitive settings. In addition, we have summarized the challenges and outlook for future trial design, with special emphasis on radiotherapy optimization and novel therapeutic options to incorporate.