Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy

CXCR6 expression correlates with radiotherapy response and immune context in triple-negative breast cancer-experimental studies

BACKGROUND

The chemokine receptor CXCR6 is critical for sustained tumor control mediated by CD8+ cytotoxic T cells (CTLs) in tumors. Previous studies have shown that ionizing radiation induces an inflamed immune contexture by upregulating CXCR6. However, the clinical significance of CXCR6 expression in triple-negative breast cancer (TNBC) and its correlation with radiotherapy remains unknown. This study aimed to clarify the prognostic value of CXCR6 and its role in the breast tumor microenvironment (TME).

METHODS

The messenger RNA and protein expression of CXCR6 in human TNBC and their association with survival were analyzed. The role of CXCR6 in the immune context was investigated using a combination of single-cell RNA sequencing, bulk transcriptome sequencing data, and fluorescence-based multiplex immunohistochemistry (mIHC) techniques.

RESULTS

Elevated CXCR6 expression correlated with better clinical outcomes and superior response to adjuvant radiotherapy and immunotherapy in TNBC. CXCR6 fostered an immunostimulatory microenvironment characterized by upregulated cytotoxic markers. We also found that CXCR6 plays a crucial role in regulating the differentiation of CD8+ T cells and the intercellular communication of immune cell subtypes, thus shaping the TME.

CONCLUSIONS

This study highlights the emerging role of CXCR6 in shaping the TME and targeting CXCR6 may be a promising strategy for improving the effectiveness of radiotherapy and immunotherapy in TNBC.

Treatment Response Biomarkers: Working Toward Personalized Radiotherapy for Lung Cancer

Owing to major advances in the field of radiation oncology, patients with lung cancer can now receive technically individualized radiotherapy treatments. Nevertheless, in the era of precision oncology, radiotherapy-based treatment selection needs to be improved as many patients do not benefit or are not offered optimum therapies. Cost-effective robust biomarkers can address this knowledge gap and lead to individuals being offered more bespoke treatments leading to improved outcome. This narrative review discusses some of the current achievements and challenges in the realization of personalized radiotherapy delivery in patients with lung cancer.

DNA methylation profiling deciphers three EMT subtypes with distinct prognoses and therapeutic vulnerabilities in breast cancer

Background: Epithelial-mesenchymal transition (EMT), deemed a pivotal hallmark of tumours, is intricately regulated by DNA methylation and encompasses multiple states along tumour progression. The potential mechanisms that drive the intrinsic heterogeneity of breast cancer (BC) via EMT transformation have not been identified, presenting a significant obstacle in clinical diagnosis and treatment. Methods: A total of 7,602 patients have been included in this study. We leveraged integrated multiomics data (epigenomic, genomic, and transcriptomic data) to delineate the comprehensive landscape of EMT in BC. Subsequently, a subtyping classifier was developed through a machine learning framework proposed by us. Results: We classified the BC samples into three methylation-driven EMT subtypes with distinct features, namely, C1 (the mammary duct development subtype with TP53 activation), C2 (the immune infiltration subtype with high TP53 mutation), and C3 (the ERBB2 amplification subtype with an unfavorable prognosis). Specifically, patients with the C1 subtype might respond to endocrine therapy or the p53-MDM2 antagonist nutlin-3. Patients with the C2 subtype might benefit from combined therapeutic regimens involving radiotherapy, PARP inhibitors, and immune checkpoint blockade therapy. Patients with the C3 subtype might benefit from anti-HER2 agents such as lapatinib. Notably, to increase the clinical applicability of the EMT subtypes, we devised a 96-gene panel-based classifier via a machine learning framework. Conclusions: Our study identified three methylation-driven EMT subtypes with distinct prognoses and biological traits to capture heterogeneity in BC and provided a rationale for the use of this classification as a powerful tool for developing new strategies for clinical trials.

Unraveling the Mechanism of Curculiginis Rhizoma in Suppressing Cisplatin Resistance in Non-Small Cell Lung Cancer: An Experimental Study

Introduction

Non-small cell lung cancer (NSCLC) stands as one of the most prevalent malignancies, and chemotherapy remains the primary treatment for advanced stages. However, the high expression of ABC binding cassette transporters, including MRP, P-gp, and LRP, along with multidrug resistance proteins, has been identified as a significant factor contributing to decreased chemotherapy drug sensitivity. This study aims to explore the impact and underlying mechanisms of Curculiginis Rhizoma [Hypoxidaceae; Curculigo orchioides Gaertn.] (CR) in combination with cisplatin on improving chemoresistance mediated by ABC binding cassette transporters and multidrug resistance proteins in NSCLC.

Methods and Results

To unravel the relationship between JNK, MRP, P-gp, and LRP in NSCLC and gain insights into the regulatory mechanism of CR, this study employs an integrated approach encompassing bioinformatics, molecular docking, molecular dynamics, animal and cellular experiments. Bioinformatics analysis revealed a significant increase in the expression levels of JNK, MRP, P-gp, and LRP subtypes in multidrug-resistant non-small cell lung cancer. Subsequent animal experiments have shown that the combination of CR with cisplatin can improve the survival rate of lung cancer mice. Molecular docking and molecular dynamics analyses demonstrated favorable binding interactions between curculigoside and the aforementioned subtypes of JNK, MRP, P-gp, and LRP. In cellular experiments, the combination of cisplatin with both curculigoside and CR extract resulted in a notable decrease in cell viability and downregulation of the expression of JNK1, JNK2, MRP1, MRP2, MRP4, P-gp, and LRP1 in A549/cis cells.

Conclusion

Remarkably, curculigoside exerted a significant downregulation effect on the expression levels of JNK1, MRP1, MRP2, MRP4, and LRP1. CR, particularly its main effective metabolite, curculigoside, has the potential to enhance the sensitivity of non-small cell lung cancer to cisplatin by regulating levels of JNK/MRP/LRP/P-gp and mitigating multidrug resistance.

Selective Organ-Targeting Hafnium Oxide Nanoparticles with Multienzyme-Mimetic Activities Attenuate Radiation-Induced Tissue Damage

Radioprotective agents hold clinical promises to counteract off-target adverse effects of radiation and benefit radiotherapeutic outcomes, yet the inability to control drug transport in human organs poses a leading limitation. Based upon a validated rank-based multigene signature model, radiosensitivity indices are evaluated of diverse normal organs as a genomic predictor of radiation susceptibility. Selective ORgan-Targeting (SORT) hafnium oxide nanoparticles (HfO2 NPs) are rationally designed via modulated synthesis by α-lactalbumin, homing to top vulnerable organs. HfO2 NPs like Hensify are commonly radioenhancers, but SORT HfO2 NPs exhibit surprising radioprotective effects dictated by unfolded ligands and Hf(0)/Hf(IV) redox couples. Still, the X-ray attenuation patterns allow radiological confirmation in target organs by dual-beam spectral computed tomography. SORT HfO2 NPs present potent antioxidant activities, catalytically scavenge reactive oxygen species, and mimic multienzyme catalytic activities. Consequently, SORT NPs rescue radiation-induced DNA damage in mouse and rabbit models and provide survival benefits upon lethal exposures. In addition to inhibiting radiation-induced mitochondrial apoptosis, SORT NPs impede DNA damage and inflammation by attenuating activated FoxO, Hippo, TNF, and MAPK interactive cascades. A universal methodology is proposed to reverse radioenhancers into radioprotectors. SORT radioprotective agents with image guidance are envisioned as compelling in personalized shielding from radiation deposition.

Hybridization Protection Reaction for Sensitive and Robust Gene Expression Profiling of Clinical Formalin-Fixed Paraffin-Embedded Samples

BACKGROUND

RNA profiling of formalin-fixed paraffin-embedded (FFPE) tumor tissues for the molecular diagnostics of disease prognosis or treatment response is often irreproducible and limited to a handful of biomarkers. This has led to an unmet need for robust multiplexed assays that can profile several RNA biomarkers of interest using a limited amount of specimen. Here, we describe hybridization protection reaction (HPR), which is a novel RNA profiling approach with high reproducibility.

METHODS

HPR assays were designed for multiple genes, including 10 radiosensitivity-associated genes, and compared with TaqMan assays. Performance was tested with synthetic RNA fragments, and the ability to analyze RNA was investigated in FPPE samples from 20 normal lung tissues, 40 lung cancer, and 30 esophageal cancer biopsies.

RESULTS

Experiments performed on 3 synthetic RNA fragments demonstrated a linear dynamic range of over 1000-fold with a replicate correlation coefficient of 0.99 and high analytical sensitivity between 3.2 to 10 000 pM. Comparison of HPR with standard quantitative reverse transcription polymerase chain reaction on FFPE specimens shows nonsignificant differences with > 99% confidence interval between 2 assays in transcript profiling of 91.7% of test transcripts. In addition, HPR was effectively applied to quantify transcript levels of 10 radiosensitivity-associated genes.

CONCLUSIONS

Overall, HPR is an alternative approach for RNA profiling with high sensitivity, reproducibility, robustness, and capability for molecular diagnostics in FFPE tumor biopsy specimens of lung and esophageal cancer.

A swallowable X-ray dosimeter for the real-time monitoring of radiotherapy

Monitoring X-ray radiation in the gastrointestinal tract can enhance the precision of radiotherapy in patients with gastrointestinal cancer. Here we report the design and performance, in the gastrointestinal tract of rabbits, of a swallowable X-ray dosimeter for the simultaneous real-time monitoring of absolute absorbed radiation dose and of changes in pH and temperature. The dosimeter consists of a biocompatible optoelectronic capsule containing an optical fibre, lanthanide-doped persistent nanoscintillators, a pH-sensitive polyaniline film and a miniaturized system for the wireless readout of luminescence. The persistent luminescence of the nanoscintillators after irradiation can be used to continuously monitor pH without the need for external excitation. By using a neural-network-based regression model, we estimated the radiation dose from radioluminescence and afterglow intensity and temperature, and show that the dosimeter was approximately five times more accurate than standard methods for dose determination. Swallowable dosimeters may help to improve radiotherapy and to understand how radiotherapy affects tumour pH and temperature.

Multimodal molecular landscape of response to Y90-resin microsphere radioembolization followed by nivolumab for advanced hepatocellular carcinoma

BACKGROUND

Combination therapy with radioembolization (yttrium-90)-resin microspheres) followed by nivolumab has shown a promising response rate of 30.6% in a Phase II trial (CA209-678) for advanced hepatocellular carcinoma (HCC); however, the response mechanisms and relevant biomarkers remain unknown.

METHODS

By collecting both pretreatment and on-treatment samples, we performed multimodal profiling of tissue and blood samples and investigated molecular changes associated with favorable responses in 33 patients from the trial.

RESULTS

We found that higher tumor mutation burden, NCOR1 mutations and higher expression of interferon gamma pathways occurred more frequently in responders. Meanwhile, non-responders tended to be enriched for a novel Asian-specific transcriptomic subtype (Kaya_P2) with a high frequency of chromosome 16 deletions and upregulated cell cycle pathways. Strikingly, unlike other cancer types, we did not observe any association between T-cell populations and treatment response, but tumors from responders had a higher proportion of CXCL9+/CXCR3+ macrophages. Moreover, biomarkers discovered in previous immunotherapy trials were not predictive in the current cohort, suggesting a distinctive molecular landscape associated with differential responses to the combination therapy.

CONCLUSIONS

This study unraveled extensive molecular changes underlying distinctive responses to the novel treatment and pinpointed new directions for harnessing combination therapy in patients with advanced HCC.

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.

Integrated multiomics analyses unveil the implication of a costimulatory molecule score on tumor aggressiveness and immune evasion in breast cancer: A large-scale study through over 8,000 patients

BACKGROUND

Although immunotherapy has revolutionised cancer management, reliable genomic biomarkers for identifying eligible patient subpopulations are lacking. Costimulatory molecules play a crucial role in mounting anti-tumour responses, and clinical trials targeting these novel biomarkers are underway. However, whether these molecules can determine tumour aggressiveness and the risk of tumour evasion in breast cancer (BC) remains largely unknown.

METHODS

The whole-tissue transcriptomic data of 8236 patients with BC from 15 independent cohorts were extracted. An integrated scoring system named 'costimulatory molecule score' (CMS) was constructed and sufficient validated using least absolute shrinkage and selection operator regression (1000 iterations) and the random survival forest algorithm (1000 trees). The correlation among CMSs, cancer genotypes and clinicopathological characteristics was examined. Extensive multiomics and immunogenomic analyses were performed to investigate and verify the association among CMSs, enriched pathways, potential intrinsic and extrinsic immune escape mechanisms, immunotherapy response and therapeutic options.

RESULTS

The predictive role of CMS model that relies on expression pattern of merely 5 costimulatory genes for prognosis is almost universally applicable to BC patients in a platform-independent manner. Through internal and external in silico validation, high CMS was characterized by favorable genotypes but decreased tumor immunogenicity, activation of stroma, immune-suppressive states and potential immunotherapeutic resistance. Similar results were observed in a real-world immunotherapy cohort and Pan-Cancer analysis.

CONCLUSION

This comprehensive characterization indicates CMS model may be complemented for predicting tumor aggressiveness and immune evasion in BC patients, underlining the future clinical potential for further exploration of resistance mechanisms and optimization of immunotherapeutic strategies.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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

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

A score of DNA damage repair pathway with the predictive ability for chemotherapy and immunotherapy is strongly associated with immune signaling pathway in pan-cancer

DNA damage repair (DDR) is critical in maintaining normal cellular function and genome integrity and is associated with cancer risk, progression, and therapeutic response. However, there is still a lack of a thorough understanding of the effects of DDR genes’ expression level in cancer progression and therapeutic resistance. Therefore, we defined a tumor-related DDR score (TR-DDR score), utilizing the expression levels of 20 genes, to quantify the tumor signature of DNA damage repair pathways in tumors and explore the possible function and mechanism for the score among different cancers. The TR-DDR score has remarkably predictive power for tumor tissues. It is a more accurate indicator for the response of chemotherapy or immunotherapy combined with the tumor-infiltrating lymphocyte (TIL) and G2M checkpoint score than the pre-existing predictors (CD8 or PD-L1). This study points out that the TR-DDR score generally has positive correlations with patients of advanced-stage, genome-instability, and cell proliferation signature, while negative correlations with inflammatory response, apoptosis, and p53 pathway signature. In the context of tumor immune response, the TR-DDR score strongly positively correlates with the number of T cells (CD4+ activated memory cells, CD8+ cells, T regs, Tfh) and macrophages M1 polarization. In addition, by difference analysis and correlation analysis, COL2A1, MAGEA4, FCRL4, and ZIC1 are screened out as the potential modulating factors for the TR-DDR score. In summary, we light on a new biomarker for DNA damage repair pathways and explore its possible mechanism to guide therapeutic strategies and drug response prediction.

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

PURPOSE

Radiotherapy (RT) is a mainstay of cancer care and accumulating evidence suggests the potential for synergism with components of the immune response. However, little data describes the tumor immune contexture in relation to RT-sensitivity. To address this challenge, we employed the radiation sensitivity index (RSI) gene signature to estimate the RT-sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI.

MATERIAL AND METHODS

We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT-sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes (DEGs) combined with single sample gene set enrichment analysis (ssGSEA). Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n=2,368) of tumors underwent DNA sequencing for mutational frequency characterization.

RESULTS

We identified a wide range of RSI values within and across various tumor types, with several demonstrating non-unimodal distributions (e.g. colon, renal, lung, prostate, esophagus, pancreas and PAM50 breast subtypes; p <0.05). Across all tumors types, stratifying RSI at a tumor type-specific median, identified 7,148 DEGs, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1β transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (e.g. CD8+ T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules.

CONCLUSION

This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.

Relationship between Macrophage and Radiosensitivity in Human Primary and Recurrent Glioblastoma: In Silico Analysis with Publicly Available Datasets

The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.