Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

Identification of key genes and pathways involved in T-DM1-resistance in OE-19 esophageal cancer cells through bioinformatics analysis

Introduction

Esophageal Cancer (EC) ranks among the most common malignancies worldwide. Most EC patients acquire drug resistance to chemotherapy either intrinsically or acquired after T-DM1 treatment, which shows that increasing or decreasing the expression of particular genes might influence chemotherapeutic sensitivity or resistance. Therefore, gaining a deeper understanding of the altered expression of genes involved in EC drug resistance and developing new therapeutic methods are essential targets for continued advancement in EC therapy.

Methods

The present study aimed to find critical regulatory genes/pathways in the progression of T-DM1 resistance in OE-19 EC cells. Expression datasets were extracted from GEO omnibus. Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then, enrichment analysis of the hub genes and network cluster analysis of the hub genes was performed. Finally, the genes were screened in the DrugBank database as therapeutic targets and molecular docking analysis was done on the selected targets.

Results

In the current study, nine hub genes were identified in TDM-1-resistant EC cells (CTGF, CDH17, THBS1, CXCL8, NRP1, ITGB5, EDN1, FAT1, and PTGS2). The KEGG analysis highlighted the IL-17 signaling pathway and ECM-receptor interaction pathway as the most critical pathways; cluster analysis also showed the significance of these pathways. Therefore, the genes involved in these two pathways, including CXCL8, FSCN1, PTGS2, SERPINE2, LEF1, THBS1, CCN2, TAGLN, CDH11, and ITGA6, were searched in DrugBank as therapeutic targets. The DrugBank analysis suggests a potential role for Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) in reducing T-DM1 drug resistance in EC. The docking results revealed that NSAIDs, including Diclofenac, Mefenamic acid, Celecoxib, Naproxen, and Etoricoxib, significantly suppress resistant cancer cells.

Conclusion

This comprehensive bioinformatics analysis deeply explains the molecular mechanisms governing TDM-1 resistance in EC. The identified hub genes and their associated pathways offer potential targets for therapeutic interventions. Moreover, the possible role of NSAIDs in mitigating T-DM1 resistance presents an intriguing avenue for further investigation. This research contributes significantly to the field and establishes a basis for further research to enhance treatment efficacy for EC patients.

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.

Predicting radiotherapy efficacy and prognosis in tongue squamous cell carcinoma through an in-depth analysis of a radiosensitivity gene signature

Background

Tongue squamous cell carcinoma (TSCC) is a prevalent tumor that affects many people worldwide. Radiotherapy is a common treatment option, but its efficacy varies greatly. This study seeks to validate the identified gene signature associated with radiosensitivity in TSCC, and its potential in predicting radiotherapy response and prognosis.

Methods

We analyzed 122 TSCC patients from TCGA database using the radiosensitivity signature and classified them into radiosensitive (RS) and radioresistant (RR) groups. Immune infiltration analysis methods were applied to investigate the immune status between different subgroups. Immunophenotype Score (IPS) and pRRophetic algorithm were employed to estimate the efficiency of treatment. A radioresistant TSCC cell line was established by gradually increasing radiation doses. Cell radiosensitivity was evaluated using the CCK-8 and colony formation assays. The expression of radiosensitivity-related genes was validated by qRT-PCR.

Results

Our study validated the predictive capacity of a previously identified "31-gene signature" in the TCGA-TSCC cohort, which effectively stratified patients into RS and RR groups. We observed that the RS group exhibited superior overall survival and progression-free survival rates relative to the RR group when treated with radiotherapy. The RS group was significantly enriched in most immune-related hallmark pathways, and may therefore benefit from immune checkpoint inhibitors. However, the RS group displayed lower sensitivity to first-line chemotherapy. A radioresistant TSCC cell line (CAL-27R) exhibited increased clonogenic potential and cell viability following irradiation, accompanied by downregulation of three radiosensitivity-related genes compared to its parental non-resistant cell (CAL-27). In addition, we constructed and validated a radiosensitivity-related prognostic index (PI) using 4 radiosensitivity-related genes associated with TSCC prognosis.

Conclusion

We assessed the ability of the radiosensitivity gene signature to predict outcomes in TSCC patients. our research provided valuable insights into the molecular pathways associated with radiosensitivity in TSCC and offered clinicians a practical tool to predict patient radiotherapy effectiveness and prognosis.

A Whole-Transcriptomic Analysis of Canine Oral Melanoma: A Chance to Disclose the Radiotherapy Effect and Outcome-Associated Gene Signature

Oral melanoma (OM) is the most common malignant oral tumour among dogs and shares similarities with human mucosal melanoma (HMM), validating the role of canine species as an immunocompetent model for cancer research. In both humans and dogs, the prognosis is poor and radiotherapy (RT) represents a cornerstone in the management of this tumour, either as an adjuvant or a palliative treatment. In this study, by means of RNA-seq, the effect of RT weekly fractionated in 9 Gray (Gy), up to a total dose of 36 Gy (4 weeks), was evaluated in eight dogs affected by OM. Furthermore, possible transcriptomic differences in blood and biopsies that might be associated with a longer overall survival (OS) were investigated. The immune response, glycosylation, cell adhesion, and cell cycle were the most affected pathways by RT, while tumour microenvironment (TME) composition and canonical and non-canonical WNT pathways appeared to be modulated in association with OS. Taking these results as a whole, this study improved our understanding of the local and systemic effect of RT, reinforcing the pivotal role of anti-tumour immunity in the control of canine oral melanoma (COM).

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.

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.

Uncovering a novel DNA repair-related radiosensitivity model for evaluation of radiotherapy susceptibility in uterine corpus endometrial cancer

Background

Uterine corpus endometrial cancer (UCEC) exhibit heterogeneity in their DNA repair capacity, which can impact their response to radiotherapy. Our study aimed to identify potential DNA repair-related biomarkers for predicting radiation response in UCEC.

Methods

We conducted a thorough analysis of 497 UCEC samples obtained from TCGA database. Using LASSO-COX regression analysis, we constructed a radiosensitivity signature and subsequently divided patients into the radiosensitive (RS) and the radioresistant (RR) groups based on their radiosensitivity index. The GSVA and GSEA were performed to explore functional annotations. The CIBERSORT and ESTIMATE algorithms were utilized to investigate the immune infiltration status of the two groups. Additionally, we utilized the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and pRRophetic algorithms to predict the effectiveness of different treatment modalities.

Results

We constructed a radiosensitivity index consists of four DNA repair-related genes. Patients in the RS group demonstrated significantly improved prognosis compared to patients in the RR group when treated with radiotherapy. We observed that the RS group exhibited a higher proportion of the POLE ultra-mutated subtype, while the RR group had a higher proportion of the copy number high subtype. GSVA enrichment analysis revealed that the RS group exhibited enrichment in DNA damage repair pathways. Notably, the RS group demonstrated a higher proportion of naïve B cells and follicular helper T cells, while regulatory T cells (Tregs) and memory B cells were more abundant in the RR group. Furthermore, patients in the RS-PD-L1-high subgroup exhibited enrichment in immune-related pathways and increased sensitivity to immunotherapy, which is likely to contribute to their improved prognosis. Additionally, we conducted in vitro experiments to validate the expression of radiosensitivity genes in non-radioresistant (AN3CA) and radioresistant (AN3CA/IR) endometrial cancer cells.

Conclusions

In conclusion, our research successfully constructed a radiosensitivity signature with robust predictive capacity. These findings shed light on the association between immune activation, PD-L1 expression, and the response to immunotherapy in the context of radiotherapy.

The value of subcutaneous xenografts for individualised radiotherapy in HNSCC: Robust gene signature correlates with radiotherapy outcome in patients and xenografts

PURPOSE

To assess the robustness of prognostic biomarkers and molecular tumour subtypes developed for patients with head and neck squamous cell carcinoma (HNSCC) on cell-line derived HNSCC xenograft models, and to develop a novel biomarker signature by combining xenograft and patient datasets.

MATERIALS AND METHODS

Mice bearing xenografts (n = 59) of ten HNSCC cell lines and a retrospective, multicentre patient cohort (n = 242) of the German Cancer Consortium-Radiation Oncology Group (DKTK-ROG) were included. All patients received postoperative radiochemotherapy (PORT-C). Gene expression analysis was conducted using GeneChip Human Transcriptome Arrays. Xenografts were stratified based on their molecular subtypes and previously established gene classifiers. The dose to control 50 % of tumours (TCD50) was compared between these groups. Using differential gene expression analyses combining xenograft and patient data, a gene signature was developed to define risk groups for the primary endpoint loco-regional control (LRC).

RESULTS

Tumours of mesenchymal subtype were characterized by a higher TCD50 (xenografts, p < 0.001) and lower LRC (patients, p < 0.001) compared to the other subtypes. Similar to previously published patient data, hypoxia- and radioresistance-related gene signatures were associated with high TCD50 values. A 2-gene signature (FN1, SERPINE1) was developed that was prognostic for TCD50 (xenografts, p < 0.001) and for patient outcome in independent validation (LRC: p = 0.007).

CONCLUSION

Genetic prognosticators of outcome for patients after PORT-C and subcutaneous xenografts after primary clinically relevant irradiation show similarity. The identified robust 2-gene signature may help to guide patient stratification, after prospective validation. Thus, xenografts remain a valuable resource for translational research towards the development of individualized radiotherapy.

Prognostic hub gene CBX2 drives a cancer stem cell-like phenotype in HCC revealed by multi-omics and multi-cohorts

Hepatocellular carcinoma (HCC) is a malignant tumor with a high prevalence and fatality rate. CBX2 has been demonstrated to impact the development and advancement of various cancers, albeit it has received limited attention in relation to HCC. In this study, CBX2 and CEP55 were screened out with the refined triple regulatory networks constructed by total RNA-seq datasets (TCGA-LIHC, GSE140845) and a robust prognostic model. Aberrantly higher expression levels of CBX2 and CEP55 in HCC may be caused by CNV alterations, promoter hypo-methylation, open chromatin accessibility, and greater active marks such as H3K4me3, H3K4me1, and H3K27ac. Functionally, CBX2, which was highly correlated with CD44, shaped a cancer stem cell-like phenotype by positively regulating cell-cycle progression, proliferation, invasion, metastasis, wound healing, and radiation resistance, revealed by combining bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown validated its role in affecting the cell cycle. Importantly, we revealed CBX2 could activate gene by cooperating with co-regulators or not rather than a recognizer of the repressive mark H3K27me3. For instance, we uncovered CBX2 bound to promoter of CTNNB1 and CEP55 to augment their expressions. CBX2 showed a highly positive correlation with CEP55 at pan-cancer level. In addition, CBX2 and CEP55 may enhance extracellular matrix reprograming via cancer-associated fibroblast. Surprisingly, patients with high expression of CBX2 or CEP55 exhibited a higher response to immunotherapy, indicating that CBX2 and CEP55 may be promising therapeutic targets for HCC patients.

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.

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.

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.

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.

Interim Analysis of a Prospective Validation of 2 Blood-Based Genomic Assessments (PPQ and NETest) to Determine the Clinical Efficacy of 177Lu-DOTATATE in Neuroendocrine Tumors

Reliable biomarkers for neuroendocrine tumor (NET) management during peptide receptor radionuclide therapy (PRRT) are lacking. We validated the role of 2 circulating biomarkers: the PRRT prediction quotient (PPQ) as a predictive marker for response and the NETest as a monitoring biomarker. Furthermore, we evaluated whether tissue-based genetic alterations are effective in predicting progression-free survival (PFS). Methods: Data were prospectively collected on patients at the Memorial Sloan Kettering Cancer Center with 177Lu-DOTATATE-treated somatostatin receptor (SSTR)-positive gastroenteropancreatic and lung NETs (n = 67; median age, 66 y; 52% female; 42% pancreatic, 39% small-bowel; 78% grade 1 or 2). All cases were metastatic (89% liver) and had received 1-8 prior treatments (median, 3), including somatostatin analogs (91%), surgery (55%), or chemotherapy (49%). Treatment response included PFS. According to RECIST, version 1.1, responders had stable disease or a partial response (disease-control rate) and nonresponders had progression. Blood was collected before each cycle and at follow-up. Samples were deidentified and assayed and underwent masked analyses. The gene expression assays included RNA isolation, real-time quantitative polymerase chain reaction, and multialgorithm analyses. The PPQ (positive predicts a responder; negative predicts a nonresponder) at baseline was determined. The NETest (0-100 score) was performed. Statistics were analyzed using Mann-Whitney U testing (2-tailed) or Kaplan-Meier survival testing (PFS). In patients with archival tumor tissue, next-generation sequencing was performed through an institutional platform (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets). Results: Forty-one patients (61%) were responders. PPQ accurately predicted 96% (64/67). The hazard ratio for prediction was 24.4 (95% CI, 8.2-72.5). Twelve-month disease control was 97% for PPQ-positive patients versus 26% for PPQ-negative patients (P < 0.0001). Median progression-free survival was not reached in those predicted to respond (PPQ-positive, n = 40) but was 8 mo in those predicted not to respond (PPQ-negative, n = 27). The NETest result in responders was 67 ± 25 at baseline and significantly (P < 0.05) decreased (-37 ± 44%) at follow-up. The NETest result in nonresponders was 44 ± 23 at baseline and significantly (P < 0.05) increased (+76% ± 56%) at progression. Overall, the NETest changes (increases or decreases) were 90% accurate. Thirty patients underwent next-generation sequencing. Tumors were microsatellite-stable, and the median mutational burden was 1.8. Alterations involved mainly the mTOR/PTEN/TSC pathway (30%). No relationship was associated with PRRT response. Conclusion: Our interim analysis confirmed that PPQ is an accurate predictor of 177Lu-DOTATATE responsiveness (radiosensitivity) and that NETest changes accurately correlated with treatment response. Tissue-based molecular genetic information had little value in PRRT prediction. Blood-based gene signatures may improve the management of patients undergoing 177Lu-DOTATATE by providing information on tumor radiosensitivity and disease course, thus allowing individualized strategies.

Molecular characteristics of incidental lower-grade glioma for treatment decision-making

OBJECTIVE

Several limitations are associated with the early diagnosis and treatment of incidental lower-grade glioma (iLGG), and due to its unknown molecular features, its management is categorized as either the "wait-and-see" strategy or immediate treatment. Therefore, in this study the authors explored iLGG's clinical and molecular landscape to improve its management.

METHODS

The authors retrospectively assessed the differences between the molecular and clinical characteristics of iLGG and symptomatic lower-grade glioma (sLGG) samples filtered based on symptom data corresponding to The Cancer Genome Atlas cohort with mutations. Thereafter, genomic and transcriptomic analysis was performed.

RESULTS

There was no significant difference between iLGG and sLGG with respect to mutation status; however, there was an increase in the interaction between major mutations in sLGG, depending on the histological subtype and the IDH1 mutation status. Furthermore, the IDH1 mutation characteristics corresponding to wild-type glioma were much more obvious in sLGG than in iLGG. Additionally, in sLGG, genes associated with malignancy, including cell proliferation-related, cell migration-related, epithelial-to-mesenchymal transition-related, and negative regulation of cell death-related genes, were significantly upregulated, and groups showing higher expression levels of these genes were associated with worse prognosis. Also, 8 of the 75 identified upregulated genes showed positive correlation with resistance to the drugs that are normally used for glioma treatment, including procarbazine, carmustine, vincristine, and temozolomide.

CONCLUSIONS

The new insights regarding the different molecular features of iLGG and sLGG indicated that the immediate management of iLGG could result in better prognosis than the wait-and-see strategy.

DKK1 affects survival of patients with head and neck squamous cell carcinoma by inducing resistance to radiotherapy and immunotherapy

BACKGROUND

Immune checkpoint inhibitors (ICIs) have been approved to treat various types of tumors, including head and neck squamous cell carcinoma (HNSC). However, most HNSC patients do not respond to ICIs. Radioimmunotherapy has been proposed to enhance the immune response rate in HNSC. Dickkopf-1 (DKK1), a secreted protein, plays important roles in the Wnt signaling pathways. Herein, we aimed to explore the effect of DKK1 on radioimmunotherapy in HNSC.

METHODS

We collected the gene expression profile and clinical information of HNSC patients from TCGA and GEO databases. The immune cell infiltration and immune score were assessed using R package CIBERSORT and ESTIMATE. The level of related pathways and biological processes were analyzed by GSEA. The signature scores of gene sets of interest were calculated by GSVA. We also performed cell viability and apoptosis assay, and clonogenic assay to investigate the radiation sensitivity of HSC-3 cells and CNE-2 cells after silencing DKK1 by siRNA.

RESULTS

We found DKK1 was significantly higher expressed in HNSC, and closely correlated with patients' survival time, especially the patients who received radiotherapy. DKK1-knockdown HSC-3 cells or CNE-2 cells showed a decrease in cell viability and colony formation, and an increase in apoptotic rate after radiation. DKK1^high tumors showed a more immunosuppressive microenvironment with lower infiltration of T cells and higher infiltration of marrow-derived suppressor cells (MDSCs).

CONCLUSION

Our data show that DKK1 can affect both radiotherapy and immunotherapy in HNSC, suggesting that DKK1 can be a potential target for radioimmunology in HNSC.

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

ABSTRACTS

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

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.

PAIP1 is a novel oncogene in human hepatocellular carcinoma

BACKGROUND

Poly(A)-binding protein interacting protein 1 (PAIP1) is a translational initiation regulatory factor that has been reported as oncogene in multiple malignant diseases. However, its role in hepatocellular carcinoma (HCC) and the potential mechanisms have not been explored.

METHODS

PAIP1 expression level in HCC cell lines were detected by real-time quantitative PCR and western blotting. The proliferation and colony formation of HCC cell lines were detected by MTT and colony formation assay. The apoptosis and cell cycle were detected by flow cytometry. The volume and growth rate of the xenograft tumors were observed. The potential mechanism of PAIP1 was analyzed by miRNA Microarray Analysis and TargetScan analysis.

RESULTS

PAIP1 is significantly upregulated in HCC cell lines. PAIP1 knockdown dramatically inhibits cell proliferation and colony formation, induces apoptosis and alters the cell cycle distribution by increasing the G2/M cell percentage. Moreover, PAIP1 knockdown significantly reduces tumorigenesis in a murine transplantation model. Bioinformatics and immunoblotting analysis reveal that PAIP1 knockdown dysregulates cyclin D pathway-related proteins.

CONCLUSION

PAIP1 plays an oncogenic role in hepatocellular carcinoma.

A Four-Gene Signature Associated with Radioresistance in Head and Neck Squamous Cell Carcinoma Identified by Text Mining and Data Analysis

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer globally, and radiotherapy plays a crucial part in its treatment. This study was designed to identify potential genes related to radiation resistance in HNSCC.

Method

We first used text mining to obtain common genes related to radiotherapy resistance and HNSCC in published articles. Functional enrichment analyses were conducted to identify the significantly enriched pathways and genes. Protein and protein interactions were performed, and the most significant gene modules were determined; then, genes in the gene modules were validated at transcriptional levels and overall survival. Gene set variation analysis (GSVA) score was calculated, and the association between GSVA score and survival/pathway was estimated. Immune cell infiltration, methylation, and genetic alteration analysis of these genes was conducted in HNSCC patients. Finally, potential sensitive anticancer drugs related to target genes were obtained.

Result

We identified 583 common genes through text mining. After further validation, a four-gene signature (EPHB2, SPP1, SERPINE1, and VEGFC) was constructed. The patients with higher GSVA scores have a worse prognosis than those with lower GSVA scores. Differences in methylation of these four genes in HNSCC tumor tissue and normal tissue were compared, with higher methylation levels of EBPH2 and SPP1 in normal tissue and higher methylation levels of SERPINE1 in the tumor. Immune cell infiltration revealed that the increased expression of these genes was closely related to the infiltration level of CD4+ T cell, neutrophil, macrophage, and dendritic cell. Thirty drugs, including 22 positively and eight negatively correlated drugs that most correlated with related genes, were available for treating HNSCC.

Conclusion

In this study, we identified four potential genes as well as corresponding drugs that might be related to radioresistance in HNSCC patients. These candidate genes may provide a promising avenue to further elevate radiotherapy efficacy.

Integrating Radiosensitivity Gene Signature Improves Glioma Outcome and Radiotherapy Response Prediction

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

Artificial intelligence in radiotherapy

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

A Novel 2-Metagene Signature to Identify High-Risk HNSCC Patients amongst Those Who Are Clinically at Intermediate Risk and Are Treated with PORT

(1) Background: Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who are biologically at high risk for the development of loco−regional recurrences after postoperative radiotherapy (PORT) but at intermediate risk according to clinical risk factors may benefit from additional concurrent chemotherapy. In this matched-pair study, we aimed to identify a corresponding predictive gene signature. (2) Methods: Gene expression analysis was performed on a multicenter retrospective cohort of 221 patients that were treated with postoperative radiochemotherapy (PORT-C) and 283 patients who were treated with PORT alone. Propensity score analysis was used to identify matched patient pairs from both cohorts. From differential gene expression analysis and Cox regression, a predictive gene signature was identified. (3) Results: 108 matched patient pairs were selected. We identified a 2-metagene signature that stratified patients into risk groups in both cohorts. The comparison of the high-risk patients between the two types of treatment showed higher loco−regional control (LRC) after treatment with PORT-C (p < 0.001), which was confirmed by a significant interaction term in Cox regression (p = 0.027), i.e., the 2-metagene signature was indicative for the type of treatment. (4) Conclusion: We have identified a novel gene signature that may be helpful to identify patients with high-risk HNSCC amongst those at intermediate clinical risk treated with PORT, who may benefit from additional concurrent chemotherapy.

Effects of prolonged treatment of TGF-βR inhibitor SB431542 on radiation-induced signaling in breast cancer cells

PURPOSE

We have earlier characterized increased TGF-β signaling in radioresistant breast cancer cells. In this study, we wanted to determine the effect of prolonged treatment of TGF-βR inhibitor SB431542 on radiation-induced signaling, viz., genes regulating apoptosis, EMT, anti and pro-inflammatory cytokines.

MATERIALS AND METHODS

Breast cancer cells were pretreated with TGF-βR inhibitor (SB 431542) followed by exposure to 6 Gy and recovery period of 7 days (D7-6G). We assessed cell survival by MTT assay, cytokines by ELISA and expression analysis by RT-PCR, flow cytometry, and western blot. We carried out migration assays using trans well inserts. We performed bioinformatics analyses of human cancer database through cBioportal.

RESULTS

There was an upregulation of TGF-β1 and 3 and downregulation of TGF-β2, TGF-βR1, and TGF-βR2 in invasive breast carcinoma samples compared to normal tissue. TGF-β1 and TNF-α was higher in radioresistant D7-6G cells with upregulation of pSMAD3, pNF-kB, and ERK signaling. Pretreatment of D7-6G cells with TGF-βR inhibitor SB431542 abrogated pSMAD3, increased proliferation, and migration along with an increase in apoptosis and pro-apoptotic genes. This was associated with hybrid E/M phenotype and downregulation of TGF-β downstream genes, HMGA2 and Snail. There was complete agreement in the expression of mRNA and protein data in genes like vimentin, Snail and HMGA2 in different treatment groups. However, there was disagreement in expression of mRNA and protein in genes like Bax, Bcl-2, E-cadherin, Zeb-1 among the different treatment groups indicating post-transcriptional and post-translational processing of these proteins. Treatment of cells with only SB431542 also increased expression of some E/M genes indicating TGF-β independent effects. Increased IL-6 and IL-10 secretion by SB431542 along with increase in pSTAT3 and pCREB1 could probably explain these TGF-β/Smad3 independent effects.

CONCLUSION

These results highlight that TGF-β-pSMAD3 and TNF-α-pNF-kB are the predominant signaling pathways in radioresistant cells and possibility of some TGF-β/Smad3 independent effects on prolonged treatment with the drug SB431542.

Association of the tissue infiltrated and peripheral blood immune cell subsets with response to radiotherapy for rectal cancer

Background

Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer.

Methods

In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution.

Results

Compared with the non-responsive group, the responsive group showed higher proportions of CD4⁺ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8⁺ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4⁺/CD8⁺ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4⁺ T cells and higher ratio of CD4⁺/CD8⁺ T cells, and lower proportion of CD8⁺ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632–0.857, better than the gene signatures.

Conclusions

Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01252-6.

An emerging role of KRAS in biogenesis, cargo sorting and uptake of cancer-derived extracellular vesicles

Extracellular vesicles (EVs) are nanovesicles secreted for intercellular communication with endosomal network regulating secretion of small EVs (or exosomes) that play roles in cancer progression. As an essential oncoprotein, Kirsten rat sarcoma virus (KRAS) is tightly regulated by its endosomal trafficking for membrane attachment. However, the crosstalk between KRAS and EVs has been scarcely discussed despite its endocytic association. An overview of the oncogenic role of KRAS focusing on its correlation with cancer-associated EVs should provide important clues for disease prognosis and inspire novel therapeutic approaches for treating KRAS mutant cancers. Therefore, this review summarizes the relevant studies that provide substantial evidence linking KRAS mutation to EVs and discusses the oncogenic implication from the aspects of biogenesis, cargo sorting, and release and uptake of the EVs.

Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer

Immunity and hypoxia are two important factors that affect the response of cancer patients to radiotherapy. At the same time, considering the limited predictive value of a single predictive model and the uncertainty of grouping patients near the cutoff value, we developed and validated a combined model based on immune- and hypoxia-related gene expression profiles to predict the radiosensitivity of breast cancer patients. This study was based on breast cancer data from The Cancer Genome Atlas (TCGA). Spike-and-slab Lasso regression analysis was performed to select three immune-related genes and develop a radiosensitivity model. Lasso Cox regression modeling selected 11 hypoxia-related genes for development of radiosensitivity model. Three independent datasets (Molecular Taxonomy of Breast Cancer International Consortium [METABRIC], E-TABM-158, GSE103746) were used to validate the predictive value of radiosensitivity signatures. In the TCGA dataset, the 10-year survival probabilities of the immune radioresistant (IRR) and hypoxia radioresistant (HRR) groups were 0.189 (0.037, 0.973) and 0.477 (0.293, 0.776), respectively. The 10-year survival probabilities of the immune radiosensitive (IRS) and hypoxia radiosensitive (HRS) groups were 0.778 (0.676, 0.895) and 0.824 (0.723, 0.939), respectively. Based on these two gene signatures, we further constructed a combined model and divided all patients into three groups (IRS/HRS, mixed, IRR/HRR). We identified the IRS/HRS patients most likely to benefit from radiotherapy; the 10-year survival probability was 0.886 (0.806, 0.976). The 10-year survival probability of the IRR/HRR group was 0. In conclusion, a combined model integrating immune- and hypoxia-related gene signatures could effectively predict the radiosensitivity of breast cancer and more accurately identify radiosensitive and radioresistant patients than a single model.

RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity

Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R2 for the published models on test data was very poor at 0.01 (range: -0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: -0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.

Circ_0008500 Knockdown Improves Radiosensitivity and Inhibits Tumorigenesis in Breast Cancer Through the miR-758-3p/PFN2 Axis

Breast cancer is one of the most common malignancies worldwide. Circular RNAs (CircRNAs) were revealed to be implicated in the development of breast cancer. In this research, we aimed to investigate the role and underlying mechanism of circ_0008500 in the development and radiosensitivity of breast cancer. Using real-time quantitative PCR (RT-qPCR) and western blot, we found that hsa_circ_0008500 (circ_0008500) and profilin 2 (PFN2) were increased, while microRNA-758-3p (miR-758-3p) was decreased in breast cancer tissues and cells. Cell viability, the number of colonies, proliferation and apoptosis were detected using CCK-8, colony formation, EdU assays and flow cytometry, respectively. Dual-luciferase reporter and RNA immunoprecipitation (RIP) assays were devoted to test the interaction between miR-758-3p and circ_0008500 or PFN2. The results showed that circ_0008500 knockdown inhibited cell growth, and facilitated cell apoptosis and radiosensitivity in breast cancer cells in vitro. Moreover, circ_0008500 regulated PFN2 expression by sponging miR-758-3p. Functionally, circ_0008500 knockdown regulated cell behaviors and radiosensitivity by targeting miR-758-3p to downregulate PFN2 expression in vitro. Additionally, in vivo tumor formation assay and immunohistochemistry (IHC) assay demonstrated that circ_0008500 knockdown enhanced the radiosensitivity and repressed tumor growth in vivo. In conclusion, circ_0008500 inhibition promoted the radiosensitivity and restrained the development of breast cancer by downregulating PFN2 expression via targeting miR-758-3p.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Purpose

We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.

Methods and Materials

Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.

Results

In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.

Conclusions

The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

Analyses of molecular subtypes and their association to mechanisms ofradioresistance in patients with HPV-negative HNSCC treated bypostoperative radiochemotherapy

PURPOSE

To assess the relation of the previously reported classification of molecular subtypes to the outcome of patients with HNSCC treated with postoperative radio(chemo)therapy (PORT-C), and to assess the association of these subtypes with gene expressions reflecting known mechanisms of radioresistance.

MATERIAL AND METHODS

Gene expression analyses were performed using the GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective patient cohort (N=128) of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) with locally advanced HNSCC treated with PORT-C. Tumours were assigned to four molecular subtypes, and correlation analyses between subtypes and clinical risk factors were performed. In addition, the classifications of eight genes or gene signatures related to mechanisms of radioresistance, which have previously shown an association with outcome of patients with HNSCC, were compared between the molecular subtypes. The endpoints loco-regional control (LRC) and overall survival (OS) were evaluated by log-rank tests and Cox regression.

RESULTS

Tumours were classified into the four subtypes basal (19.5%), mesenchymal (18.8%), atypical (15.6%) and classical (14.1%). The remaining tumours could not be classified (32.0%). Tumours of the mesenchymal subtype showed a lower LRC compared to the other subtypes (p=0.012). These tumours were associated with increased epithelial-mesenchymal transition (EMT) and overexpression of a gene signature enriched in DNA repair genes. The majority of the eight considered gene classifiers were significantly associated to LRC or OS in the whole cohort.

CONCLUSION

Molecular subtypes, previously identified on HNSCC patients treated with primary radio(chemo)therapy or surgery, were related to LRC for patients treated with PORT-C, where mesenchymal tumour presented with worse prognosis. After prospective validation, subtype-based patient stratification, potentially in combination with other molecular classifiers, may be considered in future interventional studies in the context of personalised radiotherapy and may guide the development of combined treatment approaches.

Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset

Aim: The aim of our study was to investigate the potential predictive value of the combination of radiosensitivity gene signature and PD-L1 expression for the prognosis of locally advanced head and neck squamous cell carcinoma (HNSCC).

Methods: The cohort was selected from The Cancer Genome Atlas (TCGA) and classified into the radiosensitive (RS) group and radioresistant (RR) group by a radiosensitivity-related gene signature. The cohort was also grouped as PD-L1-high or PD-L1-low based on PD-L1 mRNA expression. The least absolute shrinkage and selection operator (lasso)-based Cox model was used to select hub survival genes. An independent validation cohort was obtained from the Gene Expression Omnibus (GEO) database.

Results: We selected 288 locally advanced HNSCC patients from TCGA. The Kaplan–Meier method found that the RR and PD-L1-high group had a worse survival than others (p = 0.033). The differentially expressed gene (DEG) analysis identified 553 upregulated genes and 486 downregulated genes (p < 0.05, fold change >2) between the RR and PD-L1-high group and others. The univariate Cox analysis of each DEG and subsequent lasso-based Cox model revealed five hub survival genes (POU4F1, IL34, HLF, CBS, and RNF165). A further hub survival gene-based risk score model was constructed, which was validated by an external cohort. We observed that a higher risk score predicted a worse prognosis (p = 0.0013). The area under the receiver operating characteristic curve (AUC) plots showed that this risk score model had good prediction value (1-year AUC = 0.684, 2-year AUC = 0.702, and 3-year AUC = 0.688). Five different deconvolution methods all showed that the B cells were lower in the RR and PD-L1-high group (p < 0.05). Finally, connectivity mapping analysis showed that the histone deacetylase (HDAC) inhibitor trichostatin A might have the potential to reverse the phenotype of RR and PD-L1-high in locally advanced HNSCC (p < 0.05, false discovery rate <0.1).

Conclusion: The combination of 31-gene signature and the PD-L1 mRNA expression had a potential predictive value for the prognosis of locally advanced HNSCC who had RT. The B cells were lower in the RR and PD-L1-high group. The identified risk gene signature of locally advanced HNSCC and the potential therapeutic drug trichostatin A for the RR and PD-L1-high group are worth being further studied in a prospective homogenous cohort.

Weighted Gene Co-Expression Network Analysis and Treatment Strategies of Tumor Recurrence-Associated Hub Genes in Lung Adenocarcinoma

Despite the recent progress of lung adenocarcinoma (LUAD) therapy, tumor recurrence remained to be a challenging factor that impedes the effectiveness of treatment. The objective of the present study was to predict the hub genes affecting LUAD recurrence via weighted gene co-expression network analysis (WGCNA). Microarray samples from LUAD dataset of GSE32863 were analyzed, and the modules with the highest correlation to tumor recurrence were selected. Functional enrichment analysis was conducted, followed by establishment of a protein-protein interaction (PPI) network. Subsequently, hub genes were identified by overall survival analyses and further validated by evaluation of expression in both myeloid populations and tissue samples of LUAD. Gene set enrichment analysis (GSEA) was then carried out, and construction of transcription factors (TF)-hub gene and drug-hub gene interaction network was also achieved. A total of eight hub genes (ACTR3, ARPC5, RAB13, HNRNPK, PA2G4, WDR12, SRSF1, and NOP58) were finally identified to be closely correlated with LUAD recurrence. In addition, TFs that regulate hub genes have been predicted, including MYC, PML, and YY1. Finally, drugs including arsenic trioxide, cisplatin, Jinfukang, and sunitinib were mined for the treatment of the eight hub genes. In conclusion, our study may facilitate the invention of targeted therapeutic drugs and shed light on the understanding of the mechanism for LUAD recurrence.

Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy

Purpose

To explore the association of genes in “PD-L1 expression and PD-1 check point pathway in cancer” to radiotherapy survival benefit.

Methods and materials

Gene expression data and clinical information of cancers were downloaded from TCGA. Radiotherapy survival benefit was defined based on interaction model. Fast backward multivariate Cox regression was performed using stacking multiple interpolation data to identify radio-sensitive (RS) genes.

Results

Among the 73 genes in PD-L1/PD-1 pathway, we identified 24 RS genes in BRCA data set, 25 RS genes in STAD data set and 20 RS genes in HNSC data set, with some crossover genes. Theoretically, there are two types of RS genes. The expression level of Type I RS genes did not affect patients' overall survival (OS), but when receiving radiotherapy, patients with different expression level of Type I RS genes had varied survival benefit. Oppositely, Type II RS genes affected patients' OS. And when receiving radiotherapy, those with lower OS could benefit a lot. Type II RS genes in BRCA had strong positive correlation and closely biological interactions. When performing cluster analysis using these related Type II RS genes, patients could be divided into RS group and non-RS group in BRCA and METABRIC data sets.

Conclusions

Our study explored potential radio-sensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway and revealed a strong association between this pathway and radiotherapy survival benefit. New types of RS genes could be identified based on expanded definition to RS genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01951-x.

Radioresistance of Human Cancers: Clinical Implications of Genetic Expression Signatures

Although radiotherapy is given to more than 50% of cancer patients, little progress has been made in identifying optimal radiotherapy - drug combinations to improve treatment efficacy. Using molecular data from The Cancer Genome Atlas (TCGA), we extracted a total of 1016 cancer patients that received radiotherapy. The patients were diagnosed with head-and-neck (HNSC - 294 patients), cervical (CESC - 166 patients) and breast (BRCA - 549 patients) cancer. We analyzed mRNA expression patterns of 50 hallmark gene sets of the MSigDB collection, which we divided in eight categories based on a shared biological or functional process. Tumor samples were split into upregulated, neutral or downregulated mRNA expression for all gene sets using a gene set analysis (GSEA) pre-ranked analysis and assessed for their clinical relevance. We found a prognostic association between three of the eight gene set categories (Radiobiological, Metabolism and Proliferation) and overall survival in all three cancer types. Furthermore, multiple single associations were revealed in the other categories considered. To the best of our knowledge, our study is the first report suggesting clinical relevance of molecular characterization based on hallmark gene sets to refine radiation strategies.

A novel gene signature associated with poor response to chemoradiotherapy in patients with locally advanced cervical cancer

Objective

We aimed to investigate the distinct transcriptional landscape in poor responders to concurrent chemoradiotherapy (CCRT) and to gain mechanistic insights into treatment resistance in cervical cancer.

Methods

RNA sequencing was performed in patients with locally advanced cervical cancer treated with platinum-based CCRT. Transcriptome data of no durable benefit (NDB; progression-free period <3 years) and durable clinical benefit (DCB; progression-free period >5 years) patients were compared. The NDB score was estimated for each patient using differentially expressed genes between NDB and DCB patients. The potential response to programmed death-1 blockade was estimated using the tumor immune dysfunction and exclusion (TIDE) score and T-cell-inflamed gene expression profile (GEP).

Results

NDB patients exhibited a distinct transcriptional profile compared to DCB patients, such as higher signatures of extracellular matrix organization and epithelial-to-mesenchymal transition. The fraction of cancer-associated fibroblasts (CAFs) within the tumor was significantly higher in NDB patients than in DCB patients. High NDB scores were significantly associated with poor survival in the Cancer Genome Atlas cervical cancer cohort (n=274; p=0.015) but only in patients who received curative aim radiotherapy (p=0.002). Patients with high NDB scores displayed significantly higher TIDE prediction scores and lower T-cell-inflamed GEP scores than those with low NDB scores.

Conclusion

Patients with cervical cancer having poor CCRT or RT outcomes exhibited a distinct gene signature that could predict treatment outcomes. For poor responders, immune checkpoint inhibitors may be less effective whereas CAF-targeting treatments may be a promising approach.

• A subgroup of patients with locally advanced cervical cancer exhibit no durable benefit (NDB) after chemoradiotherapy.

• NDB patients exhibited a distinct transcriptional profile

• NDB signature score predicted poor outcome in independent cohorts.

• NDB patients may have poor response to immune checkpoint blockade.

The Combination of Radiotherapy With Immunotherapy and Potential Predictive Biomarkers for Treatment of Non-Small Cell Lung Cancer Patients

Radiotherapy is an effective local treatment modality of NSCLC. Its capabilities of eliminating tumor cells by inducing double strand DNA (dsDNA) damage and modulating anti-tumor immune response in irradiated and nonirradiated sites have been elucidated. The novel ICIs therapy has brought hope to patients resistant to traditional treatment methods, including radiotherapy. The integration of radiotherapy with immunotherapy has shown improved efficacy to control tumor progression and prolong survival in NSCLC. In this context, biomarkers that help choose the most effective treatment modality for individuals and avoid unnecessary toxicities caused by ineffective treatment are urgently needed. This article summarized the effects of radiation in the tumor immune microenvironment and the mechanisms involved. Outcomes of multiple clinical trials investigating immuno-radiotherapy were also discussed here. Furthermore, we outlined the emerging biomarkers for the efficacy of PD-1/PD-L1 blockades and radiation therapy and discussed their predictive value in NSCLC.

Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Background and Purpose

Lower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model.

Methods

In this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients.

Results

We developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate.

Conclusions

This model can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.

Establishment of a nomogram with EMP3 for predicting clinical outcomes in patients with glioma: A bi-center study

Aim

To demonstrate the clinical value of epithelial membrane protein 3 (EMP3) with bioinformatic analysis and clinical data, and then to establish a practical nomogram predictive model with bicenter validation.

Methods

The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis. Then, we analyzed EMP3 expression in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts.

Results

EMP3 was significantly higher in higher‐grade glioma and predicted poor prognosis. In multivariate analysis, high expression of EMP3 (HR = 2.842, 95% CI 1.984–4.071), WHO grade (HR = 1.991, 95% CI 1.235–3.212), and IDH1 mutant (HR = 0.503, 95% CI 0.344–0.737) were included. The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes.

Conclusion

This study demonstrated EMP3 as a novel predictor for clinical progression and clinical outcomes in glioma. Moreover, the nomogram with EMP3 expression represented a practical approach to provide individualized risk assessment for glioma patients.

More evidence for prediction model of radiosensitivity

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

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

Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy

PURPOSE

Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.

METHODS

The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.

RESULTS

FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI  1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI  1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI  1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.

CONCLUSIONS

This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.