Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data

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.

Clonal evolution of long-term expanding head and neck cancer organoid: Impact on treatment response for personalized therapeutic screening

OBJECTIVES

In biobanking based on patient-derived organoids (PDO), the genetic stability of organoid lines is critical for the clinical relevance of PDO with parental tumors. However, data on mutational heterogeneity and clonal evolution of PDO and their effects on treatment response are insufficient.

METHODS

To investigate whether head and neck cancer organoids (HNCOs) could maintain the genetic characteristics of their original tumors and elucidate the clonal evolution process during a long-term passage, we performed targeted sequencing, covering 377 cancer-related genes and adopted a sub-clonal fraction model. To explore therapeutic response variability between an early and late passage (>passage 6), we generated dose-response curves for drugs and radiation using two HNCO lines.

RESULTS

Using 3D ex vivo organoid culture protocol, we successfully established 27 HNCOs from 39 patients with an overall success rate of 70% (27/39). Their mutational profiles were highly concordant, with three of the HNCOs analyzed showing greater than 70% concordance. Only one HNCO displayed less than 50% concordance. However, many of these organoid lines displayed clonal evolution during serial passaging, although major cancer driver genes and VAF distributions were shared between early and later passages. We also found that all late passages of HNCOs tended to be more sensitive to radiation than early passages, similar to drug response results.

CONCLUSIONS

We report the establishment of HNCO lines derived from 27 patients and demonstrate their genetic concordance with corresponding parental tumors. Furthermore, we show serial changes in mutational profiles of HNCO along with long passage culture and the impact of these clonal evolutions on response to radiotherapy.

MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

BACKGROUND

. At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset.

METHODS

For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108).

RESULTS

The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage.

CONCLUSIONS

Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.

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.

A novel 3D pillar/well array platform using patient-derived head and neck tumor to predict the individual radioresponse

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Radiotherapy is a critical modality in head and neck cancer treatment.

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A novel 3D pillar/well array platform provides the individual radioresponse biomarker, RT_auc.

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Poor and good radioresponse group by RT_auc correlates with other clinical features.

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RT_auc shows potential for radioresponse biomarker, useful in clinical decision-making.

Predicting individual radiotherapy (RT) response is valuable in managing head and neck squamous cell carcinoma (HNSCC). We assessed the feasibility of our novel 3D culture platform to measure radioresponse using patient-derived cells (PDCs) from HNSCC patients.

Cells from the FaDu line and tumor samples from 39 HNSCC patients were cultivated serially in Matrigel^TM on a 3D pillar/well array culture system. The 3D tumor models were exposed to 0 to 8 Gy of radiation dose, and the radioresponse index (RT_auc, area under the dose-response curve) was measured quantitatively with Calcein AM staining of live tumor cells. Calcein AM fluorescence showed reduced density and the number of FaDu colonies as radiation increased, implying a dose-dependent effect on cell viability in the 3D pillar/well culture system. 3D tumor models using PDCs were established successfully from 39 HNSCC patient tumor samples, maintaining original genomic and pathological characteristics. These 3D tumor models were exposed to ionizing radiation on a 3D pillar/well array, with a mean period of 12 days from tumor harvest to the measurement of RT_auc. The RT_auc of all PDCs varied from 3.5 to 9.4, and the lower 40th percentile (Z-score = -0.26) was considered a good radioresponse group with a threshold RT_auc of 4.6. The good radioresponse group showed fewer adverse features than others. As of the last follow-up, recurrence-free survival was better in the good radioresponse group (p = 0.037). 3D pillar/well array platforms using PDC could rapidly quantify radioresponse index in patients with HNSCC, showing potential as a novel prognosticator.

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

INTRODUCTION

The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature.

MATERIALS AND METHODS

We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the "high immunity and low hypoxia" and "low immunity and high hypoxia" groups and those DEGs significantly associated with disease-specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia-related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups.

RESULTS

Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression-free survival, and recurrence-free survival (p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response.

CONCLUSIONS

IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Radiation Response in the Tumour Microenvironment: Predictive Biomarkers and Future Perspectives

Radiotherapy (RT) is a primary treatment modality for a number of cancers, offering potentially curative outcomes. Despite its success, tumour cells can become resistant to RT, leading to disease recurrence. Components of the tumour microenvironment (TME) likely play an integral role in managing RT success or failure including infiltrating immune cells, the tumour vasculature and stroma. Furthermore, genomic profiling of the TME could identify predictive biomarkers or gene signatures indicative of RT response. In this review, we will discuss proposed mechanisms of radioresistance within the TME, biomarkers that may predict RT outcomes, and future perspectives on radiation treatment in the era of personalised medicine.