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

Molecular subtyping of head and neck cancer - Clinical applicability and correlations with morphological characteristics

AIM

We aimed to evaluate the applicability of a customized NanoString panel for molecular subtyping of recurrent or metastatic head and neck squamous cell carcinoma (R/M-HNSCC). Additionally, histological analyses were conducted, correlated with the molecular subtypes and tested for their prognostic value.

MATERIAL AND METHODS

We conducted molecular subtyping of R/M-HNSCC according to the molecular subtypes defined by Keck et al. For molecular analyses a 231 gene customized NanoString panel (the most accurately subtype defining genes, based on previous analyses) was applied to tumor samples from R/M-HNSCC patients that were treated in the CeFCiD trial (AIO/IAG-KHT trial 1108). A total of 130 samples from 95 patients were available for sequencing, of which 80 samples from 67 patients passed quality controls and were included in histological analyses. H&E stained slides were evaluated regarding distinct morphological patterns (e.g. tumor budding, nuclear size, stroma content).

RESULTS

Determination of molecular subtypes led to classification of tumor samples as basal (n = 46, 45 %), inflamed/mesenchymal (n = 31, 30 %) and classical (n = 26, 25 %). Expression levels of Amphiregulin (AREG) were significantly higher for the basal and classical subtypes compared to the mesenchymal subtype. While molecular subtypes did not have an impact on survival, high levels of tumor budding were associated with poor outcomes. No correlation was found between molecular subtypes and histological characteristics.

CONCLUSIONS

Utilizing the 231-gene NanoString panel we were able to determine the molecular subtype of R/M-HNSCC samples by the use of FFPE material. The value to stratify for different treatment options remains to be explored in the future. The prognostic value of tumor budding was underscored in this clinically well annotated cohort.

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 and Predictive Biomarkers in Head and Neck Squamous Cell Carcinoma Treated with Radiotherapy-A Systematic Review

Background: Radiotherapy is a mainstay in head and neck squamous cell carcinoma (HNSCC) treatment but is mostly applied without stratification by molecular diagnostics. Development of reliable biomarkers may have the potential to improve radiotherapy (RT) efficacy and reduce toxicity. We conducted a systematic review to summarize the field of biomarkers in HNSCC treated by RT. Methods: Pubmed and EMBASE were searched independently by two researchers following pre-defined inclusion and exclusion criteria. Z curves were generated to investigate publication bias. OncoKB was used for identification of druggable targets. Results: 134 manuscripts remained for data extraction. 12% of tumors were AJCC/UICC stage I-II and 82% were stage III-IV. The most common biomarkers were proteins (39%), DNA (14%) and mRNA (9%). Limiting analysis to prospective data and statistically significant results, we found three potentially druggable targets: ERCC2, PTCH1 and EGFR. Regarding data quality, AJCC/UICC stage was missing in 32% of manuscripts. 73% of studies were retrospective and only 7% were based on prospective randomized trials. Z-curves indicated the presence of publication bias. Conclusion: An abundance of potential biomarkers in HNSCC is available but data quality is limited by retrospective collection, lack of validation and publication bias. Improved study design and reporting quality might accelerate successful development of personalized treatments in HNSCC.

Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) may benefit from personalised treatment, requiring biomarkers that characterize the tumour and predict treatment response. We integrate pre-treatment CT radiomics and whole-transcriptome data from a multicentre retrospective cohort of 206 patients with locally advanced HNSCC treated with primary radiochemotherapy to classify tumour molecular subtypes based on radiomics, develop surrogate radiomics signatures for gene-based signatures related to different biological tumour characteristics and evaluate the potential of combining radiomics features with full-transcriptome data for the prediction of loco-regional control (LRC). Using end-to-end machine-learning, we developed and validated a model to classify tumours of the atypical subtype (AUC [95% confidence interval] 0.69 [0.53–0.83]) based on CT imaging, observed that CT-based radiomics models have limited value as surrogates for six selected gene signatures (AUC < 0.60), and showed that combining a radiomics signature with a transcriptomics signature consisting of two metagenes representing the hedgehog pathway and E2F transcriptional targets improves the prognostic value for LRC compared to both individual sources (validation C-index [95% confidence interval], combined: 0.63 [0.55–0.73] vs radiomics: 0.60 [0.50–0.71] and transcriptomics: 0.59 [0.49–0.69]). These results underline the potential of multi-omics analyses to generate reliable biomarkers for future application in personalized oncology.

Biological Mechanisms to Reduce Radioresistance and Increase the Efficacy of Radiotherapy: State of the Art

Cancer treatment with ionizing radiation (IR) is a well-established and effective clinical method to fight different types of tumors and is a palliative treatment to cure metastatic stages. Approximately half of all cancer patients undergo radiotherapy (RT) according to clinical protocols that employ two types of ionizing radiation: sparsely IR (i.e., X-rays) and densely IR (i.e., protons). Most cancer cells irradiated with therapeutic doses exhibit radio-induced cytotoxicity in terms of cell proliferation arrest and cell death by apoptosis. Nevertheless, despite the more tailored advances in RT protocols in the last few years, several tumors show a relatively high percentage of RT failure and tumor relapse due to their radioresistance. To counteract this extremely complex phenomenon and improve clinical protocols, several factors associated with radioresistance, of both a molecular and cellular nature, must be considered. Tumor genetics/epigenetics, tumor microenvironment, tumor metabolism, and the presence of non-malignant cells (i.e., fibroblast-associated cancer cells, macrophage-associated cancer cells, tumor-infiltrating lymphocytes, endothelial cells, cancer stem cells) are the main factors important in determining the tumor response to IR. Here, we attempt to provide an overview of how such factors can be taken advantage of in clinical strategies targeting radioresistant tumors.