Patterns of recurrence in head and neck squamous cell carcinoma to inform personalized surveillance protocols

Human papillomavirus circulating tumor DNA assays as a mechanism for head and neck cancer equity in rural regions of the United States

The rates of human papillomavirus-positive oropharyngeal cancer (HPV-OPC) are rising worldwide and in the United States, particularly in rural regions including Appalachia. Rural areas face unique health challenges resulting in higher cancer incidence and mortality rates, and this includes HPV-OPC. The recent advent of highly sensitive liquid biopsies for the non-invasive detection of HPV-OPC recurrence (circulating tumor HPV DNA, HPV ctDNA) has been swiftly adopted as part of surveillance paradigms. Though knowledge gaps persist regarding its use and clinical trials are ongoing, the ease of collection and cost-effectiveness of HPV ctDNA make it more accessible for HPV-OPC survivors than usual surveillance methods of frequent exams and imaging. Herein, we discuss how implementing HPV ctDNA assays in rural regions of the United States provide one poignant example of how liquid biopsies can improve cancer care equity.

Expansion of tumor-infiltrating lymphocytes from head and neck squamous cell carcinoma to assess the potential of adoptive cell therapy

BACKGROUND

Adoptive transfer of in vitro expanded tumor-infiltrating lymphocytes (TILs) has been effective in regressing several types of malignant tumors. This study assessed the yield and factors influencing the successful expansion of tumor-infiltrating lymphocytes (TILs) from head and neck squamous cell carcinoma (HNSCC), along with their immune phenotypes.

METHODS

TILs were expanded from 47 surgically resected HNSCC specimens and their metastasized lymph nodes. The cancer tissues were cut into small pieces (1-2 mm) and underwent initial expansion for 2 weeks. Tumor location, smoking history, stromal TIL percentage, human papillomavirus infection, and programmed death-ligand 1 score were examined for their impact on successful expansion of TILs. Expanded TILs were evaluated by flow cytometry using fluorescence-activated cell sorting. A second round of TIL expansion following the rapid expansion protocol was performed on a subset of samples with successful TIL expansion.

RESULTS

TILs were successfully expanded from 36.2% samples. Failure was due to contamination (27.6%) or insufficient expansion (36.2%). Only the stromal TIL percentage was significantly associated with successful TIL expansion (p = 0.032). The stromal TIL percentage also displayed a correlation with the expanded TILs per fragment (r = 0.341, p = 0.048). On flow cytometry analysis using 13 samples with successful TIL expansion, CD4 + T cell dominancy was seen in 69.2% of cases. Effector memory T cells were the major phenotype of expanded CD4 + and CD8 + T cells in all cases.

CONCLUSION

We could expand TILs from approximately one-third of HNSCC samples. TIL expansion could be applicable in HNSCC samples with diverse clinicopathological characteristics.

Validation of a Machine Learning Model to Predict Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor's likelihood of response or a patient's likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29-0.99], p = 0.045) and progression-free (HR = 0.49 [95% CI 0.27-0.87], p = 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.