Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node-positive head and neck cancer

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

Proton Treatment Suppresses Exosome Production in Head and Neck Squamous Cell Carcinoma

Proton therapy (PT) is emerging as an effective and less toxic alternative to conventional X-ray-based photon therapy (XRT) for patients with advanced head and neck squamous cell carcinomas (HNSCCs) owing to its clustered dose deposition dosimetric characteristics. For optimal efficacy, cancer therapies, including PT, must elicit a robust anti-tumor response by effector and cytotoxic immune cells in the tumor microenvironment (TME). While tumor-derived exosomes contribute to immune cell suppression in the TME, information on the effects of PT on exosomes and anti-tumor immune responses in HNSCC is not known. In this study, we generated primary HNSCC cells from tumors resected from HNSCC patients, irradiated them with 5 Gy PT or XRT, and isolated exosomes from cell culture supernatants. HNSCC cells exposed to PT produced 75% fewer exosomes than XRT- and non-irradiated HNSCC cells. This effect persisted in proton-irradiated cells for up to five days. Furthermore, we observed that exosomes from proton-irradiated cells were identical in morphology and immunosuppressive effects (suppression of IFN-γ release by peripheral blood mononuclear cells) to those of photon-irradiated cells. Our results suggest that PT limits the suppressive effect of exosomes on cancer immune surveillance by reducing the production of exosomes that can inhibit immune cell function.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study

Background

Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers.

Methods

Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance.

Results

We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively.

Conclusions

We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.