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Purpose/objective(s): Improved stratification is needed in HPV-negative HNSCC patients with intermediate and select high-risk disease. Significant molecular and immune response features have not been well-integrated with clinicopathologic factors to predict outcomes in this population. We sought to develop and validate an integrated molecular and clinicopathologic machine learning-based predictor for OS and PFS in this population to inform a future trial design. We hypothesize that our predictor will stratify patients better than a predictor derived from standard techniques.Materials/methods: We included 253 patients from TCGA with pathologic stage III-IVB (excluded T4b; included N3a) HPV-negative HNSCC with microscopic ENE and intermediate-risk disease (close margins/LVSI/PNI), treated with surgery and RT +/- chemotherapy. We split the data into training (70%) and testing (30%) sets. We identified 29 relevant molecular features (genomic: mutation vs. no mutation, transcriptomic: ≥ vs. < upper third quartile of log2 transformed mRNA expression) associated with significant HNSCC pathways, including cellular proliferation, cellular differentiation, cell cycle control, adhesion and invasion, anti-tumor immune response, and apoptosis. We also identified 19 demographic, behavioral, and clinicopathologic factors. We performed random survival forest modeling on OS and PFS and validated the machine learning-based predictor on the testing set, using ROC/AUC. Variables of importance were identified using the "Janitza" method. We also built a predictor using standard "best" Cox proportional hazards modeling and compared AUC values for OS and PFS versus our machine learning-based values.Results: The median OS and PFS times were 4.4 years and 3 years, respectively. After accounting for pairwise correlations, we kept 38 variables for model building. Using the training set, significant variables of most importance for OS included age, female sex, PNI, LVSI, ≥ four pathologically involved lymph nodes, microscopic ENE, TP53 missense and nonsense mutations, and expression of PD1, LAG3, TIM3, L1CAM, CASP8, PIK3CA, E2F2, E2F4, FAT1, and NOTCH1 (all P-values < .05). Significant variables of importance for PFS included age, female sex, ≥ four pathologically involved lymph nodes, microscopic ENE, anatomic subsite, TP53 nonsense mutations, and expression of PD1, L1CAM, PIK3CA, and FAT1 (all P-values < .05). Using the testing set, we validated the 38-variable predictor on OS and PFS with AUC values of 0.82 and 0.75, respectively. In contrast, the "best" Cox models resulted in low AUC values for OS and PFS (0.65 and 0.66, respectively).Conclusion: We developed and validated a machine learning-based predictor for OS and PFS that outperforms standard Cox predictors in HPV-negative HNSCC. This study provides a rationale to use the predictor as a stratifier in a prospective clinical trial of surgery, radiotherapy, and PD1 blockade in patients with microscopic ENE and intermediate-risk disease. [ABSTRACT FROM AUTHOR]

Purpose/objective(s): Recent landmark trials have suggested a survival benefit to definitive tumor directed therapies (DTDT) for selected patients with metastatic disease for various malignancies. We sought 1) to confirm the prognostic significance of metachronous oligometastatic burden for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) and 2) to evaluate disease outcomes for oligometastatic HPV-associated OPSCC after upfront DTDT versus upfront systemic therapy.Materials/methods: This was a single institution retrospective observational cohort study of patients with HPV-associated OPSCC who developed metachronous metastases ≥3 months after primary treatment with trans-oral robotic surgery (TORS) from 2008-2017. At metastatic presentation, patients were classified as oligometastatic (≤5 metastases) or polymetastatic (> 5 metastases). Treatment was classified as DTDT (all metastases initially treated with surgery/radiotherapy) or as upfront systemic therapy. Overall survival (OS) was compared using log-rank tests at a significance of P < 0.05. Univariable and multivariable (MV) Cox proportional hazard models were used to assess the association of patient, disease, and treatment characteristics with survival. Variables with P < 0.15 on univariable analysis (initial metastatic treatment, metastatic site, and number of metastases) were included in the multivariable model.Results: Of 676 patients undergoing primary surgical management for HPV-associated OPSCC, 36 patients subsequently developed metachronous metastases. For those 36 patients, the median follow-up time after surgery was 27.5 months (range 4.5-127.0), and the median OS was 42.8 months. The median age at distant metastasis was 62 (range 32-86), and most patients were male (86.1%) and Caucasian (97.2%). Overall, 27 patients presented with oligometastasis and 9 with polymetastasis. Oligometastatic patients had improved median OS compared to polymetastatic patients (47.9 vs. 22.7 months, P = 0.020). For the 27 oligometastatic patients, 12 were initially treated with DTDT while 15 received systemic therapy. DTDT was associated with an improved median OS when compared to systemic therapy (median OS not reached for DTDT vs 40.7 months, P = 0.021), with 3 year OS of 90.0% and 55.0% respectively. DTDT was also associated with improved OS on MV Cox regression (hazard ratio = 0.06, 95% CI 0.004-0.73, P = 0.027) compared to systemic therapy when controlling for number of metastases and metastatic site.Conclusion: Our study findings suggest that the extent of metastatic disease at metastatic presentation is related to prognosis, with oligometastasis associated with improved overall survival compared to polymetastasis. Among patients with oligometastatic disease, initial DTDT is associated with superior overall survival when compared to upfront systemic therapy. [ABSTRACT FROM AUTHOR]