Background: Immune checkpoint blockade (ICB) with antibodies inhibiting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) (or its ligand (PD-L1)) can stimulate immune responses against cancer and have revolutionized the treatment of tumors. The influence of host germline genetics and its interaction with tumor neoantigens remains poorly defined. We sought to determine the interaction between tumor mutational burden (TMB) and the ability of a patient’s major histocompatibility complex class I (MHC-I) to efficiently present mutated driver neoantigens in predicting response ICB. Methods: Comprehensive genomic profiling was performed on 83 patients with diverse cancers treated with ICB to determine TMB and human leukocyte antigen-I (HLA-I) genotype. The ability of a patient’s MHC-I to efficiently present mutated driver neoantigens (defined by the Patient Harmonic-mean Best Rank (PHBR) score (with lower PHBR indicating more efficient presentation)) was calculated for each patient. Results: The median progression-free survival (PFS) for PHBR score < 0.5 vs. ≥ 0.5 was 5.1 vs. 4.4 months (P = 0.04). Using a TMB cutoff of 10 mutations/mb, the stable disease > 6 months/partial response/complete response rate, median PFS, and median overall survival (OS) of TMB high/PHBR high vs. TMB high/PHBR low were 43% vs. 78% (P = 0.049), 5.8 vs. 26.8 months (P = 0.03), and 17.2 months vs. not reached (P = 0.23), respectively. These findings were confirmed in an independent validation cohort of 32 patients. Conclusions: Poor presentation of driver mutation neoantigens by MHC-I may explain why some tumors (even with a high TMB) do not respond to ICB.

Higher tumor mutational burden (TMB) has been correlated with response to checkpoint blockade immunotherapy. However, it is unclear whether TMB independently serves as a prognostic biomarker for outcomes in immunotherapy-naïve patients. Here, we evaluated the relationship between TMB and overall survival in 1,415 immunotherapy-naïve patients with diverse advanced malignancies. TMB was studied both as a tiered variable (low ≤5 mutations/Mb, intermediate >5 and <20, high ≥20 and <50, and very high ≥50) and as a continuous variable. Interestingly, we observed a parabolic correlation between TMB and overall survival, in which intermediate-range TMB correlated with decreased survival, whereas low and very high TMB correlated with improved outcomes (median survival: 238, 174, 195, and 350 weeks for low, intermediate, high, and very high TMB, respectively; multivariate P < 0.01). This corresponded to an HR of 1.29 (95% confidence interval, 1.07–1.54; P < 0.01) for intermediate-range TMB on multivariable survival analysis correcting for known confounders, including primary tumor of origin. These results demonstrate that TMB may have utility as a prognostic biomarker in immunotherapy-naïve patients, with a protective effect at higher TMBs, and that studies of survival in immunotherapy-treated patients may need to stratify or randomize by TMB in a nonlinear fashion to account for this confounding.

Hydrophobic neoantigens are more immunogenic because they are better presented by the major histocompatibility complex and better recognized by T cells. Tumor cells can evade the immune response by expressing checkpoints such as programmed death ligand 1. Checkpoint blockade reactivates immune recognition and can be effective in diseases such as melanoma, which harbors a high tumor mutational burden (TMB). Cancers presenting low or intermediate TMB can also respond to checkpoint blockade, albeit less frequently, suggesting the need for biological markers predicting response. We calculated the hydrophobicity of neopeptides produced by probabilistic in silico simulation of the genomic UV exposure mutational signature. We also computed the hydrophobicity of potential neopeptides and extent of UV exposure based on the UV mutational signature enrichment (UVMSE) score in The Cancer Genome Atlas (TCGA; N = 3543 tumors), and in our cohort of 151 immunotherapy‐treated patients. In silico simulation showed that UV exposure significantly increased hydrophobicity of neopeptides, especially over multiple mutagenic cycles. There was also a strong correlation (R² = 0.953) between weighted UVMSE and hydrophobicity of neopeptides in TCGA melanoma patients. Importantly, UVMSE was able to predict better response (P = 0.0026), progression‐free survival (P = 0.036), and overall survival (P = 0.052) after immunotherapy in patients with low/intermediate TMB, but not in patients with high TMB. We show that higher UVMSE scores could be a useful predictor of better immunotherapy outcome, especially in patients with low/intermediate TMB, likely due to increased hydrophobicity (and hence immunogenicity) of neopeptides.

Background: Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse.Methods: Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique.Results: The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78).Conclusion: This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.