Search

Background: The microbiome has been shown to affect the response to Immune Checkpoint Inhibitors (ICIs) in a small number of cancers and in preclinical models. Here, we sought to broadly survey cancers to identify those in which the microbiome may play a prognostic role using retrospective analyses of patients with advanced cancer treated with ICIs.Methods: We conducted a retrospective analysis of 690 patients who received ICI therapy for advanced cancer. We used a literature review to define a causal model for the relationship between medications, the microbiome, and ICI response to guide the abstraction of electronic health records. Medications with precedent for changes to the microbiome included antibiotics, corticosteroids, proton pump inhibitors, histamine receptor blockers, non-steroid anti-inflammatories and statins. We tested the effect of medication timing on overall survival (OS) and evaluated the robustness of medication effects in each cancer. Finally, we compared the size of the effect observed for different classes of antibiotics to taxa that have been correlated to ICI response using a literature review of culture-based antibiotic susceptibilities.Results: Of the medications assessed, only antibiotics and corticosteroids significantly associated with shorter OS. The hazard ratios (HRs) for antibiotics and corticosteroids were highest near the start of ICI treatment but remained significant when given prior to ICI. Antibiotics and corticosteroids remained significantly associated with OS even when controlling for multiple factors such as Eastern Cooperative Oncology Group performance status, Charlson Comorbidity Index score, and stage. When grouping antibiotics by class, β-lactams showed the strongest association with OS across all tested cancers.Conclusions: The timing and strength of the correlations with antibiotics and corticosteroids after controlling for confounding factors are consistent with the microbiome involvement with the response to ICIs across several cancers. [ABSTRACT FROM AUTHOR]

Background We investigated the role of A2B-adenosine receptor in regulating immunosuppressive metabolic stress in the tumor microenvironment. Novel A2B-adenosine receptor antagonist PBF-1129 was tested for antitumor activity in mice and evaluated for safety and immunologic efficacy in a phase I clinical trial of patients with non-small cell lung cancer. Methods The antitumor efficacy of A2B-adenosine receptor antagonists and their impact on the metabolic and immune tumor microenvironment were evaluated in lung, melanoma, colon, breast, and epidermal growth factor receptor–inducible transgenic cancer models. Employing electron paramagnetic resonance, we assessed changes in tumor microenvironment metabolic parameters, including p O2, pH, and inorganic phosphate, during tumor growth and evaluated the immunologic effects of PBF-1129, including its pharmacokinetics, safety, and toxicity, in patients with non-small cell lung cancer. Results Levels of metabolic stress correlated with tumor growth, metastasis, and immunosuppression. Tumor interstitial inorganic phosphate emerged as a correlative and cumulative measure of tumor microenvironment stress and immunosuppression. A2B-adenosine receptor inhibition alleviated metabolic stress, downregulated expression of adenosine-generating ectonucleotidases, increased expression of adenosine deaminase, decreased tumor growth and metastasis, increased interferon γ production, and enhanced the efficacy of antitumor therapies following combination regimens in animal models (anti–programmed cell death 1 protein vs anti–programmed cell death 1 protein plus PBF-1129 treatment hazard ratio = 11.74 [95% confidence interval = 3.35 to 41.13], n = 10, P  < .001, 2-sided F test). In patients with non-small cell lung cancer, PBF-1129 was well tolerated, with no dose-limiting toxicities; demonstrated pharmacologic efficacy; modulated the adenosine generation system; and improved antitumor immunity. Conclusions Data identify A2B-adenosine receptor as a valuable therapeutic target to modify metabolic and immune tumor microenvironment to reduce immunosuppression, enhance the efficacy of immunotherapies, and support clinical application of PBF-1129 in combination therapies. [ABSTRACT FROM AUTHOR]

Background and objectives: Medical comorbidities (MC) are highly prevalent among patients with cancer and predict worse outcomes for traditional therapies. This association is poorly understood for checkpoint inhibitor immunotherapy (IO). We aimed to explore the relationship between common MC including cardiovascular disease (CVD), immune-related adverse events (irAEs), and overall survival (OS) among patients receiving IO for advanced cancer. Methods: This is a retrospective cohort study of 671 patients with any cancer who received IO at our institution from 2011 to 2018. Clinical data were abstracted via chart review and query of ICD-10 codes and used to calculate modified Charlson comorbidity index (mCCI) scores. The primary outcomes were the association of individual MC with irAEs and OS using bivariate and multivariable analyses. Secondary outcomes included association of mCCI score with irAEs and OS. Results: Among 671 patients, 62.1% had a mCCI score ≥ 1. No individual MC were associated with irAEs or OS. Increased CCI score was associated with decreased OS (p < 0.01) but not with irAEs. Grade ≥ 3 irAEs were associated with increased OS among patients without CVD (HR 0.37 [95% CI: 0.25, 0.55], p < 0.01), but not among patients with CVD. Conclusions: No specific MC predicted risk of irAEs or OS for patients receiving IO. Increased CCI score did not predict risk of irAEs but was associated with shorter OS. This suggests IO is safe for patients with MC, but MC may limit survival benefits of IO. CVD may predict shorter OS in patients with irAEs and should be evaluated among patients receiving IO. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibitors (ICIs) are associated with a unique set of immune-related adverse events (irAEs). Few studies have evaluated the risk factors and outcomes of patients who develop ICI-induced hepatitis (ICIH). Methods: We utilized an institutional database of patients with advanced cancers treated with ICI to identify patients with ICIH. irAEs were graded using the Common Terminology Criteria for Adverse Events v4. Overall survival (OS) was calculated from the date of ICI to death from any cause or the date of the last follow-up. OS with 95% confidence intervals were estimated using the Kaplan–Meier method and stratified by the occurrence of ICIH. Results: We identified 1096 patients treated with ICI. The most common ICIs were PD1/L1 (n = 774) and CTLA-4 inhibitors (n = 195). ICIH occurred among 64 (6%) patients: severity was < grade 3 in 30 and ≥ grade 3 in 24 patients (3.1% overall). Median time to ICIH was 63 days. ICIH was more frequent in women (p = 0.038), in patients treated with combination ICIs (p < 0.001), and when given as first-line therapy (p = 0.018). Occurrence of ICIH was associated with significantly longer OS, median 37.0 months (95% CI 21.4, NR) compared to 11.3 months (95% CI 10, 13, p < 0.001); there was no difference in OS between patients with ≥ grade 3 ICIH vs grade 1–2. Conclusions: Female sex, combination immunotherapy, and the first line of immunotherapy were associated with ICIH. Patients with ICIH had improved clinical survival compared to those that did not develop ICIH. There is a need for prospective further studies to confirm our findings. [ABSTRACT FROM AUTHOR]

Purpose/Objective(s): Microbiome has been shown to affect tumorigenesis by promoting inflammation. However, the association between the upper aerodigestive microbiome and head and neck squamous cell carcinoma (HNSCC) is not well established. Hypoxia is a modifiable factor associated with poor radiation response. Our study analyzed the HNSCC tumor samples from The Cancer Genome Atlas (TCGA) to investigate the relationship between different HNSCC tumor subsites, hypoxia, and local tumor microbiome composition. Results: A total of 357 patients were included [Oral cavity (OC) = 226, Oropharynx (OPx) = 53, and Larynx/Hypopharynx (LHPx) = 78], of which 12.8%, 71.7%, and 10.3%, respectively, were HPV positive. The mean (SD) hypoxia scores were 30.18 (11.10), 24.31 (14.13), and 29.53 (12.61) in OC, OPx, and LHPx tumors, respectively, with higher values indicating greater hypoxia. The hypoxia score was significantly higher for OC tumors compared to OPx (p = 0.044) and LHPx (p = 0.002). There was no significant correlation between hypoxia and HPV status. Pseudomonas sp. in OC, Actinomyces sp. and Sulfurimonas sp. in OPx, and Filifactor, Pseudomonas and Actinomyces sp. in LHPx had the strongest association with the hypoxia score. Materials/Methods: Tumor RNAseq samples from TCGA were processed, and the R package "tmesig" was used to calculate gene expression signature, including the Buffa hypoxia (BH) score, a validated hypoxia signature using 52 hypoxia-regulated genes. Microbe relative abundances were modeled with primary tumor location and a high vs. low tertile BH score applying a gamma-distributed generalized linear regression using the "stats" package in R, with adjusted p-value < 0.05 considered significant. Conclusions: In our study, oral cavity tumors were found to be more hypoxic compared to other head and neck subsites, which could potentially contribute to their radiation resistance. For each subsite, distinct microbial populations were over-represented in hypoxic tumors in a subsite-specific manner. Further studies focusing on an association between microbiome, hypoxia, and patient outcomes are warranted. [ABSTRACT FROM AUTHOR]

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets. Author summary: In this paper, we propose and describe a robust and flexible modelling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modelling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions. [ABSTRACT FROM AUTHOR]