The FITNESS study: longitudinal geriatric assessment, treatment toxicity, and biospecimen collection to assess functional disability among older adults with lung cancer.

Introduction: Older adults with chronic disease prioritize functional independence. We aimed to describe the feasibility of capturing functional disability and treatment toxicity among older adults with lung cancer using a longitudinal comprehensive geriatric assessment (CGA) and molecular biomarkers of aging. Methods: This prospective study included adults ≥60 years with any newly diagnosed non-small-cell lung cancer. Participants were recruited from central Ohio (2018-2020). Study assessments included the Cancer and Aging Research Group CGA (CARG-CGA), short physical performance battery (SPPB), and the blessed orientation-memory concentration (BOMC) test at baseline, 3, 6, and 12 months. Activities of daily living (ADLs) and instrumental ADLs (IADLs), quality of life (QoL, PROMIS 10), and treatment toxicity were captured monthly. Stool and blood were collected to characterize the gut microbiome and age-related blood biomarkers. Results: This study enrolled 50 participants with an average age of 71.7 years. Ninety-two percent of participants were Caucasian, 58% were male, and all were non-Hispanic. Most had advanced stage (stage III/IV: 90%; stage I/II: 10%), with adenocarcinoma the predominant histologic subtype (68% vs. 24% squamous). First-line treatments included chemotherapy (44%), immune checkpoint inhibitors (ICIs, 22%), chemotherapy and ICIs (30%), or tyrosine kinase inhibitors (4%). The median baseline CARG toxicity score was 8 (range 2-12). Among patients with treatment-related toxicity (n = 49), 39 (79.6%) cases were mild (grade 1-2), and 10 (20.4%) were moderate to severe (≥ grade 3). Treatment toxicity was greater among those with a CARG score ≥8 (28.0% vs. 13.6%). Higher IADL independence, QoL, and SPPB scores at baseline were positively associated with Candidatus Gastranaerophilales bacterium, Lactobacillus rogosae, and Enterobacteria phage P4. Romboutsia ilealis, Streptococcus, and Lachnoclostridium sp An138 and T cell lag3 and cd8a were associated with worse IADLs, QoL, and SPPB scores at baseline. Discussion: A longitudinal CGA and biomarker collection is feasible among older adults undergoing lung cancer treatment. Gut microbe and T cell gene expression changes correlated with subjective and objective functional status assessments. Future research will test causality in these associations to improve outcomes through novel supportive care interventions to prevent functional disability.

The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer.

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation's ability to kill cells. We hypothesized that some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. Therefore, we examined the RNA-seq data from the Oncology Research Information Exchange Network (ORIEN) database of colorectal cancer (CRC) patients treated with radiotherapy. We identified microbial RNAs for each tumor and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To experimentally investigate this concept, we implanted CT26 CRC cells into immune-competent BALB/c and immune-deficient athymic nude mice. After growth, where tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed a metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-trophic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Thus, hypoxia appears to associate with different microbial populations and elicit an adaptive transcriptional response in intratumoral microbes, potentially influencing clinical outcomes.

Obesity-associated microbiomes instigate visceral adipose tissue inflammation by recruitment of distinct neutrophils.

Neutrophils are increasingly implicated in chronic inflammation and metabolic disorders. Here, we show that visceral adipose tissue (VAT) from individuals with obesity contains more neutrophils than in those without obesity and is associated with a distinct bacterial community. Exploring the mechanism, we gavaged microbiome-depleted mice with stool from patients with and without obesity during high-fat or normal diet administration. Only mice receiving high-fat diet and stool from subjects with obesity show enrichment of VAT neutrophils, suggesting donor microbiome and recipient diet determine VAT neutrophilia. A rise in pro-inflammatory CD4+ Th1 cells and a drop in immunoregulatory T cells in VAT only follows if there is a transient spike in neutrophils. Human VAT neutrophils exhibit a distinct gene expression pattern that is found in different human tissues, including tumors. VAT neutrophils and bacteria may be a novel therapeutic target for treating inflammatory-driven complications of obesity, including insulin resistance and colon cancer.

Comparative analysis of the tumor microbiome, molecular profiles, and immune cell abundances by HPV status in mucosal head and neck cancers and their impact on survival.

Head and Neck Squamous Cell Carcinoma (HNSCC) comprises a diverse group of tumors with variable treatment response and prognosis. The tumor microenvironment (TME), which includes microbiome and immune cells, can impact outcomes. Here, we sought to relate the presence of specific microbes, gene expression, and tumor immune infiltration using tumor transcriptomics from The Cancer Genome Atlas (TCGA) and associate these with overall survival (OS). RNA sequencing (RNAseq) from HNSCC tumors in TCGA was processed through the exogenous sequences in tumors and immune cells (exotic) pipeline to identify and quantify low-abundance microbes. The detection of the Papillomaviridae family of viruses assessed HPV status. All statistical analyses were performed using R. A total of 499 RNAseq samples from TCGA were analyzed. HPV was detected in 111 samples (22%), most commonly Alphapapillomavirus 9 (90.1%). The presence of Alphapapillomavirus 9 was associated with improved OS [HR = 0.60 (95%CI: 0.40-0.89, p = .01)]. Among other microbes, Yersinia pseudotuberculosis was associated with the worst survival (HR = 3.88; p = .008), while Pseudomonas viridiflava had the best survival (HR = 0.05; p = .036). Microbial species found more abundant in HPV- tumors included several gram-negative anaerobes. HPV- tumors had a significantly higher abundance of M0 (p < .001) and M2 macrophages (p = .035), while HPV+ tumors had more T regulatory cells (p < .001) and CD8+ T-cells (p < .001). We identified microbes in HNSCC tumor samples significantly associated with survival. A greater abundance of certain anaerobic microbes was seen in HPV tumors and pro-tumorigenic macrophages. These findings suggest that TME can be used to predict patient outcomes and may help identify mechanisms of resistance to systemic therapies.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data.

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for "exogenous sequences in tumors and immune cells" to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches and DNA-amplicon-based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets. SIGNIFICANCE: The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors.

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival ≥24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Improving combination therapies: targeting A2B-adenosine receptor to modulate metabolic tumor microenvironment and immunosuppression.

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 pO2, 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.

Association of medical comorbidities and cardiovascular disease with toxicity and survival among patients receiving checkpoint inhibitor immunotherapy.

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.

Immune checkpoint inhibitor-induced hepatitis injury: risk factors, outcomes, and impact on survival.

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.

Association between Tumor Microbiome and Hypoxia across Anatomic Subsites of Head and Neck Cancers.

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.

A Geriatric Assessment Intervention to Reduce Treatment Toxicity Among Older Adults With Advanced Lung Cancer: A Subgroup Analysis From a Cluster Randomized Controlled Trial.

Introduction: More older adults die from lung cancer worldwide than breast, prostate, and colorectal cancers combined. Current lung cancer treatments may prolong life, but can also cause considerable treatment-related toxicity. Objective: This study is a secondary analysis of a cluster-randomized clinical trial which evaluated whether providing a geriatric assessment (GA) summary and GA-guided management recommendations can improve grade 3-5 toxicity among older adults with advanced lung cancer. Methods: We analyzed participants aged ≥70 years(y) with stage III & IV (advanced) lung cancer and ≥1 GA domain impairment starting a new cancer treatment with high-risk of toxicity within the National Cancer Institute's Community Oncology Research Program. Community practices were randomized to the intervention arm (oncologists received GA summary & recommendations) versus usual care (UC: no summary or recommendations given). The primary outcome was grade 3-5 toxicity through 3 months post-treatment initiation. Secondary outcomes included 6-month (mo) and 1-year overall survival (OS), treatment modifications, and unplanned hospitalizations. Outcomes were analyzed using generalized linear mixed and Cox proportional hazards models with practice site as a random effect. Trial Registration: NCT02054741. Results & Conclusion: Among 180 participants with advanced lung cancer, the mean age was 76.3y (SD 5.1), 39.4% were female and 82.2% had stage IV disease. The proportion of patients who experienced grade 3-5 toxicity was significantly lower in the intervention arm vs UC (53.1% vs 71.6%, P=0.01). More participants in the intervention arm received lower intensity treatment at cycle 1 (56.3% vs 35.3%; P<0.01). Even with a cycle 1 dose reduction, OS at 6mo and 1 year was not significantly different (adjusted hazard ratio [HR] intervention vs. UC: 6mo HR=0.90, 95% CI: 0.52-1.57, P=0.72; 1 year HR=0.89, 95% CI: 0.58-1.36, P=0.57). Frequent toxicity checks, providing education and counseling materials, and initiating direct communication with the patient's primary care physician were among the most common GA-guided management recommendations. Providing a GA summary and management recommendations can significantly improve tolerability of cancer treatment among older adults with advanced lung cancer.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.

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.

Checkpoint inhibitor immunotherapy toxicity and overall survival among older adults with advanced cancer.

OBJECTIVES: Despite growing evidence that checkpoint inhibitor immunotherapy (IO) toxicity is associated with improved treatment response, the relationship between immune-related adverse events (irAEs) and overall survival (OS) among older adults [age ≥ 70 years (y)] remains unknown. The study goal was to determine differences in OS based on age and ≥ grade 3 (G3) irAEs. MATERIALS AND METHODS: This was a retrospective cohort study of 673 patients with advanced cancer. Patients who received ≥1 dose of IO at our institution from 2011 to 2018 were eligible. The primary outcome was OS from the start of first line of IO treatment, compared between four patient groups stratified by age and ≥ G3 irAEs with adjustment for patient characteristics using a Cox proportional hazards model. RESULTS AND CONCLUSION: Among all 673 patients, 35.4% were ≥ 70y, 39.8% had melanoma, and 45.6% received single-agent nivolumab. Incidence and types of ≥G3 irAEs did not differ by age. Median OS was significantly longer for all patients with ≥G3 irAEs (unadjusted 21.7 vs. 11.9 months, P = 0.007). There was no difference in OS among patients ≥70y with ≥G3 irAEs (HR 0.94, 95% CI 0.61-1.47, P = 0.79) in the multivariable analysis. Patients <70y with ≥G3 irAEs had significantly increased OS (HR 0.33, 95% CI 0.21-0.52, P < 0.001). Younger patients, but not older adults, with high-grade irAEs experience strong survival benefit. This difference may be due to the toll of irAEs themselves or the effects of treatments for irAEs, such as corticosteroids. Factors impacting OS of older adults after irAEs must be determined and optimized.

Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients.

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes.

Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications.

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, ß-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.