Explore the vast range of titles available.

The 16S rRNA gene has been a mainstay of sequence-based bacterial analysis for decades. However, high-throughput sequencing of the full gene has only recently become a realistic prospect. Here, we use in silico and sequence-based experiments to critically re-evaluate the potential of the 16S gene to provide taxonomic resolution at species and strain level. We demonstrate that targeting of 16S variable regions with short-read sequencing platforms cannot achieve the taxonomic resolution afforded by sequencing the entire (~1500 bp) gene. We further demonstrate that full-length sequencing platforms are sufficiently accurate to resolve subtle nucleotide substitutions (but not insertions/deletions) that exist between intragenomic copies of the 16S gene. In consequence, we argue that modern analysis approaches must necessarily account for intragenomic variation between 16S gene copies. In particular, we demonstrate that appropriate treatment of full-length 16S intragenomic copy variants has the potential to provide taxonomic resolution of bacterial communities at species and strain level. Here, the authors explore the potential of the 16S gene for discriminating bacterial taxa and show that full-length sequencing combined with appropriate clustering of intragenomic sequence variation can provide accurate representation of bacterial species in microbiome datasets.

As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.

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. The role of neutrophils is increasingly being recognized in chronic inflammation and metabolic disorders. Here the authors show that visceral adipose tissue from individuals with obesity contains more neutrophils than in those without obesity and is associated with a distinct bacterial community.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states. Deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, alongside changes in the microbiome, in samples from individuals with and without prediabetes reveal insights into inter-individual variability and associations between changes in the microbiome and other factors.

PurposeFrailty in older adult cancer survivors after cancer treatments is associated with various health outcomes. However, there is less agreement on how frailty affects symptoms and health-related quality of life (HRQOL). This systematic review and meta-analysis aimed to evaluate the current literature on frailty, symptoms, and HRQOL, as well as the associations of frailty with these factors in older adult cancer survivors with chemotherapy.MethodsA review was conducted on peer-reviewed publications from 2008 to 2023, using seven electronic databases. Meta-analyses were performed using random effects models to determine pooled effect estimates for frailty prevalence, symptom severity, and HRQOL scores.ResultsA total of 26 studies involving older cancer survivors were included in the analysis. Most of these studies were conducted in Western countries and focused on White survivors, particularly those with breast cancer. The mean pooled prevalence of frailty was 43.5%. Among frail survivors, the most common symptoms reported after cancer treatments were pain (36.4%), neuropathy (34.1%), and fatigue (21.3%). Frailty was associated with higher pooled mean symptom severity (B = 1.23, p = 0.046) and lower functional HRQOL (B = − 0.31, p = 0.051, with marginal significance) after cancer treatments.ConclusionFrail older cancer survivors are at high risk of adverse symptoms and poor HRQOL after cancer treatment. Further research on screening for frailty is needed to prevent older adults from developing worse symptoms burden and maintain HRQOL. It is also essential to understand the mechanisms of the associations between frailty, symptoms and HRQOL in this population.

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.

The most common subtype of renal cell carcinoma is clear cell renal cell carcinoma (ccRCC). While localized ccRCC can be cured with surgery, metastatic disease has a poor prognosis. Recently, immunotherapy has emerged as a promising approach for advanced ccRCC. This review provides a comprehensive overview of the evolving immunotherapeutic landscape for metastatic ccRCC. Immune checkpoint inhibitors (ICIs) like PD-1/PD-L1 and CTLA-4 inhibitors have demonstrated clinical efficacy as monotherapies and in combination regimens. Combination immunotherapies pairing ICIs with antiangiogenic agents, other immunomodulators, or novel therapeutic platforms such as bispecific antibodies and chimeric antigen receptor (CAR) T-cell therapy are areas of active research. Beyond the checkpoint blockade, additional modalities including therapeutic vaccines, cytokines, and oncolytic viruses are also being explored for ccRCC. This review discusses the mechanisms, major clinical trials, challenges, and future directions for these emerging immunotherapies. While current strategies have shown promise in improving patient outcomes, continued research is critical for expanding and optimizing immunotherapy approaches for advanced ccRCC. Realizing the full potential of immunotherapy will require elucidating mechanisms of response and resistance, developing predictive biomarkers, and rationally designing combination therapeutic regimens tailored to individual patients. Advances in immunotherapy carry immense promise for transforming the management of metastatic ccRCC.

Background: Colorectal cancer (CRC) patients experience multiple types of chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking. Objectives: We conducted a systematic review and meta-analysis to determine chemotoxicity’s prevalence and risk factors in CRC. Methods: A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis. Results: Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: −1.44) and GI toxicity (β:1.85) (p-values < 0.01). Older adults (>65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (p-values < 0.05). Conclusions. Our findings highlight the importance of closely monitoring and managing chemotoxicity in CRC patients receiving chemotherapy.

Tumor-infiltrating microbes are emerging as a novel dimension of cancer biology, with growing evidence suggesting their potential as prognostic and predictive biomarkers. In this issue, Chen et al demonstrate associations between microbial signatures and treatment response in triple-negative breast cancer (TNBC). They join a growing list of examples whereby tumor-infiltrating microbes influence therapeutic efficacy, with mechanisms ranging from drug metabolism to immune modulation. Here, we explore the known mechanisms, as well as the methodological and conceptual challenges facing microbial biomarker research, including contamination risk, detection sensitivity, and the functional validation of microbial activity. As the field advances, integrating microbial profiling with genomic and immunological data, alongside foundational microbiological techniques, will be essential to clarify the role of microbes in cancer progression and treatment response. Ultimately, a deeper understanding of these microbial ecosystems may open new avenues for precision oncology in TNBC and beyond.

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.