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ObjectiveTo characterize and further compare the immune cell populations of the tumor microenvironment (TME) in both clear cell and papillary renal cell carcinoma (RCC) using heavy metal-labeled antibodies in a multiplexed imaging approach (imaging mass cytometry).Materials and methodsFormalin-fixed paraffin-embedded (FFPE) baseline tumor tissues from metastatic patients with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were retrospectively requisitioned from an institutional biorepository. Pretreated FFPE samples from 33 RCC patients (10 ccRCC, 23 pRCC) were accessioned and stained for imaging mass cytometry (IMC) analysis. Clinical characteristics were curated from an institutional RCC database. FFPE samples were prepared and stained with heavy metal-conjugated antibodies for IMC. An 11-marker panel of tumor stromal and immune markers was used to assess and quantify cellular relationships in TME compartments. To validate our time-of-flight (CyTOF) analysis, we cross-validated findings with The Cancer Genome Atlas Program (TCGA) analysis and utilized the CIBERSORTx tool to examine the abundance of main immune cell types in pRCC and ccRCC patients.ResultsPatients with ccRCC had a longer median overall survival than did those with pRCC (67.7 vs 26.8 mo, respectively). Significant differences were identified in the proportion of CD4+ T cells between disease subtypes (ccRCC 14.1%, pRCC 7.0%, p<0.01). Further, the pRCC cohort had significantly more PanCK+ tumor cells than did the ccRCC cohort (24.3% vs 9.5%, respectively, p<0.01). There were no significant differences in macrophage composition (CD68+) between cohorts. Our results demonstrated a significant correlation between the CyTOF and TCGA analyses, specifically validating that ccRCC patients exhibit higher levels of CD4+ T cells (ccRCC 17.60%, pRCC 15.7%, p<0.01) and CD8+ T cells (ccRCC 17.83%, pRCC 11.15%, p<0.01). The limitation of our CyTOF analysis was the large proportion of cells that were deemed non-characterizable.ConclusionsOur findings emphasize the need to investigate the TME in distinct RCC histological subtypes. We observed a more immune infiltrative phenotype in the TME of the ccRCC cohort than in the pRCC cohort, where a tumor-rich phenotype was noted. As practical predictive biomarkers remain elusive across all subtypes of RCC, further studies are warranted to analyze the biomarker potential of such TME classifications.

Chemotherapy and radiotherapy are first-line treatments in the management of advanced solid tumors. Whereas these treatments are directed at eliminating cancer cells, they cause significant adverse effects that can be detrimental to a patient’s quality of life and even life-threatening. Diet is a modifiable risk factor that has been shown to affect cancer risk, recurrence, and treatment toxicity, but little information is known how diet interacts with cancer treatment modalities. Although dietary interventions, such as intermittent fasting and ketogenic diets, have shown promise in pre-clinical studies by reducing the toxicity and increasing the efficacy of chemotherapeutics, there remains a limited number of clinical studies in this space. This review surveys the impact of dietary interventions (caloric restriction, intermittent and short-term fasting, and ketogenic diet) on cancer treatment outcomes in both pre-clinical and clinical studies. Early studies support a complementary role for these dietary interventions in improving patient quality of life across multiple cancer types by reducing toxicity and perhaps a benefit in treatment efficacy. Larger, phase III, randomized clinical trials are ultimately necessary to evaluate the efficacy of these dietary interventions in improving oncologic or quality of life outcomes for patients that are undergoing chemotherapy or radiotherapy.

Metawebs (networks of potential interactions within a species pool) are a powerful ion to understand how large‐scale species interaction networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.

Introduction In a joint statement, Friends of Cancer Research and the American Society of Clinical Oncology affirmed the need for broadening clinical trial eligibility criteria to expand patient access to investigational treatments and enroll cohorts more representative of the general population. Our study aimed to characterize and analyze the prevalence of overly exclusionary eligibility criteria in contemporary clinical trials involving patients with locally advanced and metastatic urothelial cancer. Methods Utilizing MeSH query terms “(metastatic OR advanced OR stage IV OR unresectable) AND (bladder cancer OR upper tract urothelial carcinoma OR upper tract urothelial cancer)” in ClinicalTrials.gov, we identified 205 interventional urothelial cancer trials activated between June 30, 2012 through June 30, 2022. We investigated the prevalence of four potentially restrictive criteria: the presence of brain metastases, HIV infection, hepatitis B/C infection, and the presence of concurrent malignancies. Fisher's Exact test was utilized to ascertain significant associations between criteria and trial characteristics. Results Of 205 trials found initially, 37 (18%) contained sufficient data for analysis. Overall, HIV infection and Hepatitis B/C infection were most restrictive, with most trials completely excluding patients with these conditions (89.2%; 56.8%). Restrictiveness for HIV infection and type of therapy were significantly associated, with most exclusionary trials involving combination or immunotherapies (39.4%; 33.3%; p = 0.003). Brain metastases were totally excluded by 35.1% of trials and had 18.9% of trials provide no explicit criteria or guidelines. Most trials specified conditions for the inclusion of patients with concurrent malignancies (91.9%). Variant histology was also underrepresented, with most trials not specifying or totally excluding all variant histology (43.2%; 8.1%). Conclusion HIV infection and hepatitis B/C infection were commonly identified in exclusion criteria across these trials despite limited evidence suggesting these criteria significantly impact therapy efficacy and tolerability. Broadening and modernization of eligibility criteria will ensure more inclusive clinical trials.

Ultrasound techniques are increasingly used to probe the internal dynamics of batteries to obtain cost-effective, real-time insights into electrochemical processes. However, prior studies have established only superficial correlations between ultrasound and electrochemical parameters, thus limiting the understanding of signal variations during cycling. In this study, the interpretability of these variations is improved by combining operando ultrasound measurements with synchrotron X-ray diffraction and nanodilatometry measurements during electrochemical cycling and relaxation. We show that at battery states of charge from 10% to 80%, ultrasound signals reflect primarily the change in the graphite electrode, particularly its elastic modulus during lithiation. At battery states of charge between 80% and 100%, the H2 → H3 phase transition in LiNi 0.8 Mn 0.1 Co 0.1 O 2 affects the ultrasound signal. This multimodal approach enhances the understanding of how mechanical and structural battery dynamics influence ultrasound signals, thus marking a step forward in the interpretation of acoustic data in commercial cells via advanced synchrotron techniques. Ultrasound helps detect early Li-ion battery failures, but linking signals to specific phenomena is difficult. Here, authors combine ultrasound with operando X-ray diffraction and nanodilatometry to correlate ultrasound signals with structural changes in battery materials.

Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient’s tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.

Supplementation with CBM588, a bifidogenic live bacterial product, has been associated with improved clinical outcomes in persons with metastatic renal cell carcinoma (mRCC) receiving nivolumab and ipilimumab. However, its effect on those receiving tyrosine kinase inhibitor-based combinations is unknown. In this open-label, randomized, investigator-initiated, phase 1 study, 30 participants with locally advanced or mRCC with histological confirmation of clear cell, papillary or sarcomatoid component were randomized in a 2:1 fashion to receive cabozantinib (an inhibitor of vascular endothelial growth factor receptor, MET and AXL) and nivolumab (anti-programmed cell death protein 1) with or without CBM588 as first-line treatment. Metagenomic sequencing was performed on stool samples to characterize their gut microbiome at baseline and 13 weeks into treatment. The primary endpoint was a change in the relative abundance of Bifidobacterium spp.; secondary endpoints included objective response rate (ORR), progression-free survival (PFS) and toxicity profile. The primary endpoint of the study was not met and the addition of CBM588 to cabozantinib and nivolumab did not result in a difference in the relative abundance of Bifidobacterium spp. or alpha diversity (as measured by the Shannon index). However, ORR was significantly higher in participants treated with CBM588 compared to those in the control arm (14 of 19, 74% versus 2 of 10, 20%; P  = 0.01). PFS at 6 months was 84% (16 of 19) and 60% (6 of 10) in the experimental and control arms, respectively. No significant difference in toxicity profile was seen between the study arms. Our results provide a preliminary signal of improved clinical activity with CBM588 in treatment-naive participants with mRCC receiving cabozantinib and nivolumab. Further investigation is needed to confirm these findings and better characterize the underlying mechanism driving this effect. ClinicalTrials.gov identifier: NCT05122546 In a randomized phase 1 trial, the addition of a live Clostridium species-containing product to a tyrosine kinase inhibitor and anti-programmed cell death protein 1 treatment combination did not increase bacterial abundance of Bifidobacterium spp. but enhanced clinical responses in participants with metastatic renal cell carcinoma.

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species—and to describe the structure, variation, and change of the ecological networks they form—we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.