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Discusses the role of denosumab, a monoclonal antibody against receptor activator of nuclear factor-κB ligand (RANKL), in modifying the pathogenesis of giant-cell tumor of bone (GCTB), and the evolving management of GCTB with the introduction of denosumab. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

Background Tertiary cancer centers offer clinical expertise and multi‐modal approaches to treatment alongside the integration of research protocols. Nevertheless, most patients receive their cancer care at community practices. A better understanding of the relationships between tertiary and community practice environments may enhance collaborations and advance patient care. Methods A 31‐item survey was distributed to community and tertiary oncologists in Southern California using REDCap. Survey questions assessed the following attributes: demographics and features of clinical practice, referral patterns, availability and knowledge of clinical trials and precision medicine, strategies for knowledge acquisition, and integration of community and tertiary practices. Results The survey was distributed to 98 oncologists, 85 (87%) of whom completed it. In total, 52 (61%) respondents were community practitioners and 33 (38%) were tertiary oncologists. A majority (56%) of community oncologists defined themselves as general oncologists, whereas almost all (97%) tertiary oncologists reported a subspecialty. Clinical trial availability was the most common reason for patient referrals to tertiary centers (73%). The most frequent barrier to tertiary referral was financial considerations (59%). Clinical trials were offered by 97% of tertiary practitioners compared to 67% of community oncologists (p = 0.001). Most oncologists (82%) reported only a minimal‐to‐moderate understanding of clinical trials available at regional tertiary centers. Conclusions Community oncologists refer patients to tertiary centers primarily with the intent of clinical trial enrollment; however, significant gaps exist in their knowledge of trial availability. Our results identify the need for enhanced communication and collaboration between community and tertiary providers to expand patients’ access to clinical trials. Further relationships between community and tertiary medical oncologists are imperative to increasing clinical trial enrollment and successful patient outcomes. This study reports significant gaps in the knowledge of clinical trial availability among oncologists and identifies a need for increased communication and collaboration between the practice settings.

A \"Think Tank for Osteosarcoma\" medical advisory board meeting was held in Santa Monica, CA, USA on February 2-3, 2024. The goal was to develop a strategic approach to prevent recurrence of osteosarcoma. Osteosarcoma metabolism and the genomic instability of osteosarcoma, immunotherapy for osteosarcoma, CAR-T cell therapy, DeltaRex-G tumor-targeted gene therapy, repurposed drugs, alternative medicines, and personalized medicine were discussed. Only DeltaRex-G was voted on. The conclusions were the following: No intervention has been demonstrated to improve survival in a clinical trial. Additionally, the consensus (10/12 in favor) was that DeltaRex-G without immunotherapy may be administered for up to one year. Phase 2/3 randomized studies of DeltaRex-G should be performed to determine whether the incidence of recurrence could be reduced in high-risk individuals. Furthermore, a personalized approach using drugs with minimal toxicity could be attempted with the acknowledgement that there are no efficacy data to base this on. Repurposed drugs and alternative therapies should be tested in mouse models of osteosarcoma. Moreover, unmodified IL-2 primed Gamma Delta (NK) cell therapy may be used to prevent recurrence. Lastly, rapid development of CAR-T cell therapy is recommended, and an institute dedicated to the study of osteosarcoma is needed.

Papillary renal cell carcinoma (pRCC) is the second-most common subtype of kidney cancer following clear cell renal cell carcinoma (ccRCC), representing 15% of kidney cancers. Despite advances in therapy, including combination strategies with targeted therapies and immune checkpoint inhibitors, progress has lagged behind that of ccRCC. This is in part due to the heterogenous nature of the various subtypes of pRCC. More recently, investigators have turned efforts towards histology and biology-based trials. In this review, we outline some of the distinct biological characteristics of pRCC and discuss the most impactful clinical trials to date. Finally, we look ahead to several highly anticipated ongoing trials in pRCC.

Background: The heterogeneity of sarcomas and resulting distinct sub-type specific characteristics, their high recurrence rates, and tendency for distant metastasis, continue to present significant challenges to providing optimal treatments. Objective: To provide a comprehensive review of current literature and clinical trials in gene and cell therapies for sarcomas. Methods: A comprehensive literature search was conducted utilizing the following databases: PubMed, Medline, Google Scholar and clinicaltrials.gov. Search terms included “gene therapy”, “cell therapy”, “NK cell therapy, “CAR-T therapy”, “virotherapy”, “sarcoma”, “gene therapy”, and “solid tumors”. Additional sources were identified through manual searching for references of relevant studies. No language restrictions were set. The NCT number, study status, condition, and phase were noted for clinical trials. Results: There are only three gene and cell therapies for sarcomas that have been approved by a federal regulatory agency. Rexin-G: the first tumor-targeted gene therapy vector designed to target all advanced solid malignancies, including chemo-refractory osteosarcomas and soft tissue sarcomas, was approved by the Philippine FDA in 2007. Gendicine was the first oncolytic virus approved for intratumoral delivery in China in 2003. Afami-cel, an innovative chimeric antigen receptor (CAR) T cell therapy, was approved for synovial sarcoma in the United States in 2024. Other promising therapies are discussed in the text. Conclusions: The future of gene and cell therapy for sarcomas holds great promise, as research moves to late-stage clinical development. The integration of gene and cell therapies into standard sarcoma treatment protocols has the potential to significantly improve the quality of life and outcomes for patients with this rare and challenging group of cancers.

Abstract Background Radiogenomics is an emerging tool with applications in screening for molecular biomarkers in diagnostic and prognostic assessment through the extraction of quantitative data from medical images (Shui et al., Front Oncol 2021). In this study, we aim to develop machine learning (ML) models to predict the most common genomic alterations present in our subset of renal cell carcinoma patients (pts). Methods Retrospectively, pts with tissue genomic testing from CT-guided biopsy samples were identified. Genomic testing was done via GEM ExTra assay, a CAP-accredited, CLIA-certified test encompassing tumor whole exome sequencing and whole transcriptome sequencing (TGen; Phoenix, AZ). Biopsy sample collection sites were identified from pre-biopsy contrast CT images, and the lesions were segmented with ITK-SNAP software, from which 510 radiomic features were extracted with Pyradiomics. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the most relevant features. Logistic regression (LR) and support vector machine (SVM) classifiers were built for the prediction of PBRM1, VHL, and SETD2 gene alterations. Multiple metrics were used to evaluate the predictive performance via leave-one-out cross-validation, including the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). Feature importance was evaluated with Shapley additive explanations (SHAP) method. Results A total of 14 RCC pts (10:4 M:F) with genomic testing from CT-guided biopsies were identified. The majority of pts were White (85.7%) and had clear cell histology (71.4%). The most common locations for the CT-guided biopsy were lung (18.2%), soft tissue (13.6%), kidney (9.1%), and bone (9.1%). The most common alterations were seen in PBRM1 (50%), VHL (43.9%), and SETD2 (35.7%) genes. The PBRM1 gene was predicted with the highest AUROC (0.84) and AUPRC (0.88) with the SVM classifier, followed by the SETD2 gene (AUROC=0.78 and AUPRC=0.66) with LR classifier and VHL gene (AUROC=0.56 and AUPRC=0.65) with SVM classifier. Notably, all three models showed good sensitivity in classifying gene mutation status (PBRM1: 0.86; VHL: 0.88; SETD2: 0.89). Among all radiomic features, first-order features, Gray Level Size Zone Matrix features, and Gray Level Dependence Matrix features were found to be the most important features for predictions. Conclusions Using a CT-based radiomics analysis of the biopsy area, we showed that SVM and LR prediction models could predict PBRM1, VHL, and SETD2 mutations with high accuracy. These models may assist in identifying potentially actionable alterations and yield ease in treatment selection for RCC. Further extensive studies are warranted to validate our findings and improve our model. CDMRP DOD Funding: no

Previous studies have suggested that the gut microbiome influences the response to checkpoint inhibitors (CPIs) in patients with cancer. CBM588 is a bifidogenic live bacterial product that we postulated could augment CPI response through modulation of the gut microbiome. In this open-label, single-center study (NCT03829111), 30 treatment-naive patients with metastatic renal cell carcinoma with clear cell and/or sarcomatoid histology and intermediate- or poor-risk disease were randomized 2:1 to receive nivolumab and ipilimumab with or without daily oral CBM588, respectively. Stool metagenomic sequencing was performed at multiple timepoints. The primary endpoint to compare the relative abundance of Bifidobacterium spp. at baseline and at 12 weeks was not met, and no significant differences in Bifidobacterium spp. or Shannon index associated with the addition of CBM588 to nivolumab–ipilimumab were detected. Secondary endpoints included response rate, progression-free survival (PFS) and toxicity. PFS was significantly longer in patients receiving nivolumab–ipilimumab with CBM588 than without (12.7 months versus 2.5 months, hazard ratio 0.15, 95% confidence interval 0.05–0.47, P  = 0.001). Although not statistically significant, the response rate was also higher in patients receiving CBM588 (58% versus 20%, P  = 0.06). No significant difference in toxicity was observed between the study arms. The data suggest that CBM588 appears to enhance the clinical outcome in patients with metastatic renal cell carcinoma treated with nivolumab–ipilimumab. Larger studies are warranted to confirm this clinical observation and elucidate the mechanism of action and the effects on microbiome and immune compartments. A randomized trial in treatment-naive patients with metastatic renal cell carcinoma shows that the addition of a live bacterial product to an immunotherapy combination elicits promising clinical benefit in association with an enrichment of bacterial species, circulating cytokines and immune cell populations in responders.

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.

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine. A multimodal analysis of patients with 22 different immune-mediated monogenic diseases versus matched healthy controls leads to the development of the immune health metric, which could be implemented broadly to predict responses to aging, vaccination and other immune perturbations.

Background Acute kidney injury (AKI) occurs commonly in pediatric septic shock and increases morbidity and mortality. Early identification of high-risk patients can facilitate targeted intervention to improve outcomes. We previously modified the renal angina index (RAI), a validated AKI prediction tool, to improve specificity in this population (sRAI). Here, we prospectively assess sRAI performance in a separate cohort. Methods A secondary analysis of a prospective, multicenter, observational study of children with septic shock admitted to the pediatric intensive care unit from 1/2019 to 12/2022. The primary outcome was severe AKI (≥ KDIGO Stage 2) on Day 3 (D3 severe AKI), and we compared predictive performance of the sRAI (calculated on Day 1) to the original RAI and serum creatinine elevation above baseline (D1 SCr > Baseline +). Original renal angina fulfillment (RAI +) was defined as RAI ≥ 8; sepsis renal angina fulfillment (sRAI +) was defined as RAI ≥ 20 or RAI 8 to < 20 with platelets < 150 × 10 3 /µL. Results Among 363 patients, 79 (22%) developed D3 severe AKI. One hundred forty (39%) were sRAI + , 195 (54%) RAI + , and 253 (70%) D1 SCr > Baseline + . Compared to sRAI-, sRAI + had higher risk of D3 severe AKI (RR 8.9, 95%CI 5–16, p  < 0.001), kidney replacement therapy (KRT) (RR 18, 95%CI 6.6–49, p  < 0.001), and mortality (RR 2.5, 95%CI 1.2–5.5, p  = 0.013). sRAI predicted D3 severe AKI with an AUROC of 0.86 (95%CI 0.82–0.90), with greater specificity (74%) than D1 SCr > Baseline (36%) and RAI + (58%). On multivariable regression, sRAI + retained associations with D3 severe AKI (aOR 4.5, 95%CI 2.0–10.2, p  < 0.001) and need for KRT (aOR 5.6, 95%CI 1.5–21.5, p  = 0.01). Conclusions Prediction of severe AKI in pediatric septic shock is important to improve outcomes, allocate resources, and inform enrollment in clinical trials examining potential disease-modifying therapies. The sRAI affords more accurate and specific prediction than context-free SCr elevation or the original RAI in this population.